CN114021295A - Multi-mode carriageway fine setting method based on branch-and-bound method - Google Patents
Multi-mode carriageway fine setting method based on branch-and-bound method Download PDFInfo
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Abstract
The invention discloses a branch-and-bound method-based multi-mode traffic lane fine setting method, which can balance multi-mode road space distribution of the trip rights and interests of various travelers such as private cars, buses, non-motor vehicles and the like from the aspect of urban multi-mode traffic network optimization by considering the optimal combination of lane widths under the existing width constraint, effectively balance urban multi-mode traffic right distribution, improve urban road space utilization efficiency and improve resident trip experience.
Description
Technical Field
The invention belongs to the technical field of road space optimization, and particularly relates to a multi-mode lane fine setting method based on a branch-and-bound method.
Background
The urban road space is a carrier space for people moving and converting and is an important infrastructure for determining whether urban people flow and logistics can be effectively and quickly transported. The specific embodiment form of urban road space distribution is a road cross section form corresponding to different road grades. The cross section of the road generally comprises a motor vehicle lane, a non-motor vehicle lane, a sidewalk, a vehicle separating belt, a facility belt, a green belt and the like, and the special cross section can also comprise an emergency lane, a road shoulder, a drainage ditch and the like. Because of high urban development strength, buildings such as businesses and residences on two sides of the existing urban road are densely built, the expansion of the urban road is often required to be matched and approved by various departments such as house and pipe departments, pipeline moving and changing parts, landscaping and the like, and is also required to be communicated with surrounding commercial tenant residents, so that the urban road expansion and changing approval time is long, the coordination difficulty is high, limited road driving resources cannot meet the increasing traffic demand, and particularly, the traffic jam is further aggravated by the characteristics that in recent years, the number of cars in the urban area is increased year by year, and the occupied road area of cars is large for transporting travelers. In order to meet the current saturated motor vehicle travel demand, many cities such as xi' an, Shanghai, Hangzhou even compress the non-motor vehicle road space to give way for the car travel. The road space distribution method taking the vehicle as the basis is not only difficult to construct a sustainable urban traffic system, but also is not beneficial to fair trip, green and low carbon and social harmony and stability by giving priority to the vehicle rather than the traveler.
The urban traffic jam can be quickly and effectively improved, and the service level of an urban road traffic system is improved. In many cities, such as Beijing and Shanghai, the special bus Lane setting place specification, the HOV (High-Occupancy Vehicle Lane) Lane implementation scheme and the like are continuously provided to promote the bus priority and lead the cars to get off the bus, thereby ensuring that a large amount of commuting requirements in the cities can be met. Compared with a car, the bus is an efficient carrying tool, more travel demands can be borne in unit area, and implementation of bus-preferred road distribution is beneficial to relieving traffic jam, reducing pollution emission and improving social welfare level. Meanwhile, in recent years, more and more patents are provided around the bus lane, for example, chinese patent CN109903564B discloses a system and method for setting the time interval and road section selection of the bus lane, and the bottleneck time interval and road section are obtained by means of the bus shift pipelining and the real-time GPS data of the buses and by using a Fisher optimal segmentation method. Chinese patent CN111161536B discloses a full-time shared bus lane system and method, which aims to allow some social vehicles to drive into the bus lane by means of the intercommunication signal of the bus signal and the roadside spike indicating device when the bus lane is idle, so as to improve the utilization efficiency of the road space. Although the method for setting the bus lane can effectively improve the running efficiency of the bus, the traffic jam cannot be effectively relieved. Firstly, because the bus way often needs wider road condition, this leads to the bus way can not keep continuous setting all the time, and this leads to the bus can only accomplish partly to be preferred. Secondly, under the existing road distribution condition, the travel concept of people is difficult to be changed from car travel to bus travel, because the bus running efficiency is not high, the punctuality is poor, the comfort is low, and the like, the people using the bus travel are few, the bus lane is not fully utilized, the car becomes more congested because of the lack of the road space, meanwhile, the non-motor vehicles and the pedestrians conflict with each other due to the insufficient road rights, and the balance of the road rights among various traffic modes cannot be achieved. At present, no research result considers the width and the number of lanes driven in different modes to be recombined under the condition of the existing road space from the perspective of the whole width of the road space, and most of the existing methods simply change one lane into other purposes and do not consider the fine adjustment for adjusting the lane width. And fourthly, the existing distribution method of urban road spaces such as bus lanes and HOV lanes only starts from a single traffic mode, a single line or even a single road section, and does not consider the optimal configuration of the global road space under a multi-mode traffic network.
The multi-mode traffic mixed travel is a remarkable characteristic of urban traffic travel in China, cars, bicycles, pedestrians, buses and the like are effective modes for urban residents to travel, and different travel subjects can enjoy the right of using urban roads to travel and cannot be as thick as and thin as each other. Under the condition of unbalanced supply and demand of urban traffic, how to scientifically and effectively distribute and manage the road right is the urgent priority of urban traffic management in China at present.
