CN101807224B - Mesoscopic-microcosmic integrated traffic simulation vehicle flow loading method - Google Patents
Mesoscopic-microcosmic integrated traffic simulation vehicle flow loading method Download PDFInfo
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- CN101807224B CN101807224B CN201010130516XA CN201010130516A CN101807224B CN 101807224 B CN101807224 B CN 101807224B CN 201010130516X A CN201010130516X A CN 201010130516XA CN 201010130516 A CN201010130516 A CN 201010130516A CN 101807224 B CN101807224 B CN 101807224B
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Abstract
The invention relates to a mesoscopic-microcosmic integrated traffic simulation vehicle flow loading method, belonging to the technical field of virtual simulation. The method comprises the steps: working out the shortest path by setting a traffic generation attractive points as a centroid point in a traffic network, and generating simulated vehicles needed by the mesoscopic-microcosmic traffic simulation by matching the departure time distribution and speed distribution of initial vehicles. The mesoscopic-microcosmic integrated traffic simulation vehicle flow loading method is convenient to transition from mesoscopic traffic simulation to microcosmic traffic simulation.
Description
Technical field
What the present invention relates to is a kind of method of virtual emulation technical field, specifically is a kind of middle microcosmic integrated traffic emulation car flow loading method.
Background technology
Traffic simulation is the The present computer technology that reproduction traffic flow time and space changes, and also is a kind of means of coming the Analysis of Complex traffic behavior by the mathematical model of setting up traffic system, belongs to the category of Computer simulation.Traffic simulation is by the careful degree of realistic model institute descriptive system and stress angle and can be divided into microscopic traffic simulation, middle sight traffic simulation, macroscopical traffic simulation.The macroscopic view traffic simulation is studied the characteristic of traffic system emphatically from overall angle; Middle sight traffic simulation is the unit with the formation that some cars constitute often to the description of traffic flow, can describe the inflow and outflow behavior of formation at highway section and node; It is very high that microscopic traffic simulation is described degree to the details of the key element of traffic system and behavior.For example: microscopic traffic simulation is to be elementary cell with the single unit vehicle to the description of traffic flow, vehicle on road with car, overtake other vehicles and microscopic behavior such as lane changing can both obtain reflecting more really.Characteristics in conjunction with above-mentioned three class traffic simulations, when carrying out traffic simulation, can middle microscopic traffic simulation is integrated, can analyze traffic behavior index in detail such as the queue length of microcosmic point and delay like this, the volume of traffic characteristic of sight aspect, routing behavior in simulating again are specially adapted to the traffic simulation of big-and-middle-sized road network.And for traffic simulation, generating wagon flow is the first step of traffic flow simulation, also is a vital step.Any one traffic simulation platform all is dispatching a car as the starting point of whole simulation.
In the traffic flow of reality, the arrival of vehicle is at random, and the time headway of arrival vehicle meets different probability distribution under the different flow situation.At present, the generation of traffic flow mainly is to adopt random number functions that operating system provides or distribute with single probability distribution such as negative exponent to obtain in traffic simulation research.Intrasystem random number functions is a kind of simple evenly distribution, and the random number that adopts a certain probability distribution also is to obtain on the basis of this uniform random number.
Through the retrieval of prior art is found, " the vehicle generation realistic model " that people such as Lei Bin delivered at computing machine and digital engineering in 2005; And people such as Kuang priori " the microscopic traffic simulation vehicle is generation model analysis and the design at random " delivered at Institutes Of Technology Of Jiangxi's journal in 2006, searching document is summarized, the existing traffic simulation method of dispatching a car mainly contains two kinds, a kind of method is to set the flow of dispatching a car in vehicle generation place, in crossing the ratio of turning that vehicle drives towards all directions is set then.For example, in Transmodeler, system by matrix or vehicle driving tabulate describe on the network or starting point and terminal point between the volume of traffic, represent the distribution of wagon flow on road network with path list or path Choice Model.The user manually adds driving path by the volume of traffic ratio at setting turning or by the path Core Generator.Another kind method is to set that vehicle produces and the position that occurs and where drive towards be at random, and each car has just determined its travel direction at each crossing when producing.For example, in Vissim, the user sets driving path decision-making starting point with the driving path decision-making mode and the driving path terminal point carries out routing, and during the simulation run, the vehicle that does not have a run routing information is assigned on the driving path when the driving path decision point by setting.
