Examining how travel distance is associated with travel mode choice is essential for understanding traveler travel patterns and the potential mechanisms of behavioral changes. Although existing studies have explored the effect of travel distance on travel mode choice, most overlook their non-linear relationship and the heterogeneity between groups. In this study, the correlation between travel distance and travel mode choice is explored by applying the random forest model based on resident travel survey data in Guiyang, China. The results show that travel distance is far more important than other determinants for understanding the mechanism of travel mode choice. Travel distance contributes to 42.28% of explanation power for predicting travel mode choice and even 63.24% for walking. Significant nonlinear associations and threshold effects are found between travel distance and travel mode choice, and such nonlinear associations vary significantly across different socioeconomic groups. Policymakers are recommended to understand the group heterogeneity of travel mode choice behavior and to make targeted interventions for different groups with different travel distances. These results can provide beneficial guidance for optimizing the spatial layout of transportation infrastructure and improving the operational efficiency of low-carbon transportation systems.

Encouraging car users who travel short distances to shift from car mode to active travel modes can effectively alleviate urban traffic congestion and reduce carbon emissions. However, few studies have examined the determinants of the travel mode choice of short-distance car users and ignored the nonlinear associations and interactions between variables. This paper conducts a questionnaire survey to investigate the short-distance travel mode choice of car users who travel less than 4km in a specific city. A random forest (RF) model is applied to examine the influence of key variables on these three travel mode choices of short-distance car users and to explore the nonlinear associations and interactions of the variables. Compared with multinomial logic model, the results of RF show that significant threshold effects exist in the relationship between the car users travel mode choice and the selected explanatory variables, mainly travel distance, road network density, distance to CBD, and number of bus stops. In particular, 1.2km is a critical turning point for car and active travel mode choice, before which car users prefer to travel by walking and cycling and after which there is a significant increase in the car use probability. When the road network density was between 2.5km/km² and 6.5km/km², the proportion of car users who chose cycling showed an increasing trend, while car use showed a decreasing trend. These findings can provide a solid basis for planning managers to develop policy measures to encourage environmentally sustainable travel.

Online car-hailing has become an indispensable transportation means for residents. The short-term origin and destination (OD) prediction of online car-hailing trips is conducive to understanding the inflow and outflow of online car-hailing trips in a region and provides data support for the delivery and scheduling of vehicles. Accordingly, this article takes the data of car-hailing trips in the central area of Haikou, China, as the research data; makes an in-depth analysis of the regularities of car-hailing trips; and divides the central area of Haikou into 84 grids with a length of 3 km. This article constructs three adjacency matrices, Am01, Sam, and Amn, to reflect the complex spatial relationships of the OD matrixes of online car-hailing from different perspectives. Then, a model, based on the graph convolutional network (GCN) and gated recurrent unit, denoted as the temporal GCN ( T-GCN ), is introduced for the grid-based short-term OD prediction. The case study in Haikou shows that T-GCNs based on the three adjacency matrices are better than other models, wherein the Amn-based T-GCN is more consistent with the OD flows’ spatial relationship, achieves the best prediction performance, and shows that there exists a proportional relationship between flows on different OD pairs. The application of the research results is beneficial for the car-hailing platform to perform the dynamic scheduling of vehicles in advance, further improving the operating efficiency and reducing the waiting time of passengers so as to effectively alleviate the problem of the imbalance between the supply and demand of online car-hailing travel.

Understanding the determinants of elderly people’s public transport usage patterns can offer new insights into elderly mobility issues and provide policy implications for planning toward an aging-friendly and sustainable city. However, few studies have examined the impact of the built environment on the trip time of the elderly using big data. Moreover, the elderly’s trip time has been mostly investigated by the multivariate linear regression model (MLR), ignoring the non-linear association between explanatory variables and trip time. Using smart card data from Nanjing in 2019, this study employs a gradient boosting regression trees (GBRT) model to probe into the correlations between the built environment and the elderly’s trip time. The results show that significant non-linear relationships exist between trip time and the selected explanatory variables, which cannot be captured by the MLR model. It suggests that relevant policy implementations should be carried out in conjunction with the elderly’s travel environment by regarding their threshold effects. Besides, interaction effects of spatial attributes on trip time are identified in our study. For example, elderly people living in the exurban area are more likely to take long-distance metro travel for their physical exercise. These findings demonstrate that planners and policymakers should not only consider one single built-environment factor, but also the interactions of various factors to enhance elderly mobility.