CN115206086A - Static traffic flow distribution method considering congestion space queuing and overflow - Google Patents
Static traffic flow distribution method considering congestion space queuing and overflow Download PDFInfo
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Abstract
The invention relates to a static traffic flow distribution method considering congestion space queuing and overflow. The invention provides a demand compression and congestion backtracking algorithm based on road segment coding improvement; a local congestion area identification algorithm for searching a congested road section in a network and marking a congestion space range is established; providing a method for selecting a minimum demand loading path in a global area and a maximum demand loading path in a local congestion area; an iterative weighted solving algorithm for static traffic flow distribution considering congestion space queuing and overflow is constructed. The method can improve the calculation efficiency of demand compression and congestion backtracking, avoid the generation of pseudo congestion areas, provide guarantee for forming user balanced distribution solution, and can be used for solving the traffic distribution of a large-scale network.
Description
Technical Field
The invention relates to the field of static traffic flow distribution, in particular to a static traffic flow distribution method considering congestion space queuing and overflow.
Background
Static traffic flow distribution (hereinafter referred to as traffic flow distribution) is used as a core part of a four-stage method for predicting traffic demand, and is used for distributing the traffic demand to a road network according to a certain rule and actual conditions and calculating the traffic volume of each road section. The research aiming at the traditional traffic flow distribution is very mature, wardrop proposes a balance principle of the traffic flow distribution, beckmann and the like construct a user balanced traffic flow distribution model, and Leblance and the like apply Frank-Wolfe algorithm to the solution of the model. The Yuanchang Wei and the like are based on the Starkeerberg game, the Huanghe game and the like, and improve the traffic flow distribution model based on the multi-class users of reliability.
However, in the conventional traffic flow distribution, since the limitation of the distribution flow rate by the passage capacity of the link is not considered, there is a case where the distribution flow rate of the link is larger than the passage capacity. For this limitation, more scholars begin to pay attention to traffic flow distribution limited by traffic capacity, that is, there are congested road sections in the distribution result, and the distribution flow of the road sections is not greater than the traffic capacity.
The vehicle queuing form can be divided into a point queuing form (point queues) and a spatial queuing form (spatial queues) according to whether road space resources are occupied or not. Thompson and Payne, smith, bliemer, etc. have constructed different point queuing traffic flow distribution models based on point queuing. Spatial queuing is divided into implicit spatial queuing (explicit queues) and explicit spatial queuing (explicit queues).
In the implicit spatial queuing research, smith, yang and Yagar, larsson and patrikson, bell, nesterov and De Palma, smith and the like propose a traffic distribution model suitable for a smaller-scale road network, and assuming that the inflow and outflow of a road section are equal, queuing delay is averaged into all required flows, and all required flows are considered to be delayed when passing through the road network. In the explicit spatial queuing research, bifulco, crisalli, lam, zhang, bliemer and the like, assuming that the outflow of a road section can be smaller than the inflow, the traffic distribution flow exceeding the traffic capacity of the road section forms explicit spatial queuing at the upstream of a road, and the influence of the spatial queuing overflow effect caused by the capacity limit of the road section on the distribution process and the distribution result is not considered; jin studies that "oscillation" in the iterative process is not subject to convergence problems.
In the explicit spatial queuing distribution model, the model can be divided into a complete congestion model and a local congestion model according to different congestion degrees of the network. In the direction of full congestion model study: building a road section impedance function under a complete congestion condition by using the Yuhao and Liuxiaoling and the like, and providing a non-equilibrium allocation method for static traffic flow increment allocation of a congested road network based on the road section impedance function; in addition, road user path selection characteristics in a complete congestion state are analyzed through Yuehao, zhangpeng and the like, a user balance and system optimization principle in the complete congestion state is given, and a static congestion traffic flow balance distribution model with the user balance and the system optimization is constructed. In the direction of local congestion model study: blimer et al increase road segment capacity limits to describe the impact of congested space queuing and overflow on traffic flow distribution, and construct a space queuing distribution model with local congestion; the method for identifying the physical bottleneck of the road network and the congestion backtracking algorithm are provided by Yuehan and Numengjie, and are respectively used for searching the network congestion bottleneck and calculating the length of congestion space queuing, and a traffic flow increment distribution algorithm considering congestion space queuing and overflow is constructed on the basis of the method; meanwhile, the distribution result of the newly-established algorithm not only comprises the whole-time and integral road network macroscopic operation state, but also comprises the interference and infiltration conditions of the specific position of the congestion bottleneck and the spatial queuing, which is different from a pseudo-dynamic traffic flow distribution model of the time slice. However, in the incremental distribution method, only the shortest-path principle and the globally shortest traffic demand loading method are considered in the calculation process, and the path selection characteristics in the local congestion area in the road network are not considered, so that the problems of iterative oscillation, difficulty in convergence to stable solution and the like in the solving process are easily caused.
