CN105427003A - Travel demand analysis-based bus station point deployment method - Google Patents
Travel demand analysis-based bus station point deployment method Download PDFInfo
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Abstract
The invention relates to a travel demand analysis-based bus station point deployment method. According to the travel demand analysis-based bus station point deployment method, travel demand characteristics of Beijing are analyzed based on massive taxi data; passenger hot areas are obtained through trajectory data; data such as road traffic congestion conditions, road types, road width and the number of roads are obtained through GIS map data extraction, and the data are adopted as consideration factors for station site deployment; and station site deployment optimization targets are processed, and a greedy algorithm is adopted to solve problems in station site deployment. Bus station sites obtained through adopting the method is the basis of intelligent bus line design; and routes can be arranged between the bus station sites, so that travel cost of passengers and road resource consumption can be effectively reduced.
Description
Technical field
The present invention designs the intelligent bus line design problem in intelligent transportation and large data analysis field, particularly intelligent transportation, is applicable to the bus station deployment issue in intelligent bus line design.
Background technology
At present, along with the development of China's economic, obtain increasing to the transport need quantitative change in city, this makes the automobile pollution per capita of resident also increase gradually.On the one hand, the increase of automobile pollution facilitates the trip of people, provides more convenient service to the life of people, and on the other hand, this result also in more traffic problems, such as traffic congestion, traffic hazard, automobile exhaust pollution etc.For this kind of problem, researchers propose to need the Changing Pattern based on existing resident trip data research Urban traffic demand, thus realize the optimization to existing Urban Traffic Modes.
Urban Traffic Modes is mainly divided into public transport, subway and taxi three kinds of modes.Subway belongs to a kind of form of urban track traffic, as the public transport passenger-traffic system of large conveying quantity, the main passenger flow of serving city assembles ground, is generally the important transit network of urban transportation, the features such as it has rapid and convenient simultaneously, the volume of passenger traffic is large, energy resource consumption is low, low in the pollution of the environment.But the travel route of subway needs to repair through long, and the travel time formulates by government is unified, and being difficult to needs to carry out real-time line adjustment according to reality trip.Bus alleviates the important measure of of urban public transport pressure, and by the end of the year 2012, Beijing will have built up more than 1,000 bar public bus networks at present, and provide about 6,000 ten thousand Bus Cards, city dweller's day bus trip amount can reach ten million person-time.Therefrom can find out that Beijing Public Transportation trip requirements amount is very large, and bus largely solves the trip problem of Beijing resident.Bus, as one of the most basic public transport form, covers substantially all trip requirements points in city, and the most of travelers for city provide trip service.Similar with subway, bus has fixing circuit and the time of departure, but compared to subway, the adjustment circuit of bus and the advantage of lower cost at the time of departure.On the other hand, taxi is also the effective means solving Urban Residential Trip demand, and since two thousand six, due to policy restriction, the cabbie population of Beijing remains on about 6.7 ten thousand, and quantity is relatively stable.2011 annual datas show that its volume of passenger traffic accounts for overall passengers transported by public traffic vehicles 9%, this illustrates that taxi is also the element that of Beijing Traffic is indispensable.And in recent years due to the rise of the software of calling a taxi such as " drip and call a taxi ", " fast calls a taxi ", the carrying efficiency of taxi there has also been larger raising.And compare with public transport with subway, taxi has dirigibility most, traffic path and travel time can be regulated flexibly according to the demand of passenger, but on the other hand, taxi price for other modes of transportation is more expensive, and carrying efficiency is relatively on the low side, this makes the Trip Costs of people relatively high.Mainly go out the feature of row mode in conjunction with above three kinds, ordinary populace can be more prone to the higher subway of selection sexual valence or public transport when selecting daily trip mode.Can find out, if will be optimized existing mode of transportation, from benefit and feasibility, public transport will be optimum selection.
On the other hand, along with the development of infotech, the trip data of resident is collected to be stored, people can find the Changing Pattern of Urban traffic demand by means such as data statistic analysis, large quantifier elimination shows, the transport need in city can change along with the change of when and where, has stronger time response and spatial character, and these characteristics can be obtained by methods such as existing data statistics and analyses.
To sum up, based on the time response of transport need and spatial character, existing mode of transportation can be optimized.Circuit and the service time Setup Cost of subway are larger, although taxi is flexible, but it is expensive to go on a journey, the Setup Cost of city bus is relatively low, therefore existing bus trip mode is optimized and there is higher feasibility, on the other hand, because public transport is a kind of important way of urban transportation, therefore Optimizing City public transport will have higher actual benefit.Existing public transport is optimized, mainly designs dynamic public bus network according to the Changing Pattern of transport need, and designed lines important prerequisite considers the installation position of bus station.
