CN113066286B - Method and device for judging vehicle running risk road sections of vehicle-road cooperative road network - Google Patents

Method and device for judging vehicle running risk road sections of vehicle-road cooperative road network Download PDF

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CN113066286B
CN113066286B CN202110290602.5A CN202110290602A CN113066286B CN 113066286 B CN113066286 B CN 113066286B CN 202110290602 A CN202110290602 A CN 202110290602A CN 113066286 B CN113066286 B CN 113066286B
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CN113066286A (en
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李锐
诸葛雪玉
薛鑫
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Hohai University HHU
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation

Abstract

The invention discloses a method and a device for judging running risk road sections of vehicles in a vehicle-road cooperation road network, which are characterized in that the average running time of the vehicles in each road section of the vehicle-road cooperation road network and the average stop time of buses at a stop are calculated according to collected running characteristic data of the vehicle-road cooperation road network; calculating to obtain the standard deviation of the whole-time running time of the vehicles on the road sections according to the average running time of the vehicles on the road sections and the average stop time of the buses at the stop; calculating the vehicle space-time operation risk degree of each road section of the vehicle-road cooperative road network according to the whole-time operation time standard deviation of the vehicles on the road sections and by combining the length of the road sections; and obtaining the vehicle and road cooperation road network vehicle operation risk road sections according to the risk degree sequencing result. The method can accurately and quickly judge the time-space reliable operation risk road sections of vehicles and roads in cooperation with the road network vehicles, thereby being beneficial to improving the operation efficiency of the urban road network in the environment of vehicle-road cooperation.

Description

Method and device for judging vehicle running risk road sections of vehicle-road cooperative road network
Technical Field
The invention relates to a road network vehicle operation risk road section determination method, in particular to a road network vehicle operation risk road section determination method and device considering vehicle-road cooperation, and belongs to the technical field of urban intelligent traffic management and control.
Background
Along with the development of urban traffic, the requirements of people on a traffic network system are increasingly increased. Unreliable road network operation will reduce the operation efficiency of traffic network system, reduce the accuracy of traffic flow prediction, influence the deployment of traffic network resources. How to improve the road network operation reliability firstly needs to find out the risk road sections of the road network vehicle operation. Since traffic flows in urban road networks have both temporal dynamics and spatial correlations, studying the risk of vehicle operation in road networks will take into account both temporal and spatial aspects.
In the prior art, the acquired traffic characteristic data is not high in precision, and the accuracy of judging the running risk road sections of the urban road network is not high, so that the identification precision of the risk road sections is required to be improved.
Disclosure of Invention
The invention mainly aims to overcome the defects in the prior art, provides a method and a device for judging vehicle running risk road sections of a vehicle-road cooperative road network, and solves the problem of low accuracy in judging running risk road sections of an urban road network.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a method for judging vehicle operation risk road sections of a vehicle-road cooperative road network comprises the following steps:
calculating the average running time of vehicles in each road section of the vehicle-road cooperative road network and the average stop time of the buses at the stop station according to the collected running characteristic data of the vehicle-road cooperative road network;
calculating to obtain the standard deviation of the whole-time running time of the vehicles on the road sections according to the average running time of the vehicles on the road sections and the average stop time of the buses at the stop;
calculating the vehicle space-time operation risk degree of each road section of the vehicle-road cooperative road network according to the whole-time operation time standard deviation of the vehicles on the road sections and by combining the length of the road sections;
and obtaining the vehicle and road cooperation road network vehicle operation risk road sections according to the risk degree sequencing result.
Further, calculating the average running time of vehicles on each road segment of the vehicle-road cooperative road network and the average stop time of the buses at the stop according to the collected running characteristic data of the vehicle-road cooperative road network comprises the following steps:
calculating the average time of the vehicle running on the r-th lane of the road section k in the y-th time period of the day according to the running characteristic data of the front W-1 week vehicle road and the road network
Figure BDA0002982387190000021
Figure BDA0002982387190000022
Calculate average transit time for a vehicle traveling on road section k for the y-th epoch of day
Figure BDA0002982387190000023
Figure BDA0002982387190000024
Calculating the average stop time of the bus of the g line running on the section k in the y period of the day
Figure BDA0002982387190000025
Figure BDA0002982387190000026
Wherein the content of the first and second substances,
Figure BDA0002982387190000027
respectively representing the operation time of the ith un-lane-changed vehicle of the r-th lane of the road section k and the stop time of the h-th bus of the stop bus line g of the road section k in the y-th time period on the d-th day of the w week;
Figure BDA0002982387190000028
respectively representing the number of vehicles which do not change lanes in the r-th lane and the bus flow of the bus route g in the road section on the y-th time period k on the d-th day of the w week; h is the number of buses of the stop bus route g on the road section k at the y-th time period of the day; rkIs the total number of lanes for road segment k; i is the number of all vehicles which do not change lanes on the r-th lane of the road section k in the y-th time period of the day.
Further, the standard deviation of the running time of the vehicles on the road section in the whole time period is calculated, and the method comprises the following steps:
and calculating the standard deviation of the running time of vehicles on the road section in the whole time period, wherein the standard deviation of the running time of the vehicles on the road section in the whole time period is calculated according to the collected running characteristic data of the W-th week vehicle and the road network.