In addition, in the static traffic flow distribution considering the queuing and overflow of the congested space, the following problems exist in the existing method:
in a congestion backtracking mechanism, a backtracking method of 'selecting shortest time to search gradually' is adopted based on the passing time from a road section exit endpoint to a bottleneck, so that the defect of complex calculation process exists, the backtracking calculation time is too long, and a pseudo-congestion area is easily caused.
The search step length for searching the optimal solution based on the feasible solution and the iterative weighting algorithm for solving the large-scale network cannot be given.
It is not possible to confirm whether a user equalization solution exists.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a static traffic flow distribution method considering the queuing and overflowing of a congested space. The method provides a demand compression and congestion backtracking algorithm based on road segment coding improvement; a local congestion area identification algorithm for searching a congested road section in a network and marking a congestion space range is established; providing a method for selecting a minimum demand loading path in a global area and a maximum demand loading path in a local congestion area; an iterative weighted solving algorithm of static traffic flow distribution considering congestion space queuing and overflow is constructed. The method can improve the calculation efficiency of demand compression and congestion backtracking, avoid the generation of pseudo congestion areas, provide guarantee for forming user balanced distribution solution, and can be used for solving the traffic distribution of a large-scale network.
In order to achieve the purpose, the invention adopts the technical scheme that:
a static traffic flow allocation method considering queuing and overflow in congested space, characterized by comprising the steps of:
step 5, finding an auxiliary path: updated as obtained in step 3
Finding the nth auxiliary path P z rs (n) obtaining the auxiliary path distribution demand flow
Based only on auxiliary path P 2 rs (n) loading traffic demands, and obtaining the nth auxiliary road network distribution result through road section number, bottleneck identification and congestion backtracking, wherein the nth auxiliary road network distribution result comprises the following steps: auxiliary distribution demand flow of road section
Auxiliary inflow rate
Auxiliary outflow rate
can be simplified as follows:
step 7, determining a new distribution path cluster: the nth auxiliary path
As the (n + 1) th path P rs (n + 1), i.e. P rs (n+1)＝P z rs (n); n +1 th path P rs The path allocation demand traffic of (n + 1) is:
i =1,2 \ 8230n paths P rs (i) The path allocation demand flow of (2) is:
Step 9, judging iteration termination conditions: if the convergence accuracy epsilon is satisfied or the maximum iteration number M is exceeded, the iteration is terminated, and the number of iterations is divided intoDistribution result is distribution demand flow for road section
Inflow rate of fluid
Outflow rate of liquid
Otherwise, enabling the iteration number n = n +1, and returning to the step 3 to continue the iteration; the precision e expression is:
and when e is less than or equal to epsilon, the precision requirement is met.
Further, the specific steps of step 2 are:
step 2-1, numbering all selected paths, slave Path P rs (n) numbering road sections on the paths from upstream to downstream in sequence from the starting road section of the path, and ensuring that the small-numbered road section of each path is required to be positioned at the upstream of the large-numbered road section;
2-2, identifying physical bottlenecks and secondary bottlenecks, and performing congestion backtracking to obtain the nth road network distribution result: distribution demand flow of road section
Inflow rate of fluid
Outflow rate of liquid
The bottleneck identification and the required traffic compression are carried out from the upstream minimum-numbered road section to the downstream from the small number to the large number; the congestion backtracking starts from a downstream bottleneck section with a large number and proceeds from the large number to a small number and upstream.
