Urban transportation systems of 25 global cities, Schemes and Mind Maps of Sustainable Development

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July 2021 Urban transportation systems of 25 global cities Elements of success 2 Contents Benchmarking methodology 7 General description of research methodology 8 Selection of target cities 10 List of metrics reviewed 12 Use of geoanalytical tools 16 Calculation of travel speed: Personal and public transport 18 Calculation of the share of residents and workplaces near an underground station or commuter railroad station 19 Calculation of road network area 20 Survey of city residents 22 Weighting and final rating 23 Comparison with 2018 results: Approach and metrics 24 General insights and observations 29 Success factors for cities with sophisticated transport systems 30 Development of transport systems over the last several years 31 Projects implemented by cities in various areas 32 Correlation between transport system sophistication and city wealth status 33 Sustainable development index 34 Correlation between objective results for city and public perceptions of city 35 City residents’ satisfaction with public and personal transport 36 City residents’ perception of changes 37 City residents’ views of metrics’ importance 38 Perceptions of transport system elements 39 Correlation between satisfaction levels and perceived importance of metrics 40 Transport system ratings 43 Ratings based on Availability metrics 44 Availability change rating, 2018–20 45 Ratings based on Affordability metrics 48 Change ratings for Affordability, 2018–20 49 Ratings based on Efficiency metrics 52 Change ratings for Efficiency, 2018–20 53 Ratings based on Convenience metrics 56 Change ratings for Convenience, 2018–20 57 Ratings based on Safety and Sustainable Development metrics 60 Change ratings for Safety and Sustainable Development, 2018–20 61 City ranking based on public-transport use 66 City ranking based on personal-transport use 67 5 Like its predecessor, the new study reported here is designed to analyze, as broadly as possible, transport systems in 25 cities of the world from a user point of view and to benchmark critical aspects of their performance that have the most effect on city residents’ transport needs and quality of life. It is distinguished by the special attention paid to the impact of the COVID-19 pandemic on objective transport-system metrics and residents’ behavior. We offer examples of successful events and projects city authorities have implemented to deal with the pandemic. One of the study’s objectives is to review the progress achieved by city transport systems since the previous study’s publication. To assure data comparability, we have left the list of examined cities unchanged. However, during the time between the two studies, the challenges faced by megapolises have changed, particularly against the backdrop of the COVID-19 pandemic. Accordingly, we have been forced to revise the list and weights of metrics under review. This has made it all but impossible to draw direct comparisons between the previous study and its current version. Nevertheless, we can compare the rate of change of metrics describing certain aspects of transport systems, such as development of road networks and improvement of fare payment systems. The report is organized into five sections and two appendixes. Section 1 describes our research methodology, while Section 2 presents the general conclusions we have drawn upon completion of the current study. Section 3 presents transport system ratings and an assessment of changes that have occurred to them since the previous study. Section 4 provides an overview of subgroups of metrics that make up the transport system ratings, and Section 5 looks at the impact of the COVID-19 pandemic on transport systems. The first appendix contains profiles of ten cities with the most efficient transport systems, and the second offers examples of significant transport system projects implemented in various cities. Research findings may prove interesting primarily for city mayors and heads of urban transportation agencies and companies. We hope our conclusions will be gainfully employed to make informed decisions regarding further development of city transport systems. Detlev Mohr Senior partner Leader of Travel, Logistics, and Infrastructure Practice Vadim Pokotilo Partner Leader of City Transport Center of Excellence Jonathan Woetzel Senior partner Director of McKinsey Global Institute (MGI) Leader of City Development Benchmarking methodology 10 Benchmarking methodology Selection of target cities Five filters were used to prepare the list of cities for examination (Exhibit 3). Population of the city must exceed five million people, and the city must play a leading role in the national economy. GRP per capita must be more than $10,000, and the number of cars must be more than 150 per 1,000 people. The city must be mentioned in international data sources. We applied these filters to approximately 13,800 cities across the world. The resultant list consists of 21 cities with comparable transport systems. Four more cities (Shanghai, Singapore, Berlin, and Hong Kong) were added to the list because of their perceived research relevance. Even though they had failed to clear one of the formal filters, those megapolises topped at least several international ratings reflecting the level of development of certain aspects of their transport systems. 11Benchmarking methodology Exhibit 3 Approach to selection of target cities Number of cities Selection criteria 1 ~13 800 Size Population of urban agglomeration is ≥5 million; city is among country’s most economically significant 2 38 Level of economic development GRP per capita is at least $10,000 3 32 Transport system features Number of cars is ≥150 per 1,000 residents 4 28 Data availability and quality >50% of data represented in international sources1 5 21 Expert assessment Leading positions in ≥2 reviewed ratings2 and population is >3 million 4 additional cities 1 This guarantees comparability of metrics across all cities. 2 Third-party transport system ratings include TomTom Traffic Index; The Future of Urban Mobility 2.0 (rating published by Arthur D. Little and International Union of Public Transport); Sustainable Cities Mobility Index (rating published by Arcadis); Urban Mobility Index Report (rating published by Qualcomm and consulting agency CEBR). 12 Benchmarking methodology List of metrics reviewed We selected five aspects of transport systems for comprehensive assessment of the level of development of city transport systems across the world. These are Availability, Affordability, Efficiency, Convenience, and Safety and Sustainable Development. Each of those aspects encompasses objective metrics joined into logical subgroups (Exhibit 4). For example, the ticketing system is part of the Convenience group, and public- transport efficiency is part of the Efficiency group. The main criteria governing inclusion of a metric in the research were availability of data for the examined cities, the evaluation of transport systems from a passenger’s perspective, and relevance of the metric in terms of assessing one of the transport system’s aspects. We did not include certain metrics in three main cases. First, we excluded any metric that does not follow the “passenger’s view” principle. For example, we do not consider the commercial efficiency of public transport for the transport operator. We also excluded data that were not available for a number of cities, so, for example, we report data on no emissions other than NO2 and no data on the share of off-street parking. Finally, we excluded metrics that involve complex relationships between various aspects of transport systems. For example, we do not consider the integration of bus and rail transport timetables and do not estimate the optimal number of taxis taking into account the specifics of a city. 15Benchmarking methodology Ticketing system Availability of a universal travel card to pay fares while using multiple modes of public transport Possibility to use remote top-up and/or remote ticketing Possibility to use an electronic travel card available on mobile devices Possibility to top up travel card and/or buy a ticket using a bank card Possibility to use contactless bank cards and/or Apple Pay, Samsung Pay, or Android Pay mobile applications directly at pay gates Possibility to use an electronic travel card to pay for nontransport services Possibility to pay fares using biometric data Need for registration following travel card top-up Safety and Sustainable Development Physical safety Number of public-road traffic accident fatalities per million people per year Number of underground accident fatalities per million people per year Safety rules compliance index Environmental safety Availability of public-transport disinfection measures Current diesel/petrol fuel quality standards Average age of cars on the roads, years Share of e-vehicles in total vehicle sales, % Concentration of NO2 in atmospheric air, molecules per cubic centimeter Number of commercial vehicles registered in the city per $1 billion of city GRP (based on purchasing power parity) Index of commercial transport-related environmental restrictions Availability of subsidies or incentive programs related to transition to more environmentally friendly fuel, e-vehicles etc. 16 Benchmarking methodology Use of geoanalytical tools To create an objective rating of transport systems, we resorted to tools used to analyze geospatial data. With those tools, we calculated values for more than 15 metrics, because traditional open-source data collection methods are not available or are available only to a limited extent. In particular, we measured average personal motor car travel time, average taxi ride cost, average public-transport travel speed, share of population living at a walking distance of less than 20 minutes from an underground station or a commuter railroad station, road network area, and so on. To enable calculation of those metrics, it was necessary to ensure that the areas of cities under analysis are comparable. To do that, in some cities we relied on areas specifically selected for research purposes, rather than on official city boundaries. We had to make certain adjustments: some urban agglomerations (for example, Paris) occupy areas that are in fact larger than those delimited by their official boundaries, while others (including Madrid) have official territories that greatly exceed the area of their densely populated parts. Had we failed to account for such deviations, they could have distorted our analytical findings. Algorithm for determining city boundaries Determination of official city boundaries. We reviewed diverse approaches applied to administrative division of each city covered by our research. For example, in Shanghai, there are various ways to measure the city’s area, ranging from seven central districts (290 km2) only partially covering the area with the highest density of population to Shanghai city area (6,341 km2). Calculation of population density. We divided the territory of the city into squares, each with an area of 1 km2. For each such square, we determined relative population density and workplace concentration, based on NASA data and municipal statistics. Adjustment of city boundaries for research purposes. As a result, sparsely populated areas were excluded from official city boundaries to enable comparability of all examined cities. 17Benchmarking methodology Therefore, we adjusted boundaries of 14 cities covered by our research: — The boundaries of Bangkok, Hong Kong, Istanbul, Madrid, Mexico City, Moscow, Saint Petersburg, São Paulo, Shanghai, Sydney, and Tokyo were narrowed to exclude sparsely 1 Here and throughout this report, references to Los Angeles mean the Los Angeles–Long Beach–Santa Ana Metro Area, references to Milan mean the Province of Milan, and references to Paris are to be construed as applying to the Métropole du Grand Paris. populated areas. — The boundaries of Los Angeles, Milan, and Paris were expanded to cover not only the cities proper, but also the nearest densely populated suburbs.1 Having determined city boundaries, we applied geospatial analysis tools to calculate the following metrics: — personal- and public-transport travel speed — road network area — share of population living and workplaces situated at a walking distance less than 20 minutes from an underground station or a commuter railroad station These tools were also used, in part, to calculate taxi waiting times and ride costs, and to assess the quality of road infrastructure. Geoanalytical tools were applied to those metrics at the initial calculation stage. They were used to identify taxi route points (with a subsequent assessment of ride costs based on data provided by the most popular taxi-booking mobile applications) and coordinates of road infrastructure facilities. In the latter case, calculations were supplemented with expert conclusions drawn in accordance with a well- established methodology on the basis of street photographs available from cartographic services. In one of the sections below, we will provide a detailed description of our geoanalytical methods and sample calculations of the above metrics. 20 Benchmarking methodology Calculation of road network area Road network area can be used to compare cities in terms of sufficiency of existing motorways. Unlike the less sophisticated metric of total road length, road area factors in the number of lanes, which has direct impact on road capacity and may have considerable influence on the ranking of the city. Note that instead of the ordinary road area metric, we use, for rating purposes, road area per motor vehicle registered in the relevant city. This metric provides a more accurate picture of the state of affairs in the examined cities than the alternative (share of city area occupied with roads), as it precludes situations where cities with large forests and parks unfairly get worse rankings. 