Further, the standard deviation of the running time of the vehicle which does not change lanes in each lane of the road section in a certain time period is as follows:
Figure BDA0002982387190000031
Figure BDA0002982387190000032
a road segment operating time standard deviation of an r-th lane-change-free vehicle representing a road segment k in the y-th time period of the day;
the standard deviation of the running time of the lane changing vehicles in each lane of the road section in a certain period is as follows:
Figure BDA0002982387190000033
Figure BDA0002982387190000034
the standard deviation of the road section operation time of the road section changing vehicle of the road section k in the y time period of the day; j is the number of vehicles changing the road on the road section k in the y-th time period of the day;
the standard deviation of the running time of the public transport vehicles on each lane of the road section in a certain time period is as follows:
Figure BDA0002982387190000035
Figure BDA0002982387190000036
the standard deviation of the section operation time of the bus of the g line of the section k in the y time period of the day;
the standard deviation of the running time of all vehicles in each lane of the road section in a certain period is as follows:
Figure BDA0002982387190000041
Figure BDA0002982387190000042
segment travel time standard deviations for all vehicles for segment k in the y-th time period of day;
Figure BDA0002982387190000043
indicates the number of r lane-change vehicles in the y time section k on the day of the W week,
Figure BDA0002982387190000044
indicates day d of week wThe travel time of the h bus of the stop bus route g of the road section k in the y time period;
Figure BDA0002982387190000045
the bus flow of a road section bus line g of a road section k at the y-th time period on the day of the W week;
the standard deviation of all vehicle running time in the section k in the whole time period is as follows:
Figure BDA0002982387190000046
wherein Y is the total number of time periods, GkIs the total number of links for segment k.
Further, the vehicle-road cooperative road network each road section vehicle space-time operation risk degree is as follows:
Figure BDA0002982387190000047
lkis the length of the link k.
On the other hand, a device for determining a vehicle operation risk road segment of a vehicle-road-network cooperation road network comprises:
the time standard deviation calculation module is used for calculating the average running time of vehicles on each road section of the vehicle-road cooperative road network and the average stop time of buses at a stop according to the collected vehicle-road cooperative road network running characteristic data; calculating to obtain the standard deviation of the whole-time running time of the vehicles on the road section;
the risk degree calculation module is used for calculating the time-space operation risk degree of vehicles on each road section of the vehicle-road cooperative road network according to the whole-time operation time standard deviation of the vehicles on the road sections and by combining the length of the road sections;
and the risk road section judging module is used for obtaining the vehicle road and road network vehicle operation risk road section in cooperation with the vehicle according to the risk degree sequencing result.
Further, calculating the average running time of vehicles on each road segment of the vehicle-road cooperative road network and the average stop time of the buses at the stop according to the collected running characteristic data of the vehicle-road cooperative road network comprises the following steps:
calculating the average time of the vehicle running on the r-th lane of the road section k in the y-th time period of the day according to the running characteristic data of the front W-1 week vehicle road and the road network
Figure BDA0002982387190000051
Figure BDA0002982387190000052
Calculate the average transit time for the vehicle to travel on section k during the y-th time period of day d
Figure BDA0002982387190000053
Figure BDA0002982387190000054
Calculating the average stop time of the bus of the g line running on the section k in the y period of the day
Figure BDA0002982387190000055
Figure BDA0002982387190000056
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002982387190000057
respectively representing the operation time of the ith un-lane-changed vehicle of the r-th lane of the road section k and the stop time of the h-th bus of the stop bus line g of the road section k in the y-th time period on the d-th day of the w week;
Figure BDA0002982387190000058
respectively representing the number of vehicles which do not change lanes in the r-th lane and the bus flow of the bus route g in the road section on the y-th time period k on the d-th day of the w week; the y time period on the way with H being dThe number of buses of the stop bus route g of the section k; rkIs the total number of lanes for road segment k; i is the number of all vehicles which do not change lanes on the r-th lane of the road section k in the y-th time period of the day;
further, the standard deviation of the running time of the vehicles on the road section in the whole time period is calculated, and the method comprises the following steps:
and calculating the standard deviation of the running time of vehicles on the road section in the whole time period, wherein the standard deviation of the running time of the vehicles on the road section in the whole time period is calculated according to the collected running characteristic data of the W-th week vehicle and the road network.
Further, the standard deviation of the running time of the vehicle which does not change lanes in each lane of the road section in a certain time period is as follows:
Figure BDA0002982387190000061
Figure BDA0002982387190000062
a road segment operating time standard deviation of an r-th lane-change-free vehicle representing a road segment k in the y-th time period of the day;
the standard deviation of the running time of the lane changing vehicles in each lane of the road section in a certain period is as follows:
Figure BDA0002982387190000063
Figure BDA0002982387190000064
the standard deviation of the road section operation time of the road section changing vehicle of the road section k in the y time period of the day; j is the number of vehicles changing the road on the road section k in the y-th time period of the day;
the standard deviation of the running time of the bus in each lane of the road section in a certain time period is as follows:
Figure BDA0002982387190000065
Figure BDA0002982387190000066
the standard deviation of the section operation time of the bus of the g line of the section k in the y time period of the day;
the standard deviation of the running time of all vehicles in each lane of the road section in a certain period is as follows:
Figure BDA0002982387190000067
Figure BDA0002982387190000068
segment travel time standard deviations for all vehicles for segment k in the y-th time period of day;
Figure BDA0002982387190000071
indicates the number of r lane-change vehicles in the y time section k on the day of the W week,
Figure BDA0002982387190000072
representing the travel time of the h bus of the stop bus route g of the road section k in the y time period on the d day of the w week;
Figure BDA0002982387190000073
the bus flow of a road section bus line g of a road section k at the y-th time period on the day of the W week;
the standard deviation of all vehicle running time in the section k in the whole time period is as follows:
Figure BDA0002982387190000074
wherein Y is the total number of time periods, GkIs the total number of links for segment k.
