CN110807919A - Urban road network traffic operation situation evaluation method based on vehicle passing data - Google Patents
Urban road network traffic operation situation evaluation method based on vehicle passing data Download PDFInfo
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Abstract
The invention provides an urban road network traffic situation evaluation method based on vehicle passing data, which is characterized by taking a driving freedom degree as a characteristic, firstly acquiring vehicle passing data of road section intersections in an investigation range from a video structured database, and processing the data into formatted data by combining an equipment point table; screening and cleaning all data of the whole road network; then, obtaining real-time travel speed of a single effective vehicle on each road section by utilizing license plate matching of vehicle passing data, geographic position information and an equipment point position table; further acquiring the free flow speed of each road section through statistical analysis, and acquiring the running freedom degree by taking the set time interval duration as an interval; and finally, calculating the real-time driving freedom degrees of the road sections, the regions and the road network by combining the weight calculation of each road section, and evaluating the traffic operation situation of the urban road network. The method has the advantages of small calculation amount, higher calculation efficiency, more accurate state evaluation and the like, and can provide accurate and reliable technical support for traffic demand analysis, traffic management and traffic policy departure.
Description
Technical Field
The invention relates to the field of urban road network traffic situation evaluation, in particular to an urban road network traffic situation evaluation method based on vehicle passing data.
Background
The root cause of urban traffic congestion is unbalanced supply and demand, and the traffic demand far exceeds the supply capacity of a road network. The quantity of motor vehicles reserved in many modern urban areas of cities is continuously increased, the connection with other urban areas and suburban areas is increasingly tight, and under the condition that the traffic demand is continuously increased, the phenomenon of imbalance of supply and demand is widely caused, and urban traffic jam is possibly caused. The traffic macroscopic state of urban roads is the key point of road traffic management attention, and when the traffic state changes, various traffic parameter data generally have a certain specific change trend, so that the traffic macroscopic state is analyzed by utilizing traffic big data to calculate the degree of freedom of road sections and the traffic situation indexes of areas, the macroscopic state of the roads is quickly and accurately judged, traffic operation information can be timely obtained by a traffic management department, evidences are provided for detecting and preventing traffic jam in key areas (such as the periphery of hospitals, schools and railway stations), an auxiliary decision is provided for traffic management, and therefore appropriate traffic management measures are taken to effectively prevent or relieve the traffic jam.
Currently, in traffic state identification, a common method is to obtain vehicle speed from GPS data, further obtain average speed of all vehicles, and evaluate traffic state. The method has the problems of low sampling rate, large data error, complex data processing and the like. Moreover, vehicles using GPS devices are usually operation vehicles, including taxies and network appointments, and the range and speed of vehicle operation cannot replace all vehicles in the whole road network, so the traffic status obtained is often one-sidedness. Therefore, traffic research researchers and traffic practitioners are always seeking an all-road network traffic state discrimination technology with higher economy, efficiency and accuracy.
With the comprehensive arrangement of urban security equipment, many cities are provided with a large number of complete intersection vehicle video detection devices which mainly comprise intersection bayonet equipment and electronic policemen. The video detection device can record information such as license plate information, passing road section number, lane number, steering and capturing time when a vehicle passes through the intersection. The recorded data is complete, the accuracy is high, and the method has the characteristics of wide coverage rate, full samples and real-time updating. The time and space information contained in the vehicle passing data acquired by the intersection detection device can acquire the activity state of the vehicle, so that the road section running freedom and the running state of the whole road network are acquired, and the vehicle passing data becomes an ideal data source for road network state identification.
Disclosure of Invention
The invention aims to provide a multidimensional urban road traffic operation situation evaluation method based on vehicle passing data, and the road network running freedom degree is taken as the representation of the operation situation. The road network running freedom degree is the ratio of the average running speed of vehicles on the road section to the road section free flow speed calculated according to the vehicle passing data, and represents the road network state. The higher the degree of freedom, the better the road network operation situation. The traffic state can be analyzed spatially from an overview (regional) and macroscopic (road network) perspective. Different time intervals can be taken in time, and the road network is judged and analyzed from a short time (15 minutes) to a long time (more than one day). The road network running freedom degree is obtained through the license plate recognition data of the large sample, the road network traffic operation situation is represented, a data source is provided for downstream information distribution, frequent congestion points are recognized according to the traffic state indexes, the intellectualization of road network high-reliability state index detection is achieved, and actual data are provided for traffic management.
