Real-time traffic operation safety evaluation method

Abstract

The invention relates to a real-time traffic operation safety evaluation method, which comprises the following steps of: acquiring vehicle track data, and generating a vehicle position time sequence for each vehicle in an observation range; determining the speed and direction of each vehicle at the current moment t based on the vehicle position time sequence; assuming that each vehicle continues to run for a time length T at the speed and direction of the moment t, and if the running tracks of any two vehicles in the time length T are overlapped, determining that the two vehicles conflict with each other; calculating the model of the speed vector difference of any two vehicles generating the conflict at the t moment, and obtaining the conflict between the two vehicles; and adding all conflicts faced by any vehicle i at the t moment in the observation range to obtain an operation safety state value of the vehicle i at the t moment. According to the method, conflicts between vehicles are innovatively defined and quantified, and the traffic operation safety condition can be accurately evaluated in real time based on the real operation state of the vehicle.

Description

Technical Field

[0001] The present invention relates to a real-time traffic operation safety evaluation method, and in particular to a method for evaluating traffic operation safety in real time based on vehicle trajectory data. Background Art

[0002] At present, the methods used to evaluate road traffic safety generally include the following categories. The first category is to evaluate potential accident risks based on statistics of traffic accidents that have occurred. This method predicts driving risks probabilistically based on the history of accidents. It cannot reflect the operating status of each vehicle at the micro level, and cannot accurately reflect the current traffic safety situation based on the current operating status of the vehicle. The second category is to evaluate traffic operation safety based on the distribution of the running speed of the detection vehicle. This method detects the speed of the vehicle and counts the number of vehicles in different speed value intervals and the time distribution to grade the traffic safety level. Although it can evaluate the current traffic safety situation to a certain extent, it can only achieve a macro evaluation at the road section level and cannot accurately reflect the operating safety of specific vehicles or specific road section locations. The third category is to evaluate traffic safety based on static traffic conflicts caused by the driving environment, such as safety risks caused by meteorological conditions or changes in road line shape or changes in traffic operation safety caused by lane changes of vehicles at the exit and entrance of the highway. This method also belongs to the macro evaluation at the road section level.

[0003] Chinese invention patent ZL202010902854.4 discloses a real-time traffic safety index dynamic comprehensive evaluation system and its construction method. A safety index model is established based on the accident risk prediction model, and data related to people, vehicles, and roads that affect traffic accidents (including historical traffic accident data, basic road attribute data, meteorological data, traffic signal cycle data, traffic flow data, vehicle performance data, and driving behavior data) are collected as independent variables. The expected number of accidents is used as the dependent variable to establish an accident risk assessment and prediction model, and further convert the expected number of accidents into a safety index. The core of this method is the probability of an accident. It is a safety evaluation from the perspective of the probability of an accident. The spatial intensity and event granularity are relatively large, such as the probability of an accident on a certain road section in the next half an hour, but it cannot evaluate the traffic safety operation status of the specific location of the road section.

[0004] Chinese invention patent application 202210032897.0 discloses a method for calculating a comprehensive traffic safety index based on multiple dimensions that affect traffic safety. It mainly collects data on four aspects, including the number of alarms received, traffic index, number of vehicles on the road, and date and time periods, generates four aspects of safety trends, and performs a weighted average of these four aspects of safety trends to obtain the final safety index to evaluate traffic safety. This method can only perform a macroscopic assessment and cannot provide real-time and accurate evaluation results.

[0005] Generally speaking, the existing road traffic safety evaluation methods can only provide macro and qualitative assessments at the road section level or predict the risk probability of traffic accidents, and cannot achieve real-time micro and accurate evaluations of specific road sections, lanes and specific vehicles.

[0006] In recent years, with the development of communication technology and intelligent terminal technology, the types of traffic data have become increasingly rich, the data quality has become increasingly accurate, and the data timeliness has become increasingly high, providing possibilities for traffic operation safety evaluation methods. However, there is no effective method for how to use these data.

[0007] Full coverage and high-precision vehicle trajectory data can be obtained through roadside monitoring equipment, such as video surveillance equipment or radar equipment. Based on signal and data processing technology and built-in algorithms, roadside monitoring equipment can identify and track multiple vehicle targets within its observation range, determine the position of each vehicle target in real time, and thus provide high-precision vehicle trajectory data.

[0008] Vehicle networking and autonomous driving are important development directions for future transportation, and they also put forward new requirements for road traffic safety evaluation. Microscopic and accurate traffic operation safety evaluation and real-time monitoring are the inevitable trend for high-quality development of road safety.

