CN110322054B - Optimized layout method of road section traffic monitor - Google Patents

Optimized layout method of road section traffic monitor Download PDF

Info

Publication number
CN110322054B
CN110322054B CN201910515286.XA CN201910515286A CN110322054B CN 110322054 B CN110322054 B CN 110322054B CN 201910515286 A CN201910515286 A CN 201910515286A CN 110322054 B CN110322054 B CN 110322054B
Authority
CN
China
Prior art keywords
travel time
node
road
highway section
shortest path
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
Application number
CN201910515286.XA
Other languages
Chinese (zh)
Other versions
CN110322054A (en
Inventor
刘建蓓
单东辉
张志伟
靳媛媛
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
CCCC First Highway Consultants Co Ltd
Original Assignee
CCCC First Highway Consultants Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by CCCC First Highway Consultants Co Ltd filed Critical CCCC First Highway Consultants Co Ltd
Priority to CN201910515286.XA priority Critical patent/CN110322054B/en
Publication of CN110322054A publication Critical patent/CN110322054A/en
Application granted granted Critical
Publication of CN110322054B publication Critical patent/CN110322054B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • 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
    • 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

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • Theoretical Computer Science (AREA)
  • General Business, Economics & Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Analytical Chemistry (AREA)
  • Chemical & Material Sciences (AREA)
  • Development Economics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses an optimized layout method of a road section traffic monitor. The method comprises the following steps: firstly, establishing a travel time error estimation model of vehicles on a highway section, and calculating travel time estimation errors of M vehicles on a highway section L; the method comprises the steps of establishing a 0-1 integer programming model for optimal layout of road monitors, and converting an optimal monitor layout problem when the travel time estimation errors of M vehicles on a road section L are minimum into a shortest path problem when the travel time estimation errors of a directed graph are minimum; and solving the shortest path of the directed graph when the travel time estimation error is minimum, wherein the node position corresponding to the shortest path is the optimal layout position of the monitor. The invention can optimally select the optimal position of the traffic detector layout under the precondition of the given number of detectors, ensures that the whole journey time estimation error of the road section is minimized, improves the monitoring accuracy of the traffic detector and has strong economy.

Description

Optimized layout method of road section traffic monitor
Technical Field
The invention belongs to the technical field of intelligent traffic information acquisition, and particularly relates to an optimized layout method of a road section traffic monitor.
Background
With the development of emerging technologies such as sensor technology, computer technology, big data and the like, the real-time collection of high-precision traffic space-time data is possible. In an intelligent traffic architecture system, traffic monitoring provides a basic data acquisition network for road users and traffic management departments by optimally arranging traffic monitors such as microwave, video and geomagnetic monitoring; therefore, traffic monitoring is a bottom foundation of the intelligent traffic architecture, is a touch perception network of the intelligent traffic system, and is one of core contents for realizing intelligent traffic. At present, the following problems exist in real-time dynamic data acquisition of highway traffic in China:
the number and the positions of the monitoring devices depend on experience, and the number and the arrangement positions of the local traffic monitoring devices are not enough; the monitoring means is single, and multiple monitoring devices cannot be effectively integrated; the full coverage is difficult to form, the traditional fixed monitor can only monitor points or sections and cannot comprehensively reflect the running condition of the whole expressway or even an expressway network; therefore, the current road section traffic monitoring has the problems of resource waste and low monitoring accuracy caused by unreasonable arrangement of monitoring equipment. How to optimally arrange the traffic monitoring equipment so as to ensure that the whole journey time estimation error of the road section is minimized, and has strong practical significance on real-time evaluation and response of road traffic management.
Disclosure of Invention
In order to solve the problems, the invention aims to provide an optimized layout method of a road section traffic monitor. On the premise of giving the number of traffic monitors, the invention converts the 0-1 integer programming model optimally arranged on the road section traffic monitors into the shortest path solving algorithm, optimally selects the optimal position for arranging the traffic monitors, and ensures that the whole-course travel time estimation error of the road section is minimized; the method solves the problem of large estimation error of the road traffic state mainly laid empirically, and improves the monitoring accuracy of the road monitor.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
An optimized layout method of a road section traffic monitor comprises the following steps:
step 1, establishing a travel time error estimation model of vehicles on a highway section L, and calculating travel time estimation errors of M vehicles on the highway section L;
the method specifically comprises the following substeps:
sub-step 1.1, dividing road units: selecting a highway section L on which a monitor is to be arranged, wherein L is the length of the highway section; equally dividing a road section L on which a monitor is to be arranged into n equal-length road units j, wherein the length of each road unit j is Deltal, namely L=nDeltal, and the n road units are sequentially numbered as 1,2, …, j … and n along the driving direction;
sub-step 1.2, dividing the monitoring time unit: selecting a total monitoring time T, and uniformly dividing the total monitoring time T into c time units delta T, namely T=c delta T; the spatiotemporal profile of the vehicle trajectory is transformed into n x c square cells.
And 1.3, establishing a travel time error estimation model of the vehicles on the highway section L, and calculating the average absolute errors of the travel time of the M vehicles on the highway section L, namely the travel time estimation errors of the M vehicles on the highway section L.
The method comprises the following specific steps:
first, M vehicles are set on the road section L, and the speed of the mth vehicle at the grid unit (j, t) is as follows
Figure BDA0002094826820000031
Then the average speed of the vehicle in the road unit j during the time interval Δt is: />
Figure BDA0002094826820000032
Next, the travel time of the mth vehicle on the highway section L is estimated as:
Figure BDA0002094826820000033
wherein ,
Figure BDA0002094826820000034
is the estimated travel time of the mth vehicle through road unit j.
Finally, calculating the average absolute error of the travel time of M vehicles on the highway section L as follows:
Figure BDA0002094826820000035
wherein ,τm The real travel time of the mth vehicle passing through the highway section L is the average travel time of the highway section L counted by history;
Figure BDA0002094826820000036
the actual monitoring section speed or the simulated section speed of the highway section L.
The estimated travel time errors epsilon of M vehicles on the highway section L are obtained.
And 2, establishing a 0-1 integer programming model for optimizing the layout of the road monitors, and converting the layout problem of the optimized monitors with the minimum travel time estimation errors of M vehicles on the road section L into the shortest path problem with the minimum travel time estimation errors of the directed graph G (V, A).
Where V is the node set and A is the set of edges formed between nodes.
The method comprises the following specific steps of:
setting the engineering budget of the installation monitor on the highway section L as C max The 0-1 integer programming model for the optimized layout of the road monitor is as follows:
the objective function is:
Figure BDA0002094826820000037
Figure BDA0002094826820000038
Figure BDA0002094826820000039
Figure BDA0002094826820000041
s.t.y 0 ＝y n+1 ＝1；
Figure BDA0002094826820000042
wherein ,
Figure BDA0002094826820000043
ε i，j representing a travel time estimation error from node i to node j; x is x i，j For the first boolean variable, represents whether node pair (i, j) is selected on the shortest path,if the value is 1, otherwise, the value is 0; y is i Representing whether the node i is on the shortest path or not for the second Boolean variable, if so, the value is 1, otherwise, the value is 0; the objective function is to minimize the travel time estimation error; c i Representing the total cost of installing the monitor at node i.
In the above model, constraints 1 and 2 ensure that the shortest path starts at node 0 and ends at node n+1; constraint condition 3 is a logic conservation constraint, which indicates that inflow is equal to outflow at a certain monitoring point position, and connectivity is ensured; constraint 4 indicates that node i belongs to the node on the shortest path if node pair (i, j) starting at node i is selected; constraint 5 is two virtual monitoring points of a starting point and an ending point; constraint 6 is a total cost constraint that can be translated with the number of devices k.
And 3, solving a shortest path with the minimum travel time estimation error of the directed graph G (V, A) by adopting a travel time error estimation model of the vehicle on the highway section L, wherein the position of the middle node corresponding to the shortest path is the optimal layout position of the monitor of the highway section L.
The method comprises the following specific steps:
firstly, setting k monitors on a highway section L, and correspondingly generating k+2 nodes and k+1 layers of acyclic graphs in a directed graph G (V, A), wherein the starting point of the directed graph G is 0, and the end point of the directed graph G is a virtual node n+1.
Second, in the acyclic graph, the acyclic graph at the middle layer contains n-k+1 candidate nodes, i.e., in the p-th layer the acyclic graph contains n-k+1 s k If the candidate upstream boundary element j of the p-th layer acyclic graph is j, j=p, …, n-k+p; the first layer and the last layer of the acyclic graph each include a node, and the node coordinates of the first layer of the acyclic graph are (1, 1), i.e., node (1, 1), and the node coordinates of the last layer thereof are (n+1, k+1), i.e., node (n+1, k+1).
wherein ,sk The candidate upstream boundary element j of (a) represents the road segment s k Starting point of (c) and last segment s k-1 All road units j between the end points of (c).
Again, the candidate section is setThe point (j, p) is the node j of the p-th layer acyclic graph, wherein the p-th layer acyclic graph correspondingly comprises a road section s k Upstream border element j of all candidates; the unidirectional connection arc between the candidate node (j, p) and the candidate node (h, p+1) corresponds to the road segment s k From the start j to the end h of (b), where j < h.
And finally, calculating the travel time estimation errors of all the nodes in the acyclic graph connected with the unidirectional arcs by adopting a travel time estimation error model of the highway section L, and selecting the shortest path with the minimum travel time estimation error from the starting point (1, 1) of the acyclic graph to the ending point (n+1, k+1) of the acyclic graph.
The position of the middle node on the shortest path is the optimal monitor layout position. The intermediate node is the rest node excluding the starting point and the end point.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, through establishing a 0-1 integer programming model for optimizing the layout of the road monitors, the problem of the layout of the optimal monitors with the minimum travel time estimation errors of M vehicles on a road section L is converted into the problem of the shortest path with the minimum travel time estimation errors of a directed graph G (V, A); solving the shortest path of the directed graph G (V, A) with the minimum travel time estimation error by using a travel time error estimation model of the vehicle on the highway section L, optimally selecting the optimal position of the traffic monitor layout, and ensuring the minimum travel time estimation error of the whole course of the road section; the method solves the problem of large estimation error of the traffic state of the road section mainly laid empirically, and improves the monitoring accuracy.
Drawings
FIG. 1 is a flow chart of steps for implementing an optimized layout method of road section traffic monitors according to the present invention;
FIG. 2 is a schematic diagram of the road segment unit and the monitored road segment division of the present invention; fig. 2 (a) is a schematic diagram of road segment unit division; FIG. 2 (b) is a monitored section s k Dividing a schematic diagram;
FIG. 3 is a spatiotemporal profile of a vehicle trajectory;
FIG. 4 is a schematic diagram of the conversion of the sensor layout problem of the present invention to a shortest path problem;
FIG. 5 is an acyclic hierarchical graph of 7 road units and 4 monitor constructions in an embodiment of the invention;
fig. 6 is a diagram illustrating a unit and a road segment corresponding to a shortest path in an acyclic hierarchical graph according to an embodiment of the invention.
Detailed Description
Embodiments and effects of the present invention are described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, an optimized layout method of a road section traffic monitor includes the following steps:
step 1, establishing a travel time error estimation model of vehicles on a highway section L, and calculating travel time estimation errors of M vehicles on the highway section L; the method specifically comprises the following substeps:
sub-step 1.1, dividing road units: selecting a highway section L on which a monitor is to be arranged, wherein L is the length of the highway section; equally dividing a road section L on which a monitor is to be arranged into n equal-length road units j, wherein the length of each road unit j is Deltal, namely L=nDeltal, and the n road units are sequentially numbered as 1,2, …, j … and n along the driving direction; as shown in fig. 2 (a).
When Deltal is small enough, the speed of the vehicle in the road unit is set to be unchanged, and the midpoint of the road unit j is the position of the monitor; setting the monitoring range of each monitor as a road section s k As shown in fig. 2 (b).
Sub-step 1.2, dividing the monitoring time unit: selecting a total monitoring time T, and uniformly dividing the total monitoring time T into c time units delta T, namely T=c delta T; the spatiotemporal profile of the vehicle trajectory is transformed into n×c square cells, typically with a time cell Δt of 20s, 30s, 60s or 5min.
When the speed data exists in the square units, representing that a vehicle passes through; when the square units have no speed data, the square units represent that no vehicle passes; thus, the travel time of the road section L is estimated from the front-rear speed data of each square cell.
And 1.3, establishing a travel time error estimation model of the vehicles on the highway section L, and calculating the average absolute errors of the travel time of the M vehicles on the highway section L, namely the travel time estimation errors of the M vehicles on the highway section L.
First, M vehicles are set on the road section L, and the speed of the mth vehicle at the grid unit (j, t) is as follows
Figure BDA0002094826820000071
Then the average speed of the vehicle in the road unit j during the time interval Δt is: />
Figure BDA0002094826820000072
Next, the travel time of the mth vehicle on the highway section L is estimated as:
Figure BDA0002094826820000073
wherein ,
Figure BDA0002094826820000074
is the estimated travel time of the mth vehicle through road unit j.
Finally, calculating the average absolute error of the travel time of M vehicles on the highway section L as follows:
Figure BDA0002094826820000075
wherein ,τm The real travel time of the mth vehicle passing through the highway section L is the average travel time of the highway section L counted by history;
Figure BDA0002094826820000076
the section speed is simulated for the actual monitored section speed or vissims of the road section L.
The estimated travel time errors epsilon of M vehicles on the highway section L are obtained.
And 2, establishing a 0-1 integer programming model for optimizing the layout of the road monitors, and converting the optimal monitor layout problem of minimizing the travel time estimation errors of M vehicles on the road section L into the shortest path problem of minimizing the travel time estimation errors of the directed graph G (V, A).
Where V is the node set and A is the set of edges formed between nodes.
The method for establishing the 0-1 integer programming model for the optimized layout of the road monitor comprises the following specific steps:
setting the engineering budget of the installation monitor on the highway section L as C max The 0-1 integer programming model for the optimized layout of the road monitor is as follows:
the objective function is:
Figure BDA0002094826820000081
Figure BDA0002094826820000082
/>
Figure BDA0002094826820000083
Figure BDA0002094826820000084
s.t.y 0 ＝y n+1 ＝1；
Figure BDA0002094826820000085
wherein ,
Figure BDA0002094826820000086
ε i，j representing a travel time estimation error from node i to node j; x is x i，j For the first boolean variable, it represents whether node pair (i, j) is selected on the shortest path, if it is a value of 1, otherwise it is 0; y is i Representing whether the node i is on the shortest path or not for the second Boolean variable, if so, the value is 1, otherwise, the value is 0; the objective function is to minimize the travel time estimation error; c i Representing the total cost of installing the monitor at node i.
In this model, constraints 1 and 2 ensure that the shortest path starts at node 0 and ends at node n+1; constraint condition 3 is a logic conservation constraint, which indicates that inflow is equal to outflow at a certain monitoring point position, and connectivity is ensured; constraint 4 indicates that node i belongs to the node on the shortest path if node pair (i, j) starting at node i is selected; constraint 5 is two virtual monitoring points of a starting point and an ending point; constraint 6 is a total cost constraint that can be translated with the number of devices k.
Then, the optimal monitor layout problem that the travel time estimation errors of the M vehicles on the highway section L are minimized is converted into the shortest path problem that the travel time estimation errors of the directed graph G (V, a) are minimized, 4 sensors are laid on the road as shown in fig. 4, and possible sensor layout positions and corresponding graphs G (V, a) are selected.
And 3, solving a shortest path with the minimum travel time estimation error of the directed graph G (V, A) by adopting a travel time error estimation model of the vehicle on the highway section L, wherein the node position corresponding to the shortest path is the optimal layout position of the monitor of the highway section L. The method comprises the following specific steps:
firstly, setting k monitors on a highway section L, and correspondingly generating k+2 nodes and k+1 layers of acyclic graphs in a directed graph G (V, A), wherein the starting point of the directed graph G is 0, and the end point of the directed graph G is a virtual node n+1;
second, in the acyclic graph, the acyclic graph at the middle layer contains n-k+1 candidate nodes, i.e., in the p-th layer the acyclic graph contains n-k+1 s k If the candidate upstream boundary element j of the p-th layer acyclic graph is j, j=p, …, n-k+p; the first layer and the last layer of the acyclic graph respectively comprise a node, the node coordinate of the first layer of the acyclic graph is (1, 1), namely the node (1, 1), and the node coordinate of the last layer of the acyclic graph is (n+1, k+1), namely the node (n+1, k+1);
wherein ,sk The candidate upstream boundary element j of (a) represents the road segment s k Starting point of (c) and last segment s k-1 All road units j between the end points of (c).
For example, the road L is composed of 7 units (n=7), 4 monitors are to be laid (k=4), the constructed acyclic graph is shown in fig. 5, a 5-layer acyclic graph is generated, the first layer and the last layer only comprise one node, and coordinates are (1, 1) and (8, 5); j=2, 3,4,5 in layer 2 (j, 2), other intermediate layers j=p, …, n-k+p; direction arcs (1, 1) to (2, 2) represent road segments s 1 The starting point is located in the unit 1, and the end point is located in the unit 2; direction arcs (1, 1) to (4, 2) represent road segments s 1 The start point is located in the unit 1, and the end point is located in the unit 4; as shown in fig. 6, the paths corresponding to fig. 6 (a) are: (1, 1) - (3, 2) - (5, 3) - (7, 4) - (8, 5); the corresponding paths of fig. 6 (b) are: (1,1) - (4,2) - (6,3) - (7,4) - (8,5).
Thirdly, setting the candidate node (j, p) as a node j of a p-th layer acyclic graph, wherein the p-th layer acyclic graph correspondingly comprises a road section s k Upstream border element j of all candidates; the unidirectional connection arc between the candidate node (j, p) and the candidate node (h, p+1) corresponds to the road segment s k From a start point j to an end point h, wherein j < h;
and finally, calculating the travel time estimation errors of all the nodes in the acyclic graph connected with the unidirectional arcs by adopting a travel time estimation error model of the highway section L, and selecting the shortest path with the minimum travel time estimation error from the starting point (1, 1) of the acyclic graph to the ending point (n+1, k+1) of the acyclic graph.
The method comprises the following specific steps: selecting a node (1, 1) of a layer 1 of the acyclic graph as a starting point, calculating a travel time estimation error epsilon between the starting point and each candidate node on a layer 2 by adopting a travel time estimation error model of a highway section L 1 Selecting a candidate node with the smallest travel time estimation error as a shortest path node of the layer 2; calculating the travel time estimation error epsilon between the shortest path node of the layer 2 and each candidate node on the layer 3 by adopting a travel time estimation error model of the highway section 2 Selecting the node with the smallest travel time estimation error as the shortest path node of the 3 rd layer; and the like, until the shortest path node of the k layer is selected, the shortest path from the starting point (1, 1) of the acyclic graph to the end point (n+1, k+1) of the acyclic graph is obtained.
In the shortest path from the starting point to the end point of the acyclic graph, nodes in other shortest paths except the virtual starting point and the virtual end point are the layout positions of the monitors with the minimum travel time estimation errors.
The invention can optimally select the optimal position of the traffic monitor layout under the precondition of the given number of detectors, and ensure that the whole journey time estimation error of the road section is minimized; the real-time evaluation and response to road traffic management have strong practical significance.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware associated with program instructions, where the foregoing program may be stored in a computer readable storage medium, and when executed, the program performs steps including the above method embodiments; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (4)

1. An optimized layout method of a road section traffic monitor is characterized by comprising the following steps:
step 1, establishing a travel time error estimation model of vehicles on a highway section L, and calculating travel time estimation errors of M vehicles on the highway section L;
step 2, establishing a 0-1 integer programming model for optimizing the layout of the road monitors, and converting the layout problem of the optimized monitors with the minimum travel time estimation errors of M vehicles on the road section L into the shortest path problem with the minimum travel time estimation errors of the directed graph G (V, A);
v is a node set, A is a set of edges formed between nodes;
the method for establishing the 0-1 integer programming model for the optimized layout of the road monitor comprises the following specific steps:
setting the engineering budget of the installation monitor on the highway section L as C max The 0-1 integer programming model for the optimized layout of the road monitor is as follows:
the objective function is:
Figure FDA0004003074560000011
Figure FDA0004003074560000012
Figure FDA0004003074560000013
Figure FDA0004003074560000014
s.t.y 0 ＝y n+1 ＝1；
Figure FDA0004003074560000015
wherein ,
Figure FDA0004003074560000016
ε i，j representing a travel time estimation error from node i to node j; x is x i，j For the first boolean variable, it represents whether node pair (i, j) is selected on the shortest path, if it is a value of 1, otherwise it is 0; y is i For the second boolean variable, it represents whether node i is on the shortest path, if it is, its value is 1, otherwise it is 0; the objective function is to minimize the travel time estimation error; c i Representing the total cost of installing the monitor by node i;
in this model, constraints 1 and 2 ensure that the shortest path starts at node 0 and ends at node n+1; constraint condition 3 is a logic conservation constraint, which indicates that inflow is equal to outflow at a certain monitoring point position, and connectivity is ensured; constraint 4 indicates that node i belongs to the node on the shortest path if node pair (i, j) starting at node i is selected; constraint 5 is two virtual monitoring points of a starting point and an ending point; constraint 6 is a total cost constraint that can be interconverted with the number of devices k;
step 3, solving a shortest path with minimum travel time estimation errors of the directed graph G (V, A) by adopting a travel time error estimation model of the vehicle on the highway section L, wherein the position of a middle node corresponding to the shortest path is an optimized layout position of a monitor of the highway section L;
the intermediate nodes are the rest nodes excluding the starting point and the end point;
the method adopts a travel time error estimation model of vehicles on a highway section L to solve and obtain a shortest path with minimum travel time estimation errors of a directed graph G (V, A), and comprises the following specific steps:
firstly, setting k monitors on a highway section L, and correspondingly generating k+2 nodes and k+1 layers of acyclic graphs in a directed graph G (V, A), wherein the starting point of the directed graph G (V, A) is node 0, and the end point is a virtual node n+1;
second, in the acyclic graph, the acyclic graph at the middle layer contains n-k+1 candidate nodes, i.e., in the p-th layer the acyclic graph contains n-k+1 s k If the candidate upstream boundary element j of the p-th layer acyclic graph is j, j=p, …, n-k+p; the first layer and the last layer of the acyclic graph respectively comprise a node, the node coordinate of the first layer of the acyclic graph is (1, 1), and the node coordinate of the last layer of the acyclic graph is (n+1, k+1);
wherein ,sk The candidate upstream boundary element j of (a) represents the road segment s k Starting point of (c) and last segment s k-1 All road units j between the end points of (a);
again, the candidate node (j, p) is set as the node j of the p-th layer acyclic graph, whereinThe p-th layer acyclic graph correspondingly comprises a road section s k Upstream border element j of all candidates; the unidirectional connection arc between the candidate node (j, p) and the candidate node (h, p+1) corresponds to the road segment s k From a start point j to an end point h, wherein j < h;
and finally, calculating the travel time estimation errors of all the nodes in the acyclic graph connected with the unidirectional arcs by adopting a travel time estimation error model of the highway section L, and selecting the shortest path with the minimum travel time estimation error from the starting point (1, 1) of the acyclic graph to the ending point (n+1, k+1) of the acyclic graph.
2. The method for optimized layout of road segment traffic monitors according to claim 1, wherein step 1 comprises the following sub-steps:
sub-step 1.1, dividing road units: selecting a highway section L on which a monitor is to be arranged, wherein L is the length of the highway section; equally dividing a road section L on which a monitor is to be arranged into n equal-length road units j, wherein the length of each road unit j is Deltal, namely L=nDeltal, and the n road units are sequentially numbered as 1,2, …, j … and n along the driving direction;
sub-step 1.2, dividing the monitoring time unit: selecting a total monitoring time T, and uniformly dividing the total monitoring time T into c time units delta T, namely T=c delta T; the spatiotemporal profile of the vehicle trajectory is transformed into n×c grid cells;
and 1.3, establishing a travel time error estimation model of the vehicles on the highway section L, and calculating the average absolute errors of the travel time of the M vehicles on the highway section L, namely the travel time estimation errors of the M vehicles on the highway section L.
3. The method for optimizing the layout of traffic monitors on highway sections according to claim 2, wherein the step of establishing a travel time error estimation model of vehicles on the highway section L comprises the following specific steps:
first, M vehicles are set on the road section L, and the speed of the mth vehicle at the grid unit (j, t) is as follows
Figure FDA0004003074560000041
Then the average speed of the vehicle in the road unit j during the time interval Δt is: />
Figure FDA0004003074560000042
Next, the travel time of the mth vehicle on the highway section L is estimated as:
Figure FDA0004003074560000043
wherein ,
Figure FDA0004003074560000044
for the estimated travel time of the mth vehicle through road unit j;
finally, calculating the average absolute error of the travel time of M vehicles on the highway section L as follows:
Figure FDA0004003074560000045
wherein ,τm The real travel time of the mth vehicle passing through the highway section L is the average travel time of the highway section L counted by history;
Figure FDA0004003074560000046
the actual monitoring section speed or the simulation section speed of the highway section L;
the estimated travel time errors epsilon of M vehicles on the highway section L are obtained.
4. The method for optimizing the layout of the traffic monitor on the highway section according to claim 1, wherein the shortest path with the minimum travel time estimation error from the start point (1, 1) of the acyclic graph to the end point (n+1, k+1) of the acyclic graph is selected, and the method comprises the following specific steps:
selecting nodes (1, 1) of a layer 1 of the acyclic graph as a starting point, calculating a travel time estimation error between the starting point and each candidate node on a layer 2 by adopting a travel time estimation error model of a highway section L, and selecting a candidate node with the minimum travel time estimation error as a shortest path node of the layer 2; calculating the travel time estimation error between the shortest path node of the layer 2 and each candidate node on the layer 3 by adopting a travel time estimation error model of the road section, and selecting the node with the minimum travel time estimation error as the shortest path node of the layer 3; and the like, until the shortest path node of the k layer is selected, the shortest path from the starting point (1, 1) of the acyclic graph to the end point (n+1, k+1) of the acyclic graph is obtained.
CN201910515286.XA 2019-06-14 2019-06-14 Optimized layout method of road section traffic monitor Active CN110322054B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910515286.XA CN110322054B (en) 2019-06-14 2019-06-14 Optimized layout method of road section traffic monitor

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910515286.XA CN110322054B (en) 2019-06-14 2019-06-14 Optimized layout method of road section traffic monitor

Publications (2)

Publication Number Publication Date
CN110322054A CN110322054A (en) 2019-10-11
CN110322054B true CN110322054B (en) 2023-04-28

Family

ID=68119639

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910515286.XA Active CN110322054B (en) 2019-06-14 2019-06-14 Optimized layout method of road section traffic monitor

Country Status (1)

Country Link
CN (1) CN110322054B (en)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110930694B (en) * 2019-11-06 2020-12-04 浙江大华技术股份有限公司 Traffic detector layout scheme generation method, computer system, and storage medium
CN112017432B (en) * 2020-08-05 2022-07-15 中交第一公路勘察设计研究院有限公司 Method for laying expressway traffic running state perception radar
CN112418079B (en) * 2020-11-20 2021-08-31 浙江科技学院 Industrial building video monitoring and installation method based on combination of map analysis and optimal path searching algorithm of security level
CN112562341B (en) * 2021-02-26 2021-06-29 深圳市城市交通规划设计研究中心股份有限公司 Road detection equipment arrangement method and device and storage medium
CN113935158B (en) * 2021-09-27 2024-04-09 北方工业大学 Traffic detector optimal layout method for multiple points of expressway accident

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105006149A (en) * 2015-07-10 2015-10-28 信融源大数据科技(北京)有限公司 Traffic road condition estimation dynamic iteration method

Family Cites Families (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7813870B2 (en) * 2006-03-03 2010-10-12 Inrix, Inc. Dynamic time series prediction of future traffic conditions
CN102306450B (en) * 2011-08-30 2014-04-16 同济大学 Layout method for traffic detectors of sparse road network
US8615107B2 (en) * 2012-01-11 2013-12-24 Ecole Polytechnique Federale De Lausanne (Epfl) Method and apparatus for multiple object tracking with K-shortest paths
CN103440764B (en) * 2013-08-19 2015-10-28 同济大学 A kind of city road network vehicle driving path reconstruction method based on automatic vehicle identification data
CN104464320B (en) * 2014-12-15 2016-09-07 东南大学 Based on true road network characteristic and the shortest path abductive approach of dynamic travel time
CN104915731B (en) * 2015-06-11 2018-04-27 同济大学 A kind of grand microcosmic integration method of vehicle driving reconstructing path based on automatic vehicle identification data
US9959339B2 (en) * 2015-07-13 2018-05-01 International Business Machines Corporation Journey time estimation
TWI619036B (en) * 2016-02-22 2018-03-21 財團法人資訊工業策進會 Traffic time forecasting system, traffic time forecasting method and traffic model establish method
CN105760960A (en) * 2016-02-29 2016-07-13 东南大学 Park and ride facility optimal siting and capacity determining method based on rail transit
CN105844038B (en) * 2016-03-31 2019-02-05 东南大学 A kind of highway polymorphic type traffic detector Combinatorial Optimization distribution method
CN109923595B (en) * 2016-12-30 2021-07-13 同济大学 Urban road traffic abnormity detection method based on floating car data
US10168176B2 (en) * 2017-03-06 2019-01-01 International Business Machines Corporation Visualizing unidirectional traffic information
CN106652483B (en) * 2017-03-06 2019-06-04 同济大学 The method for laying traffic information test point in regional highway network using detection device
CN109150629B (en) * 2018-10-12 2021-05-14 中交第一公路勘察设计研究院有限公司 Road network multi-type monitoring equipment combined layout method
CN109712398B (en) * 2019-01-22 2021-05-28 江苏智通交通科技有限公司 Expressway travel time estimation model parameter optimization method

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105006149A (en) * 2015-07-10 2015-10-28 信融源大数据科技(北京)有限公司 Traffic road condition estimation dynamic iteration method

Also Published As

Publication number Publication date
CN110322054A (en) 2019-10-11

Similar Documents

Publication Publication Date Title
CN110322054B (en) Optimized layout method of road section traffic monitor
CN109754605B (en) Traffic prediction method based on attention temporal graph convolution network
CN109544911B (en) Urban road network traffic state prediction method based on LSTM-CNN
Kurzhanskiy et al. Active traffic management on road networks: a macroscopic approach
Bar-Gera Origin-based algorithm for the traffic assignment problem
Ren et al. Mtrajrec: Map-constrained trajectory recovery via seq2seq multi-task learning
Wang et al. Efficient multiple model particle filtering for joint traffic state estimation and incident detection
Huang et al. A bimodal Gaussian inhomogeneous Poisson algorithm for bike number prediction in a bike-sharing system
Liu et al. Control method of urban intelligent parking guidance system based on Internet of Things
Lu et al. Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach
Mucsi et al. An Adaptive Neuro-Fuzzy Inference System for estimating the number of vehicles for queue management at signalized intersections
CN103052128A (en) Wireless sensor network-based energy-efficient collaborative scheduling method
CN114519932A (en) Regional traffic condition integrated prediction method based on space-time relation extraction
Huang et al. Identification and prediction of urban traffic congestion via cyber-physical link optimization
CN113762578A (en) Training method and device of flow prediction model and electronic equipment
Li Predicting short-term traffic flow in urban based on multivariate linear regression model
Xing et al. A data fusion powered bi-directional long short term memory model for predicting multi-lane short term traffic flow
Zhang et al. Travel time prediction of urban public transportation based on detection of single routes
CN113838296B (en) Traffic signal control method, device, equipment and storage medium
CN113627676B (en) Traffic prediction method and system based on multi-attention causal relationship
Zou et al. City-level traffic flow prediction via LSTM networks
Seo et al. LoRa based architecture for smart town traffic management system
Qin et al. A reliable energy consumption path finding algorithm for electric vehicles considering the correlated link travel speeds and waiting times at signalized intersections
Zhang et al. Boosted trajectory calibration for traffic state estimation
Yang et al. Spatiotemporal DeepWalk gated recurrent neural network: a deep learning framework for traffic learning and forecasting

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