Pro-environmental Attitude and Adherence to a Travel Mode in an Integrated Choice and Latent Variable (ICLV) Model: Results from a Revealed Preference Survey

Toorzani, Atieh Asgari; Rassafi, Amir Abbas

doi:10.1007/s40999-022-00757-6

Cited by 3 publications

(1 citation statement)

References 75 publications

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“…To date, discrete choice model theory has generally been classified into multinomial logit model (MNL), generalized extreme value model (GEV), mixed logit model, and multinomial probit model. Many transportation scholars have studied and applied DCM combing their own research needs, such as the multiple discrete continuous extremum model (MDCEV) [ 10 ], structural equation-based logit model (SEM-Logit) [ 11 ], and integrated choice and latent variable model (ICLV) [ 12 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Understanding Influencing Factors of Travel Mode Choice in Urban-Suburban Travel: A Case Study in Shanghai

Teng

2023

Urban Rail Transit

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After the rapid expansion of the subway system over the past two decades, some cities are preparing to build more suburban railways. The emergence of suburban railways is bound to change the choice of suburban passenger transportation. This paper studies the factors that affect the choice of travel mode at the construction stage of suburban railways, aiming to design a more rational suburban railway network and urban public transport service system. Taking Shanghai as an example, this study first surveyed revealed preference (RP) and stated preference (SP) among urban-suburban travelers. Then, we used discrete choice models (DCM) and machine learning algorithms to build a travel mode choice model based on data collection and analysis. Furthermore, the importance of each factor was analyzed, and the effects were predicted under several traffic demand management schemes. Finally, this study proposed some strategies for increasing the share of public transport. On the one hand, it is suggested that Shanghai should continue to develop suburban railways and maintain low pricing of public transport services. Considering the construction and operation costs, the government needs to provide certain subsidies to stabilize prices. On the other hand, as passengers are very sensitive to the “last mile” trips in their suburban railway travel, transport planners should strengthen the connection from and to the suburban railway stations by developing services such as shared bikes and shuttle buses. In addition, the results indicated that some traffic demand management measures can also contribute to a larger share of public transport.

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Section: Literature Reviewmentioning

confidence: 99%

Understanding Influencing Factors of Travel Mode Choice in Urban-Suburban Travel: A Case Study in Shanghai

Teng

2023

Urban Rail Transit

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Investigating work commuters mode choice behavior in Dalian city during pandemic: a hybrid choice model approach

Bhattarai,

Zhao,

Bachir Diop

et al. 2024

Transportation Planning and Technology

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Interpretable machine learning models for detecting fine-grained transport modes by multi-source data

Liu,

Chen,

Wang

et al. 2023

era

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<abstract> <p>Analysis of transport mode choice is crucial in transportation planning and optimization. Traditionally, the transport mode of individuals is detected by discrete choice models (DCMs), which rely on data regarding individual and household attributes. Using these attribute data raises privacy concerns and limits the applicability of the model. Meanwhile, the detection results of DCMs may be biased, despite providing insight into the impact of variables. The machine learning models are more effective for mode detection, but most models need more interpretability. In this study, an interpretable machine learning model is developed to detect the transport modes of individuals. The mobility features of individuals, which introduce the velocity and acceleration of the center of mass (COM) are innovatively considered in the detection model. These mobility features are combined with multi-source data, including land use mix, GDP, population and online map service data as detection features. Using the travel survey data from Nanjing, China in 2015, the effects of different machine learning models on fine-grained detection performance are investigated. The results indicate that the deep forest model presents the best detection performance and achieves an accuracy of 0.82 in the test dataset, demonstrating the effectiveness of the proposed detection model. Furthermore, t-distributed stochastic neighbor embedding (t-SNE) and ablation experiments are conducted to overcome the non-interpretability issue of the machine learning models. The results show that the mobility features of individuals are the most critical features for improving detection performance. This study is essential for improving the structure of transport modes and maintaining low-carbon and sustainable development in urban traffic systems.</p> </abstract>

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Pro-environmental Attitude and Adherence to a Travel Mode in an Integrated Choice and Latent Variable (ICLV) Model: Results from a Revealed Preference Survey

Cited by 3 publications

References 75 publications

Understanding Influencing Factors of Travel Mode Choice in Urban-Suburban Travel: A Case Study in Shanghai

Understanding Influencing Factors of Travel Mode Choice in Urban-Suburban Travel: A Case Study in Shanghai

Investigating work commuters mode choice behavior in Dalian city during pandemic: a hybrid choice model approach

Interpretable machine learning models for detecting fine-grained transport modes by multi-source data

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