Disclosure of Invention
Aiming at the defects in the prior art, the multi-mode lane fine setting method based on the branch-and-bound method solves the problems of discontinuous bus lane setting, serious non-motor lane occupation, pedestrian and non-motor vehicle conflict, optimized adjustment of road adjustment without considering width by taking a lane as a unit, and neglected global network multi-mode right of way distribution only by a single target and single road section in a single mode under the condition of limited road space resources in the conventional road space optimization technology.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a multi-mode traffic lane fine setting method based on a branch-and-bound method comprises the following steps:
s1, constructing a double-layer planning model based on a branch-and-bound method according to regional traffic basic data of a target extension region, wherein the double-layer planning model comprises an upper-layer planning model and a lower-layer traffic distribution model, the upper-layer planning model provides a planning scheme of a multi-mode road space, the lower-layer traffic distribution model simulates to obtain characteristic indexes after the planning scheme of the upper-layer planning model is implemented, and the road distribution scheme obtained based on the upper-layer model is evaluated according to the characteristic indexes;
and S2, continuously optimizing the road distribution scheme based on the evaluation result to obtain the optimal road distribution scheme and finely setting the traffic lane according to the optimal road distribution scheme.
Further, the regional traffic basic data in the step S1 includes transportation facility physical data, traffic control data, and traffic demand data.
Further, in the step S1, the method for constructing the double-layer planning model specifically includes:
a1, constructing a lower-layer traffic distribution model according to the regional traffic basic data of the target extension area and the current road distribution scheme, and performing multi-mode dynamic traffic distribution on the lower-layer traffic distribution model to further obtain a distribution result and a characteristic index; the characteristic indexes are used for evaluating the optimization degree of the road distribution scheme;
a2, sorting the street congestion degrees in the area based on the distribution result so as to screen out the preferential reconstruction streets in the area;
a3, constructing an upper-layer planning model by combining the current traffic situation of a prior reconstructed street and adopting a road space combination enumeration method;
and A4, screening out the planning scheme of the street and generating a new road allocation scheme based on the constructed upper-layer planning model.
Further, the step a1 is specifically:
a11, constructing a traveler-oriented lower-layer traffic distribution model according to various road space distribution schemes of street road sections under the current situation of a target extension area and by combining basic data of area traffic;
a12, based on regional traffic data, with user balance as an optimization target, with traveler travel decision as a variable, with capacity and transfer of a bus route as constraints, and adopting a continuous average algorithm to perform multi-mode dynamic traffic distribution on a lower-layer traffic distribution model, thereby obtaining a distribution result and characteristic indexes.
Further, the objective function of the lower traffic distribution model in the step a11 is:
wherein k represents a departure time selected by a traveler, od represents a departure/destination point of the traveler, m represents a travel mode selected by the traveler, p represents a travel route selected by the traveler,
representing the travel time of the travelers with m travel modes of starting time k, starting point k, ending point od selected travel route p,
indicating that the shortest path is used among travelers whose travel starting and ending points are od at time k,
this indicates whether or not a traveler who travels at the start/end point od at time k selects route p.
Further, in the step a2, preferentially modifying streets in the area are screened out sequentially by a road section sorting method and a street screening method;
the road section sorting method is used for sorting the congestion degrees of road sections based on the obtained travelers information in the distribution result and the lane information of each road section by taking the ratio of the number of vehicles in multiple modes to the road traffic capacity as a basis;
the street screening method carries out congestion degree grading according to the congestion degree sorting results of all road sections in the target extension area, then carries out street congestion degree sorting according to the grading results of all road sections contained in all streets, and takes the most congested street as a prior reconstruction street.
Further, the step a3 is specifically:
and generating a multi-mode road space distribution scheme pool for preferentially modifying the street, enumerating all combination schemes by setting constraint conditions, and constructing an upper-layer planning model containing all the combination schemes based on the current traffic situation.
Further, in the step a3, the objective function of the upper layer planning model is:
wherein k represents the departure time selected by the traveler, od represents the starting and ending point of the traveler, m represents the trip mode selected by the traveler,
representing travel time of a traveler (k, od, m) on the section a,
indicating the delay time of the traveler (k, od, m) at the intersection j,
indicating the waiting time of the traveler (k, od, m) at the bus stop b,
indicating the stay time of the traveler (k, od, m) at the bus stop b,
shows the time to find a parking space when a traveler (k, od, m) uses a car.
Further, the step S2 is specifically:
s21, updating a lower-layer traffic distribution model and performing multi-mode dynamic traffic distribution based on a new road distribution scheme to obtain a distribution result and a characteristic index;
s22, judging whether the new road distribution scheme generated by the current upper-layer planning model is superior to the previous road distribution scheme or not based on the characteristic indexes, if so, entering a step S23, and if not, entering a step S24;
s23, sorting the street congestion degrees in the area again based on the distribution result obtained by the current lower-layer traffic distribution model, further updating the upper-layer planning model, obtaining a new road distribution scheme, and entering the step S25;
s24, screening the next preferential reconstruction street according to the current street crowding degree sorting result, further constructing a new upper-layer planning model and obtaining a new road distribution scheme, and entering the step S25;
s25, judging whether the target extension area has no road sections to generate a new road distribution scheme or whether the iteration number reaches a set threshold and no more optimal road distribution scheme exists, if so, entering a step S26, otherwise, returning to the step S21;
and S26, taking the current road distribution scheme as the optimal road distribution scheme to carry out fine setting of the traffic lane.