21Benchmarking methodology Calculation algorithm 3 Conduct Otsu binarization. For the part of the satellite image covered by the mask, we launched the Otsu binarization algorithm, which separated homogeneous pixels under the mask (asphalt) from other objects, such as asphalt hidden by shadows and trees. 4 Make an asphalt mask. Using the homogeneous area obtained at the previous stage, we identified the color range for asphalt pixels in the satellite image (assuming that asphalt color is relatively monotonous across the entire image). Then we selected, in all satellite images, areas falling within that color range, thus producing an asphalt mask for asphalt areas visible from the satellite (showing also similarly colored areas such as rooftops) 5 Make final road map. We superimposed the mask covering asphalt visible from the satellite on the mask from the previous stage (areas looking like asphalt and visible from the satellite) and supplemented the resultant image with the mask created at the Otsu binarization stage. The latter added to the image those sections of the roads that are not visible from the satellite. As a result, we account for the area of the roads, which would be impossible to get by using just standard cartographic data. 1 Create a set of city pictures. For each city, we downloaded a set of several thousand pictures (“plates”), each covering one square kilometer. Each picture was represented in two versions: a satellite image and a map provided by a cartographic service. 2 Make a motorway mask. Binarization of the map “plate” using a number of threshold values yielded a motorway mask sketch. Then we enhanced the mask to remove artifacts produced by inscriptions and to make sure that the final mask would cover the road on the satellite image. 22 Benchmarking methodology Survey of city residents A survey was conducted among city residents. One of the key objectives of the survey was to compare results of the assessment of transport systems based on objective metrics with subjective opinions voiced by city residents. We did not use survey responses as inputs for assessment of any metrics capable of affecting the final rating, but instead compared objective metrics describing transport systems with the feedback provided by city residents. Besides that, we used survey results to draw a list of most notable transport projects, analyze the link between the number of implemented projects and the level of satisfaction of city residents with changes in transport systems, and measure the impact of the COVID-19 pandemic on current and projected mobility of city residents. About 10,000 respondents (400 from each city) took part in the survey. The survey was conducted online in local languages, and the average time required to complete the questionnaire was 15 to 20 minutes. To mitigate the risk of getting biased responses, we set quotas with respect to respondent gender, age, income level, home district, and (to eliminate sample bias in favor of, say, car owners) preferred type of transport. Exhibit 5 Structure of city resident survey Screening 10 questions Assessment of specific aspects of transport systems and changes in such systems 30 questions Awareness of transport projects implemented by city authorities 27 questions Impact of the COVID-10 pandemic on behavior of city residents 12 questions 25Benchmarking methodology Shared transport Number of bicycles used in public rental systems per million people Number of cars used in car-sharing systems per million people — External connectivity Number of regular flight routes from city airports — Affordability Public-transport affordability Number of categories of reduced-fare passengers Ratio of the cost of monthly travel card to average monthly income Ratio of the cost of a 1-kilometer taxi ride to average monthly income — Cost and barriers to using personal transport Ratio of the average cost of 2-hour paid on-street parking to average monthly income, % Existence of fees imposed on car owners entering downtown area or specific city districts Motor vehicle restrictions index: restrictions based on license plates or place of registration, prohibitive taxes or duties, mandatory availability of a reserved parking space — Efficiency Public-transport efficiency Average effective public-transport travel speed during morning rush hour, kilometers per hour Share of dedicated public-transport lanes in total length of road network, % Average land transport waiting time, minutes Underground train waiting time index Personal-transport efficiency Average traffic flow speed during morning rush hour, kilometers per hour Traffic congestion index: rush hour trip duration vs free-road trip duration Morning rush hour travel time predictability index Time lost in traffic jams (rush hour travel time vs free-road travel time), minutes — 26 Benchmarking methodology Convenience Electronic services Availability of Wi-Fi networks in underground cars and at underground stations, on buses, and at land transport stops Availability of real-time public-transport traffic information on the internet Availability of real-time public transport traffic information on electronic screens mounted at public-transport stops Availability of information on parking lots on the internet Possibility to pay parking fees online Penetration rate of the most popular official transport mobile application, % Average rating of official transport applications Transport operations big data analysis and personalization of communications Availability of Wi-Fi networks or mobile internet access at underground stations Travel comfort Average bus age, years Average underground rolling stock age, years Share of buses accessible for persons with reduced mobility, % Share of underground stations accessible for persons with reduced mobility, % — Intermodality Average distance from an underground station to the 3 closest public-transport stops, meters Average time required to switch from 1 mode of public transport to another, minutes Availability of a unified public-transport navigation for passengers — Ticketing system Availability of a universal travel card to pay fares while using multiple modes of public transport Possibility to use remote top-up and/or remote ticketing Possibility to top up travel card and/or buy a ticket using a bank card Possibility to use contactless bank cards and/or Apple Pay, Samsung Pay, or Android Pay mobile applications directly at pay gates Possibility to use an electronic travel card to pay for nontransport services Possibility to use an electronic travel card available on mobile devices Possibility to pay the fare using biometric data Need for registration following travel card top-up 27Benchmarking methodology Safety and Sustainable Development Physical safety Number of public-road traffic accident fatalities per million people per year Safety rules compliance index Number of underground accident fatalities per million people per year Availability of public-transport disinfection measures Environmental safety Current diesel/petrol fuel quality standards Share of e-vehicles in total vehicle sales, % Concentration of NO2 in atmospheric air, molecules per cubic centimeter Average age of cars on the roads, years Number of commercial vehicles registered in the city per $1 billion of city GRP (based on purchasing power parity) Index of commercial transport-related environmental restrictions Availability of subsidies or incentive programs related to transition to more environmentally friendly fuel, e-vehicles, etc 30 General insights and observations Success factors for cities with sophisticated transport systems To understand what makes the leading cities stand out from the rest and what has driven them to success, we compared their scores in all transport system operation aspects with those posted by cities in the middle (positions 11 to 18) and at the end (last seven positions) of the rating table. Exhibit 8 shows the objective and subjective (survey) ratings of cities on the 14 measured aspects of transportation systems. Where the categories are furthest apart, middle and low performers have the most need to improve. To advance to a qualitatively new level, cities at the bottom of the rating table need to improve in the areas of availability of their transport infrastructure and intermodality, as well as expand electronic services, which have already become part and parcel of living in most of the examined cities (see the highlighted chart areas marked “A”). We believe improvement of those aspects to be a top-priority task for any city in need of a better transport system. Cities desiring to rise from the middle to the top of the rating table need to painstakingly improve their ratings in Efficiency and in Safety and Sustainable Development. Superiority in these aspects differentiates the leading cities from all others (chart areas marked “B”). In the eyes of city residents, the differences between transport systems forming the middle of the rating table and those bringing up the rear are less pronounced than their common dissimilarity with transport systems operating in the leading cities. To assure that its residents have a high level of satisfaction, a city must have a truly outstanding transport system; otherwise, the difference will be hardly visible. 1 Objective metrics Residents’ perceptions Leading cities (positions 1–10) Contending cities (positions 11–18) Emerging cities (positions 19–25) 4 3 2 1 14 13 12 11 10 9 8 7 6 5 1 5 10 15 20 25 B A B Public-transport efficiency Personal-transport cost and use barriers Public-transport affordability External connectivity Road networkEnvironmental safety Physical safety Inter- modality Electronic services Ticketing system Travel comfort Personal- transport efficiency Shared transport Rail transport 4 3 2 1 14 13 12 11 10 9 8 7 6 5 1 5 10 15 20 25 Public-transport affordability Personal-transport cost and use barriers External connectivity Shared transport Road network Public-transport efficiency Personal- transport efficiency Travel comfort Ticketing system Electronic services Inter- modality Physical safety Environmental safety Rail transport Exhibit 8 Transportation system ratings by city’s overall level of development Rating positions for individual aspects Exhibit 1-7, 60 не были заменены Exhibit 8 Transportation system ratings by city’s overall level of development Rating positions for individual aspects 31General insights and observations Development of transport systems over the last several years Over the last several years, all examined cities have improved their transport systems in all key aspects. This has had positive impact on popular perception of those systems. Efficiency metrics have demonstrated the most impressive growth, in part a result of the impact of the COVID-19 pandemic. Our study shows that some of the changes may persist during the post-pandemic period. Availability metrics have sustained the least change, as this aspect requires the most significant capital investments and lengthy implementation. Cities at the bottom of the rating table have been developing relatively faster, gradually catching up with the leaders. This is partially attributable to the low base effect: underperformers retain the possibility to carry out reforms that do not require massive financial outlay or time expenditure. Emerging cities have posted the largest improvement in metrics related to efficiency and ease of use of transport systems. City residents have noticed those changes. In the cities at the bottom of the rating table, the level of satisfaction with transport systems has increased more than in the other cities (0.86 points, versus 0.53 to 0.81 points). 