张红, 徐珊, 龚恩慧. 顾及实时路况的城市浪费性通勤测算[J]. 武汉大学学报 ( 信息科学版), 2021, 46(5): 650-658. DOI: 10.13203/j.whugis20190363
引用本文: 张红, 徐珊, 龚恩慧. 顾及实时路况的城市浪费性通勤测算[J]. 武汉大学学报 ( 信息科学版), 2021, 46(5): 650-658. DOI: 10.13203/j.whugis20190363
ZHANG Hong, XU Shan, GONG Enhui. Urban Wasteful Commuting Calculation Concerning Real‑Time Traffic Information[J]. Geomatics and Information Science of Wuhan University, 2021, 46(5): 650-658. DOI: 10.13203/j.whugis20190363
Citation: ZHANG Hong, XU Shan, GONG Enhui. Urban Wasteful Commuting Calculation Concerning Real‑Time Traffic Information[J]. Geomatics and Information Science of Wuhan University, 2021, 46(5): 650-658. DOI: 10.13203/j.whugis20190363

顾及实时路况的城市浪费性通勤测算

Urban Wasteful Commuting Calculation Concerning Real‑Time Traffic Information

  • 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。

     

    Abstract:
      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. For residential POIs, the complete spatial random(CSR) sampling approach is used. That is, we choose residential randomly from the set of residential POIs. While for workplace POIs, the types of the POI are considered. In other words, the more the POIs with the given type, the more likely POI with such type will be selected.We suppose that every residential POI has only one person, and every workplace POI has only one job. Also, each people should have and only have one job. Then we derive a commuting matrix of 1 500×1 500, indicating 2 250 000 commuting pairs. For each commuting pair, we record its commuting distance and time by Amap under public transportation mode. Three navigation strategies are considered, which are the fastest, the lowest cost, and the minimum transfer. For each commuting pair, its commuting distance is defined as the average of commuting distances with three navigation strategies. Similarly, we obtain the value of commuting time. The histograms of commuting distance and cost are drawn. The spatial heterogeneity of commuting distance and time is illustrated by the kernel density maps. We also compare the excess commuting of Chengdu with other cities.And, the possible influences of the spatial pattern of road networks, the spatial distribution of metro stations and metros, bus lines, and population density are explored by qualitative analysis and regression analysis.
      Results   The experimental results show that: (1) The maximum, minimum, and average commuting time for the area within the third ring of Chengdu City are 7 418 s, 434 s, and 2 922 s, respectively. The average commuting time for the first, the second and the third ring of Chengdu are 2 126 s, 2 439 s, and 2 922 s, respectively. (2) The maximum, minimum and average commuting cost for Chengdu City are RMB ¥ 8, 0, and 3.215 respectively.The average commuting cost for the first, the second and the third ring of Chengdu are RMB ¥ 2.54, 2.84, and 3.23, respectively. (3)The wasteful commuting rate of Chengdu is 80.69%, which is very close to Aurora and Baidu extracted by big data of GPS traces. (4) There is a significant core-periphery pattern for commuting time and cost. Most residential POIs in the central urban area is of low commuting time and cost. (5)The length density and branch density are negatively related to commuting time, while the shape index of the road network has a position relationship with commuting time. Our results are also closed to other empirical studies carried out by questionnaires. Besides, areas with dense population density tend to have high commuting costs.
      Conclusions   The proposed approach could be used for near real-time estimation of excess commuting. There are significant excess commuting for Chengdu City. People who live in the urban central area are more likely to waste less in commuting. Both spatial distribution of public transportation facilities and population will impact excess commuting. Development of the public transportation helps alleviate excess commuting.

     

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