[0009] Therefore, it is necessary to design a method based on high-precision vehicle trajectory data to quantitatively evaluate the traffic operation safety status in real time, fully explore the traffic operation safety information contained in the vehicle trajectory data, and scientifically and accurately judge the vehicle's operation safety status and the traffic operation safety of the road section. Summary of the invention

[0010] In order to overcome the defects of the prior art, the present invention proposes a real-time traffic operation safety evaluation method, comprising the following steps:

[0011] Step S01: Collect vehicle trajectory data and generate a vehicle position time series for each vehicle within the observation range:

[0012] (X, Y) 0 =[(x t ,y t ), (x t-Δt ,y t-Δt ), (x t-2Δt ,y t-2Δt ), (x t-3Δt ,y t-3Δt ), …]

[0013] Among them, (x t ,y t ) represents the position of the vehicle at time t, (x t-Δt ,y t-Δt) represents the position of the vehicle at time t-Δt, (x t-2Δt ,y t-2Δt ) represents the position of the vehicle at time t-2Δt, (x t-3Δt ,y t-3Δt ) represents the position of the vehicle at time t-3Δt, x and y are used to represent the abscissa and ordinate of the vehicle position, respectively, and the subscripts t, t-Δt, t-2Δt, and t-3Δt represent the specific time when the vehicle position information is collected;

[0014] Step S02: Based on the vehicle position time series, determine the speed and direction of each vehicle at the current time t. The speed of the vehicle at time t is calculated by the following formula:

[0015]

[0016] in, is the velocity component of the vehicle along the x-axis at time t; is the velocity component of the vehicle along the y-axis at time t; Δt represents the time interval for collecting the vehicle position; v t is the speed scalar of the vehicle at time t;

[0017] Step S03: Assuming that each vehicle maintains the speed and direction at time t and continues to travel for a time length T, if the driving trajectories of any two vehicles overlap within the time length T, it is determined that there is a conflict between the two vehicles;

[0018] Step S04: Calculate the modulus of the velocity vector difference between any two vehicles that cause the conflict at time t to obtain the magnitude of the conflict between the two vehicles, that is:

[0019]

[0020] in, represents the conflict between vehicle i and vehicle j at time t, is the velocity vector of vehicle i at time t, is the velocity vector of vehicle j at time t, is the velocity component of vehicle i along the x-axis at time t, is the velocity component of vehicle j along the x-axis at time t, is the velocity component of vehicle i along the y axis at time t, is the velocity component of vehicle j along the y-axis at time t;

[0021] Step S05: For any vehicle i within the observation range, all conflicts faced by vehicle i at time t are added together to obtain the running safety status value of vehicle i at time t, that is:

[0022]

[0023] in, is the operational safety status of vehicle i at time t, is the conflict between vehicle i and any other vehicle j within the observation range at time t.

[0024] Preferably, the conflicts between all vehicles in the observation range at time t are added together to obtain the running safety state value of the observation range at time t, that is:

[0025]

[0026] Among them, E t is the operational safety status of the observation range at time t.

[0027] Preferably, the time length T is 0.5 to 1.5 seconds, and more preferably, the time length T is 1 second.

[0028] Preferably, step S01 further includes the following steps of correcting the vehicle position time series:

[0029] Step S11: Based on the vehicle position time series before correction, calculate the acceleration of the vehicle at each moment and generate a corresponding acceleration time series.

[0030]

[0031] Among them, a t-Δt is the acceleration of the vehicle at time t-Δt, is the magnitude of the vehicle’s acceleration in the x direction at time t-Δt, is the magnitude of the vehicle's acceleration in the y direction at time t-Δt, a t-2Δt is the acceleration of the vehicle at time t-2Δt, is the magnitude of the vehicle’s acceleration in the x direction at time t-2Δt, is the magnitude of the vehicle's acceleration in the y direction at time t-2Δt, a t-3Δt is the acceleration of the vehicle at time t-3Δt, is the magnitude of the vehicle’s acceleration in the x direction at time t-3Δt, is the magnitude of the vehicle's acceleration in the y direction at time t-3Δt, where Δt represents the time interval for collecting the vehicle's position;

[0032] The magnitude of the vehicle's acceleration at time t is calculated using the following formula:

[0033]

[0034]

[0035] in, is the magnitude of the vehicle’s acceleration in the x direction at time t, is the speed of the vehicle in the x direction at time t, is the speed of the vehicle in the x direction at time t-Δt, is the magnitude of the vehicle’s acceleration in the y direction at time t, is the velocity of the vehicle in the y direction at time t, is the speed of the vehicle in the y direction at time t-Δt, a t is the acceleration of the vehicle at time t;

[0036] Step S12: If the acceleration of the vehicle at any time exceeds the acceleration range [a min , a max ], the data of the vehicle at that moment is determined to be abnormal;

[0037] Step S13: Eliminate data at abnormal times from the acceleration time series and position time series of the vehicle to generate a new position time series (X, Y)′ and a new acceleration time series A′.