Further, in step S22, when a bus lane is set in the new road allocation plan, the preferred plan in the new road allocation plan and the previous road allocation plan satisfies:
when the new road distribution scheme does not include the bus lane, the optimal scheme in the new road distribution scheme and the previous road distribution scheme meets the following requirements:
in the formula, T (-) is the total travel time of all travelers corresponding to the road allocation Scheme, SchemenE is the threshold value for receiving the new road allocation plan for the nth road allocation plan.
The invention has the beneficial effects that:
(1) the invention adopts a double-layer planning model based on a branch-and-bound method to carry out multi-mode lane refined optimization design innovation, science and practicality, overcomes the defect that the traditional urban lane design method only considers the bus lane and neglects the influence of other driving, and compared with the traditional method, the model improves the evaluation of the single-mode bus lane in the road section to the optimization level of the multi-mode lane in the area network in the optimization angle, changes the change of the number of the single bus lane into the combination change of the multi-lane and the lane width in the decision method, changes the advantage of simply setting the bus lane into the evaluation considering the road space layout scheme in the influence analysis, and avoids the local optimization caused by a single evaluation means.
(2) The road space combination generation method adopted by the invention combines the actual construction precision and the standard requirements of lane widths in different travel modes, changes the solution space from continuous to discrete and improves the algorithm efficiency.
(3) The double-layer planning model based on the branch-and-bound method is adopted, comprehensive indexes of a heuristic principle are added in the branching process to accelerate the convergence speed of the algorithm, and a dynamic traffic distribution model is constructed to simulate the appearance mode of a multi-mode traveler and the implementation effect of an effective evaluation scheme of path selection.
(4) The method can provide a scientific and effective method for finely configuring the road space for the urban traffic managers, avoid the waste and unreasonable road space resources caused by singly setting the public transport lane without considering the influence of other modes, improve the traffic efficiency of the overall urban road traffic system and the utilization rate of the road space resources from the overall network efficiency, save the total travel time of all travelers in the area, ensure that all traffic modes can keep sufficient and reasonable right of way, be favorable for the fair distribution of the right of traffic travel, and reduce the negative influence caused by setting the professional lane.
Drawings
FIG. 1 is a flow chart of a multi-mode traffic lane fine setting method based on a branch-and-bound method according to the present invention.
Fig. 2 is a schematic diagram of a current situation scheme of a certain network road space in the present invention.
FIG. 3 is a diagram of the lower layer model calculation results in the present invention.
Fig. 4 is a diagram showing the result of the ranking of the congestion of each road in the present invention.
Fig. 5 is a schematic diagram of a network road space optimization scheme in the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Example 1:
as shown in fig. 1, the method for setting a multi-way lane refinement based on a branch-and-bound method in this embodiment includes the following steps:
s1, constructing a double-layer planning model based on a branch-and-bound method according to regional traffic basic data of a target extension region, wherein the double-layer planning model comprises an upper-layer planning model and a lower-layer traffic distribution model, the upper-layer planning model provides a planning scheme of a multi-mode road space, the lower-layer traffic distribution model simulates to obtain characteristic indexes after the planning scheme of the upper-layer planning model is implemented, and the road distribution scheme obtained based on the upper-layer model is evaluated according to the characteristic indexes;
and S2, continuously optimizing the road distribution scheme based on the evaluation result to obtain the optimal road distribution scheme and finely setting the traffic lane according to the optimal road distribution scheme.
The embodiment of the invention realizes the optimal target of a traffic manager and a traffic traveler in multi-mode road space distribution by constructing a double-layer planning model based on a branch-and-bound method, the upper-layer planning model minimizes the total travel time of the traffic network global traveler by providing planning schemes of different road spaces, and the lower-layer traffic distribution model evaluates the scheme provided by the upper-layer planning model by simulating the travel behavior of the multi-mode traveler so as to obtain characteristic indexes after the implementation of a future scheme; the method in the embodiment can balance the multi-mode road space distribution of the trip rights and interests of multiple travelers such as private cars, buses, non-motor vehicles and the like from the aspect of urban multi-mode traffic network optimization by considering the optimal combination of lane widths under the existing width constraint, effectively balance the urban multi-mode traffic road weight distribution, improve the urban road space utilization efficiency and improve the travel experience of residents.
Example 2:
the present embodiment is a further extension to step S1 in embodiment 1;
the regional traffic basic data in step S1 in this embodiment includes transportation facility physical data, traffic control data, and traffic demand data; the physical data of the traffic facilities comprise the bidirectional total width of road sections contained in each street, the width of lanes, green belts, road shoulders and the like, the length of the road sections, and lane attributes such as HOV lanes, bus lanes, intermittent bus lanes and the like; the traffic management data comprises intersection signal cycles, phases and lane traffic control measures at each time interval, parking charge, lane and vehicle type speed limit, vehicle type speed limit measures, minimum width requirements of each functional lane, construction precision of general lane width, hour traffic capacity of a standard lane and the like; the traffic demand data comprises the departure place to destination peak (such as early peak 7:00-8:00), peak approaching (such as 15:00-16:00) and peak leveling (such as 10: 00-11:00) of traffic travelers in the area, the traveling preference modes of travelers such as private cars, buses and the like, the average parking space searching time of car travelers, the average passenger carrying number of buses and cars, the bus line layout and operation indexes such as departure frequency, ticket price, bus conversion car coefficient and the like.
