CN111754769A - Road network traffic flow characteristic simulation method based on Manhattan city network with long-range continuous edges - Google Patents

Road network traffic flow characteristic simulation method based on Manhattan city network with long-range continuous edges Download PDF

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CN111754769A
CN111754769A CN202010438973.9A CN202010438973A CN111754769A CN 111754769 A CN111754769 A CN 111754769A CN 202010438973 A CN202010438973 A CN 202010438973A CN 111754769 A CN111754769 A CN 111754769A
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刘端阳
范鑫烨
阮中远
沈国江
刘志
杨曦
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Zhejiang University of Technology ZJUT
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Abstract

The method comprises the steps of firstly constructing a Manhattan city network, randomly setting long-range continuous edges, and setting traffic lights by adopting a single-port clockwise regular release rule; then, updating the vehicle state by adopting an update rule based on a NaSch model, and setting different maximum vehicle running speeds for the Manhattan city network and the long-range continuous edge; then, randomly selecting a starting point and an end point of the vehicle, and setting the vehicle to run according to the shortest path; and finally, setting the particle density and the total number of particles, initializing a system, and simulating the road network traffic flow characteristics by adopting a parallel updating rule. The method not only comprehensively considers the road section model, the intersection model and the road network model of the road network traffic flow characteristic, but also sets the long-distance connecting edge, the vehicle OD attribute and the shortest path driving rule, sets different maximum driving speeds of the vehicle for the long-distance connecting edge and the Manhattan city network, and better simulates the actual characteristic of the vehicle and the multi-layer property of the city road network.

Description

Road network traffic flow characteristic simulation method based on Manhattan city network with long-range continuous edges
Technical Field
The invention relates to a road network traffic flow characteristic simulation method for intelligent traffic, which can provide theoretical basis for road planning, traffic control and traffic management.
Background
With the rapid development of economy and the continuous promotion of urbanization, the quantity of motor vehicles kept continuously increases, the load of urban roads continuously increases, so that urban traffic is tested more and more severely, and the problem of traffic jam is more and more serious. How to reasonably plan traffic resources and solve the problem of urban traffic jam becomes a difficult problem which needs to be solved urgently. The traffic flow theory is a theory of analyzing the mutual relation among human, vehicle, road, environment and other factors and researching the relation among the vehicle flow, the speed and the density, and the road network traffic flow characteristic simulation is to simulate the urban road network traffic according to the traffic flow theory, so that the inherent mechanisms of various complex phenomena occurring in a traffic system can be found and explained, and a theoretical basis is provided for the reasonable planning of roads, the full utilization of traffic facilities and the control and management of the traffic system.
The cellular automaton model with a complex theory is most commonly used in the aspect of road network traffic flow characteristic simulation and can simulate road traffic. The cellular automata model has discreteness in time and space, can simulate a plurality of complex nonlinear traffic phenomena through a simple evolution rule, and is the most widely applied model in the traffic flow theory. The cellular automata model considers roads as one grid of cells, and vehicles as self-propelled particles, each grid being one cell, each cell may be occupied by one vehicle or empty. The TASEP model and the NaSch model are two cellular automata models that are commonly used, and are extensions of the 184 model. The TASEP model being ASEPThe model is a special case, which mainly comprises two parts, namely a one-dimensional lattice chain and particles, wherein the one-dimensional lattice chain is connected with the L lattices, and the particles move according to a certain rule, and describes a one-dimensional lattice gas one-way or two-way movement process with a hard nucleus repulsion effect. The TASEP model is simple and can generate abundant dynamics, so the TASEP model is widely applied to the fields of biology, traffic and the like. The NaSch model, proposed by Nagel and Schreckenberg, is widely applied to simulating road traffic. The model mainly comprises two parts, namely a one-dimensional lattice chain and particles, wherein the one-dimensional lattice chain is formed by connecting L lattices, the particles move according to a certain rule, and the particles simulate vehicles in traffic. Each cell in the model can be occupied by only one particle at most, and each cell has only two states, namely a non-empty state and an empty state. In contrast to the TASEP model, the vehicle has more than two speeds, 0 and 1, in the NaSch model. The NaSch model extends the speed of the vehicle to 0,1,2maxWherein v ismaxThe maximum running speed of the vehicle. At each time step, the particles in the system are accelerated, decelerated, and randomly slowed down, and then each bin is updated in parallel.
