| Rising gas prices, traffic congestion and environmental concerns have increased the interest in services that allow people to use their cars more wisely. Ride-sharing programs allow people to share a trip to destinations that they were going to anyway. While ride-sharing in itself is not new, the ubiquity of GPS-enabled cell phones brings dynamic ride-sharing within the realm of possibilities. Ride-sharing has to be easy, flexible, efficient and economical before it will find broad adoption. Technology, hardware and software, are key enablers. Although the hardware is currently there, a proven mechanism for optimally matching up people for rides in real-time is still lacking. The primary goal of the research is to develop algorithms to dynamically generate ride-sharing opportunities and to identify the characteristics of environments in which ride-sharing offers the greatest potential for emission and congestion reduction. To this end, we will develop decision support technology for the ride-matching service and build a realistic traffic simulation environment (RideSim) for policymakers. The resulting insights can (1) support policy makers in coming up with effective measures to reduce traffic congestion and pollution and (2) help ride-matching services to more effectively match supply and demand. |
