Abstract
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% 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.
Highlights
In recent years, with the rapid development of intelligent transportation in China, a large number of transportation resources have been transformed into data resources, of which public transport resources have increased in the share year by year
The continuous expansion of urban traffic operation scale, the growing passenger demand and high-intensity operation have brought great pressure to the urban traffic system, as a result of which, the sudden failures and equipment failures occur from time to time, The associate editor coordinating the review of this manuscript and approving it for publication was Yiming Tang
The main contributions of this paper include the following aspects: (1) For passenger information clustering, an adaptive density clustering algorithm based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is proposed to make the division of passenger spatial clustering center more reasonable; (2) An improved adaptive large neighborhood search algorithm (MALNSN) is proposed; (3) This paper, abstracting the vehicle route optimal scheduling problem into Vehicle Routing Problems with Time Windows (VRPTW) class problem, takes the cost of vehicle operation and passenger traffic as the basis of evaluation, and puts forward an improved fitness operator method for the vehicle route optimal scheduling problem
Summary
With the rapid development of intelligent transportation in China, a large number of transportation resources have been transformed into data resources, of which public transport resources have increased in the share year by year. Wang: Data-Driven Bus Route Optimization Algorithm Under Sudden Interruption of Public Transport inconvenient transfer, low density and even blind spots, emergency demand responsive public transport arises at the right moment, which flexibly adjusts the transport capacity according to passengers’ personalized travel needs, calculates the optimal line in real time by passenger flow and virtual stations, and quickly and dynamically allocates public transport capacity resources to achieve the optimal overall efficiency, effectively make up for the mismatch between transport capacity and demand of traditional public transport in specific areas and periods, improve the operation efficiency of public transport, reduce the consumption of transport capacity resources and increase the accessibility of public transport. In the case of sudden operation interruption, how to scientifically organize public transport vehicles to evacuate stranded passengers in time and reduce passenger travel delay has important theoretical and practical significance. The main contributions of this paper include the following aspects: (1) For passenger information clustering, an adaptive density clustering algorithm based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is proposed to make the division of passenger spatial clustering center more reasonable; (2) An improved adaptive large neighborhood search algorithm (MALNSN) is proposed; (3) This paper, abstracting the vehicle route optimal scheduling problem into VRPTW class problem, takes the cost of vehicle operation and passenger traffic as the basis of evaluation, and puts forward an improved fitness operator method for the vehicle route optimal scheduling problem
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