CN110188953A - A kind of O-D spatial and temporal distributions prediction technique based on space Du's guest's model - Google Patents
A kind of O-D spatial and temporal distributions prediction technique based on space Du's guest's model Download PDFInfo
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Abstract
The present invention relates to a kind of O-D spatial and temporal distributions prediction techniques based on space Du's guest's model, belong to the technical field of Urban Traffic Planning and management and intelligent transportation system.Explanatory variable of the built environment as O-D spatial and temporal distributions is added, and built environment is demonstrated for the explanatory of O-D spatial and temporal distributions by case；Give a kind of method with a certain traffic zone traffic generation or traffic attraction estimation adjacent cell Trip generation forecast or traffic attraction.The invention has the advantages that explaining the spills-over effects that built environment influences O-D spatial and temporal distributions, and this spills-over effects are decomposed into direct effect, indirect effect and gross effect, improve the precision of city O-D spatial and temporal distributions prediction result.
Description
Technical field
The invention belongs to Urban Traffic Planning and the technical fields of management, are related to traffic trip origin and destination (Origin-
Destination, abbreviation O-D) spatial and temporal distributions and ITS intelligent transport system field, especially suitable for being based on city built environment
The prediction technique of explanation and O-D spatial and temporal distributions to O-D spatial and temporal distributions.
Background technique
The existing research about the distribution of O-D demand is broadly divided into O-D data acquisition, O-D matrix constructs two steps to obtain
Obtain the O-D distribution of Traffic Systems.Alexander is using cellular triangulation location data as personal and family's daily trip rail
Mark, accuracy and the alternative traditional family's outgoing survey data of timeliness.Hadavi and Shafahi proposes a kind of base
Estimate in the O-D of traffic sensor data, using Car license recognition sensor and proposes four position models to obtain O-D
Stream.O-D matrix construction methods are broadly divided into two classes: statistical method and mathematic programming methods.Ge and Fukuda uses Maximum Entropy
Principle realizes the relevant trip O-D needs estimate of work based on cellphone GPS track data.It is excellent that Lee proposes a kind of robust
Change method is used for O-D network evaluation, the uncertainty that can be generated O-D matrix and O-D demand is overcome to be distributed.
It is more usage history under study for action either in O-D data acquisition or O-D matrix building process
The beginning and the end point data carries out that O-D is counter pushes away using mathematical method, often do not have (can not) internal factor of analyzing influence O-D distribution.Cause
This, the present invention proposes a kind of O-D spatial and temporal distributions prediction technique based on space Du's guest's model, sufficiently examines using car data is hired out
Influence of the city built environment to O-D spatial and temporal distributions is considered, to propose more accurate prediction technique.
Summary of the invention
The technical problem to be solved by the present invention is to obtain each cell O-D first with GPS data from taxi in each traffic zone
Distribution, the spills-over effects that then building space Du guest's model estimation city built environment is distributed O-D on this basis, uses city
The method of the O-D distribution number of the distribution number estimation adjacent cell of vehicle O-D in traffic zone.
Technical solution of the present invention:
A kind of O-D spatial and temporal distributions prediction technique based on space Du's guest's model, which is characterized in that steps are as follows:
(1) traffic zone divides
Traffic zone division is carried out to survey region first, administrative small towns street division or rasterizing division side can be used
Formula.
(2) city built environment elements recognition and statistics
It is needed according to research, extracts the index of various city built environment elements in traffic zone, mainly include density, soil
Ground carries out built environment in traffic zone using diversity, design of street, destination accessibility and apart from public transport facility distance
Element statistics.In addition, also to obtain the traffic generation of each traffic zone and attraction in the research period by basic data processing
Amount.