1 Objective metrics Residents’ perceptions Leading cities (positions 1–10) Contending cities (positions 11–18) Emerging cities (positions 19–25) 4 3 2 1 14 13 12 11 10 9 8 7 6 5 1 5 10 15 20 25 B A B Public-transport efficiency Personal-transport cost and use barriers Public-transport affordability External connectivity Road networkEnvironmental safety Physical safety Inter- modality Electronic services Ticketing system Travel comfort Personal- transport efficiency Shared transport Rail transport 4 3 2 1 14 13 12 11 10 9 8 7 6 5 1 5 10 15 20 25 Public-transport affordability Personal-transport cost and use barriers External connectivity Shared transport Road network Public-transport efficiency Personal- transport efficiency Travel comfort Ticketing system Electronic services Inter- modality Physical safety Environmental safety Rail transport Exhibit 8 Transportation system ratings by city’s overall level of development Rating positions for individual aspects Exhibit 1-7, 60 не были заменены 2% 5% 25% 5% 11%Safety and sustainable development Availability Affordability 0% Convenience Efficiency 25% 3% 21% 7% 10% 6% 0% 27% 3% 6% 20% 15% 8% 8% 28% Leading cities (positions 1–10) Contending cities (positions 11–18) Emerging cities (positions 19–25) City residents’ level of satisfaction with the current situation,1 scale of −10 to +10 1 Change in current level of satisfaction of city residents, based on survey results. Availability Convenience Affordability 0.60 Efficiency Safety and sustainable development 0.85 1.15 0.44 0.99 0.40 0.66 0.21 0.82 0.54 0.91 0.70 0.18 1.15 1.38 Personal transportPublic transport 2 Leading cities (positions 1–10) Contending cities (positions 11–18) Emerging cities (positions 19–25) Average: 9 Average: 9 Average: 12 Exhibit 9 Changes in cities depending on their overall level of development Objective assessment, % Average: 0.81 Average: 0.53 Average: 0.86 Changes by groups of metrics Change in current level of satisfaction of city residents, based on survey results. Exhibit 9 Changes in cities depending on their overall level of development Changes by groups of metrics 32 General insights and observations Projects implemented by cities in various areas Since the publication of the previous version of this report in 2018, the examined cities have implemented hundreds of projects designed to enhance their transport systems. Those projects cover various areas, including public-transport infrastructure development, digitization of transport system processes, and expansion of pedestrian and cycling infrastructure. On average, top ten cities implemented more projects than the other cities covered by the study. We believe that this activity is largely responsible for their leader status. The nature of projects and the tasks they pursued differ subject to the position in the rating table (Exhibit 10). Thus, leading cities implemented more transport infrastructure development projects: their share reaches 24 percent of all projects, versus 13 percent for emerging cities, possibly due to budget constraints or the complexity of such projects. In addition, cities at the bottom of the ratings rarely implemented projects related to safety and sustainable development, which may explain their weaker performance in this area. The impact from implementation of such projects is not always comparable in terms of significance. Infrastructure projects designed to boost transport accessibility are usually rather local and, accordingly, have moderate impact on the overall Availability metric. In contrast, digitization projects may affect the entire transport system, producing a more significant observable impact on Convenience metrics. Exhibit 10 Types of projects implemented in cities, by city categories 2% 2% 3% 3% 2% 3% 2% 2% 4% 5% 3% 3% 3% 5% 4% 3% 4% 2% 2% 15% 8% 8% Environmental safety Physical safety 1% 1% 1% 32% Emerging cities Freight logistics 1% Public-transport infrastructure Transit-oriented development Road infrastructure New mobility types Digitization in transport Cycling and pedestrian infrastructure Ticketing system 1% Travel comfort 1% Leading cities 45% Contending cities 23% Availability and Efficiency Efficiency Convenience Efficiency and Convenience Safety and Sustainable Development 3 101215Average number of projects per city Share of total projects, % Exhibit 10 Types of projects implemented in cities, by city categories 35General insights and observations Correlation between objective results for city and public perceptions of city On average, public opinion in the examined cities reflects actual achievements of their transport systems: the higher the city’s average rating, the higher residents’ level of satisfaction with the current situation (Exhibit 13). In some cities, though, public perceptions strongly diverge from objective metrics. In certain cities in Asia, residents’ level of satisfaction with transport systems is higher than might be expected based on objective metrics. Conversely, in certain cities in Latin America, residents are dissatisfied with objectively sound transport systems. In addition, we have analyzed residents’ level of satisfaction with changes that have occurred over the last several years. Cities with the highest levels of satisfaction with the current situation have also expressed the most satisfaction with changes. Underperformers Leaders Residents’ satisfaction with changes High Low Re si de nt s’ s at is fa ct io n w ith th e cu rr en t s itu at io n Exhibit 13 Correlation between public perceptions and the objective situation Aggregated city position based on all groups of objective metrics China South America and Russia Developing cities with GRP per capita <$40,000 (PPP) 6 Very satisfied Satisfied Neutral Dissatisfied Exhibit 13 Correlation between public perceptions and the objective situation 36 General insights and observations City residents’ satisfaction with public and personal transport We looked at how residents’ level of satisfaction relates to metrics describing various modes of transport, including public transport and personal transport. Generally, there is a strong correlation between the scores assigned to public transport and personal transport (Exhibit 14). With several notable exceptions, if residents are satisfied with the state of public transport in the city, they are satisfied with the state of personal transport, and vice versa. Three cities fall out of this pattern: Istanbul, Moscow, and Bangkok. In those cities, the residents are satisfied with public transport but rather dissatisfied with the state of personal transport. Those perceptions are not always fully consistent with the objective situation in the relevant cities. Istanbul ranks higher in terms of personal- transport use than in terms of public- transport use. Dissatisfied Satisfied Level of satisfaction with personal transport Neutral Satisfied Dissatisfied Neutral Le ve l o f s at is fa ct io n w ith p ub lic tr an sp or t Same attitude Exhibit 14 Public and personal transport: Public perceptions 7 Exhibit 14 Public and personal transport: Public perceptions 37General insights and observations City residents’ perception of changes We looked at how city residents’ level of satisfaction with changes that have occurred over the last several years depends on their perception of the current situation in their megapolises. We also assessed changes in the objective metrics posted by cities in all groups. Assessment of the current situation in a city is closely linked to the level of satisfaction with changes: the more people are satisfied with the current situation, the better is their perception of changes that have occurred over the last several years (Exhibit 15). Residents of top-ranked cities are satisfied with both the current situation and the recent changes. Conversely, residents of emerging cities, as a rule, are less happy with the current situation and the recent changes. High Low Leading cities (positions 1–10) Contending cities (positions 11–18) Emerging cities (positions 19–25) Le ve l o f s at is fa ct io n w ith c ur re nt s itu at io n Low High Level of satisfaction with changes Exhibit 15 Correlation between subjective assessment of the current situation and assessment of changes 8 Exhibit 15 Correlation between subjective assessment of the current situation and assessment of changes 40 General insights and observations Correlation between satisfaction levels and perceived importance of metrics We looked at whether city residents’ level of satisfaction of with various metrics depends on their subjective perception of importance of such metrics for the state of the urban transport system. Metrics that respondents deemed to be the most important and with which they are most satisfied include those related to safety, anti-epidemic measures, quality and condition of public transport, and availability and quality of road infrastructure (Exhibit 18). Typical city residents identify those parameters as important and are generally satisfied with the situation in the relevant areas. Examples of metrics characterized by high perceived importance and low level of satisfaction are environmental impact produced by transport, including freight transport, and traffic congestion. In addition, these metrics display average-to-high variance of satisfaction levels, meaning that there exist major differences between cities in that respect. In some cities, relative satisfaction is at an even lower level. Other metrics are classified as below average in terms of importance but are considered most problematic: are taxi fares, car ownership costs, and freight traffic on public roads. Exhibit 18 Perception of individual metrics’ importance and satisfaction with current situation Road traffic safetyBike-sharing services Distance to nearest public-transport stop Access to external transport infrastructure Transport environmental impact Public-transport fares Public-transport network coverage Car ownership costs Biking infrastructure Availability and quality offroad infrastructure Predictability of time en routewhen traveling by personal car Pedestrian infrastructure Car-sharing services Taxi fares Average surface transport waiting time Traffic congestion Quality and state of repair of public transport Convenience of payment of public-transport fares Mobile public-transport applications Convenience of payment of parking fees and fares Ease of understanding and convenience of public-transport navigation Convenience of transfer from 1 mode of public transport to another Safety of public-transport use Anti-epidemic measures in public transport Freight traffic Environmental impact produced by freight traffic Low High Importance of metric Medium Satisfied Dissatisfied Neutral HighLow MediumSatisfaction variance by cities Exhibit 18 Perception of individual metrics’ importance and satisfaction with current situation High satisfaction variance 11 Sa tis fa ct io n w ith c ur re nt s itu at io n 41General insights and observations We also analyzed the recent changes in terms of objective metrics describing the aspects under review. Generally, over the last several years, most metrics have significantly improved (Exhibit 19). The metrics that city residents perceive as the most important have changed as follows: predictability of travel time has improved, surface transport waiting times have decreased, public-transport trips have become more affordable, and traffic safety has increased. However, for some metrics, the changes have been relatively small. This is especially true for pedestrian infrastructure and for the quality and state of repair of public transport. Cities need to pay more attention to such metrics with due regard to their significance for the residents. As for the metrics generally causing the most dissatisfaction, over the last several years there has been some improvement in, for example, availability of public transport and taxis. City authorities need to keep residents informed of all positive changes and continue their efforts to improve public perceptions in those areas. Over the last several years, there has been little improvement in terms of reducing car ownership costs and overall traffic congestion; accordingly, city authorities need to focus on those aspects. Taking into consideration the need to reduce traffic congestion, it is highly likely that additional restrictions on the use of personal motor vehicles will be introduced in the next several years. To improve public perception of those aspects, city authorities must score tangible successes and clearly articulate their policies. Exhibit 19 Changes in objective metrics relative to their perceived importance Predictability of time en route when traveling by personal car Access to external transport infrastructure Bike-sharing services Ease of understanding and convenience of public-transport navigation Public transport network coverage Traffic congestion Convenience of payment of public-transport fares Distance to nearest public-transport stop Pedestrian infrastructure Availability and quality of road infrastructure Public-transport fares Taxi fares Car ownership costs Average surface transport waiting time Quality and state of repair of public transport Convenience of payment of parking fees and fares Convenience of transfer from 1 mode of public transport to another Road traffic safety Car-sharing services Very satisfied Satisfied Neutral DissatisfiedLevel of satisfaction with current situation Exhibit 19 Changes in objective metrics relative to their perceived importance 12 Low High Importance of metric Medium Improvement Deterioration No change Ch an ge a cc or di ng to o bj ec tiv e m et ric s 45Transport system ratings 100.0 Madrid Milan Moscow Beijing 47.5 61.0 Tokyo 47.1 44.4 Exhibit 21 Leading cities with top improvements in Availability Index 9 6 3 8 4 14 Change index (maximum change = 100) X Availability rank Detailed belowRail transport Shared transportRoad network External connectivity Availability change rating, 2018–20 Cities with best scores in the Availability group have bested their rivals in terms of the number of implemented rail infrastructure projects, considerably improved the quality of their road networks, increased shared-transport availability, and taken steps to boost external connectivity. The key changes include the opening of new city railway and underground stations (rail transport category) and improvement of road infrastructure (road network). Shared-transport scores were determined by the rate of growth of the number of rental bicycles and cars provided by car-sharing services, while external-connectivity scores depended on the number of destinations served by city airports. The leading cities with the highest change scores are Beijing, Moscow, and Madrid (Exhibit 21). Transformations carried out over the last several years have propelled Moscow into the top ten of the Availability index. The other cities have reaffirmed their leading positions. Change leaders have demonstrated the most impressive growth in the shared- transport category. Exhibit 21 Leading cities with top improvements in Availability Index Change index (maximum change = 100) 46 Transport system ratings Beijing Beijing has become the absolute change leader in the Availability group. This success is based on improvements in the rail transport and shared- transport subgroups (where the city’s improvement ranks fourth and first, respectively). Beijing remains one of the cities investing massively in rail infrastructure. As a result, rail transport availability for city residents has increased by four percentage points versus 2018. Three new underground lines have been opened over the last several years. The city has