[0038] Preferably, in the new acceleration time series A′ generated in step S13, the data at the abnormal moment is corrected by calculating the arithmetic mean of the data at the previous and next moments to generate a corrected acceleration time series A″, and a corrected position time series (X, Y)″ is generated based on the corrected acceleration time series A″.

[0039] Preferably, in the new acceleration time series A′ generated in step S13, the data at the time of data abnormality is corrected by triple exponential smoothing to generate a corrected acceleration time series A″, and a corrected position time series (X, Y)″ is generated based on the corrected acceleration time series A″.

[0040] Preferably, a min -8m / s 2 ~-3m / s 2 , a max 20m / s 2 ~30m / s 2 More preferably, the acceleration range [a min , a max ]Determined by vehicle type.

[0041] Preferably, the method further comprises:

[0042] Step S06: Corresponding to the position of the observation range, the operational safety status of the observation range is presented in the form of a heat map.

[0043] Preferably, the observation range is a continuous road section.

[0044] The present invention innovatively defines conflicts between vehicles and quantifies them, providing a basis for scientifically evaluating the safety status of traffic operation. Through the traffic operation safety evaluation method of the present invention, it is possible to achieve real-time and accurate evaluation of the operation safety status of road sections and individual vehicles based on the actual operation status of the vehicles, thereby achieving quantitative evaluation and analysis of the traffic operation safety status, thereby providing a scientific and reliable basis for the control of specific vehicles and the management of traffic flow.

[0045] The application value of the present invention is reflected in improving the level of traffic control from the perspectives of immediacy (real-time) and long-term (long-term). In terms of immediacy, this technology is based on microscopic vehicle trajectories, and can accurately and real-time understand the traffic operation status from the bottom up, and immediately clarify the safety status and resilience of traffic operation, and guide managers to comprehensively use control measures to formulate reasonable and efficient traffic control actions, thereby improving the efficiency of traffic control; in the long term, this technology can establish an independent historical database for each observation range, analyze and summarize the traffic operation characteristics within the observation range, and support management decision makers to formulate control strategies (such as the planning of emergency sites) in a targeted manner, optimize the allocation of emergency response resources, reduce emergency response time, and reasonably deploy control facilities, thereby improving the ability of traffic control.

[0046] The method of the present invention has the following advantages:

[0047] First, this method is real-time and can reflect the real-time traffic operation safety situation;

[0048] Second, the evaluation results are in the two-dimensional space of the road surface, achieving lane-level evaluation, breaking the previous one-dimensional linear safety evaluation;

[0049] Third, a method of using the surrounding vehicle driving vector environment to judge the vehicle's operating safety status is proposed;

[0050] Fourth, the evaluation logic of this method is bottom-up, based on a microscopic evaluation method, and the evaluation results have a wide range of applications, which can achieve evaluations of different granularities from microscopic to macroscopic;

[0051] Fifth, the data sources used are available both now and in the future.

[0052] On the one hand, this method is a bottom-up judgment method that can accurately judge the operational safety status of the road and expand the operational safety to traffic safety in the two-dimensional space of the road (the horizontal and vertical dimensions of the road), breaking the previous traffic safety in one-dimensional space, making the traffic operation safety status more flexible in analysis granularity, which can be microscopic or macroscopic; on the other hand, the judgment of the safety status is not affected by the traffic volume, and is suitable for sections with free flow and congested traffic flow, providing effective support for the realization of refined traffic control; in addition, this method has the ability to continuously expand the data set and has better scalability.

[0053] Capturing the real safe operating status of traffic flow, rather than just the safety index, is an important development direction of traffic safety evaluation. It has high data value. Analyzing the safety characteristics of traffic flow at specific locations on the road section based on the accumulated data is an important part of refined traffic operation safety evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic block diagram of a traffic operation safety evaluation method according to a preferred embodiment of the present invention is shown;

[0055] Figure 2 is a schematic diagram for determining vehicle conflicts in a traffic operation safety state according to a preferred embodiment of the present invention;

[0056] Figure 3 A schematic diagram of determining the resilience of a traffic operation safety state and the impact time according to a preferred embodiment of the present invention is shown. DETAILED DESCRIPTION

[0057] The overall concept of the present invention is to define the concept and quantification method of conflict between vehicles, emphasize the instantaneous relationship between a vehicle and other vehicles during movement, describe the instantaneous operational safety status of a single vehicle, and expand it to the instantaneous operational safety status of all vehicles on a specific road section; based on the real-time driving trajectory data of all vehicles within the observation range, analyze the fixed vector of vehicle movement, determine the instantaneous acceleration, speed and driving direction of each vehicle, judge the mutual relationship between vehicles in motion, calculate the instantaneous operational safety status of each vehicle within the observation range, and finally form the instantaneous safety status of traffic operation within the observation range.

