With the rapid development of online shopping platform, the traditional logistics distribution system is facing great challenges, which aggravates the operation pressure of urban traffic and makes the planning of logistics distribution route more complicated. However, the existing distribution route selection mainly depends on manual experience, which is difficult to achieve efficient and accurate distribution requirements. The traditional vehicle path planning model regards the road traffic status between nodes as fixed, which is difficult to make effective guidance for logistics distribution. Therefore, this paper selects a certain area of Zhengzhou City as the research object, introduces the road congestion index and the model conversion distance, and proposes a dynamic logistics distribution path optimization model considering real-time road conditions, which can quickly find the optimal distribution plan in a complex road operating environment. Firstly, the road network of the study area is drawn, and ArcGIS is used to obtain the latitude and longitude coordinates of each node of the selected road network. Secondly, Python language is used in Visual Studio Code to crawl real-time data of road traffic, including congestion degree, length of congested road section, driving speed and so on. Then, the congestion index database and the optimal distance matrix of each road section in each day from 07: 00 to 20: 00 in a week are established in hours, so as to calculate the initial distribution scheme. When the vehicle reaches a new node, the road data is re-acquired to update the distance matrix, so as to reduce the impact of emergencies on the distribution scheme. Finally, the ant colony algorithm is improved, and the algorithm is optimized in MATLAB with the goal of minimizing the distribution cost. The feasibility of the model and algorithm is verified by case analysis, which can provide new ideas and methods for the improvement of urban logistics distribution system.
Logistics Distribution Path Optimization Based on Real-Time Road Conditions
Advances in Engineering res
International Conference on Rail Transit and Transportation ; 2024 ; Jiaozuo, China October 10, 2024 - October 12, 2024
Proceedings of the 2024 International Conference on Rail Transit and Transportation (ICRTT 2024) ; Kapitel : 10 ; 86-94
15.12.2024
9 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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