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CN109147319B - A Discrimination Method of Road Emergencies Based on Multiple Traffic Data Indicators - Google Patents

A Discrimination Method of Road Emergencies Based on Multiple Traffic Data Indicators Download PDF

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CN109147319B
CN109147319B CN201810883069.1A CN201810883069A CN109147319B CN 109147319 B CN109147319 B CN 109147319B CN 201810883069 A CN201810883069 A CN 201810883069A CN 109147319 B CN109147319 B CN 109147319B
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road
data
sudden change
occupancy rate
emergency
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CN109147319A (en
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任毅龙
刘晨阳
于海洋
季楠
张路
刘帅
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Beihang University
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    • 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
    • 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

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Abstract

The patent discloses a road emergency distinguishing method based on multiple traffic data indexes, which comprises the following steps: the method comprises the following steps: acquiring historical data and calculating historical data indexes; step two: determining an index mutation judgment method; step three: and judging whether the road network has an emergency or not and finding a road section with the emergency. The method has the advantages that the problem of detection precision reduction caused by single traffic data loss is solved by adopting various data indexes in the road network to judge road emergency; the method can be used for various road network detection systems, and the selectable items of the detection types in the road network detection systems are enriched.

Description

Road emergency discrimination method based on multiple traffic data indexes
Technical Field
The invention belongs to the field of traffic state judgment, and particularly relates to a road emergency judgment method based on multiple traffic data indexes.
Background
Along with the acceleration of the urbanization process of China, the number of urban automobiles is increased rapidly, and the problem of urban traffic emergencies is more serious. Traffic emergencies become a common problem in cities across the country, and cause huge economic loss and indirect losses such as deterioration of urban environment, reduction of resident health level, reduction of urban trip satisfaction and the like.
In the past research, data adopted by the urban road traffic emergency distinguishing method is relatively single, and mainly the speed and the flow of the automobile obtained by a road detector. The road event judgment is carried out according to single traffic data, the judgment precision is often low, and the problem of data loss is often faced. The data loss can cause the detection precision to be reduced, thereby causing the failure of the traffic emergency discrimination method. In addition, the conventional traffic incident discrimination method cannot be applied to all road detection systems. The conventional road detector comprises a geomagnetic coil detector, a microwave detector, an electric alarm (video) detector and the like, and the corresponding detection data are coil data, microwave data and electric alarm data respectively; these data have different characteristics. The characteristic of data diversity makes the robustness of the conventional traffic incident discrimination method worse.
The invention provides a road emergency distinguishing method based on multiple traffic data indexes, which is used for distinguishing whether an emergency happens on an urban road or not by utilizing data such as road traffic flow, driving speed, occupancy and the like. On one hand, the travel information service can be issued to public travelers, and the travel satisfaction of residents is improved; on the other hand, the system can inform the urban traffic management department so as to intervene and manage the traffic incident in time, prevent the traffic incident from spreading and reduce the loss caused by the traffic incident, and has important practical significance.
Disclosure of Invention
The invention aims to improve a method for judging road emergency in a traffic system, and consider traffic parameters as many as possible when judging the road emergency, thereby improving the scientificity and the practicability of the judging method. Therefore, the invention provides a road emergency distinguishing method based on multiple traffic data indexes.
In the invention, historical data of road conditions in a road network is firstly obtained, and then historical data calculation indexes are calculated; secondly, determining a method for judging index mutation; and finally, determining whether any road section in the road network has an emergency according to the mutation condition of the data index.
In order to achieve the purpose, the specific technical scheme of the invention is as follows:
the method comprises the following steps: and acquiring historical data and calculating a historical data index. Acquiring corresponding traffic data by using an existing road detection system in a road network; data is subjected to processing such as workday and non-workday distinguishing, data elimination within the occurrence time range of historical events and the like; the processed data is stored as historical data. After acquiring the historical data, calculating the mean and variance of the historical data, and calculating the 3 sigma range of the historical data.
Step two: and determining an index mutation judgment method. And acquiring real-time traffic parameters of each road section in the road network, and when the traffic parameter values are out of the 3 sigma range of the historical data, considering that the traffic parameter indexes are mutated. For a certain specific traffic parameter index, when the number of indexes with mutation exceeds a set threshold value, the road section is considered to have a suspected emergency.
Step three: and judging whether the road network has an emergency or not and finding a road section with the emergency. And D, acquiring data of each road section in the current road network, calculating corresponding index values, and judging whether the current data index is mutated according to the index mutation judging method obtained in the step two. And finally, determining whether an emergency occurs in the road network and determining the road sections with the emergency according to the condition that the data indexes are subjected to sudden change.
