This paper proposes a flexible bus route optimization model for efficient public city transportation systems based on multitarget stations. The model considers passenger demands, vehicle capacities, and transportation network and aims to solve the optimal route, minimizing the vehicles running time and the passengers travel time. A heuristic algorithm based on a gravity model is introduced to solve this NP-hard optimization problem. Simulation studies verify the effectiveness and practicality of the proposed model and algorithm. The results show that the total number of vehicles needed to complete the service is 1721, the average travel time of each vehicle is 24.59 minutes, the solving time of 100 sets of data is within 25 seconds, and the average calculation time is 12.04 seconds. It can be seen that under the premise of real-time adjustment of connection planning time, the optimization model can satisfy the passengers dynamic demand to a greater extent, and effectively reduce the planning path error, shorten the distance and travel time of passengers, and the result is better than that of the flexible bus scheduling model which ignores the change of connection travel time.

In this research, 3356 alcohol-related traffic crashes were obtained from blood-alcohol test reports in Tianjin, China. Population density, intersection density, road density, and alcohol outlet densities, including retail density, entertainment density, restaurant density, company density, hotel density, and residential density, were extracted from 2114 traffic analysis zones (TAZs). After a spatial autocorrelation test, the multiple linear regression model (MLR), geographically weighted Poisson regression model (GWPR), and semi-parametric geographically weighted Poisson regression model (SGWPR) were utilized to explore the spatial effects of the aforementioned variables on drunk-driving crash density. The result shows that the SGWPR model based on the adaptive Gaussian function had the smallest AICc value and the best-fitting accuracy. The residential density and the intersection density are global variables, and the others are local variables that have different influences in different regions. Furthermore, we found that the influence of local variables in the economic–technological development area shows significantly different characteristics compared with other districts. Thus, a comprehensive consideration of spatial heterogeneity would be able to improve the effectiveness of the programs formulated to decrease drunk driving crashes.

A flexible bus route optimization scheduling model that considers the dynamic changes of passenger demand is proposed to address the large difference in demand for flexible bus passengers and real‐time variability. This model uses the heuristic algorithm based on gravity model to determine the following: passenger booking; vehicle passenger capacity; team known conditions such as size, according to the dynamic changes of passenger demand for real‐time iterative update shuttle travel time; vehicle operating costs (vehicle); and time cost for passengers (passengers waiting time for the vehicle, actual time of arrival, and the difference between expected and actual times of arrival) before minimization as the target. Finally, the practicabilities of the model and algorithm are verified by an example. Analysis results show that for 102 travel demands of 15 randomly generated demand points, completing all services requires 17–21 vehicles with average travel time of 24.59 minutes each. The solution time of 100 groups of data is within 25 seconds and the average calculation time is 12.04 seconds. Under the premise of real‐time adjustment of connection planning time, this optimization model can thus better meet the dynamic demand of passengers compared with the current scenario. The model effectively reduces the planning path error, shortens the travel distance and passenger travel time, and achieves better results than the flexible bus scheduling model that ignores changes of connection travel time.

As urban rail transit networks become denser, twin tunnels are increasingly constructed in close proximity above existing operational tunnels, making ground surface settlement control and existing tunnel protection critical challenges. Previous studies have mainly focused on the adverse effects of new tunnel excavation on existing tunnels, whereas the reverse mechanism—namely, how an existing tunnel, as a stiff continuous structure, constrains surrounding ground deformation and thereby affects surface settlement—remains insufficiently understood. Clarifying this mechanism helps explain differences in settlement between cases with and without an underlying existing tunnel and provides a basis for optimizing the overcrossing geometry, predicting settlement, and managing construction risk. This study combines numerical simulation with theoretical analysis to investigate the effects of vertical clearance, center-to-center spacing of the new twin tunnels, staggering distance between excavation faces, and crossing angle on soil deformation within the construction-affected area. The settlement control mechanism induced by the constraint of the existing tunnel is identified, and a method for calculating surface settlement is proposed. The numerical results indicate that the existing tunnel reduces surface settlement. This response is interpreted as a combination of local constraints on surrounding ground deformation and global beam-like restraint, thereby reducing ground surface settlement. Vertical clearance, the horizontal spacing of new twin tunnels, and crossing angle govern the settlement response by altering the constraint intensity, superposition of settlement troughs, and restraint provided by the embedded tunnel lining/segments, respectively, whereas the staggering distance between excavation faces mainly affects settlement evolution with excavation advance. Because the settlement profiles obtained with and without an existing tunnel are approximately linearly related, the proposed calculation method does not explicitly account for the settlement reduction associated with the existing tunnel. Verification using an engineering case study indicates that the method is suitable for preliminary settlement prediction and risk assessment in similar projects.

Drivers’ fault emergency response behavior can easily lead to crashes, resulting in significant economic and property losses. Exploring the causes of improper emergency response behavior is crucial for regulating driver behavior and preventing crash. Therefore, based on crashes data between commercial motor vehicles (CMVs) and noncommercial motor vehicles (NCMVs) that occurred in China from 2014 to 2018, this study established a binary logistic regression model. It systematically analyzed the key factors influencing drivers’ fault emergency response behaviors in terms of individuals, vehicles, road conditions, environment, and corporate management. Additionally, it compared the differences in the influencing factors of fault emergency response behaviors between drivers of CMV and NCMV. The results indicate that the model fits well. The presence of faulty emergency response behavior in drivers is significantly correlated with five factors: age, gender, fatigue driving, speeding, and weather conditions. Moreover, these factors have different impacts on CMV drivers and NCMV drivers. Fatigue driving and speeding have a more significant impact on CMV drivers, while other factors are more pronounced for NCMV drivers. This study can provide valuable insights for the development of measures aimed at reducing the severity of CMV‐NCMV crashes.

This paper investigates the internal mechanism of the trucker’s willingness to use familiar roads by constructing a structural equation model of road loyalty, in which the influence of the trucker’s “emotional value” is additionally considered. The proposed method can be used to understand the trucker’s psychological needs to improve the level of road service. Based on questionnaire data, AMOS software was used to analyze the correlations and corresponding parameters among 10 latent variables and 30 explicit variables. The model results show that, in addition to the value of roads as a commodity, the emotional dependence of truckers in the process of using them also has an impact on the perceived value of road services. The results of model index scores show that truck drivers’ loyalty to the road does not represent their satisfaction, and familiarity with the road is still “forced” to be the first choice of most truckers due to the cost and trust in the reliability of unfamiliar roads when the road service quality and experience do not meet truckers’ expectations. The latent variable scores indicate that improvements in “road safety” and “costs” are the key points to improve truckers’ overall satisfaction with road freight access.