(3) citation form of space Du guest's model
Y=ρ Wy+X β+γ WX+ ε, ε~N (0, σ2In) (1)
In formula, n is traffic zone quantity；Y is the vector of n × 1, indicates that explained variable i.e. certain traffic zone morning peak is handed over
Logical production quantity or traffic attraction；X is n × k data matrix, represents the various built environment indexs in explanatory variable i.e. certain traffic zone, and k is
City built environment element number；W is Spatial weight matrix, and ρ is the coefficient of Spatial lag dependent variable Wy, γ be Spatial lag from
The coefficient of variable WX, β reflect the influence that explanatory variable generates dependent variable y variation, and ε is stochastic error；
Spatial weight matrix W discloses the interaction between space cell, and form is as follows:
Each element in Spatial weight matrix is space weight, and the calculation method of space weight uses anti-distance weighting square
Battle array, form are as follows:
(2) effect of space Du guest's model is decomposed
For the spills-over effects in version space Du's guest's model, independent variable is introduced to the direct effect of dependent variable, indirectly effect
Should and gross effect, i.e., the change of single built environment element associated with traffic generation in any traffic zone or traffic attraction
Change, not only this cordon traffic production quantity or traffic attraction are had an impact, while the traffic for also influencing other adjoining areas indirectly is raw
At amount and traffic attraction；
For the ease of estimating direct effect and indirect effect, formula (1) is rewritten as following formula:
(In- ρ W) y=ιnα+Xβ+WXθ+ε (4)
Y=(In-ρW)-1ιnα+(In-ρW)-1X(Inβ+Wθ)+(In-ρW)-1ε (5)
Enable (In-ρW)-1=V (W), S (W)=V (W) (Inβ+W θ), then formula (6) are obtained, matrix form is formula (7), parameter effect
The expression matrix answered is formula (8):
Y=V (W) ιnα+S(W)X+V(W)ε (6)
The sum of the elements in a main diagonal is average direct effect divided by n in formula (8)；S in matrixr(W) the sum of all elements remove
Using n as average overall effect；The difference of average overall effect and average direct effect is denoted as average indirect effect；It is as follows:
In above formula, r=1,2,3 ..., k are city built environment element number, ιnIndicate the rank matrix of n × 1, M
(k)DirectIndicate built environment element in city to the direct effect of traffic generation or traffic attraction in this traffic zone, M
(k)IndirectIndicate built environment element in city to the indirect effect of traffic generation or traffic attraction in neighbouring traffic zone, M
(k)totalIndicate built environment element in city to the gross effect of traffic generation in traffic zone or traffic attraction.
Beneficial effects of the present invention: the O-D spatial and temporal distributions prediction technique addition of the invention based on space Du's guest's model is built
Explanatory variable at environment as traffic zone traffic generation and traffic attraction, it was demonstrated that built environment is for O-D spatial and temporal distributions
It is explanatory；And the spills-over effects that city built environment influences O-D distribution are demonstrated on this basis, it is quantified,
This spills-over effects are decomposed into direct effect, indirect effect and gross effect, the perfect detailed information of spatial coherence, is city
City's management carries out urban land_use plan with planning department and provides reference.
Specific embodiment
Below in conjunction with technical solution, in detail narration a specific embodiment of the invention, and simulate the implementation result of invention.
(1) research object
Selection Shenzhen's administrative region of a city is research range, and Shenzhen is not only the economic center in the whole nation, and infrastructure construction is perfect,
City built environment element is abundant and has a very wide distribution；And Pearl River Delta regional population gathers center, movement of population amount regardless of
Be it is internal or externally all quite huge, convenient for conducting a research.
(2) basic data
It completes traffic zone using ArcGIS software to divide, it is contemplated that built environment is rich inside traffic zone and grid
The operability of data, the grid that 1.5km*1.5km size is finally chosen in this research is unit traffic zone scale, obtains 1031
A traffic zone.Floating wheel paths during choosing daily 6:00-8:30-14 days on the 9th June in 2014 are programmed using Oracle
Floating Car GPS point after matching is spread in traffic zone, the Trip generation forecast of each cell is extracted in ArcGIS by origin and destination
Amount and traffic attraction, as dependent variable.Hotel's density, eating and drinking establishment's density in selection research cell, supermarket's density, pharmacy are close
Degree, mansion density, school's density, hospital's density, bank's density, government unit's density, bus station density, intersection density,
Subway station density, land use diversity and away from transport hub distance, totally 14 built environment elements as built environment from
Variable.The statistical value of each built environment attribute is provided in table 1.
The main independent variable statistical value in each traffic zone of table 2.1
(3) global return determines significant independent variable
For most number space proof analysis, spatial econometric modeling is generally opened from non-space linear regression model (LRM) first
Begin, then further discusses the model and whether need to extend to consider spatial interaction effect and then to establish spatial econometrics, because
This this research has initially set up reference of the global regression model as spatial econometric analysis.
It is because becoming with 1031 traffic zone morning peak traffic generations and traffic attraction (O-D) in global regression model
Amount, city built environment attribute are independent variable, and model calibration is completed in SPSS software.Estimated result is shown in Table 2, when t value
Absolute value be greater than 1.96 when, illustrate the independent variable on road traffic simulation amount or occurrence quantity influence be significant.
The global regression model result of table 2
Note: it is 99%, 95% and 90% that * * *, * *, *, which respectively indicate significance,.
