Method for short-term traffic risk prediction of road sections using roadside observation data
Disclosed is a method for short-term traffic risk prediction of road sections by using roadside observation data. The method includes the following steps: 1) vehicle trajectory data in the detection area is obtained by using roadside observation data; 2) according to the continuous driving trajectories in the detection area, the traffic flow indicators are counted, and the surrogate safety indicators between vehicles are calculated; 3) time to collision and deceleration are selected as identification indicators to identify conflict events with collision risk in the detection area; 4) traffic flow indicators and surrogate safety indicators within the set time before the occurrence of conflict events are extracted, and the feature screening of various extracted indicators is performed by using classification algorithms; 5) based on the selected feature indicators, the indicators with the highest importance ranking are selected as the input to build a short-term traffic risk prediction model, and the model training and testing are completed by using the identified conflict events; 6) the short-term traffic risk prediction model is used to predict the risk of road sections. The proposed method can improve the prediction accuracy rate of road sections.
1. A method for short-term traffic risk prediction of road sections using roadside observation data, including the following steps:
1) a vehicle trajectory of each vehicle in a detection area is obtained by the roadside observation data;
basic information of vehicles in the detection area is collected by roadside detection equipment; the basic information includes a timestamp, vehicle ID, vehicle position and speed;
based on the basic information of the vehicles stored in radar, position information and speed information of the vehicles are extracted frame by frame according to the vehicle ID, and finally the vehicle trajectory of each vehicle in the detection area is obtained;
2) according to the vehicle trajectory of each vehicle in the detection area, traffic flow indicators are counted and surrogate safety indicators between vehicles are calculated; the surrogate safety indicators include: deceleration, distance headway, time headway, time to collision, modified time to collision, and stopping distance;
the traffic flow indicators include: traffic flow, occupancy rate, vehicle speed, congestion index and a number of lane changes;
3) the time to collision and the deceleration are selected as identification indicators to identify conflict events with collision risk in the detection area;
4) traffic flow indicators and surrogate safety indicators within a set time before occurrence of the identified conflict events are extracted, and classification algorithms are used to perform feature screening on the extracted traffic flow indicators and surrogate safety indicators;
5) based on selected feature indicators, the selected feature indicators with a highest importance ranking are selected as input to construct a short-term traffic risk prediction model, and the identified conflict events are used to complete the model training and testing;
6) With the support of the constructed short-term traffic risk prediction model, the selected feature indicators are selected as input to predict a traffic risk configured for traffic safety control.
2. The method for short-term traffic risk prediction of road sections using roadside observation data according to claim 1 , wherein the detection area in step 1) is set according to longitudinal sensing range of the roadside detection device.
3. The method for short-term traffic risk prediction of road sections using roadside observation data according to claim 1 , in step 1), position information and speed information of the vehicle are extracted frame by frame according to the vehicle ID, and finally the vehicle trajectory of each vehicle in the detection area is obtained, as follows:
based on the basic information of the vehicle, position information and speed information of the vehicle are extracted frame by frame according to the vehicle ID, and the vehicle trajectory data of each vehicle in the detection area is obtained; the expression is as follows:
Trajectory it ={x i ,y i ,v i ,t,ID}
among them:
Trajectory it represents the trajectory data of vehicle i at time t;
x i , y i represent the location information of vehicle i;
v i represents the speed information of vehicle i;
ID represents the identification code information of vehicle i;
the trajectory association of the nearest neighbor algorithm is adopted to deal with missing or interrupted trajectory data; the moving mean filtering method is used to obtain a smooth vehicle trajectory.
4. The method for short-term traffic risk prediction of road sections using roadside observation data according to claim 3 , the trajectory association of the nearest neighbor algorithm is adopted to deal with missing or interrupted trajectory data; to supplement the missing trajectory data, the formula is as follows:
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the moving mean filtering method is used to obtain the smooth vehicle trajectory;
the mean filtering of the trajectory data is as follows:
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among them:
Δt represents a time interval of recording trajectory data,
, represents the position information of vehicle i after filtering,
{circumflex over (v)} t represents the speed information of vehicle i after filtering processing.
5. The method for short-term traffic risk prediction of road sections using roadside observation data according to claim 1 , the step 3) identifying conflict events with collision risk in the detection area, specifically as follows:
when both the time to collision and deceleration meet threshold requirements, the event is regarded as a conflict event;
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among them, Event takes 1 to indicate that the event is a conflict event, and Event takes 0 to indicate that the event is a non-conflict event;
TTC and Decel respectively represent the time to collision and deceleration value,
ttc represents a critical threshold of the time to collision, a 1% represents 1% quantile of statistical deceleration.
6. The method for short-term traffic risk prediction of road sections using roadside observation data according to claim 1 , wherein
the step 4) traffic flow indicators and surrogate safety indicators within a set time before occurrence of the identified conflict events are extracted, and classification algorithms are used to perform feature screening on the extracted traffic flow indicators and surrogate safety indicators, comprises:
for the identified conflict events, based on the timestamp, the traffic flow indicators and the surrogate safety indicators within a set time before each conflict event are extracted, and the extracted traffic flow indicators and surrogate safety indicators are aggregated for collision risk prediction modeling, as follows:
the traffic flow indicators and the surrogate safety indicators within a set time before each conflict event occurs are extracted, and the data aggregation method is used to obtain an aggregation value of each indicator;
the data aggregation method is to calculate an average value of a certain indicator of all vehicle targets in the detection area in 1 second, and then aggregate the average value over the set time length;
based on the aggregation values of the traffic flow indicators and surrogate safety indicators, according to contribution of the indicators to collision risk prediction, the random forest classification algorithm is used to sort the indicators by feature importance to complete the screening of feature indicators.
7. The method for short-term traffic risk prediction of road sections using roadside observation data according to claim 6 , wherein the data aggregation formula in step 4) is as follows:
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among them, Indicator represents a type of aggregated indicators,
T represents a length of time for aggregation,
t represents a starting point of data aggregation,
N represents a total number of aggregated indicators of a certain type,
p t,n represents a nth value of a certain indicator.
8. The method for short-term traffic risk prediction of road sections using roadside observation data according to claim 6 , in step 4), the traffic flow indicators and the surrogate safety indicators within a set time before each conflict event are extracted, and the set time is 60 to 120 seconds before the conflict event occurs.
9. The method for short-term traffic risk prediction of road sections using roadside observation data according to claim 1 , wherein the construction of a short-term traffic risk prediction model in step 5) is to select the first M feature indicators ranked by importance from screening results as input and then construct the short-term traffic risk prediction model based on support vector machines.