技术领域technical field
本发明涉及高速公路交通事件检测领域，具体涉及一种交通事件检测California算法模型改进方法。The invention relates to the field of expressway traffic event detection, in particular to a method for improving the California algorithm model of traffic event detection.
背景技术Background technique
目前，高速公路为人们提供了一个舒适快捷的生活方式，产生了巨大的社会效益和经济效益。然而日益增长的交通需求量和相对较低的道路通行能力产生了矛盾，导致平均车速下降、交通流量减少，进而导致的交通事件频发使高速公路效率降低，运行时间无法估计，严重的影响人们的出行，造成了不良的社会影响，日益成为高速公路运营的重要问题，因此为保证高速公路的运行安全和社会公众出行畅通，需要对高速公路的运行状态进行交通事件检测和应急管理。At present, highways provide people with a comfortable and fast way of life, and have produced huge social and economic benefits. However, the increasing traffic demand and the relatively low road traffic capacity have produced contradictions, leading to a decrease in average vehicle speed and a decrease in traffic flow, which in turn leads to frequent traffic incidents that reduce the efficiency of expressways, and the running time cannot be estimated, which seriously affects people. Therefore, in order to ensure the safe operation of the expressway and the smooth travel of the public, it is necessary to carry out traffic incident detection and emergency management on the operating status of the expressway.
由于道路上发生的交通事故、车辆抛锚、货物散落等偶然事件引起路段通行能力下降的交通事件存在难预测性，因此需要通过交通事件检测方法来进行判别。交通事件检测方法主要包括非自动事件检测和自动事件检测。非自动事件检测就是人通过通讯手段报警或通过摄像头监测到事故发生，这样消耗的人力非常大，而且具有滞后性。自动事件检测是利用图像处理技术等来检测交通状态的发生状况或者利用交通流参数的异常变化来对交通状态进行检测，该种算法快捷方便，及时可靠并且适应性强。因此采用自动事件检测方法及时、准确地发现事件，对减少交通延误，保障道路安全，避免二次事故的发生具有十分重要的意义。Due to accidental events such as traffic accidents, vehicle breakdowns, and cargo scattered on the road, the traffic events that reduce the capacity of the road section are difficult to predict, so it is necessary to use traffic event detection methods to identify them. Traffic incident detection methods mainly include non-automatic incident detection and automatic incident detection. Non-automatic event detection means that people call the police through communication means or monitor accidents through cameras, which consumes a lot of manpower and has a lag. Automatic event detection is to use image processing technology to detect the occurrence of traffic status or use abnormal changes in traffic flow parameters to detect traffic status. This algorithm is fast, convenient, timely, reliable and adaptable. Therefore, using automatic event detection method to detect events timely and accurately is of great significance to reduce traffic delays, ensure road safety, and avoid secondary accidents.
基于现有的技术，现阶段国内外研究的算法主要包括模式识别算法、统计算法、突变理论和人工智能事件检测算法，模式识别算法大都是在对所选定交通变量之间的关系以及变化规律进行推导、假设的基础上构建模型并利用仿真或实际数据进行检测，此类算法能在一定程度上较好地描述交通流实际运行状态，并已成功应用到实际工程中。其中California算法是一种经典的模式识别算法，它作为最早出现的高速公路事件检测算法用于洛杉矶公路管理控制中心，因该算法具有原理简单，过程直观的优势，已经成熟地应用于国内外各种工程实践中。然而在实际应用中还存在一些问题影响着检测效果，从算法模型看，检测过程过于简单，实际情况复 杂时算法的误报率会上升，影响交通管理者的判断。Based on the existing technology, the algorithms studied at home and abroad at this stage mainly include pattern recognition algorithms, statistical algorithms, catastrophe theory and artificial intelligence event detection algorithms. Models are built on the basis of derivation and assumptions, and tests are performed using simulation or actual data. Such algorithms can better describe the actual operating state of traffic flow to a certain extent, and have been successfully applied to actual projects. Among them, the California algorithm is a classic pattern recognition algorithm. As the earliest expressway incident detection algorithm, it is used in the Los Angeles highway management and control center. Because the algorithm has the advantages of simple principle and intuitive process, it has been maturely used in various fields at home and abroad. in engineering practice. However, there are still some problems that affect the detection effect in practical applications. From the perspective of the algorithm model, the detection process is too simple. When the actual situation is complicated, the false alarm rate of the algorithm will increase, which will affect the judgment of traffic managers.
