Newer modes of transportation—ride-hailing services, car sharing, bike sharing and scooter sharing—impact travel behavior and outcomes such as accessibility, traffic congestion and greenhouse gas emissions. Yet current travel modeling analyses do not adequately incorporate these modes, resulting in inaccurate predictions of their influence. This project integrated shared mobility into traffic modeling for more accurate predictions that local governments can consult to make transportation planning decisions.
New opportunities for people, especially those without cars, to reach destinations using temporary mobile options have expanded in recent years. However, studies have evaluated the impacts of shared mobility in relative isolation. Broader evaluations are needed that analyze the complex interactions between shared mobility and the entire transportation system to understand how shared mobility will impact mode use, traffic, emissions and infrastructure.
This study models changes in travel behavior initiated by shared mobility and examines the subsequent network effects. The results will inform local urban planners and policymakers as they work to improve transportation outcomes and propose strategies to optimize the use of shared mobility within existing infrastructure.
What Did We Do?
Six modes of transportation were integrated into modeling development: private vehicles, public transportation, ride-hailing services such as Uber and Lyft, car sharing, bike sharing and scooter sharing. Investigators measured the effect of shared mobility in four scenarios: a baseline of the current condition, a scenario that effectively eliminated ride-share options, another that increased shared electric vehicle use with decreased prices and the final scenario that significantly decreased the use of shared bikes and scooters.
Modeling results assessed the impact of three scenarios on congestion and emissions. The results were also used to generate maps of accessible locations from a variety of trip origins with and without shared mobility options. A cost–benefit analysis estimated the fiscal impacts associated with the three scenarios.
What Did We Learn?
Investigators successfully developed a traffic analysis model that incorporates the dynamic interactions of six transportation modes and provides insightful data on travel times, congestion levels and the types of modes used. The results of the scenario analysis highlight the trade-offs associated with the use of shared mobility. For example, the elimination of ride sharing led to the largest reduction in network congestion, primarily by eliminating empty rebalancing rides from locations with an overabundance of vehicles to areas where vehicles are needed. But ride sharing also resulted in slightly higher carbon dioxide emissions and reduced accessibility.
“As new shared mobility options emerge, this modeling can help local practitioners better understand the trade-offs of different levels of shared mobility services in their community,” said Elliott McFadden, MnDOT Emerging Mobility Unit Supervisor.
The increased use of electric vehicles in scenario two resulted in less congestion, lower carbon dioxide emissions and increased accessibility. The elimination of micromobility in scenario three increased both travel delays and emissions while decreasing first- and last-mile connectivity.
Results from the cost–benefit analysis revealed estimated savings and costs in a number of areas of all scenarios. Most notably, a 62% reduction in congestion in scenario one translated to an annual benefit of $1.6 billion, and a 45% reduction in scenario two had an annual benefit of $1.2 billion. Annual benefits from emissions reductions were $24.5 million for scenario two, whereas annual costs for increased emissions were $2.6 million and $46.9 million for scenarios one and three, respectively.
The results demonstrate how shared mobility increases the number of potential destinations, reduces travel times compared to public transportation and extends access to areas not served by conventional transit. These findings suggest that regulatory and pricing strategies for ride-sharing services such as Uber and Lyft, fleet electrification, investment in micromobility infrastructure and coordination with transit could maximize shared mobility net benefits.
What’s Next?
The transportation modeling developed in this project provides local transportation planners and engineers with an informative tool for operating a transit network that considers numerous factors. For example, if a municipality has little congestion but accessibility concerns, it may strive to increase ride-sharing opportunities.
To enhance the use of the model for policymaking decisions, real-time trip data is needed from shared mobility providers to ensure accurate results. Further, coordination between public transit and shared mobility can extend services to underserved areas and newer developments with emerging infrastructure. Overall, coordinating these shared mobility options with traditional modes and the existing network can improve a municipality’s overall transportation system.
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