WO2018122585A1 - Procédé de détection d'incident de la circulation routière urbaine sur la base de données de véhicules flottants - Google Patents

Procédé de détection d'incident de la circulation routière urbaine sur la base de données de véhicules flottants Download PDF

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WO2018122585A1
WO2018122585A1 PCT/IB2016/058105 IB2016058105W WO2018122585A1 WO 2018122585 A1 WO2018122585 A1 WO 2018122585A1 IB 2016058105 W IB2016058105 W IB 2016058105W WO 2018122585 A1 WO2018122585 A1 WO 2018122585A1
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data
time
traffic
historical
space
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PCT/IB2016/058105
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English (en)
Chinese (zh)
Inventor
杜豫川
邓富文
郑凌翰
蒋盛川
岳劲松
王晨薇
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同济大学
杜豫川
许军
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Application filed by 同济大学, 杜豫川, 许军 filed Critical 同济大学
Priority to CN201680088595.8A priority Critical patent/CN109923595B/zh
Priority to PCT/IB2016/058105 priority patent/WO2018122585A1/fr
Priority to GBGB1711408.3A priority patent/GB201711408D0/en
Priority to GBGB1909407.7A priority patent/GB201909407D0/en
Priority to GB1905907.0A priority patent/GB2569924B/en
Priority to GB2100341.3A priority patent/GB2587588B/en
Priority to PCT/IB2017/058535 priority patent/WO2018122805A1/fr
Priority to GB2009834.9A priority patent/GB2582532B/en
Priority to CN201780050765.8A priority patent/CN109844832B/zh
Priority to GB1909405.1A priority patent/GB2572717B/en
Priority to CN201780050754.XA priority patent/CN109997179A/zh
Priority to GBGB1909409.3A priority patent/GB201909409D0/en
Priority to PCT/IB2017/058531 priority patent/WO2018122801A1/fr
Priority to PCT/IB2017/058533 priority patent/WO2018122803A1/fr
Priority to PCT/IB2017/058534 priority patent/WO2018122804A1/fr
Priority to CN201780050719.8A priority patent/CN110168520A/zh
Priority to CN201780050755.4A priority patent/CN109643485B/zh
Priority to GBGB1909406.9A priority patent/GB201909406D0/en
Priority to PCT/IB2017/058536 priority patent/WO2018122806A1/fr
Priority to GB2100340.5A priority patent/GB2588556B/en
Priority to PCT/IB2017/058532 priority patent/WO2018122802A1/fr
Priority to CN201780050906.6A priority patent/CN109716414B/zh
Priority to CN201780050907.0A priority patent/CN109791729B/zh
Priority to GB2009833.1A priority patent/GB2582531B/en
Priority to GBGB1909408.5A priority patent/GB201909408D0/en
Publication of WO2018122585A1 publication Critical patent/WO2018122585A1/fr

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination

Definitions

  • the invention belongs to the technical field of traffic detection.
  • the present invention relates to a method for real-time detection of urban road traffic anomalies.
  • the spatial position information of different time points can be obtained.
  • map matching and data fusion After data preprocessing, map matching and data fusion, the travel speed probability distribution of the specific time and space range can be obtained; according to the change of the speed distribution, the position can be effectively identified.
  • Urban road traffic anomalies Background technique
  • Traffic anomaly detection is an important part of urban traffic management and one of the core functions of intelligent transportation systems. Traffic anomalies mainly include traffic accidents, vehicle breakdown, truck falling, damage or malfunction of road traffic facilities, and other special events that cause traffic flow disturbances. Such incidents are prone to traffic congestion, reduced road capacity, and severely affect the normal operation of the entire road traffic system. Through traffic anomaly detection, traffic managers can timely understand traffic anomaly information and take appropriate inducement and control measures to reduce the adverse effects of traffic anomalies.
  • Traffic anomaly detection can be divided into manual mode and automatic mode.
  • Manual methods include patrol cars, emergency telephone reporting and video surveillance. Due to the consumption of manpower and material resources and poor real-time performance, traffic management needs cannot be met.
  • the automatic method relies on the automatic event detection (AID, Automated Incidence Detection) algorithm.