发明内容Contents of the invention
有鉴于此，本发明的目的是提供一种交通事件检测California算法模型改进方法，该方法针对多种流量环境和多种路段等复杂情况，采用固定型车检器收集的交通数据，利用California算法的改进模型对交通事件进行检测。In view of this, the purpose of the present invention is to provide a kind of traffic incident detection California algorithm model improvement method, this method is aimed at complex situations such as multiple traffic environments and multiple road sections, adopts the traffic data collected by fixed vehicle detector, utilizes California algorithm The improved model to detect traffic incidents.
本发明的目的是通过以下技术方案来实现的，一种交通事件检测California算法模型改进方法，包括The object of the present invention is achieved by the following technical solutions, a traffic incident detection California algorithm model improvement method, comprising
步骤S1.获取检测路段上下游检测器的交通数据，进行数据预处理；Step S1. Obtain the traffic data of the upstream and downstream detectors of the detection road section, and perform data preprocessing;
步骤S2.将上下游检测器占有率之差与阈值K1进行比较，如果所述上下游检测器占有率之差大于K1继续步骤S3，否则转到步骤S6；Step S2. Compare the difference between the occupancy rateof the upstream and downstream detectors with the threshold K1, if the difference between the occupancy rates of the upstream and downstream detectors is greaterthan K1, continue to step S3, otherwise go to step S6;
步骤S3.将上下游检测器占有率之比与阈值K2进行比较，如果所述上下游检测器占有率之比大于K2继续步骤S4，否则转到步骤S6；Step S3. Compare the ratio of the occupancy rate of the upstream and downstream detectors with the threshold K2, if the ratio of the occupancy rate of the upstream and downstream detectors is greaterthanK2 , continue to step S4, otherwise go to step S6;
步骤S4.将上下游检测器占有率之差与下游检测器占有率之比与阈值K3进行比较，如果所述上下游检测器占有率之差与下游检测器占有率之比大于K3继续步骤S5，否则转到步骤S6；Step S4. Compare the ratio of the occupancy rate of the upstream and downstream detectors to the occupancy rate of the downstream detector with the threshold K3, if the ratioof the occupancy rate of the upstream and downstream detectors to the occupancy rate of the downstream detector is greaterthan K3 continue Step S5, otherwise go to step S6;
步骤S5.将上游检测器占有率与流量之比和下游占检测器有率与流量之比的差值与阈值K4进行比较，如果所述上游检测器占有率与流量之比和下游占检测器有率与流量之比的差值小于等于阈值K4转到步骤S6，否则转到步骤S7；Step S5. Compare the difference between the ratio of upstream detector occupancy to flow and the ratio of downstream occupancy to flow to the thresholdK4 , if the ratio of upstream detector occupancy to flow and downstream occupancy detect If the difference between the ratio of the device rate and the flow rate is less than or equal to the threshold K4 , go to step S6, otherwise go to step S7;
步骤S6.将上游车速与阈值KV进行比较，如果所述上游车速大于阈值KV则判别该检测路段处于非拥堵状态，否则继续步骤S7；Step S6. Comparing the upstream vehicle speed with the thresholdKV , if the upstream vehicle speed is greater than the thresholdKV , it is judged that the detection road section is in a non-congested state, otherwise continue to step S7;
步骤S7.判断上一周期该检测路段的交通状态是否为拥堵，是则判别该检测路段处于拥堵状态，否则为非拥堵状态。Step S7. Judging whether the traffic state of the detected road segment in the previous cycle is congested, if yes, it is judged that the detected road segment is in a congested state, otherwise it is not congested.
进一步，所述步骤S1中，对数据进行预处理包括：对原始数据进行故障的分析与判断，判定其是否为故障数据，并对故障数据进行剔除与修复。Further, in the step S1, the preprocessing of the data includes: analyzing and judging the fault of the original data, judging whether it is fault data, and removing and repairing the fault data.
