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The Effect of Travel-Chain Complexity on Public Transport Travel Intention: A Mixed-Selection Model
Yuan Yuan1, Chunfu Shao1, Zhichao Cao2
1Key Laboratory of Integrated Transportation Big Data Application Technology in Transportation Industry, Beijing Jiaotong University, Beijing 100044, China.
Summary
Trip-chain complexity negatively impacts public transport use, with higher complexity reducing subway and bus sharing rates. Understanding traveler preferences is key for optimizing mobility as a service (MaaS).
Area of Science:
- Transportation Science
- Urban Planning
- Behavioral Economics
Background:
- Urban expansion increases travel chain complexity and the need for efficient public transport.
- Mobility as a Service (MaaS) requires understanding travel behavior, preferences, and demand for service optimization.
- Existing models need enhancement to accurately predict travel intention in complex trip chains.
Purpose of the Study:
- To investigate the relationship between trip-chain complexity and travel intention in public transport.
- To develop a bounded rationality model integrating the Theory of Planned Behavior (TPB) and traveler preferences.
- To quantify the impact of trip-chain complexity on public transport mode choice and sharing rates.
Main Methods:
- K-means clustering to quantify trip-chain complexity.
- Partial Least Squares Structural Equation Model (PLS-SEM) to analyze travel intention.
- Generalized ordered Logit model to estimate travel sharing rates.
- A mixed-selection model combining PLS-SEM and generalized ordered Logit.
Main Results:
- The proposed bounded rationality model demonstrated superior fit and effectiveness compared to previous approaches.
- Trip-chain complexity significantly and negatively affected public transport usage intention through indirect pathways.
- Demographic factors like gender and vehicle ownership moderated the effects on travel intention.
- Increased trip-chain complexity reduced subway sharing by 3.89-8.30% and bus sharing by 4.63-6.03%.
Conclusions:
- Trip-chain complexity is a critical factor negatively influencing public transport adoption.
- Integrating qualitative (PLS-SEM) and quantitative (Logit) models provides a more comprehensive understanding of travel behavior.
- MaaS strategies must address trip-chain complexity to effectively promote public transport usage.
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