Traditional bus station Deployment Algorithm mainly contain based on GIS algorithm (see Zhang Feifei, the application of Xu Jianhui, Xie Xin road .GIS spacial analytical method in the addressing of bus station [J]. geospatial information, 2011 (1): 118-120; Xie Hua, all Kingcons. based on the bus station planing method [J] of optimum theory and GIS Spatial Data Analysis. Wuhan University of Technology's journal (traffic science and engineering version), 2004 (6): 907-910.), based on evaluating the algorithm of semantic ambiguity set (see Wang Lin, Chen great Peng. the analysis of bus station addressing and fuzzy evaluation [J]. transport science and techonologies are with economical, 2009 (6): 47-49.), genetic algorithm (Shi Liheng. based on MapX bus station lay with circuit emulation evaluation study [D]. Northeastern University, 2010; Qi Zhongping. based on the special lane optimization of bus station technique study [D] of GPS and IC-card data. Shandong University, 2013; Zhou Rui. based on bus station passenger flow projectional technique [D] of IC-card data. Beijing Jiaotong University, 2012), based on the algorithm (Roca-RiuM of model analysis, EstradaM, TrapoteC.Thedesignofinterurbanbusnetworksincitycenters [J] .TransportationResearchPartA-PolicyAndPractice, 2012,46 (8): 1153-1165; AlonsoB, MouraJL, Dell'OlioL, etal.BusStopLocationunderDifferentLevelsofNetworkCongest ionandElasticDemand [J] .TransportResearchJournalofVilniusGediminasTechnicalUniv ersity & LithuanianAcademyofSciences, 2011,26 (2): 141-148 etc.).Although these class methods efficiently solve the deployment issue of bus station, but it is normally for the bus station of permanent haulage line, and as intelligent bus design, do not consider the dynamic change of demand, therefore these class methods can not be applicable to the deployment of intelligent bus website completely.
And current intelligent bus site deployment algorithm mainly contains the algorithm (Ying-ShuaiLI based on timetable, YaoHY, QinL.BusStationOptimizationMethodBasedonthePrincipleofSt ationCancelingandStationCombining [J] .JournalofChongqingJiaotongUniversity, 2011; KhondakerB, WirasingheS.BusStopSpacingandLocationforaCorridorwithMul tipleBusRoutes [C] //CALGARY2013-THEMANYFACESOFTRANSPORTATION.2013.), based on algorithm (ZhangX.Bus-stopspacingoptimizationbasedonbusaccessibilit y [J] .JournalofSoutheastUniversity, 2009 of supply/demand model; GaoZW, PangHL, NiuGD, etal.Busstopspacingoptimizationbasedonunevendistribution ofpassengerflow [C] // 2009 Chinese Control and decision making meeting collection of thesis (2) .2009:2533-2538.), a combined method (appoint Hua Ling, Gao Ziyou. the Bi-level Programming Models of dynamic transit network design and algorithm research [J]. the system engineering theory and practice, 2007 (5): 82-89; NuzzoloA, CrisalliU, RussoF.ADOUBLYDYNAMICASSIGNMENTMODELFORCONGESTEDURBANTRA NSITNETWORKS [C] //TransportationPlanningMethods.1999), although these class methods solve the problem of demand dynamic, but consideration is still lacked to characteristics such as carrying hot spot-effect, urban transportation features, needs to study further.
Summary of the invention
Technology of the present invention is dealt with problems: for the deficiency of existing intelligent bus site deployment algorithm, a kind of bus station dispositions method based on travel demand analysis is proposed, by the mining analysis to magnanimity taxi data, draw the carrying hot spot region in city, it can be used as the constraint condition of disposing website, meanwhile, the deployment constraint of bus station is also contemplated, using the road conditions in city and the traffic bottlenecks attribute optimization aim as algorithm.By the bus station that this algorithm draws, as the basis of intelligent bus line design, and then urban transportation can be optimized.
Technical solution of the present invention: a kind of bus station dispositions method based on travel demand analysis, as shown in Figure 1, specific implementation step is as follows:
(1) by DBSCAN algorithm, cluster analysis is carried out to the track data of taxi, obtain the carrying hot spot region distribution of on-board and off-board.As shown in Figure 2, its concrete flow process is as follows for the process flow diagram of DBSCAN algorithm:
A) algorithm needs a point set before starting, and the minimum neighborhood that radius distance ε and set point become kernel object in epsilon neighborhood is counted MinPts.
B) first, the starting point that selected point is concentrated, is called a p.
C) travel through point set and count the point in the ε radius of a p, the radius distance namely often running into a point and p is less than ε, counter+1.
D) point set is traveled through successively, until institute is a little all traversed.
E) investigate the number being less than the point of ε with the radius distance of point and whether be greater than threshold value MinPts, if so represent that this p is kernel object, the point in the epsilon neighborhood of p is all classified as in this bunch.If not, so can think that this p is a noise spot, can be got rid of.
F) carry out the operation of (2)-(5) to other points in this bunch successively, constantly to bunch to expand, until institute is a little all traversed, and time this bunch is no longer expanded, this process terminates.
(2) by grahame method, Regularization is carried out to discrete passenger point, then by minimum outsourcing rectangle algorithm this region identified and divide, obtaining squaring hot spot region and represent.As shown in Figure 3, minimum outsourcing rectangle algorithm flow chart as shown in Figure 4 for grahame method process flow diagram.
The flow process of grahame method is as follows:
A) prepare point set P, number is n, and counter is i, and the point set of convex closure preserved by a stack, and counter is k;
B) selected point concentrates left side point P0 bottom;
C) angle and distance of each point relative to P0 is calculated;
D) ascending sort will be pressed according to the angle and distance of P0;
E) P [n-1] and P [0] is pressed in stack;
F) whether judging point set is straight line, if words jump to (6), no words k--;
Whether g) P [k-1], P [k-2], P [i] meet, and P [i] is pressed in stack by the words being, no words i--, k--;
The flow process of minimum outsourcing rectangle algorithm is as follows:
A) convex closure point set is prepared;
B) by the X of coordinate a little, the set sequence of Y;
C) minX is selected, minY, maxX, maxY
D) P1 (minX, minY) is created, P2 (minX, maxY), P3 (maxX, minY), P4 (maxX, maxY);
E) cells of rectangle according to 100m*100m is divided, calculate less than the 100m that presses of 100m.
