CN115311858A - Urban road section grading control method based on traffic flow toughness - Google Patents

Urban road section grading control method based on traffic flow toughness Download PDF

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CN115311858A
CN115311858A CN202210942349.1A CN202210942349A CN115311858A CN 115311858 A CN115311858 A CN 115311858A CN 202210942349 A CN202210942349 A CN 202210942349A CN 115311858 A CN115311858 A CN 115311858A
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CN115311858B (en
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王建军
宋明洋
卢霄娟
王赛
李冬怡
马驰骋
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Changan University
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    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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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
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    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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Abstract

The invention discloses an urban road section grading control method based on traffic flow toughness, which comprises the steps of acquiring road section intersection data; dividing the road traffic flow operation state into a plastic state, an elastic transition state and an elastic state; determining an operation state threshold value: dividing a data set of toughness values into different categories, sequencing clustering centers of the categories from small to large, wherein the average value of the clustering centers of the adjacent categories is the threshold value of each state; considering the influence of traffic flow increase and bus arrival on traffic flow driving, establishing a road section time-sharing average driving time and road service quality coefficient calculation model based on a two-flow model; and finally, obtaining a service quality evaluation standard of each road section through cluster analysis, and determining external management measures aiming at each level of service quality and toughness state. The invention is convenient and fast, and improves the road traffic service level; the self-recovery capability of the traffic flow is effectively excavated, and the cost of external management measures is saved.

Description

Urban road section grading control method based on traffic flow toughness
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a method for graded control of urban road sections based on traffic flow toughness.
Background
The method is characterized in that the urban road infrastructure is used as an important component of an urban traffic system, the existing urban is used for predicting the influence of a disturbance event on the existing urban, the adopted method is basically to divide the state of the road system into a reliable state, a degraded state, a recovery state and a recovered state, and the toughness index is used for evaluating the overall performance of a disturbance event duration system. However, the method is lack of evaluation on toughness in the traffic flow disturbed process. The normal running of the road traffic flow is always interfered by the change of the upstream traffic flow and the entering and exiting of the bus, so that the condition of deceleration or stopping and giving way occurs in the running process of the motor vehicle, the running time is increased, the running efficiency of the road is reduced, and the traffic jam is caused. Therefore, the method has better theoretical value and practical significance in evaluating the toughness of the traffic flow of the road section.
In order to consider the influence of an interference event on road traffic, it has been studied to define toughness indexes from the aspects of failure probability, disturbance absorption capacity and recovery speed, and determine the disturbed degree of road segments by simulating an event damage mode, so as to calculate the importance of each road segment in a road network. However, due to changes of the traffic flow of the road section, importance ranking by using toughness of the road section has strong time-varying property, and further, the influence effect of management measures based on the importance of the road section is small. In the method and the system for evaluating the toughness of the urban bus route disclosed in the patent CN111599180A, the service state is divided into a stable state and a collapse state by using the actual arrival time and the planned arrival time of the bus, and the toughness of the interference of a road traffic flow mainly comprising a private car on the bus arrival and departure is not considered. In the literature, "evaluation of toughness and road section importance of road traffic system" the toughness index of the road system is calculated by redistributing traffic flow in a disturbance event, but the calculation process has more parameters and large calibration difficulty.
Disclosure of Invention
Aiming at the change of travel time of road traffic flow influenced by the increase of traffic flow and the process of bus entering and leaving stations, the method for controlling the urban road sections based on the toughness of the traffic flow is provided, so that the problems that the running state indexes of the road sections in an urban road network are unreasonable, the self-recovery capability of motor vehicle flow cannot be embodied when the motor vehicle flow is interfered and the traffic control measures are redundant are solved.