Many road network traffic flow characteristic simulation methods adopt cellular automata models to simulate road networks and traffic flows thereof, and most of the methods consider the following three aspects of models: the first aspect is a road segment model, such as TASEP model, NaSch model, etc., describing traffic flow characteristics in a one-dimensional lattice chain; the second aspect is a road network model, such as a BML model, a Manhattan model and the like, which describes the network structure characteristics of the urban road network; the third aspect is an intersection model, which describes the control state of the intersection and is divided into a non-signal control intersection and a signal control intersection.
At present, the existing road network traffic flow characteristic simulation method mainly has the following problems: 1) road section models, road network models and intersection models cannot be considered comprehensively; 2) vehicle characteristics such as an OD (Origin Destination: start and end points) attributes, path selection, etc.; 3) the network multi-layer property is not considered, a real urban road network is a multi-layer complex network, and the existing network structure does not embody the characteristic.
Disclosure of Invention
The invention provides a road network traffic flow characteristic simulation method based on a Manhattan city network with long-range continuous edges, aiming at overcoming the defects in the prior art.
The method comprises the steps of firstly constructing a Manhattan city network, randomly setting long-range continuous edges, and setting traffic lights by adopting a single-port clockwise regular release rule; then, updating the vehicle state by adopting an update rule based on a NaSch model, and setting different maximum vehicle running speeds for the Manhattan city network and the long-range continuous edge; then, randomly selecting a starting point and an end point of the vehicle, and setting the vehicle to run according to the shortest path; and finally, setting the particle density and the total number of particles, initializing a system, and simulating the road network traffic flow characteristics by adopting a parallel updating rule. The method not only comprehensively considers the road section model, the intersection model and the road network model of the road network traffic flow characteristic, but also sets the long-distance connecting edge, the vehicle OD attribute and the shortest path driving rule, sets different maximum driving speeds of the vehicle for the long-distance connecting edge and the Manhattan city network, and better simulates the actual characteristic of the vehicle and the multi-layer property of the city road network.
The invention achieves the aim through the following technical scheme, namely, a road network traffic flow characteristic simulation method based on a Manhattan city network with long-distance continuous edges, which comprises the following specific implementation steps:
(1) and constructing a Manhattan city network. The manhattan city network is a square road network consisting of NxN intersections, wherein N is the number of intersections on the side of a square, two adjacent intersections are connected through a road, and the road consists of two parallel lanes with opposite driving directions. The manhattan city network has 4N (N-1) lanes, each lane is divided into L cells, namely lattices, each intersection is composed of one cell, and the value of L is set according to an empirical value. The vehicle is represented by a particle, and stays in a cell of a lane or a cell of an intersection, and the vehicle runs to the right side in the road. At any time, a cell is empty or occupied by a vehicle. Manhattan city networks are used to simulate the ground road networks of cities.
(2) And setting a long-range connecting edge. In ManhattanRandomly setting M long-range continuous-side roads in urban network, for example, arbitrarily selecting two intersections B (x) in network1,y1) And C (x)2,y2) And the two intersections do not have directly connected roads, where x1,y1And x2,y2The coordinate positions of the intersections B and C are respectively, and a long-range continuous road with two-way lanes is arranged between the intersections B and C. Therefore, the total number of lanes on the long-range continuous side in the network is 2M, and the number of cells arranged on each lane on the long-range continuous side is
Figure BDA0002503351240000041
The long-range continuous-side road is positioned at the upper layer of the Manhattan urban network, is similar to an elevated road in the urban road network, and has the same intersection but different maximum driving speeds of vehicles.