The method has the core idea that the traffic state represented by the driving freedom degree is identified by analyzing the space-time information in the vehicle passing data, so that the traffic operation situation of the whole road network is depicted in different dimensions.
In order to achieve the above object, the technical solution of the present invention is as follows: a city road network traffic situation evaluation method based on vehicle passing data is characterized by taking a driving freedom degree as a characteristic, wherein the driving freedom degree is a ratio of an average driving speed of vehicles on a road section to a free flow speed of the road section calculated according to the vehicle passing data; the method comprises the following specific steps:
(1) acquiring vehicle passing data of road section intersections in the surveyed range from a video structured database, and processing the data into formatted data by combining with an equipment point table;
(2) setting a data preprocessing threshold value according to a local license plate setting rule, and screening and cleaning all data of the whole road network obtained in the step (1);
(3) obtaining real-time travel speed of a single effective vehicle on each road section by utilizing license plate matching of vehicle passing data, geographic position information and an equipment point position table;
(4) through statistical analysis, the free flow speed of each road section defined by historical data and geographical position information is combined, the time length of a set time period is taken as an interval, and the real-time traffic operation characteristic parameter of the road section k, namely the driving freedom degree F is obtainedk；
(5) Calculating the real-time driving freedom degrees of the road sections, the regions and the road network by combining the weight calculation of each road section, and evaluating the traffic operation situation of the urban road network, wherein the method specifically comprises the following steps:
(5.1) combining the number n of vehicles in the required time dimension for each road sectionkCalculating the weight w of each road sectionkAs in the following equation:
wk＝nk×ck
and (5.2) combining weight calculation, calculating real-time traffic operation state quantitative indexes of the region and the road network, and representing the driving freedom degree of the road network.
And (5.3) evaluating the urban road network traffic operation situation by taking the road network driving freedom degree as a representation, wherein the higher the driving freedom degree is, the better the operation situation is.
Further, in the step (1), vehicle passing data of road section intersections within the surveyed range is obtained from the video structured database, and is processed into formatted data by combining with the equipment point location table, specifically:
(1.1) acquiring vehicle passing data in an investigation range from a video structured database of an electronic police or a road section gate and the like according to the calculation requirement of the driving freedom degree;
(1.2) carrying out column screening on the collected vehicle passing data, and selecting required columns comprising license plate numbers, capturing time, equipment numbers and steering information;
(1.3) acquiring an equipment point table containing camera equipment numbers and corresponding position information, corresponding vehicle passing data to corresponding intersection numbers one by one according to the equipment numbers and the position information, and arranging the vehicle passing data into formatted data, wherein each piece of data contains effective license plate information (license plate number), the time (capture time) when a vehicle enters or exits an intersection, the equipment numbers, the intersection numbers, steering information and the like;
further, in the step (2), a data preprocessing threshold is set according to a local license plate setting rule, if the length of the license plate threshold is 7-8, all data of the whole road network are subjected to data screening and cleaning, and vehicle passing data which do not accord with the number of license plates are screened out. Wherein the error data includes: no recognition (e.g., license plate recognition as "unknown") and recognition errors (e.g., license plate recognition as "Zhe B888").
Further, in the step (3), by using license plate matching of the vehicle passing data, geographic position information and an equipment point position table, the real-time travel speed of the effective vehicle on the road section can be obtained, and the method specifically comprises the following steps:
and (3.1) obtaining an effective OD pair information table, namely a road section table with all detection devices in a research range according to the geographical position information and the device point position table, wherein the table comprises information such as O point intersection numbers, D point intersection numbers, OD pair distances, OD pair road grades and the like.
