CN112767684A - Highway traffic jam detection method based on charging data - Google Patents
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Abstract
The invention discloses a highway traffic jam detection method based on charging data, which comprises the following steps: step 1: extracting traffic parameters based on the charging data; step 2: performing spatial matching on data of adjacent ETC gantries, eliminating up-and-down road or abnormal data between the adjacent ETC gantries, and calculating average travel time of a road section; and step 3: synthesizing the original vehicle travel time data using the rolling time interval; and 4, step 4: constructing an average travel speed discrimination model of the highway; and 5: carrying out fuzzy C-means clustering analysis to divide different road traffic running states; step 6: and detecting traffic jam between adjacent ETC portal frames of the highway by using the clustering result. The method overcomes the defects and shortcomings of a traffic parameter fixed time scale synthesis method, and can be suitable for estimating the road traffic jam state between two ETC portal toll stations on the highway.
Description
Technical Field
The invention belongs to the field of intelligent traffic, relates to the field of traffic data analysis and processing, and discloses a highway traffic jam detection method based on charging data.
Background
The accurate and timely detection of the occurrence of the traffic jam on the highway is a precondition that relevant departments adopt management measures to prevent secondary accidents and reduce economic loss. Due to the rise of ETC portal charging systems on expressways in recent years, massive single-vehicle driving data records are accumulated. The abundant bicycle information of each portal frame and the detailed record of the running of the vehicles on the expressway reflect the traffic state of the expressway section to a certain extent. How to effectively utilize abundant massive ETC portal data and realize the detection of traffic jam on highway sections by utilizing the ETC portal data has important theoretical and research significance for traffic managers to implement traffic control and traffic travelers to plan reasonable travel schemes.
In fact, some studies have been made to detect traffic congestion and determine traffic state by using highway networking charging data, for example, populus is used to extract road section travel time according to charging data, and a traffic congestion index reflecting road traffic state is designed based on the travel time. Meanwhile, in order to solve the problem of less data between adjacent toll stations, the Yangpofen combines the space-time layout characteristics of the toll stations, and provides a traffic jam detection algorithm for fusing basic road sections and composite road sections of the expressway. On the expressway, most toll stations are intercommunicating toll stations, and for two adjacent intercommunicating toll stations, in order to obtain the travel time of the vehicle on the road section between the two toll stations, the vehicle is required to go up from the upstream intercommunicating toll station and then go down from the downstream intercommunicating toll station. In practice, however, there are usually not so many vehicles going on and off from adjacent intercommunicating toll booths that the amount of toll data between two adjacent intercommunicating toll booths in a day is small, and the lack of data is a key factor that makes the toll data difficult to apply.
The ETC portal frame is installed on the main line road, can identify and record all vehicle information passing through the section, solves the problems that charging data between adjacent intercommunicated toll stations is less and the like, can comprehensively and practically represent the traffic state of each road section of the main line road, and is feasible to detect the traffic jam of the highway section by combining the ETC portal frame data.
Disclosure of Invention
In view of the above, the present invention provides a method for detecting traffic congestion on a highway based on charging data.
The purpose of the invention is realized by the following technical scheme:
a highway traffic jam detection method based on charging data comprises the following steps:
step 1: extracting traffic parameters based on the charging data;
step 2: performing spatial matching on data of adjacent ETC gantries, eliminating up-and-down road or abnormal data between the adjacent ETC gantries, and calculating average travel time of a road section;
and step 3: synthesizing the original vehicle travel time data using the rolling time interval;
and 4, step 4: constructing an average travel speed discrimination model of the highway;
and 5: carrying out fuzzy C-means clustering analysis to divide different road traffic running states;
step 6: and detecting traffic jam between adjacent ETC portal frames of the highway by using the clustering result.
Further, the step 1 specifically includes the following substeps:
step 11: obtaining travel time t recorded by ETC charging data of each vehiclei，tiThe travel time of the ith vehicle is the travel time of each vehicle, and the travel time is directly the OD travel time of each vehicle because the ETC portal frame executes the non-stop toll collection;
step 12: and acquiring the traffic flow of the upper ETC portal frame and the lower ETC portal frame every 5 min.