(3) And arranging a traffic signal lamp. And arranging traffic lights at each intersection, adopting a single-intersection clockwise timing release rule and not considering yellow lights. Specifically, at a certain time, for any intersection, if the signal light of a certain entrance lane is green, the signal lights of the other three entrance lanes at the intersection are red. If the signal lamp of an entrance lane is a green lamp, the vehicles of the lane can go straight, turn right, turn left or turn around. The intersection signal is four phases, the time length of each phase is T, the signal period is 4T, and the traffic signal lamps of all intersections synchronously change. For the boundary and the intersection with the long-range continuous side, the traffic signal lamp arrangement is as follows:
1) intersection at the corner: the network boundary comprises intersections with four corners, the intersections are only provided with two entrance lanes, and the entrance lanes in the other two directions are vacant; when the traffic signal lamps are arranged, the green lamp of each import lane and the green lamp in the same direction in the network are simultaneously lighted, and meanwhile, according to the clockwise timing release rule, when the signal lamp of the vacant import lane is turned to be the green lamp, the signal lamp of the opposite import lane is arranged to be the green lamp.
2) Other intersections of the boundary: other intersections on the boundary belong to T-shaped intersections, only three entrance lanes are provided, and the entrance lane in the other direction is vacant; when the traffic signal lamps are arranged, the green lamp of each import lane and the green lamp in the same direction in the network are simultaneously lighted, and meanwhile, according to the clockwise timing release rule, when the signal lamp of the vacant import lane is turned to be the green lamp, the signal lamp of the opposite import lane is arranged to be the green lamp.
3) The intersection containing the long-range continuous side comprises: additionally, a phase is added to each signal period, the phase duration is T, and the newly added phase is used for controlling the traffic flow of the upper-layer long-distance side-connected road driving to the lower-layer ground road. Therefore, the signal period of the intersection with the long-range continuous edge is 5T, the signal periods of other intersections without the long-range continuous edge are 4T, when the signal lamps of the intersections are synchronous, all the intersections with the long-range continuous edge keep the signal lamps synchronous, and other intersections without the long-range continuous edge keep the signal lamps synchronous.
(4) An update rule based on the NaSch model is adopted. When the vehicle moves, the vehicle state updating follows NaSch rules, and the updating process from time t to time t +1 is as follows:
s41, acceleration: v. ofi(t+1)→min(vi(t)+1,vmax) (ii) a Reflects the characteristic that drivers tend to run at the maximum speed in actual road traffic;
and s42, decelerating: v. ofi(t+1)→min(vi(t+1),di(t)); in order to ensure that the driver does not collide with the front vehicle in the driving process, the driver decelerates;
randomly moderating with probability p: v. ofi(t+1)→max(vi(t +1) -1, 0); simulating each moment to randomly select a part of vehicles for deceleration so as to reflect uncertain factors such as road conditions, driver moods and the like in actual traffic;
s44. location update: x is the number ofi(t+1)→xi(t)+vi(t + 1); simulating vehicle speed vi(t +1) traveling.
Wherein v isi(t) speed of the ith vehicle at time t, di(t) is the distance between the ith vehicle and the preceding vehicle at time t, xi(t) is the position of the ith vehicle at time t, vmaxThe maximum driving speed of the vehicle; p is randomThe slowing-down probability is in the value range of [0, 1%]During system simulation, each vehicle generates a value between [0,1 ]]Is compared with the probability p, and when the generated random value is less than the probability p, v is updatediThe value of (t +1) is max (v)i(t +1) -1, 0); variable vi(t)，di(t)，xi(t) and vmaxAre dimensionless values related to the number of bins, min () is the minimum function, returns the minimum value in a given input parameter, max () is the maximum function, returns the maximum value in a given input parameter, → values representing updated corresponding variables, such as vi(t+1)→min(vi(t)+1,vmax) Expressed in min (v)i(t)+1,vmax) To update the variable vi(t + 1); vehicle maximum driving speed v in Manhattan city networkmax＝vmax1Maximum running speed v of vehicle on long-distance continuous roadmax＝2vmax1，vmax1The value of (a) is set according to an empirical value; each lane except the head car, di(t)＝xi+1(t)-xi(t) -1 wherein xi+1(t) is the position of the (i +1) th vehicle at time t, and the (i +1) th vehicle is a preceding vehicle of the (i) th vehicle. D of the first vehicle on the laneiThe definition is as follows:
t1) green light: d if the next lane of the leading car is in a congested state or the intersection is occupiediThe distance from the current position of the head car to the intersection, otherwise, diThe distance from the head car to the tail car of the next lane, wherein a lane is in a crowded state if any one of the last two cells of the lane is occupied by a vehicle.
