CN113689696B - Multi-mode traffic collaborative evacuation method based on lane management - Google Patents
Multi-mode traffic collaborative evacuation method based on lane management Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
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- G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
Abstract
The invention provides a multi-mode traffic collaborative evacuation method based on lane management, which comprises the following steps: determining evacuation requirements according to disaster characteristics, and dispersing an evacuation road network into a multi-layer multi-size cellular network according to the free flow speeds of different vehicles; constructing a multi-mode traffic collaborative evacuation model by taking the shortest emptying time of the whole evacuation-required system as a target function and taking multi-mode fleet scale constraint, multi-mode traffic network loading constraint, lane distribution and lane countercurrent constraint as constraint conditions on the basis of the dispersed multi-layer multi-size cellular network; solving the multi-mode traffic collaborative evacuation model by adopting a tabu search algorithm; and performing multi-mode traffic collaborative evacuation organization according to the solving result. The method realizes the optimal coupling of the evacuation requirement, the fleet scale and the evacuation network capacity, ensures the efficiency of the evacuation scheme generation, avoids the traffic jam phenomenon in the evacuation process and reduces the secondary damage.
Description
Technical Field
The invention relates to the technical field of traffic, in particular to a multi-mode traffic collaborative evacuation method based on lane management.
Background
The large-scale evacuation by means of road networks is an effective and extensive means for dealing with various disaster events (such as hurricanes, floods, earthquakes, dangerous gas leakage, terrorist attacks, etc.), and the scientific evacuation plan can reduce the serious casualties and property loss to the greatest extent. In large scale evacuation, the primary goal is to move the affected residents to a safe area as quickly as possible while avoiding severe traffic congestion and secondary injuries. This requires managers to configure fleet and allocate road resources reasonably according to evacuation needs, while taking into account the dynamics of evacuation traffic flow to make dynamic evacuation plans.
In actual evacuation, people with low mobility, such as the elderly, the disabled, and the people without cars, often exist, and in order to achieve evacuation fairness and efficiency, a government organization fleet of cars is required to participate in evacuation, so that multi-mode traffic is difficult to avoid. The mode of adopting the public transportation guide and cooperatively evacuating more accords with the conditions that the proportion of crowds with low mobility is higher, the holding capacity and the road density of motor vehicles per capita are lower than those of developed countries, and large-scale activities and places with high crowds are more. Currently, there is less research on organizing multimode traffic evacuation, mainly including: aiming at minimizing evacuation time, traffic loads in the whole road network are balanced through a multi-ant group intelligent algorithm, but an evacuation traffic model in the method is static, traffic jam in evacuation cannot be captured, a real-time evacuation schedule cannot be provided, and a lane management scheme cannot be provided in detail; considering the shortest total travel distance of all evacuated people as a target function, establishing a bus evacuation path planning model based on reversible lane and intersection conflict elimination, and selecting a genetic algorithm to solve the model to obtain an optimal bus evacuation path planning scheme, but in the method, by setting a special lane, the influence of other vehicles on the bus is ignored, and a static evacuation traffic model is adopted, so that traffic jam cannot be captured, and a real-time evacuation schedule cannot be formulated; a double-layer planning model integrating a hybrid fleet and a lane countercurrent strategy is established, and a double-layer algorithm consisting of a heuristic algorithm and a bus priority minimum cost flow algorithm is used for solving, but the method uses a static traffic model, cannot capture phenomena such as traffic jam in evacuation and the like, has low double-layer planning solving efficiency and is not suitable for real-time evacuation.
A static traffic model is mostly adopted by the method, the dynamic evolution of various traffic flows in a network is not considered, and the traffic jam cannot be captured. In large scale evacuation, however, traffic congestion is highly likely to occur, which may reduce evacuation efficiency and cause secondary damage. In addition, the method takes the network emptying time as an objective function, but the network emptying time belongs to a nonlinear and non-convex form, and direct optimization cannot be carried out, so that a model is mostly expressed as a double-layer plan, the model cannot be directly solved by means of a commercial solver, and the large-scale application of the model is limited.