Further, step 2 comprises the following substeps:
step 21: performing space matching on the road sections between the adjacent ETC portal frames, and using the difference of the running time of the same vehicle passing through the adjacent ETC portal frames as the travel time of the road section between the corresponding two ETC portal frame toll stations;
step 22: detecting and eliminating abnormal values in the travel time of the road section; and (3) directly deleting abnormal values with negative travel time, and detecting abnormal values of the travel time of the vehicles passing through the lower lane of the exit toll station within a certain time range by adopting a Grubbs (Grubbs) rule method for the abnormal values with overlong travel time.
Further, step 4 comprises the following substeps:
step 41: carrying out space matching on adjacent ETC portal road sections; the difference of the running time of the ETC portal passing through the other adjacent ETC portal at the same time is used as the travel time between the two corresponding ETC portals;
step 42: calculating the average travel speed v of each road section at the current moment by combining the distance of each adjacent ETC portal frame and the OD travel time of each vehicle;
step 43: setting proper time interval number n, calculating the average travel speed v (t) of the road section at the current moment and the average value of the previous n time intervals
A deviation d of (d);
step 44: and calculating the standard deviation SD of the average travel speed of the road sections in the previous n time intervals.
Further, step 4 comprises the following substeps:
step 51: determining the number c of clustering categories;
step 52: input data is normalized according to the following formula:
in the formula: x is the number ofjAs raw data, xminMinimum value of sample data, xmaxIs sample maximum value, x'jFor normalized data, the normalized data all fall within [0,1 ]]Within the interval, the final input is X { (v)1',d1',SD'1),(v'2,d'2,SD'2),…(v'n,d'n,SD'n) In which v isi' (i ═ 1, 2.. times, n) denotes the average stroke speed of the i-th sample data, and d denotes the average stroke speed of the i-th sample datai' (i ═ 1, 2.., n) denotes the average travel speed v (t) of the ith sample and the average of the preceding n time intervals
Deviation of (D), SDi' (i ═ 1, 2.., n) denotes the standard deviation of the average travel speed for the ith sample time interval link;
step 53: and (3) utilizing a fuzzy C clustering algorithm to carry out an iterative solving process to obtain a membership matrix and a clustering center matrix of the sample.
Further, the specific process of step 6 is as follows: and obtaining a clustering center matrix and a membership matrix according to a clustering result, determining a final classification principle by adopting Euclidean distances between the sample data and each traffic center point, and classifying the sample data to the class closest to the Euclidean distance from the classification center, namely the minimum Euclidean distance principle:
x is theniBelonging to the k-th traffic state, wherein xiRepresents the ith sample data, dijRepresenting the euclidean distance of the ith sample from the jth cluster center.
Due to the adoption of the technical scheme, the invention has the following beneficial effects:
according to the invention, the vehicle travel speed characteristics are extracted according to the upper and lower road information in the ETC portal data, so as to represent the road traffic state. And judging the traffic state between toll stations by adopting a rolling time series traffic parameter synthesis method. According to the correlation between the travel speed and the traffic state, three distinguishing characteristics of the average travel speed of the road section at the current moment, the deviation between the average travel speed of the road section at the current moment and the average values of the previous time intervals and the standard deviation of the average travel speed of the road sections at the previous time intervals are established, a fuzzy C mean value clustering model is used for carrying out clustering analysis on the established three distinguishing characteristics, and finally the distinguishing of the road section traffic jam state is realized by utilizing a clustering result. The method overcomes the defects and shortcomings of a traffic parameter fixed time scale synthesis method, and can be suitable for estimating the road traffic jam state between two ETC portal toll stations on the highway.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
The drawings of the present invention are described below.
FIG. 1 is a schematic overall flow diagram of the present invention;
FIG. 2 is a schematic diagram of ETC portal distribution at a detection road section;
fig. 3 is a schematic diagram of a rolling time interval composition.
Detailed Description
In order to make the technical solutions, advantages and objects of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the present application.
The invention is further illustrated by the following figures and examples.
