A Trip-Chain-based approach to generate travel demands for shared autonomous vehicle systems modeling - Ruoxi Wen; Zhen Jiang; Chen Liang; Cassandra Telenko; Andrea Broaddus; Bo Wang; Yan Fu; Hua Cai (Transportation Research Interdisciplinary Perspectives)
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.