Said method has its practical value in the small-scale microscopic traffic simulation, but has limitation in extensive in the traffic simulation environment of microcosmic combination: (1) above-mentioned two kinds of methods are difficult to use in the different routing scheme of assessment; (2) above-mentioned two kinds of methods can't be induced the path of particular vehicle enforcement in emulation; (3) above-mentioned two kinds of methods are difficult to handle sudden traffic hazard and congested problem; (4) when simulation scale is big, dispatch a car a little and ratio of turning be provided with very loaded down with trivial details, and and middle sight and the combination of macroscopical Traffic Flow Simulation Models comparatively difficult, microcosmic integrated traffic simulation environment in not too being suitable for.
Summary of the invention
The present invention is directed to the prior art above shortcomings, a kind of middle microcosmic integrated traffic emulation car flow loading method is provided, by setting traffic simulation zone starting point and terminal point, obtain shortest path and pass through minimum time headway checking, cooperate the time dispatch a car to distribute again and the velocity distribution of initial vehicle, then can produce for microcosmic and the required emulation vehicle of middle sight traffic simulation, see traffic simulation microcosmic integrated traffic emulation car flow loading method in the middle microcosmic of microscopic traffic simulation transition in being convenient to.
The present invention is achieved by the following technical programs, the present invention includes following steps:
The first step, obtain the magnitude of traffic flow and adopt statistical method test samples time headway whether to meet probability distribution, then carried out for second step if meet probability distribution, otherwise counting again;
The described magnitude of traffic flow of obtaining is meant: at first determine traffic simulation zone starting point and terminal point, by the magnitude of traffic flow between counting every group of starting point of acquisition and the corresponding terminal point thereof, in the emulation cycle, record from each time interval that starting point is dispatched a car to terminal as the sample time headway as the magnitude of traffic flow;
Second step, adopt the mixed linear congruence method to be created in (0,1) to go up equally distributed random number, again according to each to the magnitude of traffic flow between starting point and the terminal point, adopt contrary method of changing to produce stochastic variable, as the emulation time headway.
The number of described random number is identical with the number of beginning or end;
Described stochastic variable meets each vehicle between starting point and terminal point is sent time interval probability distribution;
The 3rd step, the emulation time headway is carried out abnormality value removing handle, generates formation virtual to be dispatched a car, treat a time headway and an emulation vehicle thereof;
Described abnormality value removing is handled and is meant:
Time headway in the emulation time headway does not match with initial travel speed, or;
When emulation time headway during less than lower threshold,
This emulation time headway is unusual and places the virtual formation of dispatching a car of waiting.
The 4th the step, utilize Geographic Information System and GPS to obtain the middle sight transportation network of simulating area, and be the limit with the road in the middle sight transportation network, the crossing is a node, with the average stroke time on each highway section in the transportation network be weight, adopt the shortest path between Fl oyd method zequin and the terminal point, and each is imparted on the vehicle between this a pair of starting point and the terminal point the shortest path between starting point and the terminal point, thereby see driving path figure in generating;
The track of seeing in the transportation network in the 5th step, the utilization is the limit, interflow, track and shunting place are that node generates the microcosmic traffic network, wherein see under the driving path prerequisite in that vehicle is known, the spacing of node is a weight, adopt the Floyd method to calculate the microcosmic shortest path between per two nodes on the microcosmic traffic network, judge that then working as vehicle whenever enters a crossing, then give vehicle, thereby generate microcosmic driving path figure the microcosmic shortest path of this crossing and next crossing.
The 6th step, gave for the 3rd step respectively with middle sight driving path figure and microcosmic driving path figure and obtain the emulation vehicle, thereby make wagon flow can select optimum track and can normally move along road.
