CN107591003B - Urban road network dissipating capacity extraction method based on vehicle identification data - Google Patents
Urban road network dissipating capacity extraction method based on vehicle identification data Download PDFInfo
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Abstract
The invention provides an urban road network dissipating capacity extraction method based on vehicle identification data, which is characterized in that vehicle identification data in an urban road network are used as supports, a road network passing behavior track of a sample vehicle in a time dimension is constructed on the basis of continuous passing behavior tracking of the vehicle in the road network, the overall vehicle behavior characteristic analysis of a macroscopic road network in a short time interval is realized in a time convergence mode, and the road network dissipating capacity is extracted according to the change relation between the existence quantity and the dissipating quantity of the vehicle in the road network; the method is based on the current domestic traffic flow data acquisition conditions, and based on a data driving thought and from the perspective of vehicle behavior analysis, the inherent performance characteristics of the macroscopic level of the road network are extracted from the massive quantity of identification data of the number plates passing through the road network, so that the defect that the characteristic index cannot be accurately obtained by methods such as modeling at present is overcome.
Description
Technical Field
The invention relates to an urban road network dissipating capacity extracting method based on vehicle identification data.
Background
The development of the internet of things technology is promoting the progress of a refined road traffic collection technology, and with the improvement and improvement of the quality of traffic data acquired in real time, such as granularity and reliability of space-time dimensions, traffic data integrity of a road network level and the like, the international and foreign schools increasingly pay attention to the role of high-precision and high-resolution data in the research of urban road traffic flow operation performance evaluation and the like.
In view of the current mainstream urban road traffic flow operation data acquisition conditions in China, the full-sample perception and identification of vehicles in primary and secondary trunk road networks can be realized by means of high-definition checkpoint equipment and a vehicle driving management database. By detecting the whole sample of the running vehicle in the road network, the characteristics of the road network macro level can be analyzed and extracted by a big data driving idea.
The road network dissipation capacity index can reflect the service level of a road traffic system and plays an important role in traffic system planning, supply and demand analysis and traffic flow operation management, but a simple and reliable evacuation capacity extraction method is not available at present.
Disclosure of Invention
The invention aims to provide a method for extracting the dissipation capacity of an urban road network based on vehicle identification data, which is used for tracking the traffic behavior of all samples of vehicles in the urban road network based on the vehicle identification data, further extracting the dissipation capacity index of the road network through data aggregation analysis and solving the problem that a simple and reliable method for extracting the dissipation capacity is lacked in the prior art.
The data-driven road network traffic dispersion capacity extraction method achieves fine perception of vehicle driving behaviors in a road network based on vehicle identification data, captures road network dispersion capacity characteristics from actual traffic behavior expressions of road network vehicles through collective analysis of individual vehicle traffic behaviors, and provides road network traffic characteristic indexes with practical guiding significance for traffic organization planning, traffic control strategies and the like.
The technical solution of the invention is as follows:
a method for extracting the dissipation capacity of an urban road network based on vehicle identification data comprises the steps of constructing a road network traffic behavior track of a sample vehicle in a time dimension based on the vehicle identification data in the urban road network, realizing overall vehicle behavior characteristic analysis of a macroscopic road network in a short time interval in a time convergence mode, and extracting the dissipation capacity of a road network traffic flow according to the change relation between the existence quantity and the dissipation quantity of the vehicles in the road network; the method comprises the following specific steps:
step 1, acquiring vehicle identification data of each detection point location in a road network range in an analysis period, wherein the vehicle identification data comprises a vehicle number plate number, detection time and detection point locations; the detection point position is an intersection or a road section where the data acquisition equipment is installed; dividing the analysis time interval into a plurality of short time intervals, and collecting the vehicle behavior data in each short time interval in time;
step 2, carrying out quality detection and data cleaning on the vehicle passing detection data, and filtering abnormal data; the abnormal data comprises vehicle identification abnormal data and detection time stamp abnormal data;
step 4, based on the travel behavior track of the vehicle, the existence quantity E of the road network vehicles in each short time intervaltAnd dissipation amount DtCarrying out statistics; the vehicle existence amount is represented by a travel starting point of a travel behavior track and a track line segment; the vehicle dispersion amount is represented by the end point of the travel track;
step 5, according to the vehicle travel behavior state in each short time interval (E)t,Dt) Drawing a scatter diagram of the existing quantity and the dissipation quantity of the road network vehicles, and extracting a functional relation D of two parameters through curve fittingt＝f(Et) Determining E according to the traffic capacity of the road networktObtaining the maximum function value DmaxNamely the urban road network dissipation capacity.