Further, the specific steps of step 4 are:
step 4-1, calibrating the terminal of the congestion area: namely, the downstream end points of the bottleneck road sections comprise the downstream end points of the physical bottleneck road sections and the downstream end points of the secondary bottleneck road sections;
step 4-2, calibrating the starting point of the congestion area: the method comprises the following steps that backtracking is carried out on the basis of all paths passing through a physical bottleneck road section and a secondary bottleneck road section until the trail section meets the line tail road section of the path, the downstream end point of the line tail road section is the starting point of a congestion area, the line tail road section is generally a semi-unblocked semi-congested road section, and namely an unblocked part and a congested part exist on the road section;
and 4-3, calibrating the road sections between the starting point and the end point of the congestion area, wherein all the road sections on the paths between the starting point and the end point belong to the road sections in the congestion area, namely constructing and marking the congestion area.
Further, the specific steps of step 5 are:
step 5-1, calculating and searching the shortest path in the global road network, and recording as P 0 rs (n)；
Step 5-2, if P 0 rs (n) completely clear, i.e. not passing through the congested area, as an auxiliary route P z rs (n); if P 0 rs (n) passing through the partially congested area, leaving a clear portion of the path, at P 0 rs (n) searching the longest path between the starting point and the end point of the local congestion regional sub-network, and connecting with the remained clear part of the path to be used as an auxiliary path P z rs (n)；
In the step 5-3, the step of the method,
for the auxiliary path P z rs (n) a distributed demand flow equal in value to the demand flow Q rs ；
Step 5-4, based on the secondary path P only z rs (n) loading traffic demands, and obtaining auxiliary distribution results of the road network through road section number, bottleneck identification and congestion backtracking, wherein the auxiliary distribution results comprise auxiliary distribution demand flow of road sections
Auxiliary inflow rate
Auxiliary outflow rate
The static traffic flow distribution method considering the congestion space queuing and overflowing has the beneficial effects that:
1) Based on the fact that the time of queue tail passing bottlenecks is equal, which describes the macroscopic synchronism of congestion interference in space and time, the method provides a method for improving a demand compression and congestion backtracking algorithm based on road section coding, and improves the calculation efficiency of the demand compression and congestion backtracking;
2) The local congestion area identification algorithm for searching the congested road sections in the network and marking the congestion space range is provided, so that a compact congestion area is formed conveniently in the algorithm calculation process, and the generation of a pseudo congestion area is avoided;
3) Based on a user balanced distribution principle of static traffic flow distribution of a road network considering congestion space queuing and overflow, a method for selecting a minimum and maximum demand loading path of a local congestion area in a global area is provided, so that a guarantee is provided for forming a user balanced distribution solution;
4) An iterative weighted solving algorithm of static traffic flow distribution considering congestion space queuing and overflow is constructed, the exploration step length of exploring an optimal solution based on a feasible solution can be effectively given, and the iterative weighted solving algorithm is used for solving the traffic distribution of a large-scale network;
5) The method solves the problems of iterative oscillation and difficult convergence to a stable solution in the existing model solving process, and overcomes the defect that when the road resistance function is insensitive, too much traffic demand is distributed to a road section with smaller traffic capacity and the road cannot be converged effectively.
Drawings
The invention has the following drawings:
FIG. 1 is a flow of a link numbering algorithm based on a selected path;
FIG. 2 is an example of link numbering based on selected paths;
FIG. 3 illustrates a path selection method for global minimum and local maximum congestion zones;
FIG. 4 is a flow chart of an iterative weighting algorithm;
FIG. 5 is a schematic diagram of an exemplary road network;
FIG. 6 is an iterative convergence curve for different demand flows and queuing numbers.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
1. Road section numbering algorithm of selected path
In consideration of static traffic flow distribution of congestion space queuing and overflow, a demand compression and congestion backtracking algorithm improved based on road segment coding is provided, and hierarchy of bottleneck identification, demand compression and congestion backtracking is ensured by numbering all used road segments in a network based on a selected path, namely a small-numbered road segment is positioned at the upstream of a large-numbered road segment. A specific road segment numbering algorithm flow is shown in fig. 1, and an example of the road segment number based on the selected route is shown in fig. 2. The bottleneck identification and the demand traffic compression are sequentially processed step by step from the upstream minimum-numbered road section, from the small number to the large number to the downstream; and the congestion backtracking starts from a downstream bottleneck section with a large number, and queues the congestion space upstream from the large number to a small number.