[0058] Based on this concept, the traffic operation safety situation of the current observation range can be obtained in real time according to the current operation safety status of all vehicles in the observation range, providing current decision support for real-time traffic control behavior. In addition, the traffic operation safety status data at different times can be stored to form a traffic operation safety status database. Further analysis of these data can also summarize the traffic operation rules of the observation range, thereby providing a basis for the formulation of long-term traffic control strategies.

[0059] In some embodiments of the present invention, vehicle trajectory data is obtained through roadside monitoring equipment. The roadside monitoring equipment can identify and track all vehicles entering its observation range, and collect the location information of each vehicle at a frequency of not less than 10 Hz, thereby forming high-precision vehicle trajectory data. Roadside monitoring equipment includes but is not limited to video monitoring equipment or radar equipment. Those skilled in the art will understand that high-precision vehicle trajectory data can be provided by detection equipment arranged outside the vehicle (such as the roadside or other locations on the road), and can also be provided by equipment arranged on the vehicle (such as a high-precision GNSS positioning system). It can be understood that the observation areas of multiple roadside monitoring devices that monitor continuous sections of road can be combined to form a larger observation range. Therefore, in this application, the observation range refers to a continuous section of road, and is not limited to the observation area of ​​a single roadside monitoring device.

[0060] The accuracy requirement of vehicle trajectory data: the data collection frequency is not less than 10 Hz, which means that the vehicle location information is collected every 0.1 seconds at most. If the data collection frequency is 10 Hz, when the vehicle speed is in the range of [60km / h, 100km / h], the distance between adjacent trajectory points is [1.67m, 2.78m]. Under such data collection accuracy, it can ensure that the obtained vehicle trajectory has sufficient accuracy.

[0061] The most basic vehicle trajectory data obtained by roadside monitoring equipment includes at least vehicle ID, time and location. Vehicle ID is the identifier assigned by the roadside monitoring equipment to each vehicle that enters its observation range. Time refers to the specific moment when the vehicle location information is collected. Location refers to the location coordinates of the vehicle at the time of collection. For example, the geodetic coordinate system with the roadside monitoring equipment as the origin can be used as the coordinate system to identify the vehicle location.

[0062] For any vehicle within the observation range, its vehicle trajectory can be expressed as the following vehicle position time series:

[0063] (X, Y) 0 =[(x t ,y t ), (x t-Δt ,y t-Δt ), (x t-2Δt ,y t-2Δt ), (x t-3Δt ,y t-3Δt ), …]

[0064] Among them, (x t ,y t ) represents the position of the vehicle at time t, (x t-Δt ,y t-Δt) represents the position of the vehicle at time t-Δt, (x t-2Δt ,y t-2Δt ) represents the position of the vehicle at time t-2Δt, (x t-3Δt ,y t-3Δt ) represents the position of the vehicle at time t-3Δt, x and y are used to represent the horizontal and vertical coordinates of the vehicle position, respectively. The subscripts t, t-Δt, t-2Δt, and t-3Δt represent the specific times when the vehicle position information is collected.

[0065] The vehicle's speed and direction can be further calculated based on the vehicle trajectory data. The vehicle's speed at time t can be calculated using the following formula:

[0066]

[0067]

[0068] in, is the speed of the vehicle along the x-axis at time t, that is, the velocity component along the x-axis; It is the speed of the vehicle along the y-axis at time t, that is, the speed component along the y-axis; Δt represents the time interval for collecting the vehicle position; vt ​​is the speed scalar of the vehicle at time t.

[0069] Therefore, the running state of the vehicle at time t can be used to calculate the vehicle position (x t ,y t ) is the velocity fixed vector of the starting point The fixed velocity vector contains the basic information of the vehicle's motion at time t: position, velocity and direction.

[0070] The collected vehicle trajectory data may contain abnormal data points. In order to avoid the interference of abnormal data in judging the vehicle's motion state, the abnormal data in the vehicle trajectory data can be cleaned or corrected first.

[0071] For example, the maximum instantaneous acceleration and maximum braking deceleration of the vehicle can be used as the vehicle acceleration range standard [a min , a max ], identify abnormal data in vehicle trajectory data. According to vehicle performance, the maximum instantaneous acceleration of a high-performance sports car is 20-30m / s 2 The maximum instantaneous acceleration of an ordinary family car is usually 5-10m / s 2 During emergency braking, the maximum deceleration of a car is generally 7.5-8m / s 2 ; During normal braking, the average deceleration of the car should be 3-4m / s 2 Therefore, it can be considered that the vehicle acceleration is in the range [-8m / s 2 , 20m / s2 ] is normal, that is, a min =-8m / s 2 , a max =20m / s 2 ; acceleration outside this range is considered an abnormal value. In some embodiments of the present invention, a min -8m / s 2 ~-3m / s 2 , a max 20m / s 2 ~30m / s 2 In practice, for different types of vehicles, the acceleration range [a min , a max ] can be different, that is, a can be determined according to the vehicle type min and a max The value of .

