CN108647825B - Urban road construction sequence optimization method based on traffic daily variation characteristics - Google Patents
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Abstract
The invention discloses an urban road construction sequence optimization method based on traffic daily variation characteristics, which comprises the following steps: initializing parameters related to a daily traffic distribution model and a genetic algorithm; step two, generating an initial feasible solution (construction sequence) meeting the requirement of the initial population quantity; step three, calculating the fitness of a feasible solution by taking the daily traffic evolution during construction as an analysis process and the objective function provided by the invention as a fitness function; step four, judging whether the actual iteration times meet the set iteration time requirement, if so, generating an optimal solution, and stopping calculation; if not, entering the fifth step; and step five, generating a new feasible solution, and then returning to the step three. The invention has the positive effects that: the method of the invention considers the traffic time-varying characteristics, can describe the evolution process of the traffic flow from the unbalanced state to the balanced state during construction, further improves the calculation precision and better accords with the real situation.
Description
Technical Field
The invention relates to an urban road construction sequence optimization method based on traffic daily variation characteristics.
Background
The research related to the optimization of the construction sequence can be traced back to 90 years in the 20 th century, early researchers adopt a series of analytical formulas derived from a queuing theory to calculate the traffic delay caused by a single construction area in a road network, and the minimum total traffic delay of the network corresponding to a certain construction sequence is taken as a target, so that the sequential construction sequence of the construction areas is determined. Calculating traffic delay caused by completing 10 road maintenance activities by 3 construction teams within one day by Fwa and the like, and determining an optimal construction sequence by using an optimization solution method based on a genetic algorithm; subsequently, Muntavir expands the research objects to 100 road maintenance activities and 6 construction teams, prolongs the construction period to 1 week, and still adopts a genetic algorithm to calculate the optimal construction sequence. However, these studies assume that the links in the traffic network are isolated, and the interaction between traffic flows, such as congestion spread of adjacent links, is not considered from the traffic network level when calculating network traffic delays. Therefore, it only calculates the traffic delay of a single road section in turn, and cannot evaluate the influence of construction on the whole traffic network.
In 2000, the problem of optimizing the construction sequence began to be studied from the perspective of network traffic flow, taking into account the distribution of traffic in the traffic network. So far, the main focus of these studies has been on the case where the construction period is several hours and all construction projects are completed within 24 hours. In these studies, Chang et al respectively adopt dynamic traffic distribution and all-or-nothing distribution methods to calculate traffic volume distributions under different construction sequences during peak hours, and thus obtain the total network travel time, thereby determining the optimal construction sequence. The applied scene is the situation that 2 construction teams finish 6 construction tasks in 1 day, when describing the problem, the construction period of all construction areas must be the same or the construction teams must have the same start time and end time, however, the construction tasks finished by different construction teams must have certain difference, and the constraint is lower in conformity with the reality, so that the research objects of the construction teams have certain limitation. Some researchers also directly use microscopic traffic simulation tools such as PARAMICS, VISIMS and the like to calculate the network total traffic delay, and determine the optimal construction sequence by adopting some heuristic algorithms such as genetic algorithm, ant colony algorithm and the like. Their innovation point is mainly to provide a hybrid technology, i.e. to combine genetic algorithm and traffic simulation software, the core of which is still whether there is total or none distribution or UE traffic distribution, and the traffic daily-variation characteristics are not considered, if the method is also adopted for the long-term construction sequence optimization problem, the calculated amount will be greatly increased. Tang and Chien adopt a UE traffic distribution method to determine time-varying traffic transfer quantity, and consider time-varying traffic demand, variable maintenance cost, working efficiency of construction teams and other factors to optimize the length of a construction area and the construction sequence. The models proposed by the users take the geometric form of the construction area into consideration, and accurately calculate various cost increases caused by construction, but the applicability of the method in the urban traffic network is still left to be examined as the research is directed to a small-scale rural road network.
