| A nascent ridesharing industry is being enabled by new communication technologies and motivated by its many possible benefits, such as reduction in travel cost, pollution, and congestion. Understanding the complex relations between ridesharing and traffic congestion is a critical step in the evaluation of a ridesharing enterprise or of the usefulness of regulatory policies or incentives to promote ridesharing. In this research, we propose two new traffic assignment models that explicitly represent ridesharing as a mode of transportation. The objective is to analyze how ridesharing impacts traffic congestion, how people can be motivated to participate in ridesharing, and conversely, how congestion influences ridesharing, including ridesharing prices and the number of drivers and passengers.;The first model considers the scenario where drivers and passengers sharing the same ride must travel from the same origin and to the same destination. This model is built by combining a ridesharing market model with a classic elastic demand Wardrop traffic equilibrium model. It is formulated as a convex optimization problem. The Frank-Wolfe algorithm is adopted to solve the model and a heuristic approach is applied using the equilibrium condition. Our computational results show that: (1) the ridesharing base price influences the congestion level, (2) within a certain price range, an increase in the price may reduce the traffic congestion, and (3) the utilization of ridesharing increases as the congestion increases.;The second model drops the constraint of the same origin-destination (OD) pair. In this model, drivers may pick up or drop off any passenger in the middle of their trips, and they may even detour from a seemingly shortest path. In order to describe this scenario, we extend the network by doubling the nodes and tripling the arcs in size. A generalized user equilibrium is defined to represent the new network and the new constraints. The generalized user equilibrium can be formulated as a mixed complementarity problem (MiCP), and equivalently a variational inequality. It is proved that there exists one and only one solution to this model. The KNITRO solver is adopted to solve the MiCP and the computational results are promising. It can be concluded from the results that when the congestion cost decreases or the ridesharing inconvenience cost increases, more travelers would become solo drivers and thus less people would participate in ridesharing. On the other hand, when the ridesharing price increases, more travelers would become ridesharing drivers.;In conclusion, the traffic congestion, the ridesharing cost, and the number of travelers interact with each other closely in both models. Understanding their relationships enables planners to develop policies to draw more people to participate in ridesharing and thus to reduce traffic congestion. |
