摘要
本研究聚焦于利用车联网数据优化车企4S店选址策略,旨在提高选址决策的科学性和精准性。首先,收集该地区所在区域的汽车用户驾驶行为数据、交通状况数据和地理位置数据组成的多源异构数据;然后,对收集到的多源异构数据进行数据清洗、整合和标准化处理,确保数据质量;最后,基于熵权法和4S选址评价指标体系搭建模型。结果表明,基于车联网数据的选址模型能够更准确地识别高潜力区域,考虑因素更加全面,选址方案更加科学合理。同时,该模型还能够动态适应市场变化,为车企的4S店网络布局提供持续优化的建议。本研究不仅丰富了车联网数据应用的理论体系,也为车企的4S店选址实践提供了新的思路和方法。
This study focuses on optimizing the location selection strategy of 4S stores of automobile enterprises with the data of vehicle networking,aiming to improve the scientific and accurate location selection decision.Firstly,the multi-source heterogeneous data composed of driving behavior data,traffic condition data and geographical location data of car users in the region is collected.Then,the collected multisource heterogeneous data are cleaned,integrated and standardized to ensure data quality.Finally,the model is built based on entropy weight method and 4S stores location selection evaluation index system.The results show that the location selection model based on vehicle networking data can identify high-potential areas more accurately,consider more comprehensive factors,and make the location selection scheme more scientific and reasonable.At the same time,the model can also dynamically adapt to market changes,and provide continuous optimization suggestions for the 4S store network layout of auto companies.This study not only enriched the theoretical system of the application of vehicle networking data,but also provided new ideas and methods for the location selection practice of 4S stores of automobile enterprises.
作者
胡杰祥
谢婷婷
Hu Jiexiang;Xie Tingting(Chongqing Changan Automobile Co.,Ltd.,Chongqing 400000)
关键词
车联网数据
4S店选址
多源异构
熵权法
vehicle networking data
4S stores location selection
multi-source heterogeneous
entropy weight method