completed construction of a high-speed railroad linking it to Hong Kong. Work was carried out in several stages, ending in 2018. The overall length of the line is 2,439 kilometers, a distance the train covers in less than nine hours. Trains run daily. The project was part of a long-term plan to join 11 cities into a large economic cluster, using the advantages offered by efficient logistics. Beijing remains a global leader in shared transport. Over the last several years, the number of cars provided by the city’s car-sharing companies has increased dramatically. In addition, city residents can still use a huge fleet of bicycles offered for rent. The number of bicycles available for rent has stabilized at 0.9 million; no other city in the world can boast such a large fleet. Moscow Moscow has secured top ranking in improvement in several subgroups, including rail transport (where it ranks fifth), shared transport (sixth), and external connectivity (first). The city has the fourth-best change score in the Availability group. Over the last several years, Russia has been heavily investing in development of its railway infrastructure, including the underground, to make that mode of transport more accessible to city residents where they live and work. New surface lines linking the city with Moscow Region destinations have been built within the framework of the Moscow Central Diameters (MCD) project. The overall length of the first two diameters (60 stations) is 132 kilometers. In addition, the city has opened the first sections of the Large Circle Line, a new 70-kilometer underground line, one of the world’s largest underground construction projects. The line will help reduce passenger flows currently served by Moscow Metro. The new line will have a total of 31 stations, of which ten have already been opened. In addition, the new Nekrasovskaya Metro Line has been launched. As a result, another 700,000 people have gained access to Moscow Metro, while passenger traffic through other lines is going to decrease. Shared transport is posting robust growth rates in Moscow. The total number of bicycles that can be rented from the city’s Velobike service has increased from 1,000 to 6,500. Electric-scooter rental services have emerged, offering a total of 5,000 vehicles. Car-sharing fleets have posted a considerable increase from 6,500 cars to 30,000 cars (prior to the COVID-19 pandemic). In the case of car sharing, the growth can be attributed to successful development of private operators such as Yandex.Drive and Delimobil. Since 2018, Moscow has implemented several large-scale projects designed to upgrade its airport infrastructure, bringing the number of destinations with daily flights from 295 to 345. The city has taken steps to improve service quality and passenger safety. For example, Domodedovo has become Russia’s first airport to deploy baggage storage robotization systems and automated turnstiles; the air haven is currently testing a face recognition system. High-speed train connecting Beijing and Hong Kong 47Transport system ratings Madrid Madrid has shown improvements in the shared-transport and external- connectivity subgroups (putting it in third and 15th places, respectively), bringing the city’s change score up to eighth place in the Availability ranking. Madrid channels considerable investments into expanding the use of personal-mobility devices. About 3,000 bicycles were purchased and 50 new bike rentals opened in 2020 alone. City authorities hail projects designed to support shared-transport development. For example, recently city residents were granted access to 4,800 electric bikes offered for rent by private operators. Bike rental station in Madrid MCD train at a station in Moscow 50 Transport system ratings Shanghai Shanghai has shown strong improvement in public-transport affordability. The city maintains low bus and underground fares. That and growing resident incomes enable Shanghai to improve its position in the overall rating. Taxi fares remain competitive because of continued rivalry between private companies. For example, Didi, one of the largest taxi aggregators in China, has launched Huaxiaozhu, a new service targeting young customers and offering relatively low fares. The city is actively testing self-driving taxi technologies. Didi has been offering those services in certain districts since 2020. By expanding self- driving transport, Shanghai can make transport even more affordable for its residents. Over the last several years, the city has avoided the need to impose new major restrictions on the use of personal transport. The existing restrictions, however, are already quite onerous. If a car is registered outside of Shanghai, it is barred from entering certain city districts. There are certain limitations on acquisition of motor vehicles by nonpermanent residents. Mexico City Mexico City’s metrics have improved because of its balanced development. The city occupies top positions in change ratings for both Affordability subgroups. As for public-transport pricing policy, the city has not increased fares in local currency for several years, making transport services more affordable to residents. In 2013, underground fares were raised from three to five pesos, bringing the number of trips down. The fare has not changed since then. In addition, taxi services have become much more affordable. As for personal transport, the city is expanding the use of paid parking lots, which makes private car ownership more expensive. Mexico City residents have noted that transport Affordability has improved. The level of satisfaction with public- transport affordability has increased by 6 percent. Incidentally, despite the ongoing limitations on the use of personal transport, the level of satisfaction with its Affordability has risen 10 percent The average taxi fare in Shanghai currently stands at about $0.50 per kilometer 51Transport system ratings Buenos Aires Buenos Aires is the change leader in personal-transport cost and use barriers. The city also holds a top position in the public-transport affordability subgroup. The capital of Argentina continues to develop a system enabling proper measurement of motor vehicles’ environmental impact. The city has created a system of paid parking lots, and a special fee has been charged since 2018 for entry to downtown areas with excessive traffic. Drivers entering those areas between 11:00 and 16:00 on workdays must pay an annual fee of $77. In public transport, the availability of Buenos Aires taxi services is growing, with the city going up four notches in the rating table for that metric. This can be attributed to successful development of aggregator services, which recently emerged in the city. For example, Uber came to Buenos Aires only in 2016. The total number of taxis in Buenos Aires stands at about 40,000, with Uber boasting the most drivers and customers The fee for using a parking lot in Mexico City for two hours is $0.90 52 Transport system ratings Ratings based on Efficiency metrics The Efficiency Index shows how fast and predictably one can move around the city. In particular, the index comprises metrics for traffic congestion and helps assess its impact on travel times. The highest index values have been recorded by Moscow, Shenzhen, and Singapore (Exhibit 24). Moscow is holding the first place: it has high public-transport efficiency (ranking first in the subcategory), while in terms of personal-transport efficiency, it lags behind the other examined cities. Moscow is one of the top three cities in terms of underground waiting time, public-transport travel speed during the rush hour (about 21 kilometers per hour), and share of dedicated lanes (6.5 percent, versus 2.3 percent on average for all examined cities). Shenzhen is only slightly behind Moscow. It has wound up at the top of the rating because it has the highest share of dedicated bus lanes in total road length and the highest predictability of travel time during the rush hour (putting it in second place in the personal-transport efficiency subcategory). Third-ranked Singapore also scores high in public-transport efficiency (making it number two in the subcategory), while also demonstrating leading results in personal-transport travel speed during rush hour (fifth) and deviation of travel time during rush hour (seventh). An “ideal” city with the most efficient transport system would have the following characteristics: an extensive network of dedicated public-transport lanes (as in Shenzhen and Moscow); a possibility to predictably reach the point of destination, especially during the rush hour (as in Beijing); minimal underground waiting time (as in Moscow); and high travel speed during rush hour (as in Chicago). Efficiency Public-transport efficiency Personal-transport efficiency Exhibit 24 1 Components may not sum to total because of rounding. Leading cities for Efficiency Index1 (city rank on metric) Beijing 20% (2) 33% (6) Moscow 4% (24) 12% (11) 55% (1) Johannesburg 20% (3) 38% (3)Shenzhen 40% (2)Singapore 15% (6)35% (4) 9% (16)35% (5)São Paulo 22% (1) 40.8% 52.8% 18% (20)Chicago 17% (4)23% (13)Los Angeles 14% (9) 40.5% 25% (11) 50.8% Madrid 9% (19) 39.1% 58.8% 29% (8)Hong Kong 57.9% 52.8% 44.1% 37.3% 1 Components may not sum to 100% f r i 17 Exhibit 24 Leading cities for Efficiency Index1 (city rank on metric) Index metric (maximum value) Public-transport efficiency (70%) Personal-transport efficiency (30%) Пожалуйста, посмотрите легенду и подзаголовок City rank on metricIndex1 XX% (YY) 55Transport system ratings Beijing Beijing has become a change leader in this group by improving its position in public-transport efficiency. China’s capital takes the second position in this subgroup. Both public- and personal-transport travel speed in the city have increased, while waiting time for surface transport has decreased. This can be attributed, among other things, to creation of dedicated bus lanes. City authorities note that travel speed is growing because of the ongoing development of rail infrastructure and personal mobility devices. In addition, the city continues to expand its motor vehicle infrastructure, which is likely to increase motor vehicle connectivity and travel speed. Construction of the seventh ring road with a total length of about 1,000 kilometers was completed in 2018. It joins the Beijing with adjacent cities, with 38 kilometers of the road traversing Beijing. City residents have appreciated improvement of metrics in this group: their satisfaction has risen 17 percentage points. The length of bus lanes increased by 650 kilometers in Beijing 56 Transport system ratings Ratings based on Convenience metrics The Convenience Index assesses the convenience of transfers from one mode of transport to another, level of ticketing-system development, electronic-services penetration rates, internet access during the trip, and share of buses and underground stations accessible to passengers in wheelchairs. The leading cities in the Convenience category are Toronto, Hong Kong, and Singapore (Exhibit 26). Toronto comes first with its superior travel comfort metrics: city underground rolling stock is new, and the share of buses accessible to passengers in wheelchairs reaches 100 percent. In second-ranked Hong Kong, the city bus fleet is rather young, the underground uses new rolling stock, and the share of underground stations accessible to passengers in wheelchairs is very high. City authorities have launched a project designed to boost underground and bus mobility: elevators and ramps have been installed at 90 of 93 underground stations. In addition, the city has purchased new low-floor buses. Hong Kong boasts the shortest average distance from underground station to the nearest surface public-transport stop. Singapore’s presence in the top three is attributable to the fact that most of its metrics are above average: the city is developing various aspects of its transport systems at an even pace. Thus, over the last three years, it has increased the share of underground stations accessible to disabled passengers, and public transport is offering improved internet access, with buses and stops being equipped with Wi-Fi modules. An “ideal” city for Convenience would be one that deploys new technologies, offers convenient city-resident interaction mechanisms based on the use of mobile applications (to verify and pay fines and penalties, plan routes, etc.), provides passengers with continuous access to high-speed internet, minimizes fare payment efforts (e.g., through implementation of biometric technologies, as in Beijing and Shanghai), and regularly upgrades its transport fleets (as in Moscow, Istanbul, and Toronto). Convenience Electronic services Travel comfort Intermodality Ticketing system Exhibit 26 1 Components may not sum to total because of rounding. Ten leading cities for Convenience Index1 (city rank on metric) Beijing 16% (19) 19% (6) 17% (17) Milan Hong Kong 24% (13) 13% (4) 17% (9) 18% (1) Berlin 22% (18) 14% (22)31% (3) 69.4% 18% (4) 18% (6) Toronto 20% (2) 12% (8) 25% (9) 24% (15) 17% (12) 29% (4) 10% (10)29% (6) 18% (8) 19% (1) 18% (4) 17% (16) Singapore 16% (3)16% (13) 10% (12)15% (22)15% (19)32% (1)Istanbul 16% (2) 9% (16) Chicago 18% (8) 4% (24)17% (11)32% (2) 20% (3)Moscow 73.0% 13% (7)23% (17)London 16% (13) 80.6% 81.4% 76.0% 72.4% 72.1% 71.8% 71.4% 69.8% 1 Components may not sum to total because of rounding. 