2. Local congestion area identification algorithm
Traffic bottlenecks are divided into physical bottlenecks and secondary bottlenecks. The physical bottleneck is a road section which generates congestion due to the fact that the entrance traffic volume is larger than the traffic capacity, and the secondary bottleneck is a part of an upstream road section, which is congested and overflows to a downstream road section, so that congestion interference is generated on other traffic flows on the upstream road section.
When the required flow of the road section is larger than the traffic capacity of the road section, a congested space is formed at the upstream of the physical bottleneck, such as queuing, queuing overflow, congestion interference and the like, so that a congestion area composed of a plurality of congested road sections is formed. The congestion area mainly comprises three types of congestion road sections: a downstream congestion bottleneck section (comprising a physical bottleneck and a secondary bottleneck), a midstream queuing overflow section and an upstream queuing tail section. Because the congestion space queuing has overflow effect on the upstream road section, any physical bottleneck can generate a congestion area, and the road network with the congestion area can be decomposed into a local completely congested sub-network and a local completely unblocked sub-network.
And constructing a local congestion area identification algorithm according to the description for searching the congested road section and marking the congestion space range. Firstly, calibrating a terminal point of a congestion area, wherein a downstream terminal point of a bottleneck road section is the terminal point of the congestion area; secondly, calibrating the starting point of the congestion area, and backtracking based on all paths passing through the bottleneck until meeting the queue tail section of the path, wherein the downstream end point of the queue tail section is the starting point of the congestion area; and finally, calibrating the road sections between the starting point and the destination point, wherein all the road sections on the paths between the starting point and the destination point belong to the road sections in the congestion area. Through the three steps, the local congestion area is constructed and marked.
Based on the condition that multiple demanded traffic flows pass through a physical bottleneck and a secondary bottleneck, the tail of a queue in multiple congestion spaces has the same passing time when passing through the same congestion bottleneck (the tail of the queue passes through the bottleneck with equal time for short). When multiple required traffic flows pass through the congested road sections, space interference and time interference between congestion can be generated, and therefore the queuing length and the passing time are increased. The jam mutual interference describes the mutual interference of jam space queuing on the space, and the tail of the team disappears after passing through a jam bottleneck due to the dissipation of the jam; the queue tail describes mutual interference of jam space queuing in time through equal bottleneck time, the jam space queuing dissipates in time, and different demand flows pass through the same physical bottleneck in equal proportion. Therefore, the queue tail coincides with the actual observation on a macroscopic level with congestion interference through equal bottleneck time.
The non-user equilibrium state of local congestion not only reflects that the untwining (passing) time of paths existing between the same origin and destination is unequal, but also can generate the congestion multi-tail phenomenon of a pseudo smooth road section in a congestion area under special conditions. And defining the congested area without the pseudo smooth road section as a dense congested area. In a dense congestion area, the higher the demand flow of a congestion path is, the higher the congestion compression coefficient is, the higher the downstream passing traffic volume and the upstream queuing traffic volume are, and the time for untwining (passing) the road section can be shortened on the basis of a monotonically decreasing road resistance function of the congestion road section; therefore, the user equilibrium state in the local congestion area can be effectively approached.
3. Method for selecting demand loading path
When the network is in a user balance state, the whole network, local completely congested or unblocked regional subnets, the demanded traffic flow and the distributed traffic flow all meet the user balance principle. The principle of the balanced distribution of static traffic flow users of the road network considering the queuing and overflowing of the congested space is as follows: all road users accurately know the required driving time of each road section, rationally select the path with the shortest driving time, and achieve the user equilibrium state, namely: 1) From the perspective of the overall road network demand traffic, the respective used links of each 0D pair (start and end pairs) have equal and minimum travel times, and the travel times of the links not used are greater than or equal to the minimum travel time; 2) From the perspective of traffic flow distribution, within a partially completely clear subnet, each used link of each sub-OD pair has an equal and minimum travel time, and the travel time of an unused link is greater than or equal to the minimum travel time; within a partially fully congested subnet, each path of each sub-OD pair has equal travel time.
When the global road network, the local completely unblocked sub-network and the local completely blocked sub-network all meet the user balance state, the system can be in the user balance stable state, otherwise, all road users can change the traffic distribution result by changing the path so as to pursue the user balance.