However, in practical situations, there are many construction areas with construction periods of tens of days or even hundreds of days on urban road networks, and few studies are currently focused on such a construction sequence optimization problem in units of days with a long construction period. The Zheng and the like research the condition that the construction period is several months, and propose a method for carrying out UE traffic distribution by taking the months as a unit so as to calculate traffic delays under different construction sequences. However, when long-term construction is researched, the research carried out by taking months as a traffic distribution unit has some defects. Firstly, there are limitations on the objects to be studied, and in practice, the construction period of all construction areas is not an integral multiple of a month, and this method cannot cope with construction periods that are not integral multiples. More importantly, the method considers that the traffic distribution in a single month is always in an equilibrium state during construction, and actually the flow distribution on the network is mostly in an unbalanced state in a period of time after new construction starts or construction projects are completed, so that the method covers the diurnal variation process of converting the flow distribution between the unbalanced state and the equilibrium state, and compared with the real situation, the error is larger. Lee considers the time-varying characteristics of traffic distribution for the first time, determines the optimal alternative path by using a dynamic traffic distribution method embedded in VISSIM, and calculates traffic delay on a network, thereby determining the optimal construction sequence. However, when the characteristics of the traffic distribution with respect to the change of day are taken into consideration, the author surveys the route change behavior of the road user in the form of a questionnaire, thereby obtaining the traffic transfer ratio between routes within several days. This method of investigation also has certain disadvantages. Firstly, the investigation method is very heavy, it requires reasonable sample capacity and the sample is selected with enough representativeness, and it is difficult to determine the transfer ratio really and effectively due to the difference of the transfer ratio between different paths. Secondly, the network range and applicability considered by the method are limited, the method is more suitable for analyzing traffic flow change on a local network, when the network scale is large, the influence of a construction area on the whole network cannot be accurately analyzed, and particularly, the situation becomes very complicated when a plurality of construction areas exist in the network. Recently, Gong et al also studied the long-term construction sequence optimization problem and proposed a genetic algorithm-based two-layer planning model to determine the start time of each construction zone. Although they calculate the total travel delay of all travelers on the whole network by taking the day as a unit, the daily traffic flow is still calculated by the UE distribution, and the day-by-day dynamic characteristics of the traffic flow on the network are not considered, which is not substantially different from the previous research. The authors and research teams in the thesis consider the limited rational behaviors of travelers in daily traffic environment, provide a long-term urban road construction sequence optimization model based on UE traffic distribution under the framework of Cumulative Prospect Theory (Cumulative Prospect Theory), and analyze influence factors such as reference points of travelers and the number of construction teams on a network. Although the thesis calculates traffic delay caused by a limited reason traveler in long-term construction by taking days as a unit, the calculation of the traffic volume is still based on a special UE state and does not consider the characteristics of the daily change of traffic flow evolution, so that the research is yet to be further and deeply perfected.
In addition to the above studies, in order to evaluate traffic delays caused by construction, there are some commercial optimization software developed specifically for optimizing road construction sequence, which is simple to apply because the software only performs influence analysis from the street or local traffic network level where the construction area is located, and the transportation departments of several state governments in the united states develop data table-based optimization analysis tools for construction sequence problems. Although these software are convenient to use, since the adverse effect due to construction is only roughly estimated from the project level or the local street level where the construction area is located, and the traffic delay is not analyzed from the whole traffic network level, the construction sequence optimization of a plurality of construction areas on the network is performed. For example, QuickZone and steam (surface transport Efficiency Analysis model) simply evaluate traffic delays in two scenes when there is a construction area on a road section, and do not consider traffic delays on other adjacent paths caused by traffic congestion diffusion. In addition, Maryland State Software, Oregon Software, Oklahoma Software, Ohio Software, SMITE and the like have the defects. To address the shortcoming of traffic impact Evaluation from a single project perspective rather than a whole network perspective, U.S. traffic management and research engineers developed software entitled "Work zone impact and strategy Evaluation (WISE)" that enables construction sequence optimization from the traffic network level, but does not essentially take into account the characteristics of traffic flow variations during construction.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides an urban road construction sequence optimization method based on traffic daily variation characteristics, and daily traffic distribution method is adopted to obtain daily traffic on a road network during construction sequence optimization. The method can describe the day-to-day change characteristics of the network traffic flow under the construction background, and more truly reflect the gradual evolution process of the flow from the unbalanced state to the balanced state when the construction starts or ends. And on the basis, calculating the increase of the total impedance of the network in the whole time period influenced by construction, and evaluating the advantages and disadvantages of the construction sequence by comparing the increase of the total impedance of the network under different construction sequences.