  • AID Automated Incidence Detection
  • the basic principle is to identify traffic anomalies by detecting changes in road traffic at different locations.
  • AID algorithms include pattern recognition algorithms (such as California algorithm, Monica algorithm), statistical prediction algorithms (such as exponential smoothing, Kalman filtering), traffic flow model algorithms (such as McMaster algorithm), and intelligent recognition algorithms. (such as artificial neural networks, fuzzy logic algorithms).
  • the invention utilizes the trajectory data returned by the taxi and the bus GNSS positioning device, establishes a historical traffic state database and a real-time traffic state database, and identifies the traffic anomaly event by analyzing the difference of the traffic flow characteristics reflected by the two.
  • the method has the characteristics of good real-time performance, parallel processing, high recognition rate and low requirements for detection facilities. It is suitable for the detection of urban road traffic anomalies in data environment with real-time floating vehicle positioning data.
  • a US patent application, US 20160148512 discloses a composition principle and implementation method of a traffic anomaly detection and reporting system.
  • the system consists of a sensor, a communication module, a mobile processing module, and a user interaction module.
  • the sensor is used to collect relevant data around the vehicle;
  • the communication module is used for transmitting the vehicle data and receiving data of the surrounding vehicle;
  • the mobile processing module is for processing and analyzing the data of the relevant vehicle in a certain area and generating a traffic event report; user interaction
  • the module is able to provide traffic incident reports like a user.
  • the scheme is a traffic anomaly detection technology based on the vehicle and vehicle communication network, which can use various types of information collected by the sensor to identify abnormal events.
  • the sensor and the communication unit need to be separately installed and debugged, the implementation is difficult; the processing capacity of the mobile processing unit is limited; and the mobile and fixed message receiving end is required, and the system itself has a failure probability and the reliability is not good.
  • a Chinese patent application, CN 104809878 A discloses a method for detecting abnormal state of urban road traffic using bus GPS data.
  • the scheme obtains the link delay time index according to the GPS historical data, obtains the instantaneous speed, the cycle average speed, the weighted moving average speed and the multi-vehicle average speed according to the current GPS data, and uses the gauge variable analysis algorithm to detect the abnormality.
  • This program does not New testing facilities are needed and implementation is convenient.
  • the characterization of the traffic situation is too simplistic, and it is impossible to analyze the characteristics and causes of traffic anomalies. There is no basis for the division of traffic scenarios, and the influence of weather and other factors on traffic situation changes cannot be considered. Summary of the invention
  • Floating car Also known as the probe car. Refers to buses and taxis that have on-board positioning devices and are driving on city roads.
  • GNSS Global Navigation Satellite System
  • GPS Global Navigation Satellite System
  • Glonass Galileo
  • Beidou satellite navigation systems GPS, Glonass, Galileo and Beidou satellite navigation systems.
  • Space-time sub-zone A zone divided by two dimensions, time and space, reflected in a certain space within a certain period of time.
  • Historical trajectory data is trajectory data accumulated over a long period of time and stored in a database. Historical trajectory data is dynamic change data that needs to be updated in a timely manner and periodically reprocessed and analyzed to ensure the accuracy of historical traffic feature extraction. The data for each spatiotemporal sub-area can be processed in parallel to increase efficiency. In the present invention, it may be simply referred to as historical data.
  • Real-time trajectory data is a trajectory data set within a time zone that is closest to the current time. In the present invention, it may be simply referred to as real-time data.
  • Traffic situation A general term for the comprehensive situation of traffic operations within a certain period of time and within a certain space.
  • Traffic anomalies traffic turbulence caused by events such as traffic accidents, vehicle breakdown, truck falling, road traffic facilities damage or malfunctions.
  • Abnormal traffic severity The severity of traffic flow disorder is the difference in traffic flow characteristics after traffic flow and traffic anomalies in normal conditions.
  • Traffic Anomaly Index A measure of the severity of traffic. The range is 0 ⁇ 10. The larger the value, the more serious the traffic anomaly.
  • Traffic environment The sum of all external influences and forces acting on road traffic participants. This includes road conditions, transportation facilities, landforms, meteorological conditions, and transportation activities of other transportation participants.
  • Map Matching The process of associating geographic coordinates with a city road network.
  • Peak hourly traffic The maximum hourly traffic flow in a city's road section.