(3) carry out abstract to site deployment problem and describe, it is characterized in that: conclude the factor that site deployment is considered, then by the formalization of site deployment problem, concrete grammar is:
(3.1) the site deployment factor considered in comprehensive document at present, is reduced to four: traffic bottlenecks attribute, road type attribute, road width attribute, road quantitative attribute.
(3.2) based on the region recognition in the step 2 of right 1, the minimum cells getting each hot spot region is the elementary cell of site deployment, i.e. District={P
1, P
2, P
3, P
4}={ (P
i1, P
i2, P
i3, P
i4) | 0≤i≤n}.Based on the reference factor that the step 3 of right 1 draws, define each cells and have as properties:
A) traffic bottlenecks attribute bottleNeck
The span of bottleNeck is that { 1,0}, 1 represents that this cells is traffic bottlenecks, and 0 represents it is not traffic bottlenecks, and acquiescence traffic bottlenecks, all on road, therefore for the cells not having road, do not have traffic bottlenecks.
B) road type attribute roadType
The span of roadType is that { 0,1,2}, 0 represents that this road is two-way street, and 1 represents that this road is one-way road, and 2 represent do not have road.
C) road width attribute roadWidth
The span of roadWidth is that { 0,3,5.5,13,20}, 0 represents that this cells does not have road, and other represent the width of the road of this cells, if a quadrille many roads, so value is the widest that.
D) road quantitative attribute roadNumber
The span of roadNumber is that { 0,1,2,3}, 0 represents that this cells does not have road, and 1 represents that this cells has 1 road, and 2 represent that this cells has 2 or 3 roads, and 3 represent that this cells has 4 roads.
(3.3) by abstract for site deployment problem be that two constraint conditions and three optimization aim are as follows:
(3.3.1) constraint condition:
A) for Website Hosting
4≤dis (A, B)≤6, dis (A, B) represent A, the distance between B, and unit is grid number.
b)
A·roadType≠2。
(3.3.2) optimization aim:
A) bus station position is selected in road type is as far as possible on two-way road;
B) bus station position avoids traffic bottlenecks as far as possible;
C) bus station position is selected in the wider position of road width as far as possible;
D) bus station position is selected in the more position of road number as far as possible;
Wherein the priority of optimization aim is by from a) to d) from high to low.
(4) website On The Choice conversion.A cells is represented with X, and with its ID as its unique identifier, so X ∈ [1,2473].According to the definition of the step 3 of right 1, each cells has five attributes, zone number attribute AreaID, traffic bottlenecks attribute bottleNeck, road quantitative attribute roadNumber, road type attribute roadType, road width attribute roadWidth, i.e. R (X)=(AreaID, bottleNeck, roadNumber, roadType, roadWidth).
Having four optimization aim abstract is respectively that four objective functions are as follows:
Consider the priority of optimization aim, and easy in order to calculate, we are by f
1(x), f
2(x), f
3(x), f
4x the weights of () are set to α respectively, β, θ and δ, and α > β > θ > δ, alpha+beta+θ+δ=1..So the Optional Value in each region can be expressed as:
Y=F (x)=α f
1(x)+β f
2(x)+θ f
3(x)+δ f
4(x) (formula 5)
On this basis, the website On The Choice in each region is converted into: the set Φ choosing a cells in multiple territory element lattice, make the value value of all grid of Φ with maximum.In Φ, grid need meet following condition:
1) interval between any two grid must not be less than 4 grid;
2) any one grid at least and the interval of other grid be less than or equal to 6 grid;
3) value of grid must not equal β.
(5) solve website On The Choice based on greedy algorithm, the thinking of design first ensures that choosing of website meets constraint condition, under this prerequisite, ensures that the value value of the website at every turn chosen is higher as far as possible.In order to ensure that each website was traversed, being provided with a see field and marking each region, every traversed field just mark see field is 1.Specific algorithm flow process is as follows:
1) regional ensemble List according to minX, minY, maxX, maxY, value successively ascending sort.
2) revise the X in each region, Y-coordinate, and by it according to X ascending order, Y ascending order arranges.
3) amendment used attribute is 1, forbidden attribute is 0 (attribute of value=0.3 is 1), and allowed attribute is 0, see attribute is 0.
4) revising cursor vernier is 0, points to first element of List.
5) region is obtained, cursor+1.
6) the see attribute of modifier area is 1.
7) if the used attribute in region is 1, or forbidden attribute be 1 or allowed attribute be 0, get back to 5), otherwise skip to 8).
8) Adding Area is selected in set to website, and the used attribute of setting area is 1.
9) arranging with the forbidden attribute in this interregional region every being less than 4 is 1.
10) arrange interregional every being more than or equal to 4 and the allowed attribute being less than or equal to the region of 6 is 1 with this.
11) if cursor is greater than the number of regional ensemble, terminate; Otherwise skip to 5).
The process flow diagram of website Algorithms of Selecting as shown in Figure 5.
