To mitigate the problems caused by bus route overlap in the transit network, this paper proposes a new scheduling method with both large and small vehicle types based on passenger OD (Origin-Destination) data. The minimum of total cost of passenger travel time and bus company operation is taken as the optimization objective, departure intervals and vehicle types are taken as the optimization variables. The impact of route overlap on passenger travel time is analyzed. A heuristic algorithm is developed to solve the optimization model to produce the departure time and vehicle type for each bus trip. Finally, three real bus routes in Harbin city are taken as an example to validate the proposed model using peak-hour data. Compared with the model without considering route overlap, the proposed model can reduce total passenger travel time and cost by 5.2% and 8.8% respectively. INDEX TERMS Bus route overlap, scheduling method, vehicle types, optimization model. I. INTRODUCTION A. BACKGROUND
Drivers’ mistakes may cause some traffic accidents, and such accidents can be avoided if prompt advice could be given to drivers. So, how to detect driving risk is the key factor. Firstly, the selected parameters of vehicle movement are reaction time, acceleration, initial speed, final speed, and velocity difference. The ANOVA results show that the velocity difference is not significant in different driving states, and the other four parameters can be used as input variables of neural network models in deceleration zone of expressway, which have fifteen different combinations. Then, the detection model results indicate that the prediction accuracy rate of testing set is up to 86.4%. An interesting finding is that the number of input variables is positively correlated with the prediction accuracy rate. By applying the method, the dangerous state of vehicles could be released through mobile internet as well as drivers' start of risky behaviors, such as fatigue driving, drunk driving, speeding driving, and distracted driving. Numerical analyses have been conducted to determine the conditions required for implementing this detection method. Furthermore, the empirical results of the present study have important implications for the reduction of crashes.
Bus routes overlapping would lead to more than one bus entering the stop simultaneously, which may trigger bus bunching. Focusing on high frequency routes with common stops, this paper proposes a mixed scheduling method combining the all-stop service and the stop-skipping service. The method optimizes scheduling strategies for multiple routes by minimizing total passenger travel time. The optimization variables are binary variables reflecting whether the stops in the overlapping area are skipped. Three exciting bus routes are employed for case study. Results show that the proposed method reduces total passenger travel time by 21.4% compared with the current scheduling strategy. INDEX TERMS Mixed bus scheduling, common stops, high frequency routes, passenger travel time.
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