贵阳市道路拥堵状态时空分布规律研究&mdash;&mdash;基于出租车GPS大数据

doi:10.13959/j.issn.1003-2398.2018.03.014

PDF(7444 KB)

人文地理 ›› 2018, Vol. 33 ›› Issue (3) : 112-120. DOI: 10.13959/j.issn.1003-2398.2018.03.014

城市

贵阳市道路拥堵状态时空分布规律研究——基于出租车GPS大数据

贺席燕, 赵航, 黄红良

作者信息 +

THE TEMPORAL AND SPATIAL DISTRIBUTION TO THE STATE OF ROAD CONGESTION BASED ON BIG DATA OF TAXI GPS IN GUIYANG CITY

HE Xi-yan, ZHAO Hang, HUANG Hong-liang

Author information +

文章历史 +

摘要

利用贵阳市出租车GPS数据，建立ArcGIS空间模型计算路段平均行程速度，以空间数据库和ArcGIS空间分析为技术支撑，分析道路拥堵状态的时空演化规律。结果表明：①路网平均行程速度的时间分布特征具有高度相似性，且工作日与休息日分布差异大；②工作日高峰时段拥堵区域分布主要以老城区为主。休息日总体上呈现离散分布趋势，且晚高峰时段交通状况比早高峰更拥堵；③工作日早高峰交通拥堵路段呈由中心核心区逐渐向外围扩散的趋势，晚高峰呈由外围向中心核心区聚拢趋势。休息日早高峰不明显，晚高峰呈现由外围区域向内聚拢趋势。基于上述时空演化规律，从城市功能分散、城市道路网、城市空间发展、城市公交系统等方面讨论了贵阳市城市道路交通优化调整对策。

Abstract

The ArcGIS space model is established to calculate the average travel speed and divide the average travel speed level of road sections with the data of Taxi GPS and road network data of urban centers in Guiyang. The temporal and spatial laws of the traffic flow in the center of Guiyang are proposed with the spatial visualization of the spatial database and spatial analysis for ArcGIS software and the evolution processes of the temporal and spatial distribution for the traffic congestion are analyzed based on the average travel speeds of road sections for the taxis. The results show:1) The time distribution characteristics of the average travel speeds for the road network is highly similar, and it is greatly different between working day and rest day. The peak period of the working day is in the morning 6:30-10:00 period and in the evening 16:00-20:00 period and there is obvious regularity and variation for the average travel speeds of road sections in the morning and evening peak. On the rest day, the variation in morning peak is not obvious and the evening peak occurs in the 16:00-19:00 period. 2) In the peak of the working day, the congestion areas are mainly located in the inner city. The traffic states in the periphery area of Guiyang are much better than that in the inner city. Between the working day and the rest day, there is obvious difference for the spatial distribution of the average travel speed of road sections. 3) The congestion distribution of the road sections shows a trend of gradual diffusion from the inner area to the periphery area in the morning peak and gradual aggregation from the periphery area to the inner area in the evening peak.

导出引用

贺席燕, 赵航, 黄红良. 贵阳市道路拥堵状态时空分布规律研究——基于出租车GPS大数据[J]. 人文地理. 2018, 33(3): 112-120 https://doi.org/10.13959/j.issn.1003-2398.2018.03.014

HE Xi-yan, ZHAO Hang, HUANG Hong-liang. THE TEMPORAL AND SPATIAL DISTRIBUTION TO THE STATE OF ROAD CONGESTION BASED ON BIG DATA OF TAXI GPS IN GUIYANG CITY[J]. HUMAN GEOGRAPHY. 2018, 33(3): 112-120 https://doi.org/10.13959/j.issn.1003-2398.2018.03.014

中图分类号： K901

参考文献

[1] 周素红,郝新华,柳林.多中心化下的城市商业中心空间吸引衰减率验证——深圳市浮动车GPS时空数据挖掘[J].地理学报,2014, 69(12):1810-1820.[Zhou Suhong, Hao Xinhua, Liu Lin. Validation of spatial decay law caused by urban commercial center's mutual attraction in polycentric city:Spatio-temporal data mining of floating cars' GPS data in Shenzhen[J]. Acta Geographica Sinica, 2014,69(12):1810-1820.]

[2] 李乐乐,白建军,宋冰洁.西安市交通网络综合通达性研究[J].人文地理,2014,29(5):88-93.[Li Lele, Bai Jianjun, Song Bingjie. A study of comprehensive accessibility of Xi'an transport networks[J]. Human Geography, 2014,29(5):88-93.]

[3] 王波,甄峰,魏宗财.南京市区活动空间总体特征研究——基于大数据的实证分析[J].人文地理,2014,29(3):14-21,55.[Wang Bo, Zhen Feng, Wei Zongcai. The Research on characteristics of urban activity space in NanJing:An empirical analysis based on big data[J]. Human Geography, 2014,29(3):14-21,55.]

[4] Han Y F, Moutarde F. Analysis of Network-Level Traffic States Using Locality Preservative Non-Negative Matrix Factorization[C]. Washington:Institute of Electrical and Electronics Engineers, 2011:501-506.

