Analysis of Travel Choices and Scenarios for Sharing Rides
Final Report
Chapter 5. Implications for Cities
Reducing vehicle miles of travel has the potential to unlock a wide range of benefits for cities: alleviating congestion, improving travel time and travel time reliability for all road users, reducing vehicle emissions and energy use, supporting economic growth, and preventing crashes. Furthermore, shared rides offer direct benefits to several groups: lower, split costs for travelers and economized costs for transportation service providers.1
Because vehicle occupancy has a significant impact on the number of vehicles on the road, cities have an interest in encouraging higher-occupancy travel as a means of reducing vehicle miles traveled (VMT). Cities have a multitude of mechanisms for reducing VMT, from encouraging high-occupancy or active modes to pricing private vehicle travel. However, a major challenge for cities is predicting the effectiveness of various policy mechanisms on VMT reduction. Understanding this effectiveness is critical for cities because many policy mechanisms may also present challenges for transportation agencies and changes in costs for the traveling public. This study provides cities with information on the impacts of four changes in modal travel characteristics that could relate to potential policy mechanisms:
- Increase cost savings for shared transportation network company (TNC) trips relative to private TNC trips.
- Reduce travel time penalty for shared TNC trips relative to private TNC trips.
- Increase price differential of private car trips relative to all other modes.
- Reward carpooling trips but not private car trips.
Chapter 4 analyzes the first three scenarios (the fourth being experimental) and finds that changes to the relative price of single occupancy vehicle (SOV) travel (i.e., driving alone rather than with a passenger) offer the greatest opportunity for reduction in VMT. The reason for this impact is that SOV travel accounts for the majority of VMT and person trips in the United States. Scenario 3 asks what the impact would be if SOV travel were relatively more expensive than private vehicle carpool trips. The study finds, for example, that a $1/trip relative price increase for drive-alone trips could potentially save over 3.5 billion vehicle miles annually in the 15 markets studied in this report. More modest, or more targeted, interventions could also reduce VMT by focusing on particular geographies or population segments.
Chapter 3 explored carpooling incentives and the sharing of rides facilitated through carpooling apps, which informed an experimental scenario 4, discussed in chapter 4, and was provided for illustrative purposes only. In the future, this scenario analysis could be updated with other estimates of elasticity with respect to carpool rewards programs as data, such as through future randomized control trials, become more widely available.
Scenarios 1 and 2 draw upon the results of chapter 1 to test the impact of lower relative prices and faster relative travel time for shared TNC trips, respectively. The impact of these scenarios on VMT is much smaller than scenario 3 because TNCs currently represent a very small portion of total trips and total VMT (about 1 percent of all person trips in the study city regions, compared to private vehicles' much larger share of person trips in the study city regions: roughly 55 percent as a driver and a further 20 percent as a passenger). Nonetheless, these scenarios do have an impact on VMT—an impact that might have an outsized influence on congestion if TNC trips also occur in the most congested areas of a region at the most congested times. Specifically, scenario 1 finds that a $1/mile increase in price difference between private and shared TNCs could potentially reduce VMT by roughly 88 million miles per year by reducing private TNC VMT by 12.3 percent and substituting shared TNCs for that travel. This corresponds to an annual VMT savings of about 0.04 percent across all 15 study city regions. The relative effect is twice as high if the scenario is applied only to trips starting in dense office districts.
The scenario 2 example shown above demonstrates that a 15 seconds/mile decrease in travel time difference between private and shared TNCs would result in a VMT impact roughly equivalent to the $1/mile tested in scenario 1. In both scenario 1 and scenario 2, the effect of a higher or lower price/travel time difference on VMT scales linearly to the size of the difference tested. Considering all trips in all study city regions, a 15 seconds per mile reduction in travel time difference between private and shared TNCs could potentially reduce VMT by roughly 85 million miles per year by reducing private TNC miles by 11.9 percent and substituting shared TNC travel for that difference.
