Estimation of run times in a freight rail transportation network

The objective of this thesis is to improve the accuracy of individual freight train run time predictions defined as the time between departure from an origin node to arrival at a destination node not including yard time. A correlation analysis is conducted to identify explanatory variables that capture predictable sources of delay and influence run times for use in a regression model. A regression model is proposed utilizing the following explanatory variables: rolling historical average, congestion window, meets, passes, overtakes, direction, arrival headway, and departure headway to predict train run times. The performance of the proposed regression model is compared against a baseline simple historical averaging technique for a two year period of actual train operational data. The proposed regression model, though subject to specific limitations, offers substantial improvements in accuracy over the baseline technique and is recommended as justifying further exploration by the railroad to ultimately enable more accurate train schedules with subsequent improvements in railroad capacity, customer service, and asset utilization.

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering Systems Division.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2012

Authors dc:creator

• Bonsra, Kunal (Kunal Baldev)
• Harbolovic, Joseph

Advisor dc:contributor.advisor

• Başak Kalkancı and Eva M. Ponce Cueto.

• Engineering Systems Division.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/77458

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/77458