When explained variable is traffic generation in model result, Ra 2It is 0.610, illustrates that the independent variable in model can solve
Release the variation of 61.0% traffic generation；When explained variable is road traffic simulation amount, Ra 2It is 0.640, illustrates oneself in model
Variable can explain the variation of 64.0% road traffic simulation amount.
From Table 2, it can be seen that hotel quantity, mansion quantity and bus station quantity and Trip generation forecast exist significantly just
It is related；And the super quantity of quotient, diversity and exist away from transport hub distance and Trip generation forecast significant negatively correlated.Mansion quantity,
Iron website, bus station and with government's quantity and road traffic simulation exist it is significant be positively correlated, diversity and road traffic simulation exist aobvious
The negative correlation of work.
Significant independent variable is substituted into space Du's guest's model by 4
It is programmed based on Matlab, the result of space Durbin model is as shown in the following table 3 and 4.
3 space Durbin model parameter estimation result (Trip generation forecast) of table
Note: it is 99%, 95% and 90% that * * *, * *, *, which respectively indicate significance,.
4 space Durbin model parameter estimation result (road traffic simulation) of table
Note: it is 99%, 95% and 90% that * * *, * *, *, which respectively indicate significance,.
As can be seen from tables 3 and 4 that hotel density, mansion density and bus station's density and Trip generation forecast have significantly
Positive correlation, supermarket's density and Trip generation forecast have significant negative correlativing relation；Mansion density, subway station density, public transport
There are significant positive correlations with road traffic simulation for site density and government's density, consistent with global regression model result.However,
Diversity is not significant on road traffic simulation and Trip generation forecast influence, this is because the presence of Spatial weight matrix, so that diversity
It is decomposed to the effect of explained variable, and influence of the diversity to itself Trip generation forecast or attraction be not significant.
What the variable in table 4 and table 4 with W indicated is Spatial lag item, represents Trip generation forecast or the traffic of the traffic zone
Attraction is influenced by periphery traffic zone built environment variable.For Trip generation forecast, in addition to supermarket's density and bus station
Spatial lag variable it is not significant outer, remaining Spatial lag variable is significant.Specifically, peripheral cell hotel density and mansion
Density, which generates this cordon traffic, has significantly positive influence, and peripheral cell diversity generates this cordon traffic to exist and show
The negative sense of work influences.For road traffic simulation, other than the Spatial lag variable of bus station is not significant, the space of remaining variables
Lagged variable is significant.Peripheral cell mansion density and subway station density, which attract this cordon traffic, has significant positive shadow
It rings, and peripheral cell government density and diversity have significant negative sense to influence the attraction of this cordon traffic.
Spatial lag variation coefficient ρ is positive and statistically significant, when illustrating to study the influence that built environment is distributed O-D, no
Space spills-over effects can be ignored.And the trip or attraction of periphery traffic zone improve 1%, the Trip generation forecast of this cell or attraction will
Improve about 0.24% and 0.25%.
Model parameter in table 3 and table 4 is decomposed into direct effect, indirect effect, and then obtains gross effect.Decomposition computation
It the results are shown in Table shown in 5 and table 6.
Direct, the indirect and gross effect (Trip generation forecast) of 5 space Durbin model of table
Note: it is 99%, 95% and 90% that * * *, * *, *, which respectively indicate significance,.
Direct, the indirect and gross effect (road traffic simulation) of 6 space Durbin model of table
Note: it is 99%, 95% and 90% that * * *, * *, *, which respectively indicate significance,.
From the perspective of quantitative, bus station density is significant positive correlation on Trip generation forecast influence, and direct effect
The 51% of gross effect is accounted for, indirect effect accounts for the 49% of gross effect, illustrates that bus station density increases from solution Trip generation forecast
Release the double action of variable.It is to be spread centered on bus station to periphery that this, which embodies resident's daily trip, and range of scatter
It is considerable, it prompts traffic administration person that should suitably add visitor on taxi near bus station and puts to meet this part trip requirements.
In addition, diversity is regardless of to Trip generation forecast or attraction, indirect effect and the equal statistically significant of gross effect and be negative value, and seen on coefficient
Indirect effect is the absolute contribution of gross effect, illustrates that diversity is not the direct determinant that traffic generates or attracts.Generally
Think that a certain region auxiliary facility is perfect, land use pattern multiplicity, design of street is scientific and reasonable, which is considered mature
Community, consistent with previous research, mature community's diversity level is higher, will drive the multifarious raising in peripheral cell, thus
Generate less Trip generation forecast and attraction.