19 Exhibit 26 Ten leading cities for Convenience Index1 (city rank on metric) Electronic services (21%) Intermodality (19%) Travel comfort (41%) Ticketing system (19%) Index metric (maximum value) City rank on metricIndex1 XX% (YY) Пожалуйста, посмотрите легенду и подзаголовок 57Transport system ratings Change ratings for Convenience, 2018–20 Change leadership in the Convenience group comes from offering superior travel comfort to public-transport passengers, implementing of new ticketing technologies, developing electronic services, and improving intermodality metrics. Improvement of metrics in the travel comfort subgroup is explained by completion of projects designed to upgrade bus and underground car fleets and higher mobility of passengers using wheelchairs. Improvements in the ticketing system subgroup can be attributed to implementation of new payment methods and expansion of transport card functionality. To expand electronic services, cities launched applications that can be used to schedule routes for multiple modes of transport, track bus arrival times, and top up transport cards online. Intermodality metrics increased as a result of opening conveniently placed public-transport stops, enabling passengers to spend less time switching to another mode of transport, and upgrading city navigation systems. The leading cities with the highest change scores are Istanbul, Berlin, and Hong Kong (Exhibit 27). Having completed their respective transformation programs, Berlin and Istanbul made it to the top ten of the group, while Hong Kong rose to second place in the ranking table. Singapore went up by five notches and is now ranked third, while Toronto has retained its leadership. In all leading cities except Istanbul, travel comfort metrics have sustained massive changes. Exhibit 27 Leading cities with top improvements in Convenience Index Change index (maximum change = 100) 20 5 10 2 1 3 81.0 Istanbul Hong Kong 79.1 Berlin Toronto Singapore 100.0 66.2 51.4 Exhibit 27 Leading cities with top improvements in Convenience Index Change index (maximum change = 100) X Convenience rank Detailed belowTravel comfort Electronic servicesTicketing system Intermodality 60 Transport system ratings Ratings based on Safety and Sustainable Development metrics The Safety and Sustainable Development Index describes the safety level of city travel and the current environmental situation. The index comprises two groups of indicators: one related to physical safety and the other for environmental safety. The three top-ranked cities in this category are Singapore, Sydney, and Hong Kong (Exhibit 28). The cities topping the rankings have very similar performance levels for environmental protection; the key differences between them are observed in the domain of physical safety. Singapore’s leadership can be attributed to the high level of physical safety related primarily to superior road safety rules compliance record and steps taken to disinfect public transport during the COVID-19 pandemic. Sydney ranks second, slightly behind Singapore, with strong environmental- safety performance. In particular, Sydney is one of the three cities with the best levels of NO2 concentration in the atmospheric air (about 19 milligrams per cubic meter) and number of trucks relative to GRP (about 330 units per million US dollars of GRP). Third-ranked Hong Kong closely follows. The city is slightly behind Singapore with regard to physical safety. Hong Kong has demonstrated second-best environmental-safety performance as Chinese authorities have imposed more stringent car- exhaust requirements (the current standard is Euro 6). The city also sells a high amount of e-vehicles (14 percent of total vehicle sales, putting it in fourth place ). An “ideal” city would be like Singapore in terms of physical safety, like Hong Kong in terms of environmental standards for vehicles, and like Sydney in terms of air pollution and number of trucks. Safety and Sustainable Development Physical safety Environmental safety Exhibit 28 1 Components may not sum to total because of rounding. Ten leading cities for Safety and Sustainable Development Index1 (city rank on metric) 1 Components may not sum to total because of rounding. 45% (4) 41% (7) 27% (3)Singapore 49% (1) 68.9% 45% (3)Sydney 30% (2)Hong Kong 27% (4)42% (5)Shanghai Beijing 26% (6) 18% (20) London 67.3% 25% (10) 30% (1) Tokyo 61.3% Berlin 41% (6) 26% (5)38% (12)Shenzhen 75.9% 26% (8)38% (9) 26% (7)35% (13)Paris 75.5% 74.5% 67.1% 66.1% 64.6% 64.5% 49% (2) 21 Exhibit 28 Ten leading cities for Safety and Sustainable Development Index1 (city rank on metric) Index metric (maximum value) Physical safety (58%) Environmental safety (42%) City rank on metricIndex1 XX% (YY) Пожалуйста, посмотрите легенду и подзаголовок 61Transport system ratings Change ratings for Safety and Sustainable Development, 2018–20 Change leaders in the Safety and Sustainable Development group have improved city travel safety and environmental situation in their respective cities. In the physical-safety subgroup, changes were related to the reduced number of road and underground fatalities, as well as active efforts designed to ensure compliance with safety requirements. In the environmental-safety subgroup, improvements were driven by measures designed to reduce environmental pollution, imposition of more stringent restrictions on the use of petrol and diesel engines, and growth in the share of electric vehicles in total vehicle sales. Change leaders in this rating are Shanghai, Berlin, and Beijing (Exhibit 29). Transformations have enabled Shanghai to move up four levels in the rankings to take fourth place. Shanghai has gone up three levels to the sixth rank. All the leading cities except Tokyo have improved their rating positions primarily due to their better environmental-safety performance. Exhibit 29 Leading cities with top improvements in Safety and Sustainable Development Index Change index (maximum change = 100) 4 9 7 6 10 84.4 100.0 Paris Beijing Shanghai Berlin Tokyo 85.7 83.5 71.9 22 Exhibit 29 Leading cities with top improvements in Safety and Sustainable Development Index X Safety and Sustainable Development rank Detailed belowPhysical safety Environmental safety Change index (maximum change in the group = 100) 62 Transport system ratings Shanghai Shanghai has become a change leader by increasing the level of environmental safety. The city has earned the best improvement score in this subgroup. This can be attributed in particular to the growing number of more environmentally friendly motor vehicles. Since 2021, only motor vehicles that comply with the China 6 environmental- safety standard (approximately consistent with the Euro 6 standard) will be permitted for sale in Shanghai and other Chinese cities. The share of vehicles with superior environmental characteristics in total sales has been quite substantial for some time already. In addition, Chinese cities are leaders in the development of electric vehicles (EVs). Shanghai has the highest share of electric car sales. Another cause of improvement is the general initiatives designed to increase the cost of ownership of motor vehicles and reduce their total number (presence of toll roads in the city, restrictions by car plate or place of car registration, preventive tax or license on car acquisition). Those measures have contributed to the reduction of NO2 concentration in atmospheric air by 4 percent over the last several years. This has largely become possible because of a sizable decrease of emissions by motor vehicles. Residents’ overall level of satisfaction with metrics in this subgroup has increased by 15 percentage points. Berlin Berlin has become a change leader because of improvements in parameters connected with the level of environmental safety. The biggest changes in the city are associated with the development of electric transport. Over the past three years, Berlin has significantly increased the share of electric cars in total sales (from 1.6 percent to 13 percent). The support of the authorities contributes to the growth of sales of electric vehicles. Subsidies are allocated to cover part of the cost EVs hold a 19 percent share of total vehicle sales in Shanghai 65Transport system ratings Paris In the first half of 2020, the authorities of Paris announced a project called “The 15-Minute City,” which involves reducing the number of private cars in the city, turning the streets into pedestrian streets, and creating “children’s streets” near schools. On some streets, this mode is valid at certain hours (children’s streets at the beginning and end of the school day); on others, constantly. The journey time on foot or by bike to the nearest most important infrastructure facilities will be no more than 15 minutes. Green spaces and playgrounds will substitute for parking spots. By 2024, the city plans to reduce the number of street parking spaces by 72 percent or 60,000 (out of 83,500 parking spaces). The remaining seats will be reserved for residents, employees of organizations, and the disabled (the number of seats for the disabled is not reduced). In addition, Paris is already implementing a project that allows only pedestrians, public transport, and taxis to move along some city streets. As of this writing, this project involves Rue de Rivoli, the Porte d’Orléans, Boulevard Saint-Michel, Rue Saint-Jacques, and the Etoile Tunnel. Rue de Rivoli is one of the central and the most congested streets in Paris. On May 11, 2020, the authorities completely restricted the movement of personal vehicles on it, allowing only walking, cycling, and movement on public transport and taxis. (Residents are allowed to drive their own car.) At the entrance to this area are terminals that filter the traffic flow to prevent the entry of unauthorized vehicles. In 2020, 50 kilometers of roads in Paris were transformed into pedestrian and bicycle roads on a permanent basis 66 Transport system ratings City ranking based on public- transport use Two separate sub-rankings were made for public and private transport, based on selection of the parameters relevant only for the respective modes. The assessment of public transport was based on the following groups of metrics: rail transport availability, public- transport affordability, public-transport efficiency, most of the Convenience metrics, and physical safety in public transport. The three top-ranked cities are Singapore, Moscow, and Beijing (Exhibit 30). First-place Singapore has above- average values in some key metrics. In particular, it rates high in terms of Efficiency (third-best surface public- transport waiting time), Affordability (second-best ratio of cost of one- kilometer taxi ride to average monthly income), and Safety (third-best number of public-transport fatalities). Moscow ranks second, largely because of the high efficiency of its public transport: a large share of public- transport dedicated lanes and high travel speed during the rush hour. The city’s bus fleet is rather young (with average bus age of five years), and both surface and underground transport have high rates of internet penetration. Beijing, ranked third, has the highest levels of public-transport safety. For example, it is one of the four cities with the most efficient disinfection measures taken during the COVID-19 pandemic. Public transport in the Chinese capital also is very efficient, with the share of dedicated lanes reaching 9.4 percent, versus 2.3 percent on average for the other examined cities. Metrics for travel comfort metrics also are quite high: the city has the youngest underground rolling stock among all examined cities (average age, five years). In addition, the ticketing system is very convenient. For example, in Beijing it is possible to pay the fare using biometric technologies, and the Beijing travel card can be used to pay fares in other cities. Exhibit 30 1 Components may not sum to total because of rounding. Ten leading cities for public-transport use Index1 (city rank on metric) Singapore 11% (22) 11% (3) 16% (1) 20% (2)14% (13) 17% (1) 16% (3) 7% (9) 17% (8) 12% (2) 15% (14)16% (5) 15% (7) 13% (13) Beijing 16% (10) 15% (7) Moscow Tokyo 20% (2) 16% (13) 15% (11) 16% (4)10% (4)14% (9) 17% (8)17% (1)8% (8)15% (8)16% (9)Hong Kong 17% (8)13% (21) 12% (16) 5% (22) 17% (8) 17% (2)18% (6)Seoul 16% (4)11% (17) 20% (1) Shenzhen 19% (3) 15% (6)6% (15)9% (22) 14% (20) 15% (10) 79% 6% (14)18% (5) 72% New York 14% (16) 73% 7% (10)Paris 15% (8)15% (6) 75% Shanghai 75% 68% 67% 67% 66% 65%12% (14) 1 Components may not sum to total because of rounding 23 Exhibit 30 Ten leading cities for public-transport use Index1 (city rank on metric) Index metric (maximum value) Rail infrastructure (20%) Public-transport affordability (20%) Public-transport efficiency (20%) Public-transport convenience (20%) Public-transport safety (20%) City rank on metricIndex1 XX% (YY) Пожалуйста, посмотрите легенду и подзаголовок 11% (8) 15% (1) 20% (2) 50% 7% (13) 18% (5) 10% (12) 19% (3) 16% (2) 15% (3) Singapore 13% (6) 11% (4) New York Los Angeles 48% 8% (17)12% (5)Chicago 17% (1)10% (9)17% (8)9% (13) 18% (6) Madrid 42%London Johannesburg 11% (8)10% (5)Sydney 1% (25)13% (3) 53% 10% (9) 17% (7) 20% (1) 15% (3)6% (15)9% (12) 8% (11) Berlin 10% (8)19% (4) 14% (4) 8% (17) 16% (1) Toronto 9% (15)11% (7) 7% (24) 4% (22)13% (15)12% (6) 56% 53% 50% 47% 46% 44% 1 Components may not sum to total because of rounding 24 Exhibit 31 Ten leading cities for personal-transport use Index1 (city rank on metric) Index metric (maximum value) Road network (25%) Personal-transport cost and use barriers (25%) Personal-transport efficiency (15%) Safety (25%) City rank on metricIndex1 XX% (YY) Пожалуйста, посмотрите легенду и подзаголовок 67Transport system ratings City ranking based on personal-transport use The index of personal-transport use comprises the following groups of metrics: road infrastructure availability and quality, private-transport affordability, private-transport efficiency, online services for private-vehicle users, and road safety. In this analysis, unlike for the rest of the report, we scored private-transport affordability from the user’s point of view: “the cheaper the better” reflects the preferences of car