National Renewable Energy Laboratory

In this study, newly available Chicago transportation network provider data were explored to identify the extent to which different socioeconomic, spatiotemporal, and trip characteristics affect willingness to pool (WTP) in ridehailing trips. Multivariate linear regression and machine-learning models were employed to understand and predict WTP based on location, time, and trip factors. The results show intuitive trends, with income level at drop-off and pickup locations and airport trips as the most important predictors of WTP. Results from this study can help TNCs and cities devise strategies that increase pooled ride-hailing, thereby reducing adverse transportation and energy impacts from ride-hailing modes.

This paper identifies major aspects of ridesourcing services provided by Transportation Network Companies (TNCs) which influence vehicles miles traveled (VMT) and energy use. Using detailed data on approximately 1.5 million individual rides provided by RideAustin in Austin Texas, we quantify the additional miles TNC drivers travel: before beginning and after ending their shifts, to reach a passenger once a ride has been requested, and between consecutive rides (all of which is referred to as deadheading); and the relative fuel efficiency of the vehicles that RideAustin drivers use compared to the average vehicle registered in Austin.

This paper examines the relationship between ride-hailing and parking demand by looking at ride-hailing trips that otherwise would have needed parking.

This study examines the impacts of transportation network companies (TNCs) such as Uber and Lyft on trends in travel, parking, car-rental and the economy by analyzing the effects of ride-hailing at four major airports in the U.S.

This report examines several scenarios of connected and automated vehicle (CAV) adoption rates and studies their potential impacts on fuel efficiency and consumer costs. The results found massive uncertainties in potential long-term energy impacts from fully automated and highly connected vehicles in the high adoption rate scenario and similar uncertainties in the other scenarios. The authors outline the gaps in existing research and suggest routes for further research in order of importance.

This report seeks to represent a comprehensive assessment of energy savings potential for heavy trucks.