Auxiliary path selection in the demand loading process is a core problem of an iterative weighting algorithm in traffic flow distribution. In order to make the distribution result approach the equilibrium stable solution effectively, a minimum and extremely large demand loading path selection method for selecting the shortest path in the global network area and the longest path in the local congestion area is provided. The method comprises the following specific steps: when an auxiliary loading path is searched, firstly, selecting a shortest path in the whole road network, and if the shortest path does not pass through a local congestion area, directly using the shortest path as a demand loading path; if the shortest path passes through the local congestion area, marking the starting point and the ending point of the shortest path passing through the local congestion area, selecting a longest path between the starting point and the ending point to replace the local path in the original local congestion area, and forming a final auxiliary path serving as a demand loading path. Because the minimum path is selected in the network global area and the minimum path can be selected in the local completely smooth area, the path selection method for the minimum path and the maximum path is a combination of the minimum path selected in the global area, the minimum path selected in the smooth area and the maximum path selected in the congested area, and the minimum path, the minimum path and the maximum path are jointly and effectively approximated to the equilibrium solution.
Taking fig. 3 as an example, assume that there are two paths between origin-destination points 0D, which are P1 and P2, respectively. P1 does not pass through the local congestion zone, and P2 passes through the local congestion zone. If the P1 is the shortest path of the universe, selecting the P1 as an auxiliary demand loading path; if P2 is the global shortest path, r and s are respectively the starting point and the ending point of P2 passing through the local congestion area, and the longest path a is found between r and s 4 And the portion u of the path P2 passing through the clear area 1 And a 3 Are connected to form a new path P3 (a) 1 +a 4 +a 3 ) P3 is selected as the auxiliary demand load path.
In order to ensure that the used links of each 0D pair have equal and minimum travel times from the perspective of the global overall road network, the travel time of the unused links is greater than or equal to the minimum travel time, and therefore a route selection method with a very small global area is adopted as a whole. Meanwhile, a dense congestion area can be formed by the longest path selection method requiring loading in the local congestion area, and the equal time of the queue tail passing through the bottleneck is met, so that the user equilibrium state in the local congestion area can be effectively approached.
4. Iterative weighted solution algorithm
Based on the bottleneck identification, the demand compression, the congestion backtracking, the congestion area identification and the demand loading path selection method, an iterative weighting algorithm is constructed, and the detailed steps are as follows:
Specifically, the method comprises the following steps:
step 2.1, numbering all selected paths, slave Path P rs And (n) the initial road section is started, and the road sections on the paths are numbered from the upstream to the downstream in sequence, so that the small-number road section of each path is ensured to be positioned at the upstream of the large-number road section.
2.2, identifying physical bottlenecks and secondary bottlenecks, and performing congestion backtracking to obtain the nth road network distribution result: required flow of road section distribution
Inflow rate of flow
Outflow rate of liquid
The bottleneck identification and the required traffic compression are carried out from the upstream minimum-numbered road section to the downstream from the small number to the large number; the congestion backtracking starts from a downstream bottleneck section with a large number and proceeds from the large number to a small number and upstream.
Specifically, the method comprises the following steps:
step 4.1, calibrating the terminal of the congestion area: namely, the downstream end points of the bottleneck sections, including the downstream end points of the physical bottleneck sections and the secondary bottleneck sections.
Step 4.2, calibrating the starting point of the congestion area: the method comprises the steps of backtracking based on all paths passing through a physical bottleneck road section and a secondary bottleneck road section until meeting a line tail road section of the path, wherein a downstream end point of the line tail road section is a starting point of a congestion area, and the line tail road section is generally a semi-smooth semi-congested road section, namely, a smooth part and a congestion part exist on the road section.
And 4.3, calibrating the road sections between the starting point and the end point of the congestion area, wherein all the road sections on the paths between the starting point and the end point belong to the road sections in the congestion area, namely constructing and marking the congestion area.