The technical scheme adopted by the invention for solving the technical problems is as follows: an urban road construction sequence optimization method based on traffic daily variation characteristics comprises the following steps:
initializing parameters related to a daily traffic distribution model and a genetic algorithm;
step two, generating an initial feasible solution (construction sequence) meeting the requirement of the initial population quantity;
step three, calculating the fitness of a feasible solution by taking the daily traffic evolution during construction as an analysis process and the objective function provided by the invention as a fitness function;
step four, judging whether the actual iteration times meet the set iteration time requirement, if so, generating an optimal solution, and stopping calculation; if not, entering the fifth step;
and step five, generating a new feasible solution, and then returning to the step three.
Compared with the prior art, the invention has the following positive effects:
the method of the invention considers the traffic time-varying characteristics, can describe the evolution process of the traffic flow from the unbalanced state to the balanced state during construction, can further improve the calculation precision, and is more in line with the real situation.
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The invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a schematic illustration of a construction phase;
FIG. 2 is a schematic chromosome structure.
Detailed Description
The invention assumes that the working capacity of all construction teams is the same, and one construction team can only construct one construction area at the same time. On the network G, the influence of road closure construction on traffic can be divided into two stages: the direct influence stage is in the construction period, and the indirect influence stage is after the construction is finished. During construction, the traffic capacity is reduced due to the existence of the road construction zone, so that the network traffic impedance is increased, and the phenomenon of the increase of the traffic impedance is directly caused by the existence of the construction zone. Therefore, the present invention refers to this effect as a direct effect of the construction. After construction is finished, although no construction area exists, the traffic distribution still needs a certain time to reach an equilibrium state, and during the period, the network traffic impedance is still changed, the influence is not directly caused by construction, and the invention is called as indirect influence of construction.
The invention considers the sum of the impedances generated in the two stages, takes the total impedance increment of the network caused by construction as the objective function of the model, ensures that the flow on all road sections in the model meets the daily traffic evolution rule, and has certain limit on the total construction period of the construction sequence. Therefore, the urban road construction sequence optimization model considering the traffic daily variation characteristics is as follows:
S.T.
wherein, P (j) represents the increase of the total impedance of the network caused by the construction sequence j; i represents the ith construction stage; t is tiIndicating the t day in the i construction stage; t isiThe construction period of the ith construction stage is shown; TD represents the longest construction period; e is the number of days required for reaching a balanced state after construction is finished;
the traffic vector of the road section at the t day in the ith construction stage;
is the traffic vector of the road section at the t-1 th day in the ith construction stage, and when t isiWhen the number is equal to 1, the alloy is put into a container,
is the (i-1) th (i)>1, when i is equal to 1,
representing a road section flow vector before the start of construction) road section flow vector at the last day in the construction phase;
calculating traffic direction vectors of the road section on the t day in the ith construction stage according to the traffic capacity distribution of the corresponding construction stage; x is the number of0Representing a road section flow vector before construction starts; c (-) is a road section impedance vector calculated by adopting a function of the U.S. Federal road administration; dr,sThe traffic demand of OD to r, s is represented and is a fixed value;
representing the flow of a path k connecting OD pairs r, s on the t day in the ith construction stage;
representing the flow of the path k between the OD pairs r and s before construction begins;
this means that if the segment a is on the path k between the connection OD and r, s, it takes a value of 1, otherwise it takes a value of 0.
The objective function (1) in the model consists of two parts, wherein the first part represents the direct impedance and the indirect impedance caused by construction; the second part shows the total network impedance generated in the same time without construction background, and the difference between the two is the increase of the total network impedance caused by construction. Constraint (2) represents the daily traffic evolution situation under the construction influence background, and can be specifically calculated by referring to a formula (10) - (12); the constraint (3) expresses the limitation to the total construction period under a certain construction sequence; constraint (4) - (9) means similar to classical UE model constraint, and represents the relationship between road section traffic, path traffic and traffic demand.