The present invention has introduced the notion of OD matrix in macroscopical traffic simulation, by a matrix, has described dispatch a car flow and the driving path on the road network simultaneously, and microcosmic integrated traffic simulation wagon flow loading method in providing.Induce, handle the frameworks that provide the foundation such as sudden traffic hazard and congested problem for assessing the path that different routing schemes, emulation implement particular vehicle, and large-scale microscopic traffic simulation is convenient in the present invention, also is convenient to the transition toward microscopic traffic simulation of macroscopic view or middle sight traffic simulation simultaneously.
Description of drawings
Fig. 1 is that vehicle of the present invention produces process flow diagram.
Fig. 2 is that time headway of the present invention meets the process flow diagram that characteristic distribution vehicle generates.
Fig. 3 is that unusual time headway of the present invention is differentiated and processing flow chart.
Fig. 4 is middle the sight and microcosmic path product process figure of the present invention.
Fig. 5 is a driving path synoptic diagram of viewing the car in of the present invention.
Fig. 6 is a microcosmic vehicle running path synoptic diagram of the present invention.
Fig. 7 is the loading demonstration graph of dispatching a car of the present invention.
Embodiment
Below embodiments of the invention are elaborated, present embodiment is being to implement under the prerequisite with the technical solution of the present invention, provided detailed embodiment and concrete operating process, but protection scope of the present invention is not limited to following embodiment.
In order to understand the method that present embodiment proposes better, choose Shanghai City Changning District three-way intersection carry out in microcosmic integrated vehicle load simulation analysis, can be applicable to the different range road network of different cities.Present embodiment requires to provide the geometry linear of this crossing, shows with the form of GIS map, and the frequency that each entrance driveway of this crossing occurs toward the magnitude of traffic flow and the vehicle of other exit ramps in simulation time.
As shown in Figure 1, present embodiment may further comprise the steps:
(1) time headway distribution investigation on flow and starting point and the terminal point between simulating area starting point and terminal point
Because be three-way intersection, so flow has constituted three-dimensional square formation between starting point that obtains and terminal point, its flux unit is/hour, and concrete data on flows is as follows:
When obtaining each entrance driveway toward other exit ramp magnitudes of traffic flow, note the composition of all kinds vehicle in the traffic flow, when the emulation vehicle generates, this ratio situation need be reflected.
The vehicle frequency of occurrences with each entrance driveway, respectively with distribution form commonly used, as the distribution of discrete type experience, evenly distribution, normal distribution, exponential distribution and the blue distribution of ell etc., carry out statistical test, ell blue distribution in k rank appears meeting in the vehicle of finding entrance driveway 1 and 3, and the appearance index of coincidence of entrance driveway 2 vehicles distributes.
(2) the emulation time headway generates
By above-mentioned steps one, the vehicle time of occurrence of three entrance driveway in the present embodiment is obeyed blue distribution of k rank ell and exponential distribution at interval respectively as can be known.As shown in Figure 2, be distributed as example, adopt the inverse transformation method, its probability density function can be changed into the stochastic variable that meets its distribution with k rank ell orchid:
Wherein: k is the exponent number of the blue distribution variables of ell, desirable herein 1; λ is the inverse of the blue distribution variables average of k rank ell, is the magnitude of traffic flow herein; r
iBe uniform random number on (0,1) interval, can produce with method, obtain by following formula by mixed linear is surplus:
Wherein, [] expression round numbers in the formula, a, c, m all are integers.A, c, m and x
0Cycle and the statistical property thereof chosen random number have a significant impact.
The x that is obtained by top formula is the emulation time headway that each entrance driveway tallies with the actual situation.
(3) differentiation and the processing of unusual time headway
As shown in Figure 3, the vehicle that step 2 is sent is at every turn put into a virtual outgoing queue earlier, whether the time headway of judging this vehicle is greater than a certain threshold value.This threshold value can adopt the minimum time headway of each entrance driveway of field observation.Through observation, entrance driveway 1,2 and 3 minimum time headway were respectively 1.5 seconds and 1.1 seconds 1.2 seconds.With these three values threshold value lower limit that is three entrance driveway time headways, when the time headway of emulation vehicle during greater than this threshold value, vehicle is by taking out in the virtual outgoing queue, otherwise, by the time next simulation time, after the time headway of emulation vehicle added that simulation step length at interval, compare with the threshold value lower limit once more, until the time headway of emulation vehicle is determined the threshold value lower limit greater than this entrance driveway.Thereby guarantee that unusual time headway does not occur on the emulation vehicle that has set out.