Disclosure of Invention
The invention provides a multi-mode traffic collaborative evacuation method based on lane management, which aims to overcome the defects in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme.
A multi-mode traffic collaborative evacuation method based on lane management comprises the following steps:
s1, determining evacuation requirements according to disaster characteristics, and dispersing an evacuation road network into a multi-layer and multi-size cellular network according to the free flow speed of different vehicles;
s2, constructing a multi-mode traffic collaborative evacuation model by taking the shortest emptying time of the whole evacuation-required system as a target function and taking multi-mode fleet constraints, multi-mode traffic network loading constraints, lane allocation and lane countercurrent constraints as constraint conditions on the basis of the discrete multi-layer multi-size cellular network;
S3, solving the multi-mode traffic collaborative evacuation model by adopting a tabu search algorithm;
and S4, performing multi-mode traffic cooperative evacuation organization according to the solving result.
Preferably, the step of S1 includes: the method comprises the steps of taking a network source node as a disaster-affected point, taking a network sink node as a shelter, determining the positions of the source node and the sink node and the road risk level according to the characteristics of disasters, taking an evacuation area, the shelter position, the road network risk level and evacuation requirements as input parameters, loading the multi-mode traffic flow by adopting a Cellular Transmission Model (CTM), defining the length of a cell as the distance of a vehicle running at one time step under a free flow condition, dispersing a road network into a plurality of independent cellular networks according to the free flow speeds of different vehicles, and enabling each vehicle to only run in the own cellular network.
Preferably, the multi-mode collaborative evacuation model comprises:
the objective function is shown in the following equation (1):
where t is a discrete time step, Ψ is a set of discrete time steps, M is a set of vehicle types, C R A set of risk source cells;
m type of vehicles arriving at the sink cell C at time t s The number of vehicles of (1); d i,m Indicates the number of vehicles of m types of vehicles in the source cell i,
the number of the vehicles of the type m in all the evacuation source points i, namely the evacuation requirement of a certain source point; 
Meaning that the rounding is done down,
indicating whether all vehicles arrive at the sink cell at the current moment, if all vehicles arrive, namely the network is emptied,
then
Otherwise the network is not emptied,
then
Thus, the emptying time of m-type vehicles in the evacuation network is
The evacuation time of the evacuation system is the maximum value of the evacuation time of various vehicles, i.e. the evacuation system
The goal of evacuation is to minimize the maximum network airtime;
the multi-mode fleet size constraints are shown in equations (2) - (3) below:
wherein, the formula (2) is the total demand D conservation constraint, D i,m Number of vehicles, p, representing m types of vehicles in evacuation source cell i m Representing the maximum passenger capacity of a type m vehicle, C R A set of risk source cells; equation (3) is a vehicle number non-negative constraint;
the multi-mode transportation network loading constraints are as follows (4) - (14):
wherein: the equation (4) is a flow conservation constraint,
represents the number of vehicles of m types of vehicles in the cell i at the time t +1,
as the number of the current vehicles,
Γ is the number of vehicles flowing from the upstream cell k into the current cell i - (i) Is the set of upstream cells k of the current cell i,
representing i-streams from the current cellThe number of vehicles toward the downstream cell j, Γ (i) is the set of cells j downstream of the current cell i, C O Is a common cell set; equation (5) represents the flow rate from the current cell i to the downstream cell j at time t 
Number of vehicles smaller than current cell
Equation (6) represents the flow rate from the current cell i to the downstream cell j at time t
Less than the current cellular capacity limit
z i,m Is the number of lanes allocated to the m types of vehicles,
the traffic capacity of each lane of the m types at the time t; similarly to (6), equation (7) is the number of vehicles flowing from the upstream cell k into the current cell i at time t,
less than the current cellular capacity limit
Equation (8) represents the number of vehicles flowing from the upstream cell k to the current cell i at time t
Less than the limit of the remaining capacity of the current cell
z i,m Is the number of lanes allocated to the m types of vehicles,
is the traffic capacity of each lane of m types at time t，w m Is the velocity of the congestion wave, v, of the m-type vehicle m Is the free flow velocity of the m-type vehicle; equation (9) is the traffic transfer function for source cell i,
is the demand of the m-type vehicle at time t; equation (10) represents the flow rate of the source cell i
Is loaded to a source point at an initial moment, which is equivalent to that all fleets are ready when evacuation starts; the initial state cell flux is expressed by the equations (11) and (12), respectively
And link traffic
All are zero, namely the road network is emptied when evacuation starts; the cellular flux is expressed by the formulas (13) and (14)
And link traffic
Non-negative constraints of (d);
the lane assignment and lane reverse flow constraints are shown in the following equations (15) to (17):
Wherein, the expression (15) indicates that if the cell i and the cell j belong to the same road section, the lane is dividedThe same scheme is adopted, and E (i) is a set of cells contained in a road section taking the cell i as a starting point; equation (16) represents the total number of lanes shared by the M-type vehicles Z i The system comprises a bidirectional lane, and a lane needing a reverse flow design is determined according to a lane distribution result; equation (17) is an integer constraint for the lane.
