Student Thesis and Dissertations

Truck GPS Data in Freight Planning: Methodologies and Applications for Measurement and Forecasting

URL: https://digital.lib.washington.edu/researchworks/handle/1773/26109

Publication Date: 2014

Summary:

Efficient and reliable goods movement via our nation’s highway system is critical to the nation’s economy and quality of life. Truck mobility is one of the key performance measures for evaluating the conditions of goods movement and supporting freight planning. Truck GPS data can be useful in developing truck mobility measures and providing insights into freight planning. This dissertation employs truck GPS data and proposes a set of methodologies for measuring and forecasting truck mobility performance, with particular emphases on truck travel time and travel time reliability. It also examines how GPS data can be used to support freight planning, using the analysis of impacts of a tolling project on truck mobility and routing as a case study. The first part of this dissertation investigates how to measure truck travel time reliability given the characteristics of GPS data. An improved spot-speed distribution based travel time reliability measure is proposed. The proposed approach is compared with a number of commonly applied reliability measures. The correlations among these measures reveal that the reliability measures are not highly correlated, demonstrating that different measures provide different conclusions for the same underlying data and traffic conditions. The author presents recommendations of the appropriate measures for different applications. Quantitative freight project prioritization processes require both pre- and post-investment truck mobility performance. Therefore, the second part of this dissertation develops quantitative methods for forecasting truck specific travel time and travel time reliability. For travel time prediction, a speed-density based approach is proposed to predict truck travel time associated with segment density changes. Traffic regimes are segmented using a cluster analysis approach. The travel time estimates are compared with two widely applied traditional methodologies. The results demonstrate that the proposed method is able to estimate more accurate travel times. For reliability prediction, we analyze the changes of GPS spot speed distribution in response to different traffic conditions. A relationship between truck spot speed distribution coefficient of variation and segment density is proposed to forecast reliability. The approach is transferrable and sheds a light on forecasting travel time reliability. The third part of this dissertation focuses on examining how GPS data can be used to assist freight planning. The SR-520 toll bridge in the City of Seattle, Washington is selected as the case study. We quantify the toll project impacts on truck mobility and route choice. Truck GPS data is used to evaluate route choice and travel speed along SR-520 and the alternate toll-free route I-90. A logit model is developed to determine the influential factors in truck routing. The results indicate that travel time, travel time reliability and toll rate are all influential factors during both peak and off-peak periods. The values of truck travel time during different time periods are estimated, and the values vary with the definition of peak and off-peak periods. This dissertation provides decision makers with useful guidance and information on using GPS data for truck mobility measurement and forecasting. It also demonstrates the capability of GPS data in supporting freight planning.

Authors: Zun Wang

Recommended Citation:
Wang, Zun (2014). Truck GPS Data in Freight Planning: Methodologies and Applications for Measurement and Forecasting. University of Washington Doctoral Dissertation.

Thesis: Array

Paper

Using Truck Probe GPS Data to Identify and Rank Roadway Bottlenecks

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URL: https://doi.org/10.1061/(ASCE)TE.1943-5436.0000444

Publication: American Society of Civil Engineers (ASCE) Journal of Transportation Engineering

Volume: 139(1)

Pages: 7-Jan

Publication Date: 2013

Summary:

This paper describes the development of a systematic methodology for identifying and ranking bottlenecks using probe data collected by commercial global positioning system fleet management devices mounted on trucks. These data are processed in a geographic information system and assigned to a roadway network to provide performance measures for individual segments. The authors hypothesized that truck speed distributions on these segments can be represented by either a unimodal or bimodal probability density function and proposed a new reliability measure for evaluating roadway performance. Travel performance was classified into three categories: unreliable, reliably fast, and reliably slow. A mixture of two Gaussian distributions was identified as the best fit for the overall distribution of truck speed data. Roadway bottlenecks were ranked on the basis of both the reliability and congestion measurements. The method was used to evaluate the performance of Washington state roadway segments, and proved efficient at identifying and ranking truck bottlenecks.

Authors: Dr. Ed McCormack, Wenjuan Zhao, Daniel J. Dailey, Eric Scharnhorst

Recommended Citation:
Zhao, Wenjuan, Edward McCormack, Daniel J. Dailey, and Eric Scharnhorst. "Using truck probe GPS data to identify and rank roadway bottlenecks." Journal of Transportation Engineering 139, no. 1 (2012): 1-7.