Based on the above basic data of regional traffic, the data collected by investigation in this embodiment includes travel mode selection and travel OD information of travelers in traffic peak-even, peak-approaching and peak time regions, the existing total road width, lane type, combination form and road length of each road section of each street in the region, the signal timing scheme of each intersection in the region, the layout and operation parameters of the bus route in the region, and traffic management and policy measures in the region.
The construction process of the double-layer planning model in step S1 in the embodiment of the present invention is as follows:
a1, constructing a lower-layer traffic distribution model according to the regional traffic basic data of the target extension area and the current road distribution scheme, and performing multi-mode dynamic traffic distribution on the lower-layer traffic distribution model to further obtain a distribution result and a characteristic index; the characteristic indexes are used for evaluating the optimization degree of the road distribution scheme;
a2, sorting the street congestion degrees in the area based on the distribution result so as to screen out the preferential reconstruction streets in the area;
a3, constructing an upper-layer planning model by combining the current traffic situation of a prior reconstructed street and adopting a road space combination enumeration method;
and A4, screening out the planning scheme of the street and generating a new road allocation scheme based on the constructed upper-layer planning model.
Step a1 in this embodiment specifically includes:
a11, constructing a traveler-oriented lower-layer traffic distribution model according to various road space distribution schemes of street road sections under the current situation of a target extension area and by combining basic data of area traffic;
a12, based on regional traffic data, with user balance as an optimization target, with traveler travel decision as a variable, with capacity and transfer of a bus route as constraints, and adopting a continuous average algorithm to perform multi-mode dynamic traffic distribution on a lower-layer traffic distribution model, thereby obtaining a distribution result and characteristic indexes.
In step a11 of this embodiment, a lower-layer traffic distribution model facing travelers is established according to a way road space allocation scheme of each street segment under the current situation of a target extension area and in combination with a travel demand matrix, and the model assumes that all travelers can accurately know traffic information on a road network, and abandons selection of a travel way and a travel path according to the shortest individual travel time. The first principle of Wardrop is followed, i.e., it is assumed that all used routes on the regional network are no more than the cost of routes that are not used. The model meeting the principle is an optimal balanced traffic distribution model for users, and the calculation formula of an objective function is as follows:
wherein k represents a departure time selected by a traveler, od represents a departure/destination point of the traveler, m represents a travel mode selected by the traveler, p represents a travel route selected by the traveler,
representing the travel time of the travelers with m travel modes of starting time k, starting point k, ending point od selected travel route p,
indicating that the shortest path is used among travelers whose travel starting and ending points are od at time k,
this indicates whether or not a traveler who travels at the start/end point od at time k selects route p.
In step a12 of this embodiment, the multimode dynamic traffic distribution method is used to evaluate a road space distribution scheme for each road segment provided by an area, based on area traffic data, taking a Wardrop first principle (i.e., user balance) as an optimization target, taking a traveler travel decision as a variable, i.e., taking into account road impedance of multimode travelers, where motor vehicle travel impedance may be calculated by a BRP function developed by the federal highway administration in the united states, a non-motor vehicle travel impedance model may be calculated by Log-normal and Weibull models, and a signal delay at an intersection is taken into account, and the delay may be determined by calculation of a time when a traveler reaches the intersection, a queuing time, and a green light time window of a corresponding phase at the intersection; in the distribution method, the capacity constraint and the transfer constraint of the bus line are considered, and the iterative algorithm of traffic distribution adopts a continuous average algorithm (MSA) frame, so that quick and effective solution can be realized.
The above allocation process in this embodiment is specifically as follows:
after the objective function is constructed, the travel time composition of travelers in various modes is calculated, and for a bus traveler, the travel time comprises road section travel time, intersection waiting time, intermediate station residence time, station getting-on and getting-off time and the like. For car travelers, the travel time of the car travelers does not include station waiting time and stop time, and the car travelers also include parking space searching time besides the existing road section travel time and intersection stop time, wherein the calculation method of the road section travel time in each mode refers to the BPR mode, and the specific calculation method comprises the following steps:
wherein the content of the first and second substances,
the link passing time of the link a representing the mode m,
the free-flow transit time of the segment a in the mode m,
the total standard car count for the mode m on the road section a,
and the traffic capacity of the mode m on the road section a is represented, and alpha and beta are parameters of the BPR equation respectively.
Showing the travel time of the bicycle on the section a, LaThe length of the road segment a is represented,
indicating the speed of the bicycle, w, of the section aa,bikeIndicating the cycle course width of the section a, qa,bikeIndicating the number of bicycles, p, of the section aa,lbikeLight electric bicycle ratio, p, representing road section aa,mbikeIndicating the motored vehicle proportion for section a.
Furthermore, the transfer times constraint of the shortest path and the capacity limit constraint of the bus line are set, the generation of the path is ensured to meet the selection of an actual traveler, and the calculation formula is as follows:
wherein the content of the first and second substances,
indicating whether a traveler (k, od, m) gets on the bus station b, if so, it is 1, otherwise it is 0;
indicating whether a traveler (k, od, m) is on the bus at the bus station b, if so, it is 1, otherwise it is 0; sbusRepresenting the set of all bus stops in the area; capmAnd the maximum allowable number of the persons carrying the unit vehicle corresponding to the travel mode m is shown.