(6) website effect assessment.In order to illustrate that the website that this method draws can as the basis of intelligent bus line design, thus the effective operational efficiency improving urban transportation, have chosen passenger's travel cost and path resource and consume the effectiveness of two indices to the website that algorithm draws and verify.
The present invention's advantage is compared with prior art:
(1) the present invention is directed intelligent bus circuit, and traditional site deployment algorithm is normally for fixing public bus network, except considering the dynamic of city demand, also contemplate the carrying hot spot-effect in city, and the traffic characteristic in city, this bus station that the present invention is drawn is more accurate, the efficiency of the more effective raising urban transportation of energy.
(2) based on magnanimity taxi data pin, Beijing's trip requirements feature is analyzed, carrying hot spot region is obtained by track data, and obtain the data such as road traffic congestion situation, road type, road width and road number as the Consideration disposing website by the extraction of GIS map data, then site deployment position optimization target is processed, and then adopt greedy algorithm to solve site deployment problem.The bus station drawn by the present invention is the basis of intelligent bus line design, experiment shows, if arrange traffic path between these bus stations, if adopt the mode of taxi to go on a journey, average travel cost is about 34 yuan (weekend is about 33 yuan), if adopt the mode of bus to go on a journey, average unit cost is about 3 yuan.Add up the carrying tracking quantity produced at each website, then according to taxi 2 people/time and bus 10 people/time carrying efficiency calculation, when meeting the carrying demand of each website, required taxis quantity is about 2-6 (weekend is about 2-4), and required bus quantity is about 1.Can find out, between these bus stations, arrange traffic path, passenger's travel cost and path resource consumption can be effectively reduced.
Accompanying drawing explanation
Fig. 1 is realization flow figure of the present invention;
Fig. 2 is DBSCAN algorithm flow chart in the present invention
Fig. 3 is convex closure algorithm flow chart in the present invention;
Fig. 4 is minimum outsourcing rectangle algorithm flow chart in the present invention;
Fig. 5 is website Algorithms of Selecting process flow diagram in the present invention;
Fig. 6 be in the present invention working day hot spot region result figure, wherein left figure is workaday upper visitor's point cluster result figure, and right figure is drop-off point cluster result figure;
Fig. 7 be in the present invention weekend hot spot region result figure, wherein left figure is upper visitor's point cluster result figure at weekend, and right figure is drop-off point cluster result figure;
Fig. 8 is data extraction algorithm process flow diagram in the present invention;
Fig. 9 is data processing algorithm process flow diagram in the present invention;
Figure 10 is area attribute result figure in the present invention;
Figure 11 is road data result extraction figure in the present invention;
Figure 12 be in the present invention working day passenger's Trip Costs comparing result;
Figure 13 be in the present invention weekend passenger's Trip Costs comparing result figure;
Figure 14 be in the present invention working day hot spot region traffic transportation efficiency comparing result figure, wherein the period of a figure expression 13:00-13:30 on February 21, (working day)-2014 years on the 17th February in 2014 add up each website vehicle number required when meeting transport need (according to taxi 2 people/time and bus 10 people/time carrying efficiency calculation); Period of b figure expression 13:30-14:00 on February 21, (working day)-2014 years on the 17th February in 2014 add up each website vehicle number required when meeting transport need (according to taxi 2 people/time and bus 10 people/time carrying efficiency calculation); Period of c figure expression 14:00-14:30 on February 21, (working day)-2014 years on the 17th February in 2014 add up each website vehicle number required when meeting transport need (according to taxi 2 people/time and bus 10 people/time carrying efficiency calculation); Period of d figure expression 14:30-15:00 on February 21, (working day)-2014 years on the 17th February in 2014 add up each website vehicle number required when meeting transport need (according to taxi 2 people/time and bus 10 people/time carrying efficiency calculation); Period of e figure expression 15:00-15:30 on February 21, (working day)-2014 years on the 17th February in 2014 add up each website vehicle number required when meeting transport need (according to taxi 2 people/time and bus 10 people/time carrying efficiency calculation); Period of f figure expression 15:30-16:00 on February 21, (working day)-2014 years on the 17th February in 2014 add up each website vehicle number required when meeting transport need (according to taxi 2 people/time and bus 10 people/time carrying efficiency calculation).
Figure 15 be in the present invention weekend hot spot region traffic transportation efficiency comparing result figure, wherein the period of a figure expression 13:00-13:30 on February 23, (weekend)-2014 years on the 22nd February in 2014 add up each website vehicle number required when meeting transport need (according to taxi 2 people/time and bus 10 people/time carrying efficiency calculation); Period of b figure expression 13:30-14:00 on February 23, (weekend)-2014 years on the 22nd February in 2014 add up each website vehicle number required when meeting transport need (according to taxi 2 people/time and bus 10 people/time carrying efficiency calculation); Period of c figure expression 14:00-14:30 on February 23, (weekend)-2014 years on the 22nd February in 2014 add up each website vehicle number required when meeting transport need (according to taxi 2 people/time and bus 10 people/time carrying efficiency calculation); Period of d figure expression 14:30-15:00 on February 23, (weekend)-2014 years on the 22nd February in 2014 add up each website vehicle number required when meeting transport need (according to taxi 2 people/time and bus 10 people/time carrying efficiency calculation); Period of e figure expression 15:00-15:30 on February 23, (weekend)-2014 years on the 22nd February in 2014 add up each website vehicle number required when meeting transport need (according to taxi 2 people/time and bus 10 people/time carrying efficiency calculation); Period of f figure expression 15:30-16:00 on February 23, (weekend)-2014 years on the 22nd February in 2014 add up each website vehicle number required when meeting transport need (according to taxi 2 people/time and bus 10 people/time carrying efficiency calculation)
Figure 16 is the workaday bus station of parameter 1 in the present invention;
Figure 17 is the bus station at the weekend of parameter 1 in the present invention.
