Abstract
Dynamic bus scheduling is a rational solution to the urban traffic congestion problem. Most previous studies have considered a single bus line, and research on multiple bus lines remains limited. Departure schedules have been typically planned by making separate decisions regarding departure times. In this study, a joint optimization model of the bus departure time and speed scheduling is constructed for multiple routes, and a coevolutionary algorithm (CEA) is developed with the objective function of minimizing the total waiting time of passengers. Six bus lines are selected in Shenyang, with several transfer stations between them, as a typical case. Experiments are then conducted for high-, medium-, and low-intensity case of smooth, increasing and decreasing passenger flow. The results indicate that combining the scheduling departure time and speed produces better performances than when using only scheduling departure time. The total passengers waiting time of the genetic algorithm (GA) group was reduced by approximately 25%–30% when compared to the fixed speed group. The total passengers waiting time of the CEA group can be reduced by approximately 17%–24% when compared to that in the GA group, which also holds true for a multisegment convex passenger flow. The feasibility and efficiency of the constructed algorithm were demonstrated experimentally.
Highlights
E main concept of the proposed model involves decomposing the complex problem into several subproblems, which are to be solved separately using suitable evolutionary algorithms, and subsequently, performing cooperative evaluation through the cooperation of multiple populations
Intelligent algorithms can effectively solve this problem, among which genetic algorithm (GA) are widely used in major fields because of their efficient global search abilities and wide scalability. erefore, a suitable GA was first applied to solve the problem according to the model characteristics as a comparison experiment; a suitable coevolutionary algorithm (CEA) was designed to solve the problem according to the characteristics of the model in this study
Compared with GA, the optimization performance of each passenger flow model can be improved by at least 10%, up to a maximum of 25%
Summary
Bus scheduling methods are typically classified into two types: static or dynamic. Static bus scheduling is based on static information, such as scheduling of the vehicle and driver, and is mainly intended for scheduling before the initiation of the operation. Because the passengers cannot notice the reduction of the operating speed of the vehicle while running, controlling bus speeds can significantly reduce the unevenness of the time interval He et al [31] presented a new studied optimization method for the realtime scheduling of multiroute vehicles in bus hubs. Hernandez et al [8] proposed real-time scheduling of multiroute buses based on the presence of bus-only lanes on multiple routes, established a central dispatching center, determined the bus departure frequencies, and validated the results using two strategies to overcome the aforementioned drawbacks by adjusting the speeds of buses in dedicated bus lanes to stabilize the highly unstable bus lines, while reducing the waiting and traveling times of passengers. Deng et al [30] proposed a real-time speed-control model with the objective of minimizing variations in bus headway and analyzed three cases of typical road infrastructure for bus lines
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