Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (2): 531-540.doi: 10.13229/j.cnki.jdxbgxb20191153

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Forecast of urban public bicycle traffic demand by station classification

Cai-hua ZHU(),Xiao-li SUN,Yan LI()   

  1. College of Transportation Engineering,Chang′an University,Xi′an 710064,China
  • Received:2019-12-17 Online:2021-03-01 Published:2021-02-09
  • Contact: Yan LI E-mail:zhucaihua@chd.edu.cn;lyan@chd.edu.cn

Abstract:

Accurately understanding the relationship between land use and public bicycle traffic demand is essential for adjusting control measures and station operation management. Taking public bicycle stations in Xi'an as an example, first, the public bicycle stations are divided into daytime destination stations, nighttime destination stations, daytime origin stations and combined origin/destination stations through K-means clustering while treating multi-factor indicators as variables. Then the non-linear regression models of land use, bus lines, subway entrances and exits, and station centrality as explanatory variables for each station attribute separately are established. The results show that station locations, external environment variables, and land use will lead to discrepancy in passenger flow generation rates for stations with different attributes. This study can provide theoretical reference for the operation and management of urban public bicycle systems.

Key words: transportation planning and management, public bicycle system, land use, cluster analysis, nonlinear regression model

CLC Number: 

  • U491.1

Fig.1

Study area"

Fig.2

Distribution of public bicycle stations and station density in Xi'an"

Fig.3

Three methods to determine attraction of public bicycle stations"

Fig.4

Hour usage distribution"

Table 1

Correlation coefficient of passenger flow in different time periods"

时间段相关系数时间段相关系数
6∶00~7∶000.38214∶00~15∶000.517
7∶00~8∶000.69715∶00~16∶000.495
8∶00~9∶000.50316∶00~17∶000.356
9∶00~10∶000.51617∶00~18∶000.427
10∶00~11∶000.41018∶00~19∶000.728
11∶00~12∶000.55419∶00~20∶000.630
12∶00~13∶000.42920∶00~21∶000.494
13∶00~14∶000.39421∶00~22∶000.231

Table 2

Selection of clustering variables for Xi'an metro line 2 station"

变量编号变量名称变量描述
1早高峰客流量站点早高峰借出还入总量
2晚高峰客流量站点晚高峰借出还入总量
3站点总桩数站点设计布局桩数
4接驳轨道交通300 m内地铁出入口个数
5站点用地混合度车站300 m以内的土地混合熵
6站点建筑强度站点300 m建筑容积率
7接驳公交线路数100 m之内的公交线路连接数

Fig.5

Silhouette coefficients of different cluster numbers"

Fig.6

Clustering results"

Fig.7

Changes in NAB of different types of sites"

Fig.8

Centrality of different types of sites"

Table 3

Model parameter values"

参数估计值
聚类1聚类2聚类3聚类4聚类5聚类6
拟合优度(r2)0.7860.8030.7880.7940.7750.817
F68.69856.83261.76476.39791.20549.781
Sig. F0.0110.0090.0140.0160.0210.012
公交线路10.36811.11411.35212.95710.1339.941
地铁出入口21.09220.87124.13229.61822.07720.925
中心性8.1218.3647.6838.3447.9068.307
居住0.000 1960.000 4190.000 2870.000 9240.000 2460.000 271
行政办公0.001 7500.001 6880.000 3140.000 2670.002 1480.001 169
商业金融0.000 6290.000 0240.000 3180.000 2170.000 0220.000 627
文化娱乐0.000 8190.000 8470.001 0160.000 3120.000 9640.000 901
体育0.000 0110.000 0270.000 1340.000 0810.000 0590.000 067
医疗0.001 1700.001 0210.000 1270.000 0490.002 6190.000 874
教育科研0.000 1320.001 0090.000 1040.000 5030.000 9120.000 677
文物古迹0.000 2390.000 3980.000 1910.000 0990.000 7420.001 316
工业0.000 0810.000 1610.000 0720.000 0490.000 0830.000 204
其他0.000 1750.000 1020.000 0340.000 0770.000 0930.000 121
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