Example 1
As shown in fig. 1 to 3, the present embodiment provides a method for detecting traffic congestion on a highway based on charging data,
step 1: the traffic parameters are extracted based on the ETC toll data.
Step 11: obtaining the travel time t recorded by the ETC portal road sectioni,tiThe travel time of the ith vehicle.
Step 12: subtracting the recording time of the adjacent upstream ETC portal from the recording time of the downstream ETC portal to obtain the travel time of each vehicle OD, wherein the calculation formula is as follows:
tOD＝ti-td
step 2: and carrying out space matching on the data of the adjacent ETC gantries, eliminating the data of the upper and lower lanes or abnormal data between the ETC gantries, and calculating the average travel time of the road section.
Step 21: and carrying out space matching on the adjacent toll ETC portal road sections. Each ETC portal will record the information of all vehicles passing through the section, but a small number of vehicles will drive in or out on the up and down road sections between adjacent ETC portals. By combining the characteristics, the difference of the running time of the vehicle passing through the ETC portal frame and from the next adjacent ETC portal frame at the same time is used as the average travel time of the road section between the two corresponding adjacent ETC portal frames:
T(k+1,k)＝Tc(k)-Tc(k+1)|tc(k)＝tc(k+1)
in the formula, Tc(k +1) is the travel time, t, of a vehicle passing through the kth ETC portal and passing through the kth +1 th portal on a section between two adjacent ETC portalsc(k) And tc(k +1) is the time elapsed from the vehicle of the kth ETC gantry and the kth +1 gantry, respectively.
Considering that the actual conditions that the vehicles passing through the gate at the same time pass through the ETC gate k and the ETC gate k +1 respectively are too strict, the same time point can be appropriately expanded to the same time window, and the average travel time of the vehicle passing through the ETC gate and the next ETC gate within a certain time before and after the time point is calculated as T, with the time point at which the vehicle descending from the toll gate k passes through the gate as the centerc(k +1) approximation.
Step 22: and (4) eliminating the up-and-down track or abnormal data between ETC portal frames. Considering that a small number of vehicles drive out of the ramp from the main line or drive into the main line from the ramp, the vehicles cannot be completely matched between the adjacent ETC gantries, so that data which cannot be matched with the adjacent ETCs are removed. The abnormity of the matching result of the vehicles in one category is that the travel time of the road section is too long and is obviously different from other vehicles in the same time period. This may be due to an abnormal event such as a vehicle break. Such abnormal data also exists in ETC portal data, but the travel time cannot truly reflect the traffic state of the road section, and the abnormal data also needs to be eliminated. However, a long travel time may be caused by traffic jam, and in order to ensure that data in a jam state is not mistakenly deleted, abnormal values of the travel time of a vehicle passing through an adjacent ETC portal within a certain time range are detected by using a Grubbs criterion method.
The Grabbs' rule method assumes that the measurement columns are normally distributed. The data obtained by detection are arranged according to the size from small to large, and the minimum or maximum data are always suspected to be abnormal. The method comprises the following specific steps:
1) selecting a risk α;
2) calculate the value of T (T is the order statistic X)(r)Distribution of (d);
let X(1)It is questionable: order to
Let X(n)It is questionable: order to
In the formula
3) Looking up a Grabbs critical table according to the number n of data in the detection data set X and the significance level alpha to obtain a corresponding critical value T (n, alpha);
4) if T ≧ T (n, α), the suspected data is abnormal, it should be discarded, removed from the test data set X, and the above steps repeated until no abnormal values are detected.
And step 3: and synthesizing original vehicle travel time data by using a rolling time interval, and rolling and synthesizing the current traffic parameter data and the previous traffic parameter data with m sampling intervals according to a certain rule by taking the sampling interval of the original traffic parameter data as a reference. And taking the downstream exit time of the vehicle passing through the adjacent ETC portal as a reference, calculating the average time of all vehicles descending from the exit toll station in the synthesis time interval after a certain output time, and taking the average time as the travel time of the vehicles in the section in the output time interval.