Further, the vehicle-road cooperative road network each road section vehicle space-time operation risk degree is as follows:
Figure BDA0002982387190000075
lkis the length of the link k.
The invention has the beneficial effects that: the method acquires massive traffic characteristic space-time data in the vehicle-road cooperative environment, can analyze the operation rule of the road network vehicles in the vehicle-road cooperative environment, considers the influence of space-time operation to research different traffic flow characteristics, and judges the urban road network vehicle operation risk road section on the basis of the analysis, thereby improving the accuracy of judging the urban road network vehicle space-time reliable operation risk road section, optimizing the road network vehicle space-time reliable operation risk road section, and being beneficial to improving the road network reliability.
Drawings
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
FIG. 2 is a road segment division diagram of a vehicle-road cooperative road network according to an embodiment of the present invention;
FIG. 3 is a road section diagram for analyzing the spatiotemporal reliable operation of vehicles in cooperation with vehicles in road network 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 method for judging vehicle operation risk road sections of a vehicle-road cooperative road network, which comprises the following steps as shown in figure 1:
step 1, dividing a vehicle road and a road network in cooperation with a vehicle operation analysis road section;
dividing a road network into a plurality of road sections by taking each road intersection in the road network as a boundary and dividing the running direction of the road;
the method comprises the following specific steps:
as shown in fig. 2, each intersection of a road network is used as a boundary, the road running directions are separated, and the road network is divided into 1, 2, 3, …, K road segments, each of which may include a construction area, a bus stop and other road geometric scenes.
Step 2, constructing a vehicle-road cooperative road network operation characteristic data set;
the method comprises the following steps that (1) a vehicle-road cooperative road network operation characteristic data set comprises characteristics of length of each road section, number of lanes, number of public roads, traffic flow, operation time and the like, and W-cycle data (generally, an integer not less than 4) needs to be continuously collected;
in particular to a method for preparing a high-performance nano-silver alloy,
construction of vehicle-road cooperative road network operation characteristic data set Dnet＝{Dl(1),Dl(2),…Dl(k)And, a running characteristic data set D for each road sectionl(k)＝{lk,Rk,Gk,Qk,TkIn the formula, lk、Rk、GkRespectively representing the road length, the number of lanes and the number of buses passing through and stopping of the kth road section; various traffic flow characteristic data sets of road section k
Figure BDA0002982387190000081
In the formula
Figure BDA0002982387190000082
The method comprises the steps of respectively representing the number of vehicles which do not change lanes in the r-th lane, the number of vehicles which change lanes in the road section k in the Y-th time period on the d-th day (day) of the w week, and the bus flow of the road section bus line g, wherein statistics is needed; vehicle travel time data set for road section k
Figure BDA0002982387190000091
In the formula
Figure BDA0002982387190000092
Respectively representing the ith un-lane-changed vehicle running time of the r-th lane of the road section k, the jth lane-changed vehicle running time of the road section k, the h bus running time and the stop time of the stop bus line g of the road section k in the ith time interval of the d-th day (day) of the w week.
Step 3, calculating the average running time of vehicles on each road section of the vehicle-road cooperative road network and the average stop time of the buses at the stop according to the collected running characteristic data of the vehicle-road cooperative road network;
calculating the average running time of vehicles on each road section of the vehicle-road cooperative road network and the average stop time of the buses at the stop station according to the collected data of the previous (W-1) week;
in particular to a method for preparing a high-performance nano-silver alloy,
3-1) calculating the average passing time of vehicles on the road section;
from the previous (W-1) week data, the average time for the vehicle to travel on the r-th lane of the road segment k for the y-th time period of the day can be calculated
Figure BDA0002982387190000093
Figure BDA0002982387190000094
Wherein I is the number of all vehicles which do not change lanes in the r-th lane of the road section k in the y-th time period of the day, and I belongs to I;
then the average transit time of the vehicle running on the section k in the y-th period of the day d is calculated
Figure BDA0002982387190000095
Figure BDA0002982387190000096
RkIs the total number of lanes on a road segment;
3-2) calculating the average stop time of the buses on different lines at the stop;
according to the previous (W-1) week data, the average stop time of the bus of the g line running on the section k at the y time period of the day at the stop can be calculated
Figure BDA0002982387190000101
Figure BDA0002982387190000102
H is the number of buses of the stop bus line g on the road section k at the y-th time period of the day, and H belongs to H;
step 4, obtaining the whole-time-period running time standard deviation of the road vehicles according to the collected vehicle and road cooperation road network running characteristic data, and reflecting the time-space reliable running characteristics of the road vehicles;
calculating the standard deviation of the running time of the vehicles which do not change lanes, the vehicles which change lanes, the buses and all the vehicles in all lanes in the road section in a certain specific time period according to the collected Wth week data, calculating the standard deviation of the running time of the vehicles in the road section in the whole time period, and reflecting the time-space reliable running characteristics of the vehicles in the road section;
in particular to a method for preparing a high-purity sodium chloride solution,