The diurnal traffic distribution model proposed by He et al can be represented by formula (10).
xt+1＝xt+α·(yt+1-xt) (10)
In the formula, alpha (0)<Alpha is less than or equal to 1) is taken as step length; x is the number oftThe road section flow vector at the t day; x is the number oft+1The road section flow vector at the t +1 th day is obtained; y ist+1The vector that determines the direction of change of the link traffic at day t +1 is solved by equation (11).
minλc(xt)'yt+1+(1-λ)D(xt,yt+1) (11)
Wherein λ is a positive proportionality parameter and 0<λ<1；c(xt) Is a road segment impedance vector; d (x)t,yt+1) For the current road section flow vector xtAnd the target flow vector yt+1The distance of (c).
For the current road section flow vector xtAnd the target flow vector yt+1The distance of (2) is calculated by using the formula (12).
In the formula, a representsA certain path segment; l represents a set of road segments;
representing the traffic of the road section a at the time of the t day;
a directional flow rate indicating a flow rate change of the link a at the time of determining the t +1 th day; c. CaAnd (w) represents the impedance function of the road section a, and is calculated by adopting the function of the Federal road administration in the United states.
In the model of the present invention, c (x) is the value obtained when the construction period does not change for two days before and after the construction periodt) Actual road section impedance vector of the t day; c (x) if the construction stage changes in two days before and aftert) And the corresponding road section impedance vector of the flow in the t day under the background of the next construction stage is obtained.
The first half of equation (11) is a linear programming problem, i.e., minimizing the total impedance of the entire network in the case of a determined path impedance vector distribution. In essence, the linear programming problem is to find the path with the least impedance between each OD pair and distribute all traffic demands to the shortest path, which is equivalent to the all-or-nothing traffic distribution model. From the aspect of the characteristics of the daily traffic, a traveler always tends to select a path with the minimum impedance for traveling, but in practice, because the optimal path between each OD pair may not be unique, if only the traffic flow on the shortest path is used as the target flow and is not controlled, the process of the daily traffic evolution is very unrealistic. When the shortest route is searched, due to factors such as living habits and personal preferences, travelers are not willing to make changes which are not beneficial to themselves, even if the traffic capacity of some road sections changes, the travelers can select most of the road sections with the same habits as the travelers in the whole route to change a small part of the road sections for traveling, and therefore the fluctuation of the flow is reduced. Therefore, the latter half of equation (11) represents the selection habit of the traveler.
Here, the "construction phase" means a road traffic capacity state, that is, when a new construction task starts or ends in the road traffic network, the construction is shifted from the current phase to the next phase. The traffic capacity distribution of a road section at the beginning or end of a "construction phase" can change, causing the traffic impedance on the road network to change. As shown in fig. 1, when 3 construction teams complete 4 construction tasks, 5 construction stages occur according to the construction sequence given in the figure. It can be seen clearly that at the front and rear boundaries of these construction stages, there are phenomena of the completion of existing construction tasks and the start of new construction tasks on the network. Taking construction stage 1 and construction stage 2 as an example, the phenomenon of transition from construction stage 1 to construction stage 2 occurs because the tasks of construction area 1 have been completed, and here the traffic capacity reduction phenomenon due to road closure is recovered, which changes the traffic capacity distribution over the entire network. In addition, at the construction stage 5 in the figure, there is no construction phenomenon on the network, and here, in order to keep the same with the above definition, it is also taken as a stage of construction.
The solving steps of the urban road construction sequence optimization model are as follows:
(1) and (5) initializing. The method mainly determines parameters related to a daily traffic distribution model and a genetic algorithm. Namely determining the geometric topological structure, traffic capacity, free running time, the number of OD pairs and corresponding traffic demands of the road, the position of a construction area, an iteration step length, a correlation coefficient, the number of initial populations, iteration times, cross probability, variation probability and the like.