(3.2) matching the license plate with the information table by combining the effective OD to obtain the timestamp t of the vehicle passing through the effective O (origin) point and the D (destination) pairOAnd tD；
(3.3) calculating the travel time T of the vehicle i from the point O to the point D on the road section k according to the time difference between the point O and the point Di，k＝tD-tO。
(3.4) calculating the distance L from the point O to the link k at the point D according to the link numbers of the point O and the point DkCombined with the travel time T of the vehiclei，kAnd calculating the real-time travel speed of the vehicle i on the k road section
Further, in the step (4), by combining the free flow speed of each road section defined by the historical data and the geographic position information and taking the set time interval duration as an interval, the real-time traffic operation characteristic parameter of the road section, namely the driving freedom degree, is obtained, and specifically:
(4.1) obtaining the road grade of the road section k according to the road section numbers of the O point and the D point and combining the effective OD pair information table, and obtaining the free flow speed v of the road sectionf，k；
(4.2) aggregating real-time travel speeds of all vehicles on the road section k in the required time dimension, nkAnd (4) vehicles. If the number of vehicles nk> 20, mixing nkAfter the travel time of the vehicles is sequenced, the travel speeds of all the vehicles are screened by adopting an expansion strategy of a box type graph in data screening, and the condition that the travel speed is [ LowerLimit, UpperLimit ] is obtained]Valid travel speed data within the range.
UpperLimit 75% quantile + C
LowerLimit 25% quantile-C
C＝20
(4.3) obtaining the degree of freedom F of the travel of the link k in the specified time dimension according to the following formulak
The invention has the beneficial effects that: the invention provides a method for calculating the running freedom of an urban traffic road network based on vehicle passing data, which is used for evaluating the traffic state of the road network. Compared with the traditional traffic state evaluation method, the method has the advantages of small calculation amount, higher calculation efficiency, more accurate state evaluation and the like by taking the driving freedom degree as the representation, and can provide accurate and reliable technical support for traffic demand analysis, traffic management and traffic policy departure.
Drawings
FIG. 1 is a general flow diagram of the present invention;
FIG. 2 is a flow chart of calculating road segment weights in the present invention;
FIG. 3 is a flow chart of data processing for selecting valid OD pairs according to the present invention;
fig. 4 is a flow chart of the travel speed acquisition for selecting an effective OD pair according to the present invention.
Detailed Description
The invention relates to an urban road network traffic situation evaluation method based on vehicle passing data, which takes a driving freedom degree as a representation and collects and preprocesses vehicle passing data according to requirements; matching the geographical position information to obtain a travel speed; and calculating the running freedom of the whole road network, and evaluating the traffic operation situation of the urban road network according to the running freedom of the whole road network. The process is shown in figure 1 and comprises the following steps:
(1) acquiring vehicle passing data of road section intersections in the surveyed range from a video structured database, and processing the data into formatted data by combining with an equipment point table;
(2) setting a data preprocessing threshold value according to a local license plate setting rule, and screening and cleaning all data of the whole road network obtained in the step (1); in the video structured database, error data may exist for various reasons, and the error data is classified into the following cases:
(a) the light supplement lamp at the intersection is too bright, and the light reflects at the license plate, so that the license plate is exposed excessively, and cannot be identified or is identified wrongly;
(b) the light supplement lamp is not arranged at the intersection, and the license plate lamp of the vehicle is too dark, so that the license plate is not identified or is identified wrongly;
(c) when the vehicle passes through the intersection, the vehicle speed is too high, and the license plate cannot be identified by the equipment;
(d) the detection equipment fails, so that the video is too fuzzy, and the license plate cannot be identified;
(e) the license plate is intentionally shielded or the equipment installation position is not proper, so that the license plate cannot be shot completely and the license plate cannot be identified completely.
The above-mentioned condition of being unable to recognize can result in the license plate number being recognized as "unknown", and the license plate is not recognized completely, and can result in the license plate recognition digit number being too little, such as "Zhe A03", and the license plate is recognized as "Zhe B888" due to the wrong recognition. Therefore, data screening and cleaning can be carried out on all data of the whole road network according to the license plate number threshold value of 7-8, and vehicle passing data which do not accord with the license plate number can be screened out. In the practical application process, error data can be reduced by frequently maintaining intersection equipment, adjusting a light supplement lamp and the like, so that the accuracy of the evaluation method is improved.
