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Urban Freight Lab

Research Topic: Truck Travel Time Performance Measurement and Modeling

Truck travel times measure the flow of freight and identify speed trends over time. They are valuable for assessing the efficiency and performance of transportation systems and are essential for planning, designing, and building better transportation facilities.

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.
Article

Developing Roadway Performance Measures Using Commercial GPS Data from Trucks

 
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URL: https://www.researchgate.net/publication/287183573_Developing_roadway_performance_measures_using_commercial_gps_data_from_trucks
Publication: Institute of Transportation Engineers. ITE Journal,
Volume: 84(6)
Pages: 36-40
Publication Date: 2014
Summary:
Global positioning system (GPS) devices that are installed in trucks and used for fleet management are increasingly common. Raw data from these devices present an opportunity for public agencies to use these trucks as probe vehicles to better monitor roadway operations and to quantify transportation system efficiency. Several North American programs have demonstrated that these truck GPS data can be used for a variety of performance measurement applications including locating roadway bottlenecks for trucks, providing travel reliability data, and informing planning and engineering processes. This paper discusses why these private sector GPS truck data are available, suggests how a public agency might acquire these data, provides some examples of the use of these data by transportation organizations, and covers some of the steps needed to make the GPS useful. In a performance measurement program, the GPS-equipped trucks are a small subset of all trucks on the network.

 

 

Authors: Dr. Ed McCormack
Recommended Citation:
McCormack, E. (2014). Developing Roadway Performance Measures Using Commercial GPS Data from Trucks. Institute of Transportation Engineers. ITE Journal, 84(6), 36-40.
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.
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

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). 
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

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.