  • Finite Mixing Model A mathematical method of simulating complex density with simple density.
  • a finite mixed model with a set of variables y and a component number K can be expressed as:
  • Response variable A variable that changes according to an independent variable, also called a dependent variable.
  • Bayesian information criterion It is an evaluation index of the reliability of the result of correcting the probability of occurrence by using the Bayesian formula to estimate the probability of partial unknowns under incomplete intelligence. Its calculation method is:
  • a likelihood function is a function of the parameters of a statistical model. Given the output X, the likelihood function L(0 ⁇ x) on the parameter ⁇ (in numerical value) is equal to the probability of the variable after the given parameter: (
  • Kullback-Leibler divergence A measure of the difference between two probability distributions P and Q.
  • J-Shannon divergence is a symmetrized form of Kullback-Leibler divergence.
  • K-Medoids algorithm A clustering algorithm that selects such a point from the current category for each iteration to all other
  • the object of the present invention is to establish a scheme based on a floating vehicle trajectory recording system, using historical GNSS positioning data and real-time GNSS positioning data, combined with traffic environment information to identify road traffic anomalies.
  • the present invention provides the following technical solutions:
  • the implementation premise of the present invention is: a floating car (a taxi, a bus, etc.) equipped with a GNSS track recorder; a data center having a large-scale storage, calculation, and real-time task processing capability.
  • the scope of application of the present invention is: Urban roads (including ground roads and elevated roads) through which the above-mentioned floating vehicles pass.
  • the implementation steps of the present invention include:
  • the map matching algorithm is used to project the GNSS positioning points to the city map, and the matching relationship between the positioning points and the road segments is established, and the error caused by the positioning drift is corrected.
  • Historical trajectory data analysis and feature extraction Using the historical floating vehicle trajectory data, a traffic feature model is established. For each spatiotemporal sub-area, the traffic characteristics are described by the probability distribution of the travel speed, the traffic feature model is established and the parameters are estimated.
  • Real-time trajectory data analysis and feature extraction Use real-time floating car trajectory data to grasp the dynamics of traffic characteristics.
  • the current traffic characteristics are described by the travel speed probability distribution of the current space-time sub-region, and the model is established and parameter estimation is performed.
  • the Jensen-Shannon divergence is used to measure the difference between historical traffic characteristics and real-time traffic characteristics.
  • the step 1) may specifically adopt the following methods:
  • the segment size of the time dimension is determined.
  • the time segment span is a fixed value, usually 30 minutes as a time segment; the segment size of the spatial dimension is determined, and the spatial segment span is a fixed value, and a spatial grid of 200 m ⁇ 200 m is usually taken as a spatial segment.
  • Non-equidistant space-time division method For urban central areas where the road network density is greater than 2km/km 2 or the peak hour flow is greater than 1000 vehicles/hour, 30min time segments and 200mX 200m space segments are taken. For road network density less than 2km/km 2 or peak hour traffic is less than 1000. In the suburb of the city/hour, take a 30-minute time segment and a 400mX 400m space segment.
  • the step 3) specifically includes the following steps:
  • the matching scheme includes:
  • This program is suitable for high frequency floating car data.
  • the step 4) may specifically adopt the following methods:
  • the data of all driving speeds of each secondary floating car in a time and space sub-region constitutes the whole.
  • ⁇ ... ⁇ - is the first and second GNSS positioning in the space-time sub-region
  • the distance between points, ..., the distance between the n-1th and the nth GNSS anchor point, h-tange is the first in the space-time sub-region, ..., Timestamps of GNSS anchor points; data in each spatio-temporal sub-area is not filtered to form a set for subsequent processing.
  • Time-smooth sampling plan Specify the length of the time segment and the upper limit of the number of segments of the same time; search for the velocity data in each time segment in a time-space sub-region. If the number of velocity data in the time segment exceeds the upper limit, the data of the upper limit is randomly added to the data to be processed. sample.
  • the distance between the GNSS positioning points is the first time in the space-time sub-area, ..., the time stamp of the GNSS positioning point; the specified time segment length t P , the upper limit of the number of segments of the same time; 3 ⁇ 4. 1; Search for the speed data in the time and time segments of a time-space sub-region, if the number of speed data in the time segment exceeds the upper limit; 1 ⁇ 2 ⁇ , random take; 1 ⁇ 2 ⁇ data is added.
