Paper

Choosing My Own Path: Revealing Differences in Route Choice Preferences Across Long-Haul, Medium-Haul, and Short-Haul Trucking

URL: https://dx.doi.org/10.2139/ssrn.4853521

Publication Date: 2023

Summary:

The rapid growth in e-commerce activities and the constant specialization of industries have aroused an unparalleled demand for trucking in urban areas, leading to growing concern over its interference to the transportation system. Understanding truck route choice preferences across long-haul, medium-haul, and short-haul trips can offer insights for designing the truck route network tailored to specific freight demand types, so as to effectively reduce their interference to passenger transportation. However, limited research has been conducted to explore the heterogeneity or similarity of route choice preferences across those freight demand types. This study utilizes the Path Size Logit Model to explore the characteristics of preferred route across long-haul, medium-haul, and short-haul trips, and reveal the underlying route choice mechanism behind enormous trucking activities. By employing truck GPS data from China, we find that (1) although the characteristics of preferred routes vary across long-haul, medium-haul, and short-haul trips, those trips collectively reflect full consideration of travel efficiency, safety, and reliability; (2) all these freight demand types incline to the routes with shortest travel distances instead of those with shortest travel time, while short-haul trips exhibit the highest sensitivity to travel distance; (3) drivers in both long-haul and medium-haul trips favor routes that combine motorways and sub-arterial roads, while long-haul trips present higher sensitivity; (4) drivers in short-haul trips show preferences for routes featuring fewer turns, and sub-arterial roads given last-mile delivery demand. Finally, we propose suggestions for designing urban truck route network to accommodate diverse freight demand in high-density urban areas with limited road resources.

Authors: Dr. Anne Goodchild, Zhengtao Qin, Ruixu Pan, Chengcheng Yu, Tong Xiao, Chao Yang, Quan Yuan (Tongji University)

Recommended Citation:
Qin, Zhengtao and Pan, Ruixu and Yu, Chengcheng and Xiao, Tong and Yang, Chao and Goodchild, Anne and Yuan, Quan, Choosing My Own Path: Revealing Differences in Route Choice Preferences Across Long-Haul, Medium-Haul, and Short-Haul Trucking. http://dx.doi.org/10.2139/ssrn.4853521

Technical Report

Structural and Geographic Shifts in the Washington Warehousing Industry: Transportation Impacts for the Green River Valley

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URL: https://rosap.ntl.bts.gov/view/dot/17233

Publication: Transportation Northwest (TransNow)

Publication Date: 2009

Summary:

Establishment level employment data indicate that the warehousing industry has experienced rapid growth and restructuring since 1998. This restructuring has resulted in geographic shifts at the national, regional, and local scales. Uneven growth in warehousing establishments across the Pacific Northwest has likely exerted a significant impact on the regional transportation system, but the extent of these transportation impacts remains unknown. Identifying these impacts is the goal of our proposed study. Recent and ongoing research indicates that growth in the warehousing industry is profound. County Business Patterns data published by the US Census Bureau indicates that at the national level, the number of warehousing establishments grew by just over 100 percent from 1998 to 2005. In 1998 there were 6,712 warehousing establishments in the US. By 2005, that number had increased to 13,483. Although a wide range exists within the warehousing industry, interview data collected by the authors of this proposal indicate that each warehouse handles between 25 and 100 trucks, or 50 and 200 trips, hence the location of warehousing establishments has a significant impact on transportation systems. At the county level, we see that in Washington, King County experienced the strongest absolute growth, adding 59 establishments to the 61 reported in 1998. In relative terms, however, Pierce County added warehousing establishments at a faster rate (159 percent) than any other county. The preliminary data produced in Goodchild and Andreoli’s report clearly indicate that there has been strong growth in warehousing establishments at the national and state levels, but that the growth has not been even across states and counties. From a transportation perspective, these findings suggest that future research needs to focus on how these structural and geographic shifts impact regional and local transportation systems.