Preferably, the S3 step includes:
giving an initial solution containing lane allocation and fleet mixing proportion and a neighborhood corresponding to the initial solution;
determining a series of candidate solutions in the neighborhood of the initial solution, and determining the optimal candidate solution according to the objective function value;
judging the relation between the optimal candidate solution and the global optimal solution, if the objective function value of the optimal candidate solution is superior to the global optimal solution, neglecting the tabu attribute, replacing the current solution and the global optimal solution with the tabu attribute, adding the tabu attribute into a tabu table, and updating the tabu table; if the optimal candidate solution does not exist, selecting a non-taboo optimal state solution as the current solution from the candidate solutions, and updating a taboo table; and repeating iteration until the stopping criterion is met, stopping searching and obtaining the final solution.
Preferably, the stopping criterion is: satisfy the maximum iteration G, or satisfy Δ T k ＝0，ENCT k <ENCT 0 Wherein the network emptying time of different types of vehicles obtained by the k-th iteration is { T } m }，ΔT k Is the difference between the upper and lower bounds Δ T k ＝T max -T min ，ENCT k Network clearing time for kth iteration, ENCT k ＝T max ，ENCT 0 Is the initial network clearing time.
Preferably, the S4 step includes:
the method comprises the steps of calculating a fleet configuration proportion, taking network emptying time as a main time index for measuring evacuation efficiency, organizing personnel and vehicles in order according to an evacuation schedule, avoiding traffic jam and secondary injury accidents as much as possible, considering the requirements of different groups on lane allocation and lane countercurrent schemes, improving the traffic capacity in the evacuation direction and meeting the requirement of evacuation as soon as possible.
Preferably, calculating the fleet configuration ratio comprises: based on the requirement of each evacuation point, the mixed fleet scale of the whole evacuation system is solved according to the following formula (18), and the evacuation requirement is met under the condition of not wasting vehicle and road resources:
wherein the content of the first and second substances,
for the number of all evacuation source points i type m vehicles,
in terms of the size of the overall fleet m Is the scale of a type m vehicle.