  • the step 5) may specifically adopt the following method:
  • This scheme uses a mixed Gaussian model with a fixed component quantity to describe the probability distribution of vehicle speed.
  • This program uses a model-based evaluation method to select the appropriate number of components, as follows:
  • t is the number of parameters in the model, "for the total amount of data.
  • the /C smallest hybrid model is selected, and its parameter vectors 1], ⁇ , and ⁇ are recorded as the feature records of the local space-time sub-region.
  • the density curve morphology of the hybrid model is shown in Figure 6.
  • This scheme uses the same model-based evaluation method as 512), but the distribution of the sub-components and the number of components are variable, as follows:
  • the probability distribution model is chosen as the distribution type of the sub-components, including but not limited to: normal distribution, gamma distribution, Weibull distribution.
  • normal distribution gamma distribution
  • Weibull distribution a normal distribution
  • the sub-distribution function takes:
  • Historical trajectory data classification method by context. Based on temperature, precipitation, visibility and traffic control measures, historical data under no traffic anomalies are divided into different categories, models are established and parameter estimates are made.
  • the implementation method is as follows:
  • the traffic environment is divided into 5 ⁇ 8 categories, and the historical data is classified into the above categories according to the different traffic environments corresponding to historical data.
  • the processing as described in 51) is performed separately, thereby establishing a mapping relationship 3 ⁇ 4 ⁇ 7, which is a traffic situation and a traffic situation.
  • Historical data clustering method For the historical data, the difference quantization between different spatiotemporal regions is obtained by comparison between the time and space sub-regions, and the quantized differences are used for clustering. Temperature, precipitation, visibility and traffic control measures are used as characteristic factors to perform multiple Logit regressions to establish a mapping relationship between traffic environment and categories. See Figure 4 for the implementation process. The implementation steps are as follows:
  • the probability density function P, (x) of the travel speed distribution corresponding to the spatiotemporal sub-region on different dates is written, and the parameters are mixed Gaussian model as an example:
  • the traffic environment data (including temperature, precipitation, visibility, etc.) is used as an independent variable. Perform multiple logit regression to obtain the mapping relationship between traffic environment E and traffic situation category T ⁇ 7).
  • the step 6) may specifically adopt the following methods:
  • the step 7) specifically includes the following steps:
  • step 62) the historical traffic characteristic data under the category is located according to the category of the current traffic situation;
  • the step 8) specifically includes the following steps:
  • the urban road traffic anomaly detection technology based on floating car data proposed by the present invention can realize the detection of abnormal events with high accuracy, the detection rate exceeds 90%, and the false alarm rate is lower than 20%. It has achieved good detection results and can be applied to intelligent management and service of urban traffic.
  • FIG. 1 shows a schematic diagram of the components and basic principles of the present invention
  • Figure 2 is a schematic view showing the overall flow of the present invention in the implementation process
  • FIG. 3 is a schematic diagram showing an implementation manner of a fast map matching algorithm of the present invention.
  • FIG. 4 is a schematic flow chart showing a historical traffic feature extraction scheme implemented by the present invention.
  • FIG. 5 is a schematic flow chart showing a real-time traffic feature extraction scheme implemented by the present invention.
  • Figure 6 shows a schematic diagram of the morphology of the Gaussian mixture model probability distribution
  • Figure 7 shows a measurement of the difference in the comparison between historical traffic characteristics and real-time traffic characteristics.
  • the overall system architecture of the present invention includes an onboard GNSS track recorder, a data center, a GNSS satellite, and a communication system carried by a floating vehicle.
  • the GNSS here includes GPS, GLONASS, GALILEO, Beidou, IRNSS, QZSS and any similar navigation satellite positioning system.
  • GNSS track recorders equipped with floating cars, buses, etc., with a certain sampling frequency / (general requirements; ).1 ⁇ ) record the position information of the vehicle at various points during driving, and through the GPRS mobile communication network (also The use of wireless network communication technologies such as WCDMA and TD-LTE, but the cost will be correspondingly improved), the location information is sent to the data center in real time.
