With the improvement of living standards, more and more residents in China choose short leisure travel at weekends. This study aims to model the leisure travel duration to investigate the relationship between travel time and residents’ factors, which can provide guidance for the development of urban parks and improve the structure of transportation modes to promote low-carbon travel modes. The accelerated hazard model is applied to establish the model equation between travel time and various factors, to obtain the differences between weekend travel time in scenic spots and personal characteristics such as residents’ playing time and travel mode choice. First, an analysis of variance was performed on the valid data which have been collected. Then, the Kaplan–Meier test proved that weekend activity types tend to follow different survival functions. Finally, this study established the lifespan regression equation of the travel time, and we found that the best fit is the Log-normal distribution. This study shows clearly that accelerated hazard models based on fixed parameters are better at evaluating the attractiveness of urban parks, and they can also get feedback travel characteristics of primary residents. So, we can build a lifespan analysis-based model for the travel time of urban residents, to help plan the urban parks. The result of this study can be used for predicting the flow of different travel modes at urban parks in cities, which can be helpful for regional traffic organization such as regional traffic flow guidance and signal timing.

This paper analyzes the effects and potential causes of different variables on drivers’ mandatory merging decision behavior by constructing temporal and spatial dimensional merging risk indicators. This study employs the grouped latent class logit model (GLCM) and the grouped random parameter logit model with heterogeneity in means and variances (GRPMV) to analyze group heterogeneity and individual heterogeneity, respectively, and compared their goodness-of-fit and classification performance with other heterogeneity models. GLCM allows for the identification of latent classes among drivers, capturing unobserved heterogeneity by grouping drivers into distinct behavioral categories. Meanwhile, GRPMV provides a nuanced analysis of individual heterogeneity by accounting for variations in both means and variances of the parameters. The results suggest that merging risk has an important impact on the modeling of merging decision behavior, which can significantly improve the fitting performance of the model and effectively reveal unobserved group heterogeneity. In GRPMV, merging risk in the time dimension has the greatest impact on drivers’ merging decision behavior. In GLCM, individuals in different latent classes exhibit varying sensitivity to the presence of merging risk in merging situations. The parameter estimation results show the driving behavior characteristics of “conservative” and “aggressive” drivers. The results provide a theoretical basis for establishing a more comprehensive collision risk response identification function for autonomous vehicles in weaving areas and contribute to the enhancement of real-time decision-making processes in complex traffic scenarios.

The Multi-Destination Vehicle Routing Problem (MD-VRP) with a heterogeneous electric fleet is a critical challenge in optimizing commuter services for large-scale institutions and logistics operations. To address the complexities of electric fleet composition uncertainty and multi-center routing in “micro-city” campus environments, this paper establishes a robust multi-objective programming model. The model aims to simultaneously minimize three conflicting objectives, the total number of vehicles, total driving distance, and total electric energy consumption (kWh), under constraints of flow conservation and vehicle availability. Considering the nondeterministic polynomial-time hard (NP-hard) nature of the problem, a novel two-stage hybrid heuristic algorithm is proposed. In the first stage, a Modified Kruskal’s algorithm is employed to aggregate scattered stops into optimized clusters to reduce dimensionality. In the second stage, a State-Compressed Dynamic Programming (SC-DP) algorithm is applied to determine the optimal routing and electric vehicle type selection for each cluster. The methodology is validated using a case study of a large-scale campus network with 100 nodes. The optimization results identify an optimal fleet configuration of 41 campus electric commuter vehicles across three specific types (capacities of 45, 55, and 60), resulting in an annual total energy consumption of 5893.98 kWh. Compared with a global Ant Colony Optimization (ACO) baseline in this case study, the proposed framework reduces the required fleet size by 22.6% and annual energy consumption by 9.2%; however, this comparison should be interpreted as a preliminary case-study benchmark because the proposed method adopts a decomposition-based “Cluster-First, Route-Second” strategy. The results indicate that the approach achieves higher solution efficiency, offering an economically and environmentally friendly scheme for electric vehicle fleet operations.