盛宇裕, 毕硕本, 范京津, NKUNZIMANAAthanase, 许志慧. 运用交通运行状况指标分析交通热点时空模式[J]. 武汉大学学报 ( 信息科学版), 2021, 46(5): 746-754. DOI: 10.13203/j.whugis20190357
引用本文: 盛宇裕, 毕硕本, 范京津, NKUNZIMANAAthanase, 许志慧. 运用交通运行状况指标分析交通热点时空模式[J]. 武汉大学学报 ( 信息科学版), 2021, 46(5): 746-754. DOI: 10.13203/j.whugis20190357
SHENG Yuyu, BI Shuoben, FAN Jingjin, NKUNZIMANA Athanase, XU Zhihui. Analyzing Spatiotemporal Patterns of Traffic Hotspots Using Traffic Operation Indicators[J]. Geomatics and Information Science of Wuhan University, 2021, 46(5): 746-754. DOI: 10.13203/j.whugis20190357
Citation: SHENG Yuyu, BI Shuoben, FAN Jingjin, NKUNZIMANA Athanase, XU Zhihui. Analyzing Spatiotemporal Patterns of Traffic Hotspots Using Traffic Operation Indicators[J]. Geomatics and Information Science of Wuhan University, 2021, 46(5): 746-754. DOI: 10.13203/j.whugis20190357

运用交通运行状况指标分析交通热点时空模式

Analyzing Spatiotemporal Patterns of Traffic Hotspots Using Traffic Operation Indicators

  • 摘要: 城市交通热点是居民出行活动的体现,通过出租车的移动轨迹可以分析城市交通运行状况。目前,相关研究主要集中于GPS采样数据的起讫点(origin destination, OD)估计模型以及轨迹流的提取分析算法,而对交通热点的交互作用和时空模式的研究还很少。以中国江苏省南京市的出租车GPS数据为研究对象,通过轨迹流提取和地图匹配,基于宏观基本图模型构建交通运行状况指标,利用数据挖掘技术分析交通热点时空模式。通过研究发现:(1)南京市交通在早晚高峰时拥堵最严重,并且休息日的交通运行状况要优于工作日;(2)在工作日,停车场和办公楼附近最拥堵;在休息日,商场和居民小区附近最拥堵;(3)交通热点的时空交互特征主要表现在交叉路口附近,并具有显著的时空聚集性差异。上述研究结果可为交通管理部门针对不同时段和路段的交通运行状况进行治理提供帮助,并为建立现代化综合交通运输体系提供依据。

     

    Abstract:
      Objectives  Based on taxi movement trajectories, urban traffic operating conditions can be analyzed. At present, the relevant research mainly focuses on the investigation of traffic origin and destination points and the extraction of traffic hotspots, but seldom analyzes the interaction between traffic hotspots. We focus on exploring the temporal and spatial patterns of urban traffic hotspots, with the purpose of providing a scientific basis for traffic management departments for traffic management in different periods and sections.
      Methods  We take the main urban city of Nanjing City as the research area, and use taxi GPS data in 2015, 2016, and 2017 for experiments. Firstly, the effective trajectory flow is extracted from the taxi GPS data through the local anomaly test method. Secondly, based on the GeoJSON geocoding to realize map matching, the Pathlet method is used to process the trajectory flow into a sequence which can be optimally solved by dynamic programming. Finally, based on the standard for evaluation of urban traffic operation status, the macroscopic fundamental diagram model is constructed according to the relationship between traffic flow, speed and density in the road network, and the evaluation index is established by combining travel time ratio and the delay time ratio.
      Results  (1) Judging from the average of the traffic operation indicators, the main urban area of Nanjing is the most congested in the morning and evening rush hours, and the traffic is smooth in the early morning hours, and the traffic hotspots are more active on working days than on rest days. (2) Judging from the traffic operation indicators of the nine different nodes, the traffic congestion near the parking lot and office building nodes is relatively increased on weekdays, and the traffic congestion near the nodes of shopping malls and residential quarters is relatively increased on the rest days.(3) Judging from the perspective of spatial distribution of traffic hotspots in five representative periods, traffic hotspots mostly appear near intersections and ramps, and there are more interactions between main roads and express roads, but between townships and village roads and county roads. There is less interaction. (4) Judging from the perspective of the temporal and spatial patterns of traffic hotspots near the school during the morning rush hour, the temporal and spatial interaction characteristics of traffic hotspots are significant in areas with high node coverage, while the spatial distribution characteristics of traffic hotspots change unclearly with time in areas with low node coverage.
      Conclusions  Urban traffic hotspots show significant differences in time and space distribution characteristics, and their spatiotemporal interactions become stronger with time and space aggregation, which has a significant positive correlation.

     

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