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

Paper

Evaluating Spatial Inequity in Last-Mile Delivery: A National Analysis

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URL: https://doi.org/10.1108/IJPDLM-08-2023-0301

Publication: International Journal of Physical Distribution & Logistics Management

Publication Date: 2024

Summary:

Purpose
Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.

Design/methodology/approach
The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).

Findings
Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.

Originality/value
Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

Authors: Travis FriedDr. Anne Goodchild, Ivan Sanchez Diaz (Chalmers University), Michael Browne (Gothenburg University)

Recommended Citation:
Fried, T., Goodchild, A.V., Sanchez-Diaz, I. and Browne, M. (2024), "Evaluating spatial inequity in last-mile delivery: a national analysis", International Journal of Physical Distribution & Logistics Management.

Paper

Challenges in Credibly Estimating the Travel Demand Effects of Mobility Services

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URL: https://doi.org/10.1016/j.tranpol.2021.02.001

Publication: Transport Policy

Volume: 103

Pages: 224-235

Publication Date: 2021

Summary:

Mobility services including carsharing and transportation network company (TNC) services have been growing rapidly in North America and around the world. Measuring the effects of these services on traveler behavior is challenging because the results of any such analysis are sensitive to how (1) outcomes are measured and (2) counterfactuals are constructed. The lack of good control groups or randomization of assignment leaves lingering uncertainty over the contributions of selection bias and treatment effects to reported differences in travel behavior between users and non-users of these services. This paper reports on two approaches for measuring the effects of mobility service adoption on travel rate and car ownership. We first tried a pretest-posttest randomized encouragement experiment to deal with the shortcomings of poor control groups. Then, we turned to the approach of self-reported effects based on hypothetical controls to investigate whether variations in survey question presentation could influence respondents’ answers and thus lead to changes in estimated effects. The data to conduct this study came from two sources: a panel survey administered by the authors at the University of Washington (UW), and a survey by Populus Technologies, Inc. (Populus). Various statistical tests were applied to analyze the data, and the results highlight the pivotal role that the research design plays in influencing the outcomes, and manifest the fundamental challenge of establishing credible estimates of the causal effects of adopting mobility services on travel behaviors.

Authors: Dr. Andisheh Ranjbari, Xiao Wen, Fan Qi, Regina R. Clewlow, Don MacKenzie

Recommended Citation:
Xiao Wen, Andisheh Ranjbari, Fan Qi, Regina R. Clewlow, Don MacKenzie. Challenges in credibly estimating the travel demand effects of mobility services. Transport Policy, (103:224-235) 2021. https://doi.org/10.1016/j.tranpol.2021.02.001.

Paper

A Framework for Determining Highway Truck-Freight Benefits and Economic Impacts

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URL: http://dx.doi.org/10.5399/osu/jtrf.52.2.4156

Publication: Journal of the Transportation Research Forum

Volume: 52

Pages: 27-43

Publication Date: 2013

Summary:

This paper proposes a method for calculating both the direct freight benefits and the larger economic impacts of transportation projects. The identified direct freight benefits included in the methodology are travel time savings, operating cost savings, and environmental impacts. These are estimated using regional travel demand models (TDM) and additional factors. Economic impacts are estimated using a regional Computable General Equilibrium (CGE) model. The total project impacts are estimated combining the outputs of the transportation model and an economic model. A Washington State highway widening project is used as a case study to demonstrate the method. The proposed method is transparent and can be used to identify freight specific benefits and generated impacts.

Though the Washington State Department of Transportation (WSDOT) has a long standing Mobility Project Prioritization Process (MPPP) (WSDOT 2000), which is a Benefit-Cost Analysis (BCA) framework used for mobility program assessment, it does not separately evaluate or account for the truck freight benefits of proposed highway infrastructure projects. It is therefore unable to evaluate and consider the economic impacts of highway projects that accrue to freight-dependent industries (those heavily reliant on goods movement) or non-freight-dependent firms (service sector) that are perhaps indirectly impacted by the productivity of the freight system. The established evaluation criteria of any transportation project largely influences the project selection and direction, thus for freight to become an integrated component of a managing agency’s transportation program, it must be recognized and acknowledged through the project evaluation criteria (NCHRP 2007). Before implementing any freight project evaluation criteria, an agency must first be able to identify the measures that matter to freight and freight-related systems. At this time there is no known nationally accepted framework for analyzing the full range of freight-related impacts stemming from transportation infrastructure projects. Complex interactions with separate, but not isolated, effects among economic, environmental, and social components with sometimes conflicting priorities make freight impacts more difficult to measure than those of other highway users (Belella 2005).

