Skip to content
Urban Freight Lab

Publication Type: Paper

Paper

Smart Growth and Goods Movement: Emerging Research Agendas

URL: https://doi.org/10.1080/17549175.2013.875058
Publication: Journal Urbanism: International Research on Placemaking and Urban Sustainability
Volume: 2-Aug
Pages: 115-132
Publication Date: 2015
Summary:

While recent urban planning efforts have focused on the management of growth into developed areas, the research community has not examined the impacts of these development patterns on urban goods movement. Successful implementation of growth strategies has multiple environmental and social benefits but also raises the demand for intra-urban goods movement, potentially increasing conflicts between modes of travel and worsening air quality. Because urban goods movement is critical for economic vitality, understanding the relation between smart growth and goods movement is necessary in the development of appropriate policies.

This paper reviews the academic literature and summarizes the results of six focus groups to identify gaps in the state of knowledge and suggest important future research topics in five sub-areas of smart growth related to goods movement: (1) access, parking, and loading zones; (2) road channelization and bicycle and pedestrian facilities; (3) land use; (4) logistics; and (5) network system management.

Authors: Dr. Anne GoodchildDr. Ed McCormack, Erica Wygonik, Alon Bassok, Daniel Carlson
Recommended Citation:
Wygonik, Erica, Alon Bassok, Anne Goodchild, Edward McCormack, and Daniel Carlson. "Smart Growth and Goods Movement: Emerging Research Agendas." Journal of Urbanism: International Research on Placemaking and Urban Sustainability 8, no. 2 (2015): 115-132.
Paper

Reducing Train Turn Times with Double Cycling in New Terminal Designs

 
Download PDF  (0.79 MB)
URL: http://dx.doi.org/10.3141/2238-02
Publication: Transportation Research Record: Journal of the Transportation Research Board
Volume: 2238
Pages: 14-Aug
Publication Date: 2011
Summary:

North American rail terminals need productivity improvements to handle increasing rail volumes and improve terminal performance. This paper examines the benefits of double cycling in wide-span gantry terminals that use automated transfer management systems. The authors demonstrate that the use of double cycling rather than the currently practiced single cycling in these terminals can reduce the number of cycles required to turn a train by almost 50% in most cases and reduce train turn time by almost 40%. This change can provide significant productivity improvements in rail terminals, increasing both efficiency and competitiveness.

Authors: Dr. Anne Goodchild, J. G. McCall, John Zumerchik, Jack Lanigan
Recommended Citation:
Goodchild, Anne, J. G. McCall, John Zumerchik, and Jack Lanigan Sr. "Reducing Train Turn Times with Double Cycling in New Terminal Designs." Transportation Research Record 2238, no. 1 (2011): 8-14.
Paper

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

 
Download PDF  (0.41 MB)
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). 
Paper

4D Flight Trajectory Prediction using a Hybrid Deep Learning Prediction Method Based on ADS-B Technology: A Case Study of Hartsfield–Jackson Atlanta International Airport (ATL)

 
Download PDF  (1.56 MB)
URL: https://doi.org/10.1016/j.trc.2022.103878
Publication: Transportation Research Part C: Emerging Technologies
Volume: 144
Publication Date: 2022
Summary:

At the core of any flight schedule is the four dimensional (4D) trajectories which are comprised of three spatial dimensions with time added as the fourth dimension. Each trajectory contains spatial and temporal features that are associated with uncertainties that make the prediction process complex. Because of the increasing demand for air transportation, airports and airlines must have optimized schedules to best use the airports’ infrastructure potential. This is possible using advanced trajectory prediction methods. This paper proposes a novel hybrid deep learning model to extract spatial and temporal features considering the uncertainty for Hartsfield–Jackson

Atlanta International Airport (ATL). Automatic Dependent Surveillance-Broadcast (ADS–B) with a vast amount of spatial and temporal flight attribute data, are used in this paper as input to the models. This research is conducted in three steps: (a) data preprocessing; (b) prediction by a hybrid Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) along with a three dimensional (3D-CNN) model; (c) The third and last step is the comparison of the model’s performance with the proposed model by examining the experimental results. The deep model uncertainty is considered using Mont-Carlo dropout (MC-Dropout). Mont-Carlo dropouts are added

to the network layers to enhance the model’s prediction performance by a robust approach of switching off between different neurons. The results show that the proposed model has low error measurements compared to the other models (i.e., 3D CNN, CNN-GRU). The model with MCdropout reduces the error further by an average of 21 %.