The technical advantages of the invention are as follows:
the invention adopts various data indexes in the road network to judge the road emergency, and solves the problem of low detection precision caused by single traffic data loss.
The method adopts various data indexes in the road network to judge the road emergency, and has higher robustness and judgment precision compared with the existing road emergency judging method.
The invention adopts various data indexes in the road network to judge the road emergency, can be used for various road network detection systems, and enriches the selectable items of the detector types in the road network detection systems.
Detailed Description
The following describes in detail embodiments of the present patent. It should be noted that the detailed description is only an example of the preferred embodiments of the present patent, and should not be construed as limiting the scope of the present patent.
The specific embodiment provides a road emergency distinguishing method based on multiple traffic data indexes, which comprises the following steps:
the method comprises the following steps: and acquiring historical data and calculating a historical data index.
1. Historical data is acquired. The method comprises the steps of selecting a researched road network, taking data of two consecutive months measured by the road network detection system, distinguishing working days from non-working days, and taking data of 10 minutes (configurable items) before and after a current time period in history as initial historical data.
For example, the current time period is 8: 00-8: 01, historical data is historical 7: 51: 00-8: 11: 00 these 20 minutes are historical data. In order to ensure that the historical data are normal data, data within the occurrence time range of the historical events are provided in the historical data. And after the preliminary historical data are proposed, screening data around the data to serve as final historical data.
2. A historical data index is determined.
After acquiring the historical data, calculating the mean value of the historical data
Figure BDA0001754937170000031
Figure BDA0001754937170000032
Where x represents speed, flow rate, vehicle average occupancy, center lane occupancy, and upstream vehicle passing ratio.
Calculating the variance of the historical data:
Figure BDA0001754937170000033
where n is the data amount of the acquired history data.
Calculate the 3 σ range of the historical data:
Figure BDA0001754937170000034
if it is not
Figure BDA0001754937170000035
The 3 σ range is considered to be exceeded.
Step two: and determining an index mutation judgment method.
In the specific embodiment of the invention, 5 traffic parameter indexes are selected, namely speed, flow, average road vehicle occupancy, upstream vehicle passing proportion and intersection occupancy. The five index mutations are determined as follows:
1. sudden change of speed
The average speed of passing the vehicle within a 5 minute time window (configurable) was calculated for each time interval (default 1 minute). I.e. the average speed of each vehicle within a 5 minute time window.
Figure BDA0001754937170000036
Figure BDA0001754937170000041
Average speed (m/sec) of the link i over the time period t;
l: length of road section l (meters);
n: the number of vehicles matched with the upstream and downstream intersections of the road section l in the time period t;
ti,t: and after the noise is eliminated, the travel time of the vehicle i passing through the section l in the time period t.
(1) If the speed obtained in the current time period exceeds the 3 sigma range of the speed, the speed is considered to be suddenly changed.
(2) And if the speed of 5 time intervals (configurable items) is suddenly changed, 60 percent (configurable items) of the speed of 3 time intervals is considered to have sudden change, and the speed is considered to have sudden change.
(3) When the number of the data sources is 3, and the number of the data sources with mutation in the data is more than or equal to 2, the suspected emergency is considered to occur; and when the number of the data sources is less than or equal to 2, and 1 data source generates mutation, the suspected emergency is considered to occur.
2. Sudden change in flow
And (3) counting and calculating the flow of each inlet channel at the end of each period:
Figure BDA0001754937170000042
the flow rate of the inlet channel l is equal to the sum of the flow rates of the phases i of the inlet channel.
(1) And if the flow obtained in the current time period exceeds the 3 sigma range of the flow, the flow is considered to be suddenly changed.
(2) When the flow rate is suddenly changed in 5 continuous time intervals and 60 percent of the flow rate is in 3 continuous time intervals, the flow rate is considered to have a sudden change
(3) When the number of the data sources is 3, and the number of the data sources with data mutation is more than or equal to 2, the road section where the data source is located is considered to have a suspected emergency; and when the number of the data sources is less than or equal to 2, and 1 data source generates mutation, the road section where the data source is located is considered to have a suspected emergency.
3. Sudden change of average occupancy of vehicles on road
And (3) calculating the vehicle average occupancy rate during the green light period of each entrance lane at the end of each period:
Figure BDA0001754937170000043
Figure BDA0001754937170000044
average occupancy of the inlet lane l over a time period t;
n: the number of lanes;
oi,t: occupancy of phase i by the entrance lane l during time period t.
qi,t: flow through the inlet channel/phase i during time period t.