Authors: Dr. Anne Goodchild, Derek Andrioli

Recommended Citation:
Goodchild, A., & Andrioli, D. (2009). Structural and Geographic Shifts in the Washington Warehousing Industry: Transportation Impacts for the Green River Valley (No. TNW2009-04). Transportation Northwest (Organization).

Paper

A Methodology for Forecasting Freeway Travel Time Reliability Using GPS Data

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URL: https://doi.org/10.1016/j.trpro.2017.05.461

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.

Authors: Dr. Anne GoodchildDr. Ed McCormack, Zun Wang

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

Freeway Truck Travel Time Prediction for Freight Planning Using Truck Probe GPS Data

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URL: https://journals.open.tudelft.nl/ejtir/article/view/3114/3301

Publication: European Journal of Transport and Infrastructure Research.

Volume: 16

Pages: 76-94

Publication Date: 2016

Summary:

Predicting truck (heavy vehicle) travel time is a principal component of freight project prioritization and planning. However, most existing travel time prediction models are designed for passenger vehicles and fail to make truck specific forecasts or use truck specific data. Little is known about the impact of this limitation, or how truck travel time prediction could be improved in response to freight investments with an improved methodology. In light of this, this paper proposes a pragmatic multi-regime speed-density relationship based approach to predict freeway truck travel time using empirical truck probe GPS data (which is increasingly available in North American and Europe) and loop detector data. Traffic regimes are segmented using a cluster analysis approach. Two case studies are presented to illustrate the approach. The travel time estimates are compared with the Bureau of Public Roads (BPR) model and the Akçelik model outputs. It is found that the proposed method is able to estimate more accurate travel times than traditional methods. The predicted travel time can support freight prioritization and planning.

Authors: Dr. Anne GoodchildDr. Ed McCormack, Zun Wang

Recommended Citation:
Wang, Zun, Anne V. Goodchild, and Edward McCormack. "Freeway truck travel time prediction for freight planning using truck probe GPS data." European Journal of Transport and Infrastructure Research 16, no. 1 (2016).

Presentation

Scheduling Double Girder Bridge Crane with Double Cycling in Rail-Based Transfer Automated Container Terminals

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Publication: Second Institute for Operations Research and the Management Sciences (INFORMS) Transportation Science and Logistics Society Workshop

Volume: 13-Jun

Publication Date: 2016

Summary:

In automated container terminals, rail based horizontal transfer systems are newly proposed and regarded to be more suitable to intermodal transportation [1]. However, improvements are required in operations scheduling in rail based transfer automated container terminals (RBT-ACT) to take advantage of the infrastructure improvement [2].

In this paper a double girder bridge crane (DGBC) is introduced, whose benefits can be obtained with modest investments, such as combining the existing twin 40-ft double trolley container cranes with a double girder [3]. Each girder has one independent spreader, and the two spreaders work on containers in adjacent bays simultaneously with no change to the safety distance constraints. As a result, operating costs are reduced, potential collision of QCs can be avoided and the vessel service time is reduced.

Most research in this area aims to minimizing crane cycles, not processing times [4], however is it processing time that is of ultimate interest [5]. Our objective is to minimize total processing time, and the sequence dependent setup time is considered [6]. It is well established that double cycling can greatly improve quay crane productivity [7], and we consider its performance in the scheduling strategy for DGBC.

Authors: Dr. Anne Goodchild, Dandan Wang, Xiaoping Li

Recommended Citation:
Wang, D., Goodchild, A., & Li, X. (2013, June). Scheduling double girder bridge crane with double cycling in rail based transfer automated container terminals. In Logistics Society Workshop (p. 91).