In order to achieve the above object, the present invention provides a technical solution,
the method for hierarchical control of urban road sections based on traffic flow toughness comprises the following steps:
s1, data acquisition; the dynamic data comprises road traffic flow dynamic intersection data, and the static data comprises intersection coordinate positions, bus station coordinate positions and urban road network center lines;
s2, preprocessing data; calculating the traffic volume of the road sections in the checkpoint data according to the acquired data, matching the traffic volume of the road sections with the travel time, and counting the number of bus stations on each road section;
s3, dividing the toughness state of the traffic flow; calculating the full-day time-sharing toughness value of the road traffic flow based on the travel time, dividing the traffic toughness into a plastic state, an elastic transition state and an elastic state, and determining the threshold values of the three toughness states by performing k-means clustering on the toughness value;
s4, calculating a road section service quality index; based on a two-stream theory mesoscopic traffic flow model, obtaining a relational expression between the average travel time of the road section, the average shortest travel time of the road section and the average travel time of the road section by using the road section service quality index; then based on the road section traffic volume and the toughness value, considering the influence of the bus station entrance and exit on the road section traffic flow running, and calculating the road section daily time-sharing average running time and the road service quality index;
s5, managing the road section operation in a grading manner; dividing the service quality grades into three levels, and then determining threshold values corresponding to the three levels of service quality grades by performing k-means clustering on the service quality indexes; and determining road section traffic flow management measures respectively according to the traffic flow toughness state and the road service quality level.
Further, the specific process of the step S2 is as follows:
s21, number of lanes of road section: counting the number of lanes in the same direction of the gate and recording the corresponding lane number;
s22, extracting road traffic volume in the checkpoint data: calculating the traffic flow sum of each lane in the same driving direction corresponding to the same gate in a unit statistical time period by using the lane number;
s23, matching road traffic volume with travel time: matching longitude and latitude information of the gate with information of a road center line by Arcgis software, judging the driving direction of traffic according to the node number of the road center line and the start node and the end node of the traffic in the travel time data, and corresponding the travel time data to the data of the same road segment number and the driving direction in the same time segment in the traffic one by one;
s24, the number of bus stations between road section nodes: in the coordinate information of urban bus lines and bus stops, firstly, stations with different lines and the same longitude and latitude are screened, and the number of the stations is counted; and adding the bus station coordinates with the overlapped station coordinate information removed into a road center line loaded by Arcgis software, and finally counting the number of bus stations on road sections with different numbers.
Further, the specific process of calculating the full-time-of-day toughness value of the road traffic flow based on the travel time in the step S3 is as follows:
setting the average travel time of the vehicle at the initial zero moment as t 0 (ii) a If the average travel time t of the vehicle at the next statistical moment 1 ≤t 0 Then the first minimum occurs at time t 0_low (ii) a If the average travel time t of the vehicle is counted next time 1 ＞t 0 Then t is 0 ＝t 0_low (ii) a The ith maximum value of the average travel time of the vehicle is t i_high (ii) a The average travel time of the vehicle at the next statistical time after the maximum value is t i_low ,(i＝1,2…)；
From above, t (i-1)_low ＜t i_high ，t i_low ＜t i_high (ii) a Ith time period delta t of road traffic flow i The toughness value of (a) can be calculated as:
Figure BDA0003786214600000031
further, in step S4, based on the two-stream theory observed traffic flow model, the road segment service is utilizedQuality index, obtained average travel time T of road section r Average shortest travel time T of road section m And the relation between the average travel time T of the road sections is as follows:
Figure BDA0003786214600000041
in the formula, n is a road traffic service quality index;
calculating the average travel time of the road section in the time of the whole day and the road service quality index, wherein the formula is as follows:
Figure BDA0003786214600000042
in the formula (I), the compound is shown in the specification,
Figure BDA0003786214600000043
is Δ t i Average travel time of inner road section, V T Is Δ t i The average amount of traffic in the inner road section,
Figure BDA0003786214600000044
the shortest driving time of the road section corresponds to the traffic volume, p is the total number of the bus stations at one side of the road section in the same direction, R i For a section of road Δ t i The value of the toughness of the time period,
Figure BDA0003786214600000045
is Δ t i The time for the kth bus to get in and out of the station in the section; if the bus station is a bay station,
Figure BDA0003786214600000046
is Δ t i The bus queuing arrival time of the section; if the bus stop is a non-estuary stop,
Figure BDA0003786214600000047
is Δ t i The sum of the bus queuing time of the bus on the section and the time of waiting for passengers to get on the bus of the last bus.
