Principal Investigator: Jing Dong
About this Project
Brief Project Description & Background
In the event of natural or artificial disasters, part of the freight transportation network (or a certain mode) might be impacted, which leads to reduced terminal service rates and/or link capacities. As a result, longer delays are expected during such major disruptions. Moreover, the terminal delays will change with time-of-day, reflecting the time-dependence of shipment flows. Thus, it is essential for an intermodal freight transportation network model to explicitly capture the time-varying, non-stationary delays at terminals.
This research project plans to build a data-driven freight transportation network model that incorporates a GIS-based intermodal network and the assignment of commodity flows on each route.
The decision support tool developed in the proposed project can help emergency managers, security and emergency personnel to make timely impact evaluation, comprehensive vulnerability assessments and collaborative recovery planning for freight transportation networks under disruption. Moreover, real-time information and recommendation of alternative routes and transportation modes to freight shippers, receivers, and carriers, will encourage collaborative decision-making to improve transportation efficiency and mitigate disruption impacts.
This research project plans to build a data-driven freight transportation network model that incorporates a GIS-based intermodal network and the assignment of commodity flows on each route. In the event of natural catastrophes or man-made disasters, part of the network will be closed or operated at a reduced capacity. A fluid-based dynamic queueing approximation is used to perform a quick and relatively accurate estimation of the delays at classification yards, ports, locks or intermodal terminals caused by such disruption in the network. By simulating commodity movements on the disrupted freight transportation network, the proposed network model enables (1) estimation of freight transportation network performance under disruptions, (2) evaluation of emergency response and recovery plans in the immediate aftermath, (3) information provision regarding alternative shipping route and mode for shippers, receivers and carriers; and (4) vulnerability and resiliency analysis of the freight transportation network, identification of the vulnerable links and development of proactive strategies. To demonstrate the operational effectiveness of the proposed modeling approach, a risk area and what-if scenarios will be generated. Vulnerability and resiliency analysis of the study area will be conducted. A set of emergency response and recovery plans will also be evaluated and compared in terms of delays, economic impacts and recovery time. Though the incident affects only a small area, the freight flows throughout the nation might be impacted, which will be captured by the national network model.