进一步，采用当前路段的实测数据与历史数据的加权方式得出的值来对所述故障数据进行剔除与修复；Further, the fault data is eliminated and repaired by using the value obtained by weighting the measured data of the current road section and the historical data;
其中，为t时段的数据修复值；x(t-1)为t-1时段的实际检测值；x'‘(t)为同一时刻前n天的采集数据的历史均值；α为遗忘因子，α∈[0,1]。in, is the data restoration value of period t; x(t-1) is the actual detection value of period t-1; x''(t) is the historical average value of the collected data of n days before the same moment; α is the forgetting factor, α∈ [0,1].
由于采用了上述技术方案，本发明具有如下的优点：Owing to adopting above-mentioned technical scheme, the present invention has following advantage:
本发明通过固定检测器所得到的交通信息，对交通事件检测California算法模型进行改进，提高了算法检测率，降低误报率。The invention improves the traffic event detection California algorithm model through the traffic information obtained by the fixed detector, improves the algorithm detection rate, and reduces the false alarm rate.
附图说明Description of drawings
为了使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明作进一步的详细描述，其中：In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with the accompanying drawings, wherein:
图1为模型改进方法的整体流程图；Fig. 1 is the overall flowchart of the model improvement method;
图2为单一流量下道路处于异常状态时上下游交通占有率变化图；Figure 2 is a diagram of the change of upstream and downstream traffic occupancy when the road is in an abnormal state under a single traffic flow;
图3为单一流量下道路处于异常状态时上下游车速变化图；Figure 3 is a diagram of the speed change of the upstream and downstream vehicles when the road is in an abnormal state under a single flow rate;
图4为单一流量下道路处于异常状态时上下游交通流量变化图；Figure 4 is a diagram of the upstream and downstream traffic flow changes when the road is in an abnormal state under a single flow;
图5为不同流量下道路处于异常状态时上下游交通占有率变化图；Figure 5 is a diagram of the change of upstream and downstream traffic occupancy when the road is in an abnormal state under different flow rates;
图6为不同流量下道路处于异常状态时上下游车速变化图；Figure 6 is a diagram of the speed change of the upstream and downstream vehicles when the road is in an abnormal state under different flow rates;
图7为不同流量下道路处于异常状态时上下游交通流量变化图。Figure 7 is a diagram of the upstream and downstream traffic flow changes when the road is in an abnormal state under different flow rates.
具体实施方式detailed description
以下将结合附图，对本发明的优选实施例进行详细的描述；应当理解，优选实施例仅为了说明本发明，而不是为了限制本发明的保护范围。The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings; it should be understood that the preferred embodiments are only for illustrating the present invention, rather than limiting the protection scope of the present invention.
交通事件检测California算法模型改进方法的流程图如图1所示。以下是实施的具体过程：The flow chart of the improvement method of the California algorithm model for traffic incident detection is shown in Figure 1. The following is the specific process of implementation:
步骤一、原始数据获取Step 1. Raw data acquisition
(1)获取检测路段上下游固定车检器的交通数据，进行数据预处理。可以通过高速集团提供的车检器数据，直接得到检测断面在5min内车辆的平均车速、车检器编码、行车方向等。高速公路交通数据字段定义如表1所示：(1) Obtain the traffic data of the fixed vehicle detectors on the upstream and downstream of the detection road section, and perform data preprocessing. Through the vehicle detector data provided by the Expressway Group, the average speed of the vehicle within 5 minutes of the detection section, the code of the vehicle detector, and the driving direction can be directly obtained. The definition of expressway traffic data fields is shown in Table 1:
表1：高速公路交通数据字段定义表Table 1: Freeway traffic data field definition table
(2)获取固定车检器数据后需对数据进行一定的预处理，主要是对原始数据进行故障的分析与判断，判定其是否为故障数据，并对故障数据进行剔除与修复。主要有：(2) After obtaining the data of the fixed vehicle detector, it is necessary to perform certain preprocessing on the data, mainly to analyze and judge the fault of the original data, determine whether it is fault data, and eliminate and repair the fault data. There are:
1)对于原始数据进行故障的分析与判断，判定其是否为故障数据。由于检测器或传输线路出现故障会产生数据失真或数据丢失，车速和占有率同时很高时判断为失真，流量车速占有率均为0时判断为丢失。1) Carry out fault analysis and judgment on the original data, and determine whether it is fault data. Data distortion or data loss will occur due to the failure of the detector or transmission line. When the vehicle speed and occupancy rate are high at the same time, it is judged as distortion, and when the traffic and vehicle speed occupancy rate are both 0, it is judged as loss.