T2) red light: diThe distance from the current position of the head car to the intersection.
(5) A start point and an end point of the vehicle are selected. When the vehicle runs to the terminal, the system allocates a new destination for the vehicle again, namely a new starting and terminal point, the starting point is the original terminal point, the new terminal point is the newly allocated destination, and the total number of the vehicles in the system is kept unchanged.
(6) The vehicle travels according to the shortest route. The shortest path for a vehicle is determined by the intersection it passes through. When the vehicle is about to pass through a certain intersection, the vehicle may have a plurality of shortest paths to travel to the next intersection, and at the moment, the vehicle randomly selects one of the shortest paths as a travel path.
(7) And (5) simulating and simulating road network traffic flow characteristics. Setting a particle density rho and a particle total number 4NL rho (N-1) according to the models and rules determined in the steps (1) to (6), initializing a system, and simulating road network traffic flow characteristics by adopting a parallel updating rule, wherein the particles are used for simulating self-driven vehicles, and the parallel updating rule means that all the particles are updated synchronously. And during system simulation, setting different particle densities rho and different long-distance continuous edge quantities for analyzing and evaluating road network traffic flow characteristics and the influence of the long-distance continuous edge quantities on urban road network traffic flow characteristics.
Preferably, in step (4), the random slowing-down probability p is 0.1.
The invention has the advantages that: (1) the method fully considers three aspects of the traffic flow model of the urban road network, adopts a NaSch model as a road section model, adopts a clockwise timing release rule of a single intersection as an intersection signal rule, and adopts a Manhattan urban network containing long-distance connecting edges as a road network model; (2) the OD attribute is set for the vehicle, and the vehicle runs according to the shortest path, so that the actual characteristics of the vehicle are better simulated; (3) the invention sets different maximum driving speeds of vehicles for the Manhattan city network and the long-distance connecting edge, and better simulates the multi-layer property of the city road network.
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FIG. 1 is a general flow diagram of the process of the present invention.
Fig. 2 is a schematic diagram of a network model of the method of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
The invention discloses a road network traffic flow characteristic simulation method based on a Manhattan city network with long-range continuous edges, which comprises the following specific implementation steps:
(1) and constructing a Manhattan city network. The manhattan city network is a square road network consisting of NxN intersections, wherein N is the number of intersections on the side of a square, two adjacent intersections are connected through a road, and the road consists of two parallel lanes with opposite driving directions. The manhattan city network has 4N (N-1) lanes, each lane is divided into L cells, namely lattices, each intersection is composed of one cell, and the value of L is set according to an empirical value. The vehicle is represented by a particle, and stays in a cell of a lane or a cell of an intersection, and the vehicle runs to the right side in the road. At any time, a cell is empty or occupied by a vehicle. Manhattan city networks are used to simulate the ground road networks of cities.
(2) And setting a long-range connecting edge. Randomly setting M long-range continuous-side roads in Manhattan city network, for example, arbitrarily selecting two intersections B (x) in the network1,y1) And C (x)2,y2) And the two intersections do not have directly connected roads, where x1,y1And x2,y2The coordinate positions of the intersections B and C are respectively, and a long-range continuous road with two-way lanes is arranged between the intersections B and C. Therefore, the total number of lanes on the long-range continuous side in the network is 2M, and the number of cells arranged on each lane on the long-range continuous side is
Figure BDA0002503351240000081
The long-range continuous-side road is positioned at the upper layer of the Manhattan urban network, is similar to an elevated road in the urban road network, and has the same intersection but different maximum driving speeds of vehicles.
