The primary purpose of this paper is to explore the mechanism of combined travel mode choice in multimodal networks. To meet the objective, stated preference survey and revealed preference survey are designed under short, middle, and long travel distance scenarios. Data including travelers’ socio-economic/personal information, trip characteristics, and mode choice are collected and analyzed. To recognize the influential factors of mode choice, a nested logit model is established. A value of time estimation and sensitivity analysis are conducted to quantify the influencing degree. The results reveal that cost has a significant influence on the short-distance travel mode; waiting time is perceived as the most important factor in short-distance scenario, and transfer-walking time as the most significant in middle and long distance scenario. Moreover, the traveler is more sensitive to the decrease of the transfer walking time than increase. Regarding socio-economic/personal information, travelers aged 40–50 prefer to choose combined travel mode than other ages; female travelers have a greater acceptance of metro-based transfer travel than male; individuals with higher economic level have a positive image of metro than bus.

Commuting by transfer in the public transit network is a green travel choice compared to private cars which should be encouraged when direct transit lines cannot take the commuters to their destinations. Therefore, transfer commuting attitudes are important for finding appropriate ways to attract more transfer commuters. Firstly, since attitudes are usually unobserved, a combined revealed preference (RP) and stated preference (SP) survey was conducted in Nanjing, China to obtain the observed attitudinal variables. Then the market segmentation approach including the factor analysis, the structural equation modelling (SEM) model and the K-means clustering method was used to identify the underlying attitudinal factors and variables and analyze the interrelationship between them. Six segments were identified by four key factors including the willingness to transfer, the sensitivity to time, the need for flexibility and the desire for comfort. The sensitivity to time is the most important factor for commuters influencing their willingness to transfer. The socio-economic features of each segment were also analyzed and compared. The result shows that socio-economic features have a great impact on the willingness to transfer. Corresponding policy and strategy implications to increase transfer commuting proportion were finally proposed.

Sharing bus rapid transit (BRT) exclusive lanes with conventional buses is being considered to solve the problem of low utilization rate of BRT-exclusive lanes. However, the quantitative conditions and threshold that determine when to share need to be study. This paper took the common section of BRT and conventional bus lines as its research object. Practical investigation was conducted to analyze shared characteristics from multiple perspectives and explore influencing factors and mechanisms for sharing implementation. Based on the survey results, analytical models were established to quantify the influencing factors from three perspectives of road section, intersection, and bus stop. We selected departure volume of conventional buses as a threshold index and then summarized the constraints and the calculation process of sharing threshold. Finally, numerical examples of different scenarios were used to verify the feasibility and effectiveness of the method. The operation efficiency of the road section on exclusive lanes was the constraint on the lower limit of the shared threshold, while the upper limit was constrained by queuing probability or bus operation time under different intersections and stop spacing, which can provide reference for the shared setting of exclusive bus lanes.

This study presents a data-driven framework for optimizing parking space allocation and pricing in large commercial complexes, addressing persistent spatial imbalances in occupancy between high- and low-demand zones. A mixed Logit (ML) model with interaction terms is estimated from stated preference survey data to capture heterogeneous user preferences across trip purposes. A dual clustering algorithm is then applied to generate spatially coherent pricing zones, integrating geometric, functional, and occupancy-based attributes. Two differential pricing strategies are formulated: an administered model with regulatory price bounds and a market-based model without such constraints. Both pricing models are solved using an improved multi-objective Particle Swarm Optimization–Grey Wolf Optimizer (PSO–GWO) algorithm that jointly optimizes spatial zoning and zone–time pricing schedules. Using data from the Kingmo Complex in Nanjing, China, the results show that both strategies significantly reduce spatio-temporal occupancy variance and improve utilization balance. The administered strategy reduces variance by up to 67% on weekdays, with only a 1% increase in revenue, making it suitable for contexts prioritizing regulatory compliance and price stability. In contrast, the market-based strategy reduces variance by over 40% while generating substantially higher revenue, particularly during periods of high and uneven demand. The proposed framework demonstrates the potential of integrating behavioral modeling, spatial clustering, and multi-objective optimization to improve parking efficiency. The findings provide practical guidance for operators and policymakers seeking to implement adaptive pricing strategies in large-scale parking facilities.