A Improved A* Algorithm for the Path Planning Problem of Urban Taxi-Carpooling
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In order to address the issue of taxi carpooling path planning on urban roads, this study suggests an improved A* algorithm and a model based on node weight. The path planning model enables us to implement carpool path planning after carpool passengers, taxi passengers, and taxi drivers have gathered. It uses a vector city traffic road network, city road vector map topology, dynamic road weight functions, node weight tables of the road, and an improved A* algorithm. Our evaluation of the model involves comparing its path planning computation time and total travel time with the traditional A* algorithm using Nanjing taxi trajectory data. The comparison shows that the proposed algorithm significantly outperforms the traditional A* algorithm. Results show that the taxi path planning model proposed in this paper can provide a reference for carpool passengers, taxi passengers, and taxi drivers in choosing a carpool.
Abstract
In order to address the issue of taxi carpooling path planning on urban roads, this study suggests an improved A* algorithm and a model based on node weight. The path planning model enables us to implement carpool path planning after carpool passengers, taxi passengers, and taxi drivers have gathered. It uses a vector city traffic road network, city road vector map topology, dynamic road weight functions, node weight tables of the road, and an improved A* algorithm. Our evaluation of the model involves comparing its path planning computation time and total travel time with the traditional A* algorithm using Nanjing taxi trajectory data. The comparison shows that the proposed algorithm significantly outperforms the traditional A* algorithm. Results show that the taxi path planning model proposed in this paper can provide a reference for carpool passengers, taxi passengers, and taxi drivers in choosing a carpool.