In conclusion, the urban road section hierarchical control method based on the traffic flow toughness has the following advantages: 1) The invention combines the road traffic gate data to obtain the traffic flow running time and the traffic volume, does not need to specially set other monitoring systems, and is convenient and quick; 2) The invention combines the concept of material deformation to accurately divide the running state into three types, determines various threshold values, excavates the resistance of the traffic flow to interference and can effectively reduce redundant external control measures; 3) According to the method, corresponding management measures are made according to the toughness states and service quality levels of different road sections in each time period through the traffic flow growth rate and the interference of the bus station entering and exiting processes on the traffic flow, so that the management efficiency is improved, and the resource cost is saved.
Drawings
FIG. 1 is a flowchart of an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a toughness status determination of a traffic flow according to an embodiment of the present disclosure;
FIG. 3 is a diagram illustrating a hierarchical management approach according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the drawings and the embodiment.
The method for urban road section graded control based on traffic flow toughness as shown in fig. 1 comprises the following parts:
1. data acquisition
And acquiring data of the road section card port, wherein the data is divided into dynamic data and static data. The dynamic data comprises road traffic flow dynamic bayonet data, and the static data comprises bayonet coordinate positions, bus station coordinate positions and urban road network center lines. Taking the data of the network interface of the inner road in a certain day in Xuan city, anhui province, for example, tables 1-3, the obtained data includes the gate number, the lane number, the initial observation time, the end observation time, the road section number, the initial node of vehicle driving, the end node of vehicle driving, the average traffic flow, the average travel time and the lane driving direction. The coordinate position of the bus station and the central line data of the urban road network can be obtained by purchasing or calling a map interface.
TABLE 1 average traffic volume in partial section lane
Figure BDA0003786214600000051
TABLE 2 average travel time for part of links
Figure BDA0003786214600000052
Figure BDA0003786214600000061
TABLE 3 part of road section Bayonet observing lane number and driving direction
Figure BDA0003786214600000062
2. Data pre-processing
The method comprises the following steps of preprocessing static data and dynamic block data of road traffic flow, and matching the dynamic data with the static data, wherein the process comprises the following steps:
s21, counting the number of lanes in the road section: counting the number of lanes in the same direction of IDs of different gates and recording the corresponding lane numbers;
s22, extracting road section traffic volume data in the checkpoint record: calculating the traffic flow sum of all lanes in the same driving direction corresponding to the same gate ID in the same section by using the lane numbers in the same direction recorded in the S21;
s23, matching road traffic with node average travel time: matching longitude and latitude information of the card port with information of a road center line by using Arcgis software, judging the driving direction of the traffic flow according to the node number of the road center line and a starting node and a stopping node of the traffic flow driving in the travel time data, and finally corresponding the travel time data to the data of the same road segment number and the driving direction in the same time period in the traffic volume one by one;
s24, counting the number of bus stations between road section nodes: in the coordinate information of urban bus lines and bus stops, firstly, stations with different lines and the same longitude and latitude are screened, and the number of the stations is counted; and adding the bus station coordinates without the overlapped station coordinate information into a road center line loaded by Arcgis software, and finally counting the number of bus stations on road sections with different numbers.
3. Calculation of road traffic flow toughness index
And S31, determining the index.
The method comprises the following steps of acquiring travel time of a traffic flow passing a road section through road section bayonet data, and calculating a full-day time-sharing toughness value of the road section traffic flow, wherein the method specifically comprises the following steps:
setting the average travel time of the vehicle at the initial zero moment as t 0 Then, there are the following cases:
(1) If the average travel time t of the vehicle at the next statistical moment 1 ≤t 0 Then the 1 st minimum occurs at time t 0_low ；
(2) If the average travel time t of the vehicle at the next statistical moment 1 ＞t 0 Then t is 0 ＝t 0_low 。
The ith maximum value of the average travel time of the vehicle is t i_high (ii) a The average travel time of the vehicle at the next statistical time after the maximum value is t i_low ,(i＝1,2…)。
From above, t (i-1)_low ＜t i_high ，t i_low ＜t i_high . Ith time period delta t of road traffic flow i The toughness value of (a) can be calculated as:
Figure BDA0003786214600000071
in the formula, R i For a section of road at Δ t i A toughness value for a time period; Δ t i Is t (i-1)_low And t i_low The statistical time interval of (c).