(3) Obtaining real-time travel speed of a single effective vehicle on each road section by utilizing license plate matching of vehicle passing data, geographic position information and an equipment point position table;
(4) through statistical analysis, combining the free flow speed of each road section defined by historical data and geographical position information, and taking the set time interval duration as an interval, obtaining real-time traffic operation characteristic parameters of the road section, namely the running freedom degree;
(5) and calculating the real-time driving freedom degrees of the road sections, the regions and the road network by combining the weight calculation of each road section, and evaluating the traffic operation situation of the urban road network. As shown in fig. 2, specifically:
(5.1) calculating the weight w of each road section by combining the number of vehicles of each road section in the required time dimension and the road grade of the road sectionkAs in the following equation:
wk＝nk×ck
and (5.2) combining weight calculation, calculating real-time traffic running state quantitative indexes of the region and the road network, and representing the degree of freedom of the road network.
And (5.3) evaluating the urban road network traffic operation situation by taking the driving freedom degree as a representation, wherein the higher the driving freedom degree is, the better the operation situation is.
Specifically, as a preferable scheme, the specific operation of the step (1) is as follows:
(1.1) acquiring vehicle passing data in an investigation range from a video structured database of an electronic police or a road section gate and the like according to the requirement of the driving freedom degree;
(1.2) carrying out column screening on the collected vehicle passing data, and selecting required columns comprising license plate numbers, capturing time, equipment numbers and steering information;
(1.3) acquiring an equipment point table containing camera equipment numbers and corresponding position information, corresponding vehicle passing data to corresponding intersection numbers one by one according to the equipment numbers and the position information, and arranging the vehicle passing data into formatted data, wherein each piece of data contains effective license plate information (license plate number), the time (capture time) when a vehicle enters or exits an intersection, the equipment numbers, the intersection numbers, steering information and the like; as shown in table 1:
table 1: formatted data table
In the steering information, generally, 1 is a left turn, 2 is a straight line, 3 is a right turn, and 4 is a u-turn.
As shown in fig. 3 and 4, in steps (3) to (4), the driving freedom of each road section is obtained by designing an OD pair information table, and the following steps are specifically adopted:
(3.1) obtaining an effective OD pair information table according to the geographical position information and the equipment point position table; the OD pair information table is a road section table with all detection devices in the research range, and the table contains information such as O point intersection numbers, D point intersection numbers, OD pair distances, OD pair road grades and the like. Wherein the meaning of the effective OD to the information table is as follows: the links with the start point of O and the end point of D do not include 2 or more intersections and do not include the superposition of multiple level links. By setting the rule, the OD pairs can be prevented from being too long, complex driving behaviors such as detour, intersection signal lamp coordination and the like of the vehicle between the O point and the D point are prevented, the travel time is closer to an actual value, the calculated amount is reduced, and the accuracy of road network driving freedom degree calculation is improved.
Table 2: effective OD to information table
(3.2) matching the license plate with the information table by combining the effective OD to obtain the timestamp t of the vehicle passing through the effective O (origin) point and the D (destination) pairOAnd tD；
(3.3) calculating the travel time T of the vehicle i from the point O to the point D on the road section k according to the time difference between the point O and the point Di,k＝tD-to。
According to the data in the table, the travel time of the vehicle Zhe A12345 from the O point 322910944 to the D point 322910955 link k can be calculated to be 30 min.
(3.4) calculating the distance L from the point O to the link k at the point D according to the link numbers of the point O and the point DkCombined with the travel time T of the vehiclei，kAnd calculating the real-time travel speed of the vehicle i on the k road section
(4.1) obtaining the road grade of the road section k according to the road section numbers of the O point and the D point and combining the effective OD pair information table, and obtaining the free flow speed v of the road sectionf，kThe following table:
table 2: road grade and free flow speed meter
| Road grade | Design driving speed (km/h) | Velocity v of free flowf(km/h) |
| Express way | 60～100 | 80 |
| Main road | 40～60 | 50 |
| Secondary trunk road | 30～50 | 40 |
| Branch circuit | 20～40 | 35 |
(4.2) aggregating real-time travel speeds of all vehicles on the road section k in the required time dimension, nkAnd (4) vehicles. If the number of vehicles nk> 20, mixing nkAfter the travel time of the vehicles is sequenced, the travel speeds of all the vehicles are screened by adopting an expansion strategy of a box type graph in data screening, and the condition that the travel speed is [ LowerLimit, UpperLimit ] is obtained]Valid travel speed data within the range.
