Application and Validation of Dynamic Freight Simulation–Assignment Model to Large-Scale Intermodal Rail Network

Zhang, Kuilin; Nair, Rahul; Mahmassani, Hani S.; Miller-Hooks, Elise; Arcot, Vishnu Charan; Kuo, April Y.; Dong, Jing; Lu, Chung-Cheng

doi:10.3141/2066-02

Cited by 25 publications

(15 citation statements)

References 15 publications

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“…it to a Pan-European rail network (16). Using bilevel programming, in which a lower-level problem finds the multimodal, multiclass user traffic assignment and an upper-level problem determines the maximum benefit-cost ratio yielding network improvement actions, Yamada et al developed a multimodal freight network model for strategic transportation planning (17).…”

Section: Formulationmentioning

confidence: 99%

Freight Traffic Assignment Methodology for Large-Scale Road–Rail Intermodal Networks

Uddin

Huynh

2015

Transportation Research Record: Journal of the Transportation R

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A methodology is proposed for freight traffic assignment in large-scale road–rail intermodal networks. To obtain the user–equilibrium freight flows, a path-based assignment algorithm (gradient projection) was proposed. The developed methodology was tested on the U.S. intermodal network by using the 2007 freight demand for truck, rail, and road–rail intermodal from the Freight Analysis Framework, Version 3 (FAF3). The results indicate that the proposed methodology's projected flow pattern is similar to the FAF3 assignment. The proposed methodology can be used by transportation planners and decision makers to forecast freight flows and to evaluate strategic network expansion options.

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Section: Formulationmentioning

confidence: 99%

Freight Traffic Assignment Methodology for Large-Scale Road–Rail Intermodal Networks

Uddin

Huynh

2015

Transportation Research Record: Journal of the Transportation R

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“…Xu et al (2003) developed a freight traffic simulator as part of their TTMNet module that receives pseudo-real time information from a dynamic supply chain network equilibrium model. Mahmassani et al (2007) and Zhang et al (2008) developed an intermodal, dynamic freight network simulator that accounts for load scheduling and vehicle routing. Their system has been applied to an intermodal corridor in Europe to illustrate the analysis of several policies such as reducing technological/communicative barriers across national borders and improving infrastructure by increasing maximum rail speeds.…”

Section: Other Innovationsmentioning

confidence: 99%

State-of-the art of freight forecast modeling: lessons learned and the road ahead

2010

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In this paper we review freight forecasting models and current advances and needs with respect to data and model development. We then present a case study to suggest which models should be developed for the State of California in the US. We suggest several alternatives including an aggregate commodity flow model, a disaggregate regional logistics model and a hybrid regional logistics model with a truck touring model. We point out however, that the data requirements for the latter model would be extensive. In addition, the development of hybrid models, for example progress in the integration of regional logistics models with urban truck touring models, will introduce new problems such as reconciling the outputs of multiple models for consistency.

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“…At a certain future traffic level, the combination of decreased fuel efficiency, prolonged transit times and poor service reliability associated with congested rail lines and terminals may degrade the efficiency of the rail mode to the point where there is no longer any incentive for highly competitive shipments to move by rail and they may return to truck, as seen by point B in Figure 2. The loss of traffic will ease some rail network congestion and drive traffic back to the rail mode until equilibrium is reached, point C. This equilibrium has been studied by several researchers via mode choice models that include current truck and rail fuel efficiency metrics as input parameters [12][13][14]. In order for these models to adequately describe future scenarios and congested rail traffic levels, proper assumptions must be made to capture potential variation in passenger and freight rail fuel efficiency under these conditions.…”

Section: Figure 2 Relative Efficiency Of Rail and Highway Transportamentioning

confidence: 99%

Congestion as a Source of Variation in Passenger and Freight Railway Fuel Efficiency

Fullerton

DiDomenico

Shih

et al. 2014

2014 Joint Rail Conference

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Recent railway industry campaigns have highlighted the relative average fuel efficiency of freight and passenger trains as a key benefit of the railway transportation mode. These efficiencies are anticipated to increase rail market share as rising energy costs make less efficient competing modes less attractive. However, the fuel consumption and energy efficiency of a specific passenger or freight rail system, and even individual trains, depend on many factors. Changes in these factors can have various effects on the overall fuel consumption and efficiency of the system. One of these factors is the amount of congestion and delay due to increased traffic on the line. Thus, it is possible that the additional traffic anticipated to shift to the rail mode due to its energy benefits may increase congestion and actually have a negative impact on overall network energy efficiency. Such a case would tend to dampen the future shift of traffic to the rail mode. While simple train performance calculators can evaluate the energy efficiency of a train for an ideal run, more powerful train dispatching simulation software is required to simulate the performance of trains in realistic operating scenarios on congested single-track lines. Using this software, the relative impact of congestion on efficiency can be analyzed and compared to changes in factors related to fuel consumption. In this study, several factors affecting the efficiency of both passenger and freight rail systems were selected for analysis. Rail Traffic Controller (RTC), a train dispatching software, simulated representative single-track rail subdivisions to determine the performance of specific passenger and freight trains under different combinations of factor level settings. For passenger rail, the effects of traffic volume and station spacing on fuel consumption were analyzed while the effects of traffic volume and average speed were analyzed for freight rail. Each system was analyzed on level track and on territory with grades. Preliminary results suggest that passenger trains, if given priority, maintain their efficiency until large numbers of passenger trains are present on the network, while freight trains experience degradation in energy efficiency as congestion increases. These results will be used to develop a factorial experiment to evaluate the relative sensitivity of freight and passenger rail efficiency to congestion and other system parameters. The paper concludes with a brief discussion of possible technologies to improve efficiency and offset potential losses due to future congestion.

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Application and Validation of Dynamic Freight Simulation–Assignment Model to Large-Scale Intermodal Rail Network

Cited by 25 publications

References 15 publications

Freight Traffic Assignment Methodology for Large-Scale Road–Rail Intermodal Networks

Freight Traffic Assignment Methodology for Large-Scale Road–Rail Intermodal Networks

State-of-the art of freight forecast modeling: lessons learned and the road ahead

Congestion as a Source of Variation in Passenger and Freight Railway Fuel Efficiency

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