(4) driving path of viewing the car in generates
As shown in Figure 4 and Figure 5, be the limit with the road, the crossing is a node, sees transportation network in can constituting.Can get by inquiry, the average stroke time on entrance driveway 1,2 and the 3 place roads was respectively 17.6 seconds, 20.5 seconds and 15.3 seconds.With the average stroke time be weight, adopt the Flody method to calculate shortest path between each entrance driveway, in the Flody method, the weight matrix of employing is:
| Entrance driveway 1 | Entrance driveway 2 | Entrance driveway 3 | |
| Entrance driveway 1 | 0 | 38.1 | 32.9 |
| Entrance driveway 2 | 38.1 | 0 | 35.8 |
| Entrance driveway 3 | 32.9 | 35.8 | 0 |
Its algorithm steps is as follows:
A. initialization.Make the C=weight matrix; To all node i and j, order: vij=i.
B. to all point of crossing k, do:
To all nodes, comprise point of crossing and PA point i (i ≠ k), do
(j ≠ i), k does to all node j
If cik+ckj＜cij, cij=cik+ckj then, vij=vkj.
C. algorithm finishes.
Then can obtain brachymedial between each an entrance driveway driving path of viewing the car, green path is brachymedial between entrance driveway 1 and 2 driving path of viewing the car among Fig. 5.This seeks the middle sight transportation network that shortest-path method is suitable for any range size.
(5) the microcosmic vehicle running path generates
As Fig. 4 and shown in Figure 6, be the limit with the track, the interflow in track and split point are node, can generate the microcosmic traffic network.Length with the track is weight, adopts the Flody method equally, calculates the microcosmic vehicle running path under the driving path of viewing the car in each.The grey path is the microcosmic vehicle running path from entrance driveway 1 toward entrance driveway 2 among Fig. 6.From Fig. 5 and Fig. 6 as can be seen, in a driving path of viewing the car provided the direction of vehicle ', but and the concrete guiding vehicle of microcosmic vehicle running path travels on suitable track.For fairly large network, adopt this method that the track is selected to have higher efficient.
(6) generation of traffic simulation wagon flow
As shown in Figure 7, give the emulation vehicle that is produced by (1), (2) and (3), make vehicle to carry out simulation run according to certain distribution, flow and path by middle sight and microcosmic vehicle running path that (4) and (5) are generated.This simulation result, induce, handle the frameworks that provide the foundation such as sudden traffic hazard and congested problem for assessing the path that different routing schemes, emulation implement particular vehicle, and large-scale microscopic traffic simulation is convenient in the present invention, also is convenient to the transition toward microscopic traffic simulation of macroscopic view or middle sight traffic simulation simultaneously.
Claims (3)
1. microcosmic integrated traffic emulation car flow loading method in a kind is characterized in that, may further comprise the steps:
The first step, obtain the magnitude of traffic flow and adopt statistical method test samples time headway whether to meet probability distribution, then carried out for second step if meet probability distribution, otherwise counting again;
The described magnitude of traffic flow of obtaining is meant: at first determine traffic simulation zone starting point and terminal point, by the magnitude of traffic flow between counting every group of starting point of acquisition and the corresponding terminal point thereof, in the emulation cycle, record from each time interval that starting point is dispatched a car to terminal as the sample time headway as the magnitude of traffic flow;
Second step, employing mixed linear congruence method are created in (0,1) goes up equally distributed random number in order to simulating vehicle, again according to each to the magnitude of traffic flow between starting point and the terminal point, adopt contrary method of changing to produce to meet each that vehicle between starting point and terminal point is sent the stochastic variable of time interval probability distribution, as the emulation time headway;
The 3rd step, the emulation time headway is carried out abnormality value removing handle, generates formation virtual to be dispatched a car, treat a time headway and an emulation vehicle thereof;
The 4th the step, utilize Geographic Information System and GPS to obtain the middle sight transportation network of simulating area, and be the limit with the road in the middle sight transportation network, the crossing is a node, with the average stroke time on each highway section in the transportation network be weight, adopt the shortest path between Floyd method zequin and the terminal point, and each is imparted on the vehicle between this a pair of starting point and the terminal point the shortest path between starting point and the terminal point, thereby see driving path figure in generating;
The track of seeing in the transportation network in the 5th step, the utilization is the limit, interflow, track and shunting place are that node generates the microcosmic traffic network, wherein see under the driving path prerequisite in that vehicle is known, the spacing of node is a weight, adopt the Floyd method to calculate the microcosmic shortest path between per two nodes on the microcosmic traffic network, judge that then working as vehicle whenever enters a crossing, then give vehicle, thereby generate microcosmic driving path figure the microcosmic shortest path of this crossing and next crossing;
The 6th step, gave for the 3rd step respectively with middle sight driving path figure and microcosmic driving path figure and obtain the emulation vehicle, thereby make wagon flow can select optimum track and can normally move along road.