And S32, judging.
Combining the traffic flow passing time variation of the road section and the time-sharing toughness valueThe self-adaptive function of the through-flow operation divides the road section passing state into three states, which are respectively expressed as plasticity (0, R) p ) Elastic transition state (R) p ,R e ) And elasticity (R) e , + ∞). Wherein R is p Upper limit value of toughness, R, representing plastic change of road traffic flow running time e And the toughness threshold value represents the elastic change of the road traffic flow running time.
And S33, determining an operation state threshold value.
The K-means clustering algorithm divides the data set into different categories through an iterative process, so that a criterion function for evaluating the clustering performance is optimal, each generated cluster is compact, and the categories are independent. The algorithm comprises the following steps:
s331, determining an initial cluster center for each cluster, wherein K initial cluster centers exist;
s332, distributing each sample to the nearest cluster according to the minimum distance principle;
in calculating the distance between samples, commonly used distance principles include euclidean distance, manhattan distance, cosine distance, and the like. The embodiment selects the Euclidean distance d (x) in actual calculation i ,x j ) As follows:
Figure BDA0003786214600000081
in the formula, x i ＝(x i1 ,x i2 ,…,x im ),x j ＝(x j1 ,x j2 ,…,x jm ) Each sample having m attribute values; x is the number of ik Is the k attribute value, x, of the ith sample jk Is the kth attribute value of the jth sample.
In this embodiment, the samples are all one-dimensional data, and the distance between two samples is the absolute value of the sample value difference. The smaller the distance between the samples, the higher the similarity of the two samples, and vice versa the lower the similarity.
S333, using the sample mean value in each cluster as a new cluster center;
s334, repeating S332 and S333 until the cluster center is not changed any more;
and S335, finishing clustering to obtain K clusters.
And finally, sorting the clustering centers of all categories obtained by using a K-means mean algorithm from small to large, wherein the mean value of the clustering centers of adjacent categories is the threshold value of each state. Setting the sorted clustering centers as h 1 ,h 2 ,h 3 Then, the state thresholds are as follows:
Figure BDA0003786214600000082
taking the gate data of a road network of a city in a certain day of the Xuan city in Anhui province as an example, setting an initial clustering value to be 3 and randomly giving 3 initial clustering centers by using Python. The time-sharing toughness value data of each road section is input, the final values of the three clustering centers can be obtained through continuous iteration and circulation, the threshold value corresponding to each state can be determined according to the mean value of each category of clustering centers, and the result is shown in table 4.
TABLE 4 road traffic flow toughness State threshold
Figure BDA0003786214600000091
The road section traffic flow plasticity state threshold value in table 4 is 0.045. And when the ratio of the recovery degree after the traffic flow passing time of a certain road section has the maximum value to the recovery time is less than 0.045, the current operation state is considered to be a plastic state.
4. Road section service quality calculation method based on two-stream theory
S41, establishing a two-stream theory medium traffic flow model.
Vehicles in traffic flow are classified into two categories: one is a moving vehicle and one is a stopped vehicle. Suppose that:
(1) The average running speed of the vehicles in the road network is proportional to the proportion of the running vehicles;
(2) The parking time proportion of the circulating test vehicles (traffic observation vehicles) in the road network is equal to the parking time proportion of the vehicles running at the same time segment in the road network.
Based on the assumption (1), the relationship between the average traveling speed of the vehicle and the specific gravity of the traveling vehicle can be obtained:
U r ＝U m f r n
in the formula of U r For average running speed, U m Maximum average traveling speed, f r N is the road traffic service quality index.