  • the data center establishes a historical road traffic characteristic database through data preprocessing, data fusion, and through a specific algorithm; establishes a real-time traffic feature database for the recently received real-time data; and determines whether the current traffic feature is abnormal through the mapping relationship between the historical database and the real-time database And visualize the display through the processing terminal and generate a traffic anomaly event report.
  • the overall process of the scheme is shown in Figure 2, including the acquisition and storage of GNSS trajectory data, the establishment of spatiotemporal sub-areas, historical traffic feature extraction, real-time traffic feature extraction, and anomaly identification.
  • Collecting and storing GNSS trajectory data is the data foundation of the whole scheme. Due to the huge amount of data, a distributed storage scheme should be adopted.
  • the basic assumption of establishing a spatiotemporal sub-area is that it has the same traffic characteristics in a specific area and a specific time period. This assumption is generally applicable after long-term observation.
  • Historical traffic feature extraction the principle is to use the GNSS trajectory data to calculate the travel speed, use a large number of travel speed data in the same space-time sub-region, establish a probability distribution model of vehicle speed, and estimate the parameters, and characterize the traffic characteristics with a small number of parameters.
  • Real-time traffic feature extraction the principle is to process and analyze the speed data in the current time period, and also establish the current vehicle speed probability distribution model.
  • the anomaly identification is to use the difference measure to judge the degree of change of the real-time feature compared to the historical feature, and determine whether a traffic anomaly event occurs according to whether it reaches the threshold.
  • Embodiment 1 According to the combination of the embodiments of the invention, the implementation is given below. Embodiment 1
  • Step 11 Determine the segment size of the time dimension by using the equidistant space-time division method, and the time segment span is a fixed value, usually 30 minutes.
  • the spatial segment span is a fixed value, usually takes a spatial grid of 200mX200m as a spatial segment.
  • Step 12 Perform data preprocessing to perform data cleaning, data integration, data conversion, and data reduction on the GNSS positioning data to improve the structural degree of the data.
  • the anchor point is matched to the spatio-temporal sub-area in combination with the timestamp data of the coordinates of the positioning point.
  • the distance between the GNSS positioning points ..., the distance between the -1 and the nth GNSS positioning point, which is the first in the space-time sub-region, ..., the first Timestamp of the GNSS anchor point; the data in each spatio-temporal sub-area is not filtered to form a set for subsequent processing.
  • Step 15 the historical data under the condition of no traffic anomaly is used to establish a traffic feature model and estimate the parameters.
  • t is the number of parameters in the model, "for the total amount of data.
  • Step 16 Perform real-time traffic data model establishment and parameter estimation to obtain a characteristic function of the current traffic condition.
  • the method is the same as step 1-5, and the parameter vectors t] rt , ⁇ , a rt are recorded.
  • Step 18 normalize the speed of each space-time sub-region by ⁇ 1
  • Step 21 Using the equidistant space-time division method, determining the segment size of the time dimension, the time segment span is a fixed value, usually taking 30 minutes as a time segment; determining the segment size of the spatial dimension, the spatial segment span is a fixed value, usually taking 200m ⁇ 200m
  • the spatial grid acts as a spatial fragment.
  • Step 22 Perform data preprocessing to perform data cleaning, data integration, data conversion, and data reduction on the GNSS positioning data to improve the structural degree of the data.
  • the anchor point is matched to the spatio-temporal sub-area in combination with the timestamp data of the coordinates of the positioning point.
  • Step 24 Calculate the travel speed of each vehicle in the space-time sub-region: ⁇ .
  • ⁇ , 2... ⁇ -1 " is the distance between the first and second GNSS positioning points in the space-time sub-area, . ., the distance between the -1 and the GNSS anchor points, ⁇ is the first time in the space-time sub-area, ..., the time stamp of the GNSS anchor points ;
  • the upper limit of the number of segments of the data segment ⁇ Search for the time data in a space-time sub-region. The velocity data in each time segment. If the number of velocity data in the time segment exceeds the upper limit ⁇ , the random data is added to V.
  • Step 25 the historical data under the condition of no traffic anomaly is used to establish the traffic characteristic model and estimate the parameters.
  • t is the number of parameters in the model, "for the total amount of data.