(3) And arranging a traffic signal lamp. And arranging traffic lights at each intersection, adopting a single-intersection clockwise timing release rule and not considering yellow lights. Specifically, at a certain time, for any intersection, if the signal light of a certain entrance lane is green, the signal lights of the other three entrance lanes at the intersection are red. If the signal lamp of an entrance lane is a green lamp, the vehicles of the lane can go straight, turn right, turn left or turn around. The intersection signal is four phases, the time length of each phase is T, the signal period is 4T, and the traffic signal lamps of all intersections synchronously change. For the boundary and the intersection with the long-range continuous side, the traffic signal lamp arrangement is as follows:
1) intersection at the corner: the network boundary comprises intersections with four corners, the intersections are only provided with two entrance lanes, and the entrance lanes in the other two directions are vacant; when the traffic signal lamps are arranged, the green lamp of each import lane and the green lamp in the same direction in the network are simultaneously lighted, and meanwhile, according to the clockwise timing release rule, when the signal lamp of the vacant import lane is turned to be the green lamp, the signal lamp of the opposite import lane is arranged to be the green lamp.
2) Other intersections of the boundary: other intersections on the boundary belong to T-shaped intersections, only three entrance lanes are provided, and the entrance lane in the other direction is vacant; when the traffic signal lamps are arranged, the green lamp of each import lane and the green lamp in the same direction in the network are simultaneously lighted, and meanwhile, according to the clockwise timing release rule, when the signal lamp of the vacant import lane is turned to be the green lamp, the signal lamp of the opposite import lane is arranged to be the green lamp.
3) The intersection containing the long-range continuous side comprises: additionally, a phase is added to each signal period, the phase duration is T, and the newly added phase is used for controlling the traffic flow of the upper-layer long-distance side-connected road driving to the lower-layer ground road. Therefore, the signal period of the intersection with the long-range continuous edge is 5T, the signal periods of other intersections without the long-range continuous edge are 4T, when the signal lamps of the intersections are synchronous, all the intersections with the long-range continuous edge keep the signal lamps synchronous, and other intersections without the long-range continuous edge keep the signal lamps synchronous.
(4) An update rule based on the NaSch model is adopted. When the vehicle moves, the vehicle state updating follows NaSch rules, and the updating process from time t to time t +1 is as follows:
s41, acceleration: v. ofi(t+1)→min(vi(t)+1,vmax) (ii) a Reflects the characteristic that drivers tend to run at the maximum speed in actual road traffic;
and s42, decelerating: v. ofi(t+1)→min(vi(t+1),di(t)); to ensureThe driver can not collide with the front vehicle in the running process, and the speed is reduced;
randomly moderating with probability p: v. ofi(t+1)→max(vi(t +1) -1, 0); simulating each moment to randomly select a part of vehicles for deceleration so as to reflect uncertain factors such as road conditions, driver moods and the like in actual traffic;
s44. location update: x is the number ofi(t+1)→xi(t)+vi(t + 1); simulating vehicle speed vi(t +1) traveling.
Wherein v isi(t) speed of the ith vehicle at time t, di(t) is the distance between the ith vehicle and the preceding vehicle at time t, xi(t) is the position of the ith vehicle at time t, vmaxThe maximum driving speed of the vehicle; p is the random moderation probability, p is 0.1, and the value range is [0, 1%]During system simulation, each vehicle generates a value between [0,1 ]]Is compared with the probability p, and when the generated random value is less than the probability p, v is updatediThe value of (t +1) is max (v)i(t +1) -1, 0); variable vi(t)，di(t)，xi(t) and vmaxAre dimensionless values related to the number of bins, min () is the minimum function, returns the minimum value in a given input parameter, max () is the maximum function, returns the maximum value in a given input parameter, → values representing updated corresponding variables, such as vi(t+1)→min(vi(t)+1,vmax) Expressed in min (v)i(t)+1,vmax) To update the variable vi(t + 1); vehicle maximum driving speed v in Manhattan city networkmax＝vmax1Maximum running speed v of vehicle on long-distance continuous roadmax＝2vmax1，vmax1The value of (a) is set according to an empirical value; each lane except the head car, di(t)＝xi+1(t)-xi(t) -1 wherein xi+1(t) is the position of the (i +1) th vehicle at time t, and the (i +1) th vehicle is a preceding vehicle of the (i) th vehicle. D of the first vehicle on the laneiThe definition is as follows:
t1) green light: d if the next lane of the leading car is in a congested state or the intersection is occupiediIs the current position of the head carDistance to intersection, otherwise, diThe distance from the head car to the tail car of the next lane, wherein a lane is in a crowded state if any one of the last two cells of the lane is occupied by a vehicle.
