The Urban Mobility Metric

In Transportation Transformation I observe that urban transportation must transform. New urban mobility will emerge as a result of this transformation and will result in providing the movement of consumers and goods as a service using vehicles of various form factors. In this piece, I introduce the Urban Mobility Metric as a composite metric for assessing an urban area’s progress toward new mobility.

Megatrends such as congestion, pollution, noise, climate change, the future of work, and aging societies always influence our decisions on where and how to live. Consequently, they have a direct impact on mobility. Think of the multi-hour congestion-induced commute times in Sao Paulo, or Mumbai, and the pollution-related transportation restrictions in Beijing. Today we are hampered in effectively addressing how megatrends impact mobility because we approach urban transportation as a collection of silos: personally owned vehicles of any type represent one silo, public transportation represents a second silo, and on-demand mobility services, used for people transportation or goods delivery, a third. This approach impedes the optimized allocation of mobility resources, the effective utilization, and the operation of the available transportation infrastructures, as well as the quality of the overall mobility experience. In my book Transportation Transformation, I identify these as important reasons for reimagining and transforming urban mobility. The result of this transformation will be an integrated system for frictionless, safe, affordable, and convenient mobility of passengers and goods through which the movement of people and goods. This system will bring together intelligent transportation infrastructures with multimodal fleets of public and private vehicles that are automated, autonomous, connected, and electrified. For such a system to become a reality automakers, mobility services companies, and cities need to collaborate. This post introduces the Urban Mobility Metric and presents some of its uses.

Cities must lead in the introduction and broad adoption of new mobility. This will not come easy for every city because leading will require transformation. Some, like New York, Los Angeles, and Seattle in the US, Paris, Berlin, and London in Europe, and Singapore, Shanghai, and Tokyo in Asia have already embraced new mobility and are proceeding with ambitious transformation projects. Others may need to first see the results of early adopters before deciding to follow. There are four reasons for cities to transform.

First, through their transformation, they must define the role they would want to play in new urban mobility, including how they will utilize their public transportation systems. Some cities in the US have already started partnering with mobility services companies in an effort to provide frictionless multimodal transportation in a way that addresses the needs of more citizens. In the process, they are also attempting to reduce the growing losses of their public transportation systems.

Second, they must learn to manage effectively and in an integrated manner their transportation infrastructure resources: curbs, sidewalks, parking spaces, and roads. As new urban mobility advances, these resources, particularly the curb and the sidewalk, are becoming extremely important for passenger pickup and drop off, as well as goods pickup and delivery. By properly managing these resources, cities will be able to monetize them and thus fund their participation in new mobility.

Third, they must address jurisdictional boundaries which today often negatively impact convenience and user experience and will have an even larger negative impact in the case of offering Mobility as a Service (MaaS). Finally, as part of their transformation, cities must determine how to regulate MaaS while enabling it to thrive.

Depending on the transformations they decide to undertake, cities will emerge as intelligent transportation infrastructure providers, transportation coordinators that actively manage traffic flow across their intelligent infrastructures, or transportation orchestrators that control the efficient operation of resources used in passenger transportation and goods delivery very much like air traffic control systems do in the case of air transportation.

To assess their progress towards achieving the integrated system that provides frictionless, safe, affordable, and convenient mobility to urban populations, cities (or metropolitan areas), automakers, and mobility services companies need a new metric. The Urban Mobility Metric is one such metric that captures:

  1. The trips a consumer makes during a specific period, e.g., a month, within a geofence (typically a metropolitan area), as well as the trips that were not made because of using delivery services. For example, shopping trips to the supermarket that were not made because groceries were delivered using a groceries delivery service.
  2. The modality, or modalities, used during each trip. For example, a daily commute may involve walking, riding a local train, and using an on-demand micromobility service to the final destination. The distance traveled using each modality is incorporated and the use of each modality, or combination of modalities, is correlated to the weather conditions at the time of the trip, and the congestion levels along the routes used.
  3. When available, the metric can also incorporate the value of the traveler’s time. For example, the value of a pharmaceutical company’s representative who is trying to visit a specific number of doctor offices during a day may be higher than that of a retired person’s who needs to go grocery shopping.
  4. The amount spent on each transportation modality used during the period of interest. This reflects the percentage of the consumer’s mobility budget that was spent on each modality utilized for transportation and goods delivery but also reflects the modality’s mileage share in the consumer’s mobility plans.

The Urban Mobility Metric enables:

  • Assessing the state of new mobility in an area of interest, i.e., assessing progress towards the system to provide frictionless, safe, affordable, and convenient mobility.
  • Understanding how heavily each transportation modality is being used, and how frequently it is used by each individual;
  • Comparing the use of personally driven vehicles (owned, leased, subscribed) to the use of public transportation and mobility/delivery services and establishing the trip types for which each modality is used;
  • Establishing how heavily the consumer utilizes services instead of traveling to accomplish particular goals;
  • Measuring the affordability of each mobility/delivery service for each population segment of interest;
  • Assessing the consumer’s loyalty towards each service utilized and calculating the individual’s lifetime value to that service.

