Value of ITS Information for Congestion Avoidance in Inter-Modal Transportation Systems: Focus Area: Infrastructure Utilization Year 4
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Value of ITS Information for Congestion Avoidance in Inter-Modal Transportation Systems: Focus Area: Infrastructure Utilization Year 4

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      Final Report
    • Abstract:
      This Year 4 Final Report focuses on infrastructure utilization in the study of intelligent transportation system (ITS) information for congestion avoidance in intermodal transportation systems. In this report, intermodal freight refers to the shipment of freight involving more than one mode of transportation (road, rail, air, and sea) during a single, seamless journey. Three major milestones were addressed in year 3: (1) Compare the performance of static and dynamic models through the case studies; (2) Develop extensive scenarios based on loading levels at inter-modal facilities and transportation network, disruption and incident states, and fidelity of realtime information, etc.; (3) Apply Static and Dynamic models for the different scenarios in a simulation test-bed to perform cost/benefit analysis. In Section A of this report, the authors consider a freight forwarder's operational implementation of alternative access airport policy in a multi-airport region for air cargo transportation to evaluate static and dynamic routing policies. Given a set of heterogeneous air cargo customers and their air-cargo characteristics, the forwarder's problem is to simultaneously select air cargo flight itineraries and schedule the pickup and delivery of customer loads to the airport(s) such that the cargo is delivered to the airport on-time for the assigned flight itineraries. This problem is formulated as a novel pickup and delivery problem, where the delivery cost is both destination and time dependent. An efficient solution method based on Lagrangian decomposition and variable target method with backtracking is developed. Results of computational experiments and a practical case study in the Southern California demonstrate the merits of the model and show that the proposed algorithm is very efficient and obtains near-optimal solutions. In section B, the authors consider disruptions in intermodal facilities used by a company to transport its freight. Facing a disruption scenario, the company re-allocates the flow of goods through those facilities that survive as well as resort to emergency alternatives (e.g., expediting). For this problem, the authors present a novel hybrid method, swarm intelligence based sample average approximation (SIBSAA), for solving the capacitated reliable facility location problem (CRFLP). The CRFLP extends the well-known capacitated fixed-cost facility problem by accounting for the unreliability of facilities. The standard SAA procedure, while effectively used in many applications, can lead to poor solution quality if the selected sample sizes are not sufficiently large. With larger sample sizes, however, the SAA method is not practical due to the significant computational effort required. The proposed SIBSAA method addresses this limitation by using smaller samples and repetitively applying the SAA method while injecting social learning in the solution process inspired by the swarm intelligence of particle swarm optimization. The authors report on experimental study results showing that the SIBSAA improves the computational efficiency significantly while attaining same or better solution quality than the SAA method. The results of computational experiments also indicate that the benefit of having flexibility in the inter-modal transportation system increases with increasing failure likelihood and severity. The authors also note that the flexibility levels depend on the capacity as well as various cost factors such as recourse costs.
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