Paper

An Agent-Based Simulation Assessment Of Freight Parking Demand Management Strategies For Large Urban Freight Generators

URL: https://doi.org/10.1016/j.rtbm.2022.100804

Publication: Research in Transportation Business & Management

Volume: 42

Publication Date: 2022

Summary:

A growing body of research looks specifically at freight vehicle parking choices for purposes of deliveries to street retail, and choice impacts on travel time/uncertainty, congestion, and emissions. However, little attention was given to large urban freight traffic generators, e.g., shopping malls and commercial buildings with offices and retail. These pose different challenges to manage freight vehicle parking demand, due to the limited parking options. To study these, we propose an agent-based simulation approach which integrates data-driven parking-choice models and a demand/supply simulation model. A case study compares demand management strategies (DMS), influencing parking choices, and their impact in reducing freight vehicle parking externalities, such as traffic congestion. DMS include changes to parking capacity, availability, and pricing as well as services (centralized receiving) and technology-based solutions (directed parking). The case study for a commercial region in Singapore shows DMS can improve travel time, parking costs, emission levels and reducing the queuing. This study contributes with a generalizable method, and to local understanding of technology and policy potential. The latter can be of value for managers of large traffic generators and public authorities as a way to understand to select suitable DMS.

Authors: Dr. Giacomo Dalla Chiara, Andre Alho, Simon Oh, Ravi Seshadri, Wen Han Chong, Takanori Sakai, Lynette Cheah, Moshe Ben-Akiva

Recommended Citation:
Alho, A., Oh, S., Seshadri, R., Dalla Chiara, G., Chong, W. H., Sakai, T., Cheah, L., & Ben-Akiva, M. (2022). An agent-based simulation assessment of freight parking demand management strategies for large urban freight generators. Research in Transportation Business & Management, 42, 100804. https://doi.org/10.1016/j.rtbm.2022.100804

Paper

Lessons from Tests of Electronic Container Door Seals

URL: https://trid.trb.org/view/880858

Publication: Transportation Research Board 88th Annual Meeting

Publication Date: 2009

Summary:

A series of field operational tests completed by Washington State over a 10-year period has shown that electronic container door seals (E-seals) can increase the efficiency and improve the security of containerized cargo movement. Universal use of E-seals, along with the associated infrastructure, could provide notable improvements in security, container tracking, and transaction cost reductions. Testing in ports, border facilities, and on roadways proved that E-Seal technology works: E-Seals can accurately and automatically report on container status at choke points, and the records can be accessed online to verify seal location, status (tampered or untampered), date, and time. However, a number of institutional barriers are likely to delay or even forestall the adoption of E-seals. A lack of standards is a major issue, since the E-seals available today use many different frequencies, hindering their applicability to international trade flows. A further barrier is the acceptability and cost of E-seals to the container industry. Routine use of seals would require new software linkages and container sealing procedures, which could slow acceptance. Disposable seals, which eliminate the need to recycle E-seals, are not common because they need to be produced in large quantities to be low cost. E-seals acceptable to the industry also need to be proved in a real world trade environment and need to be functionally simple to reduce routine operational problems. Compatibility with existing highway transponders systems might also promote E-seal acceptance, since containers could be tracked on the roadway system.

Authors: Dr. Ed McCormack, Mark Jensen, Al Hovde

Recommended Citation:
McCormack, E., Jensen, M., & Hovde, A. (2009). Lessons from Tests of Electronic Container Door Seals (No. 09-0821).

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.

Presentation

Development and Application of a Framework to Classify and Mitigate Truck Bottlenecks to Improve Freight Mobility

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URL: https://trid.trb.org/view/1494739

Publication: Transportation Research Record: Journal of the Transportation Research Board

Volume: TRN Annual Meeting

Publication Date: 2018

Summary:

This paper presents a framework to classify and mitigate roadway bottlenecks and that is designed to improve freight mobility. This is in recognition that roadway operations for trucks are under studied, truck-only bottlenecks are often not identified and freight-specific problem areas are therefore often overlooked. The framework uses four-steps:

Step 1: identifies and locates the roadway sections where vehicle travel time is in excess of what would normally occur.

Step 2: made possible by increasingly available truck probe data, identifies bottlenecks for all vehicles or for trucks only. This is necessary to identify bottlenecks that notably impact freight mobility and might not be identified by car-based approaches.

Step 3: classifies bottlenecks as travel speed-based or process-based. This selects the mitigation treatments as mainly due to operational or roadway limitations.

Step 4: which is the core of the paper, supports the mitigation process by determining the cause of the bottleneck. The bottlenecks are identified as due to congestion, limitations where roadway design slows all vehicles, or where a truck’s size or weight can slow vehicles (such as tight curves or bridge restrictions).

The paper present a review of specific roadway attributes that limit a truck’s mobility and is used to suggest mitigation. The framework is demonstrated using a case study. The framework is designed to be applied by planning and infrastructure agencies who want to locate and address freight bottlenecks in a systematic manner using available resources as well as by researchers interested in linking roadway attributes to truck mobility.

Authors: Dr. Ed McCormackDr. Anne Goodchild, William Eisele, Mark Hallenbeck

Recommended Citation:
McCormack, Edward, Anne Goodchild, W. Eisele, and Mark Hallenbeck. "Development and Application of a Framework to Classify and Mitigate Truck Bottlenecks to Improve Freight Mobility." TRN Annual Meeting, Washington D.C. 2018.