Embodiment
As shown in Figure 1, a kind of bus station Deployment Algorithm based on travel demand analysis is made up of 4 parts altogether: hot spot region cluster, road/traffic bottlenecks data are extracted, website On The Choice is changed, chosen bus station based on greedy algorithm.First the present invention has drawn the carrying hot spot region of taxi by hot spot region cluster, and in this, as the basis at cloth station; Then extract road/traffic bottlenecks data, and be treated to the territory element attribute of carrying hot spot region; Afterwards by formalized description, website On The Choice is changed; Finally select bus station based on greedy algorithm.
One, basic embodiment of the present invention is introduced below.
1, first, need the taxi GPS track data (at least one week) obtaining city, cluster analysis is carried out by the GPS track data of DBSCAN algorithm to taxi, obtain hot spot region as shown in Figure 6 and Figure 7, wherein Fig. 6 is the on-board and off-board hot spot region drawn by workaday taxi track data cluster, Fig. 7 is the on-board and off-board hot spot region drawn by the taxi track data cluster at weekend, and black color dots part wherein represents carrying hot spot region.Disposal route is specific as follows:
(1) DBSCAN cluster
This part can pass through the DBSCAN clustering algorithm carried in existing Software of Fuzzy Clustering Analysis (such as R software), cluster analysis is carried out to data set, this part output is the set of a discrete point, if need to represent region, also needs to do further process.
(2) hot spot region identifies
On the basis of (1), further regular in order to do region, need to carry out regular to discrete point set, first the convex closure of point set is asked for based on grahame method, then on this basis, ask the minimum outsourcing rectangle MBR of convex closure, in order to the follow-up convenience asking for site location, need to divide MBR, namely divide according to the area of 100*100, calculate according to the region of 100*100 less than the region of 100*100.
2, then, need to extract road/traffic bottlenecks data by GIS map data/Beijing Communication official website data, and inputted the attribute as each site location.One has four website attributes, as follows respectively:
A) traffic bottlenecks attribute bottleNeck
The span of bottleNeck is that { 1,0}, 1 represents that this cells is traffic bottlenecks, and 0 represents it is not traffic bottlenecks, and acquiescence traffic bottlenecks, all on road, therefore for the cells not having road, do not have traffic bottlenecks.
B) road type attribute roadType
The span of roadType is that { 0,1,2}, 0 represents that this road is two-way street, and 1 represents that this road is one-way road, and 2 represent do not have road.
C) road width attribute roadWidth
The span of roadWidth is that { 0,3,5.5,13,20}, 0 represents that this cells does not have road, and other represent the width of the road of this cells, if a quadrille many roads, so value is the widest that.
D) road quantitative attribute roadNumber
The span of roadNumber is that { 0,1,2,3}, 0 represents that this cells does not have road, and 1 represents that this cells has 1 road, and 2 represent that this cells has 2 or 3 roads, and 3 represent that this cells has 4 roads.
By data extraction algorithm, these four attributes are processed respectively and corresponds on cells.Processing Algorithm as shown in Figure 8 and Figure 9.Fig. 8 contains 3 algorithms altogether, wherein leftmost algorithm extracts road information algorithm, and it is road network object data from GIS map extracting data, comprises road number, road quantity, road type and road width attribute; Middle algorithm is extracted the GPS information of road, and object is in order to by the attribute of road object and longitude and latitude data correspondence; Rightmost algorithm have read traffic bottlenecks data, comprises starting point and the end point of traffic bottlenecks, and creates traffic bottlenecks object from starting point and end point; Fig. 9 contains 2 algorithms altogether, the algorithm process on left side road information, and the longitude and latitude data being about to road object data and the road read before map; The algorithm process traffic bottlenecks data on the right, are about in the traffic bottlenecks data that read before and area data correspondence.
3, then, need to change website On The Choice according to optimization object function.
Based on following formula, the attribute of each grid is treated to website and chooses value value.
Y=F (x)=α f
1(x)+β f
2(x)+θ f
3(x)+δ f
4(x) (formula 5)
In formula, α, β, θ and δ are f
1(x), f
2(x), f
3(x), f
4the weights of (x), and α > β > θ > δ, alpha+beta+θ+δ=1.; BottleNeck represents traffic bottlenecks attribute, and roadNumber represents road quantitative attribute, and roadType represents road type attribute, and roadWidth represents road width attribute.
Result as shown in Figure 10.Figure 10 comprises 7 fields altogether, AreaID, ID, minX, minY, maxX, maxY, value, wherein AreaID represents the numbering attribute in region, ID represents the identity property of territory element, minX represents the minimum X-coordinate of unit, and minY represents the minimum Y-coordinate of unit, and maxX represents the maximum X-coordinate of unit, maxY represents the maximum Y-coordinate of unit, and value represents that treated regional choice is worth.
4, finally site deployment position is chosen based on greedy algorithm.