By taking advantage of everything the Urban Mobility Metric enables at the traveler level, a city or an entire metropolitan area can determine the steps it must take to succeed in achieving its new mobility goals. Potential steps include the incentives an area’s agencies may need to offer, the restrictions they may need to impose, and how to price its infrastructure at different times of the day by type of user, e.g., private citizen versus fleet operator. The Urban Mobility Metric can be as valuable to an automaker, a company offering on-demand mobility/delivery services, or companies from other industries such as insurance, financial services, retail, and telecommunications which can use it as part of their customer-centricity initiatives.

The Urban Mobility Metric can be measured at many different levels, such as household, demographic group, or even the population of a geographic area, e.g., a specific neighborhood. A city’s, and ultimately a country’s, overall Mobility Metric is very dependent on the state of the public transportation system, the cost of the mobility services operating in its territory, and the cost of owning and operating a vehicle. Some from our research at the country level are shown in Table 1 (POV/1000 means Private Vehicles Owned per 1000 citizens).

Country POVs/1000 POV Miles/capita Public Transport rides/user/year Annual mobility services users (million) Mobility services rides/user/year
US 800 12000 40 42 35
Germany 561 6300 177 11 5
France 480 6200 154 12.6 34
China 181 620 108 521 3
Japan 615 3900 246 12.6 14
Table 1: Country-level transportation modality data

From the country level data shown in Table 1 one can see, for example, that Germany and France have an Urban Mobility Metric that is more evenly distributed between privately-owned vehicles, public transportation, and on-demand mobility services compared to the US where transportation is dominated by privately-owned vehicles, and Japan, where consumer mobility is dominated by public transportation.

Analyzing data at the city level is even more illuminating because it provides more insight about how demographics, urban structure, private vehicle ownership, the state of the city’s transportation infrastructure, and the quality of its public transportation system impact the modalities consumers utilize for their mobility. Table 2 provides data from four different cities collected by Deloitte, and Frost and Sullivan (the second number in each cell). In some cases there are significant differences between the numbers provided by each firm that may be due to the differences in the population of the metropolitan area’s geofence being measured. For example, for Paris Deloitte measures the transportation data for a population of 7.2 million whereas Frost and Sullivan measures it for a population of 10.9 million. In other cases the difference may be due to the year of the surveys. Deloitte reports data from their 2019 survey whereas Frost and Sullivan reports for 2016 or 2017.

City POV/1000 POV (%) Public
Transit (%)
Walking (%) Biking (%)
Berlin 329 30/32 22/27 31/24 13/17
Frankfurt * 515 46 41 6 7
Paris 430 26/34 25/51 46/10 2/5
Tokyo 206 12/29 47/59 24/11 17/9
Table 2: City-level percent of trips by transportation modality

* Only Frost and Sullivan data

The data reveals interesting differences. Compare, for example, Berlin to Frankfurt. First, you will notice that in Frankfurt the number of POV/1000 is closer to Germany’s national average (Table 1), whereas in Berlin it is much lower. Second, the modalities usage between the two cities is very different. Then consider the differences between Berlin, Paris, and Tokyo. The Paris data is closer to France’s national average, whereas Berlin’s and Tokyo’s diverges significantly from the national average. You will also notice that at the time the measurements were taken neither Paris nor Tokyo would be considered micromobility-friendly cities. Paris is changing that by significantly enhancing its network of micromobility lanes.

Cities that are transforming to become transportation coordinators are investing aggressively in smart transportation infrastructures, urban planning aimed to reduce traveling long distances, multimodal public transportation, and intelligent blending of public transportation with on-demand mobility services. Such cities can use the data they collect to generate their Urban Mobility Metric and provide their citizens and businesses specific incentives with the goal to bring their mobility behavior closer to the area’s mobility goal. Examples may include free public transportation during the morning and afternoon commute periods, congestion pricing, or even limiting access to privately owned vehicles in certain parts of the area. Success can then be measured in several ways: increasing the population’s use of multimodal mobility even if it doesn’t rely on public transportation, reducing the population’s miles traveled annually, and moving specific population segments, e.g., commuters, to shared transportation modalities.

Our firm has collaborated with urban planners and transportation planners, automakers, and mobility/delivery services companies on the Urban Mobility Metric. This work enabled us to reach the following conclusions:

  1. New urban mobility will be implemented differently around the world. Its implementation will be influenced by the urban landscape, the local population’s characteristics, and the region’s approach toward transportation investment, environmental conservation, and regulation. Regardless of its implementation the use of the Urban Mobility Metric provides an important way to measure the collective progress toward transportation transformation. 
  2. Once a private vehicle is acquired the use of other modalities decreases and the self-interest in making the personally owned vehicle experience most convenient, rewarding, and pleasant increases. People appear to prefer congestion, the safety and consistency of the experience provided by their privately owned vehicle, to using public transportation.
  3. Though it varies by country, trips completed by the members of a population (city, metro area, county, etc.) on a daily basis are dominated by commuting and errands (various forms of shopping, taking the kids to school, coffee and meal breaks, etc.) tend to be short.
  4. Cities and automakers are in a perennial conflict. Cities plan their public transportation systems assuming that consumer logic will prevail and their systems will be the preferred modalities. Automakers try to sell as many vehicles as possible viewing everything that doesn’t support this goal as an obstacle that needs to be eliminated. Once a region’s per capita annual miles traveled passes a certain level private vehicle ownership dominates every other modality.


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