T2) red light: diThe distance from the current position of the head car to the intersection.
(5) A start point and an end point of the vehicle are selected. When the vehicle runs to the terminal, the system allocates a new destination for the vehicle again, namely a new starting and terminal point, the starting point is the original terminal point, the new terminal point is the newly allocated destination, and the total number of the vehicles in the system is kept unchanged.
(6) The vehicle travels according to the shortest route. The shortest path for a vehicle is determined by the intersection it passes through. When the vehicle is about to pass through a certain intersection, the vehicle may have a plurality of shortest paths to travel to the next intersection, and at the moment, the vehicle randomly selects one of the shortest paths as a travel path.
(7) And (5) simulating and simulating road network traffic flow characteristics. Setting a particle density rho and a particle total number 4NL rho (N-1) according to the models and rules determined in the steps (1) to (6), initializing a system, and simulating road network traffic flow characteristics by adopting a parallel updating rule, wherein the particles are used for simulating self-driven vehicles, and the parallel updating rule means that all the particles are updated synchronously. And during system simulation, setting different particle densities rho and different long-distance continuous edge quantities for analyzing and evaluating road network traffic flow characteristics and the influence of the long-distance continuous edge quantities on urban road network traffic flow characteristics.
Referring to FIG. 1, the overall flow chart of the present invention is shown. Firstly, constructing a Manhattan urban network, designing a square road network consisting of NxN intersections according to a cellular automata model, dividing cells, simulating an urban ground road network, setting long-range connecting edges and simulating an urban elevated road; then, setting traffic lights, determining phase change and signal period of each intersection, including corners, boundaries and intersections with long-range continuous edges, and synchronously updating all vehicle states by adopting an update rule based on a NaSch model; then, randomly selecting a starting point and an end point for each vehicle, and specifying that the vehicles run according to the shortest path; finally, according to the model and the rule determined in the previous step, the particle density and the total number of particles are set, a system is initialized, and the road network traffic flow characteristics are simulated and simulated by adopting the parallel updating rule, so that the road network traffic flow characteristics are analyzed and evaluated.
Referring to fig. 2, a schematic diagram of a network model of the method of the present invention is a manhattan city network composed of 5 × 5 intersections, two adjacent intersections in the network are connected by two lanes in parallel and in opposite driving directions, each lane is divided into L-10 cells, each intersection is composed of one cell, M-2 long-distance connecting edges are set in the model, the 1 st long-distance connecting edge between the intersections B (2,2) and C (1,3), and the number of cells set on the lane is 2
Figure BDA0002503351240000121
Item 2 is the long-distance connecting edge between the intersections D (1,1) and E (3,3), and the number of the cells arranged on the lane is
Figure BDA0002503351240000122
And the total number of lanes on the long-range continuous side in the model is 2M-4. In the figure, a vehicle occupies a black grid, the starting point is origin, the end point is destination, and when the vehicle travels to the end point, the system will reassign a new destination to the vehicle. The vehicle will travel according to the shortest path, which is equivalent to the shortest path from the intersection a of the starting lane to the intersection C of the ending lane, and since there is a long-range side between the intersections B and C, the shortest path from the intersection a to C is: the vehicle runs from an intersection A to an intersection B through a lower-layer ground road, runs to the intersection C through a long-distance connecting edge between the intersections B and C, and finally runs to a destination, and the shortest path of two paths from A to B can be selected, namely the path directions S1 and S2 indicated by two black arrows in the figure, and the vehicle randomly selects one path to run.
