An agent-based simulation assessment of freight parking demand management strategies for large urban freight generators

André Alho; Simon Oh; Ravi Seshadri; Giacomo Dalla Chiara; Wen Han Chong; Takanori Sakai; Lynette Cheah; Moshe Ben-Akiva

doi:10.1016/j.rtbm.2022.100804

An agent-based simulation assessment of freight parking demand management strategies for large urban freight generators

André Alho^*, Simon Oh, Ravi Seshadri, Giacomo Dalla Chiara, Wen Han Chong, Takanori Sakai, Lynette Cheah, Moshe Ben-Akiva

^*Corresponding author for this work

Department of Technology, Management and Economics

Research output: Contribution to journal › Journal article › Research › peer-review

Abstract

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.

Original language	English
Article number	100804
Journal	Research in Transportation Business and Management
Volume	43
Number of pages	15
ISSN	2210-5395
DOIs	https://doi.org/10.1016/j.rtbm.2022.100804
Publication status	Published - 2022

Bibliographical note

Funding Information:
This research was partially supported by the Singapore Ministry of National Development and the National Research Foundation, Prime Minister's Office under the Land and Liveability National Innovation Challenge (L2 NIC) Research Programme (L2 NIC Award No. L2 NICTDF1-2016-1). We would also like to thank partner agencies, the Urban Redevelopment Authority of Singapore, the Land Transport Authority of Singapore and JTC Corporation for their support. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Singapore Ministry of National Development, National Research Foundation, Prime Minister's Office, Singapore, the Urban Redevelopment Authority of Singapore, JTC Corporation, Land Transport Authority of Singapore. This research was also supported by the National Research Foundation under its CREATE program and the Singapore-MIT Alliance for Research and Technology, Future Urban Mobility Interdisciplinary Research Group.

Funding Information:
This research was partially supported by the Singapore Ministry of National Development and the National Research Foundation , Prime Minister's Office under the Land and Liveability National Innovation Challenge (L2 NIC) Research Programme (L2 NIC Award No. L2 NICTDF1-2016-1). We would also like to thank partner agencies, the Urban Redevelopment Authority of Singapore , the Land Transport Authority of Singapore and JTC Corporation for their support. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Singapore Ministry of National Development , National Research Foundation , Prime Minister's Office, Singapore , the Urban Redevelopment Authority of Singapore , JTC Corporation , Land Transport Authority of Singapore . This research was also supported by the National Research Foundation under its CREATE program and the Singapore-MIT Alliance for Research and Technology, Future Urban Mobility Interdisciplinary Research Group .

Publisher Copyright:
© 2022 Elsevier Ltd

Keywords

Agent-based simulation
City logistics
Demand management strategies
Freight parking
Parking choice

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.rtbm.2022.100804

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Cite this

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title = "An agent-based simulation assessment of freight parking demand management strategies for large urban freight generators",

abstract = "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.",

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note = "Funding Information: This research was partially supported by the Singapore Ministry of National Development and the National Research Foundation, Prime Minister's Office under the Land and Liveability National Innovation Challenge (L2 NIC) Research Programme (L2 NIC Award No. L2 NICTDF1-2016-1). We would also like to thank partner agencies, the Urban Redevelopment Authority of Singapore, the Land Transport Authority of Singapore and JTC Corporation for their support. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Singapore Ministry of National Development, National Research Foundation, Prime Minister's Office, Singapore, the Urban Redevelopment Authority of Singapore, JTC Corporation, Land Transport Authority of Singapore. This research was also supported by the National Research Foundation under its CREATE program and the Singapore-MIT Alliance for Research and Technology, Future Urban Mobility Interdisciplinary Research Group. Funding Information: This research was partially supported by the Singapore Ministry of National Development and the National Research Foundation , Prime Minister's Office under the Land and Liveability National Innovation Challenge (L2 NIC) Research Programme (L2 NIC Award No. L2 NICTDF1-2016-1). We would also like to thank partner agencies, the Urban Redevelopment Authority of Singapore , the Land Transport Authority of Singapore and JTC Corporation for their support. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Singapore Ministry of National Development , National Research Foundation , Prime Minister's Office, Singapore , the Urban Redevelopment Authority of Singapore , JTC Corporation , Land Transport Authority of Singapore . This research was also supported by the National Research Foundation under its CREATE program and the Singapore-MIT Alliance for Research and Technology, Future Urban Mobility Interdisciplinary Research Group . Publisher Copyright: {\textcopyright} 2022 Elsevier Ltd",

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AU - Chong, Wen Han

AU - Sakai, Takanori

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AU - Ben-Akiva, Moshe

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AB - 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.

KW - Agent-based simulation

KW - City logistics

KW - Demand management strategies

KW - Freight parking

KW - Parking choice

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DO - 10.1016/j.rtbm.2022.100804

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SN - 2210-5395

VL - 43

JO - Research in Transportation Business and Management

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ER -

An agent-based simulation assessment of freight parking demand management strategies for large urban freight generators

Abstract

Bibliographical note

Keywords

UN SDGs

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