CN115311858B - Urban road section hierarchical control method based on traffic flow toughness - Google Patents
Urban road section hierarchical control method based on traffic flow toughness Download PDFInfo
- Publication number
- CN115311858B CN115311858B CN202210942349.1A CN202210942349A CN115311858B CN 115311858 B CN115311858 B CN 115311858B CN 202210942349 A CN202210942349 A CN 202210942349A CN 115311858 B CN115311858 B CN 115311858B
- Authority
- CN
- China
- Prior art keywords
- road section
- road
- traffic flow
- time
- toughness
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- 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
- G08G1/0137—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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a traffic flow toughness-based urban road section hierarchical control method, which comprises the steps of obtaining road section bayonet data; dividing the traffic flow running state of the road section into a plastic state, an elastic transition state and an elastic state; operating state threshold determination: dividing the data set of the toughness value into different categories, sorting each category of clustering centers from small to large, and taking the average value of the adjacent category of clustering centers as each state threshold value; considering the influence of traffic flow increase and bus arrival on traffic flow running, and establishing a road section time-sharing average running time and road service quality coefficient calculation model based on a two-flow model; and finally, obtaining the service quality evaluation standard of each road section through cluster analysis, and determining external management measures aiming at the service quality and toughness states of each level. The invention is convenient and quick, and improves the road traffic service level; the traffic flow self-recovery capacity is effectively excavated, and the cost of external management measures is saved.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a method for urban road section hierarchical control based on traffic flow toughness.
Background
The urban road infrastructure is used as an important component of an urban traffic system, the influence of disturbance events on the urban road infrastructure is predicted at present, the road system state is basically divided into a reliable state, a degradation state, a recovery state and a recovery state by adopting a method, and the overall performance of the disturbance event duration system is evaluated by using toughness indexes. However, the method lacks evaluation of toughness in the traffic flow disturbance process. The normal running of the road traffic flow is always interfered by the change of the upstream traffic flow and the entrance and exit of buses, so that the situation of decelerating or stopping and yielding occurs in the running process of the motor vehicle, the running time is increased, the road running efficiency is reduced, and the traffic jam is caused. Therefore, the evaluation of the road traffic flow toughness has good theoretical value and practical significance.
In order to consider the influence of disturbance events on road traffic, there have been studies to define toughness indexes in terms of failure probability, absorption disturbance capacity and recovery speed, determine the disturbance degree of road segments by simulating event destruction modes, and thus calculate the importance of each road segment in a road network. However, the change of the traffic flow of the road section causes the importance degree ordering by using the toughness of the road section to have strong time variability, so that the effect of the management measures based on the importance degree of the road section is less. In the method and the system for evaluating the toughness of the urban bus route of the patent CN111599180A, the service state is divided into the stable state and the collapse state by utilizing the actual arrival time and the planned time of the bus, and the toughness of the road traffic flow which mainly uses private vehicles to interfere the bus arrival and departure is not considered. The literature on evaluation of toughness and road section importance of a road traffic system calculates the toughness index of the road system by redistributing traffic flow under disturbance events, but the parameters in the calculation process are more, and the calibration difficulty is high.
Disclosure of Invention
Aiming at the travel time change of road section traffic flow affected by the increase of traffic flow and the arrival and departure processes of buses, the urban road section grading control method based on traffic flow toughness is provided, so that the problems that the running state index of road section traffic flow in an urban road network is unreasonable, the self-recovery capability cannot be embodied when the motor traffic flow is interfered, and traffic control measures are redundant are solved.