Step 5, finding an auxiliary path: updated as obtained in step 3
Finding the nth auxiliary path P z rs (n) obtaining the auxiliary path distribution demand flow
Based only on auxiliary path P z rs (n) loading traffic demands, and obtaining the nth auxiliary road network distribution result through road section number, bottleneck identification and congestion backtracking, wherein the nth auxiliary road network distribution result comprises the following steps: auxiliary distribution demand flow of road section
Auxiliary inflow rate
Auxiliary outflow rate
Specifically, the method comprises the following steps:
step 5.1, calculating and searching the shortest path in the global road network, and recording as P 0 rs (n)。
Step 5.2, if P 0 rs (n) completely clear, i.e. not passing through the congested area, as an auxiliary route P z rs (n); if P is 0 rs (n) passing through the partially congested area, leaving a clear portion of the path, at P 0 rs (n) searching the longest path between the starting point and the end point of the local congestion area sub-network, connecting with the reserved smooth part of the path, and using the longest path as an auxiliary path P z rs (n)。
And (4) in a step 5.3,
as an auxiliary path P z rs (n) a distributed demand flow equal in value to the demand flow Q rs 。
Step 5.4, based on the auxiliary path P only z rs (n) loading traffic demands, and obtaining auxiliary distribution results of the road network through road section number, bottleneck identification and congestion backtracking, wherein the auxiliary distribution results comprise auxiliary distribution demand flow of road sections
Auxiliary inflow rate
Auxiliary outflow rate
can be simplified as follows:
step 7, determining a new distribution path cluster: the nth auxiliary path P z rs (n) as the (n + 1) th path P rs (n + 1), i.e. P rs (n+1)＝P z rs (n); n +1 th path P rs The path allocation demand traffic of (n + 1) is:
i =1,2 \ 8230n paths P rs (i) The path distribution demand flow is:
and 8, updating the road network distribution result: the method comprises the steps of loading traffic demands on all i =1,2 \ 8230in a new path cluster, wherein n +1 selected paths are subjected to traffic demand loading, and obtaining the road network distribution result of the (n + 1) th time through road section numbers, bottleneck identification and congestion backtracking, wherein the road network distribution result of the (n + 1) th time comprises the following steps: required flow of road section distribution
Inflow rate of fluid
Outflow rate of liquid
Step 9, judging iteration termination conditions: if the convergence precision epsilon is met or the maximum iteration number M is exceeded, the iteration is terminated, and the distribution result is the required flow for the road section distribution
Inflow rate of fluid
Outflow rate of liquid
Otherwise, enabling the iteration number n = n +1, and returning to the step 3 to continue the iteration; the precision e expression is:
and when e is less than or equal to epsilon, the precision requirement is met.
It should be noted that, in order to describe the phenomena of queuing and overflow in the congested space, the iterative weighting algorithm introduces core problems such as bottleneck identification, demand compression, congestion backtracking, congestion area identification, and longest path selection in the congested area in the process of the traditional static traffic flow distribution iterative weighting algorithm. Each subprogram is calculated in parallel in each step of iteration process, and nested calculation does not exist between the subprograms, so the time complexity and the space complexity of the calculation of the whole algorithm are similar to those of the traditional static traffic flow distribution iteration weighting algorithm. The iterative weighting algorithm flow is shown in fig. 4.
Example (b):
in order to describe the data processing and operation process of the method, simple calculation examples are constructed for calculation. The arithmetic road network consists of 5 road segments and a pair of start and end points (1 and 5), and has 2 paths P 1 (1-2-4-5) and P 2 (1-2-3-4-5) (as shown in FIG. 5),the link numbers and attributes are shown in table 1. The impedance functions of the unblocked section and the congested section are respectively
Wherein x is a Inflow for an open road section, y a Is the outflow of congested road segments.
TABLE 1 example basic attribute table for road segments
In the algorithm calculation process: t is t a Time of passage, X, for road section a a Demanded flow, x, for road section a a The inflow of the section a, y a Is the outflow of the section a, k a The smooth ratio (k is more than or equal to 0) of the road section a a ≤1)，S a The number of vehicles queued for the point on road segment a,
starting with the destination r is the allocated demand traffic on the nth path between s.
When the convergence accuracy epsilon =0.3%, the calculation iteration convergence process under different demand flows and queuing numbers is shown in fig. 6. When the flow rate Q is required rs =4, number of queues N rs If =40, the required flow rate of each link is smaller than the link traffic capacity, and no congestion occurs.
Let Q rs -8，N rs =80. When n =1, the shortest path is P 1 (ii) a The demand flow begins to be compressed at the downstream end of segment C, creating a spatial queue on segment C where the proportion of the clear portion of segment C is 0.88. When n =2, path P 2 Is selected at this time
The bottleneck point is the upstream end point of the section E; on the upstream of the bottleneck, the road section C and the road section D respectively form a spatial queue, and the proportion of the unblocked part is as follows: 0.93 and 0.95. When n =6, convergence accuracy is satisfied. At this time, the allocation results of the paths are respectively
The transit times were 54.60 and 62.36, respectively. Space queues are formed on the road sections C and D, the passing time of the tail of the line is 16, and the proportion of the occupied road sections is 0.09 and 0.02 respectively.