This part is the core of this algorithm, and principle is the principle of greedy algorithm, and algorithm process flow process is as follows:
1) regional ensemble List according to minX, minY, maxX, maxY, value successively ascending sort.
2) revise the X in each region, Y-coordinate, and by it according to X ascending order, Y ascending order arranges.
3) amendment used attribute is 1, forbidden attribute is 0 (attribute of value=0.3 is 1), and allowed attribute is 0, see attribute is 0.
4) revising cursor vernier is 0, points to first element of List.
5) region is obtained, cursor+1.
6) the see attribute of modifier area is 1.
7) if the used attribute in region is 1, or forbidden attribute be 1 or allowed attribute be 0, get back to 5), otherwise skip to 8).
8) Adding Area is selected in set to website, and the used attribute of setting area is 1.
9) arranging with the forbidden attribute in this interregional region every being less than 4 is 1.
10) arrange interregional every being more than or equal to 4 and the allowed attribute being less than or equal to the region of 6 is 1 with this.
11) if cursor is greater than the number of regional ensemble, terminate; Otherwise skip to 5).
3, last, the present invention effectively can process the site deployment problem of intelligent bus in smart city, specifically has:
(1) time response of demand and spatial character problem
It is carry out for fixing public bus network that traditional bus station is disposed, do not consider time response and the spatial character problem of demand, and this algorithm is for the site deployment problem of the intelligent bus line design in smart city, because it considers time response and the spatial character problem of demand, the traffic efficiency in city therefore more effectively can be improved.
(2) road conditions problem
In the deployment issue of bus station, road conditions is the factor usually needing to consider.This algorithm considers road width, road number, road type and traffic bottlenecks factor when disposing website, and the site location therefore disposing out can be more excellent.
Two, for making the present invention easier to understand, promote effect more directly perceived, then the present invention is further elaborated in conjunction with an algorithm examples, but this example does not form any limitation of the invention.
1, Primary Stage Data process
According in embodiment one introduce performing step, result as shown in Figure 10, respectively by AreaID, ID, minX, minY, maxX, maxY, roadNumber, roadType, roadWidth, bottleNeck10 field represents, wherein minX, minY, maxX, maxY represent the minimum cells of division, roadNumber, roadType, roadWidth, bottleNeck represents the road quantity of cells respectively, road type, the attribute such as road width and traffic bottlenecks.
In order to make the result of website algorithm more accurate, the data at working day and weekend are processed respectively.What Figure 16 illustrated workaday bus station chooses result, can find out to have selected altogether 41 bus stations by the present invention.What Figure 17 illustrated the bus station at the weekend under first group of parameter chooses result, can find out, select altogether 33 bus stations by the present invention.
2, result verification
In order to verify the validity of the website chosen herein, the passenger's travel cost and path resource of bus station being laid to front and back being consumed two indices and contrasts.
In order to contrast the impact of laying on the Trip Costs of passenger before and after bus station, based on the bus station position candidate selected by a upper joint, based on the carrying record on February 23 ,-2014 years on the 17th February in 2014, the OD added up between site location shifts track, and calculates its travel cost according to the valuation rule of bus and the valuation rule of taxi respectively.As is illustrated by figs. 11 and 12, between hot spot region, arrange traffic path, on weekdays, if adopt the mode of taxi to go on a journey, average travel cost is about 34 yuan, if adopt the mode of bus to go on a journey, average unit cost is about 3 yuan.At weekend, if adopt the mode of taxi to go on a journey, average travel cost is about 33 yuan, if adopt the mode of bus to go on a journey, average unit cost is about 3 yuan, will reduce the Trip Costs of passenger greatly as seen in laying bus station, hot spot region after designing corresponding public bus network.
Have chosen the carrying record under the period of 13:00-15:30 on the 23rd February-2014 years on the 17th February in 2014, a time period of per half an hour adds up the carrying tracking quantity produced at each website, then according to taxi 2 people/time and bus 10 people/time carrying efficiency calculation Different periods under, the vehicle number that each website is required when meeting transport need.As shown in Figure 14 and Figure 15, when meeting the carrying demand of each website, taxis quantity needed for working day is about 2-6, weekend, passenger demand amount was less, be about 2-4, but no matter working day or weekend, and average each website only needs 1 bus just can meet carrying demand, this is for the traffic pressure alleviating city, and the traffic efficiency improving city has larger meaning.
Comprehensive above Performance comparision, intelligent bus website of the present invention lays the Trip Costs that algorithm will reduce passenger effectively, improves the traffic efficiency in city, thus effectively alleviates the traffic pressure in city.
The above is embodiments of the present invention; certainly the interest field of the present invention can not be limited with this; should be understood that; for those skilled in the art, under the premise without departing from the principles of the invention, some improvement and variation can also be made; as changed the setting of message taxis preference; according to the situation setting vehicle node cache size of actual vehicle, difference emulation or actual development platform realize, and these improve and variation is also considered as protection scope of the present invention.