Average travel speed U for U r f r Representing, the above formula in combination is available:
U＝U m f r n+1
and f is r +f s ＝1，f s For parking proportion, substituted can be obtained
U＝U m (1-f s ) n+1
Converting the above formula into the relation of average travel time, wherein T represents the average travel time, T r Denotes the average travel time, T m Indicating the average shortest travel time. Unit distance T =1/U, T r ＝1/U r ，T m ＝1/U m Substituting the speed relation can obtain:
T＝T m (1-f s ) -(n+1)
based on assumption (2), the parking time T of available test vehicles in the road network s Instead of the parking time of all vehicles:
Figure BDA0003786214600000101
the time is substituted into the time relation to obtain,
T＝T m [1-(T s /T)] -(n+1)
because T = T r +T s Therefore, it is possible to
Figure BDA0003786214600000102
For calibration, the natural logarithm is taken at both sides of the equation:
Figure BDA0003786214600000103
and S42, considering the influence of the traffic flow growth rate and the bus arrival on the traffic flow running of the road section based on the road section service quality and the running toughness state.
The average shortest travel time for the link is assumed to be equal to the average shortest travel time for the link.
Figure BDA0003786214600000104
In the formula (I), the compound is shown in the specification,
Figure BDA0003786214600000105
is Δ t i Average travel time of inner road section, V T Is Δ t i The average amount of traffic in the inner road section,
Figure BDA0003786214600000106
the shortest driving time of the road section corresponds to the traffic volume, p is the total number of the bus stations at one side of the road section in the same direction, R i For a section of road Δ t i The value of the toughness of the time period,
Figure BDA0003786214600000107
is Δ t i The time for the kth bus to get in and out of the station in the section; if the bus station is a bay station,
Figure BDA0003786214600000108
is Δ t i The bus queuing arrival time of the section; if the bus stop is a non-estuary stop,
Figure BDA0003786214600000109
is Δ t i The sum of the time of bus queue-in and the time of waiting for passengers to get on the bus of the last bus in the section.
And calculating the average travel time of the road section in the whole day and the road service quality index by combining the relational expression in the S41, and referring to a table 5.
TABLE 5 time-sharing average travel time and road quality index for a certain section
Figure BDA0003786214600000111
5. And (4) road section operation hierarchical management considering the traffic flow toughness state and the road service quality level.
S51, road section service quality grade division: dividing the service quality of the road into a first level, a second level and a third level, and carrying out the treatment on each road segment delta t in the urban road network i The k-means clustering method described in S33 is performed on the service quality index in the table 6 to obtain a threshold corresponding to the service quality level of each road segment.
TABLE 6 quality of service class thresholds for certain road segment
Figure BDA0003786214600000112
S52, road section time-sharing operation optimization management measures: respectively for each road section delta t i And determining road section traffic flow management measures according to the internal traffic flow toughness classification and the road service quality grade, and the figure is 3.
The above is a specific embodiment of the present invention, but the scope of the present invention should not be limited thereto. Any changes or substitutions that can be easily made by those skilled in the art within the technical scope of the present invention are included in the protection scope of the present invention, and therefore, the protection scope of the present invention is subject to the protection scope defined by the appended claims.

Claims (4)

1. The method for controlling the urban road sections in a grading manner based on the toughness of the traffic flow is characterized by comprising the following steps:
s1, data acquisition; the dynamic data comprises road traffic flow dynamic intersection data, and the static data comprises intersection coordinate positions, bus station coordinate positions and urban road network center lines;
s2, preprocessing data; calculating the traffic volume of the road section in the checkpoint data according to the acquired data, matching the traffic volume of the road section with the travel time, and counting the number of bus stations on each road section;
s3, dividing the toughness state of the traffic flow; calculating the full-day time-sharing toughness value of the road traffic flow based on the travel time, dividing the traffic toughness into a plastic state, an elastic transition state and an elastic state, and determining the threshold values of the three toughness states by performing k-means clustering on the toughness value;
s4, calculating a road section service quality index; based on a two-stream theory mesoscopic traffic flow model, obtaining a relational expression between the average travel time of the road section, the average shortest travel time of the road section and the average travel time of the road section by using the road section service quality index; then based on the road section traffic volume and the toughness value, considering the influence of the bus station entrance and exit on the road section traffic flow driving, calculating the road section time-sharing average driving time and the road service quality index all day long;
s5, managing the road section operation in a grading manner; dividing the service quality grades into three levels, and then determining threshold values corresponding to the three levels of service quality grades by performing k-means clustering on the service quality indexes; and determining road traffic flow management measures respectively according to the traffic flow toughness state and the road service quality level.
