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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. [ABSTRACT FROM AUTHOR]

In modern cities, public transit is important for meeting people's transportation needs. Dynamic bus dispatching plays an important role in a transit system, especially when the system experiences extreme weather, fluctuations in passenger flows, etc. Consequently, the assumption presented in the literature that departure timetables of buses are pre-determined at stops is not applicable to transit systems in developing countries, where traffic conditions vary frequently and dynamically. With the development of new technology, e.g. the internet of things, real-time information for public transit systems can be obtained conveniently. Considering multiple types of real-time information such as dynamic passenger flows and road traffic status, we propose an optimization model for dynamic bus dispatching to minimize the overall waiting time of passengers in a transit system. In our model, multiple bus routes and waiting times at the transfer stations are taken into consideration. Accordingly, we develop a genetic algorithm with memory-based initialization to solve the model. The effectiveness of the proposed approach is verified for different scenarios via numerical experiments. [ABSTRACT FROM AUTHOR]