In order to make optimal use of the capacity, rail networks in urban areas are extremely complicated, consisting of multiple trackage configurations of single, double, and, in high traffic zones, even triple-track. Another complicating factor of rail networks in urban areas is the existence of multiple speed limits at different points in the network because of physical contours, cross-overs, or other safety considerations as opposed to travel in rural areas where a single speed limit may apply for long stretches of travel. Therefore, there is a need to develop theory and techniques to model and develop policies to streamline operations in these types of complicated rail networks.

Determining the optimal dispatch policies that minimizes train delays and ensures deadlock-free operations is NP-hard. Therefore, most of the research efforts in this direction have focused on developing simple heuristics as well as detailed simulations to plan dispatching policies. Optimization efforts thus far have only addressed simple trackage configurations like single, double, and partially double rail lines. However complicating entities in a rail system like junctions, crossovers, sidings, speed limits and goods priorities need to be considered in order to extend the current theory and scheduling algorithms to large-scale urban rail networks. The current state-of-the art in scheduling these complicated networks includes only simple insertion type algorithms. To address this need, our proposed research will consist of the following tasks:

(1) Development of a simulation model that can be implemented using commercially available simulation software. The current train simulation model used by the PI is based on old simulation software that is no longer supported and is outdated.

(2) Development of real-time dynamic scheduling algorithms for complex general rail networks, commonly found in urban areas.

The main impact of the proposed research will have a significant bearing on real-world urban rail operations, many of whose pertinent complexities have been ignored in theoretical studies thus far.

Task Descriptions
(1) Data Collection and Literature Review

(2) Simulation Model Development

(3) Real-Time Dynamic Scheduler

(4) Validation and Feasibility Assessment

Milestones, Dates:
(1) August 16, 2009 - December 1, 2009

(2) August 16, 2009 - January 1, 2010

(3) October 1, 2009 - June 1, 2010

(4) April 1, 2010 - July 31, 2010

Total Budget:
$83,033

Student Involvement:
One graduate student at 50% effort, 12 months

Relationship to Other Research Projects:
Related to 99-03, 01-06, 07-11; part of the goods movement focus area

Technology Transfer Activities:
Project report will be posted soon

Potential Benefits of the Project:
Improving Complex Train Network Scheduling and Throughput

TRB Keywords:
Optimization, real-time, dynamic algorithms, scheduling

Primary Subject:
3p.7 To develop real-time dynamic algorithms that can be used to schedule trains for complicated rail networks