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With the continuous expansion of urban traffic operation scale, public transport emergencies occur from time to time, causing serious traffic jams and potential safety hazards in a short time. In view of this, a new adaptive bus route optimization strategy based on the emergency demand responsive public transport is proposed. Firstly, in order to improve the fine-grained passenger carrying capacity of emergency demand responsive public transport and build a clustering model of passenger information, this paper proposes an adaptive clustering algorithm, which considers the main influencing factors such as vehicle capacity, passenger travel time window and the number of stations visited. Aiming at minimizing the cost of vehicle operation and passenger traffic, a multi-objective optimization model of emergency bus route is constructed based on Vehicle Routing Problems with Time Windows (VRPTW) to ensure the operation efficiency of emergency bus. Secondly, a Modified Adaptive Large Neighborhood Search with Nearest Vehicle Dispatch (NVD) algorithm (MALNSN) is proposed, which is an extension of the Adaptive Large Neighborhood Search algorithm (ALNS), by improving the generation rules of initial solutions with NVD and operator selection strategy with Modified Choice Function (MCF), and the effectiveness of algorithm is analyzed according to the Solomon benchmark. The average gain of the proposed MALNSN algorithm is 17.11&#x0025; higher than that of the original algorithm. Finally, based on the actual road network, experiments are carried out to compare the proposed algorithm with the representative algorithms. The experimental results show that the MALNSN algorithm proposed in this paper can not only ensure the stability of the algorithm, but also formulate a reasonable route optimization strategy in a shorter time, effectively reducing the consumption of transport capacity resources, improving the operation efficiency of public transport and increasing the accessibility of public transport. The theoretical analysis was consistent with the experimental results.

Data-Driven Bus Route Optimization Algorithm Under Sudden Interruption of Public Transport