UpperLimit 75% quantile with C
LowerLimit 25% quantile-C
C＝20
(4.3) obtaining the degree of freedom F of the travel of the link k in the specified time dimension according to the following formulak
The driving freedom degree F can be accurately and quickly calculated and obtained by arranging the effective OD pair information tablek。
Example (b):
taking a certain city as an example, the method is applied to calculate 09 in 2019, 12 months and 7 days: and the road network has the running freedom of 30, the time dimension is 30 minutes, and the space dimension is the whole road network.
Step C1:
(1) acquiring vehicle passing data in an investigated range from a video structured database of an electronic police or a road section gate and the like according to the requirement of the driving freedom degree;
(2) the collected vehicle passing data is subjected to column screening, and the selected required columns comprise license plate numbers, capturing time, equipment numbers and steering information;
(3) acquiring an equipment point table containing camera equipment numbers and corresponding position information, and according to the equipment numbers and the position information, enabling vehicle passing data to correspond to corresponding intersection numbers one by one and arranging the vehicle passing data into formatted data, wherein each piece of data contains effective license plate information (license plate number), the time (capturing time) when a vehicle enters or exits an intersection, the equipment numbers, the intersection numbers, steering information and the like;
step C2:
and setting a data preprocessing threshold value according to a local license plate setting rule, if the length of the license plate threshold value is 7-8, screening and cleaning all data of the whole road network, and screening out the passing data which do not accord with the number of license plates. Such as deleting the following data:
step C3:
(1) and obtaining an effective OD pair information table, namely a road section table with all detection equipment in the research range according to the geographical position information and the equipment point position table, wherein the table comprises information such as O point intersection numbers, D point intersection numbers, OD pair distances, OD pair road grades and the like. As in the following table:
(2) matching the license plate by combining the effective OD to the information table to obtain the timestamp t of the vehicle passing through the effective O (origin) point and D (destination) pairoAnd tDThe following are:
(2) the travel time Ti of the vehicle is calculated to be 81(s) from the time difference between the point O (intersection 322910124) and the point D (intersection 322910452)
(3) Calculating the distance L from the point O to the road section k of the point D according to the crossing numbers of the point O (crossing 322910124) and the point D (crossing 322910452) and combining the geographical position informationk721 (m). Calculating the real-time travel speed of the vehicle by combining the travel time Ti of the vehicle with 81(s)
Step C4:
(1) obtaining the road grade of the road section k according to the road section numbers of the O point (the intersection 322910124) and the D point (the intersection 322910452) and combining the historical data and the geographical position information, and obtaining the free flow speed v of the road sectionfThe following table:
| road grade | Design driving speed (km/h) | Velocity v of free flowf(km/h) |
| Express way | 60～100 | 80 |
| Main road | 40～60 | 50 |
| Secondary trunk road | 30～50 | 40 |
| Branch circuit | 20～40 | 35 |
Road grade of link k is the main road, so vf，k＝50(km/h)≈13.9(m/s)
(2) Aggregating the real-time travel speeds of all vehicles on a road segment k, n in the required time dimensionkVehicle, n in this examplek＝40
| Numbering | Speed of travel (m/s) | Time of capture | Road segment numbering |
| 01 | 8.9 | 2018/12/7 09:13:50 | k |
| 02 | 10.6 | 2018/12/7 09:15:32 | k |
| ... | ... | ... | ... |
| 40 | 7.2 | 2018/12/7 09:29:32 | k |
If the number of vehicles nkAnd if the speed is more than 20, screening the travel speeds of all vehicles by adopting an expansion strategy of a box diagram in data screening to obtain effective travel speed data. In this example, the data with the stroke speed of the last 5% and the data with the stroke speed of the first 5% are removed, and 4 pieces of data are rejected as abnormal values.