[0072] The magnitude of the vehicle's acceleration at time t is calculated using the following formula:

[0073]

[0074] in, is the magnitude of the vehicle’s acceleration in the x direction at time t, is the speed of the vehicle in the x direction at time t, is the speed of the vehicle in the x direction at time t-Δt, is the magnitude of the vehicle’s acceleration in the y direction at time t, is the velocity of the vehicle in the y direction at time t, is the speed of the vehicle in the y direction at time t-Δt, a t is the acceleration of the vehicle at time t, and Δt represents the time interval for collecting the vehicle position.

[0075] For any vehicle within the observation range, based on its vehicle position time series, the acceleration of the vehicle at each moment can be calculated, thereby generating the corresponding acceleration time series.

[0076]

[0077] Among them, a t-Δt is the acceleration of the vehicle at time t-Δt, is the magnitude of the vehicle’s acceleration in the x direction at time t-Δt, is the magnitude of the vehicle's acceleration in the y direction at time t-Δt, a t-2Δt is the acceleration of the vehicle at time t-2Δt, is the magnitude of the vehicle’s acceleration in the x direction at time t-2Δt, is the magnitude of the vehicle's acceleration in the y direction at time t-2Δt, a t-3Δt is the acceleration of the vehicle at time t-3Δt, is the magnitude of the vehicle’s acceleration in the x direction at time t-3Δt, is the magnitude of the vehicle’s acceleration in the y direction at time t-3Δt.

[0078] If the vehicle's acceleration at any time exceeds the acceleration range [a min , a max ], it can be determined that the data of the vehicle at that moment is abnormal. The data corresponding to that moment is removed from the acceleration time series and position time series of the vehicle, and a new position time series (X, Y)′ and acceleration time series A′ are generated.

[0079] In some embodiments of the present invention, the speed and direction of travel of the vehicle can be determined based on the new position time series (X, Y)′, and on this basis, it can be determined whether there is a conflict between vehicles and the size of the conflict, thereby further determining the operating safety status of the vehicle and the observation range.

[0080] In some embodiments of the present invention, the data at the time of data anomaly can be corrected to generate a corrected acceleration time series A″ and a corrected position time series (X, Y)″ as a basis for determining conflicts and the operating safety status of the vehicle and the observation range.

[0081] In some embodiments, the correction method is to calculate the arithmetic average of the data before and after the abnormal data moment in the new acceleration time series A′, and use it to replace the data at the abnormal data moment, thereby generating a corrected acceleration time series A″; then use the formula

[0082] and The position data at the time of data anomaly is calculated to generate a corrected position time series (X, Y)". Compared with simply removing abnormal data, the correction method of calculating the arithmetic mean of the data at the previous and subsequent moments can avoid the loss of data at some moments in the acceleration time series and the position time series, and effectively reduce the complexity of subsequent calculations; at the same time, the calculation load caused by the correction process itself is small, so that the interference caused by abnormal data can be avoided with smaller computing resources.

[0083] In other embodiments, the correction method is to use triple exponential smoothing to correct the data at the abnormal time, that is, to perform triple exponential smoothing on the new acceleration time series A' to generate a corrected acceleration time series A", and then use the formula

[0084] and The position data at the time of data anomaly is calculated to generate a corrected position time series (X, Y)″.

[0085] In general, the use of triple exponential smoothing can better correct abnormal data, thereby effectively reducing the error of subsequent calculations. For example, the following method can be used to perform triple exponential smoothing on the acceleration time series (taking the x direction as an example):

[0086]

[0087] is the smoothing value of the first smoothing time t, is the smoothing value of the first smoothing time t-1; is the smoothing value of the second smoothing time t, is the smoothing value of the second smoothing time t-1; is the smoothing value of the third smoothing time t, is the smoothing value of the third smoothing time t-1; α is the weighting coefficient, and its value range is [0, 1]. The smaller the value, the stronger the smoothing effect, and the slower the response to the change of actual data; x t is the actual observed value at time t.

[0088] The following further describes how to determine the vehicle operation safety status and traffic operation safety status based on the vehicle trajectory data.

[0089] For a specific vehicle, the vehicle's operating safety status at a certain moment is directly related to the traffic operating environment in which it is located. Specifically, it is manifested in whether the vehicle will collide with surrounding vehicles or fixed objects while maintaining its current state of motion. If a collision occurs, it is considered that there is a "conflict". This application quantifies this "conflict" to characterize the vehicle's operating safety status. Similarly, the traffic operating safety status of the observation range is determined by the operating safety status of all vehicles within the observation range, and can be represented by the sum of the "conflicts" of all vehicles and the distribution of the vehicle's operating safety status.