2)对于故障数据的剔除与补充修复，采用当前路段的实测数据与历史数据的加权方式得出的值来对故障数据进行补充修复，公式如下：2) For the elimination and supplementary repair of fault data, the value obtained by weighting the actual measurement data and historical data of the current road section is used to supplement and repair the fault data. The formula is as follows:
其中，为t时段的数据修复值；in, is the data repair value of period t;
x(t-1)为t-1时段的实际检测值；x(t-1) is the actual detection value in the t-1 period;
x′(t)为同一时刻前n天的采集数据的历史均值；x'(t) is the historical mean value of the collected data of n days before the same moment;
α为遗忘因子，α∈[0,1]，α取值的大小决定对于历史的数据依赖程度。α is the forgetting factor, α∈[0,1], the value of α determines the degree of dependence on historical data.
步骤二、上下游检测器占有率的绝对差判断Step 2. Absolute difference judgment of the occupancy ratio of upstream and downstream detectors
计算上下游检测器占有率的绝对差，和阈值K1比较，如果超过K1继续步骤三，否则转到步骤六，公式如下：Calculate the absolute difference of the occupancy rate of the upstream and downstream detectors, and compare it with the threshold K1 , if it exceeds K1 , continue to step 3, otherwise go to step 6, the formula is as follows:
OCCDF＝OCC(i,t)-OCC(i+1,t)≥K1OCCDF＝OCC(i,t)-OCC(i+1,t)≥K1
其中，OCC(i,t)为t时刻上游检测器的占有率；Among them, OCC(i,t) is the occupancy rate of the upstream detector at time t;
OCC(i+1,t)为t时刻下游检测器的占有率。OCC(i+1,t) is the occupancy rate of the downstream detector at time t.
步骤三、上下游占有率之比判断Step 3. Judgment of the ratio of upstream and downstream occupancy
计算上下游占有率之比，和阈值K2比较，如果超过K2继续步骤四，否则转到步骤六。Calculate the ratio of upstream and downstream occupancy and compare it with the threshold K2 , if it exceeds K2 go to step four, otherwise go to step six.
由图2-4采样的交通流参数可知，当道路交通状态发生异常时，上下游占有率之差，上下游占有率之差/下游占有率的值会产生明显的突变，但是由于异常状态发生时上游占有率和上下游占有率之差都会增大，因此此时上下游占有率之差/上游占有率的值不会发生明显的变化。当道路交通状态发生异常时，上游占有率会显著的提高，而下游占有率的变化不是特别的明显，因此我们可以将原始加州算法对上下游占有率之差/下游占有率的判定规则修改为如下公式：From the traffic flow parameters sampled in Figure 2-4, it can be seen that when the road traffic state is abnormal, the difference between the upstream and downstream occupancy, the difference between the upstream and downstream occupancy / the value of the downstream occupancy will produce a significant mutation, but due to the abnormal state At this time, the upstream occupancy rate and the difference between the upstream and downstream occupancy rates will both increase, so the value of the difference between the upstream and downstream occupancy rates/upstream occupancy rate will not change significantly at this time. When the road traffic state is abnormal, the upstream occupancy rate will increase significantly, but the change in the downstream occupancy rate is not particularly obvious, so we can modify the original California algorithm to determine the difference between the upstream and downstream occupancy rates/downstream occupancy rate as The formula is as follows:
其中，OCC(i,t)为t时刻上游检测器的占有率；Among them, OCC(i,t) is the occupancy rate of the upstream detector at time t;
OCC(i+1,t)为t时刻下游检测器的占有率。OCC(i+1,t) is the occupancy rate of the downstream detector at time t.