  • the /C smallest hybrid model is selected, and its parameter vectors 1], ⁇ , and ⁇ are recorded as the feature records of the local space-time sub-region.
  • the probability density function p'Q) of the travel speed distribution corresponding to the spatio-temporal sub-region on different dates is written:
  • the distance matrix is used as the input of the K-Medoids algorithm to obtain clustering results and index the categories.
  • category index is used as the response variable, and multiple logit regressions are performed to obtain the mapping relationship R(E ⁇ T) between the traffic environment E and the traffic situation category T.
  • the same type of data is aggregated, and the hybrid model is re-established with the new data set after aggregation, and the parameter estimation is performed to obtain the final historical traffic characteristic data set.
  • Step 26 Obtain a characteristic function of the traffic condition, and obtain current information such as temperature, precipitation, visibility, traffic control measures, and the type of the current traffic condition.
  • Step 28 Denormalize the speed of each time and space sub-region
  • Step 31 Using a non-equidistant space-time division method, for a central area of the city where the road network density is greater than 2 km/km 2 or the peak hour flow is greater than 1000 vehicles/hour, take a 30 min time segment and a 200 mX 200 m spatial segment for the road network density. For suburban areas of less than 2km/km 2 or peak hour traffic of less than 1000 vehicles per hour, 30 min time segments and 400 mX 400 m space segments are taken.
  • Step 32 Perform data preprocessing, perform data cleaning, data integration, data conversion, and data reduction on the GNSS positioning data to improve the structural degree of the data.
  • ⁇ is satisfied, complete the match; otherwise, search for other road segments Until the conditions are met.
  • the projection line equation is: yy A ⁇ ( ⁇ _ ) ky A - ky t + k 2 x t + x A
  • the anchor point is matched to the spatio-temporal sub-area in combination with the timestamp data of the coordinates of the positioning point.
  • Step 34 Calculate the travel speed of each vehicle in the space-time sub-region: , where ⁇ 3 ⁇ 4, 2... ⁇ -1," is the distance between the first and second GNSS positioning points in the space-time sub-region, . ., the distance between the -1 and the GNSS anchor points, ... is the first time in the space-time sub-region, ..., the time of the GNSS anchor point Poke; specify the time segment length at the same time segment number of data segments upper limit ⁇ search for a time-space sub-region time ⁇ speed data within each time segment, if the number of velocity data within the time segment exceeds the upper limit ⁇ random data is added to V .
  • Step 35 Perform historical traffic data without traffic abnormality as a whole, and establish traffic feature model and parameter estimation.
  • t is the number of parameters in the model, "for the total amount of data.
  • the /C smallest hybrid model is selected, and its parameter vectors 1], ⁇ , and ⁇ are recorded as the feature records of the local space-time sub-region.
  • the probability density function p'Q) of the travel speed distribution corresponding to the spatio-temporal sub-region on different dates is written:
  • the distance matrix is used as the input of the K-Medoids algorithm to obtain clustering results and index the categories.
  • the traffic environment data (including temperature, precipitation, visibility, etc.) is used as an independent variable to perform multiple logit regression to obtain the mapping relationship between the traffic environment E and the traffic situation category T R ⁇ E ⁇ T).
  • the same type of data is aggregated, and the hybrid model is re-established with the new data set after aggregation, and the parameter estimation is performed to obtain the final historical traffic characteristic data set.
  • Step 36 Obtain a characteristic function of the traffic condition, and obtain current information such as temperature, precipitation, visibility, traffic control measures, and the type of the current traffic condition.
  • Step 38 Denormalize the speed of each time and space sub-region

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Abstract

La présente invention concerne un procédé de détection d'incident de la circulation routière urbaine sur la base de données de véhicules flottants qui peut utiliser des dispositifs de positionnement GNSS embarqués de véhicules flottants pour acquérir les informations de position spatiale des véhicules flottants à différents moments, et qui peut par conséquent réaliser la détection intelligente d'incidents de la circulation routière urbaine par analyse et exploration des informations massives de suivi de véhicules flottants. La technologie de détection d'incident de la circulation routière urbaine utilise la distribution de probabilité de vitesse de déplacement pour caractériser des conditions de circulation, utilise l'indice de mesure de différence de distribution de probabilité pour refléter la différence dans les conditions de circulation, présentant ainsi les avantages de principes clairs, de facilité de mise en œuvre et de haute vitesse de détection.
