|M.Sc Student||Bahat Oren|
|Subject||Incorporating Ride Sharing in the Static Traffic|
|Department||Department of Civil and Environmental Engineering||Supervisor||Professor Shlomo Bekhor|
|Full Thesis text|
Among the different ways proposed to manage travel demand, Dynamic Ride Sharing is one of the more trendy ones. Dynamic Ride Sharing tries to minimize the number of private vehicles on the road, by matching drivers and passenger who share similar travel demand needs. Ride sharing services appear around the world, usually with a lot of green buzz, but have a limited success. In terms of research, there are hardly any quantitative tools to assess the expected success of ride sharing services. We develop a model that incorporates ride sharing as an option in a mode choice model, and also combine this into a traffic assignment model. A discrete choice model that includes ride sharing is developed, taking into account the need to wait for a ride, the cost of a ride, and the chances for a driver to being assigned a passenger. The traffic assignment model is a static user equilibrium that interacts with the discrete choice model through level of service variables. The model is formulated as an extension of the variable demand static traffic assignment problem, and demonstrated to have similar properties. An iterative algorithm was implemented and used to run the model in various networks and conditions. The results indicate that the quantity of ride sharing drivers is a key parameter to the service success, and below a critical mass of drivers, it is unlikely that passengers will find the service valuable. It is also shown that ride sharing has the ability to reduce in-vehicle times for all the users, although passenger may suffer from longer door-to-door times, having to wait for their ride. Having a full scale network model also enables the characterization of places and conditions in which ride sharing is more likely to succeed. For example, we have seen that longer routes and areas that suffer from congestion patterns benefit more from ride sharing than others. This model can be broadened to include additional modes, better operational assumptions, and more sophisticated models. Together, they could serve as a planning and prediction tool for ride sharing services.