步骤四、上下游占有率之差与下游占有率之比判断Step 4. Judging the ratio of the difference between the upstream and downstream occupancy rate and the downstream occupancy rate
计算上下游占有率之差与下游占有率之比，和阈值K3进行比较，如果超过K3继续步骤五，否则转到步骤六，公式如下：Calculate the difference between the upstream and downstream occupancy rate and the ratioof the downstream occupancy rate, and compare it with the threshold K3, if it exceeds K3, continue to step five, otherwise go to step six, the formula is as follows:
其中，OCC(i,t)为t时刻上游检测器的占有率；Among them, OCC(i,t) is the occupancy rate of the upstream detector at time t;
OCC(i+1,t)为t时刻下游检测器的占有率。OCC(i+1,t) is the occupancy rate of the downstream detector at time t.
步骤五、上游占有率与流量之比和下游占有率与流量之比的差值判断Step 5. Judging the difference between the ratio of upstream occupancy rate to flow rate and the ratio of downstream occupancy rate to flow rate
计算上游占有率与流量之比和下游占有率与流量之比的差值，和阈值K4进行比较，如果未超过K4转到步骤六，否则转到步骤七。Calculate the difference between the ratio of the upstream occupancy rate to the flow rate and the ratio of the downstream occupancy rate to the flow rate, and compare it with the threshold K4, if it does not exceed K4, go to step six, otherwise go to step seven.
由图5-7可知，由于交通状态发生异常时，占有率的变化受到流量变化的影响，因此不能单纯地从占有率的变化去判断。为了提高检测精度，将在判断规则中加入占有量与流量的比值进行判断，具体公式如下：It can be seen from Figure 5-7 that when the traffic state is abnormal, the change of the occupancy rate is affected by the change of the flow rate, so it cannot be judged simply from the change of the occupancy rate. In order to improve the detection accuracy, the ratio of occupancy and flow will be added to the judgment rule for judgment. The specific formula is as follows:
其中，FLOW(i,t)为t时刻的上游检测器的车流量；Among them, FLOW (i, t) is the traffic flow of the upstream detector at time t;
FLOW(i+1,t)为t时刻的下游检测器的车流量。FLOW(i+1,t) is the traffic flow of the downstream detector at time t.
步骤六、上游车速判断Step 6. Judgment of upstream vehicle speed
比较上游车速与阈值KV，如果超过KV则判别该检测路段处于非拥堵状态，否则继续步骤七。Compare the upstream vehicle speed with the threshold KV , if it exceeds KV , it is judged that the detection road section is in a non-congested state, otherwise, continue to step seven.
当道路交通状态发生异常时，上游车速存在明显的下降趋势，异常消散时上游车速大副度回升。因此在模型中加入上游车速的判断环节，阈值设置为KV，公式如下：When the road traffic condition is abnormal, the upstream vehicle speed has an obvious downward trend, and when the abnormality dissipates, the upstream vehicle speed rises sharply. Therefore, the judgment link of the upstream vehicle speed is added to the model, and the threshold is set as KV , the formula is as follows:
SPEED(i,t)≤KVSPEED(i,t)≤KV
其中，SPEED(i,t)为t时刻的上游检测器的车速Among them, SPEED(i,t) is the vehicle speed of the upstream detector at time t
步骤七、重复性检验Step 7. Repeatability test
判断上一周期该检测路段的交通状态是否为拥堵，是则判别该检测路段处于拥堵状态，否则为非拥堵状态。It is judged whether the traffic state of the detected road section in the previous cycle is congestion, if yes, it is judged that the detection road section is in a congestion state, otherwise it is a non-congestion state.
以上所述仅为本发明的优选实施例，并不用于限制本发明，显然，本领域的技术人员可以对本发明进行各种改动和变型而不脱离本发明的精神和范围。这样，倘若本发明的这些修改和变型属于本发明权利要求及其等同技术的范围之内，则本发明也意图包含这些改动和变型在内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and equivalent technologies thereof, the present invention also intends to include these modifications and variations.
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| CN115440029A (en)* | 2022-07-29 | 2022-12-06 | 重庆大学 | Vehicle inspection device data restoration method considering distribution of detection equipment |
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| RJ01 | Rejection of invention patent application after publication | Application publication date:20171024 |