About me
Real-Time Large-Scale Incentivized Ridesharing - In this study we propose an online and large-scale rideshare system that can dynamically match passenger requests with drivers and provide efficient routes to the drivers. We develop a greedy insertion-based routing procedure to route thousands of requests in an hour. We incorporate flexible meeting point selection into our framework, which can reduce travel distances for both drivers and passengers. We implement an online incentive and cost-sharing system that can incentivize drivers and passengers for their ride time limit violations and share the cost of a rideshare trip among the passengers fairly. To get the most updated travel time and study the effects of ridesharing in a road network, we also incorporate a simulation approach into our framework. Numerical experiments performed on the New York Taxicab dataset and a rural dataset based on Kern and Tulare Counties, California, show that our proposed framework is effective, matching thousands of requests per hour within minutes. Results also show that ridesharing can cost significantly less compared to ride-hailing services such as Uber or Lyft, and incorporating flexible meeting points can reduce travel distance by 4% on average. Simulation studies show that ridesharing can reduce total vehicle miles traveled by 13% in Manhattan and 31% in Kern and Tulare Counties on average. Our proposed framework can help transportation officials design real-time and city-scale rideshare systems to alleviate traffic congestion and transportation accessibility problems in California.
