摘要
通勤是城市居民的基本交通需求,也是联系城市居民居住地和就业地的重要桥梁。城市居民的就业地与居住地很少能完全重叠,从而导致了浪费性通勤。现有浪费性通勤研究主要通过问卷调查进行,存在样本有限、成本较高、分辨率不足等问题。提出了一种顾及实时路况的城市浪费性通勤测算思路,并将其用于中国四川省成都市浪费性通勤测算。通过调用高德地图应用程序接口(application programming interface,API)获取成都市居住地和就业地的兴趣点(point of interest,POI)数据,然后分别使用完全随机抽样和分层抽样方法对居住地与就业地POI进行抽样,生成通勤点对。编写网络爬虫程序,在高德地图中基于实时路况自动批量查询公共交通模式(公交与地铁)下最快捷、最经济、最少换乘情况时各通勤点对的实时通勤时间与通勤费用,计算出任一通勤点对的通勤时间与通勤费用的平均值。使用线性规划方法计算所有通勤点的理论最小通勤时间,分析成都市浪费性通勤时间与费用的统计与空间分布特征,揭示通勤薄弱环节和通勤供需严重不平衡地区。结果表明:成都市一环、二环、三环内的平均通勤时间分别为2 126 s、2 439 s、2 922 s。成都市老城区三环内浪费性通勤率为80.69%,通勤容量使用率69.34%。这与极光、百度等网站使用轨迹大数据分析的结果,以及其他学者的经验研究结果十分接近。
Objectives: Commuting is arguably the most common behavior for urban residents. It describes the traveling behaviors of urban residents between residential and working places. Theoretically, every people will find a job near to his/her residential place. However, due to housing prices, children.s education,and other reasons, most people do not live close to their working place. Therefore, there is usually excess commuting. There is a quite rich literature on excess commuting from the perspective of definition, calculation, social, and spatial dimensions of excess commuting, etc. Most of these studies are carried out based on two types of data. The first is the survey data, which is obtained by census survey, questionnaire and a face-to-face interview. This kind of data is very detailed and with socio-economic information, but it is time-consuming and with low spatiotemporal resolution. Besides, its sample size is usually limited. The second type of data is big data such as the GPS traces of taxis, the smart card data, and mobile phone data,etc. Such kind of data has a very high spatiotemporal resolution,small-scale, and covers a broad geographical range. The limitation is that such kind of data is usually hard to access and update, and with a lack of socio-economic information. Methods: Considering the limitation of the above two types of data, we propose a new approach to estimate excess commuting based on open-source data, which is near real-time and easily accessible. We investigate the spatial heterogeneity of excess commuting based on distance and time. We also explore the possible influencing factors of excess commuting. The main data involved are residential and workplace point of interest(POI), road networks, bus stops, metro stations and, metro lines, which are all downloaded from application programming interface(API). The population of each district is also collected. We define the POI sampling size as 1 500 and use a spatial sampling approach to derive representative residential and workplace POIs. Fo
作者
张红
徐珊
龚恩慧
ZHANG Hong;XU Shan;GONG Enhui(School of Urban and Regional Science,East China Normal University,Shanghai 200062,China;College of Surveying and Geo-informatics,Tongji University,Shanghai 200092,China;Faculty of Geosciences and Environmental Engineering,Southwest Jiaotong University,Chengdu 611756,China)
出处
《武汉大学学报（信息科学版）》
EI
CAS
CSCD
北大核心
2021年第5期650-658,共9页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金(41471383,51878558)
四川省科技支撑计划(2020YJ0325)
成都市重点研发支撑计划(2019-YF05-02119-SN)。
关键词
浪费性通勤
实时路况
线性规划
职住平衡
wasteful commuting
real-time road conditions
linear programming
jobs-housing balance