4-1) obtaining the standard deviation of the running time of the vehicles in each lane of the road section in a certain specific time period without changing the lane according to the running characteristic data of the vehicles and the road cooperated with the road network;
for the non-lane-changing vehicles on different lanes on different road sections, the running characteristics of the non-lane-changing vehicles in different time periods are different, so the standard deviation of the road section running time of the r-th lane non-lane-changing vehicle on the road section k in the y-th time period of the day is utilized
Figure BDA0002982387190000103
To reflect its spatio-temporal reliable operation characteristics over a certain period of time,
Figure BDA0002982387190000104
4-2) obtaining the standard deviation of the running time of the road-section vehicle in a certain specific time period according to the running characteristic data of the vehicle and the road cooperated with the road network;
for the lane-changing vehicles on different road sections, the running characteristics of the lane-changing vehicles in different time periods are different, so the standard deviation of the road section running time of the lane-changing vehicles using the road section k in the y time period of the day
Figure BDA0002982387190000111
To reflect its time in a specific periodThe characteristics of the air-to-air reliable operation,
Figure BDA0002982387190000112
j is the number of lane changing vehicles on the road section k in the y-th time period of the day, and J belongs to J;
4-3) obtaining the standard deviation of the running time of the bus on the road section at a certain time period according to the running characteristic data of the bus road and the road network;
for buses with different road sections and different lines, the running characteristics of the buses in different time periods are different, so the standard deviation of the road section running time of the bus using the g line of the road section k in the y time period of the day
Figure BDA0002982387190000113
To reflect its spatio-temporal reliable operating characteristics over a certain period of time,
Figure BDA0002982387190000114
4-4) obtaining the standard deviation of the running time of all vehicles in a certain time period of the road section according to the standard deviation of the running time of the vehicles which do not change lanes, the vehicles which change lanes and the buses in the certain time period of the road section;
because the occupation ratios of different types of vehicles in the road section are different, the occupation ratios of various types of vehicles in the road section are comprehensively considered, and the standard deviation of the road section operation time of all vehicles in the y time section of the day is utilized
Figure BDA0002982387190000115
To reflect its spatio-temporal reliable operating characteristics over a certain period of time,
Figure BDA0002982387190000116
4-5) obtaining the running time standard deviation of all vehicles on the road section in the whole time period according to the running time standard deviation of all vehicles on the road section in a certain specific time period;
since the vehicle operation characteristics are different on different days of the week and at different time periods of the day, the time factor influence needs to be comprehensively considered, and the standard deviation delta of the section operation time of all vehicles of the section k in the time of one week is utilizedkTo reflect the space-time reliable operation characteristics of the whole time period,
Figure BDA0002982387190000121
step 5, calculating the space-time operation risk degree of vehicles on each road section of the vehicle-road cooperative road network according to the whole-time operation time standard deviation of the vehicles on the road section and by combining the length of the road section;
vehicle-road cooperative road network each road section vehicle space-time operation risk degree EkComprises the following steps:
Figure BDA0002982387190000122
and 6, judging to obtain the vehicle operation risk road sections of the vehicle-road cooperation road network according to the vehicle space-time operation risk degree sequencing result of each road section of the vehicle-road cooperation road network.
Sequencing the vehicle space-time operation risk degrees of the vehicles on the road network and the road network in cooperation with each road section from large to small, wherein the front alpha percent (generally 10-20 percent) of the road section is the road section with the vehicle operation risk of the road network;
in particular to a method for preparing a high-performance nano-silver alloy,
according to the vehicle-road cooperative road network each road section vehicle space-time operation risk degree EkThe road sections are sorted from large to small, and the first alpha percent (generally 10-20 percent) of the road sections are the road network vehicle operation risk road sections, so that the road network vehicle operation risk road sections can be optimized, and the reliability of a road network is improved.
For the risk road section, the operation risk degree of the risk road section can be reduced by traffic control measures (such as optimizing an intersection signal timing scheme, adjusting a road lane setting scheme, strengthening road intelligent transformation and the like), so that the road traffic operation efficiency is improved.
Example 2:
a vehicle road cooperation road network vehicle operation risk road section determination device comprises:
the time standard deviation calculation module is used for calculating the average running time of vehicles on each road section of the vehicle-road cooperative road network and the average stop time of buses at a stop according to the collected vehicle-road cooperative road network running characteristic data; calculating to obtain the standard deviation of the whole-time running time of the vehicles on the road section;
the risk degree calculation module is used for calculating the time-space operation risk degree of vehicles on each road section of the vehicle-road cooperative road network according to the whole-time operation time standard deviation of the vehicles on the road sections and by combining the length of the road sections;
and the risk road section judging module is used for obtaining the vehicle road and road network vehicle operation risk road section in cooperation with the vehicle according to the risk degree sequencing result.
