A Trip-Chain-based approach to generate travel demands for shared autonomous vehicle systems modeling
To inform decision making and guide the development of smart transportation systems towards urban sustainability, it is critical to model how travelers may use shared autonomous vehicles (SAV). Such models need two key components − travel demands with high spatiotemporal resolutions and travelers’ s...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Transportation Research Interdisciplinary Perspectives |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590198225001046 |
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| author | Ruoxi Wen Zhen Jiang Chen Liang Cassandra Telenko Andrea Broaddus Bo Wang Yan Fu Hua Cai |
| author_facet | Ruoxi Wen Zhen Jiang Chen Liang Cassandra Telenko Andrea Broaddus Bo Wang Yan Fu Hua Cai |
| author_sort | Ruoxi Wen |
| collection | DOAJ |
| description | To inform decision making and guide the development of smart transportation systems towards urban sustainability, it is critical to model how travelers may use shared autonomous vehicles (SAV). Such models need two key components − travel demands with high spatiotemporal resolutions and travelers’ sociodemographic information – to determine travelers’ acceptance and participation in SAV system. Existing SAV operations models used travel demand generation methods that either lack travelers’ demographics or only generate trips at a zonal level on a case-by-case basis. A scalable approach that can generate travel demands with higher resolution and linked household- and person-level sociodemographic is needed to enable better analysis of trips’ shareability and support SAV operations modeling. To address this gap, we propose a Household and Individual Trip-chain-based (HIT) travel demand generation model. The travel demands of household members are generated as chains of trips with spatial and temporal details that match the travel patterns of the individual’s as well as the household’s demographic profile. Using Miami as a case study city, we compared the proposed HIT model with a state-of-the-art activity-based model (ABM) to demonstrate its feasibility and validity. Results show that HIT model captures more complex travel patterns. We also used the travel demands generated by both methods as inputs to simulate SAV operation and found that using ABM to input travel demands in SAV operation models may overestimate the benefits of SAVs. Additionally, the proposed HIT model has the advantage of only requiring publicly available data as inputs, making it scalable nationwide. |
| format | Article |
| id | doaj-art-4eb52ad91bd2480f865d4ef6c911cb51 |
| institution | OA Journals |
| issn | 2590-1982 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Research Interdisciplinary Perspectives |
| spelling | doaj-art-4eb52ad91bd2480f865d4ef6c911cb512025-08-20T02:10:02ZengElsevierTransportation Research Interdisciplinary Perspectives2590-19822025-05-013110142510.1016/j.trip.2025.101425A Trip-Chain-based approach to generate travel demands for shared autonomous vehicle systems modelingRuoxi Wen0Zhen Jiang1Chen Liang2Cassandra Telenko3Andrea Broaddus4Bo Wang5Yan Fu6Hua Cai7Industrial Engineering, Purdue University, West Lafayette, IN, United StatesGlobal Data Insights and Analytics, Ford Motor Company, Dearborn, MI, United States; Ford Greenfield Labs, Ford Motor Company, Palo Alto, CA, United StatesGlobal Data Insights and Analytics, Ford Motor Company, Dearborn, MI, United StatesGlobal Data Insights and Analytics, Ford Motor Company, Dearborn, MI, United States; Ford Greenfield Labs, Ford Motor Company, Palo Alto, CA, United StatesRobotics and Mobility Research Group, Ford Motor Company, Palo Alto, CA, United StatesGlobal Data Insights and Analytics, Ford Motor Company, Dearborn, MI, United StatesGlobal Data Insights and Analytics, Ford Motor Company, Dearborn, MI, United StatesIndustrial Engineering, Purdue University, West Lafayette, IN, United States; Environmental and Ecological Engineering, Purdue University, West Lafayette, IN, United States; Corresponding author.To inform decision making and guide the development of smart transportation systems towards urban sustainability, it is critical to model how travelers may use shared autonomous vehicles (SAV). Such models need two key components − travel demands with high spatiotemporal resolutions and travelers’ sociodemographic information – to determine travelers’ acceptance and participation in SAV system. Existing SAV operations models used travel demand generation methods that either lack travelers’ demographics or only generate trips at a zonal level on a case-by-case basis. A scalable approach that can generate travel demands with higher resolution and linked household- and person-level sociodemographic is needed to enable better analysis of trips’ shareability and support SAV operations modeling. To address this gap, we propose a Household and Individual Trip-chain-based (HIT) travel demand generation model. The travel demands of household members are generated as chains of trips with spatial and temporal details that match the travel patterns of the individual’s as well as the household’s demographic profile. Using Miami as a case study city, we compared the proposed HIT model with a state-of-the-art activity-based model (ABM) to demonstrate its feasibility and validity. Results show that HIT model captures more complex travel patterns. We also used the travel demands generated by both methods as inputs to simulate SAV operation and found that using ABM to input travel demands in SAV operation models may overestimate the benefits of SAVs. Additionally, the proposed HIT model has the advantage of only requiring publicly available data as inputs, making it scalable nationwide.http://www.sciencedirect.com/science/article/pii/S2590198225001046Shared autonomous vehiclesTravel demand generationAgent-based modelTrip chainSmart transportation system modeling |
| spellingShingle | Ruoxi Wen Zhen Jiang Chen Liang Cassandra Telenko Andrea Broaddus Bo Wang Yan Fu Hua Cai A Trip-Chain-based approach to generate travel demands for shared autonomous vehicle systems modeling Transportation Research Interdisciplinary Perspectives Shared autonomous vehicles Travel demand generation Agent-based model Trip chain Smart transportation system modeling |
| title | A Trip-Chain-based approach to generate travel demands for shared autonomous vehicle systems modeling |
| title_full | A Trip-Chain-based approach to generate travel demands for shared autonomous vehicle systems modeling |
| title_fullStr | A Trip-Chain-based approach to generate travel demands for shared autonomous vehicle systems modeling |
| title_full_unstemmed | A Trip-Chain-based approach to generate travel demands for shared autonomous vehicle systems modeling |
| title_short | A Trip-Chain-based approach to generate travel demands for shared autonomous vehicle systems modeling |
| title_sort | trip chain based approach to generate travel demands for shared autonomous vehicle systems modeling |
| topic | Shared autonomous vehicles Travel demand generation Agent-based model Trip chain Smart transportation system modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2590198225001046 |
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