Intelligent Bus Scheduling Control Based on On-Board Bus Controller and Simulated Annealing Genetic Algorithm

Abstract

The stable and fast service of a bus network is one of the important indicators of the service quality and management level of urban public transport. With the continuous expansion of cities, the bus network complexity has been increasing accordingly. The application of new technologies such as self-driving buses has made the bus network more complex and its vulnerability more obvious. Therefore, how to collect information on passenger flow, traffic flow, and transport distribution using intelligent means, and how to establish an effective intelligent bus scheduling control method have been important questions surrounding the improvement of the level of urban bus operation. To address this challenge, this paper proposes the design method of a bus controller based on data collection and the edge computing requirements of autonomous driving buses; and installs them widely on buses. In addition, an intelligent bus control scheduling method based on the simulated annealing genetic algorithm was developed according to the current scheduling requirements. The proposed method combines the strong local search ability of the simulated annealing algorithm, which prevents the search process from falling into a local optimum, and the strong search ability of the genetic algorithm in the overall search process, leading an intelligent bus control scheduling method based on the simulated annealing genetic algorithm. The proposed method was verified by experiments on the optimal scheduling of multi-destination public transport as an example, we verified the research method, and finally, simulated it using historical data. There is good model prediction of the experimental results. Therefore, the intelligent traffic control can be realized through efficient bus scheduling, thus improving the robustness of the bus network operation.

Joint design of timetabling and capacity for mixed transit system with autonomous and human-driven buses considering passengers’ acceptance

“…Yan et al (2023) addressed the above problem as a Markov decision model and proposed a multi-agent deep reinforcement learning framework to overcome the problems of high computational cost and low efficiency. Yu et al (2022) and Liu et al (2023) worked to more accurately characterize the variation of passenger demand in space and time. But not all passengers are able to be served by the first arriving bus.…”

Design of an Intelligent Shop Scheduling System Based on Internet of Things

“…Additionally, the AGV uses the chromosome sequence to determine the city number of the gene. Assuming there are three sets of AGV, the breakpoints for 1 and 10 are [2,5,7,1,12,9,6,8,10,11], respectively. Routes for AGVs 1 and 2 are [0-2-5-7-1], [0-12-9-6-8-10], and [0-11-3-4-0].…”

“…Zhang et al [5] developed a genetic algorithm with the Energies 2023, 16, 6310 2 of 13 optimization objective of optimizing overall cost capable of simultaneously scheduling single-load and multi-load AGVs. Yu Xingbao et al [6] introduced a simulated annealing genetic algorithm to improve the response time of the scheduling system. They created a new population using a genetic algorithm and applied the metropolis algorithm to track population state changes until stabilization was reached.…”