[0090] First, based on the current fixed speed vector of each vehicle, if the vehicle trajectories overlap in space within the effective reaction time of the driver, it is considered that a conflict has occurred between the vehicles; the magnitude of the conflict is determined by the speed and collision angle of the vehicle. The effective reaction time of the driver is the time taken by the driver to accurately perceive the driving environment and make correct operations. When the driver faces an emergency, the effective reaction time is the indicator most directly related to driving safety. Usually, the effective reaction time of the driver is between 0.3 and 1 second. In order to make the determination of the conflict consistent with the driving behavior of most drivers, in a preferred embodiment of the present invention, the maximum value of the effective reaction time of the driver is used as the time threshold in the definition of the conflict. That is to say, within 1 second after time t (the maximum effective reaction time of the driver), if the trajectories of the two vehicles overlap in space, there is a conflict between the two vehicles at time t.

[0091] like Figure 2 As shown, for example, on a one-way three-lane road, vehicle 3 driving in the third lane changes its driving direction at time t to change lanes, and will have a conflict with vehicle 2 driving normally in the second lane in the next second; vehicles 1 and 2 driving in the second lane will have a conflict in the next second due to the large speed difference at time t. In other words, if each vehicle maintains the moving state at time t, vehicle 2 will encounter two conflicts in the next second.

[0092] The severity of the conflict or the size of the conflict is directly related to the speed of the vehicle. Therefore, the magnitude of the conflict can be taken as the modulus of the speed vector difference between the two vehicles causing the conflict, that is:

[0093]

[0094] in, represents the conflict between vehicle i and vehicle j at time t, is the fixed velocity vector of vehicle i at time t, is the fixed velocity vector of vehicle j at time t, is the velocity component of vehicle i along the x-axis at time t, is the velocity component of vehicle j along the x-axis at time t, is the velocity component of vehicle i along the y axis at time t, is the velocity component of vehicle j along the y-axis at time t.

[0095] For a specific vehicle, the vehicle operation safety state is the sum of the conflicts faced by the vehicle. Therefore, the operation safety state of any vehicle i in the observation range at time t can be expressed by calculating the sum of the conflicts between vehicle i and all other vehicles j in the observation range at time t, that is:

[0096]

[0097] in, is the operational safety status of vehicle i at time t, is the conflict between vehicle i and any other vehicle j within the observation range at time t.

[0098] Furthermore, the operational safety state of the observation range at time t is the sum of the conflicts of all vehicles within the observation range at time t, that is:

[0099]

[0100] Among them, E t is the operational safety status of the observation range at time t.

[0101] Through the above method, the operating safety status of a single vehicle and the traffic operating safety status within the observation range can be quantified into specific conflict values ​​according to the movement of the vehicle itself on the road section; its unit is the same as the unit of speed, that is, km / h.

[0102] In the present invention, conflict has a clear physical meaning: the magnitude of the velocity vector difference of the vehicles that are in conflict (or may collide) under the current traffic environment. Its physical meaning can express the magnitude of the collision intensity to some extent and present the magnitude of the danger the vehicle is in. At the same time, under this definition, the operating safety status of vehicles can be directly compared with each other without the need for standardized processing.

[0103] Since the operational safety status of vehicles or observation ranges can be directly compared in size, they can be intuitively presented in the form of a heat map, that is, different colors are used to represent the values ​​or value ranges of different vehicle operational safety status or operational safety status of the observation range. For example, based on the corresponding relationship of positions, the operational safety status heat map can be directly superimposed on the two-dimensional plane map of the road to form a traffic operational safety status distribution map of the observation area.

[0104] As the vehicle moves, the vehicle operation safety status and the operation safety status of the observation range will also change, and these changes can be reflected in real time on the traffic operation safety status distribution map. In a preferred embodiment of the present invention, the operation safety status is divided into different levels according to the size of the value, and each corresponds to a different color. For example, if the operation safety status value is less than 20km / h, it is represented by light green; between 20km / h and 40km / h, it is represented by yellow; between 40km / h and 60km / h, it is represented by orange; between 60km / h and 80km / h, it is represented by red; and if it exceeds 80km / h, it is represented by dark brown. In this way, when the operation safety status value changes within a small range, its corresponding level will not change, thereby shielding the fluctuation of the operation safety status within a small range and avoiding too frequent changes in the traffic operation safety status distribution map. At the same time, one or more safety thresholds can be pre-set. When the vehicle operation safety status or the operation safety status of the observation range exceeds a specific safety threshold, the corresponding position or area is presented in a flashing or other manner that is easy to attract attention, so that the alarm function for a specific traffic operation safety status can be realized.

[0105] Through the above-mentioned method of determining the number of conflicts and calculating the size of the conflict value, the total number of vehicle conflicts within the observation range at the current moment can be used as a traffic safety status indicator, and the traffic safety status of the observation range can be output in real time. As a result, managers can make immediate and effective responses, thereby improving the efficiency of traffic control.