(3) Obtaining the degree of freedom of travel of a link k in a given time dimension (30 minutes)
Step C5:
(1) calculating the weight w of each road section by combining the number of vehicles of each road section in the required time dimension and the road grade of each road sectionkE.g. weight w of link kk＝nk×ck＝40×1.0＝40
(2) Combining with weight calculation, calculating the road network driving freedom degree of the urban road network at 9 points and 30 points in 12, 7 and 7 in 2019
(3) The method is characterized by evaluating the traffic operation situation of the urban road network by taking the driving freedom degree as a characteristic, wherein the higher the driving freedom degree is, the better the operation situation is. The urban road network has the road network driving freedom S of 0.66 at 9 points and 30 points in 12, 7 and 7 in 2019, and the situation is good.
Compared with the traditional traffic state evaluation method, the method has the advantages of small calculation amount, higher calculation efficiency, more accurate state evaluation and the like by taking the driving freedom degree as the representation, and can provide accurate and reliable technical support for traffic demand analysis, traffic management and traffic policy departure.
Claims (5)
1. A city road network traffic situation evaluation method based on vehicle passing data is characterized in that a driving freedom degree is taken as a representation, and the driving freedom degree is a ratio of an average driving speed of vehicles on a road section to a free flow speed of the road section calculated according to the vehicle passing data; the method comprises the following steps:
(1) acquiring vehicle passing data of road section intersections in the surveyed range from a video structured database, and processing the data into formatted data by combining with an equipment point table;
(2) setting a data preprocessing threshold value according to a local license plate setting rule, and screening and cleaning all data of the whole road network obtained in the step (1);
(3) obtaining real-time travel speed of a single effective vehicle on each road section by utilizing license plate matching of vehicle passing data, geographic position information and an equipment point position table;
(4) through statistical analysis, the free flow speed of each road section defined by historical data and geographical position information is combined, the time length of a set time period is taken as an interval, and the real-time traffic operation characteristic parameter of the road section k, namely the driving freedom degree F is obtainedk；
(5) Calculating the real-time driving freedom degrees of the road sections, the regions and the road network by combining the weight calculation of each road section, and evaluating the traffic operation situation of the urban road network, wherein the method specifically comprises the following steps:
(5.1) combining the number n of vehicles in the required time dimension for each road sectionkCalculating the weight w of each road sectionkAs in the following equation:
wk＝nk×ck
and (5.2) combining weight calculation, calculating real-time traffic operation state quantitative indexes of the region and the road network, and representing the driving freedom degree of the road network.
And (5.3) evaluating the urban road network traffic operation situation by taking the road network driving freedom degree as a representation, wherein the higher the driving freedom degree is, the better the operation situation is.
2. The urban road network traffic situation evaluation method based on vehicle passing data according to claim 1, characterized in that: in the step (1), vehicle passing data of road section intersections in the surveyed range is obtained from the video structured database, and is processed into formatted data by combining with an equipment point location table, wherein the method specifically comprises the following steps:
(1.1) acquiring vehicle passing data in an investigation range from a video structured database of an electronic police or a road section gate and the like according to the calculation requirement of the driving freedom degree;
(1.2) carrying out column screening on the collected vehicle passing data, and selecting required columns comprising license plate numbers, capturing time, equipment numbers and steering information;
and (1.3) acquiring an equipment point table containing camera equipment numbers and corresponding position information, corresponding the vehicle passing data to corresponding intersection numbers one by one according to the equipment numbers and the position information, and arranging the vehicle passing data into formatted data, wherein each piece of data contains effective license plate information (license plate number), the time (capture time) when the vehicle enters or exits the intersection, the equipment numbers, the intersection numbers, steering information and the like.
3. The urban road network traffic situation evaluation method based on vehicle passing data according to claim 1, characterized in that: and (2) screening and cleaning all data of the whole road network when the threshold length of the license plate is 7-8, and screening out the passing data which do not accord with the number of the license plate.
4. The urban road network traffic situation evaluation method based on vehicle passing data according to claim 1, characterized in that: in the step (3), by utilizing license plate matching of the vehicle passing data, geographic position information and an equipment point position table, the real-time travel speed of the effective vehicle on the road section can be obtained, and the method specifically comprises the following steps:
and (3.1) obtaining an effective OD pair information table, namely a road section table with all detection devices in a research range according to the geographical position information and the device point position table, wherein the table comprises information such as O point intersection numbers, D point intersection numbers, OD pair distances, OD pair road grades and the like.
