Demand-responsive bus scheduling optimisation considering candidate pick-up and drop-off points

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Improved system efficiency leads to more efficient public transportation operations, especially in peak times.
Candidate points allow for better bus routing and response to varying demand in real-time.
Adaptive large neighbourhood search algorithm tackles complexities of bus scheduling while accommodating flexible pick-up.
Utilizing simulated annealing reduces local optima in scheduling solutions, enhancing overall performance.

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Analysis shows enhanced system efficiency by 11% from candidate drop-off points, indicating better scheduling in traffic jams.

Zheng et al. (2025) studied this question.

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