Let Q rs -8，N rs -1200, when n =18, satisfying convergence accuracy. The allocation results of the paths are respectively
Let Q rs ＝8，N rs =2000, when n =189, convergence accuracy is satisfied, and
the road sections B, C, D, E form a local fully congested area. The longest way will be selected for allocation within the region. At this time, P 1 And P 2 The line tail of the local congestion area is merged into one line tail on the road section A, and the length errors E = (381.59) - (382.37) of the path C + E and the path B + D + E in the local congestion area meet the error of the congestion equilibrium solution in the full congestion area.
Let Q rs -8，N rs And =2400. When n =172, convergence accuracy is satisfied, and
at this time, not only all the links are in a fully congested state, but also a point forming a starting point at the starting point 1 is queued. In the fully congested area, the path P 1 And path P 2 Length error of satisfying user equilibrium solutionAnd (4) error.
In the iterative process of calculating traffic flow distribution, along with the increase of the iteration number n, the distribution result gradually approaches to a stable solution, and the passing time difference of each path gradually decreases, so that the feasibility of the method is fully explained. When n is larger than the critical value, the convergence speed and the convergence effect gradually become slow, more iteration times are needed when the convergence to the preset precision is achieved, and the solving efficiency is limited. The method provides a basic iterative algorithm for a solving method of a balanced distribution model considering the congestion space queuing and overflowing, and enriches and develops a static traffic flow distribution theory considering the congestion space queuing and overflowing.
In addition, the first and second substrates are,
(1) The principle of representing the calculation order based on the coding rule is adopted, and other known methods are used for numbering road segments or nodes passing through a road network or a path in a forward sequence or a reverse sequence so as to ensure the hierarchy and the secondary nature of bottleneck identification, demand compression and congestion backtracking, and the method also belongs to the protection scope of the method;
(2) Based on the principle that the time of queue tails passing through bottlenecks in a congested area is equal, the method for effectively approximating the equilibrium state of users in a local congested area by using other known calculation methods also belongs to the protection range of the method;
(3) Based on the principle that the integral area of the unobstructed area, the jammed area and the whole area is used for solving the extreme value, other known methods are used for calculating the optimal solution of the user, and the method also belongs to the protection range of the method;
(4) The convergence condition is judged based on the required flow, the inflow flow and the outflow flow of the road section distribution, and the method also belongs to the protection scope of the method.
Those not described in detail in this specification are within the skill of the art.
Claims (4)
1. A static traffic flow allocation method considering queuing and overflow in congested space, characterized by comprising the steps of:
step 1, initialization: number of iterations n =1, in accordance with
Selecting the shortest path as the first iteration path P rs (n) for the required flow rate Q rs Carrying out all-existence or all-nonexistence distribution to obtain the required flow of path distribution
Step 2, loading an initial traffic demand: first, link numbering is performed: numbering road sections of the selected path from the initial road section; secondly, identifying a physical bottleneck and a secondary bottleneck, and carrying out congestion backtracking; finally, obtaining the road network distribution result of the nth time, wherein the road network distribution result of the nth time comprises the following steps: required flow of road section distribution
Inflow rate of fluid
Outflow rate of liquid
Step 3, updating the impedance of each road section:
step 4, marking a congestion area: determining the congestion state of the used road section, and marking a congestion area; carrying out congestion backtracking based on a path passing through a bottleneck road section, and finding out a downstream endpoint of a line tail road section as a congestion area starting point and a downstream endpoint of the bottleneck road section as a congestion area terminal point;
step 5, finding an auxiliary path: updated as obtained in step 3
Finding the nth auxiliary path P z rs (n) obtaining the auxiliary path distribution demand flow
Based only on auxiliary path P z rs (n) loading traffic demands, and obtaining the nth road network auxiliary distribution result through road section number, bottleneck identification and congestion backtracking, wherein the nth road network auxiliary distribution result comprises the following steps: auxiliary distribution demand flow of road section
Auxiliary inflow rate
Auxiliary outflow rate
Step 6, determining the flow transfer coefficient: λ is the flow transfer coefficient, the value of which is related to the number of iterations and can be obtained by the following dichotomy:
can be simplified as follows:
step 7, determining a new distribution path cluster: the nth auxiliary path P z rs (n) as the (n + 1) th path P rs (n + 1), i.e. P rs (n+1)＝P z rs (n); n +1 th path P rs The path allocation demand traffic of (n + 1) is:
i =1,2 8230n, n paths P rs (i) The path distribution demand flow is:
step 8, updating the road network distribution result: the method comprises the steps of loading traffic demands on all i =1,2 \ 8230in a new path cluster, wherein n +1 selected paths are subjected to traffic demand loading, and obtaining the road network distribution result of the (n + 1) th time through road section numbers, bottleneck identification and congestion backtracking, wherein the road network distribution result of the (n + 1) th time comprises the following steps: required flow of road section distribution
Inflow rate of fluid
Outflow rate of liquid
Step 9, judging iteration termination conditions: if the convergence precision epsilon is met or the maximum iteration number M is exceeded, the iteration is terminated, and the distribution result is the required flow for the road section distribution
Inflow rate of fluid
Outflow rate of liquid
Otherwise, enabling the iteration number n = n +1, and returning to the step 3 to continue the iteration; the precision e expression is:
accuracy of
And when e is less than or equal to epsilon, the precision requirement is met.
2. A static traffic flow distribution method considering congestion space queuing and overflow as claimed in claim 1 wherein: the specific steps of the step 2 are as follows:
step 2-1, numbering all selected paths, slave Path P rs (n) numbering road sections on the paths from upstream to downstream in sequence from the starting road section of the path, and ensuring that the small-numbered road section of each path is required to be positioned at the upstream of the large-numbered road section;
2-2, identifying physical bottlenecks and secondary bottlenecks, and performing congestion backtracking to obtain the nth road network distribution result: required flow of road section distribution
Inflow rate of fluid
Outflow rate of liquid
The bottleneck identification and the required traffic compression are carried out from the upstream minimum-numbered road section to the downstream from the small number to the large number; the congestion backtracking starts from a downstream bottleneck section with a large number and proceeds from the large number to a small number and upstream.
3. The method of claim 1, wherein the static traffic flow distribution method takes into account queuing and overflow in congested space, further comprising: the specific steps of the step 4 are as follows:
step 4-1, calibrating the terminal of the congestion area: namely, the downstream end points of the bottleneck road sections comprise the downstream end points of the physical bottleneck road sections and the downstream end points of the secondary bottleneck road sections;
step 4-2, calibrating the starting point of the congestion area: the method comprises the following steps that backtracking is carried out on the basis of all paths passing through a physical bottleneck road section and a secondary bottleneck road section until the trail section meets the line tail road section of the path, the downstream end point of the line tail road section is the starting point of a congestion area, the line tail road section is generally a semi-unblocked semi-congested road section, and namely an unblocked part and a congested part exist on the road section;
and 4-3, calibrating the road sections between the starting point and the end point of the congestion area, wherein all the road sections on the paths between the starting point and the end point belong to the road sections in the congestion area, namely constructing and marking the congestion area.
4. The method of claim 1, wherein the static traffic flow distribution method takes into account queuing and overflow in congested space, further comprising: the specific steps of the step 5 are as follows:
step 5-1, calculating and searching the shortest path in the global road network, and recording as P 0 rs (n)；
Step 5-2, if P 0 rs (n) completely clear, i.e. not passing through the congested area, as an auxiliary route P z rs (n); if P is 0 rs (n) passing through the partially congested area, leaving a clear portion of the path, at P 0 rs (n) searching the longest path between the starting point and the end point of the local congestion area sub-network, connecting with the reserved smooth part of the path, and using the longest path as an auxiliary path P z rs (n)；
In the step 5-3, the step of,
as an auxiliary path P z rs (n) a distributed demand flow equal in value to the demand flow Q rs ；
Step 5-4, based on the auxiliary path P only z rs (n) loading traffic demands, and obtaining auxiliary distribution results of the road network through road section number, bottleneck identification and congestion backtracking, wherein the auxiliary distribution results comprise auxiliary distribution demand flow of road sections
Auxiliary inflow rate
Auxiliary outflow rate
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