The experimental results show that: compared with other simulation methods, the road network traffic flow characteristic simulation method based on the Manhattan city network with the long-distance continuous edges is effective, and compared with other simulation methods, the road network traffic flow characteristic simulation method based on the Manhattan city network with the long-distance continuous edges adopts a NaSch model as a road section model, a single-port clockwise timed release rule as an intersection signal rule, and the Manhattan city network with the long-distance continuous edges as a road network model, and comprehensively considers the road section model, the intersection model and the road network model for road network traffic flow characteristic simulation. Meanwhile, the invention sets the OD attribute of the vehicle and the shortest path driving rule, and sets different maximum driving speeds of the vehicle for long-distance continuous-edge and Manhattan city networks, thereby better simulating the actual characteristics of the vehicle and the multi-layer property of the city road network.
The embodiments described in this specification are merely illustrative of implementations of the inventive concept and the scope of the present invention should not be considered limited to the specific forms set forth in the embodiments but rather by the equivalents thereof as may occur to those skilled in the art upon consideration of the present inventive concept.

Claims (2)

1. The road network traffic flow characteristic simulation method based on the Manhattan city network with long-range continuous edges comprises the following steps:
(1) constructing a Manhattan city network; the Manhattan city network is a square road network consisting of NxN intersections, wherein N is the number of intersections on the side of a square, two adjacent intersections are connected through a road, and the road consists of two parallel lanes with opposite driving directions; the manhattan urban network has 4N (N-1) lanes, each lane is divided into L cells, namely lattices, each intersection is composed of one cell, and the value of L is set according to an empirical value; the vehicle is represented by particles, stays in the cells of the lane or the cells of the intersection, and runs to the right side in the road; at any time, one cell is empty or occupied by one vehicle; the Manhattan city network is used for simulating a ground road network of a city;
(2) setting a long-range connecting edge; randomly setting M long-range continuous-side roads in Manhattan city network, for example, arbitrarily selecting two intersections B (x) in the network1,y1) And C (C: (a)x2,y2) And the two intersections do not have directly connected roads, where x1,y1And x2,y2The coordinate positions of the intersections B and C are respectively, and a long-range continuous road with two-way lanes is arranged between the intersections B and C; therefore, the total number of lanes on the long-range continuous side in the network is 2M, and the number of cells arranged on each lane on the long-range continuous side is
Figure FDA0002503351230000011
The long-range continuous road is positioned at the upper layer of the Manhattan urban network, is similar to an elevated road in the urban road network, and has the same intersection but different maximum driving speeds of vehicles;
(3) arranging a traffic signal lamp; setting a traffic signal lamp at each intersection, adopting a single-intersection clockwise timing release rule and not considering yellow lamps; specifically, at a certain time, for any intersection, if the signal lamp of a certain entrance lane is a green lamp, the signal lamps of the other three entrance lanes at the intersection are red lamps; if the signal lamp of a certain entrance lane is a green lamp, the vehicle of the lane can go straight, turn right, turn left or turn around; the intersection signal is four phases, the time length of each phase is T, the signal period is 4T, and the traffic signal lamps of each intersection synchronously change; for the boundary and the intersection with the long-range continuous side, the traffic signal lamp arrangement is as follows:
1) intersection at the corner: the network boundary comprises intersections with four corners, the intersections are only provided with two entrance lanes, and the entrance lanes in the other two directions are vacant; when traffic signal lamps are set, the green lamp of each import lane and the green lamp in the same direction in the network are simultaneously lighted, and meanwhile, according to the clockwise timing release rule, when the signal lamp of the vacant import lane is turned to be the green lamp, the signal lamp of the opposite import lane is set to be the green lamp;
2) other intersections of the boundary: other intersections on the boundary belong to T-shaped intersections, only three entrance lanes are provided, and the entrance lane in the other direction is vacant; when traffic signal lamps are set, the green lamp of each import lane and the green lamp in the same direction in the network are simultaneously lighted, and meanwhile, according to the clockwise timing release rule, when the signal lamp of the vacant import lane is turned to be the green lamp, the signal lamp of the opposite import lane is set to be the green lamp;