Paper

Using the Truck Appointment System to Improve Yard Efficiency in Container Terminals

URL: https://doi.org/10.1057/mel.2012.23

Publication: Maritime Economics & Logistics

Volume: 15

Pages: 101-119

Publication Date: 2013

Summary:

This article considers the effectiveness of a truck appointment system in improving yard efficiency in a container terminal. This research uses the truck appointment information obtained from an appointment system to improve import container retrieval operation and reduce container rehandles by adopting an advanced container location assignment algorithm. By reducing container rehandles, the terminal could improve yard crane productivity and reduce truck transaction time. A hybrid approach of simulation and queuing theory was developed to model the container retrieval operation and estimate the crane productivity and truck turn-time. Various configurations of the truck appointment system are modeled to investigate how those factors affect the effectiveness of the truck information. The research results illustrate a clear benefit for terminals utilizing a truck appointment system to manage their yard operation. Reducing the duration of the appointment time window or increasing the appointment lead time could further enhance system performance. Furthermore, the truck information is still effective in improving system efficiency, even if a good portion of trucks miss their appointments.

Authors: Dr. Anne Goodchild, Wenjuan Zhao

Recommended Citation:
Zhao, W., & Goodchild, A. V. (2013). Using the Truck Appointment System to Improve Yard Efficiency in Container Terminals. Maritime Economics & Logistics, 15(1), 101-119.

Paper

Evaluating Global Positioning System (GPS) Data Usability for Freight Performance Measures

URL: https://trid.trb.org/view.aspx?id=1438595

Publication: Transportation Research Board 96th Annual Meeting - Transportation Research Board

Volume: 17-04053

Publication Date: 2017

Summary:

Freight Performance Measures (FPM) are of interest to transportation planning agencies. One of the key tools that aids in the study of freight system activity is the data from Global Positioning System (GPS) devices located in trucks and cars. While commercially available GPS data has a common basic output format, the level of aggregation of the raw data, impacts the data’s ultimate usability and applications. This paper categorizes the different level of GPS data – from raw to highly aggregate and highlights the different strength, weakness, and applications of the data. Based on the insights learned from previous studies related to GPS data types, the authors make recommendations for how to match the GPS data to different analytical needs.

Authors: Dr. Anne GoodchildDr. Ed McCormackSaravanya (Sara) Sankarakumaraswamy, Mark Hallenbeck

Recommended Citation:
Sankarakumaraswamy, Saravanya. Edward McCormack, Anne Goodchild, and Mark Hallenbeck. Evaluating Global Positioning System (GPS) Data Usability for Freight Performance Measures. No. 17-04053. 2017.

Paper

Measuring Truck Travel Time Reliability Using Truck Probe GPS Data

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URL: https://doi.org/10.1080/15472450.2014.1000455

Publication: Journal of Intelligent Transportation Systems: Technology, Planning, and Operations

Publication Date: 2015

Summary:

Truck probe data collected by global positioning system (GPS) devices has gained increased attention as a source of truck mobility data, including measuring truck travel time reliability. Most reliability studies that apply GPS data are based on travel time observations retrieved from GPS data. The major challenges to using GPS data are small, nonrandom observation sets and low reading frequency. In contrast, using GPS spot speed (instantaneous speed recorded by GPS devices) directly can address these concerns. However, a recently introduced GPS spot-speed-based reliability metric that uses speed distribution does not provide a numerical value that would allow for a quantitative evaluation. In light of this, the research described in this article improves the current GPS spot speed distribution-based reliability approach by calculating the speed distribution coefficient of variation. An empirical investigation of truck travel time reliability on Interstate 5 in Seattle, WA, is performed. In addition, correlations are provided between the improved approach and a number of commonly used reliability measures. The reliability measures are not highly correlated, demonstrating that different measures provide different conclusions for the same underlying data and traffic conditions. The advantages and disadvantages of each measure are discussed and recommendations of the appropriate measures for different applications are presented.

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

Recommended Citation:
Wang, Zun. Anne Goodchild, and Edward McCormack. "Measuring truck travel time reliability using truck probe GPS data." Journal of Intelligent Transportation Systems 20, no. 2 (2016): 103-112.

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.