PCT/IB2016/058105 2016-12-30 2016-12-30 Procédé de détection d'incident de la circulation routière urbaine sur la base de données de véhicules flottants WO2018122585A1 (fr)

Priority Applications (25)

Application Number Priority Date Filing Date Title
CN201680088595.8A CN109923595B (zh) 2016-12-30 2016-12-30 一种基于浮动车数据的城市道路交通异常检测方法
PCT/IB2016/058105 WO2018122585A1 (fr) 2016-12-30 2016-12-30 Procédé de détection d'incident de la circulation routière urbaine sur la base de données de véhicules flottants
GBGB1711408.3A GB201711408D0 (en) 2016-12-30 2016-12-30 Early entry
GBGB1909407.7A GB201909407D0 (en) 2016-12-30 2017-12-30 Multimodal road traffic anomaly detection method
GB1905907.0A GB2569924B (en) 2016-12-30 2017-12-30 Travel time distribution-based multimodal traffic anomaly detection method
GB2100341.3A GB2587588B (en) 2016-12-30 2017-12-30 A method for detecting traffic anomaly with non-equidistant spatial-temporal division based on peak hour traffic flow rate
PCT/IB2017/058535 WO2018122805A1 (fr) 2016-12-30 2017-12-30 Procédé de détection d'anomalies de trafic sur la base de la répartition dans le temps des trajets
GB2009834.9A GB2582532B (en) 2016-12-30 2017-12-30 Method for detecting traffic anomaly of urban road based on peak hour traffic flow rate
CN201780050765.8A CN109844832B (zh) 2016-12-30 2017-12-30 一种基于行程时间分布的多模态交通异常检测方法
GB1909405.1A GB2572717B (en) 2016-12-30 2017-12-30 A Method for Urban Traffic Incident Detecting
CN201780050754.XA CN109997179A (zh) 2016-12-30 2017-12-30 一种非等距时空划分的道路交通异常检测方法
GBGB1909409.3A GB201909409D0 (en) 2016-12-30 2017-12-30 Traffic anomaly detection method based on time distribution of travels
PCT/IB2017/058531 WO2018122801A1 (fr) 2016-12-30 2017-12-30 Procédé de détection d'anomalie de circulation d'une route urbaine
PCT/IB2017/058533 WO2018122803A1 (fr) 2016-12-30 2017-12-30 Procédé de détection d'anomalie de trafic routier intelligent
PCT/IB2017/058534 WO2018122804A1 (fr) 2016-12-30 2017-12-30 Procédé de détection d'anomalies de trafic routier par division temps/espace non isométrique
CN201780050719.8A CN110168520A (zh) 2016-12-30 2017-12-30 一种智能化道路交通异常检测方法
CN201780050755.4A CN109643485B (zh) 2016-12-30 2017-12-30 一种城市道路交通异常检测方法
GBGB1909406.9A GB201909406D0 (en) 2016-12-30 2017-12-30 Multimodal road traffic anomaly detection method
PCT/IB2017/058536 WO2018122806A1 (fr) 2016-12-30 2017-12-30 Procédé de détection d'anomalies de trafic multimodal à base de répartitions de temps de trajet
GB2100340.5A GB2588556B (en) 2016-12-30 2017-12-30 A method for detecting traffic anomaly based on travel time distribution with equidistant spatial-temporal division
PCT/IB2017/058532 WO2018122802A1 (fr) 2016-12-30 2017-12-30 Procédé de détection d'anomalie de trafic routier multimodal
CN201780050906.6A CN109716414B (zh) 2016-12-30 2017-12-30 一种多模态道路交通异常检测方法
CN201780050907.0A CN109791729B (zh) 2016-12-30 2017-12-30 一种基于行程时间分布的交通异常检测方法
GB2009833.1A GB2582531B (en) 2016-12-30 2017-12-30 Method for detecting traffic anomally of urban road with equidistant spatial-temporal division
GBGB1909408.5A GB201909408D0 (en) 2016-12-30 2017-12-30 Road traffic anomaly detection method using non-isometric time/space divison

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