(3.2) matching the license plate with the information table by combining the effective OD to obtain the timestamp t of the vehicle passing through the effective O (origin) point and the D (destination) pairOAnd tD；
(3.3) calculating the travel time T of the vehicle i from the point O to the point D on the road section k according to the time difference between the point O and the point Di,k＝tD-tO。
(3.4) calculating the distance L from the point O to the link k at the point D according to the link numbers of the point O and the point DkCombined with the travel time T of the vehiclei,kAnd calculating the real-time travel speed of the vehicle i on the k road section
5. The urban road network traffic situation evaluation method based on vehicle passing data according to claim 1, characterized in that: and (4) obtaining real-time traffic operation characteristic parameters of the road sections, namely the driving freedom degree, by combining the free flow speed of each road section defined by historical data and geographical position information and taking the set time interval duration as an interval, wherein the method specifically comprises the following steps:
(4.1) obtaining the road grade of the road section k according to the road section numbers of the O point and the D point and combining the effective OD pair information table, and obtaining the free flow speed v of the road sectionf,k；
(4.2) aggregating lanes in the desired time dimensionReal-time travel speeds of all vehicles on segment k, nkAnd (4) vehicles. If the number of vehicles nk> 20, mixing nkAfter the travel time of the vehicles is sequenced, the travel speeds of all the vehicles are screened by adopting an expansion strategy of a box type graph in data screening, and the condition that the travel speed is [ LowerLimit, UpperLimit ] is obtained]Valid travel speed data within the range.
UpperLimit 75% quantile + C
LowerLimit 25% quantile-C
C＝20
(4.3) obtaining the degree of freedom F of the travel of the link k in the specified time dimension according to the following formulak
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| CN113837446A (en) * | 2021-08-30 | 2021-12-24 | 航天科工广信智能技术有限公司 | Multi-source heterogeneous data-based airport land side area traffic situation prediction method |
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| CN116994441A (en) * | 2023-09-26 | 2023-11-03 | 北京清创美科环境科技有限公司 | Traffic flow information data processing method and system |
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| CN112015837A (en) * | 2020-08-24 | 2020-12-01 | 安徽科力信息产业有限责任公司 | Urban road high-frequency path analysis method, system and storage medium |
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| CN112150045A (en) * | 2020-11-05 | 2020-12-29 | 北京阿帕科蓝科技有限公司 | Method for judging urban vehicle supply and demand relationship based on vehicle position statistics and monitoring system thereof |
| CN112150045B (en) * | 2020-11-05 | 2023-12-01 | 北京阿帕科蓝科技有限公司 | Method for judging supply and demand relationship of city shared vehicle based on vehicle position statistics and monitoring system thereof |
| CN112820108A (en) * | 2021-01-12 | 2021-05-18 | 南京睿思交通信息科技有限公司 | Self-learning road network traffic state analysis and prediction method |
| CN113192342B (en) * | 2021-04-27 | 2022-03-22 | 中山大学 | Method for determining percentage vehicle speed of urban road in free flow state based on floating vehicle data |
| CN113192342A (en) * | 2021-04-27 | 2021-07-30 | 中山大学 | Method for determining percentage vehicle speed of urban road in free flow state based on floating vehicle data |
| CN113837446A (en) * | 2021-08-30 | 2021-12-24 | 航天科工广信智能技术有限公司 | Multi-source heterogeneous data-based airport land side area traffic situation prediction method |
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| WO2023216504A1 (en) * | 2022-05-11 | 2023-11-16 | 华南理工大学 | Digital road network traffic state reckoning method based on multi-scale calculation |
| CN115410375A (en) * | 2022-11-02 | 2022-11-29 | 华路易云科技有限公司 | Fusion traffic index set generation method based on fusion traffic data of thunder card |
| CN116994441A (en) * | 2023-09-26 | 2023-11-03 | 北京清创美科环境科技有限公司 | Traffic flow information data processing method and system |
| CN116994441B (en) * | 2023-09-26 | 2023-12-12 | 北京清创美科环境科技有限公司 | Traffic flow information data processing method and system |
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