基于大数据的大学校园停车系统规划——以大连理工大学凌水主校区为例

Campus Parking System Planning Based on Big Data:A Case Study of Lingshui Main Campus of Dalian University of Technology

为解决大学校园停车系统混乱以及用于相关规划的基础数据样本量小、精度不高、时效性差等问题,应用大学校园停车调查数据、各出入口车辆记录数据以及各院系教职工信息数据进行多源大数据分析,提出了一种基于大数据的大学校园停车系统规划方法。以大连理工大学凌水主校区收集到的数据为例,首先从静态角度利用各出入口车辆记录数据和校园停车调查数据获取校园整体停车需求,进而利用各院系教职工信息数据分析各部门的停车需求。然后,从动态角度考虑停车位的周转问题,利用各出入口车辆记录数据分析校园出入车辆停车时段分布状况。最后,根据多源大数据分析所得校园停车系统现状,得出相应的校园停车系统规划结果。研究结果表明:基于大数据的大学校园停车系统规划方法可有效解决传统方法中基础数据样本量小、精度不高、时效性差等问题,提高了规划结果的实用性和科学性。

In order to solve the problem of chaos in campus parking system and relative basic data with small sample size, low precision and poor timeliness, a method about campus parking system planning was proposed based on multi-source information analysis using the data of campus parking survey, vehicle records at gate and staff information of each department. Taking the data collected in Lingshui main campus of Dalian University of Technology as an example, firstly the campus overall parking demand was obtained from the data of campus parking survey and vehicle records at gate in a static perspective, then the campus parking demand of each department was obtained from the staff information of each department. Secondly, considering the turnover rates of parking spaces in a dynamic perspective, the campus vehicle parking time distribution was gotten by analyzing the vehicle records at gate. Finally, based on the current situation of campus parking system obtained from the analysis of muhi-source data, the corresponding campus parking system planning was proposed. The results show that the campus parking system planning method based on big data can effectively solve the problems of small sample size, low precision and poor timeliness existing in the basic data in traditional method, and improve the practicability and scientificity of the planning results.