(1) And if the vehicle average occupancy rate obtained by the microwave equipment in the current time period exceeds the 3 sigma range of the historical data, the vehicle average occupancy rate of the microwave equipment road section is considered to be suddenly changed.
(2) And (3) the same microwave equipment continuously has 5 time intervals, and the average vehicle occupancy rate of 60 percent, namely 3 time intervals has sudden change, so that the average vehicle occupancy rate is considered to have sudden change.
(3) And if the occupancy rate of any microwave equipment vehicle is suddenly changed, the road section corresponding to the microwave equipment is considered to have a suspected emergency.
4. Upstream vehicle passing proportion mutation
(1) If the upstream vehicle passing proportion exceeds the 3 sigma range of the historical data at the current time point, the inlet road upstream vehicle passing proportion is considered to be changed suddenly.
(2) And if 60% of the vehicles passing upstream of the entrance road in the 5 continuous time intervals, namely 3 time intervals, suddenly change, the road section corresponding to the entrance road is considered to have a suspected emergency.
5. Intersection occupancy catastrophe
(1) If the intake tract occupancy exceeds the 3 sigma range of the historical data for the current time period, the intake tract occupancy is considered to be mutated.
(2) And if 60% of the inlet channel occupancy of the same inlet channel in 5 continuous time intervals, namely 3 time intervals, has mutation, the inlet channel occupancy is considered to have mutation, and the road section corresponding to the inlet channel is considered to have suspected emergency.
Step three: and judging whether the road network has an emergency or not and finding a road section with the emergency.
(1) And D, calculating historical data index values of 5 traffic parameters of the road section average speed, the entrance road flow, the road section average vehicle occupancy, the upstream vehicle passing proportion and the entrance road occupancy and the 3 sigma range thereof according to the calculation method of the historical data index determined in the step I.
(2) The method comprises the steps of obtaining data of all road sections in a road network at the current moment, and respectively calculating values of 5 traffic parameter indexes including average speed of the road sections at the current moment, flow of an inlet road, average road section and vehicle occupancy, upstream vehicle passing proportion and inlet road occupancy.
(3) And according to the index mutation judgment method determined in the step two, comparing the current time values and the historical values of the 5 data indexes, and judging the mutation condition of each index.
(4) Each time interval (default per minute) is scored according to table 1 for each index.
If the index considers that a suspected emergency happens, the score is 1; if the index has no data source, the index is scored as 0; if the index is within the historical normal range, the index is scored as-1. And accumulating all the index scores to obtain a total score, wherein the road section with the total score being more than gamma (default to 0) is the road section where the emergency happens.
TABLE 1 score recording sheet for each index
Figure BDA0001754937170000061
And if the road section has an emergency, recording the occurrence time of the event, the occurrence road section, the sudden change index of the road section and the historical mean and standard deviation corresponding to the index, which are identified by the system.
And (5) continuously performing time intervals, wherein the road section score is less than or equal to 0, judging that the event is ended by the system, and recording the event ending time.

Claims (2)

1.一种基于多交通数据指标的道路突发事件判别方法，其特征在于，所述方法包括：1. a road emergency discrimination method based on multiple traffic data indicators, is characterized in that, described method comprises: 步骤一：获取历史数据并计算历史数据指标Step 1: Obtain historical data and calculate historical data indicators 首先，获取历史数据；对于选定的路网，取该路网检测系统所测量的连续第一时间段的历史数据，数据区分工作日和非工作日；并在该历史数据中取历史上当前时段前后各第一预定时刻的数据作为初步历史数据；对初步历史数据进行提出后，筛选为期一个月的数据作为最终的历史数据；First, obtain historical data; for the selected road network, obtain the historical data of the first consecutive time period measured by the road network detection system, and the data distinguish working days and non-working days; The data at the first predetermined moments before and after the time period is used as the preliminary historical data; after the preliminary historical data is proposed, the data for a period of one month is screened as the final historical data; 然后，确定历史数据指标；计算历史数据的均值