Generally, a traffic network flow distribution model needs to satisfy a flow conservation principle, so that the total flow of all used paths is equal to the total traffic demand of the area; meanwhile, all used paths should be the shortest paths in all path sets of the OD corresponding to the path, and the calculation method is as follows:
wherein the content of the first and second substances,
indicating whether the traveler (k, od, m) uses the path p, if yes, it is 1, otherwise it is 0; q represents the total travel demand number in the area.
In this embodiment, after the model is constructed, the programming language Python 3.8 and the traffic simulation software Aimsun can be used for secondary development, and the dynamic traffic distribution module and the MSA algorithm are called for fast and effective solution.
In step a12 of the present embodiment, the multimode dynamic traffic distribution result includes data such as the number of users in each lane of each road segment, the delay time distribution of each traveler, the number of passengers on the bus route and the car, the traveling speed, the traveling delay, and the traveling distance.
In step a12 in the present embodiment, the characteristic indexes used for evaluating the optimization degree of the road allocation plan include total travel time of all travelers, travel delays of various types, average travel speed, and the like.
In step a2 of this embodiment, the preferential reconstruction streets in the area are screened out sequentially by the road section sorting method and the street screening method; the road section sorting method is used for sorting road section congestion degrees according to the ratio of the number of multi-mode vehicles to road traffic capacity on the basis of the obtained traveler information and the information of each lane of each road section, wherein the number of the multi-mode vehicles is obtained by calculating the number of the multi-mode travelers according to the average number of passenger carriers in a corresponding mode, and the multi-parameter conversion for determining the traffic capacity comprises lane width, lane position, lane type and the like and can be calculated by referring to a method of a United states traffic capacity manual (HCM) 2010; the street screening method carries out congestion degree grading according to the congestion degree sorting results of all road sections in the target extension area, then carries out street congestion degree sorting according to the grading results of all road sections contained in all streets, and takes the most congested street as a prior reconstruction street.
In step a2 of this embodiment, to improve the speed and accuracy of the branch-and-bound method, in this embodiment, for each road segment included in the streets in the area, a comprehensive index is constructed by using the number of people, the road segment traffic capacity, and the vehicle conversion coefficient, and is sorted, so as to determine that the street is preferentially modified, and the calculation method of the comprehensive index is as follows:
in the formula (I), wherein,
an evaluation index representing the link a mode m,
the number of vehicles representing the link a mode m,
representing the capacity of the section a mode m. The higher the index is, the more crowded the road section where the corresponding mode is located, the higher the priority of the road space reconstruction will be.
In step a2 of the embodiment of the present invention, according to the result of the ranking of congestion of each road segment, the most congested street is screened out and used as the street that is to be modified preferentially, and the congestion degree of each street can be obtained by accumulating the congestion degrees of the road segments included in the street, and the calculation method is as follows:
wherein, IsAn evaluation index of the street s is shown.
In step A3 of this embodiment, in the branching stage of the branch-and-bound method, based on the preferential reconstruction streets screened out from the sorting result in step a2, a plurality of possible road cross-sectional layout schemes corresponding to the preferential reconstruction streets are selected by a road space enumeration method. Based on this, step a3 in this embodiment is specifically:
and generating a multi-mode road space distribution scheme pool for preferentially modifying the street, enumerating all combination schemes by setting constraint conditions, and constructing an upper-layer planning model containing all the combination schemes based on the current traffic situation.
Specifically, in this embodiment, enumeration is performed according to the constraints of the available total width of the road, the requirements of the urban road engineering design specifications CJJ37-2012 on the minimum width and the maximum width of each type of traffic lane, the local urban road design specifications, the traffic lane marking construction accuracy and the like, so as to obtain the cross-sectional layout scheme of the street under each width. In all possible street road space combination schemes, an upper-layer planning model is constructed by combining the current traffic situation of the street, the travel time of all travelers in the area is minimized, and the objective function is as follows:
wherein k represents the departure time selected by the traveler, od represents the starting and ending point of the traveler, m represents the trip mode selected by the traveler,
representing travel time of a traveler (k, od, m) on the section a,
indicating the delay time of the traveler (k, od, m) at the intersection j,
indicating the waiting time of the traveler (k, od, m) at the bus stop b,
indicating the stay time of the traveler (k, od, m) at the bus stop b,
shows the time to find a parking space when a traveler (k, od, m) uses a car.
In step a4 of this embodiment, based on the constructed upper layer planning model, a combination scheme satisfying the following rules is further optimized as a branching stage in the branch-and-bound method, where the optimization rules are:
for streets without public transport lines, the new road space allocation scheme should meet
I.e. the new solution should be considered to provide a number of lanes of cars not less than the original solution.
For streets with bus routes, the new road space allocation scheme should meet the following conditions:
wherein the content of the first and second substances,
and whether the n-th road space allocation scheme of the street s-way type is implemented or not is 1 if the n-th road space allocation scheme is implemented, and 0 is not implemented. The types of traffic lanes can be divided into three types, wherein the type of the traffic lane is ebl and represents special busAnd (4) indicating a car lane by using a lane, wherein the type is car, and the type is mix and indicates a mixed driving lane of the bus and the car.
In step a4 of the present embodiment, for each street if all alternatives fail to meet the above requirements, the street will be skipped. Otherwise, if a feasible new scheme exists, the scheme is adopted as a planning scheme and a new road distribution scheme is obtained.