Further, in step 1, the vehicle identification data is dynamically collected by a traffic detection system which is installed at the detection point and can automatically identify the passing vehicle or is extracted from a data storage database of the traffic detection system.
Further, in step 1, the analysis period is a time period greater than 1 hour and not more than 24 hours.
Further, in step 3, the vehicle travel time interval is a time range with continuous traffic records; the vehicle travel behavior time sequence track is a virtual track capable of representing the continuous vehicle passing state in a time coordinate system.
Further, step 3 specifically includes collecting all sample data in the analysis time period according to the number plate number, arranging the identification data in the analysis time period according to a time sequence for any vehicle with valid identification data, marking the time points of the vehicle with vehicle passing detection records on a time axis, determining continuous travel time intervals of the vehicle according to the distribution conditions of the marked points on the time axis, and drawing travel behavior tracks of the vehicle.
Further, the analysis method of the continuous travel time interval comprises the following steps: taking the first mark point as the starting point of the continuous travel interval; calculating the interval length between the subsequent adjacent mark points one by one, if the interval length is larger than a judgment threshold value TgapJudging that the vehicle has two travel behaviors in the time interval, and respectively taking two end points of the interval as a previous travel terminal and a next travel terminal; analyzing all the mark points by the method, and taking the last mark point of the vehicle in the analysis time period as the final point of the last trip of the vehicle; and determining a continuous travel time interval according to the time scales corresponding to the starting point and the end point.
Further, the step of drawing the travel behavior trajectory of the vehicle is specifically to draw a line segment connecting a starting point and an end point of the vehicle according to the continuous travel time interval of the vehicle.
Further, in step 4, the existing amount and the dissipated amount of the vehicle in each short time interval are counted one by one, specifically,
according to formula Et＝No+NlCalculating the quantity of vehicles in road network, wherein EtFor the presence of vehicles in the road network, NoNumber of starting points for track, NlThe number of the trace lines is; according to Dt＝NDCalculating the amount of vehicle dissipation, wherein DtFor the amount of dispersion of vehicles in the road network, NDThe number of the track end points is.
The invention has the beneficial effects that: the urban road network dissipating capacity extraction method based on the vehicle identification data is characterized in that massive vehicle passing identification data are used as supports, the time characteristics of vehicle passing behaviors in a road network are expressed in an abstract and visual visualization method through drawing a virtual travel behavior track form, the aggregation analysis of individual vehicle behavior characteristics is realized in a time aggregation mode, and the road network dissipating capacity index is extracted through the fitting of the relation between the existence quantity and the dissipating quantity of road network vehicles. The method is based on the current domestic traffic flow data acquisition conditions, and based on a data driving thought and from the perspective of vehicle behavior analysis, the inherent performance characteristics of a road network macroscopic level are extracted from massive vehicle identification data in the road network, so that the defect that the characteristic index cannot be accurately obtained by methods such as modeling at present is overcome.
Drawings
Fig. 1 is a schematic flow chart of a method for extracting urban road network dissipating capacity based on vehicle identification data according to an embodiment of the invention.
FIG. 2 is a diagram showing abnormal data in the embodiment.
Fig. 3 is a schematic diagram of a travel behavior trajectory in the embodiment.
FIG. 4 is a scatter plot and a curve fit of the relationship of amount present to amount dissipated in the examples.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Examples
According to the urban road network dissipation capacity extraction method based on the vehicle identification data, the vehicle identification data in the urban road network is used as a support, the road network traffic behavior track of a sample vehicle in a time dimension is constructed on the basis of the behavior track tracking of the vehicle in the road network, the overall vehicle behavior characteristic analysis of a macroscopic road network in a short time interval is realized in a time convergence mode, and the road network traffic flow dissipation capacity is extracted according to the change relation between the existence quantity and the dissipation quantity of the vehicle in the road network.
Referring to fig. 1, the specific steps of the embodiment method are as follows:
step 1, acquiring vehicle identification data of each detection point location in a road network range in an analysis period, wherein the vehicle identification data comprises a vehicle number plate number, detection time and detection point locations; the detection point position is an intersection or a road section where the data acquisition equipment is installed; the vehicle identification data is dynamically acquired by a traffic detection system which is arranged at the detection point position and can automatically identify the passing vehicle.
In one example, the vehicle identification data can be acquired from an electronic police system for detecting the section passing situation of the road section, an intelligent high-definition checkpoint and an intersection entrance way through vehicle passing snapshot and number plate identification; in another example, license plate number and location information are obtained from roadside RFID devices; in another example, the vehicle identification data is extracted from a data storage database of the traffic detection system.
