CN114023067B - Road travel time prediction method for internet of vehicles environment robustness guidance - Google Patents
Road travel time prediction method for internet of vehicles environment robustness guidance Download PDFInfo
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Abstract
The invention discloses a road travel time prediction method for vehicle networking environment robustness guidance, which comprises the following steps: dividing a vehicle road operation analysis road section in a vehicle networking environment; synchronously detecting a target vehicle by means of a traffic detector, and collecting starting and ending vehicle running characteristic data of a road section; determining a service area vehicle speed influence correction coefficient of a road section; determining the influence coefficient of the vehicle speed of the entrance ramp of the road section; determining the vehicle speed influence coefficient of an exit ramp of a road section; calculating the average speed of the target vehicle on the road section, and predicting the travel time of the road section under the normal road condition; and dynamically correcting the road travel time of the target vehicle under the emergency traffic incident. The invention provides a road travel time prediction method for the robustness guidance of an Internet of vehicles environment, which can provide technical support for the rapid and robust prediction of road travel time in the Internet of vehicles environment, thereby providing reliable high-quality travel service for road traffic travelers.
Description
Technical Field
The invention relates to a road vehicle travel time prediction method, in particular to a road travel time prediction method for vehicle networking environment robustness guidance, and belongs to the technical field of intelligent traffic management and control.
Background
With the development of roads, the demand of people for road travel information service is increasing day by day. And the travel time prediction should be one of the most valuable contents in the road travel information service. The prior art has the defects of inaccurate and unstable acquisition of traffic flow operation data, low accuracy of road travel time prediction, and creates conditions for accurately predicting the road travel time by acquiring massive traffic flow operation data in real time in an internet of vehicles environment.
Disclosure of Invention
The invention mainly aims to overcome the defects in the prior art, provide a road travel time prediction method for vehicle networking environment robustness guidance, predict road vehicle travel time by utilizing dynamic data in a vehicle networking environment, and effectively improve the robustness and reliability of prediction.
In order to achieve the purpose, the invention adopts the technical scheme that:
a road travel time prediction method for vehicle networking environment robustness guidance comprises the following steps:
1) vehicle road operation analysis road section under division vehicle networking environment
Setting a length d (generally not more than 10 kilometers) according to a road section to be predicted, and dividing a vehicle road operation analysis section in an Internet of vehicles environment;
2) collecting starting and ending point vehicle running characteristic data of road section
Synchronously detecting a target vehicle at the start and end point of a road operation analysis road section by means of a traffic detector, and acquiring the instantaneous speed of the target vehicle passing through the start and end point of the road section;
3) determining influence correction coefficients of service area vehicle speed, entrance ramp vehicle speed and exit ramp vehicle speed of road section
Calculating the shadow correction coefficient of the target vehicle on the travel time of the road section in the service area by detecting the number of vehicles entering and leaving the service area at the starting and ending point detection time at the entrance and exit of the service area;
calculating the influence correction coefficient of the entrance ramp on the travel time of a target vehicle on the road section by detecting the number of vehicles entering the main line road from the entrance ramp at the start and end point detection period of the entrance ramp;
calculating an influence correction coefficient of the exit ramp on the travel time of a target vehicle on the road section by detecting the number of vehicles leaving the main line road from the exit ramp at the start and end point detection period of the exit ramp;
4) predicting vehicle road section travel time under normal road condition
Calculating the average speed of the target vehicle on a road section by determining the instantaneous speed of the target vehicle at the starting point and the ending point, and predicting the travel time of the road section under the normal road condition;
5) dynamically correcting vehicle road travel time under emergency traffic events
The method comprises the steps of dynamically correcting the travel time of the emergency traffic incident on the road section of the vehicle by recording the running time of the target vehicle, wherein the speed of the target vehicle is less than the threshold speed in the road section running process, so that the travel time of the emergency traffic incident on the road section of the vehicle is dynamically corrected.
The invention is further configured to: in the step 1), vehicle road operation analysis road sections in the vehicle networking environment are divided, specifically,
if the road distance s needing to be predicted does not exceed the set length d, predicting the road distance s as a road section;
if the road distance s to be predicted exceeds the set length d, dividing the road to be predicted into J road sections, setting each road section according to the set length d, and predicting each road section respectively; each segment is defined as J, J ∈ J, J ═ 1,2,3, and ….