And 4, step 4: and constructing the discrimination characteristics of the average travel speed of the highway.
Step 41: calculating the average travel speed v (t) of the current road section:
in the formula, L is the mileage length of a road section between adjacent ETC gantries, and T is the average travel time.
Step 42: setting a proper time interval number n, generally taking a value of 3-5, and calculating the average travel speed v (t) of the road section at the current moment and the average value of the previous n time intervals
Deviation of (2):
step 43: calculating the standard deviation SD of the average travel speed of the previous n time interval road sections:
and 5: and carrying out fuzzy C-means clustering analysis to divide different road traffic running states.
Step 51: and determining the number c of the clustering categories. The average travel speed of the road section at the current moment reflects the running state of the road traffic flow, the deviation of the average travel speed of the road section at the current moment and the average value of the previous n time intervals represents the change degree of the average travel speed of the road section at the current moment relative to the previous n time intervals, and the standard deviation of the average travel speed of the road section at the previous n time intervals represents the stability degree of the average travel speed in the previous n time intervals; and (3) dividing the road traffic state into 4 conditions of smooth traffic, transition from smooth to congested, traffic jam, transition from congested to smooth and the like by combining the meanings of the three distinguishing features, and thus determining the number C of the classes of the fuzzy C-means cluster to be 4.
Step 52: input data are normalized, and the normalization formula is as follows:
in the formula: x is the number ofjAs raw data, xminMinimum value of sample data, xmaxIs sample maximum value, x'jFor normalized data, the normalized data all fall within [0,1 ]]Within the interval, the final input is X '{ (v'1,d'1,SD'1),(v'2,d'2,SD'2),…(v'n,d'n,SD'n) In which v isi' (i ═ 1, 2.. times, n) denotes the average stroke speed of the i-th sample data, and d denotes the average stroke speed of the i-th sample datai' (i ═ 1, 2.., n) denotes the average travel speed v (t) of the ith sample and the average of the preceding n time intervals
Deviation of (D), SDi' (i ═ 1, 2.., n) denotes the standard deviation of the average travel speed of the i-th sample time interval link.
Step 53: and determining a fuzzy weighting index m, setting a maximum iteration time T and an iteration stop threshold epsilon, and enabling the iteration time T to be 0.
Step 54: calculating a membership matrix U according to the following formula:
wherein r is the number of iterations,
representing the membership matrix at the r-th iteration,
and representing the Euclidean distance between the ith sample and the jth cluster center during the ith iteration, wherein m is a fuzzy weighting index, and the higher the value of m is, the lower the classification fuzzy degree is.
Step 55: calculating a new cluster center V according to the following formula(r+1)。
Step 56: if V | |(r+1)-V(r)And if not, making r equal to r +1, and returning to the step 54 until the algorithm is ended.
Step 6: and detecting the traffic jam of the road section between the adjacent ETC gantries of the expressway by utilizing the clustering result. And inputting the actual data x into the model, and calculating the nearest cluster center, thereby realizing the traffic state estimation. The sample data is classified into one class of classification centers with the nearest Euclidean distance, namely the Euclidean distance minimum principle:
x is theniBelonging to the k-th traffic state. In the formula xiRepresents the ith sample data, dijRepresenting the euclidean distance of the ith sample from the jth cluster center.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered in the protection scope of the present invention.
Claims (6)
1. A highway traffic jam detection method based on charging data is characterized by comprising the following steps:
step 1: extracting traffic parameters based on the charging data;
step 2: performing spatial matching on data of adjacent ETC gantries, eliminating up-and-down road or abnormal data between the adjacent ETC gantries, and calculating average travel time of a road section;
and step 3: synthesizing the original vehicle travel time data using the rolling time interval;
and 4, step 4: constructing an average travel speed discrimination model of the highway;
and 5: carrying out fuzzy C-means clustering analysis to divide different road traffic running states;
step 6: and detecting traffic jam between adjacent ETC portal frames of the highway by using the clustering result.