Further, calculating the average running time of vehicles on each road segment of the vehicle-road cooperative road network and the average stop time of the buses at the stop according to the collected running characteristic data of the vehicle-road cooperative road network comprises the following steps:
calculating the average time of the vehicle running on the r-th lane of the road section k in the y-th time period of the day according to the running characteristic data of the front W-1 week vehicle road and the road network
Figure BDA0002982387190000131
Figure BDA0002982387190000132
Calculate the average transit time for the vehicle to travel on section k during the y-th time period of day d
Figure BDA0002982387190000133
Figure BDA0002982387190000134
Calculating the average stop time of the bus of the g line running on the section k in the y period of the day
Figure BDA0002982387190000135
Figure BDA0002982387190000136
Wherein the content of the first and second substances,
Figure BDA0002982387190000137
respectively representing the operation time of the ith un-lane-changed vehicle of the r-th lane of the road section k and the stop time of the h-th bus of the stop bus line g of the road section k in the y-th time period on the d-th day of the w week;
Figure BDA0002982387190000141
respectively representing the number of vehicles which do not change lanes in the r-th lane and the bus flow of the bus route g in the road section on the y-th time period k on the d-th day of the w week; h is the number of buses of the stop bus route g on the road section k at the y-th time period of the day; rkIs the total number of lanes for road segment k; i is the number of all vehicles which do not change lanes on the r-th lane of the road section k in the y-th time period of the day;
calculating to obtain the standard deviation of the running time of the vehicles on the road section in the whole time period, wherein the method comprises the following steps:
and calculating the standard deviation of the running time of vehicles on the road section in the whole time period, wherein the standard deviation of the running time of the vehicles on the road section in the whole time period is calculated according to the collected running characteristic data of the W-th week vehicle and the road network.
Further, the standard deviation of the running time of the vehicle which does not change lanes in each lane of the road section in a certain time period is as follows:
Figure BDA0002982387190000142
Figure BDA0002982387190000143
when the r-th lane of the road section k in the y-th period of the day runs, the road section of the vehicle is not changedThe standard deviation between;
the standard deviation of the running time of the lane changing vehicles in each lane of the road section in a certain period is as follows:
Figure BDA0002982387190000144
Figure BDA0002982387190000145
the standard deviation of the road section operation time of the road section changing vehicle of the road section k in the y time period of the day; j is the number of vehicles changing the road on the road section k in the y-th time period of the day;
the standard deviation of the running time of the public transport vehicles on each lane of the road section in a certain time period is as follows:
Figure BDA0002982387190000151
Figure BDA0002982387190000152
the standard deviation of the section operation time of the bus of the g line of the section k in the y time period of the day;
the standard deviation of the running time of all vehicles in each lane of the road section in a certain period is as follows:
Figure BDA0002982387190000153
Figure BDA0002982387190000154
segment travel time standard deviations for all vehicles for segment k in the y-th time period of day;
Figure BDA0002982387190000155
indicates the number of r lane-change vehicles in the y time section k on the day of the W week,
Figure BDA0002982387190000156
representing the travel time of the h bus of the stop bus route g of the road section k in the y time period on the d day of the w week;
Figure BDA0002982387190000157
the bus flow of the road section bus line g of the y time section k on the day of the d day of the W week;
the standard deviation of all vehicle running time in the section k in the whole time period is as follows:
Figure BDA0002982387190000158
wherein Y is the total number of time periods, GkIs the total number of links for segment k.
Further, the vehicle-road cooperative road network each road section vehicle space-time operation risk degree is as follows:
Figure BDA0002982387190000159
lkis the length of the link k.
Example 3:
a method for judging vehicle operation risk road sections of a vehicle-road cooperative road network comprises the following steps:
s1: and dividing the vehicle roads and cooperating with the road network to analyze the road sections in a time-space reliable operation mode.
S11: with reference to fig. 3, each road intersection in the road network is used as a boundary, the road running directions are separated, the road network is divided into 12 road segments, each road segment comprises a bus stop and a geometric road scene in a construction area, the road network is a bidirectional four-lane road, and the number of road segments and roads is as follows: the number of the B1 and B2 road sections is 0, the number of the road sections A1, A2, C1, C2, the number of the E1 and the number of the E2 road sections are 1, and the number of the road sections D1, D2, F1 and F2 are 2 bus lines.
S2: and constructing a vehicle-road cooperative road network operation characteristic data set.
Data from monday to sunday (11/2/2020/11/8/2020/11/2020/9/2020/11/15/2020/11/2020/16/2020/11/2/2020/11/23/2020/11/29/2020) for a total of 4 weeks are investigated by field research. The intervals were 1 hour apart, and each day (24 hours) was divided into 24 intervals. The length, the number of lanes and the number of stopped buses are shown in table 1, the various traffic flow characteristic data of the road section are shown in table 2 (listing part data), the vehicle running time data of the road section are shown in table 3 (listing part data), and the bus running and stop time data table of the road section is shown in table 4 (listing part data).
TABLE 1 road section Length and number of lanes and number of transit lines
Road segment numbering Road section length/m Number of lanes/number of road sections Number of lines/strip of public traffic
A1 600 2 1
A2 590 2 1
B1 830 2 0
B2 800 2 0
C1 700 2 1
C2 720 2 1
D1 680 2 2
D2 680 2 2
E1 545 2 1
E2 540 2 1
F1 650 2 2
F2 650 2 2
Table 2 road section various traffic flow characteristic data table
Figure BDA0002982387190000171
Figure BDA0002982387190000181
TABLE 3 vehicle run time data sheet for road segments
Figure BDA0002982387190000182
Figure BDA0002982387190000191
Table 4 bus running and stop time data table
Figure BDA0002982387190000192
Figure BDA0002982387190000201
S3: and calculating the average running time of vehicles on each road section of the vehicle-road cooperative road network.