Claims (1)
1. a kind of O-D spatial and temporal distributions prediction technique based on space Du's guest's model, which is characterized in that steps are as follows:
(1) traffic zone divides
Traffic zone division is carried out to survey region first, using the division of administrative small towns street or rasterizing division mode；
(2) city built environment elements recognition and statistics
According to needs, the index of various city built environment elements in traffic zone, including density, land use multiplicity are extracted
Property, design of street, destination accessibility and apart from public transport facility distance, and carry out built environment element in traffic zone and count；
In addition, also to obtain the traffic generation and traffic attraction of each traffic zone in the research period by basic data processing；
(3) citation form of space Du guest's model
Y=ρ Wy+X β+γ WX+ ε, ε~N (0, σ2In) (1)
In formula, n is traffic zone quantity；Y is the vector of n × 1, indicates that explained variable i.e. certain traffic zone morning peak traffic is raw
At amount or traffic attraction；X is n × k data matrix, represents the various built environment indexs in explanatory variable i.e. certain traffic zone, k is city
Built environment element number；W is Spatial weight matrix, and ρ is the coefficient of Spatial lag dependent variable Wy, and γ is Spatial lag independent variable
The coefficient of WX, β reflect the influence that explanatory variable generates dependent variable y variation, and ε is stochastic error；
Spatial weight matrix W discloses the interaction between space cell, and form is as follows:
Each element in Spatial weight matrix is space weight, and the calculation method of space weight uses anti-distance weighting matrix,
Form is as follows:
(2) effect of space Du guest's model is decomposed
In order to version space Du guest's model in spills-over effects, introduce independent variable to the direct effect of dependent variable, indirect effect and
Gross effect, i.e., the variation of single built environment element associated with traffic generation in any traffic zone or traffic attraction, no
Only this cordon traffic production quantity or traffic attraction are had an impact, at the same also influence indirectly other adjoining areas traffic generation and
Traffic attraction；
For the ease of estimating direct effect and indirect effect, formula (1) is rewritten as following formula:
(In- ρ W) y=ιnα+Xβ+WXθ+ε (4)
Y=(In-ρW)-1ιnα+(In-ρW)-1X(Inβ+Wθ)+(In-ρW)-1ε (5)
Enable (In-ρW)-1=V (W), S (W)=V (W) (Inβ+W θ), then formula (6) are obtained, matrix form is formula (7), the square of parameter effects
Battle array is expressed as formula (8):
Y=V (W) ιnα+S(W)X+V(W)ε (6)
The sum of the elements in a main diagonal is average direct effect divided by n in formula (8)；S in matrixr(W) the sum of all elements are divided by n
Average overall effect；The difference of average overall effect and average direct effect is denoted as average indirect effect；It is as follows:
In above formula, r=1,2,3 ..., k are city built environment element number, ιnIndicate the rank matrix of n × 1, M (k)DirectIt indicates
City built environment element is to the direct effect of traffic generation or traffic attraction in this traffic zone, M (k)IndirectIndicate city
Built environment element is to the indirect effect of traffic generation or traffic attraction in neighbouring traffic zone, M (k)totalIndicate that city is built up
Gross effect of the environmental element to traffic generation in traffic zone or traffic attraction.
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| CN110728305A (en) * | 2019-09-16 | 2020-01-24 | 南京信息工程大学 | Taxi passenger-carrying hot spot region mining method based on grid information entropy clustering algorithm |
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| CN114331058A (en) * | 2021-12-15 | 2022-04-12 | 东南大学 | Method for evaluating influence of built-up environment on traffic running condition |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110728305A (en) * | 2019-09-16 | 2020-01-24 | 南京信息工程大学 | Taxi passenger-carrying hot spot region mining method based on grid information entropy clustering algorithm |
| CN110728305B (en) * | 2019-09-16 | 2024-01-05 | 南京信息工程大学 | Mining method for taxi passenger carrying hot spot area based on grid information entropy clustering algorithm |
| CN111401612A (en) * | 2020-03-06 | 2020-07-10 | 山东省交通规划设计院有限公司 | Highway traffic generation analysis and modeling method under economic new and normal states |
| CN114331058A (en) * | 2021-12-15 | 2022-04-12 | 东南大学 | Method for evaluating influence of built-up environment on traffic running condition |
| CN114331058B (en) * | 2021-12-15 | 2023-04-21 | 东南大学 | Assessment method for influence of built environment on traffic running condition |
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