According to the technical scheme provided by the lane management-based multi-mode traffic collaborative evacuation method, the multi-mode evacuation scene is considered, the dynamic property of the traffic flow is described by adopting a cellular transmission model in traffic flow loading, the traffic jam can be captured, and a dynamic evacuation schedule is provided; the method comprises the steps of providing an objective function for describing a multi-mode evacuation system, establishing a multi-mode traffic collaborative evacuation model with multi-mode fleet configuration, traffic network loading, lane allocation and lane countercurrent control, and under the condition that only evacuation requirements are given, solving the model by adopting a heuristic algorithm, so that a feasible evacuation scheme is obtained under the condition of limited time requirement; under the condition that the evacuation demand and the fleet scale are given, the model is degenerated into a mixed integer linear programming model, the model is rapidly solved by adopting the existing commercial solving software (Gurobi or CPLEX), the efficiency of evacuation scheme generation is improved, the traffic jam phenomenon in the evacuation process is changed, secondary damage is reduced, evacuation planning is provided for urban road networks or expressway networks with notification disasters (such as hurricanes, floods, harmful substance leakage and the like), and the method has a wide application prospect.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a multi-mode collaborative evacuation method based on lane management according to an embodiment;
FIG. 2 is a schematic diagram of an Nguyen-Dupuis evacuation network;
FIG. 3 is a schematic diagram of a cellular structure for discretizing a road segment;
FIG. 4 is a schematic diagram of cellular network flow diversion and merging, lane distribution and lane counterflow;
fig. 5 is a schematic flow chart of a solution process of the multi-mode collaborative evacuation model according to the embodiment.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary only for explaining the present invention and are not construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It should be understood that the term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding of the embodiments of the present invention, the following description will be further explained by taking specific embodiments as examples with reference to the drawings, and the embodiments of the present invention are not limited thereto.
Examples
Fig. 1 is a schematic flow chart of a multi-mode collaborative evacuation method based on lane management according to this embodiment. It should be noted that the present embodiment is implemented based on the following preconditions:
(1) there are a sufficient number of cars and buses to be evacuated, each type of vehicle having the same size and driveability; all evacuees listen to the manager's direction, the extent of evacuation and the total evacuation demand at each staging point are known, D i The number of persons at evacuation source point i.
(2) Before the evacuation starts, the road network of the disaster area is completely emptied, and the interleaving conflict between the evacuation vehicles and the social vehicles is avoided.
(3) In the evacuation process, the refuge position and the intersection control scheme are determined according to the emergency plan, and a manager only needs to decide the mixed fleet scale and the lane management scheme according to the evacuation requirement.
(4) Due to the fixed infrastructure of the roads, it is difficult to change the topology of the road network in a short time. The embodiment describes the lane variable as a static integer variable, and the lane assignment scheme is the same for the same road section (between two intersections).
Referring to fig. 1, the method of the present embodiment includes the following steps:
s1, determining evacuation requirements according to disaster characteristics, and dispersing the evacuation requirement road network into a multi-layer multi-size cellular network according to the free flow speed of different vehicles.
And enabling a multi-source multi-sink hybrid traffic road network G (N, A), wherein N is an intersection, and A represents a road section. The network source node is used as a disaster-affected point, the network sink node is used as a shelter, the positions of the source node and the sink node and the road risk level are determined according to the characteristics of disasters, schematically, fig. 2 is a schematic diagram of an Nguyen-Dupuis evacuation network under a certain disaster, and under the diffusion scenes of hurricanes, floods and toxic gases, the disaster area range and the risk level can be calculated by adopting a meteorological method, hydraulic analysis and plume modeling. The evacuation area, the refuge location, the road network risk level and the evacuation demand are used as input parameters, a Cellular Transmission Model (CTM) is adopted to load the multi-mode traffic flow, the CTM can simulate the evacuation dynamics and capture the traffic jam propagation, the cell length is defined as the driving distance of vehicles in one time step under the condition of free flow, the model stability and the cells are ensured to be emptied in time, the road network G (N, A) is dispersed into a plurality of independent cellular networks G (C, B) according to the free flow speed of different vehicles, and each vehicle can only drive in the cellular network of the vehicle. And converting the multi-source and multi-sink network into a multi-source and single-sink network by introducing the virtual road section. Wherein C represents a set of cells comprising C O Set of common cells, C R Set of risk source cells, C S A set of refuge cells; b represents a cell link set.