3) the intersection containing the long-range continuous side comprises: additionally adding a phase to each signal cycle, wherein the phase duration is T, and the newly added phase is used for controlling the traffic flow of the upper-layer long-distance side-connected road to the lower-layer ground road; in this way, the signal period of the intersection with the long-range continuous edge is 5T, the signal periods of other intersections without the long-range continuous edge are 4T, when the signal lamps of the intersections are synchronous, all the intersections with the long-range continuous edge keep the signal lamps synchronous, and the other intersections without the long-range continuous edge keep the signal lamps synchronous;
(4) adopting an update rule based on a NaSch model; when the vehicle moves, the vehicle state updating follows NaSch rules, and the updating process from time t to time t +1 is as follows:
s41, acceleration: v. ofi(t+1)→min(vi(t)+1,vmax)；
And s42, decelerating: v. ofi(t+1)→min(vi(t+1),di(t))；
Randomly moderating with probability p: v. ofi(t+1)→max(vi(t+1)-1,0)；
s44. location update: x is the number ofi(t+1)→xi(t)+vi(t+1)；
Wherein v isi(t) speed of the ith vehicle at time t, di(t) is the distance between the ith vehicle and the preceding vehicle at time t, xi(t) is the position of the ith vehicle at time t, vmaxThe maximum driving speed of the vehicle; p is the random moderation probability and the value range is [0,1 ]]During system simulation, each vehicle generates a value between [0,1 ]]Is compared with the probability p, and when the generated random value is less than the probability p, v is updatediThe value of (t +1) is max (v)i(t +1) -1, 0); variable vi(t)，di(t)，xi(t) and vmaxAre dimensionless values related to the number of bins, min () is a minimum function, returns the minimum value in a given input parameter, max () is a maximum function, returns a given input parameterIs "→ denotes a value for updating the corresponding variable, such as vi(t+1)→min(vi(t)+1,vmax) Expressed in min (v)i(t)+1,vmax) To update the variable vi(t + 1); vehicle maximum driving speed v in Manhattan city networkmax＝vmax1Maximum running speed v of vehicle on long-distance continuous roadmax＝2vmax1，vmax1The value of (a) is set according to an empirical value; each lane except the head car, di(t)＝xi+1(t)-xi(t) -1 wherein xi+1(t) is the position of the (i +1) th vehicle at the time t, and the (i +1) th vehicle is a front vehicle of the (i) th vehicle; d of the first vehicle on the laneiThe definition is as follows:
t1) green light: d if the next lane of the leading car is in a congested state or the intersection is occupiediThe distance from the current position of the head car to the intersection, otherwise, diThe distance from the head car to the tail car of the next lane, wherein if any one of the last two cells of one lane is occupied by the car, the lane is in a crowded state;
t2) red light: diThe distance between the current position of the head car and the intersection is taken as the distance;
(5) selecting a starting point and an end point of the vehicle; at the beginning, a starting point and an end point are randomly selected for each vehicle, the starting point and the end point are both on a lower-layer ground road and cannot be connected with the road at an intersection and a long distance, when the vehicle runs to the end point, the system allocates a new destination for the vehicle again, namely a new starting point and end point, the starting point is the original end point, the new end point is the newly allocated destination, and the total number of the vehicles in the system is kept unchanged;
(6) the vehicle runs according to the shortest path; the shortest path for a vehicle, determined by the intersection it passes through; when the vehicle is about to pass through a certain intersection, the vehicle may have a plurality of shortest paths to travel to the next intersection, and at the moment, the vehicle randomly selects one of the shortest paths as a traveling path;
(7) simulating and simulating road network traffic flow characteristics; setting a particle density rho and a particle total number 4NL rho (N-1) according to the models and rules determined in the steps (1) to (6), initializing a system, and simulating road network traffic flow characteristics by adopting a parallel updating rule, wherein the particles are used for simulating self-driven vehicles, and the parallel updating rule means that all the particles are synchronously updated; and during system simulation, setting different particle densities rho and different long-distance continuous edge quantities for analyzing and evaluating road network traffic flow characteristics and the influence of the long-distance continuous edge quantities on urban road network traffic flow characteristics.
2. The road network traffic flow characteristic simulation method based on the Manhattan city network with long-distance continuous edges as claimed in claim 1, characterized in that: in step (4), the random slowing-down probability p is 0.1.
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