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Clustering of Rail Transit Stations in Chengdu

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Clustering of Rail Transit Stations in Chengdu JECET; December 2019- February 2020; Sec. C; Vol.9. No.1, 074-079. E-ISSN: 2278–179X [DOI: 10.24214/jecet.C.9.1.07479.] Journal of Environmental Science, Computer Science and Engineering & Technology An International Peer Review E-3 Journal of Sciences and Technology Available online at www.jecet.org Section C: Engineering & Technology Research Article Clustering of rail transit stations in Chengdu Xing Zhaomin, Zhao Xinhao, Guo Aixin, Zhi Yingchong and Zhao Jinbao Shandong University Of Technology, Zibo,Shandong,China Received: 03 December 2019; Revised: 10 December 2019; Accepted: 22 December 2019 Abstract: In recent years, with the rapid development of China's transportation, urban rail transit has become more and more a means of transportation to share a large volume of traffic. At the same time, the connection modes of rail transit are becoming more and more diverse. However, with the rapid development of intelligent transportation, didi chuxing stands out. The combination of the two can not only save travel time, but also improve travel speed. The effective connection between rail transit and didi chuxing is influenced by many potential factors, among which the type of land use as the source and destination will have a huge impact on the travel motivation. This thesis with orbit traffic of Chengdu and drabs travel big data as the research object, through clustering analysis of rail transit site first divided into residential, commercial, entertainment, schools and hospitals, five main land use types and then drops travel big data filtering, get the site around 200 m within the scope of the travel data and carries on the depth of mining, different land use types hop on and off the site in the morning and evening peak. Keywords: didi chuxing; rail transit; clustering analysis; land use type INTRODUCTION With the continuous development of China's social economy and the acceleration of urbanization level, people's economic activities become more and more frequent, accompanied by the rise of travel demand. The continuous increase of the traffic volume makes the traffic supply seriously insufficient or even lagging behind. The sharp increase of traffic demand and the relative lack of traffic facilities aggravate the contradiction between the traffic supply and the traffic demand, which seriously restricts the sustainable development of the city. 74 JECET; December 2019- February 2020; Sec. C; Vol.9. No.1, 074-079. DOI: 10.24214/jecet.C.9.1.07479. Clustering … Xing Zhaomin et al. Urban rail transit is an independent rail transit system, which is no longer affected by road conditions on the ground. It has the advantages of large traffic volume, punctuality, environmental protection, high safety and small footprint, and has become one of the first choices for people to travel. Along with our country urbanization accelerates, as of the beginning of 2016, there have been 40 cities of rail transit construction projects approved by state, operating now total mileage of 3000 kilometers, the year has more than 100 passengers choose to rail transportation, urban rail transit has entered a stage of vigorous development, transportation function increasingly highlighted in the position in the public transport system, is an important way to travel in the process of residents travel [1].Despite the rapid development of urban rail transit alleviate the pressure of road traffic, subway stations coverage is low, but the height of the vehicle comfort is poorer, too crowded, and the special structure and space form of rail transit and land resources utilization situation, determine the rail transit in terms of coverage and wire mesh density to independence from other modes of transport development. Therefore, in order to ensure certain operational efficiency of rail transit, strong support and effective connection and coordination of other transportation modes are necessary. RESEARCH CONTENT (1) Didi Chuxing Development：Didi chuxing, as the representative of ride-hailing, can effectively match drivers with passengers according to orders, avoiding the phenomenon that drivers spend too much time on empty cars. It can also shorten the distance a passenger has to travel before getting into a vehicle. Didi chuxing can not only improve the utilization of resources, but also shorten people's travel time, bringing convenience to citizens' travel. Didi chuxing can solve the problem of using private cars to some extent. It can not only save families' private car expenses, but also relieve the traffic