Claims (8)
1., based on a bus station dispositions method for travel demand analysis, it is characterized in that performing step is as follows:
Step one, carries out cluster analysis by DBSCAN algorithm to the track data of taxi, and obtain the carrying hot spot region distribution of on-board and off-board, the data mode of this hot spot region is a discrete point set with zone number, is also the input of step 2;
Step 2, on the basis of the input obtained in step one, by grahame method, Regularization is carried out to discrete point set, identify by the minimum region of outsourcing rectangle algorithm to the discrete point set with zone number and divide again, obtain squaring hot spot region to represent, output is a rectangular area set;
Step 3, based on the rectangular area set in step 2, carry out abstract to site deployment problem and describe, namely induction-arrangement being carried out to the factor that site deployment is considered, merge into " road width, road number, road type, traffic bottlenecks " four reference factors; Then site deployment problem form is turned to two constraint conditions and four optimization aim, two constraint conditions are that distance between restriction adjacent sites is between 400m to 600m and can not at the local cloth station not having road; Four optimization aim are that bus station position is selected on two-way street as far as possible, avoid traffic bottlenecks, are selected in the wider and position that number is more of road width;
Step 4, on the basis of step 3, by linear weight sum method by abstract for four optimization aim be four objective functions, for each objective function assigns weight, the Optional Value value of each territory element is calculated by Optional Value function, again website On The Choice is converted to following principle: the set Φ choosing a cells in multiple territory element lattice, make the value value of all grid of Φ with maximum; In Φ, grid need meet following condition: the interval 1) between any two grid must not be less than 4 grid (cells of 100m*100m); 2) any one grid at least and the interval of other grid be less than or equal to 6 grid (cells of 100m*100m); 3) value of grid must not equal β;
Step 5, on the basis of step 4, chooses bus station based on greedy algorithm.
2. the bus station dispositions method based on travel demand analysis according to claim 1, is characterized in that: the flow process of the DBSCAN algorithm in described step one is as follows:
(1) arrange two parameters, the minimum neighborhood that radius distance ε and set point become kernel object in epsilon neighborhood is counted out MinPts;
(2) first, the starting point that random selecting point is concentrated, is called a p;
(3) travel through point set and count the point in the ε radius of a p, the radius distance namely often running into a point and p is less than ε, counter+1;
(4) point set is traveled through successively, until institute is a little all traversed;
(5) investigate the number being less than the point of ε with the radius distance of point and whether be greater than threshold value MinPts, if so represent that this p is kernel object, the point in the epsilon neighborhood of p is all classified as in this bunch; If not, so then think that this p is a noise spot, can be got rid of;
(6) successively other points in this bunch are carried out to the operation of (2)-(5), constantly to bunch to expand, until institute is a little all traversed, and time this bunch is no longer expanded, this process terminates, and obtains the discrete point set with zone number.
3. the bus station dispositions method based on travel demand analysis according to claim 1, is characterized in that: the grahame method flow process in described step 2 is as follows:
(1) number choosing point set P is n, and counter is i, and the point set of convex closure preserved by a stack, and counter is k;
(2) selected point concentrates left side point P0 bottom;
(3) angle and distance of each point relative to P0 is calculated;
(4) ascending sort will be pressed according to the angle and distance of P0;
(5) P [n-1] and P [0] is pressed in stack;
(6) whether judging point set is straight line, if words jump to (6), no words k--;
(7) whether P [k-1], P [k-2], P [i] meet, and P [i] is pressed in stack by the words being, no words i--, k--;
(8) point set in stack is outputted to file, the point set of namely obtained Regularization.
4. the bus station dispositions method based on travel demand analysis according to claim 1, is characterized in that: in described step 2, minimum outsourcing rectangle algorithm flow is as follows:
(1) the discrete point set with zone number is prepared;
(2) point is concentrated the X of the coordinate of all points, the set sequence of Y;
(3) minimum X-coordinate and minX is selected, minimum Y-coordinate and minY, maximum X-coordinate and maxX, maximum Y-coordinate and maxY;
(4) creating four points of minimum outsourcing rectangle, is P1 (minX, minY), P2 (minX, maxY) respectively, P3 (maxX, minY), P4 (maxX, maxY);
(5) cells of rectangle according to 100m*100m is divided, calculate less than the 100m that presses of 100m, finally export a rectangular area set.
5. the bus station dispositions method based on travel demand analysis according to claim 1, is characterized in that: in described step 3, by site deployment problem formalization concrete grammar is:
(5.1) comprehensive considered site deployment factor, is reduced to four reference factors by site deployment factor: traffic bottlenecks, road type, road width, road quantity;
(5.2) according to the region recognition in step 2, the minimum cells getting each hot spot region is the elementary cell of site deployment, i.e. District={P
1, P
2, P
3, P
4}={ (P
i1, P
i2, P
i3, P
i4) | 0≤i≤n}, wherein P
1, P
2, P
3, P
4represent four summits of minimum outsourcing rectangle respectively, P
i1, P
i2, P
i3, P
i4four summits of the zonule unit after being respectively division, zonule cellar area is less than or equal to 100m*100m, based on drawn reference factor, determines that each cells has as properties:
A) traffic bottlenecks attribute bottleNeck
The span of bottleNeck be 1,0}, 1 represents that this cells is traffic bottlenecks, and 0 represents it is not traffic bottlenecks, and acquiescence traffic bottlenecks, all on road, therefore for the cells not having road, do not have traffic bottlenecks;
B) road type attribute roadType
The span of roadType be 0,1,2}, 0 represents that this road is two-way street, and 1 represents that this road is one-way road, and 2 represent do not have road;
C) road width attribute roadWidth
The span of roadWidth be 0,3,5.5,13,20}, 0 represents that this cells does not have road, and other represent the width of the road of this cells, if a quadrille many roads, so value is the widest that;
D) road quantitative attribute roadNumber
The span of roadNumber be 0,1,2,3}, 0 represents that this cells does not have road, and 1 represents that this cells has 1 road, and 2 represent that these cells have 2 or 3 roads, and 3 represent that these cells have 4 roads;
The output of this step is the set of a series of territory element, and each unit is expressed as (AreaID, P
1, P
2, P
3, P
4, bottleNeck, roadType, roadWidth, roadNumber), wherein AreaID represents the numbering attribute in region, P
1, P
2, P
3, P
4represent four summits in region respectively, bottleNeck, roadType, roadWidth, roadNumber represent traffic bottlenecks respectively, road type, road width and road quantity four attributes, and the output of this step is the input of 5.3;
(5.3) by abstract for site deployment problem be that two constraint conditions and four optimization aim are as follows:
(5.3.1) constraint condition:
A) for Website Hosting
4≤dis (A, B)≤6, dis (A, B) represent A, the distance between B, and unit is grid number;
b)
A·roadType≠2；
(5.3.2) optimization aim:
A) bus station position is selected in road type is as far as possible on two-way road;
B) bus station position avoids traffic bottlenecks as far as possible;
C bus station position is selected in the wider position of road width as far as possible;
D) bus station position is selected in the more position of road number as far as possible
Wherein the priority of optimization aim is by from a) to d) from high to low;
Finally obtain two constraint conditions and four optimization aim.