(2) An initial feasible solution (construction sequence) is generated. The algorithm adopts a natural number coding-based genetic mode to respectively number each construction area, and 0 is used as a boundary of tasks of different construction teams. According to the evolution characteristics in biology, the invention uses a chromosome to represent a feasible solution, and FIG. 2 shows a chromosome structure diagram when two construction teams undertake a plurality of construction tasks. In this step, an initial number of feasible solutions that meets the initial population quantity requirements should be generated.
(3) And calculating the fitness of the feasible solution. And determining the construction stage condition corresponding to each feasible solution in the population, and calculating the daily variable traffic volume corresponding to each feasible solution by adopting the daily variable traffic distribution model (10) - (12), thereby further obtaining the network total impedance increment. The numerical meaning of the model objective function is the size of adverse effect caused by construction, the larger the objective function value is, the larger the adverse effect caused by construction is, and the smaller the probability of inheritance to the next generation is. However, according to the iteration rule of the genetic algorithm, the higher the fitness is, the higher the possibility that the fitness is inherited to the next generation is, so that the fitness function of the model takes the reciprocal of the objective function, namely 1/P (j).
(4) And (6) judging convergence. Judging whether the actual iteration times meet the set iteration time requirement, if so, generating an optimal solution, and stopping calculation; if not, continuing the next step.
(5) A new feasible solution is generated. And (4) determining the individuals in the next generation population by adopting an adaptive proportion selection method according to the fitness calculated in the step (3), so that the higher the fitness, the higher the chance that the individuals are inherited to the next generation. Meanwhile, in order to reduce the possibility that the problem converges to the local optimal solution, reasonable probability is adopted for carrying out operations such as partial reflection intersection, inversion mutation and the like on the individuals generated by the parent, so that the individuals in the offspring are continuously updated, and the diversity of the solution is increased. And (4) returning to the step (3) after the new feasible solution is generated.
Claims (3)
1. An urban road construction sequence optimization method based on traffic daily variation characteristics is characterized by comprising the following steps: the method comprises the following steps:
initializing parameters related to a daily traffic distribution model and a genetic algorithm;
step two, generating an initial feasible solution meeting the requirement of the initial population quantity:
numbering each construction area respectively by adopting a natural number coding genetic mode, using 0 as a boundary of tasks of different construction teams, and using a chromosome to represent a feasible solution;
step three, calculating the fitness of the feasible solution: determining the construction stage condition corresponding to each feasible solution in the population, calculating the daily variable traffic volume corresponding to each feasible solution by adopting a daily variable traffic distribution model, further obtaining the total impedance increment of the network, and taking the reciprocal of an objective function as a fitness function, wherein:
the method for calculating the total impedance increment of the network comprises the following steps:
(1) determining an objective function of an urban road construction sequence optimization model based on traffic daily variation characteristics:
(2) determining the constraint condition of the objective function:
the conditions are as follows,
The second condition,
The third condition,
The condition IV,
Conditions V,
The six conditions,
The conditions are seven,
The conditions are eight,
Wherein, P (j) represents the increase of the total impedance of the network caused by the construction sequence j; i represents the ith construction stage; t is tiIndicating the t day in the i construction stage; t isiThe construction period of the ith construction stage is shown; TD represents the longest construction period; e is the number of days required for reaching a balanced state after construction is finished;
the traffic vector of the road section at the t day in the ith construction stage;
the traffic vector of the road section at the t-1 th day in the ith construction stage;
the traffic direction vector of the road section on the t day in the ith construction stage; x is the number of0Representing a road section flow vector before construction starts; c (-) is a road segment impedance vector; dr,sRepresenting the traffic demand between OD to r, s;
representing the flow of a path k connecting OD pairs r, s on the t day in the ith construction stage;
representing the flow of the path k between the OD pairs r and s before construction begins;
if the road section a is on a path k between the connection OD and r, s, the value is 1, otherwise, the value is 0;
step four, judging whether the actual iteration times meet the set iteration time requirement, if so, generating an optimal solution, and stopping calculation; if not, entering the fifth step;
and step five, generating a new feasible solution, and then returning to the step three.