2. The urban road segment hierarchical control method according to claim 1, wherein the step S2 is specifically performed by:
s21, number of lanes of road section: counting the number of lanes in the same direction of the gate and recording the corresponding lane number;
s22, extracting road traffic volume in the checkpoint data: calculating the traffic flow sum of each lane in the same driving direction corresponding to the same gate in a unit statistical time period by using the lane number;
s23, matching road traffic volume with travel time: matching longitude and latitude information of the card port with information of a road center line by using Arcgis software, judging the driving direction of the traffic flow according to the node number of the road center line and a starting node and a stopping node of the traffic flow driving in the travel time data, and finally corresponding the travel time data to the data of the same road segment number and the driving direction in the same time period in the traffic volume one by one;
s24, the number of bus stations between the road section nodes is as follows: in the coordinate information of urban bus lines and bus stops, firstly, stations with different lines and the same longitude and latitude are screened, and the number of the stations is counted; and adding the bus station coordinates without the overlapped station coordinate information into a road center line loaded by Arcgis software, and finally counting the number of bus stations on road sections with different numbers.
3. The urban road segment hierarchical control method according to claim 2, wherein the specific process of calculating the full-time-of-day toughness value of the road segment traffic flow based on the travel time in step S3 is as follows:
setting the average travel time of the vehicle at the initial zero moment as t 0 (ii) a If the average travel time t of the vehicle at the next statistical moment 1 ≤t 0 Then the first minimum occurs at time t 0_low (ii) a If the average travel time t of the vehicle at the next statistical moment 1 ＞t 0 Then t is 0 ＝t 0_low (ii) a The ith maximum value of the average travel time of the vehicle is t i_high (ii) a The average travel time of the vehicle at the next statistical moment after the maximum value is t i_low ,(i＝1,2…)；
From above, t (i-1)_low ＜t i_high ，t i_low ＜t i_high (ii) a Ith time period delta t of road traffic flow i The toughness value of (a) can be calculated as:
Figure FDA0003786214590000021
4. the urban road section grading control method according to claim 3, wherein in the step S4, the average driving time T of the road section is obtained by using the road section service quality index based on a two-stream theory macroscopic traffic flow model r Average shortest travel time T of road section m And the relation between the average travel time T of the road sections is as follows:
Figure FDA0003786214590000022
in the formula, n is a road traffic service quality index;
calculating the average travel time of the road section in the time sharing and the road service quality index, wherein the formula is as follows:
Figure FDA0003786214590000031
in the formula (I), the compound is shown in the specification,
Figure FDA0003786214590000032
is Δ t i Average travel time of inner road section, V T Is Δ t i The average amount of traffic in the inner road section,
Figure FDA0003786214590000033
the shortest driving time of the road section corresponds to the traffic volume, p is the total number of the bus stations at one side of the road section in the same direction, R i For a section of road Δ t i The value of the toughness of the time period,
Figure FDA0003786214590000034
is Δ t i The time for the kth bus to get in and out of the station in the section; if the bus station is a bay station,
Figure FDA0003786214590000035
is Δ t i The time of bus queue arrival in the road section; if the bus stop is a non-estuary stop,
Figure FDA0003786214590000036
is Δ t i The sum of the time of bus queue-in and the time of waiting for passengers to get on the bus of the last bus in the section.
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