In order to achieve the above purpose, the invention provides the following technical scheme,
the urban road section hierarchical control method based on traffic flow toughness comprises the following steps:
s1, data acquisition; the dynamic data comprise road section traffic flow dynamic gate data, and the static data comprise gate 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 gate data according to the acquired data, matching the traffic volume of the road sections with the travel time, and simultaneously counting the number of bus stations on each road section;
s3, dividing traffic flow toughness states; calculating the whole-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 thresholds of the three toughness states by carrying out k-means clustering on the toughness values;
s4, calculating a road section service quality index; based on a mesoscopic traffic flow model of the two-flow theory, obtaining a relational expression among the average running time of the road section, the average shortest running time of the road section and the average travel time of the road section by using the service quality index of the road section; then calculating the whole-day time-sharing average running time of the road and the road service quality index based on the road traffic volume and the toughness value and considering the influence of the bus in and out on the running of the road traffic flow;
s5, road section operation hierarchical management; dividing the quality of service class into three levels, and then determining a threshold value corresponding to the three levels of quality of service class by carrying out k-means clustering on the quality of service indexes; and determining road traffic flow management measures according to the traffic flow toughness state and the road service quality grade.
Further, the specific process of the step S2 is as follows:
s21, number of road section lanes: counting the number of lanes in the same direction of the bayonet and recording the corresponding lane numbers;
s22, extracting road section traffic volume in the bayonet data: calculating the traffic flow sum of all lanes in the same running direction corresponding to the same bayonet in the unit statistical period by using the lane number;
s23, matching the road section traffic volume with the travel time: the method comprises the steps of utilizing Arcgis software, matching longitude and latitude information of a bayonet with information of a road center line, judging the running direction of a traffic flow according to a node number of the road center line and a starting node and a stopping node of the traffic flow in the running time data, and finally enabling the running time data to correspond to data of the same road section number and the running direction in a time period in traffic flow one by one;
s24, number of bus stops among road section nodes: in the coordinate information of urban public transport lines and public transport stops, stations with different lines and same longitude and latitude are screened at first, and the number of stations is counted; and adding the bus stop coordinates with overlapping station coordinate information removed to the road center line loaded by Arcgis software, and finally counting the number of bus stops on the road sections with different numbers.
Further, the specific process of calculating the whole-day time-sharing 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 time ast 0 The method comprises the steps of carrying out a first treatment on the surface of the If the average travel time t of the vehicle is the next statistical moment 1 ≤t 0 The moment when the first minimum appears is t 0_low The method comprises the steps of carrying out a first treatment on the surface of the If the average travel time t of the vehicle is the next statistical moment 1 ＞t 0 Then t 0 ＝t 0_low The method comprises the steps of carrying out a first treatment on the surface of the The ith maximum value of the average travel time of the vehicle is t i_high The method comprises the steps of carrying out a first treatment on the surface of the The average travel time of the vehicle at the next statistical moment after the occurrence of 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 The method comprises the steps of carrying out a first treatment on the surface of the Road section traffic flow ith time period delta t i The toughness value of (c) can be calculated as:
further, in step S4, based on the mesoscopic traffic flow model of the two-flow theory, the average travel time T of the road segment is obtained by using the road segment quality of service index r Average shortest travel time T of road section m The relation between the average travel time T of the road section is as follows:
wherein n is a road traffic service quality index;
the whole-day time-sharing average running time and the road service quality index of the road section are calculated, and the formula is as follows:
in the method, in the process of the invention,is delta t i Average travel time of inner road section, V T Is delta t i Average traffic volume of inner road section->For the traffic quantity corresponding to the shortest running time of the road section, p is the total number of bus stations at one side of the same direction of the road section, R i For the road section delta t i The toughness value of the time period is set,is delta t i The time for the kth bus to get in and out of the road section; if the bus station is an estuary station, < > for>Is delta t i Queuing and entering time of buses at the road section; if the bus station is a non-estuary station, < > for the bus station>Is delta t i And the sum of the queuing and arrival time of buses in the road section and the waiting time of the last bus for passengers to get on.
In summary, the urban road section grading control method based on traffic flow toughness has the following advantages: 1) According to the invention, the traffic flow running time and traffic volume are obtained by combining the road traffic gate data, and other monitoring systems are not required to be specially arranged, so that the method is convenient and quick; 2) The invention combines the concept of material deformation to accurately divide the running state into three types and determine various thresholds, and the invention can excavate the resistance of traffic flow to interference, thereby effectively reducing redundant external control measures; 3) According to the invention, through the traffic flow increase rate and the interference of the bus in and out process on the traffic flow, corresponding management measures are formulated aiming at the toughness states and the service quality grades of different road segments in each period, so that the management efficiency is improved, and the resource cost is saved.