[0106] In addition, the observed vehicle trajectory data can be structured to form a standardized data set. For example, the smallest data unit of the data set may include: vehicle ID, time t, horizontal coordinate of vehicle position, vertical coordinate of vehicle position, lateral speed, longitudinal speed, lateral acceleration, longitudinal acceleration, number of conflicts and operational safety status. Such a data unit reflects the vehicle operating status of a specific vehicle at a specific moment. As the observation time increases, the accumulated vehicle trajectory data gradually increases, and the data set continues to grow. Through such a data set, not only the operational safety status of the vehicle at each moment can be reproduced, but also the traffic operational safety status of the entire observation range or section can be reproduced, thereby supporting the analysis and summary of traffic operation characteristics and laws, and can effectively support the improvement of traffic control efficiency:

[0107] (1) According to the historical data set of the observation area, the traffic flow can recover in a short time when it is disturbed by this scale at the current moment, and the conflict level will not be too high during the recovery process, which will not cause long-term congestion or safety hazards to the traffic. In other words, in the face of this disturbance, the traffic system can recover itself relatively smoothly within a period of time (such as a short-term slowdown of the road section caused by emergency braking of the vehicle). For this type of problem, the manager does not need to take any management actions, but can do a good job of monitoring and let the traffic system recover by itself;

[0108] (2) Traffic operation status can be restored in a short time, but the conflict level will be too high during the restoration process. That is, in the face of this interference, the traffic system can be restored in a short time, but the safety risk is high during the restoration process (such as a vehicle breakdown and stopping in the middle of the road). For this type of problem, managers first solve the management and control methods of reducing and controlling the excessive conflict level, such as providing information reminders on variable information boards, dynamically adjusting the speed limit of road sections, arranging personnel and emergency vehicles for on-site command and rescue, etc.;

[0109] (3) The traffic operation status cannot be restored in a short time, and the number of affected vehicles exceeds the acceptable range. In other words, in the face of this interference, the resilience of the traffic system is low and it takes a long time to recover (such as traffic congestion caused by multiple vehicle collisions under large traffic flow). For this type of problem, management should conduct traffic control from three aspects: first, the removal of interference sources, second, the control of traffic flow, and third, the diversion of traffic. Comprehensive use of traffic control measures can reduce the impact time and the number of affected vehicles.

[0110] Through the vehicle trajectory dataset, not only can the changes of traffic operation safety status over time be reproduced, but also the spatial distribution of traffic operation safety status can be obtained.

[0111] For a specific road section or observation range, the trajectory data of all vehicles passing through within a certain period of time forms a two-dimensional matrix corresponding to the road plane space where the specific road section or observation range is located. By superimposing the operation safety status values ​​of different vehicles at the same matrix position, the traffic operation safety status spatial distribution matrix (referred to as matrix B) of the specific road section or observation range within the time period can be formed.

[0112] Based on matrix B, the traffic operation safety status of any location in the road section or observation range can be obtained, which is convenient for summarizing the characteristics of the traffic operation safety status of the road section or observation range, such as high-incidence points and areas of conflict, high-incidence time of conflict, and conflict characteristics of different levels. Managers can plan emergency responses based on the conflict characteristics shown in the data, optimize resource layout and quantity allocation from the perspective of minimizing the overall time for personnel and emergency rescue equipment in the area to arrive at the scene, shorten the emergency response time after a specific interference event occurs, thereby improving the response ability of managers, that is, improving traffic control capabilities.

[0113] It can be seen that the present invention can improve the level of traffic control in both the short-term and long-term aspects. In the short term, through the instant information output, managers are provided with instant and effective data to support the decision-making of current traffic control behaviors, and the efficiency is improved in terms of event reflection time and event control. In the long term, through the continuous accumulation of observation area data sets, traffic operation characteristics gradually become clear and obvious, and characteristics such as where events occur frequently, what types of events occur frequently, and what types of events occur frequently where gradually become prominent. Based on these characteristics, the layout and quantity of emergency resources are optimized from the perspective of reducing emergency response time.

[0114] Those skilled in the art should appreciate that the embodiments described herein are not illustrative but rather non-limiting, and the present invention is not limited to the specific embodiments described above.