S31: and (3) calculating the average vehicle running time of each road section of the road and road cooperative road network by using the data (11/month 2/2020-11/month 8/2020, 11/month 9/2020-11/month 15/2020, and 11/month 16/2020-11/month 2/2020) of the counted 4-week data. The average running time of the vehicle on each lane of each road section in each time period from monday to sunday is obtained by combining the vehicle-road cooperation road network running characteristic data set constructed in step S2, and the average passing time of the vehicle on each road section in each time period from monday to sunday is obtained as shown in table 5 (listing partial data).
S32: and (3) calculating the average stop time of the buses on all road sections of the road synergetic network at the stop station by using the statistical data of the first 3 weeks of 4 weeks (11/month 2/2020-11/month 8/2020, 11/month 9/2020-11/month 15/2020, and 11/month 16/2020-11/month 2/2020). The average stop time of the buses running on each road segment in each time period from monday to sunday at the stop is obtained by combining the vehicle road and road network running characteristic data set constructed in the step S2, and is shown in table 5 (listing partial data).
TABLE 5 average running time of vehicles on road section and average stop time of buses on each road section
Figure BDA0002982387190000211
Figure BDA0002982387190000221
S4: and analyzing the space-time reliable operation characteristics of vehicles on the road section. And calculating the standard deviation of the running time of the vehicles, the vehicles changing the lanes, the public transport vehicles and all the vehicles in the road section in a specific time period by using the data of the 4 th week (11/23/2020-11/29/2020) in the 4 th week data of the statistics, and calculating the standard deviation of the running time of the vehicles in the road section in the whole time period, thereby analyzing the space-time reliable running characteristics of the vehicles in the road section.
S41: for the lane-change-free vehicles on different lanes on different road sections, the running characteristics of the lane-change-free vehicles in different time periods are different, so that the road section running time standard deviation of the lane-change-free vehicles on each road section in each time period from Monday to Sunday is utilized to reflect the space-time reliable running characteristics of the lane-change-free vehicles in a specific time period, and the data of S3 is combined to obtain the data (listed part of data) shown in Table 6.
S42: for the lane-changing vehicles on different road sections, the running characteristics of the lane-changing vehicles in different time periods are different, so that the time-space reliable running characteristics of the lane-changing vehicles in specific time periods are reflected by the standard deviation of the road section running time of the lane-changing vehicles on each road section in each time period from Monday to Sunday, and the data of S3 are combined to obtain the data shown in the table 6 (listing partial data).
S43: for buses on different routes on different road sections, the running characteristics of the buses in different time periods are different, so that the time-space reliable running characteristics of the buses in a specific time period are reflected by the standard deviation of the road section running time of the buses on each route in each time period from Monday to Sunday, and the data of S3 are combined to obtain the data shown in the table 6 (listing partial data).
TABLE 6 Standard deviation of running time of various vehicles in road section in specific time period
Figure BDA0002982387190000231
Figure BDA0002982387190000241
S44: since the occupation ratios of different types of vehicles in the road sections are different, the occupation ratios of the vehicles in the road sections are comprehensively considered, and the space-time reliable operation characteristics of all vehicles in the road sections in each time period from Monday to Sunday are reflected by the standard deviation of the road section operation time of all vehicles in the road sections in each time period from Monday to Sunday, as shown in Table 7 (listing part of data).
TABLE 7 Standard deviation of running time of all vehicles in a section of road in a specific time period
Figure BDA0002982387190000242
Figure BDA0002982387190000251
Figure BDA0002982387190000261
S45: since the vehicle operation characteristics are different on different days of the week and at different time periods of the day, the time factor influence needs to be considered comprehensively, and the standard deviation of the road section operation time of all vehicles in each road section in one week time is used for reflecting the space-time reliable operation characteristics at the whole time period, as shown in table 8.
TABLE 8 Standard deviation of the Total time running time of the road section
Road segment numbering Standard deviation/s of road section running time
A1 95
A2 103
B1 126
B2 95
C1 66
C2 95
D1 123
D2 115
E1 142
E2 120
F1 160
F2 123
S5: and quantizing the space-time reliable running degree of the vehicles in each road section.
And calculating the vehicle space-time operation risk degree of each road section of the vehicle-road cooperative road network according to the road section vehicle whole-time operation time standard deviation obtained in the step S45 and by combining the road section length, as shown in the table 9.
TABLE 9 vehicle and road cooperative road network each road section vehicle space-time operation risk degree
Figure BDA0002982387190000262
Figure BDA0002982387190000271
S6: and judging the vehicle road and road network vehicle operation risk road sections.
And sorting the vehicle space-time running risk degrees of the vehicles at each road section of the vehicle-road cooperative road network obtained according to the step S5 from large to small, as shown in a table 10.