2. The urban road construction sequence optimization method based on traffic daily variation characteristics according to claim 1, characterized in that: the parameters comprise the geometric topological structure of the road, the traffic capacity, the free running time, the number of OD pairs and corresponding traffic demands, the position of a construction area, the iteration step length, the correlation coefficient, the initial population number, the iteration times, the cross probability and the variation probability.
3. The urban road construction sequence optimization method based on traffic daily variation characteristics according to claim 2, characterized in that: the daily traffic distribution model comprises the following steps:
xt+1＝xt+α·(yt+1-xt)
in the formula, alpha (0)<Alpha is less than or equal to 1) is taken as step length; x is the number oftThe road section flow vector at the t day; x is the number oft+1The road section flow vector at the t +1 th day is obtained; y ist+1The vector of the road section flow change direction at the t +1 th day is obtained by solving the following formula:
minλc(xt)'yt+1+(1-λ)D(xt,yt+1)
wherein λ is a positive proportionality parameter and 0<λ<1；c(xt) Is a road segment impedance vector; d (x)t,yt+1) For the current road section flow vector xtAnd the target flow vector yt+1The distance (c) is calculated by the following formula:
in the formula, a represents a certain road section; l represents a set of road segments;
representing the traffic of the road section a at the time of the t day;
indicates to decidethe direction flow of the flow change of the road section a in t +1 day; c. Ca(w) represents the impedance function of the section a.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102750427A (en) * | 2012-07-23 | 2012-10-24 | 中国地质大学（武汉） | Urban traffic planning simulation method and system based on multi-agent activity model |
| CN103150913A (en) * | 2013-02-07 | 2013-06-12 | 东莞中国科学院云计算产业技术创新与育成中心 | Dynamic self-adapting traffic signal control apparatus and method |
| CN105117800A (en) * | 2015-08-28 | 2015-12-02 | 国网天津市电力公司 | Method for optimizing power transmission and transformation project construction network plan on basis of genetic algorithm |
| CN107450426A (en) * | 2017-08-01 | 2017-12-08 | 中国建筑第八工程局有限公司 | Foundation pit construction deformable statistical analysis method and system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7930198B2 (en) * | 2005-05-09 | 2011-04-19 | Siemens Corporation | Maintenance event planning and scheduling for gas turbines |
| US9342982B2 (en) * | 2013-09-09 | 2016-05-17 | International Business Machines Corporation | Traffic control agency deployment and signal optimization for event planning |
-
2018
- 2018-05-11 CN CN201810446757.1A patent/CN108647825B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102750427A (en) * | 2012-07-23 | 2012-10-24 | 中国地质大学（武汉） | Urban traffic planning simulation method and system based on multi-agent activity model |
| CN103150913A (en) * | 2013-02-07 | 2013-06-12 | 东莞中国科学院云计算产业技术创新与育成中心 | Dynamic self-adapting traffic signal control apparatus and method |
| CN105117800A (en) * | 2015-08-28 | 2015-12-02 | 国网天津市电力公司 | Method for optimizing power transmission and transformation project construction network plan on basis of genetic algorithm |
| CN107450426A (en) * | 2017-08-01 | 2017-12-08 | 中国建筑第八工程局有限公司 | Foundation pit construction deformable statistical analysis method and system |
Non-Patent Citations (5)
| Title |
|---|
| Link-based Day-to-day Traffic Assignment Model;XiaozhengHe等;《Transportation Research Part B: Methodological》;20100531;第44卷(第4期);第597-608页 * |
| On a link-based day-to-day traffic assignment model;LanshanHan等;《Transportation Research Part B: Methodological》;20120131;第46卷(第1期);第72-84页 * |
| 交通事故持续期内行程时间的可靠性研究;陈玲娟等;《西南交通大学学报》;20130415(第02期);第376-382页 * |
| 地铁区间隧道盖挖逆筑法设计施工方案研究;刘建国等;《四川建筑》;20101028(第05期);第204-206页 * |
| 考虑有限理性和认知更新的日变交通分配模型;祝俪菱等;《北京交通大学学报》;20180215(第01期);第75-80页 * |
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