Modeling Urban Transport Choices: Incorporating Sociocultural Aspects
Salazar-Serna, Kathleen, Cadavid, Lorena, Franco, Carlos J.
–arXiv.org Artificial Intelligence
By understanding how users decide on their commuting modes, it is possible to identify factors that can be influenced to change travel behavior and promote the adoption of more sustainable transportation modes. Agent-based modeling (ABM) is particularly valuable for this purpose, as it can represent complex systems like transportation and identify emerging collective behaviors resulting from the autonomous decisions of transport users interacting among them and with the environment (Kagho, Balac, and Axhausen 2020). These capabilities make ABM suitable for analyzing the impacts of transport policies (Wise, Crooks, and Batty 2017). However, the application of ABM in analyzing transport mode choices has been limited and studies have been conducted predominantly in developed countries (Cadavid and Salazar-Serna 2021; Salazar-Serna, Cadavid, Franco, and Carley 2023). The effectiveness of these findings may not extend seamlessly to developing regions due to different contextual policy needs and the distinct ways socioeconomic and cultural factors influence human behavior (Carley 1991; Salazar-Serna et al. 2023). Therefore, policies that have been successful in one setting might not achieve similar outcomes in another. Previous studies in transportation have identified various determinants affecting mode choice. These factors can be grouped into several categories: sociodemographic characteristics such as age, sex, occupation, and income level (Ashalatha et al. 2013); travel habits including distance traveled, travel time, origin-destination pairs, and trip purpose (Madhuwanthi et al. 2016); and attributes of the built environment like design, density, and capacity (Ewing and Cervero 2010). Additionally, attitudes and perceptions regarding transport modes, which cover aspects such as comfort, cost, security, safety, quality, and reliability, play a crucial role (Fu 2021).
arXiv.org Artificial Intelligence
Jul-30-2024
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