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| SPATIAL PATTERN OF TRAFFIC FLOW INTENSITY AMONG CITIES IN CHINA |
| CHEN Wei1, XIU Chun-liang1, CHEN Jin-xing2, WANG Nv-ying1, WEI Ye1 |
1. School of Geographical Sciences, Northeast Normal University, Changchun 130024, China;
2. Institute of Remote Sensing and Digital Earth Chinese, Academy of Sciences, Beijing 100094, China |
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Abstract This paper aims to reveal the spatial pattern of urban external connection intensity from the respective of traffic flow. Previous urban studies on traffic flow laid more emphasis on single type of traffic flow while multi-flow synthesis was not taken into account. Moreover, the scale of prefecture-level city was not be covered before. Therefore, a further analysis on spatial pattern of multi-type traffic intensity between cities in China is required. To detail and comprehensively consider the spatial pattern of cities in China, we narrow the study scale to prefecture-level city. According to the list of prefecture-level divisions of China, this paper firstly takes 321 cities as basic study units. Data crawling was implemented under the C# language environment, which collects the runs number of three transport modes as basic data. Then we measure external connection index of each city respectively through bus, railway and flight schedules. In the following analysis, kernel density estimation was employed to describe spatial distribution pattern of urban external connection intensity, which implies a concentration trend. The number of runs shows bus > train > flight. Furthermore, Rank-size Rule was applied to portrait distribution variations of urban external connection intensity, among them flight schedules show significant rank-size feature, while train schedules come last. From the perspective of the whole urban system, the distribution of urban size is concentrated. Specifically, top-ranking cities have considerable scales, while small and medium-size cities need further development. After that, we use ESDA (Exploratory Spatial Data Analysis) to examine the spatial agglomeration. There is apparently positive spatial correlation between urban external connection index based on bus schedules data and that based on train schedules data. Spatially, a gradually weakening trend appears from coastal to inland. At the same time, core-periphery structure can be recognized, which identifies the national railway artery as core area and areas along the railway as periphery.
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Received: 23 June 2014
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2015, Vol. 30 