Drawings
FIG. 1 is a flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating traffic flow toughness status determination according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a hierarchical management approach in an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
The urban road section hierarchical control method based on traffic flow toughness as shown in fig. 1 comprises the following steps:
1. data acquisition
Road section bayonet data acquisition is divided into dynamic data and static data. The dynamic data comprise road section traffic flow dynamic bayonet data, and the static data comprise bayonet coordinate positions, bus station coordinate positions and urban road network center lines. Taking the data of the network interface card in a certain city of Xuan city of Anhui province as an example, as shown in tables 1-3, the obtained data comprises a gate number, a lane number, a starting observation time, an ending observation time, a road section number, a starting node of vehicle running, a stopping node of vehicle running, an average vehicle flow, an average travel time and a lane running direction. The bus station coordinate position and the urban road network central line data can be obtained by purchasing or calling a map interface.
TABLE 1 average traffic volume for partial road segments
TABLE 2 average journey time for partial road segments
TABLE 3 part of road section Bayonet observation lane number and travel direction
2. Data preprocessing
Preprocessing static data and road traffic flow dynamic gate data, and matching dynamic data with the static data, wherein the process is as follows:
s21, counting the number of lanes of the road section: counting the number of lanes in the same direction of different gate IDs and recording corresponding lane numbers;
s22, extracting road section traffic volume data in the bayonet records: calculating the sum of traffic flows of all lanes in the same driving direction corresponding to the same bayonet ID in the same time period by using the lane numbers in the same direction recorded in the S21;
s23, matching the road section traffic volume with the average travel time of the nodes: the method comprises the steps of utilizing Arcgis software, matching longitude and latitude information of a bayonet with information of a road center line, judging the running direction of a traffic flow according to a node number of the road center line and a starting node and a stopping node of the traffic flow in the running time data, and finally enabling the running time data to correspond to data of the same road section number and the running direction in a time period in traffic flow one by one;
s24, counting the number of bus stations among road section nodes: in the coordinate information of urban public transport lines and public transport stops, stations with different lines and same longitude and latitude are screened at first, and the number of stations is counted; and adding the bus stop coordinates with overlapping station coordinate information removed to the road center line loaded by Arcgis software, and finally counting the number of bus stops on the road sections with different numbers.
3. Road section traffic flow toughness index calculation
S31, determining an index.
The travel time of the traffic flow passing through the road section is obtained through the road section bayonet data, and the whole-day time-sharing toughness value of the traffic flow of the road section is calculated, which specifically comprises the following steps:
let the average travel time of the vehicle at the initial zero time t 0 There are the following cases:
(1) If the average travel time t of the vehicle is the next statistical moment 1 ≤t 0 The time at which the 1 st minimum appears is t 0_low ；
(2) If the average travel time t of the vehicle is the next statistical moment 1 ＞t 0 Then t 0 ＝t 0_low 。
The ith maximum value of the average travel time of the vehicle is t i_high The method comprises the steps of carrying out a first treatment on the surface of the The average travel time of the vehicle at the next statistical moment after the occurrence of 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 . Road section traffic flow ith time period delta t i The toughness value of (c) can be calculated as:
wherein R is i At delta t for road section i Toughness value of time period; Δt (delta t) i At t (i-1)_low And t i_low Is provided.
S32, judging.
The traffic state of the road section is divided into three states from the viewpoint of the traffic flow operation self-adaption capability by combining the time variation of the traffic flow of the road section and the magnitude of the time-division toughness value, and the three states are respectively expressed as plasticity (0, R p ) State of elastic transition (R) p ,R e ) And elasticity (R) e , + -infinity A kind of electronic device. Wherein R is p Toughness upper limit value R for showing plastic change of road traffic flow time e And the toughness threshold value represents the elastic change of the traffic flow time of the road section.