Figure FDA0002371640890000011
Figure FDA0002371640890000012
其中，x表示速度、流量、车均占有率、路口中间车道占有率、上游车辆通过比例；计算历史数据的方差：
Figure FDA0002371640890000013
其中，n为所获取历史数据的数据量；计算历史数据的3σ范围：
Figure FDA0002371640890000014
当当前数据xi满足
Figure FDA0002371640890000015
则认为超出3σ范围；Then, determine historical data indicators; calculate the mean of historical data
Figure FDA0002371640890000011
Figure FDA0002371640890000012
Among them, x represents speed, flow, vehicle occupancy rate, middle lane occupancy rate at intersection, and upstream vehicle passing rate; calculate the variance of historical data:
Figure FDA0002371640890000013
Among them, n is the data volume of the acquired historical data; calculate the 3σ range of the historical data:
Figure FDA0002371640890000014
When the current data xi satisfies
Figure FDA0002371640890000015
It is considered to be beyond the 3σ range; 步骤二：确定指标突变判别方法Step 2: Determine the indicator mutation discrimination method 选取速度、流量、车均占有率、上游车辆通过比例、路口中间车道占有率，5个交通指标参数的突变作为判定依据；Select speed, flow, average vehicle occupancy rate, upstream vehicle passing ratio, occupancy rate of the middle lane at the intersection, and the sudden change of five traffic index parameters as the judgment basis; 对于速度突变，每第一时间间隔计算第一时间窗内的过车平均速度；即第一时间窗内每一辆车的平均速度，
Figure FDA0002371640890000016
其中
Figure FDA0002371640890000017
为第l路段在时间段t内的平均速度，L为第l路段长度、单位为米，n为在时间段t内第l路段上下游路口匹配到的车辆数，ti,t为剔除噪声后、时间段t内通过第l路段的车辆i的行程时间；当当前时段得到的速度超出速度的3σ范围时，判断速度发生第一突变；当连续N个时间间隔时，有预定比例的时间间隔的速度发生突变，判定速度发生第二突变；当数据源数量为3时，有突变的数据源数量≥2，则认为发生第一疑似突发事件；当数据源数量≤2时，有1个数据源产生突变，则认为发生第二疑似突发事件；For a sudden change in speed, the average speed of passing vehicles in the first time window is calculated every first time interval; that is, the average speed of each vehicle in the first time window,
Figure FDA0002371640890000016
in
Figure FDA0002371640890000017
is the average speed of the lth road segment in the time period t, L is the length of the lth road segment in meters, n is the number of vehicles matched at the upstream and downstream intersections of the lth road segment in the time period t, t i, t is the noise removal Then, the travel time of vehicle i passing through the lth road segment in the time period t; when the speed obtained in the current period exceeds the 3σ range of the speed, it is judged that the speed has a first sudden change; when there are N consecutive time intervals, there is a predetermined proportion of the time When the speed of the interval changes suddenly, it is determined that the second sudden change occurs in the speed; when the number of data sources is 3, and the number of data sources with mutation is ≥2, it is considered that the first suspected emergency has occurred; when the number of data sources is less than or equal to 2, there is 1 If a sudden change occurs in each data source, it is considered that a second suspected emergency has occurred; 对于流量突变；每一路口信号控制器的信号周期结束统计计算各进口道流量：第l进口道的流量＝该进口道各相位i流量和；当前时段得到的流量超出流量的3σ范围时，判断流量发生第一突变；当连续N个时间间隔时，有预定比例的时间间隔的流量发生突变，判定流量发生第二突变；当数据源数量为3时，有突变的数据源数量≥2，则认为发生第一疑似突发事件；当数据源数量≤2时，有1个数据源产生突变，则认为发生第二疑似突发事件；For the sudden change of flow; the signal cycle of the signal controller at each intersection ends the statistical calculation of the flow of each inlet: the flow of the lth inlet = the sum of the flow of each phase i of the inlet; when the flow obtained in the current period exceeds the 3σ range of the flow, it is judged that The first sudden change occurs in the traffic; when there are consecutive N time intervals, the traffic with a predetermined proportion of the time interval changes suddenly, and it is determined that the second sudden change occurs in the traffic; when the number of data sources is 3, and the number of data sources with sudden changes is greater than or equal to 2, then It is considered that the first suspected emergency has occurred; when the number of data sources is less than or equal to 2, and there is a mutation in one data source, the second suspected emergency is considered to have occurred; 对于路段车均占有率突变；每一路口信号控制器的信号周期结束计算各进口道绿灯期间车均占有率:
Figure FDA0002371640890000021
其中
Figure FDA0002371640890000022