Taking a network formed by cars and buses as an example, fig. 3 depicts a schematic diagram of dispersing road sections into cells, and fig. 4 depicts a schematic diagram of converting intersections into flow-dividing and flow-converging cells and lane distribution and lane backflow. Specifically, 4 cars in the cell a at the upstream in fig. 4 occupy 4 lanes, of which 2 lanes are countercurrent lanes; the cellular B is a normal road section, and only one lane is occupied by one bus; the vehicles of the cells A and B are converged and enter the cells C at the downstream, the cars occupy 2 lanes, one lane is a reverse flow lane, the buses use one lane, and one lane is not used; the cell C is then shunted to the cells D and E at the downstream, the cell D is a normal road section, and the cell E comprises a reverse lane occupied by a car. In the embodiment, lane allocation schemes in the cells, the number of vehicles of different types and the number of vehicles flowing into and out of the cells are obtained by optimizing a multi-mode collaborative evacuation model.
S2, based on the discrete multilayer multi-size cellular network, a multi-mode traffic collaborative evacuation model is constructed by taking the shortest emptying time of the whole evacuation-required system as an objective function and taking multi-mode fleet scale constraint, multi-mode traffic network loading constraint, lane allocation and lane countercurrent constraint as constraint conditions.
The multimode traffic collaborative evacuation model comprises the following steps:
the objective function is from a time point of view aimed at shortening the evacuation process so that the last group of evacuees arrive at the shelter as early as possible. The objective function is shown in the following equation (1):
where t is a discrete time step, Ψ is a set of discrete time steps, M is a set of vehicle types, C R A set of risk source cells;
m type of vehicles arriving at the sink cell C at time t s The number of vehicles of (1); d i,m Indicates the number of vehicles of m types of vehicles in the source cell i,
the number of the vehicles of the type m in all the evacuation source points i, namely the evacuation requirement of a certain source point;
meaning that the rounding is done down,
indicating whether all vehicles arrive at the sink cell at the current moment, if all vehicles arrive, namely the network is emptied,
then
Otherwise the network is not emptied,
then
The emptying time of m types of vehicles in the evacuation network is
The total emptying time of the evacuation system is the maximum value of the evacuation time of various vehicles, namely max
The goal of evacuation is to minimize the maximum network airtime;
the multi-mode fleet size constraints are shown in equations (2) - (3) below:
wherein, the formula (2) is the total demand D conservation constraint, D i,m Number of vehicles, p, representing m types of vehicles in evacuation source cell i m Representing the maximum passenger capacity of a type m vehicle, C R A set of risk source cells; equation (3) is a vehicle number non-negative constraint.
The multi-mode transportation network loading constraints are as follows (4) - (14):
wherein, the formula (4) is the flow conservation constraint,
representing m-type vehicles in a cell i at time t +1The number of the vehicles is counted,
as the number of the current vehicles,
Γ - (i) is the set of upstream cells k of the current cell i, which is the number of vehicles flowing from the upstream cell k into the current cell i,
denotes the number of vehicles flowing from the current cell i to the downstream cell j, Γ (i) is the set of downstream cells j of the current cell i, C O Is a common cell set; equation (5) represents the flow rate from the current cell i to the downstream cell j at time t
Number of vehicles smaller than current cell
Equation (6) represents the flow rate from the current cell i to the downstream cell j at time t
Less than the current cellular capacity limit
z i,m Is the number of lanes allocated to the m types of vehicles,
the traffic capacity of each lane of the m types at the time t; similarly to (6), equation (7) is the number of vehicles flowing from the upstream cell k into the current cell i at time t,
less than the current cellular capacity limit
Equation (8) represents that the cell flows from the upstream cell k at time tNumber of vehicles with front cell i
Less than the limit of the remaining capacity of the current cell
z i,m Is the number of lanes allocated to the m types of vehicles,
is the traffic capacity of each lane of m types at time t, w m Is the velocity of the congestion wave, v, of the m-type vehicle m Is the free flow velocity of the m-type vehicle; equation (9) is the traffic transfer function of the source cell i,
is the demand of the m-type vehicle at time t; equation (10) represents the flow rate of the source cell i
Is loaded to a source point at an initial moment, which is equivalent to that all fleets are ready when evacuation starts; the initial state cell flux is expressed by the equations (11) and (12), respectively
And link traffic
All are zero, namely the road network is emptied when evacuation starts; the cellular flux is expressed by the formulas (13) and (14)
And link traffic
Is not negatively constrained.