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, and each generated cluster is compact and the categories are mutually independent. The algorithm comprises the following steps:
s331, determining an initial cluster center for each cluster, so that K initial cluster centers exist;
s332, distributing each sample to the nearest neighbor clusters according to the minimum distance principle;
in calculating the distance between samples, the distance principle is usually Euclidean distance,Manhattan distance, cosine distance, etc. The present embodiment selects the Euclidean distance d (x i ,x j ) The following is shown:
wherein x is i ＝(x i1 ,x i2 ,…,x im ),x j ＝(x j1 ,x j2 ,…,x jm ) Each sample has m attribute values; x is x ik The kth attribute value, x, for the ith sample jk The kth attribute value for 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 samples, the higher the similarity between the two samples, and vice versa.
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;
s335, ending the clustering to obtain K clusters.
And finally, sorting the cluster centers of all types obtained by using a K-means mean value algorithm from small to large, wherein the mean value of the cluster centers of adjacent types is the threshold value of each state. Let the cluster centers after the sorting be h respectively 1 ,h 2 ,h 3 Each state threshold is represented by the following formula:
taking the bayonet data of the urban road network in a certain day of the Xuan city of Anhui province as an example, setting an initial cluster value as 3 by using Python and randomly giving 3 initial cluster centers. The time-sharing toughness value data of each road section is input, the values of the final three clustering centers can be obtained through continuous iteration circulation, the threshold value corresponding to each state can be determined according to the average value of each class of clustering centers, and the result is shown in table 4.
TABLE 4 road segment traffic flow toughness status threshold
The road segment traffic flow plasticity state threshold in table 4 is 0.045. And when the ratio of the recovery degree after the maximum value of the traffic flow time of a certain road section is smaller than 0.045, the current running state is considered to be a plastic state.
4. Road section service quality calculation method based on two-stream theory
S41, establishing a mesoscopic traffic flow model of the two-flow theory.
Vehicles in traffic flow are divided into two categories: one is a moving vehicle and one is a stopped vehicle. Assume that:
(1) The average running speed of the vehicle in the road network is proportional to the proportion of the running vehicle;
(2) The parking time proportion of the circulating test vehicle (traffic observation vehicle) in the road network is equal to that of the running vehicle in the same period in the road network.
Based on assumption (1), a relationship between the average running speed of the vehicle and the specific gravity of the running vehicle can be obtained:
U r ＝U m f r n
in U r For average travelling speed, U m F is the maximum average running speed r For the specific gravity of the driving vehicle, n is the road traffic service quality index.
Average travel speed U r f r The expression, in combination with the above formula, can be obtained:
U＝U m f r n+1
and f r +f s ＝1，f s Is substituted into the parking proportion to obtain
U＝U m (1-f s ) n+1
Converting the above formula into a relationship of average travel time, T representing the average travel time, T r Represents average travel time, T m Representing the average shortest travel time. Unit distance t=1/U, T r ＝1/U r ，T m ＝1/U m Substitution into a velocity relation is obtained:
T＝T m (1-f s ) -(n+1)
based on assumption (2), available test vehicle parking time T in road network s Instead of the parking time of all vehicles:
substituting the obtained product into a time relation to obtain,
T＝T m [1-(T s /T)] -(n+1)
because T=T r +T s Therefore, it is
For easy calibration, natural logarithms are taken from both sides of the equation:
s42, considering the influence of the traffic flow growth rate and the bus arrival on the road section traffic flow running based on the road section service quality and the running toughness state.
It is assumed that the link average shortest travel time is equal to the link average shortest travel time.
In the method, in the process of the invention,is delta t i Average travel time of inner road section, V T Is delta t i Average traffic volume of inner road section->For the traffic quantity corresponding to the shortest running time of the road section, p is the total number of bus stations at one side of the same direction of the road section, R i For the road section delta t i The toughness value of the time period is set,is delta t i The time for the kth bus to get in and out of the road section; if the bus station is an estuary station, < > for>Is delta t i Queuing and entering time of buses at the road section; if the bus station is a non-estuary station, < > for the bus station>Is delta t i And the sum of the queuing and arrival time of buses in the road section and the waiting time of the last bus for passengers to get on.