To successfully compete in a new funding world with significantly reduced monies for transportation infrastructure, states must become even more pragmatic about the means by which they emphasize and prioritize investments. Identification of the necessity to include freight performance measures in local, state, and national transportation plans, and rise above anecdotal understandings of system performance, is becoming evident as more municipalities and state agencies move toward implementing freight-related plans (MnDOT 2008, Harrison et al. 2006). Therefore, WSDOT has undertaken the development of an improved methodology to assess highway truck-freight project benefits designed to be integrated into the department’s existing prioritization processes. This paper lays out the development process of this effort and the resulting methodology. The contribution of this paper to the literature is to present a methodology that includes a truck-specific determination of the economic value of a project in addition to the economic impacts captured by a regional Highway Truck-Freight Benefits 28 computable general equilibrium (CGE) framework. The proposed method is transparent, and can be used to identify freight-specific benefits and generated impacts.

The remainder of this paper is organized as follows: the second section provides a brief review of the state of practice in the evaluation of transportation infrastructure investments; the third section details the process by which the benefits to be included in the analysis were selected and the methodology subsequently developed; the next section applies the methodologies to a case study and provides its result; the last section offers conclusions of the proposed methodology as well as the limitations of the study and directions for future work on fully incorporating freight into state DOT investment decisions.

Authors: Dr. Anne Goodchild

Recommended Citation:
Wang, Zun, Jeremy Sage, Anne Goodchild, Eric Jessup, Kenneth Casavant, and Rachel L. Knutson. "A framework for determining highway truck-freight benefits and economic impacts." In Journal of the Transportation Research Forum, vol. 52, no. 1424-2016-118048, pp. 27-43. 2013.

Chapter

Guide for Identifying, Classifying, Evaluating, and Mitigating Truck Freight Bottlenecks

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URL: http://www.trb.org/Publications/Blurbs/176115.aspx

Publication: Transportation Research Board - NCHRP Research Report

Volume: 854

Publication Date: 2017

Summary:

The demand for truck transportation increases alongside growth in population and economic activity. As both truck and passenger traffic outstrip roadway capacity, the result is congestion, which the freight community experiences as truck bottlenecks. This NCHRP project produced a Guidebook that provides state-of-the-practice information to transportation professionals on practices and measures for identifying, classifying, evaluating, and mitigating truck freight bottlenecks. The intent is to help decision-makers in developing cost-effective solutions to address different types of truck freight bottlenecks.

The Guidebook is designed for use by transportation planners and research and operational staff. Its contents

Define a common language related to truck freight bottlenecks
Classify truck freight bottleneck categories based on causal and contributing factors
Describe truck bottleneck state of the practice
Provide highlights from several case studies related to truck bottlenecks
Describe data sources used for truck bottleneck analysis
Provide a spatially scalable methodology for identifying truck freight bottlenecks
Describe quantitative measures for truck freight bottleneck categories for determining bottleneck severity, impact, and ranking and subsequent decision-making
Describe mitigation options for truck freight bottlenecks
Describe how to integrate freight bottleneck analysis into the planning process.

The Guidebook embraces a broad term for “truck freight bottlenecks” as any condition that acts as an impediment to efficient truck travel, thereby leading to travel times in excess of what would normally occur. This definition encompasses a wide range of events and conditions, all of which add time to the delivery of truck freight shipments, from the time those shipments leave their origin to the time they arrive at their destination.

The Guidebook describes two methodologies for identifying truck freight bottlenecks:

A travel speed-based delay methodology, and
A process or operation delay-based methodology.

The bottleneck analysis described in the Guidebook focuses on utilizing truck probe data rather than traditional travel demand models. Truck probe speed data can be used in conjunction with other data sources (e.g., crash data, weather data, volume data) to identify the causes of bottlenecks. The methodologies are scalable in multiple ways, and this will allow agencies to use their available data resources regardless of the source or size of those resources. In addition, the same analytical approach will work whether the analysis is performed for an entire state highway network, a regional network, or even a specific city. The recommended approach can also be applied to a single road segment, multiple roads within a geographic corridor, an entire region, to all roads in the state, or to all roads in a multistate region. Finally, the methodology can be used to demonstrate the benefit of bottleneck improvements to truckers, policy decision-makers, and the general public. This is particularly true for bottlenecks based on operational restrictions (such as geometric or height restrictions or truck bans).

Authors: Dr. Anne GoodchildDr. Ed McCormack, Dike Ahanotu, Richard Margiotta, Bill Eisele, Mark Hallenbeck

Recommended Citation:
Ahanotu, Dike, Richard Margiotta, Bill Eisele, Mark Hallenbeck, Anne Goodchild, and Ed McCormack. (2017) Guide for Identifying, Classifying, Evaluating, and Mitigating Truck Freight Bottlenecks. Transportation Research Board. Project 08-98. 2017.

Paper

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

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URL: https://doi.org/10.3141/2411-14

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.