为第l进口道在时间段t内的平均占有率、n为车道数oi,t为时间段t内通过第l进口道相位i的占有率、qi,t为时间段t内通过第l进口道相位i的流量；当前时段流量监控设备得到的车均占有率超出历史数据的3σ范围时，则认为该流量监控设备路段车均占有率发生第一突变；同一流量监控设备连续5个时间间隔，有60％时间间隔的车均占有率发生突变，则认为车均占有率发生第二突变；任意流量监控设备车均占有率有突变，则认为流量监控设备对应的该路段有疑似突发事件；For the sudden change in the average vehicle occupancy rate of the road section; the average vehicle occupancy rate during the green light period of each entrance road is calculated at the end of the signal cycle of each intersection signal controller:
Figure FDA0002371640890000021
in
Figure FDA0002371640890000022
is the average occupancy rate of the lth entrance in the time period t, n is the number of lanes o i, t is the occupancy rate of the phase i passing through the lth entrance road in the time period t, q i,t is the pass through the first entrance in the time period t. l The flow of phase i at the entrance; when the average vehicle occupancy rate obtained by the flow monitoring device in the current period exceeds the 3σ range of the historical data, it is considered that the first sudden change in the average vehicle occupancy rate of the road section of the flow monitoring device; the same flow monitoring device has 5 consecutive If there is a sudden change in the average vehicle occupancy rate of 60% of the time interval, it is considered that the second sudden change in the average vehicle occupancy rate; if any traffic monitoring equipment has a sudden change in the average vehicle occupancy rate, it is considered that the road section corresponding to the traffic monitoring equipment has a suspected sudden change. event; 对于上游车辆通过比例突变，如果当前时间点，上游车辆通过比例超过历史数据的3σ范围，则认为该进口道上游车辆通过比例发生第一突变；连续5个时间间隔有60％的时间间隔的进口道上游车辆通过比例发生突变，则认为该进口道对应路段有疑似突发事件；For the sudden change in the passing ratio of upstream vehicles, if the passing ratio of upstream vehicles exceeds the 3σ range of historical data at the current time point, it is considered that the first sudden change occurs in the passing ratio of upstream vehicles at the entrance; If there is a sudden change in the passing ratio of vehicles on the upstream side of the road, it is considered that there is a suspected emergency in the corresponding road section of the entrance road; 对于路口占有率突变，如果当前时段进口道占有率超出历史数据的3σ范围，则认为进口道占有率发生第一突变；同一进口道连续5个时间间隔，有60％的时间间隔的进口道占有率发生突变，则认为进口道占有率有突变，认为该进口道所对应的路段有疑似突发事件；For the sudden change in the occupancy rate of the intersection, if the occupancy rate of the entrance road in the current period exceeds the 3σ range of the historical data, it is considered that the occupancy rate of the entrance road has the first sudden change; the same entrance road has 5 consecutive time intervals, and 60% of the time intervals are occupied by the entrance road. If there is a sudden change in the rate, it is considered that the occupancy rate of the entrance has a sudden change, and the road section corresponding to the entrance is considered to have a suspected emergency; 步骤三：判定路网中是否有突发事件并找到发生突发事件的路段Step 3: Determine whether there is an emergency in the road network and find the road section where the emergency occurs 获取当前时刻路网中各路段的数据，分别计算当前时刻路段平均速度、进口道流量、路段车均占有率、上游车辆通过比例、进口道占有率5个交通参数指标的数值；根据步骤二中确定的指标突变判别方法，将5个数据指标的当前时刻值和历史数值进行比较，判断各个指标突变情况；每一时间间隔各个指标累积权重数值；如果该指标根据步骤二的判断，认为发生或疑似发生疑似突发事件，则其权重数值+1；如果该指标没有数据源，则该指标权重数不变；如果该指标在历史正常范围内，则该指标权重数值-1；将所有指标的权重数值累加，得到总权重数值，总权重数值＞γ的路段即为突发事件发生路段。Obtain the data of each road section in the road network at the current moment, and calculate the values of five traffic parameter indicators, namely, the average speed of the road section at the current moment, the traffic flow of the entrance road, the average occupancy rate of vehicles on the road section, the passing ratio of upstream vehicles, and the occupancy rate of the entrance road; according to step 2 The method for determining the mutation of indicators is to compare the current moment values of the five data indicators with the historical values to determine the mutation of each indicator; each indicator accumulates weight values at each time interval; If a suspected emergency event is suspected, the weight value of the indicator is +1; if the indicator has no data source, the indicator weight remains unchanged; if the indicator is within the historical normal range, the indicator weight value is -1; The weight values are accumulated to obtain the total weight value, and the road section with the total weight value>γ is the road section where the emergency event occurred. 2.根据权利要求1所述的一种基于多交通数据指标的道路突发事件判别方法，其特征在于，在步骤一中，所述第一时间段为一个月，所述第一预定时刻为10分钟。2 . The method for identifying road emergencies based on multiple traffic data indicators according to claim 1 , wherein, in step 1, the first time period is one month, and the first predetermined time is 10 minutes.
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