The lane assignment and lane reverse flow constraints are shown in the following equations (15) to (17):
wherein, formula (15) indicates that if cell i and cell j belong to the same road segment, the lane assignment scheme is the same, e (i) is a set of cells included in the road segment with cell i as a starting point; equation (16) represents the total number of lanes shared by the M-type vehicles Z i The system comprises a bidirectional lane, and a lane needing a reverse flow design is determined according to a lane distribution result; equation (17) is an integer constraint for the lane.
S3, solving the multi-mode traffic collaborative evacuation model by adopting a tabu search algorithm.
Since the multi-mode collaborative traffic evacuation model of the present embodiment is a mixed integer nonlinear programming (MINLP) problem, a heuristic algorithm is specifically adopted to solve the problem. The solving step comprises the following steps:
Giving an initial solution containing lane allocation and fleet mixing proportion and a neighborhood corresponding to the initial solution;
determining a series of candidate solutions in the neighborhood of the initial solution, and determining the best candidate solution according to the objective function value;
judging the relation between the optimal candidate solution and the global optimal solution, if the objective function value of the optimal candidate solution is superior to the global optimal solution, neglecting the tabu attribute, replacing the current solution and the global optimal solution with the tabu attribute, adding the tabu attribute into a tabu table, and updating the tabu table; if the optimal candidate solution does not exist, selecting a non-taboo optimal state solution as the current solution from the candidate solutions, and updating a taboo table; and repeating iteration until the stopping criterion is met, stopping searching and obtaining the final solution.
Fig. 5 is a schematic diagram of a solving process of the multi-mode collaborative evacuation model of the embodiment, and with reference to fig. 5, the method specifically includes the following steps:
(1) initialization
The total number of the road sections is K, and each road section comprises a plurality of cells. In the case of unknown fleet size, the objective function is nonlinear, and an initial solution is obtained according to the mixed integer programming of the objective function of equation (18) below
Wherein the emptying time of the m-type vehicle is
The difference between the emptying times of different types of vehicles is DeltaT 0 The system objective function is ENCT 0 . Defining an initial tabu list
The length of the list is given as
Wherein p is m Indicates the maximum passenger capacity of the m-type vehicle, and indicates that the cells i at time t (excluding the sink cell C) S ) A vehicle number objective function (18) for m types of vehicles, minimizing the total travel time for all evacuated persons; the constraint conditions are equations (2) - (17).
(2) Neighborhood search
If the initial solution is not the best solution, a neighborhood search is performed to find possible ENCT acquisitions * The lane assignment scheme of (1). The number of neighborhood solutions is K/4, depending on the scale of the problem.
First, a variation position set of the lane assignment schemes R (N, Ca) representing different search directions is randomly generated. Then, a new neighborhood solution is obtained based on the current optimal solution:
if it is not
Means thatMore lanes are required to be covered by the car, then
If it is not
Meaning that the bus requires more lanes, then
ENCT to obtain all neighborhood solutions k And Δ T k Then, half of the best neighborhood solution is retained as the candidate solution.
(3) Scoffle standard
If ENCT k <ENCT 0 Replacing the initial solution, updating the Tabu list and the length TL of the Tabu list, and turning to the step (5); otherwise, go to step (4).
(4) Tabu list
Judging the Tabu state of the candidate solution mutation position, if the candidate solution mutation position is not in the Tabu list, selecting the best neighborhood solution as the current best solution, and updating Tabu and TL. (the tabu list is to prevent falling into a locally optimal solution.)
(5) Stopping criterion
The stopping criteria include two rules: satisfy the maximum iteration G, or satisfy Δ T k ＝0，ENCT k <ENCT 0 Wherein the network emptying time of different types of vehicles obtained by the k-th iteration is { T } m }，ΔT k Is the difference between the upper and lower bounds Δ T k ＝T max -T min ，ENCT k Network clearing time for kth iteration, ENCT k ＝T max ，ENCT 0 Is the initial network clearing time. Once any one of the rules is satisfied, the search will end upAnd outputting the best solution.