And (4) calculating the average running time and the road service quality index of the road section in a time-sharing way on a whole day by combining the relational expression in the step (S41), wherein the average running time and the road service quality index are shown in a table 5.
TABLE 5 time-sharing average travel time and road quality index for a segment
5. And (3) carrying out hierarchical management on road sections taking the traffic flow toughness state and the road service quality level into consideration.
S51, grading the service quality of the road section: the service quality of road sections is divided into a first level, a second level and a third level, and each road section delta t in the urban road network is divided into i And (3) carrying out the k-means clustering method described in S33 on the service quality indexes in the road sections to obtain the threshold value corresponding to the service quality grade of each road section, wherein the threshold value is shown in Table 6.
TABLE 6 QoS class threshold for certain road segment
S52, carrying out time-sharing operation optimization management measures on the road sections: respectively for each road section deltat i The toughness classification of the internal traffic flow and the road service quality level determine the traffic flow management measures of the road section, see fig. 3.
The foregoing 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 would be obvious to one skilled in the art are deemed to be within the scope of the present invention, and the scope is defined by the appended claims.
Claims (1)
1. The urban road section hierarchical control method based on traffic flow toughness is characterized by comprising the following steps of:
s1, data acquisition; the dynamic data comprise road section traffic flow dynamic gate data, and the static data comprise gate 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 gate data according to the acquired data, matching the traffic volume of the road sections with the travel time, and simultaneously counting the number of bus stations on each road section;
s2, the specific process is as follows:
s21, counting the number of lanes of the road section: counting the number of lanes in the same direction in the data recorded by the gate, and numbering lanes in the same direction respectively;
s22, extracting road section traffic volume in the bayonet data: calculating the traffic flow sum of all lanes in the same running direction corresponding to the same bayonet in the unit statistical period by using the lane number;
s23, matching the road section traffic volume with the travel time: the method comprises the steps of utilizing Arcgis software, matching longitude and latitude information of a bayonet with information of a road center line, judging the running direction of a traffic flow according to a node number of the road center line and a starting node and a stopping node of the traffic flow in the running time data, and finally enabling the running time data to correspond to data of the same road section number and the running direction in a time period in traffic flow one by one;
s24, counting the number of bus stations among the nodes of the road section: in the coordinate information of urban public transport lines and public transport stops, stations with different lines and same longitude and latitude are screened at first, and the number of stations is counted; adding bus stop coordinates with overlapping station coordinate information removed to a road center line loaded by Arcgis software, and finally counting the number of bus stops on road sections with different numbers;
s3, dividing traffic flow toughness states; calculating the whole-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 thresholds of the three toughness states by carrying out k-means clustering on the toughness values;
and S3, calculating the whole-day time-sharing toughness value of the road section traffic flow based on the travel time, wherein the specific process is as follows:
let the average travel time of the vehicle at the initial zero time t 0 The method comprises the steps of carrying out a first treatment on the surface of the If the average travel time t of the vehicle is the next statistical moment 1 ≤t 0 The moment when the first minimum appears is t 0_low The method comprises the steps of carrying out a first treatment on the surface of the If the average travel time t of the vehicle is the next statistical moment 1 ＞t 0 Then t 0 ＝t 0_low The method comprises the steps of carrying out a first treatment on the surface of the The ith maximum value of the average travel time of the vehicle is t i_high The method comprises the steps of carrying out a first treatment on the surface of the The average travel time of the vehicle at the next statistical moment after the occurrence of 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 The method comprises the steps of carrying out a first treatment on the surface of the Road section traffic flow ith time period delta t i The toughness value of (c) can be calculated as:
s4, calculating a road section service quality index; based on a mesoscopic traffic flow model of the two-flow theory, obtaining a relational expression among the average running time of the road section, the average shortest running time of the road section and the average travel time of the road section by using the service quality index of the road section; then calculating the whole-day time-sharing average running time of the road section and the service quality index of the road section based on the traffic volume and the toughness value of the road section and considering the influence of the bus in and out on the running of the traffic flow of the road section;
s4, obtaining the average running time T of the road section by using the road section service quality index based on the mesoscopic traffic flow model of the two-flow theory r Average shortest travel time T of road section m The relation between the average travel time T of the road section is as follows:
wherein n is a road section service quality index;
the whole-day time-sharing average running time and the road section service quality index of the road section are calculated, and the formula is as follows:
in the method, in the process of the invention,for average travel time of road section within Deltati, V T Is delta t i Average traffic volume of inner road section->For the traffic quantity corresponding to the shortest running time of the road section, p is the total number of bus stations at one side of the same direction of the road section, R i For the road section delta t i The toughness value of the time period is set,is delta t i The time for the kth bus to get in and out of the road section; if the bus station is an estuary station, < > for>Is delta t i Queuing and entering time of buses at the road section;if the bus station is a non-estuary station, < > for the bus station>Is delta t i The sum of the queuing and arrival time of buses at the road section and the waiting time of the last bus for passengers to get on;
s5, road section operation hierarchical management; dividing the road service quality grade into three levels, and then determining a threshold value corresponding to the three levels of road service quality grade by carrying out k-means clustering on the road section service quality indexes, wherein the threshold value is specifically as follows: for each road section delta t in urban road network i Carrying out a k-means clustering method on the road section service quality indexes in the road sections to obtain a threshold value corresponding to the road service quality grade of each road section;
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, and each generated cluster is compact and the categories are mutually independent;
the algorithm comprises the following steps:
(1) Determining an initial cluster center for each cluster, so that K initial cluster centers exist;
(2) Distributing each sample to the nearest neighbor clusters according to the minimum distance principle;
the distance principle used in calculating the distance between samples is Euclidean distance d (x i ,x j ) The following is shown:
wherein x is i ＝(x i1 ,x i2 ,…,x im ),x j ＝(x j1 ,x j2 ,…,x jm ) Each sample has m attribute values; x is x ik The kth attribute value, x, for the ith sample jk A kth attribute value for a jth sample;
(3) Using the sample mean value in each cluster as a new cluster center;
(4) Repeating (2) and (3) until the cluster center is no longer changed;
(5) Ending the clustering to obtain K clusters;
finally, sorting the cluster centers of all types obtained by using a K-means mean algorithm from small to large, wherein the mean value of the cluster centers of adjacent types is the threshold value of each state; let the cluster centers after the sorting be h respectively 1 ,h 2 ,h 3 Each state threshold is represented by the following formula:
the road traffic flow management measures are determined according to the traffic flow toughness state and the road service quality level, specifically: when the traffic flow plastic state operates, the number of times of bus stops at the road section is reduced, and the traffic volume at the road section is reduced; when the traffic flow is in an elastic transition state, the number of times of bus stops at the road section is reduced, the traffic volume at the road section is controlled, and the sudden increase of the traffic volume is avoided; when the traffic flow bullet state operates, no external management measures are needed; for the three-level road service quality level, the number of times of entering a bus at a road section is reduced; for the secondary road service quality level, signal timing and road channeling should be optimized; for the first-level road service quality level, no external management measures are needed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210942349.1A CN115311858B (en) | 2022-08-08 | 2022-08-08 | Urban road section hierarchical control method based on traffic flow toughness |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210942349.1A CN115311858B (en) | 2022-08-08 | 2022-08-08 | Urban road section hierarchical control method based on traffic flow toughness |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115311858A CN115311858A (en) | 2022-11-08 |
