ride-hailing

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.

This report focuses on the analyses of the data collected in this project, in particular on the differences in attitudes towards transportation and the environment among different generational groups, the adoption and use of shared mobility services, and their relationship with vehicle ownership, the interest in the adoption of alternative fuel vehicles, and the interest in the future adoption of connected and automated vehicles.

This paper: (1) uses empirical data and conducts descriptive and comparative analysis using a spatial lag model to analyze the factors influencing average cruising time (ACT) related to parking search, and (2) proposes a novel framework to predict grid-based ACT and to estimate average emission metrics (AEM).