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Estimating Truck Trips with Product Specific Data: A Disruption Case Study in Washington potatoes

Publication: Transportation Letters
Volume: 4 (3)
Pages: 153-166
Publication Date: 2012
Summary:

Currently, knowledge of actual freight flows in the US is insufficient at a level of geographic resolution that permits corridor-level freight transportation analysis and planning. Commodity specific origins, destinations, and routes are typically estimated from four-step models or commodity flow models. At a sub-regional level, both of these families of models are built on important assumptions driven by the limited availability of data. This study was motivated by a desire to determine whether efforts to gather corridor-level freight movement data will bring significant new insights over current approaches to freight transportation modeling. Through a case study of Washington State’s potato and value added potato products industry, we show that significant insight can be gained by collecting commodity-specific truck trip generation and destination data: the approach allows product specific truck trips to be estimated for each roadway link. When considering a network change, the number of affected trips can be identified, and their re-route distance quantified.

Authors: Dr. Anne Goodchild, Derik Andreoli, Eric Jessup
Recommended Citation:
Derik Andreoli, Anne Goodchild & Eric Jessup (2013). Estimating Truck Trips with Product Specific Data: A Disruption Case Study in Washington Potatoes, Transportation Letters, 4:3, 153-166, DOI: 10.3328/TL.2012.04.03.153-166
Chapter

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

 
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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

Identifying Truck Route Choice Priorities: The Implications for Travel Models

Publication: Transportation Letters
Volume: 6 (2)
Pages: 98-106
Publication Date: 2014
Summary:

This article identifies the truck routing priorities of freight companies through a survey of Washington state shippers, carriers, and receivers. To elicit these priorities, the survey prompted the respondents to rate 15 items believed to affect route choice decision making with respect to each item’s influence on route choice. Item response theory (IRT) and latent class analysis (LCA) highlights priorities that were common among all survey respondents and priorities that were different among the sample.

Minimizing cost and meeting customer requirements were priorities for all. The influence of other items such as road grade, hours of service limits, and driver availability depended on whether the respondent was best described as a long-haul, local-regional, or urban trucking provider. These three classes of companies were derived from the LCA, and each class has a distinct response pattern to the 15 routing items. This result suggests that truck routing priorities are not constant and uniform across a state’s trucking industry but rather variable and largely dependent on trip length. The paper concludes with practical recommendations as to how these priorities can be implemented within a truck routing model.

Authors: Dr. Anne Goodchild, Maura Rowell, Andrea Gagliano
Recommended Citation:
Rowell, Maura, Andrea Gagliano, and Anne Goodchild. "Identifying Truck Route Choice Priorities: The Implications for Travel Models." Transportation Letters 6, no. 2 (2014): 98-106.