users. The top-ranked cities for personal transport are Los Angeles, Chicago, and Madrid (Exhibit 31). Los Angeles comes out on top because it enjoys high-quality road infrastructure (89 of 100 points) and is one of the leaders in terms of cost of using personal transport (primarily due to low parking fees, averaging about $4 per two hours). Los Angeles is characterized by high travel speed during the rush hour: more than 50 kilometers per hour). This is largely because the agglomeration has an extensive network of high-speed highways. Chicago ranks second. The city has reached this position due to steadily high scores on most metrics under review. Its main difference from the other cities lies in personal-transport efficiency. Chicago is the leader in this group of metrics, as it has one of the highest motor car travel speeds during the rush hour: more than 40 kilometers per hour. Madrid has taken the third position, largely because of the city’s leadership in road safety. Madrid has one of the lowest road fatality levels, which is consistent with very sophisticated legislation designed to maintain road safety. Madrid authorities are actively working on reducing noxious environmental emissions: they have restricted entry in downtown areas during certain times of the day for some categories of vehicles. Exhibit 31 1 Components may not sum to total because of rounding. Ten leading cities for personal-transport use Index1 (city rank on metric) 11% (8) 15% (1) 20% (2) 50% 7% (13) 18% (5) 10% (12) 19% (3) 16% (2) 15% (3) Singapore 13% (6) 11% (4) New York Los Angeles 48% 8% (17)12% (5)Chicago 17% (1)10% (9)17% (8)9% (13) 18% (6) Madrid 42%London Johannesburg 11% (8)10% (5)Sydney 1% (25)13% (3) 53% 10% (9) 17% (7) 20% (1) 15% (3)6% (15)9% (12) 8% (11) Berlin 10% (8)19% (4) 14% (4) 8% (17) 16% (1) Toronto 9% (15)11% (7) 7% (24) 4% (22)13% (15)12% (6) 56% 53% 50% 47% 46% 44% 1 Components may not sum to total because of rounding 24 Exhibit 31 Ten leading cities for personal-transport use Index1 (city rank on metric) Index metric (maximum value) Road network (25%) Personal-transport cost and use barriers (25%) Personal-transport efficiency (15%) Safety (25%) City rank on metricIndex1 XX% (YY) Пожалуйста, посмотрите легенду и подзаголовок 70 Analysis of specific aspects of transport systems Rail transport To measure rail transport availability, we assessed pedestrian coverage of underground and commuter train networks. The index of rail transport availability is based on the share of population residing within 30 minutes’ walking distance from the nearest underground or commuter train station and the share of workplaces located at the same distance. When measuring both metrics, we took into consideration the existence of actual pedestrian routes that can be used to reach the relevant stations. Among the examined cities, the three top-ranked cities for rail transport availability leaders are Tokyo, Madrid, and Paris (Exhibit 32). In those cities, rail transport pedestrian availability areas cover more than 80 percent of total population and more than 94 percent of total workplaces. In most examined cities, residents are satisfied with rail transport availability, and satisfaction levels are generally consistent with objective metrics. However, in some leading cities, including Tokyo, Paris, and Buenos Aires, the level of satisfaction with the transport system proved to be much lower than it should be based on objective metrics. We also see that, with the exception of Moscow, Hong Kong, and Seoul, the level of satisfaction with changes in the cities with the best objective metrics is somewhat lower than in the midranked cities (such as Singapore, Sydney, and Shanghai). This assumption is supported by data on large-scale projects in those cities. For example, Sydney’s first underground line (13 stations) was opened in 2019. Its launch became a landmark event: implementation of the construction project took three years. The line is part of a bigger project envisaging construction of an underground network capable of carrying up to 40,000 passengers per hour. It is expected that all 31 stations will have been opened in 2024. Exhibit 32 Perception and reality: Rail transport availability Availability Rail transport Road network Shared transport External connectivity In this geospatial analysis of Singapore, rail transport covers 83 percent of city’s total population 25 1520 10 5 1 Hong Kong Buenos Aires Paris London Madrid Moscow Seoul New YorkBerlin Tokyo 25 Exhibit 32 Perception and reality: Rail transport availability Neutral Dissatisfied Underperformers Leaders Satisfied Score based on objective metrics Satisfaction with changes Very satisfiedSatisfied LLee vvee ll oo ff cc uurr rree nntt ss aatt iiss ffaa cctt iioo nn ooff rree ssii ddee nntt ss 1525 20 15 10 Paris Singapore London Madrid Chicago New York MilanBerlin Sydney Los Angeles 26 Exhibit 33 Perception and reality: Road network Asia Latin America and Africa Neutral Dissatisfied Leaders Satisfied Score based on objective metrics Satisfaction with changes LLee vvee ll oo ff cc uurr rree nntt ss aatt iiss ffaa cctt iioo nn ooff rree ssii ddee nntt ss Neutral Very satisfiedSatisfied Underperformers 71Analysis of specific aspects of transport systems Road network The road network assessment was based on metrics critical for various types of users, including motor vehicles, surface public transport, cyclists, and pedestrians. The road network index comprises five metrics: road network area per motor vehicle, motor vehicle infrastructure cohesion index, pedestrian infrastructure cohesion index, road network quality index, and share of bicycle lanes in total road network length. The top-ranked cities for road network development are Milan, Paris, and London (Exhibit 33). In those cities, the road network is best adapted to the needs of various road traffic participants: the motor vehicle infrastructure cohesion index does not exceed 1.48, and the pedestrian infrastructure cohesion index does not exceed 1.45. Residents of the cities in the upper half of the ranking table are satisfied with the current situation and take a positive view of relevant changes. Residents of the remaining cities have a neutral or negative attitude toward the current state of their road networks. Generally, objective and subjective assessments are correlated, with some notable exceptions. For example, city residents in Asia have a propensity to assess the state of the road network more positively, while people in Latin America and Africa take a more skeptical view. Exhibit 33 Perception and reality: Road network Availability Rail transport Road network Shared transport External connectivity Tokyo has the largest road network area per motor vehicle (68 square meters). The picture shows the downtown area between the Tokyo Imperial Palace and the Sumida River 1525 20 15 10 Paris Singapore London Madrid Chicago New York MilanBerlin Sydney Los Angeles 26 Exhibit 33 Perception and reality: Road network Asia Latin America and Africa Neutral Dissatisfied Leaders Satisfied Score based on objective metrics Satisfaction with changes LLee vvee ll oo ff cc uurr rree nntt ss aatt iiss ffaa cctt iioo nn ooff rree ssii ddee nntt ss Neutral Very satisfiedSatisfied Underperformers 72 Analysis of specific aspects of transport systems Shared transport Over the last several years, the role of shared transport has increased significantly, as has its availability to city residents. The shared-transport index comprises two metrics: the number of cars used by car-sharing services and the number of bicycles used by public bicycle rentals. Both indicators were scaled per million people to enable a comparison of cities of different sizes. Limits were set on the maximum useful number of motor vehicles and bicycles to make sure that they support demand without creating excessive load on the city transport system. Shared-transport leaders among the examined cities are Beijing, Berlin, and Shanghai (Exhibit 34). In those cities, there are more than 500 rented cars and more than 3,000 rented bicycles per million people. Objective metrics observed in the cities are generally consistent with public perceptions. However, residents of Berlin display a lower level of satisfaction with the current situation than might be expected based on objective data. In almost every city, residents note that large changes have occurred over the last several years. Residents of the cities in the top half of the ranking table based on objective metrics in this subgroup are also satisfied with the current situation. The only exception is Moscow, where residents say there are not enough bicycle rentals in the city, although people note improvements in that area. Still, Moscow has considerably expanded its bicycle rental network: about 2,900 new bicycles have been purchased over the last three years, and 119 new bicycle rental stations have emerged in 2020 alone. Exhibit 34 Perception and reality: Shared transport Availability Rail transport Road network Shared transport External connectivity More than 5,000 bicycles per million people are available for rent in Beijing 25 20 15 10 5 1 Shenzhen Singapore Madrid Moscow Milan Tokyo Berlin Beijing Toronto Shanghai 27 Exhibit 34 Perception and reality: Shared transport Neutral Dissatisfied Leaders Satisfied Score based on objective metrics Satisfaction with changes LLee vvee ll oo ff cc uurr rree nntt ss aatt iiss ffaa cctt iioo nn ooff rree ssii ddee nntt ss Underperformers Very satisfiedSatisfied 2025 15 10 5 1 London Moscow Paris Madrid Chicago Milan New York Beijing Shanghai Istanbul 28 Exhibit 35 Perception and reality: External connectivity Neutral Dissatisfied Underperformers Leaders Satisfied Score based on objective metrics Satisfaction with changes LLee vvee ll oo ff cc uurr rree nntt ss aatt iiss ffaa cctt iioo nn ooff rree ssii ddee nntt ss Very satisfiedSatisfied 75Analysis of specific aspects of transport systems Personal-transport cost and use barriers This index of personal-transport cost and use represents the costs associated with the use of personal transport and the restrictions imposed on its owners in the examined cities. It takes into account the following factors: existence of fees charged for entering the city or specific districts, ratio of average two- hour parking fee to average individual income level, and total number of restrictions related to owning a motor vehicle (requirement to have a parking space, additional car purchase tax, etc.). The highest ratings went to cities where the real costs associated with personal- transport ownership are determined from a public perspective and financial barriers on the use of such transport are imposed subject to such costs. The leaders in personal-transport cost and use barriers among the examined cities are Tokyo, São Paulo, and Beijing (Exhibit 37). In São Paulo, the two-hour parking fee relative to average individual income level is the highest among examined cities. Tokyo and Beijing have imposed onerous restrictions on personal-transport owners. The capital of Japan is the only city covered by our study where anyone wishing to purchase a motor car must have an individual parking space. Beijing regularly holds license plate acquisition lotteries. However, at the end of 2020, the Chinese government called on city administrations to relax those restrictions in order to support the local automotive industry and help it overcome the aftermath of the COVID-19 pandemic. Beijing authorities responded by adding to the lottery pool another 20,000 license plates for hybrid vehicles and EVs. Objective metrics of the examined cities are inversely correlated to subjective perception of the current situation by city residents. This is logical, as they are forced to pay for the right to use their personal transport over and above its purchase price. In US cities, the level of satisfaction is above average, even though restrictions are among the least stringent among the examined cities. Chinese cities stand out, as the level of satisfaction with the current situations and its recent changes is rather high despite the existence of considerable restrictions. Exhibit 37 Perception and reality: Personal-transport cost and use barriers Affordability Public-transport affordability Personal-transport cost and use barriers London has the highest parking fees among the examined cities: about $12 for 2 hours 2025 15 10 5 1 Paris Singapore Beijing Tokyo Mexico City Shanghai Sao PauloBuenos Aires Bangkok Shenzhen 30 Exhibit 37 Perception and reality: Personal-transport cost and use barriers Neutral Attitude Dissatisfied Underperformers Leaders Satisfied Score based on objective metrics Satisfaction with changes LLee vvee ll oo ff cc uurr rree nntt ss aatt iiss ffaa cctt iioo nn ooff rree ssii ddee nntt ss DissatisfiedVery dissatisfied Very satisfiedSatisfiedNeutral 76 Analysis of specific aspects of transport systems Public-transport efficiency The public-transport efficiency index shows the speed and predictability of movement across the city. It comprises the following metrics: average public- transport travel speed during the morning rush hour, average surface transport waiting time, underground- train waiting time index, and share of dedicated public-transport lanes in total road network length. Public-transport efficiency leaders among the examined cities are Moscow, Singapore, and Shenzhen (Exhibit 38). Moscow has the third-best average public-transport travel speed during the rush hour and, together with Shenzhen, is part of the top three in terms of the share of dedicated bus lanes. About 100 kilometers of dedicated lanes have been put in operation in the Russian capital over the last three years, and this is one of the reasons for the high average public-transport travel speed noted