[5] Duan H L, Li Z H, Li L, et al. Network-wide traffic state observation and analysis method using pseudo-color map[J]. Journal of Transportation Systems Engineering & Information Technology, 2009,9(4):46-52.

[6] liu S, Liu Y, Lionel N, et al. Detecting crowdedness spot in city transportation[J]. IEEE Transactions on Vehiculai Technology, 2013, 62(4):1527-1539.

[7] Giannotti F, Nanni M, Pedreschi D, et al. Unveiling the complexity of human mobility by querying and mining massive trajectory data[J]. Vldb Journal, 2011,20(5):695-719.

[8] Pan G, Qi G, Zhang W, et al. Trace analysis and mining for smart cities:Issues, methods, and applications[J]. IEEE Communications Magazine, 2013,51(6):120-126.

[9] Cheu R L, Lee D H, Xie C. An Arterial Speed Estimation Model Fusing Data from Stationary and Mobile Sensors[C]. Oakland:Institute of Electrical and Electronics Engineers, 2001:573-578.

[10] Bishop R. Intelligent vehicle applications worldwide[J]. Intelligent Systems & Their Applications IEEE, 2000,15(1):78-81.

[11] Castro P S, Zang D, Chen C, et al. From taxi GPS traces to social and community dynamics:A survey[J]. ACM Computing Surveys, 2013,46(2):17-30.

[12] 郝新华,周素红,柳林.广州市高峰小时居住用地交通产生与分布——基于出租车GPS起讫关联的分析[J].人文地理,2016,31(6):109-114.[Hao Xinhua, Zhou Suhong, Liu Lin. Traffic generation and distribution of residential of land of Guangzhou in commuting peak hour:Based on the analysis of O-D associated data of taxi GPS[J]. Human Geography, 2016,31(6):109-114.]

[13] 陈世莉,陶海燕,李旭亮,等.基于潜在语义信息的城市功能区识别——广州市浮动车GPS时空数据挖掘[J].地理学报,2016,71(3):471-483.[Chen Shili, Tao Haiyan, Li Xuliang, et al. Discovering urban functional regions using latent semantic information:Spatiotemporal data mining of floating cars GPS data of Guangzhou[J]. Acta Geographical Sinica, 2016,71(3):471-483.]

[14] 张苏,常芳,李九全,等.大数据支持下城市社区生活空间质量的可获性图谱法初探——以西安为例[J].人文地理,2016,31(3):52-59.[Zhang Su, Chang Fang, Li Jiuquan, et al. Preliminary study of GEO-informatic tupu method for urban community quality of life space based on big data:A case study of Xi'an[J]. Human Geography, 2016,31(3):52-59.]

[15] 高悦儿,陈舒婷,郑承于,等.基于浮动车数据的旅游景点周边路网容量研究——以厦门岛为例[J].地理科学进展,2016,35(12):1529-1537.[Gao Yueer, Chen Shuting, Zhen Chengyu, et al. Road network capacity of tourist site's periphery based on FCD:Taking Xiamen island as an example[J]. Progress in Geography, 2016,35(12):1529-1537.]

[16] 申悦,柴彦威.基于GPS数据的北京市郊区巨型社区居民日常活动空间[J].地理学报,2013,68(4):506-516.[Shen Yue, Chai Yanwei. Daily activity space of suburban mega-community residents in Beijing based on GPS data[J]. Acta Geographica Sinica, 2013,68(4):506-516.]

[17] 申悦,柴彦威.基于GPS数据的城市居民通勤弹性研究——以北京市郊区巨型社区为例[J].地理学报,2012,67(6):733-744.[Shen Yue, Chai Yanwei. Study on commuting flexibility of residents based on GPS data:A case study of suburban mega-communities in Beijing[J]. Acta Geographica Sinica, 2012,67(6):734-744.]

[18] 柴彦威,申悦,马修军,等.北京居民活动与出行行为时空数据采集与管理[J].地理研究,2013,32(3):441-451.[Chai Yanwei, Shen Yue, Ma Xiujun, et al. The collection and management of space-time data of individual behavior based on location-based technologies:A case study of activity-travel survey in Beijing[J]. Geographical Research, 2013,32(3):441-451.]

[19] 陈青.基于GPS浮动车的城市道路交通状态判别技术研究[D].西安:长安大学,2009:1-59.[Chen Qing. Study on the Technology of Traffic State Identification for Urban Road Based on GPS Equipped Floating Car[D]. Xi'an:Chang'an University, 2009:1-59.]

[20] 廖孝勇.浮动车交通参数检测及在道路交通状态分析中的应用研究[D].重庆:重庆大学,2011:1-122.[Liao Xiaoyong. Study on FCD Based Traffic Parameters Detection and Applications in Traffic Condition Analysis[D]. Chongqing:Chongqing University, 2011:1-122.]

[21] 苟锡荣.基于GPS浮动车的城市交通状态时空分布规律研究[D]. 昆明:昆明理工大学,2013:1-86.[Gou Xirong. Study of Spatial and Temporal Distribution of the Urban Traffic Based on GPS Floating Car Data[D]. Kunming:Kunming University of Science and Technology, 2013:1-86.]