Example 3:
the present embodiment is a further extension to step S2 in embodiment 1;
the method for obtaining the optimal road allocation plan in step S2 in this embodiment specifically includes:
s21, updating a lower-layer traffic distribution model and performing multi-mode dynamic traffic distribution based on a new road distribution scheme to obtain a distribution result and a characteristic index;
s22, judging whether the new road distribution scheme generated by the current upper-layer planning model is superior to the previous road distribution scheme or not based on the characteristic indexes, if so, entering a step S23, and if not, entering a step S24;
s23, sorting the street congestion degrees in the area again based on the distribution result obtained by the current lower-layer traffic distribution model, further updating the upper-layer planning model, obtaining a new road distribution scheme, and entering the step S25;
s24, screening the next preferential reconstruction street according to the current street crowding degree sorting result, further constructing a new upper-layer planning model and obtaining a new road distribution scheme, and entering the step S25;
s25, judging whether the target extension area has no road sections to generate a new road distribution scheme or whether the iteration number reaches a set threshold and no more optimal road distribution scheme exists, if so, entering a step S26, otherwise, returning to the step S21;
and S26, taking the current road distribution scheme as the optimal road distribution scheme to carry out fine setting of the traffic lane.
Step S21 in this embodiment belongs to the delimiting stage in the branch-and-bound method, and updates the input parameters of the lower traffic distribution model according to the new road allocation plan, including the combination of the lane width, type and number of each street, and substitutes the lower traffic distribution model again on the basis to calculate the characteristic indexes of total travel time, travel delay of each type, average travel speed, and the like of all travelers in the area.
In step S22 of this embodiment, the calculation results of the upper layer planning model in the previous iteration are combined to evaluate the two road allocation models, and when a bus lane is set in the new road allocation scheme, the optimal scheme in the new road allocation scheme and the previous road allocation scheme satisfies:
when the new road distribution scheme does not include the bus lane, the optimal scheme in the new road distribution scheme and the previous road distribution scheme meets the following requirements:
in the formula, T (-) is the total travel time of all travelers corresponding to the road allocation Scheme, SchemenE is the threshold value for receiving the new road allocation plan for the nth road allocation plan.
In this embodiment, when the upper-level planning model parameter is updated, the road traffic capacity under different width combinations is further modified based on the determined preferential reconstruction of the street, and the upper-level planning model parameter is updated by the following updating method:
wherein the content of the first and second substances,
a standard lane capacity representing the lane type i,
indicating the number of available lanes of lane type l for road segment a,
a lane width correction coefficient indicating a lane type l of a link a,
a traffic rate correction factor representing the lane type l of the section a,
representing the proportion of the type of lane l bus in the road section a to the number of vehicles, EHVRepresenting the number of buses converted into standard cars.
Example 4:
in the embodiment of the present invention, taking the current situation scheme of the network road space shown in fig. 2 as an example, the road space optimization is performed on the network road space, which specifically includes the following steps:
the method comprises the following steps: the traffic facility physical data, the traffic control data, and the traffic demand data of the target reconstruction and extension area shown in fig. 2 are obtained.
The physical data of the transportation facility in the first step of this embodiment includes the total bidirectional width of the road sections included in each street, the width of the lane, the green belt, the road shoulder and the like, the length of the road sections, and the lane attributes such as HOV lane, bus lane, intermittent bus lane and the like, and these data can be obtained through field survey measurement records.
The traffic control data in the first step of this embodiment includes intersection signal cycles and phases at various time intervals, lane traffic control measures, parking charges, lane and vehicle type speed limits, vehicle type speed limit measures, minimum width requirements of various functional lanes, construction accuracy of general lane widths, hour traffic capacity of standard lanes, and the like, and these data can be obtained through field investigation and urban design specification standards.
The traffic demand data in the first step of this embodiment includes the departure amount of the departure place to the destination peak in the morning and evening, the peak approaching and the peak leveling of the departure place of the internal traffic traveler, the trip preference mode of the traveler such as private car, bus and the like, the average parking space searching time of the car traveler, the average passenger number of the bus and the car, the bus line layout and operation indexes such as departure frequency, fare, bus conversion factor and the like, and these data can be obtained through the intention survey of the traveler and the consultation of the public transport company.
In the embodiment, the travel demands of residents at three time periods, namely, peak hours, temporary peak hours and flat peak hours, are considered, and the region comprises 25 triggering and attracting points corresponding to 600 OD pairs. The travel modes comprise public transport and car travel. In order to simplify the model, the bus station is assumed to be arranged at a position close to the intersection, and the outermost lane of the road section is used for providing a bus and a car for mixed use.
Step two: and establishing a lower-layer planning model by combining a resident OD trip demand matrix of the region according to the road space distribution scheme of each mode of each street section of the current region. By combining with a programming language python, a program is constructed for operation, and specific travel modes, travel paths, various travel smog of all travelers such as road section travel time, bus waiting time, time for getting on and off buses, time for finding parking spaces of cars, intersection delay time and the like can be obtained, as shown in fig. 3.
Step three: and obtaining data such as the number of traffic users, delay time distribution of travelers, the number of passengers, traveling speed, traveling delay, traveling distance and the like of the bus route and the car according to the lower-layer multi-mode dynamic traffic distribution result.
In the third step of this embodiment, for each road segment included in the streets in the area, a comprehensive index is constructed and ranked according to the number of users, the road segment traffic capacity, and the vehicle conversion coefficient, and the calculation method of the comprehensive index is as follows:
the higher the index value is, the more crowded the link where the corresponding mode is located is, the higher the priority of the road space reconstruction is, and the sorted result is shown in fig. 4.