2. The method for detecting the traffic jam of the expressway based on the charging data as recited in claim 1, wherein the step 1 specifically comprises the following substeps:
step 11: obtaining travel time t recorded by ETC charging data of each vehiclei，tiThe travel time of the ith vehicle is the travel time of each vehicle, and the travel time is directly the OD travel time of each vehicle because the ETC portal frame executes the non-stop toll collection;
step 12: and acquiring the traffic flow of the upper ETC portal frame and the lower ETC portal frame every 5 min.
3. The method for detecting the traffic jam on the highway based on the charging data as recited in claim 2, wherein the step 2 comprises the following substeps:
step 21: performing space matching on the road sections between the adjacent ETC portal frames, and using the difference of the running time of the same vehicle passing through the adjacent ETC portal frames as the travel time of the road section between the corresponding two ETC portal frame toll stations;
step 22: detecting and eliminating abnormal values in the travel time of the road section; and (3) directly deleting abnormal values with negative travel time, and detecting abnormal values of the travel time of the vehicles passing through the lower lane of the exit toll station within a certain time range by adopting a Grubbs (Grubbs) rule method for the abnormal values with overlong travel time.
4. The method for detecting the traffic jam of the expressway based on the charging data as recited in claim 3, wherein the step 4 comprises the substeps of:
step 41: carrying out space matching on adjacent ETC portal road sections; the difference of the running time of the ETC portal passing through the other adjacent ETC portal at the same time is used as the travel time between the two corresponding ETC portals;
step 42: calculating the average travel speed v of each road section at the current moment by combining the distance of each adjacent ETC portal frame and the OD travel time of each vehicle;
step 43: setting proper time interval number n, calculating the average travel speed v (t) of the road section at the current moment and the average value of the previous n time intervals
A deviation d of (d);
step 44: and calculating the standard deviation SD of the average travel speed of the road sections in the previous n time intervals.
5. The method for detecting the traffic jam of the expressway based on the charging data as recited in claim 4, wherein the step 4 comprises the substeps of:
step 51: determining the number c of clustering categories;
step 52: input data is normalized according to the following formula:
in the formula: x is the number ofjAs raw data, xminMinimum value of sample data, xmaxIs the maximum value of the samples and is,x′jfor normalized data, the normalized data all fall within [0,1 ]]Within the interval, the final input is X '{ (v'1,d′1,SD′1),(v′2,d′2,SD′2),…(v′n,d′n,SD′n) V 'therein'i(i ═ 1, 2.. times, n) denotes an average travel speed of the ith sample data, d'i(i 1, 2.. times.n) represents the average travel speed v (t) of the ith sample and the average of the previous n time intervals
Deviation of (2), SD'i(i ═ 1, 2.., n) denotes the standard deviation of the average travel speed for the ith sample time interval link;
step 53: and (3) utilizing a fuzzy C clustering algorithm to carry out an iterative solving process to obtain a membership matrix and a clustering center matrix of the sample.
6. The method for detecting the traffic jam of the expressway based on the charging data as recited in claim 5, wherein the specific process of the step 6 is as follows: and obtaining a clustering center matrix and a membership matrix according to a clustering result, determining a final classification principle by adopting Euclidean distances between the sample data and each traffic center point, and classifying the sample data to the class closest to the Euclidean distance from the classification center, namely the minimum Euclidean distance principle:
x is theniBelonging to the k-th traffic state, wherein xiRepresents the ith sample data, dijRepresenting the euclidean distance of the ith sample from the center of the jth cluster of the cluster.
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| CN113850991A (en) * | 2021-08-31 | 2021-12-28 | 北京北大千方科技有限公司 | Traffic condition identification method and device for toll station, storage medium and terminal |
| CN115424432A (en) * | 2022-07-22 | 2022-12-02 | 重庆大学 | Upstream shunting method under highway abnormal event based on multi-source data |
| CN115424432B (en) * | 2022-07-22 | 2024-05-28 | 重庆大学 | Upstream diversion method based on multisource data under expressway abnormal event |
| CN115311865A (en) * | 2022-09-05 | 2022-11-08 | 中铁长江交通设计集团有限公司 | Method for acquiring traffic volume survey data based on ETC portal frame and charging data |
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