in our study. Singapore has demonstrated excellent surface transport waiting times, ranking third for this metric. City residents’ level of satisfaction of and the objective results posted by the cities in this subgroup are generally positively correlated. In most leading cities, residents are satisfied or very satisfied with improvements in public-transport efficiency. Moscow is a notable exception from that rule: despite its star-quality objective performance, Muscovites’ level of satisfaction with the current situation is rather low. Johannesburg and São Paulo are similar in that their residents have failed to notice significant changes and see the current situation in a more negative light than residents of most other cities. Exhibit 38 Perception and reality: Public-transport efficiency The length of dedicated bus lanes in Shenzhen is 1,057 kilometers Efficiency Public-transport efficiency Personal-transport efficiency 25 20 15 5 110 Sao Paulo SingaporeLondon Paris Hong Kong Moscow Seoul Beijing Johannesburg Shenzhen Exhibit 38 Perception and reality: Public-transport efficiency 31 Neutral Dissatisfied Underperformers Leaders Satisfied Score based on objective metrics LLee vvee ll oo ff cc uurr rree nntt ss aatt iiss ffaa cctt iioo nn ooff rree ssii ddee nntt ss Satisfaction with changes Neutral Satisfied Very satisfied 25 1020 515 1 Madrid Chicago Los Angeles Sydney Beijing Toronto Shanghai Buenos Aires Johannesburg Shenzhen Exhibit 39 Perception and reality: Personal-transport efficiency 32 Neutral Dissatisfied Underperformers Leaders Satisfied Score based on objective metrics LLee vvee ll oo ff cc uurr rree nntt ss aatt iiss ffaa cctt iioo nn ooff rree ssii ddee nntt ss Satisfaction with changes Neutral SatisfiedDissatisfied Very satisfied 77Analysis of specific aspects of transport systems Personal-transport efficiency To assess personal transport efficiency, we have used the following metrics: average traffic flow speed, time en route predictability index during the morning rush hour, traffic congestion index, and time lost in traffic jams per motor vehicle trip. Personal-transport efficiency leaders among the examined cities are Chicago, Shenzhen, and Johannesburg (Exhibit 39). In Chicago, the traffic congestion index is one of the lowest, while travel speed is one of the highest, reaching 40 kilometers per hour, versus an average 28.6 kilometers per hour for all examined cities. This is one of the reasons why Chicago has one of the lowest indicators of time lost in traffic jams (about three minutes on average). As for the other leading cities, Shenzhen has a high index for time en route predictability during the morning rush hour, and Johannesburg boasts high average personal-transport travel speed during the rush hour (46 kilometers per hour). We note a strong positive correlation between objective personal-transport efficiency metrics and city residents’ level of satisfaction with that aspect. One notable exception is Buenos Aires, where residents have a rather low level of satisfaction with both the current situation and the recent changes. In addition, unlike in the other subgroups of metrics, this one has a rather high share of respondents who fail to note occurrence of any changes over the last three years. This may testify to the fact that traffic congestion in the examined cities is still high, and that remains a concern for city residents. Indeed, the average traffic congestion index for all cities has increased from 1.39 in 2018 to 1.43 in 2021. Exhibit 39 Perception and reality: Personal-transport efficiency To reduce traffic congestion, New York authorities charge drivers a special fee for entering certain city districts Efficiency Public-transport efficiency Personal-transport efficiency 25 1020 515 1 Madrid Chicago Los Angeles Sydney Beijing Toronto Shanghai Buenos Aires Johannesburg Shenzhen Exhibit 39 Perception and reality: Personal-transport efficiency 32 Neutral Dissatisfied Underperformers Leaders Satisfied Score based on objective metrics LLee vvee ll oo ff cc uurr rree nntt ss aatt iiss ffaa cctt iioo nn ooff rree ssii ddee nntt ss Satisfaction with changes Neutral SatisfiedDissatisfied Very satisfied 80 Analysis of specific aspects of transport systems Electronic services In the course of our analysis of electronic services implemented in various cities, we assessed transport applications, real-time availability of transport-related information, and availability of high-speed internet on vehicles and at stops. In particular, we reviewed the following metrics: average rating of official transport applications, penetration rates of the most popular applications, and Wi-Fi availability on trains, at underground stations, on buses, and at bus stops. The electronic-services leaders among examined cities are Madrid, Hong Kong, and Moscow (Exhibit 42). Madrid and Moscow have rather sophisticated official transport applications, as confirmed by high user ratings and high download numbers. In addition, high- speed internet is available at bus stops and on buses in all leading cities. Residents of most cities are generally satisfied with changes in electronic services, which is consistent with objective metrics. The exceptions are three Latin American cities: Buenos Aires, Mexico City, and São Paulo. Despite the low level of satisfaction with the current situation and changes in electronic services, São Paulo is part of the top ten for this group of metrics. As with certain other subgroups of metrics, Asian cities post the highest levels of satisfaction with the current situation. Exhibit 42 Perception and reality: Electronic services In the Moscow Metro, Wi-Fi works even between stations Convenience Travel comfort Ticketing system Electronic service Intermodality 2025 515 110 Singapore Paris New York Hong Kong MadridMoscow Shanghai Toronto Sao Paulo Shenzhen Exhibit 42 Perception and reality: Electronic services 35 Neutral Dissatisfied Underperformers Leaders Satisfied Score based on objective metrics LLee vvee ll oo ff cc uurr rree nntt ss aatt iiss ffaa cctt iioo nn ooff rree ssii ddee nntt ss Satisfaction with changes Neutral Satisfied Very satisfied 125 20 15 10 5 Berlin Singapore Paris Hong Kong London Chicago New York Milan Toronto Buenos Aires Exhibit 43 Perception and reality: Intermodality 36 Neutral Dissatisfied Underperformers Leaders Satisfied Score based on objective metrics LLee vvee ll oo ff cc uurr rree nntt ss aatt iiss ffaa cctt iioo nn ooff rree ssii ddee nntt ss Satisfaction with changes Neutral Satisfied Very satisfied 81Analysis of specific aspects of transport systems Intermodality To assess intermodality, we used metrics measuring convenience of switching from one mode of transport to another. The index comprises the following metrics: average distance from an underground station to the three nearest surface transport stops, average time required to switch from one mode of public transport to another, and availability of unified public-transport navigation for passengers. The intermodality leaders among the examined cities are Toronto, Chicago, and Milan (Exhibit 43). In all those cities, it takes less than one minute to switch from one mode of public transport to another, while the average value across all examined cities reaches two minutes. Toronto also boasts the shortest distance from an underground station to the three nearest surface transport stops: less than 90 meters, versus the average of 135 meters. In all but one city, residents are satisfied with intermodality changes. Similarly, in most cities (except two), the current situation is perceived in a positive way. Those perception data are consistent with objective metrics. In two Latin American cities (Buenos Aires and São Paulo), the level of satisfaction with the current situation and recent changes is rather low, even though the capital of Argentina is one of the top ten cities in this subgroup of metrics. Exhibit 43 Perception and reality: Intermodality Chicago has one of the highest intermodality levels among the examined cities Convenience Travel comfort Ticketing system Electronic service Intermodality 125 20 15 10 5 Berlin Singapore Paris Hong Kong London Chicago New York Milan Toronto Buenos Aires Exhibit 43 Perception and reality: Intermodality 36 Neutral Dissatisfied Underperformers Leaders Satisfied Score based on objective metrics LLee vvee ll oo ff cc uurr rree nntt ss aatt iiss ffaa cctt iioo nn ooff rree ssii ddee nntt ss Satisfaction with changes Neutral Satisfied Very satisfied 82 Analysis of specific aspects of transport systems Physical safety To assess physical safety, we analyzed the following metrics: number of traffic accident fatalities on public roads per million population, public-transport disinfection measures, number of underground accident fatalities, and safety rules compliance index. The leaders in this subgroup are Singapore, Tokyo, and Sydney (Exhibit 44). For example, Singapore and Hong Kong have excellent scores for safety rules compliance, while Tokyo has the lowest number of traffic accident fatalities per million people: 9.5, versus the average of 34. Objective metrics for the physical- safety index have a strong positive correlation with city residents’ level of satisfaction with the current situation in that area. Satisfaction increases as relevant metrics improve. Thus, in cities with superior objective metrics, the level of satisfaction with physical safety is generally higher. Curiously, dissatisfaction with changes is particularly noticeable in cities with the worst physical-safety index values. Residents of those cities are also dissatisfied with the current situation. Exhibit 44 Perception and reality: Physical safety A speed limit of 30 kilometers per hours is in effect on more than 80 percent of Madrid’s streets Safety and Sustainable Development Physical safety Environmental safety 25 520 115 10 Singapore Berlin Sydney Hong Kong London Seoul Beijing Toronto Tokyo Shanghai Exhibit 44 Perception and reality: Physical safety 37 Neutral Dissatisfied Underperformers Leaders Satisfied Score based on objective metrics LLee vvee ll oo ff cc uurr rree nntt ss aatt iiss ffaa cctt iioo nn ooff rree ssii ddee nntt ss Satisfaction with changes Neutral SatisfiedDissatisfied Very satisfied 25 20 1015 5 1 Singapore Paris Hong KongLondon Shenzhen Madrid Berlin Beijing Shanghai Sydney Exhibit 45 Perception and reality: Environmental safety 38 Neutral Dissatisfied Underperformers Leaders Satisfied Score based on objective metrics Satisfaction with changes Neutral SatisfiedDissatisfied LLee vvee ll oo ff cc uurr rree nntt ss aatt iiss ffaa cctt iioo nn ooff rree ssii ddee nntt ss Very satisfied Baan of ee oes 19 cera 86 Impact of the COVID-19 pandemic COVID-19’s impact on urban transport systems The COVID-19 pandemic has drastically changed habitual living conditions, ways of thinking, and human behavior, especially in the urban environment. The implications of the pandemic are not limited to introduction of lengthy lockdowns, advancement of remote working formats, and a ubiquitous decline in social activity. The pandemic has affected, quite literally, all domains of economic and public life, including the operation of urban transport systems. At this time, we are observing several key trends: — lower mobility with an increasing share of private cars in the modal split of most transport systems — lower popularity of public transport, as it is more frequently perceived as associated with risks of COVID-19 infection — declining revenue of transport systems due to lower mobility and, therefore, inevitably poorer service in urban public transport In the long run, these trends may lead to further growth in the number of private cars on the roads, which will place an additional burden on transport systems and cause an even more substantial decrease in the ticket revenue of public transport. We anticipate that city governments will respond with projects designed to improve the sustainability of their transport systems. Exhibit 46 lays out the interaction of these trends in terms of six key insights. This section discusses the key short- term and long-term trends engendered by COVID-19, presents a comparative description of measures taken by city authorities to combat COVID-19 (showing how city residents perceive those measures), and identifies the key activities carried out in cities to assure long-term sustainable development of their transport systems. 87Impact of the COVID-19 pandemic 1. Restrictive measures and an increased share of remote employment resulted in much lower mobility (hereinafter, mobility shall mean the number of trips). Thus, at the peak of restrictions, the average mobility of residents in the examined cities was just 32 percent of the level recorded before the pandemic outbreak. At the time of this writing, mobility has not recovered fully; in the examined cities, it averages merely 69 percent of the level observed before the pandemic. Considering an ever-greater spread of remote working formats, one may also presume that mobility might not recover even after the pandemic is over. 2. Since people now use public transport less frequently, giving preference to their private cars, ticket revenue of transport systems covered by the study has dropped by 37 percent on average, which made some administrations reduce public-transport service levels. 3. As follows from data on the eight cities we examined, the lower the level of service, the lower city residents’ satisfaction with public transport and its popularity. This drives up the share of private cars in the modal split. In the cities where the level of service is the same or growing, however, we observe a less notable decline in the share of public transport in the modal split. 