Step four: screening out the most crowded streets according to the congestion sorting result of each road section obtained in the third step, wherein the congestion degree of each street can be obtained by accumulating the congestion degrees of the road sections contained in the streets, and the calculation method is as follows:
in step four of this embodiment, the road cross-sectional layout plan enumeration is performed for the preferred street according to the sorting result. According to the total width of the street, the design specification of urban road engineering, the design specification of local urban road, the construction precision of forming the road marking line and other constraints, and the retrograde enumeration, the enumeration method further obtains the cross section layout scheme of the street under each width as shown in the table 1:
table 1: road space layout scheme table for traffic lanes of different streets
In step four of this embodiment, an upper layer planning model is further constructed, considering the goal of minimizing the appearance time of all travelers in the area, as follows:
recording the upper-layer planning target value of the current scheme, and further preferably selecting a combination scheme meeting the following rules as a branch stage in a branch-and-bound method for the situation that no public transport existsThe newly obtained road space distribution scheme of the streets passed by the lines is satisfied
Namely, the new scheme is considered to provide the number of small automobile lanes not less than the original scheme, and for the streets which are passed by the bus routes, the new road space allocation scheme meets the following conditions:
wherein the content of the first and second substances,
and whether the n-th road space allocation scheme of the street s-way type is implemented or not is 1 if the n-th road space allocation scheme is implemented, and 0 is not implemented. The driving lane types can be divided into three types, wherein the type is ebl to represent a bus lane, and the type is car to represent a car lane type and mix to represent a bus and car mixed driving lane.
For each street traversed, if all alternatives fail to meet the above requirements, that street is skipped. Otherwise, if a feasible new scheme exists, the scheme is selected to be adopted, and the next calculation is carried out.
Step five: and updating the input parameters of the lower-layer dynamic traffic distribution model according to the new regional road space combination scheme obtained in the fourth step, wherein the input parameters comprise the combination form of the lane width, the type and the number of each street. On the basis, the lower-layer model is substituted again, the total occurrence time of all travelers in the research area, the travel delay of each type, the travel speed evaluation and other indexes are calculated.
In step five of this embodiment, a new road space plan is further evaluated in combination with the upper layer model calculation result of the iterative plan, and the specific evaluation method is as follows:
if the bus special lane is set in the new scheme, the scheme is the optimal scheme and should meet the requirement
If the new scheme does not contain a bus lane, the optimal scheme should meet
If the new solution meets the preferred solution criteria, the solution is located as the currently preferred solution. And sequencing the crowdedness degree of each street section after the implementation according to the current scheme, finding the most crowded street in the next order as a next generation candidate street, skipping the scheme if the new scheme does not meet the preference scheme standard, and determining the most crowded street in the next order as the next generation candidate street according to the crowdedness degree of each street section after the implementation of the previous generation.
Step six: and C, carrying out algorithm convergence judgment, correspondingly correcting the road traffic capacity under different width combinations according to the new candidate streets obtained in the step five, and updating the parameters of the upper layer model, wherein the updating method comprises the following steps:
wherein the content of the first and second substances,
a standard lane capacity representing the lane type i,
indicating the number of available lanes of lane type l for road segment a,
representing road sectionsa lane width correction coefficient of lane type l,
a traffic rate correction factor representing the lane type l of the section a,
representing the proportion of the type of lane l bus in the road section a to the number of vehicles, EHVRepresenting the number of buses converted into standard cars.
And meanwhile, trying to carry out the step four, if no road section can generate a feasible road space distribution scheme or the number of branch limiting branches reaches the maximum iteration number in the current research area, considering that the algorithm is converged, and taking the current scheme as the optimal scheme. Otherwise, substituting the step four, and repeating the steps until the algorithm is converged. An optimal area road space allocation scheme is given as shown in fig. 5.
In the embodiment, two schemes of not allowing to set the bus lane and allowing to set the bus lane are optimized according to the traffic demands of each time period in the area, so that the implementation effect of the invention under each situation is shown in table 2;
table 2:
Claims (10)
1. a multi-mode traffic lane fine setting method based on a branch-and-bound method is characterized by comprising the following steps:
s1, constructing a double-layer planning model based on a branch-and-bound method according to regional traffic basic data of a target extension region, wherein the double-layer planning model comprises an upper-layer planning model and a lower-layer traffic distribution model, the upper-layer planning model provides a planning scheme of a multi-mode road space, the lower-layer traffic distribution model simulates to obtain characteristic indexes after the planning scheme of the upper-layer planning model is implemented, and the road distribution scheme obtained based on the upper-layer model is evaluated according to the characteristic indexes;
and S2, continuously optimizing the road distribution scheme based on the evaluation result to obtain the optimal road distribution scheme and finely setting the traffic lane according to the optimal road distribution scheme.
2. The method for setting up the multi-way traffic lane refinement based on the branch-and-bound method as claimed in claim 1, wherein the basic data of regional traffic in the step S1 includes physical data of transportation facilities, traffic control data and traffic demand data.
