ride-hailing

Traffic congestion is a significant problem in major metropolitan areas in the United States. According to the Urban Mobility Report, in 2019 commuters on average lost about 54 hours in traffic congestion. The researchers developed three approaches to rideshare routing and evaluated their effectiveness in combating traffic congestion.

The research team used the Los Angeles MATSim model to evaluate the travel, greenhouse gas(GHGs), and equity impacts of single-and multiple-passenger automated taxi scenarios, including free transit fares and a VMT tax.

This research explicitly models taste heterogeneity towards pooled ridehailing. In addition, unlike existing studies either at the person level or employing stated-preference data, a trip-level analysis is performed in connection with revealed preferences, which generates more realistic and relevant implications to policy and practice.

With rapid population growth and urban development, traffic congestion has become an inescapable issue in large metropolitan regions. This research project studies the problem of offering incentives to organizations to change the behavior of their individual drivers (or individuals using their organization’s services).

Researchers at the University of Southern California developed a distributed algorithm for offering incentives to organizations to make socially optimal routing decisions designed to lower the traffic flow of congested roads without creating new congestion in other parts of the road network.