In one example, the analysis time interval is a time interval which is longer than 1 hour and does not exceed 24 hours, such as a peak time interval of 8:00-10:00, 5 minutes is taken as a time unit for data processing and analysis, the analysis time interval is divided into a plurality of short time intervals, and vehicle behavior data in each short time interval are subjected to time aggregation.
In one example, the detection point location where the vehicle identification data is summarized is a number or name of an intersection or a road section where the detection device is arranged; in one example, the detection point location is determined by the device number and the mapping relationship processing of the device and the point location.
Step 2, carrying out quality detection and data cleaning on the vehicle passing detection data, and filtering abnormal data; the abnormal data comprises vehicle identification abnormal data and detection time stamp abnormal data.
In one embodiment, original data, such as the number plate number field of the vehicle identification data collected and acquired by the high-definition bayonet system is "unidentified", the data of the detection time field is obviously abnormal, and the like, need to be deleted in the step, and fig. 2 shows partial abnormal data.
The vehicle travel time interval is a time range with continuous traffic records; the vehicle travel behavior time sequence track is a virtual track capable of representing the continuous vehicle passing state under a time one-dimensional coordinate system.
In one example, all sample data in the analysis period is classified by number, and any sample data with valid identification data existsFor a vehicle, identification data of the vehicle in an analysis period are arranged according to a time sequence, time points of the vehicle with vehicle passing detection records are marked on a time axis, a continuous travel time interval of the vehicle is determined according to distribution conditions of the marked points on the time axis, and a travel behavior track of the vehicle is drawn. The continuous travel time interval analysis method comprises the following steps: taking the first mark point as the starting point of the continuous travel interval; calculating the interval length between the subsequent adjacent mark points one by one, if the interval length is larger than a judgment threshold value Tgap(in this example, the threshold length is 20min), judging that the vehicle has two travel behaviors in the time interval, and taking two end points of the interval as a previous travel end point and a next travel start point respectively; analyzing all the mark points by the method, and taking the last mark point of the vehicle in the analysis time period as the final point of the last trip of the vehicle; and determining a continuous travel time interval according to the time scales corresponding to the starting point and the end point. According to the continuous travel time interval, a line segment is drawn to connect the starting point and the end point, as shown in fig. 3.
Step 4, based on the travel behavior track of the vehicle, the existence quantity E of the road network vehicles in each short time intervaltAnd dissipation amount DtCarrying out statistics; the vehicle existence amount is represented by a travel starting point of a travel behavior track and a track line segment; the amount of vehicle dispersion is characterized by the end of the travel trajectory.
In one example, 5min is taken as a short time interval length, a research interval of 8:00-10:00 is divided into 24 short time intervals according to a formula Et＝No+NlCalculating the quantity of vehicles in road network, wherein EtFor the presence of vehicles in the road network, NoNumber of starting points for track, NlThe number of the trace lines is; according to Dt＝NDCalculating the amount of vehicle dissipation, wherein DtFor the amount of dispersion of vehicles in the road network, NDCounting the number of track end points; each vehicle can be counted at most once in the existing quantity or the dissipation quantity in a short time interval; the method for judging the starting point comprises the following steps: a point exists, and a line is connected behind the time sequence and is used as a starting point of a vehicle running track; the method for judging the end point comprises the following steps: there is one point and a line is connected in front of the time sequence, or there is oneAnd (4) judging the point as the vehicle driving track end point when the point is not connected with the front and the back of the time sequence by lines. The method is used for counting the vehicle existence amount and the vehicle dissipation amount in each short time interval one by one.
Step 5, according to the vehicle travel behavior state in each short time interval (E)t,Dt) Drawing a scatter diagram of the existing quantity and the dissipation quantity of the road network vehicles, and extracting a functional relation D of two parameters through curve fittingt＝f(Et) Determining E according to the traffic capacity of the road networktObtaining the maximum function value DmaxNamely the urban road network dissipation capacity.
In one example, E according to step 4t、DtCounting the cases, will correspond to (E)t,Dt) As the vehicle travel behavior state in each short time interval, a scatter diagram is drawn in a coordinate system of the existence amount-the dissipation amount, and E is obtained by curve fittingt、DtA relationship function; the traffic capacity S of the road network can be determined EtHas an effective interval of [0, S]In this interval, E is extractedt、DtRelation function f (E)t) Maximum value of DmaxI.e. the dissipation capability of the road network.
