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Presentation

Development and Application of a Framework to Classify and Mitigate Truck Bottlenecks to Improve Freight Mobility

 
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Publication: Transportation Research Record: Journal of the Transportation Research Board
Volume: TRN Annual Meeting
Publication Date: 2018
Summary:

This paper presents a framework to classify and mitigate roadway bottlenecks and that is designed to improve freight mobility. This is in recognition that roadway operations for trucks are under studied, truck-only bottlenecks are often not identified and freight-specific problem areas are therefore often overlooked. The framework uses four-steps:

Step 1: identifies and locates the roadway sections where vehicle travel time is in excess of what would normally occur.

Step 2: made possible by increasingly available truck probe data, identifies bottlenecks for all vehicles or for trucks only. This is necessary to identify bottlenecks that notably impact freight mobility and might not be identified by car-based approaches.

Step 3: classifies bottlenecks as travel speed-based or process-based. This selects the mitigation treatments as mainly due to operational or roadway limitations.

Step 4: which is the core of the paper, supports the mitigation process by determining the cause of the bottleneck. The bottlenecks are identified as due to congestion, limitations where roadway design slows all vehicles, or where a truck’s size or weight can slow vehicles (such as tight curves or bridge restrictions).

The paper present a review of specific roadway attributes that limit a truck’s mobility and is used to suggest mitigation. The framework is demonstrated using a case study. The framework is designed to be applied by planning and infrastructure agencies who want to locate and address freight bottlenecks in a systematic manner using available resources as well as by researchers interested in linking roadway attributes to truck mobility.

Authors: Dr. Ed McCormackDr. Anne Goodchild, William Eisele, Mark Hallenbeck
Recommended Citation:
McCormack, Edward, Anne Goodchild, W. Eisele, and Mark Hallenbeck. "Development and Application of a Framework to Classify and Mitigate Truck Bottlenecks to Improve Freight Mobility." TRN Annual Meeting, Washington D.C. 2018.
Paper

A Methodology for Forecasting Freeway Travel Time Reliability Using GPS Data

 
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Publication: Transportation Research Procedia
Volume: 25
Pages: 842-852
Publication Date: 2017
Summary:

The objective of this paper is to develop a methodology for forecasting freeway vehicle travel time reliability for transportation planning using probe GPS data. Travel time reliability is measured using the coefficient of variation of the GPS spot (instantaneous) speed distribution. The proposed approach establishes relationships between travel time reliability and roadway traffic density in order to forecast reliability given future traffic conditions. The travel time reliability and traffic density datasets are segmented into different homogenous groups using the K-means cluster algorithm and the corresponding reliability-density relationship of each cluster is fitted by minimizing squared errors. This paper employs a truck probe GPS dataset as an example to demonstrate the proposed approach. The approach can be applied with any GPS datasets for forecasting reliability.

Recommended Citation:
Wang, Zun, Anne Goodchild, and Edward McCormack. A Methodology for Forecasting Freeway Travel Time Reliability Using GPS Data. Transportation Research Procedia, (25) 842–852. https://doi.org/10.1016/j.trpro.2017.05.461
Paper

GPS Data Analysis of the Impact of Tolling on Truck Speed and Routing: A Case Study in Seattle, WA

 
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Publication: Journal of the Transportation Research Board
Volume: 2411:01:00
Pages: 112-119
Publication Date: 2014
Summary:

Roadway tolls are designed to raise revenue to fund transportation investments and manage travel demand and as such may affect transportation system performance and route choice. Yet, limited research has quantified the impact of tolling on truck speed and route choice because of the lack of truck-specific movement data. Most existing tolling impact studies rely on surveys in which drivers are given several alternative routes and their performance characteristics and asked to estimate route choices. The limitations of such an approach are that the results may not reflect actual truck route choices and the surveys are costly to collect. The research described in this paper used truck GPS data to observe empirical responses to tolling, following the implementation of a toll on the State Route 520 (SR-520) bridge in Seattle, Washington. Truck GPS data were used to evaluate route choice and travel speed along SR-520 and the alternate toll-free Route I-90. It was found that truck travel speed on SR-520 improved after tolling, although travel speed on the alternative toll-free Route I-90 decreased during the peak period. A set of logit models was developed to determine the influential factors in truck routing. The results indicated that travel time, travel time reliability, and toll rate were all influential factors during peak and off-peak periods. The values of truck travel time during various time periods were estimated, and it was found that the values varied with the definition of peak and off-peak periods.

Authors: Dr. Anne Goodchild, Zun Wang
Recommended Citation:
Wang, Zun, and Anne V. Goodchild. “GPS Data Analysis of the Impact of Tolling on Truck Speed and Routing.” Transportation Research Record: Journal of the Transportation Research Board, vol. 2411, no. 1, 2014, pp. 112–119., doi:10.3141/2411-14.