MetroFreight Researchers Maged Dessouky and Lunce Fu from the University of Southern California,Viterbi School of Engineering published their interim report titled Integrating Management of Truck and Rail Systems in Los Angeles. This research is supported by the Volvo Research and Education Foundations through the MetroFreight Center of Excellence at METRANS Transportation Center.

Dessouky and Fu note that according to the Federal Railroad Administration, the American railway system will experience a 22% increase in tonnage carried from 2010 to 2035. However, the capital cost of expanding railroad infrastructure is very expensive. This research project aims to enhance rail system efficiency by designing a Positive Train Control (PTC) Algorithm, improving truck and train movement.

Existing simulation modeling cannot represent dynamic headway control since the number and length of nodes in the model may vary as the train travels through the network at different speeds and rates of acceleration and deceleration. The researchers proposed a new simulation framework to represent dynamic headways.  They formulated an optimization model and provided a heuristic method to schedule trains in the new framework. Dessouky and Fu introduced the PTC system in order to achieve optimal routing, scheduling, and speed of each train. In addition, the use of this algorithm will reduce human labor expenditures, an indicator of improved efficiency.

Three essential decisions are made to achieve dynamic control:

1. Routing decision, or the choice of the next headway node,
2. Headway decision, the number of headway nodes needed, and
3. Velocity decision, the speed at the next decision point.

Dessouky and Fu tested their theory using one-year data from the Los Angeles area rail network that they collected at the beginning of the project. Their results show that the dynamic headway approach will significantly increase system efficiency as compared to the constant headway method. This new approach is also applicable to networks with smaller node size.

The full interim report can be found here.

Maged M. Dessouky is a professor of Industrial and Systems Engineering at the University of Southern California, and the director of the Epstein Institute. He received B.S. and M.S. degrees from Purdue University and a Ph.D. in Industrial Engineering from the University of California, Berkeley. He is area/associate editor of the IEEE Transactions on Intelligent Transportation Systems, Transportation Research Part B: Methodological, the IIE Transactions and Computers and Industrial Engineering, is on the editorial board of Transportation Research Part E: Logistics and Transportation Review, and previously served as area editor of the ACM Transactions of Modeling and Computer Simulation. He is a Fellow of IIE and was awarded the 2007 Transportation Science and Logistics Best Paper Prize.

Lunce Fu is a Ph.D. candidate in Industrial and Systems Engineering at the University of Southern California. He received his B.S. in Mathematics from Peking University in 2011.

Nicole Guo

Nicole (Haichao) Guo is a first-year graduate student majoring in transportation engineering at the University of Southern California. Her interests are Intelligent Transportation Systems, High-Speed Rail, and active transportation. She is also an active student member of WTS-Los Angeles. She can be reached at [email protected].