Paper

Examining Carrier Categorization in Freight Models

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URL: http://dx.doi.org/10.1016/j.rtbm.2014.06.006

Publication: Research in Transportation Business & Management

Volume: 11

Pages: 116-122

Publication Date: 2014

Summary:

Travel demand models are used to aid infrastructure investment and transportation policy decisions. Unfortunately, these models were built primarily to reflect passenger travel and most models in use by public agencies have poorly developed freight components. Freight transportation is an important piece of regional planning, so regional models should be improved to more accurately capture freight traffic. Freight research has yet to fully identify the relationships between truck movements and company characteristics in a manner sufficient to model freight travel behavior. Through analyzing the results of a survey, this paper sheds light on the important transportation characteristics that should be included in freight travel demand models and classifies carriers based on their role in the supply chain. The survey of licensed motor carriers included 33 questions and was conducted in Oregon and Washington. Respondents were asked about their vehicle fleets, locations served, times traveled, time windows, types of deliveries, and commodities. An assessment of how the relationships found can be integrated into existing models is offered.

Authors: Dr. Anne Goodchild, Maura Rowell, Andrea Gagliano

Recommended Citation:
Rowell, Maura, Andrea Gagliano, and Anne Goodchild. Examining Carrier Categorization in Freight Models. Research in Transportation Business & Management 11 (2014): 116-122.

Paper

Understanding Freight Trip Chaining Behavior Using Spatial Data Mining Approach with GPS Data

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URL: http://dx.doi.org/10.3141/2596-06

Publication: Transportation Research Record: Journal of the Transportation Research Board

Volume: 2596

Pages: 44-54

Publication Date: 2016

Summary:

Freight systems are a critical yet complex component of the transportation domain. Understanding the dynamic of freight movements will help in better management of freight demand and eventually improve freight system efficiency. This paper presents a series of data-mining algorithms to extract an individual truck’s trip-chaining information from multi-day GPS data. Individual trucks’ anchor points were identified with the spatial clustering algorithm for density-based spatial clustering of applications with noise. The anchor points were linked to construct individual trucks’ trip chains with 3-day GPS data, which showed that 51% of the trucks in the data set had at least one trip chain. A partitioning around medoids nonhierarchical clustering algorithm was applied to group trucks with similar trip-chaining characteristics. Four clusters were generated and validated by visual inspection when the trip-chaining statistics were distinct from each other. This study sheds light on modeling freight-chaining behavior in the context of massive freight GPS data sets. The proposed trip chain extraction and behavior classification algorithms can be readily implemented by transportation researchers and practitioners to facilitate the development of activity-based freight demand models.

Authors: Dr. Ed McCormack, X. Ma, W. Yong, and Yinhai Wang

Recommended Citation:
Ma, Xiaolei & Wang, Yong & McCormack, Edward & Wang, Yinhai. (2016). Understanding Freight Trip-Chaining Behavior Using a Spatial Data-Mining Approach with GPS Data. Transportation Research Record: Journal of the Transportation Research Board. 2596. 44-54. 10.3141/2596-06.

Student Thesis and Dissertations

Improving Statewide Freight Routing Capabilities for Sub-National Commodity Flows

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URL: https://www.wsdot.wa.gov/Research/Reports/700/792.1.htm

Publication: Washington State Transportation Center (TRAC)

Publication Date: 2012

Summary:

The ability to fully understand and accurately characterize freight vehicle route choices is important in helping to inform regional and state decisions. This project recommends improvements to WSDOT’s Statewide Freight GIS Network Model to more accurately characterize freight vehicle route choice. This capability, when combined with regional and sub-national commodity flow data, will be a key attribute of an effective statewide freight modeling system. To come to these recommendations, the report describes project activities undertaken, and their outcomes, including 1) a review of commercially available routing software, 2) an evaluation of the use of statewide GPS data as an input for routing analysis, and 3) the design, implementation, and evaluation of a survey of shippers, carriers, and freight forwarders within the state. The software review found that routing software assumes least cost paths while meeting user specified constraints, and it identified criteria for evaluation in the subsequent survey. The GPS data evaluation showed that significant temporal shifting occurs rather than spatial route shifting, and it revealed significant limitations in the use of GPS data for evaluating routing choices, largely because of the read rate. Among the survey results was that the first priority of shippers, carriers, and freight forwarders is to not only meet customer requirements, but to do so in the most cost-efficient way. From a latent class analysis of routing priorities, we discovered that distance-based classification best clusters similar routing behavior. The report includes recommendations for implementing this within the Statewide Freight GIS Network Model.