TABLE 10 sequencing results of vehicle space-time running risk degree of vehicles on each road section of vehicle-road cooperative road network
Road segment numbering Space-time operation risk degree (s/m)
E1 0.26
F1 0.25
E2 0.22
F2 0.19
D1 0.18
A2 0.17
D2 0.17
A1 0.16
B1 0.15
C2 0.13
B2 0.12
C1 0.09
And taking the first 10% of road sections, and then E1 is the road network vehicle operation risk road section.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (2)

1. A method for judging vehicle operation risk road sections of a vehicle-road cooperative road network is characterized by comprising the following steps:
calculating the average running time of vehicles in each road section of the vehicle-road cooperative road network and the average stop time of the buses at the stop station according to the collected running characteristic data of the vehicle-road cooperative road network;
calculating to obtain the standard deviation of the whole-time running time of the vehicles on the road sections according to the average running time of the vehicles on the road sections and the average stop time of the buses at the stop;
calculating the vehicle space-time operation risk degree of each road section of the vehicle-road cooperative road network according to the whole-time operation time standard deviation of the vehicles on the road sections and by combining the length of the road sections;
obtaining vehicle and road cooperation road network vehicle operation risk road sections according to the risk degree sequencing result;
calculating the average running time of vehicles at each road section of the vehicle-road cooperative road network and the average stop time of the buses at the stop according to the collected running characteristic data of the vehicle-road cooperative road network, and the method comprises the following steps:
calculating the average time of the vehicle running on the r-th lane of the road section k in the y-th time period of the day according to the running characteristic data of the front W-1 week vehicle road and the road network
Figure FDA0003527537420000011
Figure FDA0003527537420000012
Calculate the average transit time for the vehicle to travel on section k during the y-th time period of day d
Figure FDA0003527537420000013
Figure FDA0003527537420000014
Calculating the average stop time of the bus of the g line running on the section k in the y period of the day
Figure FDA0003527537420000015
Figure FDA0003527537420000021
Wherein the content of the first and second substances,
Figure FDA0003527537420000022
respectively representing the operation time of the ith un-lane-changed vehicle of the r-th lane of the road section k and the stop time of the h-th bus of the stop bus line g of the road section k in the y-th time period on the d-th day of the w week;
Figure FDA0003527537420000023
respectively representing the number of vehicles which do not change lanes in the r-th lane and the bus flow of the bus route g in the road section on the y-th time period k on the d-th day of the w week; h is the number of buses of the stop bus route g on the road section k at the y-th time period of the day; rkIs a section of road kTotal number of lanes of; i is the number of all vehicles which do not change lanes on the r-th lane of the road section k in the y-th time period of the day;
calculating to obtain the standard deviation of the running time of the vehicles on the road section in the whole time period, wherein the method comprises the following steps:
calculating the standard deviation of the running time of vehicles on the road section in a certain period, such as non-lane-change vehicles, buses and all vehicles on each lane of the road section according to the collected W-th week vehicle and road network running characteristic data, and calculating the standard deviation of the running time of the vehicles on the road section in the whole period;
the standard deviation of the running time of the vehicle which does not change lanes in each lane of the road section in a certain period is as follows:
Figure FDA0003527537420000024
Figure FDA0003527537420000025
a road segment operating time standard deviation of an r-th lane-change-free vehicle representing a road segment k in the y-th time period of the day;
the standard deviation of the running time of the lane changing vehicles in each lane of the road section in a certain period is as follows:
Figure FDA0003527537420000026
Figure FDA0003527537420000027
the standard deviation of the road section operation time of the road section changing vehicle of the road section k in the y time period of the day; j is the number of vehicles changing the road on the road section k in the y-th time period of the day;
Figure FDA0003527537420000031
the travel time of the jth lane-changing vehicle on the ith hour road section k on the w week d;
the standard deviation of the running time of the public transport vehicles on each lane of the road section in a certain time period is as follows:
Figure FDA0003527537420000032
Figure FDA0003527537420000033
the standard deviation of the section operation time of the bus of the g line of the section k in the y time period of the day;
the standard deviation of the running time of all vehicles in each lane of the road section in a certain period is as follows:
Figure FDA0003527537420000034
Figure FDA0003527537420000035
segment travel time standard deviations for all vehicles for segment k in the y-th time period of day;
Figure FDA0003527537420000036
indicates the number of r lane-change vehicles in the y time section k on the day of the W week,
Figure FDA0003527537420000037
representing the travel time of the h bus of the stop bus route g of the road section k in the y time period on the d day of the w week;
Figure FDA0003527537420000038
the bus flow of a road section bus line g of a road section k at the y-th time period on the day of the W week;
the standard deviation of all vehicle full-time running time of the road section k is as follows:
Figure FDA0003527537420000039
wherein Y is the total number of time periods, GkThe total number of lines of the road section k;
the vehicle-road cooperative road network each road section vehicle space-time operation risk degree is as follows:
Figure FDA00035275374200000310
lkis the length of the link k.
