Using Real Time Traveler Demand Data to Optimize Commuter Rail Feeder Systems
Yao Yu and Randy Machemehl, University of Texas at Austin, August 2012, 111 pp.
This report focuses on real time optimization of the Commuter Rail Circulator Route Network Design Problem (CRCNDP). The route configuration of the circulator system – where to stop and the route among the stops – is determined on a real-time basis by employing adaptive Tabu Search to timely solve a Mixed Integer Program (MIP) problem with an objective to minimize total cost incurred to both transit users and transit operators. Numerical experiments are executed to find the threshold for the minimum fraction of travelers that would need to report their destinations via smart phone to guarantee the practical value of optimization based on real-time collected demand against a base case defined as the average performance of all possible routes. The adaptive Tabu Search Algorithm is also applied to three real-size networks abstracted from the Martin Luther King (MLK) station of the new MetroRail system in Austin, Texas.
Keywords: Commuter Rail Circulator Network Design Problem (CRCNDP), Austin Texas, Tabu Search, Mixed Integer Program
ENTIRE REPORT (Adobe Acrobat File – 1.6 MB)