Claims (8)
1. A city road network dissipation capacity extraction method based on vehicle identification data is characterized by comprising the following steps: based on vehicle identification data in an urban road network, constructing a road network traffic behavior track of a sample vehicle in a time dimension, realizing overall vehicle behavior characteristic analysis of a macroscopic road network in a short time interval in a time convergence mode, and extracting road network traffic flow dissipation capacity according to a change relation between the existence amount of the road network vehicles and the dissipation amount; the method comprises the following specific steps:
step 1, acquiring vehicle identification data of each detection point location in a road network range in an analysis period, wherein the vehicle identification data comprises a vehicle number plate number, detection time and detection point locations; the detection point position is an intersection or a road section where the data acquisition equipment is installed; dividing the analysis time interval into a plurality of short time intervals, and collecting the vehicle behavior data in each short time interval in time;
step 2, carrying out quality detection and data cleaning on the vehicle passing detection data, and filtering abnormal data; the abnormal data comprises vehicle identification abnormal data and detection time stamp abnormal data;
step 3, extracting number plate number information from the data subjected to data cleaning, and analyzing the travel behavior characteristics of each sample vehicle according to the number plate data; the travel behavior characteristic analysis comprises vehicle travel time interval determination and vehicle travel behavior time sequence track drawing;
step 4, based on the travel behavior track of the vehicle, the existence quantity E of the road network vehicles in each short time intervaltAnd dissipation amount DtCarrying out statistics; the vehicle existence amount is represented by a travel starting point of a travel behavior track and a track line segment; the vehicle dispersion amount is represented by the end point of the travel track;
step 5, according to the vehicle travel behavior state in each short time interval (E)t,Dt) Drawing a scatter diagram of the existing quantity and the dissipation quantity of the road network vehicles, and extracting a functional relation D of two parameters through curve fittingt＝f(Et) Determining E according to the traffic capacity of the road networktObtaining the maximum function value DmaxNamely the urban road network dissipation capacity.
2. The urban road network dissipating capacity extracting method based on vehicle identification data according to claim 1, wherein: in the step 1, vehicle identification data are dynamically acquired by a traffic detection system which is arranged at a detection point position and can automatically identify a passing vehicle or extracted from a data storage database of the traffic detection system.
3. The urban road network dissipating capacity extracting method based on vehicle identification data according to claim 1, wherein: in step 1, the analysis period is a time period greater than 1 hour and not greater than 24 hours.
4. The urban road network dissipating capacity extracting method based on vehicle identification data according to claim 1, wherein: in the step 3, the vehicle travel time interval is a time range with continuous traffic records; the vehicle travel behavior time sequence track is a virtual track capable of representing the continuous vehicle passing state in a time coordinate system.
5. The urban road network dissipating capacity extracting method based on vehicle identification data according to claim 1, wherein: and 3, specifically, collecting all sample data in the analysis time period according to the number plate number, arranging the identification data in the analysis time period according to the time sequence for any vehicle with effective identification data, marking the time points of the vehicle with vehicle passing detection records on a time axis, determining the continuous travel time interval of the vehicle according to the distribution condition of the marked points on the time axis, and drawing the travel behavior track of the vehicle.
6. The method of extracting urban road network dissipating capacity based on vehicle identification data according to claim 5, wherein: the analysis method of the continuous travel time interval comprises the following steps: taking the first mark point as the starting point of the continuous travel time interval; calculating the time interval length between the subsequent adjacent mark points one by one, if the time interval length is larger than the judgment threshold value TgapJudging that the vehicle has two travel behaviors in the time interval, and respectively taking two end points of the time interval as a previous travel end point and a next travel start point; analyzing all the mark points by the method, and taking the last mark point of the vehicle in the analysis time period as the final point of the last trip of the vehicle; and determining a continuous travel time interval according to the time scales corresponding to the starting point and the end point.
7. The method of extracting urban road network dissipating capacity based on vehicle identification data according to claim 5, wherein: the step of drawing the travel behavior track of the vehicle is specifically to draw a line segment connecting a starting point and an end point of the vehicle according to the continuous travel time interval of the vehicle.
8. The method of extracting urban road network dissipating capacity based on vehicle identification data according to any of claims 1 to 7, characterized in that: in step 4, the existing quantity and the dissipated quantity of the vehicles in each short time interval are counted one by one, specifically,
according to formula Et＝No+NlCalculating the quantity of vehicles in road network, wherein EtFor the presence of vehicles in the road network, NoNumber of starting points for track, NlThe number of the trace lines is; according to Dt＝NDCalculating the amount of vehicle dissipation, wherein DtFor the amount of dispersion of vehicles in the road network, NDThe number of the track end points is.
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