6. the bus station dispositions method based on travel demand analysis according to claim 1, it is characterized in that: described step 4, website On The Choice conversion method: represent a cells with X, and with its ID as its unique identifier, so X ∈ [1, 2473], define each cells and there are five attributes, zone number attribute AreaID, traffic bottlenecks attribute bottleNeck, road quantitative attribute roadNumber, road type attribute roadType, road width attribute roadWidth, i.e. R (X)=(AreaID, bottleNeck, roadNumber, roadType, roadWidth),
Having four optimization aim abstract is respectively that four objective functions are as follows:
F
1x () represents the optimization object function of road type, f
2x () represents the optimization object function of traffic bottlenecks, f
3x () represents the optimization object function of road width, f
4x () represents the optimization object function of road quantity, roadType represents the road type attribute of territory element, bottleNeck represents the traffic bottlenecks attribute of territory element, roadWidth represents the road width attribute of territory element, and roadNumber represents the road quantitative attribute of territory element;
Consider the priority of optimization aim, and easy in order to calculate, by f
1(x), f
2(x), f
3(x), f
4x the weights of () are set to α respectively, β, θ and δ, and α > β > θ > δ, alpha+beta+θ+δ=1., the Optional Value in each region is expressed as:
Y=F (x)=α f
1(x)+β f
2(x)+θ f
3(x)+δ f
4(x) (formula 5)
Y and F (x) represents the Optional Value function in each region, f
1x () represents the optimization object function of road type, f
2x () represents the optimization object function of traffic bottlenecks, f
3x () represents the optimization object function of road width, f
4x () represents the optimization object function of road quantity, α, β, θ and δ represent f respectively
1(x), f
2(x), f
3(x), f
4the Optional Value weight of (x);
On this basis, the website On The Choice in each region is converted into: the set Φ choosing a cells in multiple territory element lattice, make the Optional Value value value of all grid gathered in Φ with maximum, in Φ, grid need meet following condition:
1) interval between any two grid must not be less than 4 grid;
2) any one grid at least and the interval of other grid be less than or equal to 6 grid;
3) value of grid must not equal β.
7. the bus station dispositions method based on travel demand analysis according to claim 1, it is characterized in that: described step 5, based on greedy algorithm, website On The Choice is solved, the thinking of design first ensures that choosing of website meets constraint condition, under this prerequisite, ensure that the value value of the website at every turn chosen is higher as far as possible; In order to ensure that each website was traversed, being provided with a see field and marking each region, every traversed field just mark see field is 1, and specific algorithm flow process is as follows:
1) regional ensemble List according to minimum X-coordinate minX, minimum Y-coordinate minY, maximum X-coordinate maxX, maximum Y-coordinate maxY, Optional Value value ascending sort successively;
2) revise the X in each region, Y-coordinate, and by it according to X ascending order, Y ascending order arranges;
3) revising used (representing that this region is selected) attribute is 1, forbidden (representing that this region can not be selected) attribute is 0, the attribute of value=0.3 is 1, allowed (representing that this region can be selected) attribute is 0, see (representing that this region was traversed) attribute is 0;
4) revising cursor vernier is 0, points to first element of List;
5) region is obtained, cursor+1;
6) the see attribute of modifier area is 1;
7) if the used attribute in region is 1, or forbidden attribute be 1 or allowed attribute be 0, get back to 5), otherwise skip to 8);
8) Adding Area is selected in set to website, and the used attribute of setting area is 1;
9) arranging with the forbidden attribute in this interregional region every being less than 4 is 1;
10) arrange interregional every being more than or equal to 4 and the allowed attribute being less than or equal to the region of 6 is 1 with this;
11) if cursor is greater than the number of regional ensemble, terminate; Otherwise skip to 5);
Result is chosen in final bus station of must arriving.
8. the bus station dispositions method based on travel demand analysis according to claim 1, is characterized in that: the area of a rectangular area in described step one is no more than 100m*100m.
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