4. City residents believe the risk of viral infection in public transport is much higher than in a private car. Thanks to safety measures and competent communication with the people, some city authorities have been able to soften perception of public transport as a “hazard” and have sustained its popularity during the pandemic. 5. As private cars are perceived to be safer and people are less satisfied with public transport, the share of private cars in the modal split in the examined cities has grown from 40 to 48 percent on average. In a poll of city residents, most of them indicated that they will remain active users of their private cars after the pandemic. With a further decrease in the share of remote employment, this trend may lead to serious adverse effects, including lower efficiency of transport systems and higher early-death rates. 6. Sustainable development of urban environment will be facilitated by projects designed to reduce the use of private cars and popularize the modes of travel associated with physical activity (walking, cycling, etc.). 1. Lower mobility 3. Lower satisfaction with public transport 5. Higher share of private cars in the modal split 2. Lower transport system income and service level 4. Perception of public transport as being more hazardous in terms of contracting the virus 6. Implementation of projects designed to improve transport system sustainability COVID-19 pandemic Exhibit 46 Impact of COVID-19: Key insights 90 Impact of the COVID-19 pandemic The declining share of remote employment combined with people’s reluctance to use public transport may cause street and road traffic in some cities to exceed the values recorded before the pandemic. Throughout the year, we have observed significant fluctuations in the number of cars driven in all examined cities. Street and road traffic levels also have been changing. For example, at the peak of restrictions in the examined cities, the number of motor vehicles on the roads decreased to unprecedented lows. Later on, personal-transport mobility gradually recovered, but with the second wave of the pandemic in the 2020, new restrictions were put in place. The existing conditions enabled us to assess the correlation between traffic volume and personal transport mobility. For the examined cities, this correlation is described in the form of exponential functions, which differ due to city specifics, such as street and road building density, maturity of smart transport systems, and other characteristics (Exhibit 51). 100 95 75%65% 35% 25% At this time 100 80% 20% 65% Before the pandemic 35% 65 Active private-car users Active public-transport users Share of private-car tripsShare of public-transport trips 40 48 47 60 52 53 At this timeBefore the pandemic After the pandemic Mode used or intended to use, % of trips Personal transport Public transport Пожалуйста, посмотрите легенду. Похож на 42, 44 Нет заголовка для графиков внизу «Exhibit 50 Trips by mode of transport: [City name] example» Exhibit 50 Trips by mode of transport: city-level example 40 48 47 60 52 53 After the pandemic Before the pandemic At this time Mode used or intended to use, % of trips Personal transport Public transport Active private-car users Active public-transport users 100 35% 75%65% 25% 95 80% 65 100 20% 65% Before the pandemic At this time 35% Share of private-car trips Share of public-transport trips Exhibit 49 Modal split change in the examined cities due to the pandemic To assess the situation, we must understand the reasons for which the use of public transport has decreased. Was there a change in the preferences of active users of public transport, or did they just travel less in 2020? Our analysis of data on one of the examined cities shows that the observed change had several causes. Active personal-transport users currently use public transport even less frequently than before the pandemic, while their overall mobility has changed insignificantly. Their number of personal-transport trips has increased by nine percentage points, while overall mobility has decreased by 5 percent (Exhibit 50). Among residents who are more active in using public-transport services, overall mobility has decreased by 35 percent, and they now choose public transport as the preferred way of travel less frequently: its share in the modal split has decreased by 15 percentage points. Therefore, one may conclude that the observed trend is the outcome of a complex totality of behavioral changes. 91Impact of the COVID-19 pandemic Exhibit 51 Observed changes in mobility and traffic congestion index and their description using exponential functions 20 100 0 0 40 12060 80 140 160 10 20 30 40 50 60 70 80 90 0 80 20 1406040 80 120100 160 0 70 10 20 30 40 50 60 90 100 0 60 10 40 30 20 50 70 80 90 100 10020 800 40 60 120 140 160 10 60 0 50 20 30 70 40 80 90 100 1400 1206020 40 80 100 160 Traffic congestion index Personal-transport mobility Mexico CityIstanbul Paris London Exhibit 51 Observed changes in mobility and traffic congestion index and their description using exponential functions Actual mobility and traffic congestion index values in 2020 Simulated values of correlation between traffic volume and mobility based on 2020 data Personal-transport mobility Personal-transport mobility Personal-transport mobility Traffic congestion index 45 Having analyzed correlation between the traffic congestion index and mobility and forecasts ventured by city residents with respect to their own mobility when the share of remote employment decreases, we assessed post-pandemic road and street traffic in the examined cities. In some cities, residents expect the traffic to be higher than before the pandemic (Exhibit 52). But in most cities, city residents were quite cautious in their assessment of the potential decrease in the share of remote employment, so one may expect lower traffic on the roads in those cities. The modal-split shift toward personal transport, however, may lead to a sharp increase of the traffic congestion index as the pre-pandemic overall mobility level is reached. 100 95 75%65% 35% 25% At this time 100 80% 20% 65% Before the pandemic 35% 65 Active private-car users Active public-transport users Share of private-car tripsShare of public-transport trips 40 48 47 60 52 53 At this timeBefore the pandemic After the pandemic Mode used or intended to use, % of trips Personal transport Public transport Пожалуйста, посмотрите легенду. Похож на 42, 44 Нет заголовка для графиков внизу «Exhibit 50 Trips by mode of transport: [City name] example» 40 48 47 60 52 53 After the pandemic Before the pandemic At this time Mode used or intended to use, % of trips Personal transport Public transport Active private-car users Active public-transport users 100 35% 75%65% 25% 95 80% 65 100 20% 65% Before the pandemic At this time 35% Share of private-car trips Share of public-transport trips 92 Impact of the COVID-19 pandemic Exhibit 52 Projected post-pandemic traffic congestion index vs. 2019 index values recorded in the examined cities One of the critical tasks currently facing most examined transport systems is to restore the popularity of public transport both in the short term and in the long term. In some cities, authorities have already scored some successes in making their public transport more attractive, primarily by informing residents about the safety of public transport in an effective manner and maintaining a high level of service throughout 2020. Exhibit 52 Projected post-pandemic traffic congestion index vs. 2019 index values recorded in the examined cities Pre-pandemic traffic congestion index Post-pandemic traffic congestion index 46 50 10 25 35 30 5 40 15 20 45 55 60 Sã o Pa ul o M ad rid N ew Y or k To ky o Jo ha nn es bu rg Lo nd on B ue no s Ai re s Si ng ap or e M os co w Is ta nb ul M ex ic o Ci ty Sa in t P et er sb ur g B an gk ok Sy dn ey Lo s An ge le s H on g Ko ng M ila n To ro nt o Pa ris B er lin Ch ic ag o Index increase vs pre- pandemic values Index decrease vs pre-pandemic values Pre-pandemic traffic congestion index Post-pandemic traffic congestion index 95Impact of the COVID-19 pandemic Exhibit 55 Correlation between perceived risk of infection while traveling by public transport and passenger traffic decrease in 2020 Perceived risk of infection while traveling by public transport, % Chinese cities High Average Low Medium Low High Public transport mobility during the pandemic vs mobility before restrictions, % 48 However, analysis of data collected in some of the examined cities precludes statements to the effect that the number of trips by public transport substantially affects the spread rate of the infection. In some megacities, including Paris, Singapore, and Hong Kong, effective processes were put in place to track affected-person contacts, and no serious outbreaks attributable to public-transport systems were reported. According to multiple experts, biological safety measures also play an important role in the reduction of COVID-19 incidence among public-transport passengers. The most widespread measures include mandatory use of personal protective equipment, more frequent disinfection of vehicles and infrastructure facilities, and introduction of social-distancing rules in public transport. The examined cities have different sets of measures, though. For convenience of analysis, those measures are divided into six categories (Exhibit 56). Efficacy of such activities was noted in multiple studies published in 2020, but the fact that they are being carried out in a city does not have direct impact on how city residents perceive the risk of infection in public transport. Despite the existence of an indirect relationship between the two metrics, in some cities the measures have had a less significant impact on perception of biological risks associated with public- transport travel (Exhibit 57). This might be because city authorities have not effectively distributed information on such initiatives. Exhibit 56 Activities carried out in cities to improve public-transport safety Mandatory use of PPE by passengers and employees Frequent disinfection and implementation of effective innovations Stickers, markers, and barriers to support social distancing Same or higher public-transport frequency Better cycling infrastructure Limits on the number of passengers in public transport Personal protective equipment (PPE) Disinfection Social distancing Service level Cycling infrastructure Limited number of passengers Number of cities actively taking safety measures City actively taking measures in the respective category 96 Impact of the COVID-19 pandemic Exhibit 57 Correlation between the number of biological safety activities carried out in a city and perceived risk of infection in public transport Exhibit 57 Correlation between the number of biological safety activities carried out in a city and perceived risk of infection in public transport >6 <3 4–5 49 Number of safety measures taken in cities Perceived risk of infection while traveling by public transport, % Number of cities actively taking safety measures Exhibit 56 Activities carried out in cities to improve public-transport safety Disinfection Social distancing Service level Cycling infrastructure Personal protective equipment (PPE) Limited number of passengers Mandatory use of PPE by passengers and employees Frequent disinfection and implementa- tion of effective innovations Stickers, markers, and barriers to support social distancing Same or higher public-transport frequency Better cycling infrastructure Limits on the number of passengers in public transport City actively taking measures in the respective category Low levelAverage levelHigh level Exhibit 58 Correlation between perceived risk of infection while traveling by public transport and visibility of biological safety measures Exhibit 58 Correlation between perceived risk of infection while traveling by public transport and visibility of biological safety measures Perceived risk of infection while traveling by public transport, % Exhibit 59 Correlation between perceived risk of infection while traveling by public transport and number of new infection cases Medium Low High HighAverageLow Visibility of safety measures for city residents Number of new cases of COVID-19 per 1,000 population 50 Perceived risk of infection while traveling by public transport, % High levelAverage levelLow level Medium Low High The data we acquired during our study testify to the fact that it is the visibility of biological safety measures that immediately influences how residents perceive the level of risks associated with traveling by public transport and, therefore, what modes of transport they prefer to use. In cities where authorities managed to arrange effective communication, and the residents were more aware of relevant activities, respondents assess the risk of infection in public transport to be lower (Exhibit 58). 97Impact of the COVID-19 pandemic Exhibit 59 Correlation between perceived risk of infection while traveling by public transport and number of new infection cases Exhibit 58 Correlation between perceived risk of infection while traveling by public transport and visibility of biological safety measures Perceived risk of infection while traveling by public transport, % Exhibit 59 Correlation between perceived risk of infection while traveling by public transport and number of new infection cases Medium Low High HighAverageLow Visibility of safety measures for city residents Number of new cases of COVID-19 per 1,000 population 50 Perceived risk of infection while traveling by public transport, % High levelAverage levelLow level Medium Low High However, the total number of COVID- 19 infection cases also is an important factor for determining the risk of infection by population. The perceived risk of infection is lower in cities with fewer infection cases (Exhibit 59).