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Traffic congestion traceability method based on multi-source data
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A technology of traffic congestion and multi-source data, which is applied in the traffic control system of road vehicles, traffic flow detection, traffic control system, etc., and can solve problems such as position deviation, lack of map matching algorithm, and insufficient data sample size
Active Publication Date: 2022-07-01
SOUTHEAST UNIV
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These algorithms all show the problem of insufficient matching accuracy when facing sparse AVI data
[0005] Problems existing in the existing technology: On the one hand, the existing traffic congestion traceability methods mostly use GPS data, but there are limitations such as insufficient data sample size and location deviation, and it is impossible to realize traffic congestion traceability based on large-scale samples
On the other hand, although a quasi-full sample of traffic congestion can be traced by matching massive AVI trajectory data to the traffic network, there is a lack of map matching algorithms for sparse AVI data.
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Embodiment 1
[0147] (1) Data collection and extraction
[0148] Road network: In the experiment, the road network in Shenzhen, China, with a total length of 21,985 kilometers, was used. The city-wide network map covers a spatial extent of 40×50 kilometers, including 237,440 nodes and 215,771 road connections.
[0149] AVI dataset: A dataset of AVI detected by 715 stationary sensors was collected in Shenzhen from September 1 to October 31, 2016. The locations of these fixed sensors are as Figure 9 shown in the dots. The distribution histograms of the temporal and spatial gaps of the AVI observation pairs were calculated, respectively as follows Figure 10 and Figure 11 shown. The mean, median and variance of the time gap are 12.18min, 5.54min and 252.83min, respectively 2 , the corresponding statistics for the spatial separation are 16.25 km, 10.23 km and 292.93 km, respectively 2 . The various statistical indicators of AVI data are shown in Table 1. The temporal and spatial inter...
Embodiment 2
[0161] (1) Experimental data collection
[0162] Nigang Road is an east-west expressway in Shenzhen, with a total length of 3.4 kilometers. It starts from Nigang Overpass in the west, connects with Beihuan Avenue, and ends at Honghu Overpass in the east and connects with Buxin Road. There is Nigang in the middle. Hongling Interchange can enter Yuping Avenue (Qingping Expressway) and Honggang Road to the north, Hongling North Road to the south, and there are ramps to the south to enter Qingshuihe Third Road and Honghu West Road.
[0163] The Shenzhen road network, the cellular grid network, and the position of the video bayonet with data are composited in QGIS to form a base map, and the corresponding cellular grids and OD points are numbered, such as Figure 14 shown.
[0164] The circles represent different ramp entrances, representing 8 OD communities; the small dots represent 6 video bayonet ports where data can be collected in and around Nigang Road, see Table 3 for speci...
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Abstract
The invention proposes a traffic congestionsource tracing method based on multi-source data, and the method includes a multi-source data-based map matchingalgorithm (AVI-MM) and a multi-level traffic congestionsource tracingalgorithm. The AVI‑MM algorithm fuses sparse AVI data and massive GPS data. First, a set of vehicle path candidates is generated based on a random walk algorithm, then a priori probability is determined based on the Logit path selection model with the greatest utility, and a spatiotemporal fusion analysis is used to define the conditional probability. The sub-path with the highest probability of connection matching obtains the final full path; the multi-level traffic congestionsource tracing algorithm calculates the OD distribution of multi-level vehicle trajectories of road sections, nodes and regions based on the map matching results. The method of the invention realizes the high-precision matching of the quasi-full-sample vehicle trajectory and the driving path, and based on the path matching result of the quasi-full-sample AVI data, quantitatively analyzes the starting and ending distribution and path distribution of the traffic flow in the congested road section.
Description
technical field [0001] The invention belongs to the field of urban traffic control and management, and in particular relates to a traffic congestion source tracing method based on multi-source data. Background technique [0002] Tracing the source of traffic congestion refers to tracing the starting and ending positions and driving paths of vehicles outside a certain spatial range, and providing strong support for the formulation of traffic control strategies by tracing the source and destination of traffic flow. [0003] Map matching is to calibrate the vehicle position information by representing road sections on a digital map. First, the sample vehicle trajectory data is matched to the road network through the map matching algorithm, and then the traffic flow sources passing through the observed road sections, intersections or areas can be obtained. And whereabouts, and ultimately realize the traceability of vehicle congestion. Therefore, map matching is an important tec...
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