Authors: Amelia Regan, Hesam Shafienya
Recommended Citation:
Shafienya, H., & Regan, A. C. (2022). 4D flight trajectory prediction using a hybrid Deep Learning prediction method based on ADS-B technology: A case study of Hartsfield–Jackson Atlanta International Airport (ATL). Transportation Research Part C: Emerging Technologies, 144, 103878. https://doi.org/10.1016/j.trc.2022.103878
Paper

The Automated Driver as a New Road User

 
Download PDF  (2.40 MB)
URL: https://doi.org/10.1080/01441647.2020.1861124
Publication: Transport Reviews
Pages: 23-Jan
Publication Date: 2020
Summary:

Although road infrastructure has been designed to accommodate human drivers’ physiology and psychology for over a century, human error has always been the main cause of traffic accidents. Consequently, Advanced Driver Assistance Systems (ADAS) have been developed to mitigate human shortcomings. These automated functions are becoming more sophisticated allowing for Automated Driving Systems (ADS) to drive under an increasing number of road conditions. Due to this evolution, a new automated road user has become increasingly relevant for both road owners and the vehicle industry alike. While this automated driver is currently operating on roads designed for human drivers, in the future, infrastructure policies may be designed specifically to accommodate automated drivers. However, the current literature on ADSs does not cover all driving processes. A unified framework for human and automated driver, covering all driving processes, is therefore presented. The unified driving framework, based on theoretical models of human driving and robotics, highlights the importance of sensory input in all driving processes. How human and automated drivers sense their environment is therefore compared to uncover differences between the two road users relevant to adapt road design and maintenance to include the automated driver. The main differences identified between human and automated drivers are that (1) the automated driver has a much greater range of electromagnetic sensitivity and larger field of view, and (2) that the two road users interpret sensory input in different ways. Based on these findings, future research directions for road design and maintenance are suggested.

Authors: Dr. Ed McCormack, Ane Dalsnes Storsaeter, Kelly Pitera
Recommended Citation:
Storsæter, A. D., Pitera, K., & McCormack, E. D. (2020). The automated driver as a new road user. Transport Reviews, 1–23. https://doi.org/10.1080/01441647.2020.1861124
Paper

GPS Truck Data Performance Measures Program in Washington State

 
Download PDF  (1.12 MB)
URL: https://wadot.wa.gov/NR/rdonlyres/B7A5D60C-BF99-412F-9444-BA513768DC93/0/TPMFinalreportver2_17June2011.pdf
Publication: Washington State Transportation Center (TRAC)
Publication Date: 2011
Summary:

The Washington State Department of Transportation (WSDOT), Transportation Northwest at the University of Washington (UW), and the Washington Trucking Associations (WTA) have partnered on a research effort to collect and analyze global positioning systems (GPS) truck data from commercial, invehicle, truck fleet management systems. This effort was funded by the Washington State Legislature, and its purpose is to develop a statewide freight performance measures program for use by WSDOT. This document reviews the program’s previous phases and provides details about the latest phase of the program. The report also provides references to the technical documents that support the program.

Authors: Dr. Ed McCormack, Wenjuan Zhao
Recommended Citation:
McCormack, E. D., Zhao, W., & Tabat, D. (2011). GPS Truck Data Performance Measures Program in Washington State. Washington State Department of Transportation, Office of Research.
Paper

Measuring Delivery Route Cost Trade-Offs Between Electric-Assist Cargo Bicycles and Delivery Trucks in Dense Urban Areas

 
Download PDF  (3.79 MB)
 
Download Slides PDF  (0.88 MB)
URL: https://doi.org/10.1186/s12544-019-0349-5
Publication: European Transport Research Review
Volume: 11
Publication Date: 2019
Summary:

Introduction

Completing urban freight deliveries is increasingly a challenge in congested urban areas, particularly when delivery trucks are required to meet time windows. Depending on the route characteristics, Electric Assist (EA) cargo bicycles may serve as an economically viable alternative to delivery trucks. The purpose of this paper is to compare the delivery route cost trade-offs between box delivery trucks and EA cargo bicycles that have the same route and delivery characteristics, and to explore the question, under what conditions do EA cargo bikes perform at a lower cost than typical delivery trucks?

