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Bestmile has introduced a new Service Design Offering that enables mobility providers to test how a planned on-demand service will perform before deployment using realistic demand data. A new white paper, “On-Demand Mobility Service Success by Design,” walks through three case studies in which the offering uses taxi demand data from Chicago to analyze the performance of on-demand micro-transit, airport shuttle, and ride-sharing services with multiple fleet sizes and criteria for allowable wait times and excess ride times. The performance of simulated shared services is compared with unshared taxi services.

The Service Design Offering allows mobility providers to view and assess the many trade-offs that can be made to optimize a mobility service. The white paper shows how multiple variables can be tested to measure their impacts on Key Performance Indicators (KPIs) like ride acceptance rates, ride times, wait times, and vehicle utilization.


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Solving the “How” of New Mobility Services

The big-picture benefits of new mobility services predicted by industry leaders and pundits are well-known. The promise of empty streets and clean air resulting from the ability to move more people with fewer vehicles and with fewer kilometers driven are expected to dramatically transform urban living.


For transportation planners and service providers, however, there are many unknowns as to how these services can achieve these goals. These unknowns include how shared, on-demand services will work in a specific city, with unique geography, population, traffic patterns, and existing transit operations. The logistics issues — how many vehicles will be needed and where they will be located — need to be solved. There are also questions surrounding the service levels for travelers in the form of wait times and excess ride times that will work for a given city.



Simulation-Based Service Design

Bestmile has helped multiple public and private mobility providers design, deploy, and manage optimized shared mobility services with autonomous and human-driven fleets. The company’s Mobility Services Platform uses advanced matching and routing algorithms that make it possible to adjust a host of fleet efficiency metrics that help find the right balance between the number of vehicles needed, passenger comfort, and cost-effectiveness.


“On-Demand Mobility Service Success by Design” explains how service providers can use the same platform to design and analyze these services in advance in order to accurately simulate how they will perform. The service design process can use known or approximated demand data to evaluate the performance of mobility services.



Balancing Service Levels and Fleet Efficiency

This white paper shows in detail how Bestmile’s Service Design Offering can be used to vary service design parameters, the service area specifications, and the assumptions about the travel demand and view the impacts of these changes on service performance. Because no two service areas are alike, even within the same city, the white paper explains the simulation of three services in three different areas in Chicago:


  • Micro-transit for Lincoln Park: Service within the downtown area of Lincoln Park involves more than 4,000 short rides (5 minutes direct ride time average) per day dispersed throughout the service area.


  • Shuttles to and from O’Hare airport: Service to and from Chicago’s O’Hare airport averages more than 2,600 trips per day and is characterized by longer distance travel averaging 40 minutes.


  • City ride sharing using a uniform sample of Chicago: The uniform sample of the city’s traffic demand takes half the 30,000 daily taxi rides — 15,000 rides per day.


In each case, the Service Design Offering simulates how a shared mobility service would perform compared to an unshared service. The cases studies analyze the performance of multiple fleet sizes for each service. To read the results of the service design process for each service area, download the white paper here.



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