Claims

1. A real-time traffic operation safety evaluation method, comprising the following steps: Step S01: Collect vehicle trajectory data and generate a vehicle position time series for each vehicle within the observation range: (X,Y) 0 =[(x t ,and t ),(x t-Δt ,and t-Δt ),(x t-2Δt ,and t-2Δt ),(x t-3Δt ,and t-3Δt ),……] in, (x t ,y t ) represents the position of the vehicle at time t, (x t-Δt ,y t-Δt ) represents the position of the vehicle at time t-Δt, (x t-2Δt ,y t-2Δt ) represents the position of the vehicle at time t-2Δt, (x t-3Δt ,y t-3Δt ) represents the position of the vehicle at time t-3Δt, x and y are used to represent the abscissa and ordinate of the vehicle position, respectively, and the subscripts t, t-Δt, t-2Δt, and t-3Δt represent the specific time when the vehicle position information is collected; Step S02: Based on the vehicle position time series, determine the speed and direction of each vehicle at the current time t. The speed of the vehicle at time t is calculated by the following formula: in, is the velocity component of the vehicle along the x-axis at time t; is the velocity component of the vehicle along the y-axis at time t; Δt represents the time interval for collecting the vehicle position; v t is the speed scalar of the vehicle at time t; Step S03: Assuming that each vehicle maintains the speed and direction at time t and continues to travel for a time length T, if the driving trajectories of any two vehicles overlap within the time length T, it is determined that there is a conflict between the two vehicles; Step S04: Calculate the modulus of the velocity vector difference between any two vehicles that cause the conflict at time t to obtain the magnitude of the conflict between the two vehicles, that is: in, represents the conflict between vehicle i and vehicle j at time t, is the velocity vector of vehicle i at time t, is the velocity vector of vehicle j at time t, is the velocity component of vehicle i along the x-axis at time t, is the velocity component of vehicle j along the x-axis at time t, is the velocity component of vehicle i along the y axis at time t, is the velocity component of vehicle j along the y-axis at time t; Step S05: For any vehicle i within the observation range, all conflicts faced by vehicle i at time t are added together to obtain the running safety status value of vehicle i at time t, that is: in, is the operational safety status of vehicle i at time t, is the conflict between vehicle i and any other vehicle j within the observation range at time t.

2. The method according to claim 1, further comprising: Add up the conflicts between all vehicles in the observation range at time t to obtain the running safety status value of the observation range at time t, that is: Among them, E t is the operational safety status of the observation range at time t.

3. The method according to claim 1 or 2, wherein: The time length T is 0.5 to 1.5 seconds.

4. The method according to claim 3, wherein: The time length T is 1 second.

5. The method according to claim 1 or 2, wherein: Step S01 further includes the following steps of correcting the vehicle position time series: Step S11: Based on the vehicle position time series before correction, calculate the acceleration of the vehicle at each moment and generate the corresponding acceleration time series. Among them, a t-Δt is the acceleration of the vehicle at time t-Δt, is the magnitude of the vehicle’s acceleration in the x direction at time t-Δt, is the magnitude of the vehicle's acceleration in the y direction at time t-Δt, a t-2Δt is the acceleration of the vehicle at time t-2Δt, is the magnitude of the vehicle’s acceleration in the x direction at time t-2Δt, is the magnitude of the vehicle's acceleration in the y direction at time t-2Δt, a t-3Δt is the acceleration of the vehicle at time t-3Δt, is the magnitude of the vehicle’s acceleration in the x direction at time t-3Δt, is the magnitude of the vehicle's acceleration in the y direction at time t-3Δt, where Δt represents the time interval for collecting the vehicle's position; The magnitude of the vehicle's acceleration at time t is calculated using the following formula: in, is the magnitude of the vehicle’s acceleration in the x direction at time t, is the speed of the vehicle in the x direction at time t, is the speed of the vehicle in the x direction at time t-Δt, is the magnitude of the vehicle’s acceleration in the y direction at time t, is the velocity of the vehicle in the y direction at time t, is the speed of the vehicle in the y direction at time t-Δt, a t is the acceleration of the vehicle at time t; Step S12: If the acceleration of the vehicle at any time exceeds the acceleration range [a min ,a max ], the data of the vehicle at that moment is determined to be abnormal; Step S13: Eliminate the data at abnormal times from the acceleration time series and position time series of the vehicle to generate a new position time series (X, Y) ′ and the new acceleration time series A ′ .

6. The method according to claim 5, wherein: The new acceleration time series A generated in step S13 ′ In the method of calculating the arithmetic mean of the data before and after the data, the data at the abnormal time is corrected to generate a corrected acceleration time series A", and a corrected position time series (X, Y)" is generated based on the corrected acceleration time series A".

7. The method according to claim 5, wherein: The new acceleration time series A generated in step S13 ′ In the method of triple exponential smoothing, the data at the abnormal moment is corrected to generate a corrected acceleration time series A″, and a corrected position time series (X, Y)″ is generated based on the corrected acceleration time series A″.

8. The method according to claim 5, wherein: a min -8m / s 2 ~-3m / s 2 , a max 20m / s 2 ~30m / s 2 .

9. The method according to claim 5, wherein: The acceleration range [a min ,a max ]Determined by vehicle type.

10. The method according to claim 1 or 2, further comprising: Step S06: Corresponding to the position of the observation range, the operational safety status of the observation range is presented in the form of a heat map.

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