| CN115311858B true CN115311858B (en) | 2023-07-21 |
Family
ID=83860237
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210942349.1A Active CN115311858B (en) | 2022-08-08 | 2022-08-08 | Urban road section hierarchical control method based on traffic flow toughness |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115311858B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115985088B (en) * | 2022-11-30 | 2024-01-26 | 东南大学 | Traffic flow stability improving method based on vehicle collision time feedback |
| CN116307260B (en) * | 2023-05-11 | 2023-08-08 | 深圳市规划和自然资源数据管理中心(深圳市空间地理信息中心) | Urban road network toughness optimization method and system for disturbance of defective road sections |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4811063B2 (en) * | 2006-03-10 | 2011-11-09 | アイシン・エィ・ダブリュ株式会社 | Traffic data calculation system |
| CN110634287B (en) * | 2019-08-26 | 2021-08-17 | 上海电科智能系统股份有限公司 | Urban traffic state refined discrimination method based on edge calculation |
| CN111144727B (en) * | 2019-12-18 | 2023-07-11 | 东南大学 | Urban rail transit arrival passenger flow toughness evaluation system and method |
| CN111599180B (en) * | 2020-06-02 | 2020-12-01 | 悉地（苏州）勘察设计顾问有限公司 | Urban bus line toughness assessment method and system |
| CN113409578B (en) * | 2021-06-25 | 2022-05-13 | 北京航空航天大学 | Traffic network health portrait method and system based on fuzzy clustering |
| CN114093168B (en) * | 2021-11-18 | 2022-11-11 | 长安大学 | Method for evaluating urban road traffic running state based on toughness visual angle |
| CN114446051A (en) * | 2022-01-06 | 2022-05-06 | 东南大学 | Method for identifying weak toughness of urban road network traffic |
-
2022
- 2022-08-08 CN CN202210942349.1A patent/CN115311858B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN115311858A (en) | 2022-11-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102737508B (en) | Urban road traffic state detection method combined with support vector machine (SVM) and back propagation (BP) neural network | |
| CN102750824B (en) | Urban road traffic condition detection method based on voting of network sorter | |
| CN115311858B (en) | Urban road section hierarchical control method based on traffic flow toughness | |
| Liu et al. | Bus arrival time prediction based on LSTM and spatial-temporal feature vector | |
| Luo et al. | Queue length estimation for signalized intersections using license plate recognition data | |
| CN110197588A (en) | A kind of truck driving behavior appraisal procedure and device based on GPS track data | |
| CN102136190B (en) | Dispatching management system and method for event emergency response of urban bus passenger transport | |
| CN108346292B (en) | Urban expressway real-time traffic index calculation method based on checkpoint data | |
| Hellinga et al. | Assessing expected accuracy of probe vehicle travel time reports | |
| CN109191849B (en) | Traffic jam duration prediction method based on multi-source data feature extraction | |
| CN112700072A (en) | Traffic condition prediction method, electronic device, and storage medium | |
| CN102750825B (en) | Urban road traffic condition detection method based on neural network classifier cascade fusion | |
| CN109686090B (en) | Virtual traffic flow calculation method based on multi-source data fusion | |
| CN108417023A (en) | Traffic zone central point choosing method based on taxi on-board and off-board space of points cluster | |
| CN105825310A (en) | Taxi passenger-searching path recommendation method based on information entropy | |
| CN104778834A (en) | Urban road traffic jam judging method based on vehicle GPS data | |
| CN113436433B (en) | Efficient urban traffic outlier detection method | |
| CN115691120A (en) | Congestion identification method and system based on highway running water data | |
| Oh et al. | Real-time inductive-signature-based level of service for signalized intersections | |
| Xu et al. | Utilizing structural equation modeling and segmentation analysis in real-time crash risk assessment on freeways | |
| CN109544967B (en) | Bus network running state monitoring method based on low-frequency AVL data | |
| CN110766940A (en) | Method for evaluating running condition of road signalized intersection | |
| CN105575120B (en) | The floating car data parking behavior pattern cleaning method calculated towards road real-time speed | |
| CN115662124A (en) | GPS track data road section flow matching method based on network coding | |
| CN113408833A (en) | Public traffic key area identification method and device and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |