CN111931317A - Regional congestion road network boundary control method based on vehicle-mounted GPS data - Google Patents

Regional congestion road network boundary control method based on vehicle-mounted GPS data Download PDF

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CN111931317A
CN111931317A CN202010495328.0A CN202010495328A CN111931317A CN 111931317 A CN111931317 A CN 111931317A CN 202010495328 A CN202010495328 A CN 202010495328A CN 111931317 A CN111931317 A CN 111931317A
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刘攀
何子昂
徐铖铖
李志斌
季彦婕
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Southeast University
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Abstract

The invention discloses a regional congestion road network boundary control method based on vehicle-mounted GPS data, which is characterized in that a regional road network microscopic simulation model is constructed by means of the vehicle-mounted GPS data and a small amount of actually measured data, a corresponding network basic graph (NFD) is obtained, a road network accumulated vehicle number critical value range is determined on the basis, road network boundary control is further carried out through a signal lamp arranged at the upstream of a road network boundary, the road network accumulated vehicle number is kept near a target vehicle number, and the running efficiency of a road network is kept at an optimal level. The method realizes regional road network modeling and NFD acquisition based on vehicle-mounted GPS data, and solves the problem of NFD instability caused by direct control of the road network boundary intersection by arranging the control intersection at the upstream of the road network boundary intersection. The regional congestion road network boundary control method based on vehicle-mounted GPS data has practical engineering application value in modeling of urban regional congestion road networks and boundary control based on signal lamps.

Description

Regional congestion road network boundary control method based on vehicle-mounted GPS data
Technical Field
The invention belongs to the field of regional road network traffic control, and particularly relates to a regional congestion road network boundary control method based on vehicle-mounted GPS data, in particular to VISSIM (visual identification system) simulation modeling based on vehicle-mounted GPS data and regional road network boundary total control including control delay.
Background
Along with the increasing popularization of motor vehicles, large and medium cities in China face frequent traffic congestion, large-area traffic congestion often occurs in small cities and even villages and towns on holidays, and the urban traffic problem increasingly becomes a social focus problem. In order to solve the problem of increasingly systematized and global traffic jam, the problems of traffic model construction and traffic management strategy formulation need to be explored from a network level urgently. In this context, Network Fundamental map theory (NFD) is used to analyze and solve the problem of urban regional road Network traffic congestion, where congestion is common. The NFD is considered not to depend on the trusted network OD data, is independent of individual traffic behavior, has the characteristics of simplicity and feasibility, and can be used for developing traffic management strategies by means of real-time traffic data.
In order to make a reasonable regional road network traffic management strategy, a traffic model capable of reflecting actual conditions needs to be constructed firstly, and the most important of the traffic model is the problem of data sources. For the flow of each road section in the regional road network, the method for respectively surveying each road has huge workload, the start and stop information of vehicles is difficult to determine, and domestic urban roads have few vehicle detection devices. And the data acquisition and processing technology based on the vehicle-mounted GPS which is raised in recent years provides a new idea for establishing a network traffic model. Through analysis and processing of a large amount of vehicle-mounted GPS data, the space-time trajectory data of the vehicle can be obtained, and then the flow and the speed of each road section can be obtained through the permeability, so that a traffic model which can reflect actual traffic conditions better can be obtained.
After the well-calibrated regional road network traffic model is obtained, the control intersection is directly arranged at the road network boundary intersection to cause the instability of NFD, so that the control intersection is arranged at the upstream intersection of the protected road network boundary intersection. Therefore, there is a problem of control delay from the control intersection to the protected road network, and the conventional processing method often ignores the existence of control delay. In the real-time dynamic complex traffic network situation, the problem of road network boundary control parameter estimation under the condition of control delay needs to be researched according to the actual layout situation of control intersections, and various limitations such as maximum and minimum phase time of each control intersection, the proportion of traffic flowing into a protected road network and the like need to be specifically considered, so that the requirements in actual road network control are practically met.
Disclosure of Invention
In order to solve the above problems, the present invention provides a regional congestion road network boundary control method based on vehicle-mounted GPS data, which firstly solves the problem of establishing a regional road network VISSIM traffic model based on vehicle GPS data, and on the basis of the problem, develops a road network total amount control method for controlling flow through an intersection upstream of a road network boundary, which realizes a control target for keeping the number of vehicles in the road network near an optimal value in consideration of actual conditions in regional road network control, particularly in consideration of control time delay, and for this purpose, the present invention provides a regional congestion road network boundary control method based on vehicle-mounted GPS data, and is characterized in that the method comprises the following steps:
step 1) collecting real-time data of a vehicle carrying a GPS (global positioning system), including data of license plate ID, time, position, direction, speed and the like, and collecting field measurement data, including vehicle type proportion, permeability of a selected GPS vehicle, conditions of a road and a lane containing channeling, a signal timing scheme and other traffic management strategy conditions containing traffic restriction;
step 2) clustering the peak time GPS data according to the weather condition and the attribute of the holiday and the festival, cleaning the data, and then processing the data in a certain time step;
step 3) map matching is carried out on the GPS data points by adopting ArcGIS software on the basis of Openmap map data, the track of the vehicle and the space-time corresponding relation with the road are determined on the basis of a single vehicle, and the flow and the average speed data of the GPS vehicle selected by each road section in a single time step are obtained;
step 4) assuming that the permeabilities of the selected GPS vehicles in the road network are consistent, obtaining total flow data in each step length of each road section according to the average permeability of the measured data arrangement, and taking the average speed of the selected GPS vehicles as corresponding speed data;
step 5) reversely deducing OD data according to the road section flow data to establish a VISSIM (visual identification system) road network model containing a dynamic distribution module;
step 6) constructing a VISSIM (visual static identity module) containing a dynamic distribution module according to the actual road line type, channeling, lanes, signal timing, other traffic management measures, vehicle type proportion and the OD (origin-destination) conditions, wherein the dynamic distribution module adopts an improved Logit model, namely a Kirchhoff formula for path selection, and the specific formula is as follows:
Figure BDA0002522573150000031
wherein, P (R)j) Is the probability that the path j is selected,
Figure BDA0002522573150000032
is the utility of path j, CjIs the total cost of path j, k is the sensitivity coefficient of the model;
step 7) calibrating the model according to the obtained flow and speed data of each road section of the road network;
step 8), obtaining the road network vehicle accumulation quantity N (t) and the road network output vehicle number parameter P (t) in each step to obtain the NFD of the road network, and determining the road network accumulated vehicle number critical range according to the graph;
step 9) determining a road network vehicle number balance equation containing control delay, which is as follows:
Figure BDA0002522573150000033
wherein D isi(t) internal traffic demand at time t of road network, Iu(t) is the rate of entry of traffic from the uncontrolled boundary intersection at time t,
Figure BDA0002522573150000034
is the delay time tau for controlling the intersection m to enter the road networkgDelay time, UI, for whole road network to enter road networkg(t) and
Figure BDA0002522573150000035
respectively the total amount of the boundary intersections controlled at the upstream intersection and the flow of the individual M entering the road network at the time t, wherein M is the total amount of the upstream intersections with the control boundary, and G (N (t)) is an expression of NFD;
step 10) discretizing the formula (2) by an Euler formula to obtain:
ΔN(k+1)＝AΔN(k)+B[ΔUIg(k-dg)+ΔIu(k)+ΔDi(k)]formula (3)
Wherein, taug＝dgT, T being the time of a single step, dgIs an integer, assuming Δ I within a time stepu(k)+ΔDi(k) 0, a and B are calculated by parameter estimation based on the least squares sum;
step 11) further deducing a standard PI control system through the formula (3) to obtain the expected traffic volume total quantity change quantity delta UI of the road network entering from the control intersectiong(k) The PI control system is specifically as follows:
Figure BDA0002522573150000041
wherein, KPAnd KIRespectively non-negative proportional and integral increments, the delay d at different times being obtained by the change in zgUnder the condition of KP、KIIn relation to the parameter A, B,
Figure BDA0002522573150000042
is in the critical range of the optimal number of vehicles in the road networkThe value of the selected specific vehicle is selected as the value which minimizes the road network delay through trial calculation
Figure BDA0002522573150000043
A final value of;
step 12) taking the signal phase for controlling the intersection to go straight into the road network as the object of flow control, and determining the maximum green time of each relevant phase
Figure BDA0002522573150000044
Minimum green time
Figure BDA0002522573150000045
Limiting conditions such as traffic demands of entering a road network;
step 13) determining the proportion rho of the traffic volume entering the road network to the total traffic volume in the straight-going road network phase entering each control intersectionm；
Step 14) calculating the distributed entering flow change value and the green light time length of the control intersection m within the k step length, wherein the specific calculation method comprises the following steps:
Figure BDA0002522573150000046
Figure BDA0002522573150000047
wherein,
Figure BDA0002522573150000048
is the total change of the traffic volume of the updated control intersection entering the road network, is determined by the step 15) in the last iteration, and the initial value of the total change is equal to delta UIg(k)，lanemThe number of the lanes for controlling the intersection to go straight into the road network,
Figure BDA0002522573150000049
and
Figure BDA00025225731500000410
respectively controlling the traffic demand and the traffic capacity of a downstream boundary intersection corresponding to the intersection m in the kth time step length gm() Is the green time necessary for the total dismissal of the corresponding vehicle number;
step 15) pre-distributing flow change values and green light time lengths to the control intersections reaching the limiting conditions in the step 12), and calculating the updated total traffic change amount of the control intersections entering the road network
Figure BDA00025225731500000411
Step 16) iterates between step 14) and step 15) until a final desired control total apportioned flow value and corresponding phase green time period satisfying the constraints are obtained.
As a further improvement of the invention, the GPS data refreshing frequency is not less than 30 s.
As a further improvement of the invention, the time step of the GPS data processing is 2 minutes.
As a further improvement of the invention, the maximum value of the delay time of all the controlled intersections entering the road network is used as the whole control delay time of the road network.
As a further improvement of the invention, the signal phase for controlling the intersection to enter the road network directly is taken as the object of flow control.
The invention discloses a regional congestion road network boundary control method based on vehicle-mounted GPS data, which is characterized in that a regional road network microscopic simulation model is constructed by means of the vehicle-mounted GPS data and a small amount of actual measurement data, a corresponding network basic graph (NFD) is obtained, a road network accumulated vehicle number critical range is determined on the basis, road network boundary control is further carried out through a signal lamp arranged at the upstream of a regional road network boundary, the road network accumulated vehicle number is kept near the critical vehicle number, and the running efficiency of a road network is kept at the optimal level. The method solves the problems of regional road network modeling and NFD acquisition based on vehicle-mounted GPS data, and the problem of unstable NFD caused by direct control of the road network boundary intersection is solved by arranging the control intersection at the upstream of the road network boundary intersection. The regional congestion road network boundary control method based on vehicle-mounted GPS data has practical engineering application value in modeling of urban regional congestion road networks and boundary control based on signal lamps.
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FIG. 1 is a schematic diagram of road networks and control intersection points in Shanghai forest and field areas;
FIG. 2 is a flow chart of a road network boundary control method with delays based on vehicle GPS data;
fig. 3 is a graph comparing changes in the cumulative number of vehicles in the road network.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the invention provides a regional congestion road network boundary control method based on vehicle-mounted GPS data, which solves the problem of establishing a regional road network VISSIM traffic model based on vehicle GPS data, and develops a road network total amount control method for controlling flow through an upstream intersection of a road network boundary on the basis of the problem.
The following will be further described with reference to the accompanying drawings in the embodiments of the present invention, wherein fig. 2 is a flowchart of a road network boundary control method with delay based on vehicle-mounted GPS data, and the specific steps are as follows;
1) collecting real-time data of a taxi carrying a GPS device, wherein the real-time data comprises data such as a license plate, time, position, direction, speed and the like, and the data refreshing frequency is 10 s;
collecting field measurement data including vehicle type proportion, GPS vehicle permeability used, road and lane conditions including channeling, signal timing scheme, and other traffic management strategy conditions including traffic restrictions;
2) clustering the GPS data at the peak time during the working day under the general weather condition, then cleaning the data, and then processing the data by taking 2 minutes as the time step;
3) on the basis of Openmap map data, map matching operation is carried out on GPS data points by adopting ArcGIS software, the track of a vehicle and the space-time corresponding relation with a road are determined on the basis of a single vehicle, and the selected GPS vehicle flow and average vehicle speed data of each road section in a single time step are obtained;
4) assuming that the permeabilities of the selected GPS vehicles in the road network are consistent, obtaining total flow data in each step length of each road section through the average permeability obtained by the actual measurement data arrangement, and taking the average speed of the selected GPS vehicles as corresponding speed data;
5) reversely deducing OD data according to the road section flow data;
6) according to the actual road line type, channeling, lane, signal timing, other traffic management measures, vehicle type proportion and the OD conditions, a VISSIM (visual static subscriber identity module) comprising a dynamic distribution module is constructed, an improved Logit model (Kirchoff formula) is adopted in the dynamic distribution module for path selection, and the specific formula is as follows:
Figure BDA0002522573150000061
wherein, P (R)j) Is the probability that the path j is selected,
Figure BDA0002522573150000062
is the utility of path j, CjIs the total cost of path j, k is the sensitivity coefficient of the model;
7) calibrating the model according to the obtained flow and speed data of each road section of the road network;
8) obtaining road network vehicle accumulation amount and road network output vehicle number parameters in each step length to obtain the NFD of the road network, and determining a road network accumulated vehicle number critical range according to the graph;
9) determining a balance equation of the number of road network vehicles including control delay, which is specifically as follows:
Figure BDA0002522573150000071
wherein D isi(t) internal traffic demand at time t of road network, Iu(t) is from the upstream intersection at time tThe flow rate at the border crossings without control,
Figure BDA0002522573150000072
is the delay time tau for controlling the intersection m to enter the road networkgDelay time, UI, for whole road network to enter road networkg(t) and
Figure BDA0002522573150000073
respectively the total amount of the boundary intersections controlled at the upstream intersection and the flow of the individual M entering the road network at the time t, wherein M is the total amount of the upstream intersections with the control boundary, and G (N (t)) is an expression of NFD;
10) discretizing the formula (2) by an Euler formula to obtain:
ΔN(k+1)＝AΔN(k)+B[ΔUIg(k-dg)+ΔIu(k)+ΔDi(k)]formula (3)
Wherein, taug＝dgT, T is the time step, dgIs an integer;
assume Δ I within a time stepu(k)+ΔDi(k) 0, a and B are calculated by parameter estimation based on the least squares sum;
11) the standard PI control system is further derived by equation (3), as follows:
Figure BDA0002522573150000074
wherein, KPAnd KIRespectively non-negative proportional and integral increments, the delay d at different times being obtained by the change in zgUnder the condition of KP、KIA relationship with parameter A, B;
Figure BDA0002522573150000075
the method is characterized in that a specific vehicle value selected from the range of the critical vehicle number of the optimal road network is selected as the value which minimizes the road network delay through trial calculation
Figure BDA0002522573150000076
A final value of;
calculating the total change quantity delta UI of the traffic volume expected to enter the road network from the control intersectiong(k)；
12) Taking the phase of the signal for controlling the intersection to go straight into the road network as the object of flow control, and determining the maximum green time of each relevant phase
Figure BDA0002522573150000077
Minimum green time
Figure BDA0002522573150000078
Limiting conditions such as traffic demands of entering a road network;
13) determining the proportion rho of the traffic volume entering the road network to the total traffic volume in the straight-going road network phase entering each control intersectionm；
14) Calculating the change value of the distributed entering flow and the green light duration of the control intersection m within the k step length, wherein the specific calculation method comprises the following steps:
Figure BDA0002522573150000081
Figure BDA0002522573150000082
wherein,
Figure BDA0002522573150000083
is the total change of the traffic volume of the updated control intersection entering the road network, is determined by the step 15) in the last iteration, and the initial value of the total change is equal to delta UIg(k)；
Wherein, lanemThe number of the lanes for controlling the intersection to go straight into the road network,
Figure BDA0002522573150000084
and
Figure BDA0002522573150000085
respectively controlling the traffic demand and the traffic capacity of a downstream boundary intersection corresponding to the intersection m in the kth time step length gm() Is the green time necessary for passing the corresponding number of vehicles;
15) pre-distributing the traffic change value and the green light duration to the control intersection reaching the limiting condition in the step 12), and calculating the updated total traffic change amount of the control intersection entering the road network
Figure BDA0002522573150000086
16) And repeating the iteration from the step 14) to the step 15) until a final distribution flow value meeting the limiting conditions and the corresponding phase green light time length are obtained.
The invention is illustrated below by way of an example of a land net for a Shanghai field area.
The area of the road network is 3.1 square kilometers, and the road network comprises 116 roads and 53 intersections, including 11 boundary upstream signalized intersections for implementing control;
selecting 4.5 hours of late peak to study based on about 13000 taxi GPS data and actual measurement data from 15 days to 31 days at 3 months in 2015, wherein the road network in the selected time period faces serious traffic jam;
through processing taxi GPS data and combining with actually measured data, a VISSIM simulation model containing a dynamic allocation module is established as shown in figure 1;
in the example, the maximum distance from the control intersection to the road network is 876m, the delay time is 315s, d is calculated according to the average speed of 10km/hg3 (in case, 100s is the control time step);
derived from z transform with Kp＝A/(6B)，KI＝(1-A)/(6B)；
By using the least squares sum parameter estimation method, K is obtained when N1430, a 0.667, and B0.009 are each set to onepAnd KIThe best boundary control effect can be obtained when the number is 12 and 4 respectively;
calculating the total expected traffic volume change delta UI of the road network in each control step length according to the parametersg(k)；
Determining maximum green time of each control intersection relative phase
Figure BDA0002522573150000091
Minimum green time
Figure BDA0002522573150000092
The traffic demand of entering a road network and the like;
calculating the change value of the distributed entering flow and the green light duration of the control intersection m within the k step length, wherein the specific calculation method comprises the following steps:
Figure BDA0002522573150000093
Figure BDA0002522573150000094
pre-distributing flow change values and green light time lengths to the control intersections reaching the limiting conditions in the previous steps, and calculating the updated total traffic change amount of the control intersections entering the road network
Figure BDA0002522573150000095
And repeating iteration among the steps until a final distribution flow value meeting the limiting conditions and the corresponding phase green light time length are obtained.
In the case, the implementation of regional congestion road network boundary control based on vehicle-mounted GPS data keeps the number of vehicles in the road network at (or below) the expected number of vehicles, and a good control effect is obtained, as shown in fig. 3 specifically;
the above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.

Claims (5)

1. The regional congestion road network boundary control method based on vehicle-mounted GPS data is characterized by comprising the following steps of:
step 1) collecting real-time data of a vehicle carrying a GPS (global positioning system), including data of license plate ID, time, position, direction, speed and the like, and collecting field measurement data, including vehicle type proportion, permeability of a selected GPS vehicle, conditions of a road and a lane containing channeling, a signal timing scheme and other traffic management strategy conditions containing traffic restriction;
step 2) clustering the peak time GPS data according to the weather condition and the attribute of the holiday and the festival, cleaning the data, and then processing the data in a certain time step;
step 3) map matching is carried out on the GPS data points by adopting ArcGIS software on the basis of Openmap map data, the track of the vehicle and the space-time corresponding relation with the road are determined on the basis of a single vehicle, and the flow and the average speed data of the GPS vehicle selected by each road section in a single time step are obtained;
step 4) assuming that the permeabilities of the selected GPS vehicles in the road network are consistent, obtaining total flow data in each step length of each road section according to the average permeability of the measured data arrangement, and taking the average speed of the selected GPS vehicles as corresponding speed data;
step 5) reversely deducing OD data according to the road section flow data to establish a VISSIM (visual identification system) road network model containing a dynamic distribution module;
step 6) constructing a VISSIM (visual static identity module) containing a dynamic distribution module according to the actual road line type, channeling, lanes, signal timing, other traffic management measures, vehicle type proportion and the OD (origin-destination) conditions, wherein the dynamic distribution module adopts an improved Logit model, namely a Kirchhoff formula for path selection, and the specific formula is as follows:
Figure FDA0002522573140000011
wherein, P (R)j) Is the probability that the path j is selected,
Figure FDA0002522573140000012
is a pathEffect of j, CjIs the total cost of path j, k is the sensitivity coefficient of the model;
step 7) calibrating the model according to the obtained flow and speed data of each road section of the road network;
step 8), obtaining the road network vehicle accumulation quantity N (t) and the road network output vehicle number parameter P (t) in each step to obtain the NFD of the road network, and determining the road network accumulated vehicle number critical range according to the graph;
step 9) determining a road network vehicle number balance equation containing control delay, which is as follows:
Figure FDA0002522573140000021
wherein D isi(t) internal traffic demand at time t of road network, Iu(t) is the rate of entry of traffic from the uncontrolled boundary intersection at time t,
Figure FDA0002522573140000022
is the delay time tau for controlling the intersection m to enter the road networkgDelay time, UI, for whole road network to enter road networkg(t) and
Figure FDA0002522573140000023
respectively the total amount of the boundary intersections controlled at the upstream intersection and the flow of the individual M entering the road network at the time t, wherein M is the total amount of the upstream intersections with the control boundary, and G (N (t)) is an expression of NFD;
step 10) discretizing the formula (2) by an Euler formula to obtain:
ΔN(k+1)＝AΔN(k)+B[ΔUIg(k-dg)+ΔIu(k)+ΔDi(k)]formula (3)
Wherein, taug＝dgT, T being the time of a single step, dgIs an integer, assuming Δ I within a time stepu(k)+ΔDi(k) 0, a and B are calculated by parameter estimation based on the least squares sum;
step 11) Further deducing a standard PI control system by the formula (3) to obtain the expected traffic total quantity change delta UI of entering the road network from the control intersectiong(k) The PI control system is specifically as follows:
Figure FDA0002522573140000026
wherein, KPAnd KIRespectively non-negative proportional and integral increments, the delay d at different times being obtained by the change in zgUnder the condition of KP、KIIn relation to the parameter A, B,
Figure FDA0002522573140000028
the method is characterized in that a specific vehicle value selected in the critical range of the optimal road network vehicle number is selected by trial calculation to minimize road network delay
Figure FDA0002522573140000027
A final value of;
step 12) taking the signal phase for controlling the intersection to go straight into the road network as the object of flow control, and determining the maximum green time of each relevant phase
Figure FDA0002522573140000024
Minimum green time
Figure FDA0002522573140000025
Limiting conditions such as traffic demands of entering a road network;
step 13) determining the proportion rho m of the traffic volume entering the road network to the total traffic volume when each control intersection directly enters the road network;
step 14) calculating the distributed entering flow change value and the green light time length of the control intersection m within the k step length, wherein the specific calculation method comprises the following steps:
Figure FDA0002522573140000031
Figure FDA0002522573140000032
wherein,
Figure FDA0002522573140000033
is the total change of the traffic volume of the updated control intersection entering the road network, is determined by the step 15) in the last iteration, and the initial value of the total change is equal to delta UIg(k)，lanemThe number of the lanes for controlling the intersection to go straight into the road network,
Figure FDA0002522573140000034
and
Figure FDA0002522573140000035
respectively controlling the traffic demand and the traffic capacity of a downstream boundary intersection corresponding to the intersection m in the kth time step length gm() Is the green time necessary for the total dismissal of the corresponding vehicle number;
step 15) pre-distributing flow change values and green light time lengths to the control intersections reaching the limiting conditions in the step 12), and calculating the updated total traffic change amount of the control intersections entering the road network
Figure FDA0002522573140000036
Step 16) iterates between step 14) and step 15) until a final desired control total apportioned flow value and corresponding phase green time period satisfying the constraints are obtained.
2. The regional congested road network boundary control method based on vehicle-mounted GPS data as claimed in claim 1, wherein a GPS data refresh frequency is not less than 30 s.
3. The method for controlling the regional congested road network boundary based on vehicle-mounted GPS data as claimed in claim 1, wherein the time step of GPS data processing is 2 minutes.
4. The regional congested road network boundary control method based on vehicle-mounted GPS data according to claim 1, characterized in that a maximum value of the road network entering delay time of all control intersections is used as the whole road network control delay time.
5. The regional congested road network boundary control method based on vehicle-mounted GPS data according to claim 1, characterized in that a signal phase for controlling an intersection to go straight into the road network is taken as a target of flow control.
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