2. The utility model provides a vehicle road is road network vehicle operation risk highway section decision maker which characterized in that: the method comprises the following steps:
the time standard deviation calculation module is used for calculating the average running time of vehicles on each road section of the vehicle-road cooperative road network and the average stop time of buses at a stop according to the collected vehicle-road cooperative road network running characteristic data; calculating to obtain the standard deviation of the whole-time running time of the vehicles on the road section;
the risk degree calculation module is used for calculating the time-space operation risk degree of vehicles on each road section of the vehicle-road cooperative road network according to the whole-time operation time standard deviation of the vehicles on the road sections and by combining the length of the road sections;
the risk road section judging module is used for obtaining a vehicle road and road network vehicle operation risk road section in cooperation with the vehicle according to the risk degree sequencing result;
calculating the average running time of vehicles at each road section of the vehicle-road cooperative road network and the average stop time of the buses at the stop according to the collected running characteristic data of the vehicle-road cooperative road network, and the method comprises the following steps:
calculating the average time of the vehicle running on the r-th lane of the road section k in the y-th time period of the day according to the running characteristic data of the front W-1 week vehicle road and the road network
Figure FDA0003527537420000041
Figure FDA0003527537420000042
Calculate the average transit time for the vehicle to travel on section k during the y-th time period of day d
Figure FDA0003527537420000043
Figure FDA0003527537420000044
Calculating the average stop time of the bus of the g line running on the section k in the y period of the day
Figure FDA0003527537420000045
Figure FDA0003527537420000046
Wherein the content of the first and second substances,
Figure FDA0003527537420000051
respectively representing the operation time of the ith un-lane-changed vehicle of the r-th lane of the road section k and the stop time of the h-th bus of the stop bus line g of the road section k in the y-th time period on the d-th day of the w week;
Figure FDA0003527537420000052
respectively representing the number of vehicles which do not change lanes in the r-th lane and the bus flow of the bus route g in the road section on the y-th time period k on the d-th day of the w week; h is the number of buses of the stop bus route g on the road section k at the y-th time period of the day; r iskIs the total number of lanes for road segment k; i is the number of all vehicles which do not change lanes on the r-th lane of the road section k in the y-th time period of the day;
calculating to obtain the standard deviation of the running time of the vehicles on the road section in the whole time period, wherein the method comprises the following steps:
calculating the standard deviation of the running time of vehicles on the road section in a certain period, such as non-lane-change vehicles, buses and all vehicles on each lane of the road section according to the collected W-th week vehicle and road network running characteristic data, and calculating the standard deviation of the running time of the vehicles on the road section in the whole period;
the standard deviation of the running time of the vehicle which does not change lanes in each lane of the road section in a certain period is as follows:
Figure FDA0003527537420000053
Figure FDA0003527537420000054
a road segment operating time standard deviation of an r-th lane-change-free vehicle representing a road segment k in the y-th time period of the day;
the standard deviation of the running time of the lane changing vehicles in each lane of the road section in a certain period is as follows:
Figure FDA0003527537420000055
Figure FDA0003527537420000056
the standard deviation of the road section running time of the road section changing vehicle of the road section k in the y-th time period of the day; j is the number of vehicles changing the road on the road section k in the y-th time period of the day;
Figure FDA0003527537420000057
the travel time of the jth lane-changing vehicle on the yth hour road section k on the w week d;
the standard deviation of the running time of the public transport vehicles on each lane of the road section in a certain time period is as follows:
Figure FDA0003527537420000061
Figure FDA0003527537420000062
the standard deviation of the section operation time of the bus of the g line of the section k in the y time interval of the day;
the standard deviation of the running time of all vehicles in each lane of the road section in a certain period is as follows:
Figure FDA0003527537420000063
Figure FDA0003527537420000064
segment travel time standard deviations for all vehicles for segment k in the y-th time period of day;
Figure FDA0003527537420000065
indicates the number of r lane-change vehicles in the y time section k on the day of the W week,
Figure FDA0003527537420000066
representing the travel time of the h bus of the stop bus route g of the road section k in the y time period on the d day of the w week;
Figure FDA0003527537420000067
the bus flow of a road section bus line g of a road section k at the y-th time period on the day of the W week;
the standard deviation of all vehicle running time in the section k in the whole time period is as follows:
Figure FDA0003527537420000068
wherein Y is the total number of time periods, GkThe total number of lines of the road section k;
the vehicle-road cooperative road network each road section vehicle space-time operation risk degree is as follows:
Figure FDA0003527537420000069
lkis the length of the link k.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105006147A (en) * 2015-06-19 2015-10-28 武汉大学 Road segment travel time deducing method based on road space-time incidence relation
CN105608895A (en) * 2016-03-04 2016-05-25 大连理工大学 Local abnormity factor-based urban heavy-traffic road detection method
US9672734B1 (en) * 2016-04-08 2017-06-06 Sivalogeswaran Ratnasingam Traffic aware lane determination for human driver and autonomous vehicle driving system
CN108629973A (en) * 2018-05-11 2018-10-09 四川九洲视讯科技有限责任公司 Road section traffic volume congestion index computational methods based on fixed test equipment
CN111292535A (en) * 2020-02-19 2020-06-16 河海大学 Road network traffic state evaluation method for passenger travel in vehicle and road cooperation environment

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105006147A (en) * 2015-06-19 2015-10-28 武汉大学 Road segment travel time deducing method based on road space-time incidence relation
CN105608895A (en) * 2016-03-04 2016-05-25 大连理工大学 Local abnormity factor-based urban heavy-traffic road detection method
US9672734B1 (en) * 2016-04-08 2017-06-06 Sivalogeswaran Ratnasingam Traffic aware lane determination for human driver and autonomous vehicle driving system
CN108629973A (en) * 2018-05-11 2018-10-09 四川九洲视讯科技有限责任公司 Road section traffic volume congestion index computational methods based on fixed test equipment
CN111292535A (en) * 2020-02-19 2020-06-16 河海大学 Road network traffic state evaluation method for passenger travel in vehicle and road cooperation environment

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于速度的城市快速路交通拥堵预测研究;邢珊珊 等;《交通信息与安全》;20160428;第34卷(第2期);1-9 *

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