2. microcosmic integrated traffic emulation car flow loading method in according to claim 1 is characterized in that the number of described random number is identical with the number of beginning or end.
3. microcosmic integrated traffic emulation car flow loading method in according to claim 1 is characterized in that, described abnormality value removing is handled and is meant:
Time headway in the emulation time headway does not match with initial travel speed, or;
When emulation time headway during less than lower threshold,
This emulation time headway is unusual and places the virtual formation of dispatching a car of waiting.
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| CN104517491B (en) * | 2014-12-16 | 2018-12-04 | 北京中交兴路车联网科技有限公司 | A kind of vehicle driving simulation system and method |
| CN104616509B (en) * | 2015-02-03 | 2016-08-17 | 上海交通大学 | Urban signalized intersections optimization method based on microscopic traffic simulation and system |
| CN104820763B (en) * | 2015-05-25 | 2017-12-15 | 西华大学 | A kind of traffic accident three-dimensional emulation method based on VISSIM |
| CN105047057B (en) * | 2015-07-09 | 2017-07-28 | 同济大学 | A kind of high through street Macro-traffic Flow emulation mode of multi-user's multilane |
| CN106781461A (en) * | 2016-12-02 | 2017-05-31 | 交通运输部公路科学研究所 | A kind of freeway net operation situation deduces thermal starting technology online |
| CN108337644B (en) * | 2017-01-18 | 2020-11-27 | 腾讯科技（深圳）有限公司 | Data processing method and device |
| CN108595846A (en) * | 2018-04-26 | 2018-09-28 | 苏州城方信息技术有限公司 | The traffic simulation method docked with VISSIM based on SCATS |
| CN108922168B (en) * | 2018-05-29 | 2019-10-18 | 同济大学 | A kind of mid-scale view Frequent Accidents road sentences method for distinguishing |
| CN109033583A (en) * | 2018-07-12 | 2018-12-18 | 中国科学院计算技术研究所 | A kind of the traffic rapid deduction method and system of data-driven |
| CN110118661B (en) * | 2019-05-09 | 2024-03-26 | 腾讯科技（深圳）有限公司 | Method and device for processing driving simulation scene and storage medium |
| CN111881557B (en) * | 2020-07-01 | 2023-09-29 | 浙江中控信息产业股份有限公司 | Traffic flow simulation method based on average speed of road |
| CN112365706B (en) * | 2020-09-14 | 2021-11-23 | 江苏大学 | Abnormal vehicle distance identification method based on multi-source information |
| CN114627650B (en) * | 2022-05-11 | 2022-08-23 | 深圳市城市交通规划设计研究中心股份有限公司 | Urban public transport priority simulation deduction system, method, electronic equipment and storage medium |
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| CN1937001A (en) * | 2006-10-19 | 2007-03-28 | 上海交通大学 | Method for obtaining everage speed of city road section traffic flow |
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| CN1937001A (en) * | 2006-10-19 | 2007-03-28 | 上海交通大学 | Method for obtaining everage speed of city road section traffic flow |
| CN101533455A (en) * | 2009-04-24 | 2009-09-16 | 昆明理工大学 | Driving analog synthesis simulation method based on microscopic traffic flow simulation model |
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