The invention is further configured to: in the step 2), the operation characteristic data of the vehicles at the starting and ending points of the road section is collected, specifically,
2-1) collecting traffic flow characteristic data at starting point of road section
When the target vehicle (the vehicle needing to predict the travel time of the road section) is away from the start detector of the road section j by a certain distance L (generally 500-
When the Mth vehicle, namely the target vehicle passes through the starting point detector of the section j, the instantaneous speed of the starting point is recorded as
Finishing vehicle speed detection, wherein the starting point instantaneous vehicle speeds of M vehicles are detected at the moment, wherein M belongs to M, and M is 1,2,3 and …;
2-2) collecting traffic flow characteristic data at the end point of the road section
The end point detector of the section j and the starting point detector start detecting at the same time, when the vehicle passes through the end point detector of the section j, the end point instantaneous vehicle speed of the nth vehicle is recorded
And finishes the detection simultaneously with the starting point detector, and the terminal instantaneous vehicle speeds of N vehicles are detected, wherein N belongs to N, and N is 1,2,3 and ….
The invention is further configured to: in the step 3), determining a vehicle speed influence correction coefficient of the service area of the road section, specifically,
if no service area exists in the road section j, the vehicle speed influence correction coefficient k of the service area s_j Taking 1; if the road section j comprises the service area, the influence of the service area on the travel time of the target vehicle on the road section needs to be further considered;
3-1a) collecting data of vehicles at entrance and exit of service area
The number of vehicles entering and leaving the service area in the time period is detected by the service area entrance and exit at the same time of detecting the starting point and the end point of the road section j by the detector
3-2a) calculating the influence correction coefficient of the speed of the vehicle in the service area of the road section
Calculating the speed influence correction coefficient k of the road section j service area according to the number N of vehicles detected by the road section j end point detector and the data of the vehicles at the entrance and the exit of the service area s_j ，
The invention is further configured to: in the step 3), determining an influence correction coefficient of the vehicle speed of the entrance ramp of the road section, specifically,
if no entrance ramp exists in the road section j, the influence of the speed of the entrance ramp on the correction coefficient k R(i)_j Taking 1; if the road section j comprises the entrance ramp, the influence of the entrance ramp on the travel time of the target vehicle on the road section needs to be further considered;
3-1b) collecting data of vehicles on the ramp of the entrance
Detecting the number Q of vehicles entering the main line road through the ramp in the time period at the entrance ramp while detecting the starting point and the end point of the road section j by using the detector R(i)_j ；
3-2b) calculating influence correction coefficient of vehicle speed of road section entrance ramp
Calculating the speed influence correction coefficient k of the ramp at the entrance of the road section j according to the number N of the vehicles detected by the end point detector of the road section j and the data of the vehicles on the ramp at the entrance R(i)_j ，
The invention is further configured to: in the step 3), determining the correction coefficient of the influence of the speed of the exit ramp of the road section, specifically,
if no exit ramp is present in the road section j, the speed of the exit ramp affects the correction factor k R(o)_j Taking 1; if the road section j comprises the exit ramp, the influence of the exit ramp on the travel time of the target vehicle on the road section needs to be further considered;
3-1c) collecting exit ramp vehicle data
Detecting the number Q of vehicles leaving the main line road through the ramp in the time period at the exit ramp while detecting the starting point and the end point of the road section j by using the detector R(o)_j ；
3-2c) calculating the influence correction coefficient of the vehicle speed of the exit ramp of the road section
Calculating the speed influence correction coefficient k of the exit ramp of the road section j according to the number N of vehicles detected by the road section j end point detector and the data of the vehicles on the exit ramp R(o)_j ，
The invention is further configured to: in the step 4), the travel time of the vehicle section under the normal road condition is predicted, specifically,
4-1) calculating the instantaneous speed sequence of the target vehicle at the starting point of the road section
The starting point instantaneous speed of M vehicles
According to the sequence from small to large, the instantaneous speed of the target vehicle at the starting point of the road section j is determined
Order of the order of bits
And calculating the ranking proportion of the instantaneous speed of the target vehicle at the starting point of the road section j
4-2) determining the speed of the target vehicle corresponding to the same level as the starting point in the road section end point detection vehicles
The ranking proportion of the corresponding instantaneous speed of the target vehicle at the terminal point of the road section j is
The order of the bits is
The terminal instantaneous speed of M vehicles
Sorting according to the sequence from small to large, and according to the sorting condition and
determining a vehicle speed of a target vehicle at a road segment j end-point detector
4-3) predicting the travel time of the target vehicle in the road section under the condition of normal road conditions
Calculating the average speed of the target vehicle on the road section j
Predicting the travel time of the target vehicle on the road section j under the condition of normal road conditions
The invention is further configured to: in the step 5), the vehicle road travel time under the emergency traffic event is dynamically corrected, specifically,
5-1) acquisition of influence time of sudden traffic incident on road section
The speed of the target vehicle is less than the threshold speed in the running process of the road section j
When the vehicle is in the emergency, the vehicle is judged to be influenced by the emergency traffic incident so as to reduce the running speed of the vehicle;
when the k time of the instantaneous speed of the target vehicle is less than
Record the time of day
When the instant vehicle speed is greater than k times
Time recording the time
The travel time modification value of the target vehicle in the road section j under the influence of the kth traffic incident is t A(k)_j ，
Wherein K belongs to K, K is 1,2,3, …;
5-2) dynamically correcting the road section travel time of the target vehicle
Combining the j travel time of the road section under the condition of normal road conditions, dynamically correcting the j travel time of the target vehicle on the road section under the influence of the emergency traffic incident
5-3) dynamically correcting the road travel time of the target vehicle
Combined with the travel time of the target vehicle on the section j
Dynamically correcting road travel time of target vehicle affected by sudden traffic incident
Wherein J ∈ J, J ═ 1,2,3, and ….
The foregoing is only an overview of the technical solutions of the present invention, and in order to more clearly understand the technical solutions of the present invention, the present invention is further described below with reference to the accompanying drawings.
The invention has the beneficial effects that:
the method acquires massive traffic flow operation data in real time in the Internet of vehicles environment, can dynamically analyze the change rule of the road vehicle entering and exiting ramps and service area process operation time in the Internet of vehicles environment, and quantizes the influence of sudden traffic accidents on the road vehicle travel time, thereby improving the reliability and robustness of road vehicle travel time prediction in the Internet of vehicles environment and providing reliable high-quality travel service for road travelers.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a vehicle operation analysis road section division diagram of the vehicle networking road not exceeding the set length d in step 1) of the invention;
FIG. 3 is a vehicle operation analysis road section division diagram of the vehicle networking road exceeding the set length d in step 1) of the invention;
FIG. 4 is a schematic diagram of the start and end point detection position of the target vehicle on the road section j in step 2) of the invention;
FIG. 5 is a vehicle operation analysis road segment division diagram of a vehicle networking road according to an embodiment of the present invention;
fig. 6 is a schematic diagram of the start-end point detection position of the target vehicle on the road segment #1 according to the embodiment of the invention.
Detailed Description
The invention is further described with reference to the accompanying drawings.
Example 1:
the invention provides a road travel time prediction method for vehicle networking environment robustness guidance, which comprises the following steps of:
1) vehicle road operation analysis road section under division vehicle networking environment
Setting a length d (generally not more than 10 kilometers) according to a road section to be predicted, and dividing a vehicle road operation analysis section in an Internet of vehicles environment;
in particular to a method for preparing a high-performance nano-silver alloy,
as shown in fig. 2, if the road distance s to be predicted does not exceed the set length d, it is predicted as one link only;
as shown in fig. 3, if the road distance s to be predicted exceeds a set length d, dividing the road to be predicted into J links, setting each link according to the set length d, and respectively predicting each link; here, taking the J-th link as an example, the travel time is predicted, J ∈ J, J ═ 1,2,3, and ….
2) Collecting starting and ending point vehicle running characteristic data of road section
Synchronously detecting a target vehicle at the start and end point of a road operation analysis road section by means of a traffic detector, and acquiring the instantaneous speed of the target vehicle passing through the start and end point of the road section;
in particular to a method for preparing a high-performance nano-silver alloy,
2-1) collecting traffic flow characteristic data at starting point of road section
As shown in FIG. 4, when the target vehicle (the vehicle whose travel time of the link is to be predicted) is a certain distance L (typically 500-
When Mth vehicle is the targetWhen the vehicle passes through the starting point detector of the road section j, the instantaneous speed of the vehicle is recorded as
Finishing the vehicle speed detection, and detecting the instantaneous vehicle speeds of M vehicles at the moment, wherein M belongs to M, and M is 1,2,3 and …;
2-2) collecting traffic flow characteristic data at the end point of the road section
The end point detector of the section j and the starting point detector start detecting at the same time, when the vehicle passes through the end point detector of the section j, the instantaneous speed of the nth vehicle is recorded
And finishes the detection simultaneously with the starting point detector, and the instantaneous speed of N vehicles is detected, wherein N belongs to N, and N is 1,2,3 and ….
3) Determining service area vehicle speed influence correction coefficient of road section
Calculating the shadow correction coefficient of the service area to the travel time of the target vehicle on the road section by detecting the number of vehicles entering and leaving the service area at the starting and ending point detection time interval at the entrance and the exit of the service area;
in particular to a method for preparing a high-performance nano-silver alloy,
if no service area exists in the road section j, the vehicle speed influence correction coefficient k of the service area s_j Taking 1; if the road section j comprises the service area, the influence of the service area on the travel time of the target vehicle on the road section needs to be further considered;
3-1) collecting data of vehicles at the entrance and the exit of the service area
The number of vehicles entering and leaving the service area in the time period is detected by the service area entrance and exit at the same time of detecting the starting point and the end point of the road section j by the detector
3-2) calculating the influence correction coefficient of the speed of the vehicle in the service area of the road section
Calculating the speed influence correction coefficient k of the road section j service area according to the number N of vehicles detected by the road section j end point detector and the data of the vehicles at the entrance and the exit of the service area s_j ，
4) Determining correction coefficient for influence of vehicle speed on entrance ramp of road section
Calculating the influence correction coefficient of the entrance ramp on the travel time of a target vehicle on the road section by detecting the number of vehicles entering the main line road from the entrance ramp at the start and end point detection period of the entrance ramp;
in particular to a method for preparing a high-performance nano-silver alloy,
if no entrance ramp exists in the road section j, the speed influence correction coefficient k of the entrance ramp R(i)_j Taking 1; if the road section j comprises the entrance ramp, the influence of the entrance ramp on the travel time of the target vehicle on the road section needs to be further considered;
4-1) collecting data of vehicles on the ramp of the entrance
Detecting the number Q of vehicles entering the main line road through the ramp in the time period at the entrance ramp while detecting the starting point and the end point of the road section j by using the detector R(i)_j ；
4-2) calculating influence correction coefficient of vehicle speed of road section entrance ramp
Calculating the speed influence correction coefficient k of the ramp at the entrance of the road section j according to the number N of the vehicles detected by the end point detector of the road section j and the data of the vehicles on the ramp at the entrance R(i)_j ，
5) Determining exit ramp vehicle speed influence correction coefficient for road section
Calculating the influence correction coefficient of the exit ramp on the travel time of the target vehicle on the road section by detecting the number of vehicles leaving the main line road from the exit ramp at the start and end point detection period of the exit ramp;
in particular to a method for preparing a high-performance nano-silver alloy,
if no exit ramp is present in the road section j, the speed of the exit ramp affects the correction factor k R(o)_j Taking 1; if an exit ramp is included in road segment j, then it is necessaryFurther considering the influence of the exit ramp on the travel time of the target vehicle on the road section;
5-1) collecting data of vehicles on the exit ramp
Detecting the number Q of vehicles leaving the main line road through the ramp in the time period at the exit ramp while detecting the starting point and the end point of the road section j by using the detector R(o)_j ；
5-2) calculating influence correction coefficient of vehicle speed of road section exit ramp
Calculating the speed influence correction coefficient k of the exit ramp of the road section j according to the number N of vehicles detected by the road section j end point detector and the data of the vehicles on the exit ramp R(o)_j ，
6) Predicting vehicle road section travel time under normal road condition
Calculating the average speed of the target vehicle on a road section by determining the instantaneous speed of the target vehicle at the starting point and the ending point, and predicting the travel time of the road section under the normal road condition;
in particular to a method for preparing a high-performance nano-silver alloy,
6-1) calculating the instantaneous speed sequence of the target vehicle at the starting point of the road section
Will be provided with
According to the sequence from small to large, the instantaneous speed of the target vehicle at the starting point of the road section j is determined
Order of the order of bits
And calculating the ranking proportion of the instantaneous speed of the target vehicle at the starting point of the road section j
6-2) determining the vehicle speed corresponding to the target vehicle with the same position as the starting point in the road section end point detection vehicles
The rank proportion of the corresponding instantaneous speed of the target vehicle at the terminal point of the road section j is
The order of the bits is
Will be provided with
Sorting according to the sequence from small to large, and according to the sorting condition and
determining a vehicle speed of a target vehicle at a road segment j end-point detector
6-3) predicting the travel time of the target vehicle in the road section under the condition of normal road conditions
Calculating the average speed of the target vehicle on the road section j
Predicting the travel time of the target vehicle on the road section j under the condition of normal road conditions
7) Dynamically correcting vehicle road travel time under emergency traffic events
The method comprises the steps of dynamically correcting the travel time of the emergency traffic incident on the road section of the vehicle by recording the running time of the target vehicle, wherein the speed of the target vehicle is less than the threshold speed in the road section running process, so that the travel time of the emergency traffic incident on the road section of the vehicle is dynamically corrected. In particular to a method for preparing a high-performance nano-silver alloy,
7-1) acquisition of influence time of sudden traffic incident on road section
The speed of the target vehicle is less than the threshold speed in the running process of the road section j
When the vehicle is in the emergency, the vehicle is judged to be influenced by the emergency traffic incident so as to reduce the running speed of the vehicle;
when the k time of the instantaneous speed of the target vehicle is less than
Recording time
When its instantaneous vehicle speed is kth greater than
Recording time
The travel time modification value of the target vehicle in the road section j under the influence of the kth traffic incident is t A(k)_j ，
Wherein K belongs to K, K is 1,2,3, …;
7-2) dynamically correcting the road section travel time of the target vehicle
Combining the j travel time of the road section under the condition of normal road conditions, dynamically correcting the j travel time of the target vehicle on the road section under the influence of the emergency traffic incident
7-3) dynamically correcting the road travel time of the target vehicle
Combined with the travel time of the target vehicle on the section j
Dynamically correcting road travel time of target vehicle affected by sudden traffic event
Example 2:
the internet-of-vehicles environment robustness oriented road travel time prediction method of the present invention is further illustrated by an example,
according to the specific steps of the road travel time prediction method for the environment robustness guidance of the Internet of vehicles, the road travel time is predicted.
S1: vehicle road operation analysis road section under division vehicle networking environment
According to the investigation, the distance of the road to be predicted is 30km, which exceeds the set length by 10km, and as shown in fig. 5, the distance of the road to be predicted is divided into 3 road sections, which are road sections #1, #2 and #3, respectively, each road section has a length of 10km, the road section #1 has an entrance ramp, the road section #2 has an entrance ramp, an exit ramp and a service area, and the road section #3 has an exit ramp.
S2: collecting starting and ending point vehicle running characteristic data of road section
S21: as shown in fig. 6, taking the link #1 as an example, the start point detector starts detecting the vehicle instantaneous speed when the target vehicle is 1km away from the link start point detector, and ends the detection when the target vehicle passes the start point detector. The detection time is selected from the working day early peak time (8:00-9:00), and the starting point vehicle operation characteristic data of the road section is shown in the table 1. The target vehicle is the 23 rd vehicle on the link #1, the 26 th vehicle on the link #2, and the 30 th vehicle on the link # 3.
Table 1 road section starting point vehicle running characteristic data table
Note: "/" indicates that this vehicle does not exist for that road segment.
S22: the end point detector starts detecting the instantaneous speed of the vehicle at the same time as the start point detector and ends the detection at the same time, and the end point vehicle operation characteristic data of the road section is shown in table 2.
TABLE 2 road segment end point vehicle operating characteristic data sheet
Note: "/" indicates that this vehicle is not present on the road segment.
S3: determining service area vehicle speed influence correction coefficient of road section
S31: at the same time of the detection of the starting point and the end point of the road section #2, the service area entrance and exit respectively detect the number of 4 vehicles entering the service area in the period, and the number of vehicles leaving the service area is 3.
S32: according to the number of vehicles detected by the road section #2 end point detector and the service area import and export vehicle data, the calculation result of the service area vehicle speed influence correction coefficient of the road section is as follows:
s4: determining an entry ramp vehicle speed impact correction factor for a road segment
S41: the number of vehicles entering the main line road through the ramp in the time period detected at the entrance ramp while the end point detectors detect the links #1, #2 is shown in table 3.
S42: the calculation results of the vehicle speed influence correction coefficients of the road section on the entrance ramp according to the number of vehicles detected by the road section end point detector and the data of the vehicles on the entrance ramp are shown in table 3.
TABLE 3 vehicle speed influence correction coefficient of road section entrance ramp
| Road segment numbering | Number of vehicles on entrance ramp | Number of vehicles at destination | Service area vehicle speed influence correction coefficient |
| Road segment #1 | 4 | 25 | 0.86 |
| Road segment #2 | 5 | 24 | 0.83 |
S5: determining exit ramp vehicle speed influence correction coefficient for road section
S51: the number of vehicles entering the main line road through the ramp in the time period detected at the exit ramp while the end point detectors detect the links #2, #3 is shown in table 4.
S52: the calculation results of the vehicle speed influence correction coefficient of the exit ramp of the road section according to the number of vehicles detected by the road section end point detector and the exit ramp vehicle data are shown in table 4.
TABLE 4 vehicle speed influence correction coefficient of road section exit ramp
| Road segment numbering | Number of vehicles on exit ramp | Number of vehicles at destination | Service area vehicle speed influence correction coefficient |
| Road segment #2 | 2 | 24 | 1.08 |
| Road segment #3 | 2 | 27 | 1.07 |
S6: predicting vehicle road section travel time under normal road condition
S61: the instantaneous vehicle speeds at the start point positions of the road sections are ranked as shown in table 5, and the ranking order and the ranking ratio of the instantaneous speed of the target vehicle at the start point of the road section are obtained as shown in table 6, for example.
TABLE 5 detected vehicle speed sequence of road segment starting point location
Note: "/" indicates that this vehicle is not present on the road segment.
TABLE 6 speed ranking order and ranking proportion of target vehicles at starting point position of road section
| Road segment numbering | Rank order of vehicle speed | Vehicle speed ranking proportion |
| Road segment #1 | 15 | 0.65 |
| Road segment #2 | 10 | 0.38 |
| Road segment #3 | 12 | 0.40 |
S62: the instantaneous vehicle speeds at the road section end point positions are ranked as shown in a table 7, and ranking proportion and ranking rank of the instantaneous vehicle speeds corresponding to the target vehicle at the road section end point are calculated by combining various speed influence correction coefficients, so that the vehicle speeds of the target vehicle at the road section end point detector are obtained and are shown in a table 8.
TABLE 7 sequencing of vehicle speed for detection of link end position
Note: "/" indicates that this vehicle is not present on the road segment.
TABLE 8 speed of target vehicle at end of road segment Detector
S63: the average speed of the target vehicle on the road section is calculated according to the speed of the target vehicle at the starting and ending points, and the travel time of the target vehicle on the road section under the normal road condition is predicted as shown in table 9.
TABLE 9 travel time of target vehicle on road segment
S7: dynamically correcting vehicle road travel time under emergency traffic events
S71: the threshold vehicle speed of the target vehicle during the road segment operation is calculated in conjunction with the speed of the target vehicle at the start and end of the road segment as shown in table 10.
TABLE 10 threshold vehicle speeds during vehicle operation over a road segment
| Road segment numbering | Threshold vehicle speed (km/h) |
| Road segment #1 | 85.85 |
| Road segment #2 | 83.30 |
| Road segment #3 | 86.70 |
According to the operation time that the vehicle speed of the target vehicle is less than the threshold vehicle speed in the road section operation process, it can be known that the target vehicle encounters 1 time of the emergency traffic event in the road section #2, and the travel time correction value of the target vehicle in the road section under the influence of the traffic event is obtained as shown in table 11.
TABLE 11 travel time correction values for a target vehicle in a road segment
S72: combining the road section travel time under the normal road condition, dynamically correcting the travel time of the target vehicle at the road section, which is influenced by the emergency traffic incident, and obtaining the following result:
link # 1:
segment # 2:
segment # 3:
s73: and dynamically correcting the road travel time of the target vehicle influenced by the sudden traffic incident by combining the travel time of the target vehicle on the road section, wherein the calculation result is as follows:
Claims (4)
1. a road travel time prediction method for vehicle networking environment robustness guidance is characterized by comprising the following steps:
1) vehicle road operation analysis road section under division vehicle networking environment
Setting a length d according to a road section to be predicted, and dividing a vehicle road operation analysis section in the Internet of vehicles environment;
2) collecting starting and ending point vehicle running characteristic data of road section
Synchronously detecting a target vehicle at the start and end point of a road operation analysis road section by means of a traffic detector, and acquiring the instantaneous speed of the target vehicle passing through the start and end point of the road section;
3) determining influence correction coefficients of service area vehicle speed, entrance ramp vehicle speed and exit ramp vehicle speed of road section
Calculating an influence correction coefficient of the service area on the travel time of the target vehicle on the road section by detecting the number of vehicles entering and leaving the service area at the starting and ending point detection time period at the entrance and exit of the service area;
calculating the influence correction coefficient of the entrance ramp on the travel time of a target vehicle on the road section by detecting the number of vehicles entering the main line road from the entrance ramp at the start and end point detection period of the entrance ramp;
calculating an influence correction coefficient of the exit ramp on the travel time of a target vehicle on the road section by detecting the number of vehicles leaving the main line road from the exit ramp at the start and end point detection period of the exit ramp;
4) predicting vehicle road section travel time under normal road condition
Calculating the average speed of the target vehicle on a road section by determining the instantaneous speed of the target vehicle at the starting point and the ending point, and predicting the travel time of the road section under the normal road condition;
5) dynamically correcting vehicle road travel time under emergency traffic events
The method comprises the steps that the running time of a target vehicle, the speed of which is less than a threshold speed in the running process of a road section, is recorded, and the travel time of the emergency traffic incident on the road section of the vehicle is dynamically corrected, so that the travel time of the emergency traffic incident on the road of the vehicle is dynamically corrected;
in the step 3), determining a vehicle speed influence correction coefficient of a service area of the road section, specifically comprising the following steps,
if no service area exists in the road section j, the vehicle speed influence correction coefficient k of the service area s_j Taking 1; if the road section j comprises the service area, further considering the influence of the service area on the travel time of the target vehicle on the road section;
3-1a) collecting data of vehicles at entrance and exit of service area
The number of vehicles entering and leaving the service area in the time period is detected by the service area entrance and exit at the same time of detecting the starting point and the end point of the road section j by the detector
3-2a) calculating the influence correction coefficient of the speed of the vehicle in the service area of the road section
Number of vehicles detected by end point detector of link jN and data of vehicles at the entrance and the exit of the service area, and calculating a speed influence correction coefficient k of the road section j in the service area s_j ，
In the step 3), determining the correction coefficient of the influence of the vehicle speed of the entrance ramp of the road section, specifically comprising the following steps,
if no entrance ramp exists in the road section j, the influence of the speed of the entrance ramp on the correction coefficient k R(i)_j Taking 1; if the road section j comprises the entrance ramp, further considering the influence of the entrance ramp on the travel time of the target vehicle on the road section;
3-1b) collecting data of vehicles on the ramp of the entrance
Detecting the number Q of vehicles entering the main line road through the ramp in the time period at the entrance ramp while detecting the starting point and the end point of the road section j by using the detector R(i)_j ；
3-2b) calculating influence correction coefficient of vehicle speed of road section entrance ramp
Calculating the speed influence correction coefficient k of the ramp at the entrance of the road section j according to the number N of the vehicles detected by the end point detector of the road section j and the data of the vehicles on the ramp at the entrance R(i)_j ，
In the step 3), determining the correction coefficient of the influence of the speed of the exit ramp on the road section, specifically comprising the following steps,
if no exit ramp is present in the road section j, the speed of the exit ramp affects the correction factor k R(o)_j Taking 1; if the road section j comprises the exit ramp, further considering the influence of the exit ramp on the travel time of the target vehicle on the road section;
3-1c) collecting exit ramp vehicle data
Detecting the number Q of vehicles leaving the main line road through the ramp in the time period at the exit ramp while detecting the starting point and the end point of the road section j by using the detector R(o)_j ；
3-2c) calculating influence correction coefficient of vehicle speed of road section exit ramp
Calculating the speed influence correction coefficient k of the exit ramp of the road section j according to the number N of vehicles detected by the road section j end point detector and the data of the vehicles on the exit ramp R(o)_j ，
In the step 4), the travel time of the vehicle road section under the normal road condition is predicted, and the specific steps are,
4-1) calculating the instantaneous speed sequence of the target vehicle at the starting point of the road section
The starting point instantaneous speed of M vehicles
According to the sequence from small to large, the instantaneous speed of the target vehicle at the starting point of the road section j is determined
Order of the order of bits
And calculating the ranking proportion of the instantaneous speed of the target vehicle at the starting point of the road section j
4-2) determining the speed of the target vehicle corresponding to the same level as the starting point in the road section end point detection vehicles
The ranking proportion of the corresponding instantaneous speed of the target vehicle at the terminal point of the road section j is
The order of the bits is
Wherein k is s_j Correction coefficient, k, for the influence of the vehicle speed in the service area R(i)_j Correction coefficient, k, for the effects of the speed on the ramp R(o)_j Correcting coefficients for the influence of the speed of the exit ramp;
the terminal instantaneous speed of M vehicles
Sorting according to the sequence from small to large, and according to the sorting condition and
determining a vehicle speed of a target vehicle at a road segment j end-point detector
4-3) predicting the travel time of the target vehicle in the road section under the condition of normal road conditions
Calculating the average speed of the target vehicle on the road section j
Target vehicle in road section j under normal road condition predictionTravel time of vehicle
Wherein d is the set length of the road section needing to be predicted.
2. The vehicle networking environment robustness oriented road travel time prediction method according to claim 1, characterized by: in the step 1), vehicle road operation analysis road sections in the vehicle networking environment are divided, and the specific steps are as follows,
if the road distance s needing to be predicted does not exceed the set length d, predicting the road distance s as a road section;
if the road distance s to be predicted exceeds the set length d, dividing the road to be predicted into J road sections, setting each road section according to the set length d, and predicting each road section respectively; each segment is defined as J, J ∈ J, J ═ 1,2,3, and ….
3. The internet of vehicles environment robustness oriented road travel time prediction method according to claim 1, characterized by: in the step 2), the operation characteristic data of the vehicles at the starting and ending points of the road section is collected, and the specific steps are,
2-1) collecting traffic flow characteristic data at starting point of road section
When the target vehicle is at a distance L from the starting point detector of the road section j, the detection is started by the traffic detector of the starting point of the road section, and the instantaneous speed of the vehicle passing through the starting point of the mth vehicle of the detector is
When the Mth vehicle, namely the target vehicle passes through the starting point detector of the section j, the instantaneous speed of the starting point is recorded as
Finishing vehicle speed detection, wherein the starting point instantaneous vehicle speeds of M vehicles are detected at the moment, wherein M belongs to M, and M is 1,2,3 and …;
2-2) collecting traffic flow characteristic data at the end point of the road section
The end point detector of the section j and the starting point detector start detecting at the same time, when the vehicle passes through the end point detector of the section j, the end point instantaneous vehicle speed of the nth vehicle is recorded
And finishes the detection simultaneously with the starting point detector, and the terminal instantaneous vehicle speeds of N vehicles are detected, wherein N belongs to N, and N is 1,2,3 and ….
4. The vehicle networking environment robustness oriented road travel time prediction method according to claim 1, characterized by: in the step 5), the vehicle road travel time under the emergency traffic incident is dynamically corrected, and the specific steps are,
5-1) acquisition of influence time of sudden traffic incident on road section
The speed of the target vehicle in the running process of the road section j is less than the threshold speed
When the vehicle is in the emergency, the vehicle is judged to be influenced by the emergency traffic incident so as to reduce the running speed of the vehicle;
when the instant vehicle speed of the target vehicle is lower than the kth time
Record the time of day
When the instant vehicle speed is greater than k times
Record the time of day
The travel time modification value of the target vehicle in the road section j under the influence of the kth traffic incident is t A(k)_j ，
Wherein K belongs to K, K is 1,2,3, …;
5-2) dynamically correcting the road section travel time of the target vehicle
Combining the travel time of the road section j under the condition of normal road conditions, dynamically correcting the travel time of the target vehicle influenced by the emergency traffic incident on the road section j
5-3) dynamically correcting the road travel time of the target vehicle
Combined with the travel time of the target vehicle on the section j
Dynamically correcting road travel time of target vehicle affected by sudden traffic incident
Wherein J ∈ J, J ═ 1,2,3, and ….
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