And S4, performing multi-mode traffic collaborative evacuation according to the solving result.
The method comprises the following steps: the method comprises the steps of calculating a fleet configuration proportion, taking network emptying time as a main time index for measuring evacuation efficiency, organizing personnel and vehicles in order according to an evacuation schedule, avoiding traffic jam and secondary injury accidents as much as possible, considering the requirements of different groups on lane allocation and lane countercurrent schemes, improving the traffic capacity in the evacuation direction and meeting the requirement of evacuation as soon as possible.
Based on the requirement of each evacuation point, the mixed fleet scale of the whole evacuation system is solved according to the following formula (18), and the evacuation requirement is met under the condition of not wasting vehicle and road resources:
wherein the content of the first and second substances,
for the number of all evacuation source points i type m vehicles,
in terms of the size of the overall fleet m Is the scale of a type m vehicle.
It will be appreciated by those skilled in the art that the number of various network elements shown in fig. 2 for simplicity only may be less than that in an actual network, but such omissions are clearly not to be considered as a prerequisite for a clear and complete disclosure of embodiments of the invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in 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 (1)
1. A multi-mode traffic collaborative evacuation method based on lane management is characterized by comprising the following steps:
s1, determining evacuation requirements according to disaster characteristics, and dispersing an evacuation road network into a multi-layer and multi-size cellular network according to the free flow speed of different vehicles; the method specifically comprises the following steps:
taking a network source node as a disaster-affected point, taking a network sink node as a shelter, determining the positions of the source node and the sink node and the road risk level according to the characteristics of disasters, taking the evacuation area, the shelter position, the road network risk level and the evacuation requirement as input parameters, loading a multi-mode traffic flow by adopting a Cellular Transmission Model (CTM), defining the length of a cell as the distance of a vehicle running at one time step under a free flow condition, dispersing a road network into a plurality of independent cellular networks according to the free flow speeds of different vehicles, and enabling each vehicle to only run in the own cellular network;
s2, constructing a multi-mode traffic collaborative evacuation model by taking the shortest emptying time of the whole evacuation-required system as a target function and taking multi-mode fleet constraints, multi-mode traffic network loading constraints, lane allocation and lane countercurrent constraints as constraint conditions on the basis of the discrete multi-layer multi-size cellular network; the multi-mode collaborative evacuation model comprises:
The objective function is shown in the following equation (1):
where t is a discrete time step, Ψ is a set of discrete time steps, M is a set of vehicle types, C R A set of risk source cells;
m type of vehicles arriving at the sink cell C at time t s The number of vehicles of (1); d i,m Indicates the number of vehicles of m types of vehicles in the source cell i,
the number of the vehicles of the type m in all the evacuation source points i, namely the evacuation requirement of a certain source point;
meaning that the rounding is done down,
indicating whether all vehicles arrive at the sink cell at the current moment, if all vehicles arrive, namely the network is emptied,
then
Otherwise the network is not emptied,
then
Thus, the emptying time of m-type vehicles in the evacuation network is
The evacuation time of the evacuation system is the maximum value of the evacuation time of various vehicles, i.e. the evacuation system
The goal of evacuation is to minimize the maximum network airtime;
the multi-mode fleet size constraints are shown in equations (2) - (3) below:
wherein, the formula (2) is the total demand D conservation constraint, D i,m Number of vehicles, p, representing m types of vehicles in evacuation source cell i m Representing the maximum passenger capacity of a type m vehicle, C R A set of risk source cells; equation (3) is a vehicle number non-negative constraint;
the multi-mode transportation network loading constraints are as follows (4) - (14):
wherein: the equation (4) is a flow conservation constraint, 
Indicates the number of m types of vehicles in the cell i at the time t +1,
the number of the current vehicles is the number of the current vehicles,
Γ is the number of vehicles flowing from the upstream cell k into the current cell i - (i) Is the set of upstream cells k of the current cell i,
