An Automated Real-Time Rideshare Network

According to the Commuting in America report, more than 88% of American workers commute to work in private vehicles, which accounts for a daily sum of 166 million miles. The report also indicates that more than 76% of the commuters drive alone, resulting in inefficient use of the transportation infrastructure. Development of programs that encourage ridesharing can alleviate this problem; however, past efforts to promote ridesharing have not achieved full potential due to rigid spatial and temporal requirements of the travel schedules of participating parties. A dynamic rideshare system that takes advantage of real-time passenger demand, vehicle supply, and travel time information can overcome these issues. Real-time rideshare matching differs from the classical rideshare matching in two ways. First, traditional systems assume that the travelers have a fixed schedule and a fixed set of origins and destinations. Real-time systems must take into account each trip individually and be able to match the rides to arbitrary origins and destinations based on the passengers' and drivers' preferences. The second major difference is that real time rideshare systems must be able to respond to instant requests in a very short period of time. Numerous papers exist that deal with various aspects of ridesharing; however, few studies have considered the rideshare problem as an optimization problem. Recent technological advances in information technology, communication, and the improvements in the ITS infrastructure (i.e., availability of real time travel time information and live accident and congestion reports) have added a new dimension to the ridesharing problem. Motivated by the use of technology to improve mobility through efficient use of existing transportation capacity, this project proposes an optimization framework for Automated Real-Time Rideshare Network. In the first phase of the project, an optimization model and an iPhone application for localizing the users and communicating with the server were developed. In this phase the following tasks will be accomplished: 1. An efficient solution algorithm will be developed as the main component of the optimization engine for solving the rideshare matching problem in real-time. 2. A simulation framework that uses UMCP campus commuter data to demonstrate the real-time rideshare framework in action and evaluate its impacts on the campus transportation system will be developed. 3. Extensive testing and verification of the model will be undertaken.