pressure to some extent, as shown in figure1. Figure1: Road hierarchy diagram (2) Rail Traffic Data: This paper conducts further research on the basis of the rail transit in Chengdu. By the end of 2016, the rail transit line in Chengdu has a total length of 108.52km, the average daily passenger flow is about 2.1535 million, the transfer coefficient is 1.45, and the average ride distance 75 JECET; December 2019- February 2020; Sec. C; Vol.9. No.1, 074-079. DOI: 10.24214/jecet.C.9.1.07479. Clustering … Xing Zhaomin et al. is 11.51 km. As of 2016, there are four rail transit lines in Chengdu. In November 2016, about 100 stations of the four Chengdu rail transit lines undertook a total traffic volume of 56.0505 million person-times, this main object of study is the line 1. The specific traffic volume is shown in figure2. Figure2: Line 1 From the perspective of travel chain, travelers are more inclined to choose the combination of convenient access modes at both ends of rail transit when making unified decisions on the access modes at both ends of rail transit. In other words, travelers are more sensitive to the convenience of access modes at the arrival end than at the departure end, so the car access mode is more popular among people. Therefore, the connection between rail transit and didi chuxing has a very important relationship with the land nature of the place of origin and destination. RESEARCH METHOD Based on the above analysis of relevant background knowledge, this paper divides the land use types along the rail transit lines in Chengdu, and analyzes the data samples of didi chuxing in Chengdu, China supported by the gaia plan. (1) Data Preparation：Up to November 2016, chengdu metro line 1 has opened and put into operation a total of 22 stations, excluding the mutual influence between other lines, taking into account its own factors and the surrounding environment, mainly selecting the following clustering variable. such as: site size, distance from center, number of entrances and exits, site spacing, volume fraction, building density, number of feeder buses. (2) Data Standardization: In the data analysis of the above clustering variables, the dimensionality of various variables will have a certain impact on the final processing results, so the above variables need to be standardized. The data standardization method adopted in this paper is z-score standardization method. A standard score, also known as a z-score, is the difference between a score and the mean divided by the standard deviation. Can be expressed as: 76 JECET; December 2019- February 2020; Sec. C; Vol.9. No.1, 074-079. DOI: 10.24214/jecet.C.9.1.07479. Clustering … Xing Zhaomin et al. z = (xij − μj)/σ푗。 Where, z —the variable value after standardization; xij — the variable value; µ푗 — the mean value; σ푗 — the standard deviation. The quantity of z value represents the distance between the original score and the mean of the matrix, calculated in units of standard deviation. When the original score is lower than the mean, Z is negative and vice versa. The result of the original data normalization is shown in table 1. Table 1: The result of the original data normalization 编号 ZSco01 ZSco02 ZSco03 ZSco04 ZSco05 ZSco06 ZSco07 F1 -0.58787 -0.28428 -0.08536 1.97793 -1.57706 -3.11324 -2.04853 F2 0.78968 -0.51612 2.73157 1.37773 1.24388 0.14495 2.66975 F3 -0.92543 -0.72863 -1.02434 1.67783 0.15446 0.42224 1.36815 F4 2.12292 -1.01843 1.79259 0.17733 -0.49486 0.56089 0.22926 F5 -0.77235 -1.13434 -1.02434 -1.32317 2.10242 0.49156 -0.74694 F6 1.18607 -1.33527 -0.08536 -1.02307 -0.13413 -0.54828 -0.42154 F7 -0.36412 -1.18999 -0.08536 -1.62327 1.30881 0.63021 -0.25884 F8 -0.13856 -1.03775 -1.02434 -0.72297 -1.36062 0.56089 0.55466 F9 0.27145 -0.82523 1.79259 -0.42287 0.05345 0.63021 0.39196 F10 -0.67064 -0.65135 -1.02434 -0.42287 0.94086 0.69953 -0.42154 F11 -0.9725 -0.43884 -0.08536 0.17733 0.16167 0.49156 0.22926 F12 2.10037 -0.207 0.85362 0.77753 0.15446 0.35292 -0.09614 F13 -1.15968 0.02483 -0.08536 1.07763 0.94808 -0.20167 0.71736 F14 -1.26952 0.25667 -0.08536 -0.42287 -0.8556 0.69953 0.22926 F15 -0.4555 0.41123 -1.02434 -1.53324 -0.92774 0.28359 -0.42154 F16 -0.93008 0.5851 -0.08536 0.56746 -0.49486 -2.62798 -0.74694 F17 -0.07593 0.85558 -0.08536 0.53745 -1.57706 -0.54828 0.71736 F18 0.15418 1.02946 -0.08536 -0.36285 0.94086 0.0063 0.22926 F19 -0.09332 1.24197 -0.08536 0.47743 -1.21633 0.21427 -0.25884 F20 -0.39353 1.47381 -0.08536 0.17733 0.06788 -0.06302 0.71736 F21 1.20164 1.66701 -0.08536 -0.72297 0.33483 0.42224 -0.90964 F22 0.98271 1.82156 -1.02434 -0.42287 0.22661 0.49156 -1.72313 (2) K-Means: In this paper, k-means algorithm is adopted for clustering analysis.
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