Fine identification method for urban traffic jam based on mobile clustering

A fine identification and urban traffic technology, applied in the traffic control system of road vehicles, traffic control system, traffic flow detection, etc., can solve the problem that the recognition accuracy depends on the extraction effect of the congestion trajectory segment, lacks the fine identification method of dynamic traffic congestion, and cannot reveal The dynamic evolution process of traffic congestion and other issues

Active Publication Date: 2020-01-31
CENT SOUTH UNIV
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Problems solved by technology

However, the recognition accuracy of such methods is heavily dependent on the extraction of congestion trajectory segments, and cannot reveal the dynamic evolution of traffic congestion
[0005] To sum up, identifying urban traffic congestion based on vehicle trajectory big data has become one of the important technical means to alleviate and control congestion. However, there is still a lack of a dynamic traffic congestion fine-grained identification method based on vehicle trajectory big data.

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  • Fine identification method for urban traffic jam based on mobile clustering
  • Fine identification method for urban traffic jam based on mobile clustering
  • Fine identification method for urban traffic jam based on mobile clustering

Examples

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Embodiment 1

[0039] The flow process of the technical method proposed in this embodiment is as follows: figure 1 shown. In order to illustrate the specific implementation process of this embodiment by using the taxi track data of a certain district in a certain city in China on May 1, 2014:

[0040] (1) In the embodiment, a certain district of a certain city is selected as the research area, and the data used are taxi trajectory data. The data time is May 1, 2014, and the average time resolution of track points is 1 minute. Part of the track data is consistent with the research area such as figure 2 shown.

[0041] (2) Clean the data in the trajectory data outside the study area, time anomalies and repeated records, and use the ST-Matching algorithm to match the vehicle trajectory to the urban road network; in addition, the time interval △t is set to 1 minute, and a day is divided into 1440 time slice, and project the trajectory points that have been matched to the road network into th...

Embodiment 2

[0057] In order to solve the problem that the existing traffic jam identification method is difficult to accurately identify the space-time range and dynamic evolution process of traffic jams, this embodiment provides a mobile clustering-based fine urban traffic jam identification method, which mainly includes the following steps:

[0058] Step 1: Data Preprocessing

[0059] Perform data cleaning and road network matching on the trajectory data, and project the matched trajectory data into the corresponding time slice. Specifically include:

[0060] 1.1 Data cleaning and road network matching.

[0061] First, track data outside the study area, temporal anomalies, and duplicate records were deleted. Furthermore, considering the geometric structure of the road network, topological information and vehicle speed constraints, a map matching algorithm ST-Matching for low sampling rate trajectory points is used to match the vehicle trajectory with the urban road network, so that an...

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Abstract

The invention discloses a fine recognition method for urban traffic jam based on mobile clustering. The method comprises the steps: 1, preprocessing track data by carrying out data cleaning and road network matching on the track data and projecting the matched track data to a corresponding time slice; 2, extracting a space cluster with significant high density from each time slice, further measuring the inter-cluster similarity of adjacent time slices, and extracting a candidate jam space-time cluster; and 3) calculating the average speed and the growth duration of the space-time cluster, andif the calculated average speed and the growth duration reach preset conditions, identifying the space-time cluster as a space-time region containing a traffic jam phenomenon. According to the method,the characteristics of fine space-time range, jam scale, survival time and the like of traffic jam in a road network environment can be dug, and the full-life-cycle process of the traffic jam from occurrence to end can be effectively identified based on low-cost vehicle track big data.

Description

technical field [0001] The invention relates to the technical fields of big data mining and mobile sensor networks, in particular to a fine identification method for urban traffic congestion based on mobile clustering. Background technique [0002] With the rapid development of my country's economy, the number of urban vehicles continues to grow. Urban roads cannot expand infinitely in limited urban land space, and urban road networks (especially intersections) are difficult to meet the smooth driving of a large number of vehicles in a special time period, which makes urban traffic congestion more serious, and hinders the city's sustainable development. Continuous development. Therefore, the comprehensive and accurate identification of urban traffic congestion is of great guiding significance for the realization of efficient traffic management, dynamic planning of driving routes, and optimization of road network structure. [0003] Traditional methods usually rely on traff...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01G06K9/62
CPCG08G1/0125G06F18/23213G06F18/22
Inventor 石岩王达邓敏唐建波陈袁芳
Owner CENT SOUTH UNIV
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