Using real time traveler demand data to optimize commuter rail feeder systems
Commuter rail systems, operating on unused or under-used railroad rights-of-way, are being introduced into many urban transportation systems. Since locations of available rail rights-of-way were typically chosen long ago to serve the needs of rail freight customers, these locations are not optimal for commuter rail users. The majority of commuter rail users do not live or work within walking distance of potential commuter rail stations, so provision of quick, convenient access to and from stations is a critical part of overall commuter decisions to use commuter rail. Minimizing access time to rail stations and final destinations is crucial if commuter rail is to be a viable option for commuters. Well-designed feeder routes or circulator systems are regarded as potential solutions to provide train station to ultimate destination access. Transit planning for main line or feeder routes relies upon static demand estimates describing a typical day. Daily and peak-hour demands change in response to the state of the transport system, as influenced by weather, incidents, holiday schedules and many other factors. Recent marketing successes of “smart phones” might provide an innovative means of obtaining real time data that could be used to identify optimal paths and stop locations for commuter rail circulator systems. Such advanced technology could allow commuter rail users to provide real-time final destination information that would enable real time optimization of feeder routes. This dissertation 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 an 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.