3. The method for setting up a multi-way traffic lane refinement based on the branch-and-bound method according to claim 2, wherein in the step S1, the method for constructing the double-layer planning model specifically comprises:
a1, constructing a lower-layer traffic distribution model according to the regional traffic basic data of the target extension area and the current road distribution scheme, and performing multi-mode dynamic traffic distribution on the lower-layer traffic distribution model to further obtain a distribution result and a characteristic index; the characteristic indexes are used for evaluating the optimization degree of the road distribution scheme;
a2, sorting the street congestion degrees in the area based on the distribution result so as to screen out the preferential reconstruction streets in the area;
a3, constructing an upper-layer planning model by combining the current traffic situation of a prior reconstructed street and adopting a road space combination enumeration method;
and A4, screening out the planning scheme of the street and generating a new road allocation scheme based on the constructed upper-layer planning model.
4. The method for setting up the multi-way traffic lane refinement based on the branch-and-bound method as claimed in claim 3, wherein the step A1 is specifically as follows:
a11, constructing a traveler-oriented lower-layer traffic distribution model according to various road space distribution schemes of street road sections under the current situation of a target extension area and by combining basic data of area traffic;
a12, based on regional traffic data, with user balance as an optimization target, with traveler travel decision as a variable, with capacity and transfer of a bus route as constraints, and adopting a continuous average algorithm to perform multi-mode dynamic traffic distribution on a lower-layer traffic distribution model, thereby obtaining a distribution result and characteristic indexes.
5. The method for setting up the multi-way traffic lane refinement based on the branch-and-bound method as claimed in claim 4, wherein the objective function of the lower traffic distribution model in the step A11 is:
wherein k represents a departure time selected by a traveler, od represents a departure/destination point of the traveler, m represents a travel mode selected by the traveler, p represents a travel route selected by the traveler,
representing the travel time of the travelers with m travel modes of starting time k, starting point k, ending point od selected travel route p,
indicating that the shortest path is used among travelers whose travel starting and ending points are od at time k,
this indicates whether or not a traveler who travels at the start/end point od at time k selects route p.
6. The method for setting the multi-mode vehicle driving to the refinement according to the branch and bound method of claim 3, wherein the preferential reconstruction streets in the area are screened out sequentially through a road section sorting method and a street screening method in the step A2;
the road section sorting method is used for sorting the congestion degrees of road sections based on the obtained travelers information in the distribution result and the lane information of each road section by taking the ratio of the number of vehicles in multiple modes to the road traffic capacity as a basis;
the street screening method carries out congestion degree grading according to the congestion degree sorting results of all road sections in the target extension area, then carries out street congestion degree sorting according to the grading results of all road sections contained in all streets, and takes the most congested street as a prior reconstruction street.
7. The method for setting up the multi-way traffic lane refinement based on the branch-and-bound method as claimed in claim 3, wherein the step A3 is specifically as follows:
and generating a multi-mode road space distribution scheme pool for preferentially modifying the street, enumerating all combination schemes by setting constraint conditions, and constructing an upper-layer planning model containing all the combination schemes based on the current traffic situation.
8. The method for setting up the multi-way traffic lane refinement based on the branch-and-bound method as claimed in claim 7, wherein in the step a3, the objective function of the upper-level planning model is:
wherein k represents the departure time selected by the traveler, od represents the starting and ending point of the traveler, m represents the trip mode selected by the traveler,
representing travel time of a traveler (k, od, m) on the section a,
indicating the delay time of the traveler (k, od, m) at the intersection j,
indicating the waiting time of the traveler (k, od, m) at the bus stop b,
indicating the stay time of the traveler (k, od, m) at the bus stop b,
shows the time to find a parking space when a traveler (k, od, m) uses a car.
9. The method for setting up a multi-way traffic lane refinement based on the branch-and-bound method according to claim 3, wherein the step S2 is specifically as follows:
s21, updating a lower-layer traffic distribution model and performing multi-mode dynamic traffic distribution based on a new road distribution scheme to obtain a distribution result and a characteristic index;
s22, judging whether the new road distribution scheme generated by the current upper-layer planning model is superior to the previous road distribution scheme or not based on the characteristic indexes, if so, entering a step S23, and if not, entering a step S24;
s23, sorting the street congestion degrees in the area again based on the distribution result obtained by the current lower-layer traffic distribution model, further updating the upper-layer planning model, obtaining a new road distribution scheme, and entering the step S25;
s24, screening the next preferential reconstruction street according to the current street crowding degree sorting result, further constructing a new upper-layer planning model and obtaining a new road distribution scheme, and entering the step S25;
s25, judging whether the target extension area has no road sections to generate a new road distribution scheme or whether the iteration number reaches a set threshold and no more optimal road distribution scheme exists, if so, entering a step S26, otherwise, returning to the step S21;
and S26, taking the current road distribution scheme as the optimal road distribution scheme to carry out fine setting of the traffic lane.
10. The method for fine setting of the multi-way traffic lane based on the branch-and-bound method as claimed in claim 9, wherein in step S22, when a bus lane is set in the new road distribution scheme, the preferable scheme of the new road distribution scheme and the previous road distribution scheme satisfies:
when the new road distribution scheme does not include the bus lane, the optimal scheme in the new road distribution scheme and the previous road distribution scheme meets the following requirements:
in the formula, T (-) is the total travel time of all travelers corresponding to the road allocation Scheme, SchemenE is the threshold value for receiving the new road allocation plan for the nth road allocation plan.
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