Authors: Dr. Anne Goodchild, Maura Rowell, Andrea Gagliano, Zun Wang, Jeremy Sage, Eric Jessup

Recommended Citation:
Rowell, Maura K., Andrea Gagliano, Zun Wang, Anne V. Goodchild, Jeremy L. Sage and Eric L. Jessup. “Improving Statewide Freight Routing Capabilities for Sub-National Commodity Flows.” (2012). University of Washington Doctoral Dissertation.

Paper

Impact of Truck Arrival Information on System Efficiency at Container Terminals

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URL: http://dx.doi.org/10.3141/2162-03

Publication: Transportation Research Record: Journal of the Transportation Research Board

Volume: 2162

Publication Date: 2014

Summary:

This paper quantifies the benefits to drayage trucks and container terminals from a data-sharing strategy designed to improve operations at the drayage truck-container terminal interface. This paper proposes a simple rule for using truck information to reduce container rehandling work and suggests a method for evaluating yard crane productivity and truck transaction time. Various scenarios with different levels of information quality are considered to explore how information quality affects system efficiency (i.e., truck wait time and yard crane productivity). Different block configurations and truck arrival rates are also investigated to evaluate the effectiveness of truck information under various system configurations. The research demonstrates that a small amount of truck information can significantly improve crane productivity and reduce truck delay, especially for those terminals operating near capacity or using intensive container stacking, and that complete truck arrival sequence information is not necessary for system improvement.

Authors: Dr. Anne Goodchild, Wenjuan Zhao

Recommended Citation:
Zhao, Wenjuan, and Anne V. Goodchild. "Impact of truck arrival information on system efficiency at container terminals." Transportation Research Record 2162, no. 1 (2010): 17-24.

Student Thesis and Dissertations

Enhancing Performance Measurement: Implementing Computable General Equilibrium Models in Truck-Freight Network Investment Prioritization

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URL: http://ses.wsu.edu/wp-content/uploads/2015/07/FPTI-16.pdf

Publication: Freight Policy Transportation Institute

Publication Date: 2013

Summary:

The adoption of defensible performance measures and establishment of proven results has become a necessity of many state Transportation Departments. A major factor in demonstrating results is the impact a transportation infrastructure improvement project has on the region’s economic climate. Though often previously underrepresented in policy and planning of transportation systems, freight movement plays a critical role in the transference of infrastructure improvement benefits into regional economic impacts. The degree of impact influenced by freight movement improvements is dependent upon location and geographic scale of evaluation. This paper assesses the geographic scale considerations in selecting the modeling framework to evaluate economic impacts. Specifically, we consider the results of regional input-output (I-O) models as compared to those of computable general equilibrium (CGE) models in response to reduced travel time and operating costs in the freight highway network. Though widely used for policy and planning purposes, I-O models have come under criticism for their inability to realistically model the behaviors of a regional economy. Despite their increased flexibility in real-world modeling, CGE models have been resisted due to their complexity of use. We consider the implications of selecting between ease of use and model flexibility at scales ranging from a single county to statewide.

Authors: Dr. Anne Goodchild, Jeremy Sage, John Maxwell, Zun Wang, Ken Casavant

Recommended Citation:
Sage, Jeremy. John Maxwell, Zun Wang, Ken Casavant, and Anne Goodchild. "Enhancing Performance Measurement: Implementing Computable General Equilibrium Models in Truck-Freight Network Investment Prioritization." University of Washington Master's degree thesis.