denotes the number of vehicles flowing from the current cell i to the downstream cell j, Γ (i) is the set of downstream cells j of the current cell i, C O Is a common cell set; equation (5) represents the flow rate from the current cell i to the downstream cell j at time t
Number of vehicles smaller than current cell
Equation (6) represents the flow rate from the current cell i to the downstream cell j at time t
Less than the current cellular capacity limit
z i,m Is the number of lanes allocated to the m types of vehicles,
the traffic capacity of each lane of the m types at the time t; equation (7) represents the number of vehicles flowing from the upstream cell k to the current cell i at time t
Less than the current cellular capacity limit
Equation (8) represents the number of vehicles flowing from the upstream cell k to the current cell i at time t
Less than the limit of the remaining capacity of the current cell
z i,m Is the number of lanes allocated to the m types of vehicles,
is the traffic capacity of each lane of m types at time t, w m Is the velocity of the congestion wave, v, of the m-type vehicle m Is the free flow velocity of the m-type vehicle; equation (9) is the traffic transfer function of the source cell i,
is the demand of the m-type vehicle at time t; equation (10) represents the flow rate of the source cell i 
Is loaded to a source point at an initial moment, which is equivalent to that all fleets are ready when evacuation starts; the initial state cell flux is expressed by the equations (11) and (12), respectively
And link traffic
All are zero, namely the road network is emptied when evacuation starts; the cellular flux is expressed by the formulas (13) and (14)
And link traffic
Non-negative constraints of (d);
the lane assignment and lane reverse flow constraints are shown in the following equations (15) to (17):
wherein, formula (15) indicates that if cell i and cell j belong to the same road segment, the lane assignment scheme is the same, e (i) is a set of cells included in the road segment with cell i as a starting point; equation (16) represents the total number of lanes shared by the M-type vehicles Z i The system comprises a bidirectional lane, and a lane needing a reverse flow design is determined according to a lane distribution result; equation (17) is an integer constraint for the lane;
s3, solving the multi-mode traffic collaborative evacuation model by adopting a tabu search algorithm; the method specifically comprises the following steps:
giving an initial solution containing lane allocation and fleet mixing proportion and a neighborhood corresponding to the initial solution;
determining a series of candidate solutions in the neighborhood of the initial solution, and determining the optimal candidate solution according to the objective function value;
judging the relation between the optimal candidate solution and the global optimal solution, if the objective function value of the optimal candidate solution is superior to the global optimal solution, neglecting the tabu attribute, replacing the current solution and the global optimal solution with the tabu attribute, adding the tabu attribute into a tabu table, and updating the tabu table; if the optimal candidate solution does not exist, selecting a non-taboo optimal state solution as the current solution from the candidate solutions, and updating a taboo table; repeating iteration until a stopping criterion is met, stopping searching and obtaining a final solution;
The stopping criterion is: satisfy the maximum iteration G, or satisfy Δ T k ＝0，ENCT k <ENCT 0 Wherein the network emptying time of different types of vehicles obtained by the k-th iteration is { T } m }，ΔT k Is the difference Δ T between the upper and lower bounds k ＝T max -T min ，ENCT k Network clearing time for kth iteration, ENCT k ＝T max ，ENCT 0 Initial network clearing time;
s4 performing multi-mode traffic collaborative evacuation organization according to the solving result; the method specifically comprises the following steps:
calculating the fleet configuration proportion, taking the network emptying time as a main time index for measuring the evacuation efficiency, organizing personnel and vehicles in order according to an evacuation schedule, avoiding traffic jam and secondary injury accidents as much as possible, considering the requirements of different groups on lane allocation and lane countercurrent schemes, improving the traffic capacity in the evacuation direction and realizing the requirement of evacuation as soon as possible;
the calculating the fleet configuration ratio comprises: based on the requirement of each evacuation point, the mixed fleet scale of the whole evacuation system is solved according to the following formula (18), and the evacuation requirement is met under the condition of not wasting vehicle and road resources:
wherein the content of the first and second substances,
for the number of all evacuation source points i type m vehicles,
in terms of the size of the overall fleet m Is the scale of a type m vehicle.
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