Methods

The independent variables, constant variables, and assumptions used for the cost function comparison model were gathered through data collection and a literature review. A delivery route in Seattle was observed and used as the base case; the same route was then modeled using EA cargo bicycles.

Four separate delivery scenarios were modeled to evaluate how the following independent route characteristics would impact delivery route cost – distance between a distribution center (DC) and a neighborhood, number of stops, distance between each stop, and number of parcels per stop.

Results

The analysis shows that three of the four modeled route characteristics affect the cost trade-offs between delivery trucks and EA cargo bikes. EA cargo bikes are more cost effective than delivery trucks for deliveries in close proximity to the DC (less than 2 miles for the observed delivery route with 50 parcels per stop and less than 6 miles for the hypothetical delivery route with 10 parcels per stop) and at which there is a high density of residential units and low delivery volumes per stop.

Conclusion

Delivery trucks are more cost effective for greater distances from the DC and for large volume deliveries to one stop.

 

Authors: Polina ButrinaDr. Anne GoodchildManali ShethDr. Ed McCormack
Recommended Citation:
Sheth, Manali, Polina Butrina, Anne Goodchild, and Edward McCormack. "Measuring delivery route cost trade-offs between electric-assist cargo bicycles and delivery trucks in dense urban areas." European Transport Research Review 11, no. 1 (2019): 11.
Paper

A Description of Fatal Bicycle Truck Accidents in the United States: 2000 to 2010

URL: https://trid.trb.org/view/1394216
Publication: Transportation Research Board 95th Annual Meeting
Volume: 16-5911
Publication Date: 2016
Summary:

Bicycling is being encouraged across the US and the world as a low-impact, environmentally friendly mode of transportation. In the US, many states and cities, especially cities facing congestion issues, are encouraging cycling as an alternative to automobiles. However, as cities grow and consumption increases, freight traffic in cities will increase as well, leading to higher amounts of interactions between cyclists and trucks. This paper will describe where and how accidents between cyclists and trucks occur. From 2000 to 2010, 807 bicyclists were killed the United States in accidents involving trucks. In 2009, trucks accounted for 9.5% of fatal bicycle accidents, despite trucks only accounting for 4.5% of registered vehicles. The typical fatal bike-truck accident happens in an urban area on an arterial street with a speed limit of 35 or 45 mph. It is about equally likely to occur mid-block or at an intersection. Most accidents involved trucks going straight (56%), and right-turning trucks were involved in a much larger number of accidents (24%) than left turning trucks (7%). Methods such as providing bicycle lanes, or even physically separated bicycle tracks, will not be sufficient to address bicycle-truck collisions, as a significant number of accidents (49%) occur in intersections or are intersection related. Cities with a higher mode-share of bicycling had a lower rate of bicycle-truck fatality accidents.

Authors: Dr. Anne Goodchild, Jerome Drescher
Recommended Citation:
Drescher, Jerome and Anne Goodchild. (2016), "A Description of Fatal Bicycle Truck Accidents in the United States: 2000 to 2010," Accepted for presentation at the 95th Transportation Research Board Annual Meeting, Washington DC, January 10-14. [Paper # 16-5911]
Paper

Examining the Differential Responses of Shippers and Motor Carriers to Travel Time Variability

URL: http://dx.doi.org/10.4018/jal.2012010103
Publication: International Journal of Applied Logistics
Volume: 3 (1)
Pages: 39-53
Publication Date: 2012
Summary:

Shippers and motor carriers are impacted by and react differently to travel time variability due to their positions within the supply chain and end goals. Through interviews and focus groups these differences have been further examined. Shippers, defined here as entities that send or receive goods, but do not provide the transportation themselves, are most often concerned with longer-term disruptions, which are typically considered within the context of transportation system resilience. Motor carriers, defined here as entities engaged in transporting goods for shippers, are most often concerned with daily travel time variability from events such as congestion. This paper describes the disparity in concerns and the strategies shippers and motor carriers are likely to engage in to address time travel variability. This knowledge allows for a better understanding of how investments to mitigate travel time variability will impact shippers and motor carriers.

Authors: Dr. Anne GoodchildDr. Ed McCormack, Kelly Pitera
Recommended Citation:
Goodchild, Anne V., Kelly Pitera, and Edward McCormack. "Examining the differential responses of shippers and motor carriers to travel time variability." International Journal of Applied Logistics (IJAL) 3, no. 1 (2012): 39-53.
Paper

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

 
Download PDF  (2.26 MB)
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.