CN113611105A - Urban traffic travel demand total quantity prediction method - Google Patents
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Abstract
The invention discloses a method for predicting the total demand of urban traffic trips, which comprises the following steps: collecting relevant data of urban traffic travel demands; constructing a traffic system co-evolution model; calibrating parameters in the traffic system co-evolution model by using the acquired data; and substituting the calibrated parameters into the traffic system co-evolution model to predict the total urban traffic travel demand. The invention is suitable for cities with any structural characteristics, and provides basis and support for urban traffic planning, traffic development policy making and traffic network design.
Description
Technical Field
The invention belongs to the field of urban traffic planning, and particularly relates to a method for predicting total demand of urban traffic travel.
Background
The prediction of the demand of the trip is one of the core contents in the traffic planning. The formulation of traffic development policies, traffic network design and scheme evaluation are all closely related to the traffic travel demand prediction. Traffic generation forecast is the first stage of traffic demand four-stage forecast, and is one of the most basic parts in traffic demand analysis work. The prediction of the traffic demand total amount is used as important constraint data of traffic generation prediction, and the prediction precision of the prediction can directly influence the precision of a subsequent prediction stage and even the whole prediction process, so that the method for predicting the traffic demand total amount has higher practical research value.
Disclosure of Invention
In order to solve the technical problems mentioned in the background art, the invention provides a method for predicting the total quantity of urban traffic travel demands.
In order to achieve the technical purpose, the technical scheme of the invention is as follows:
a total demand forecasting method for urban transportation travel comprises the following steps:
(1) collecting relevant data of urban traffic travel demands;
(2) constructing a traffic system co-evolution model;
(3) calibrating parameters in the traffic system co-evolution model by using the data acquired in the step (1);
(4) and substituting the calibrated parameters into the traffic system co-evolution model to predict the total urban traffic travel demand.
Further, in the step (1), the urban transportation travel demand related data includes the consumption level data f of residents of the past yeariMaximum value F achievable by future development of the consumption level of the residents0Historical city GDP data giMaximum G that urban GDP can reach in future development0Number n of urban households over the yearsiMaximum value N that can be reached in the future of the number of family members0Number of urban population p of the yeariMaximum P that the city population can reach in future development0Urban road area c of the yeariThe maximum value that the urban road area can reach in future development and the total quantity q of the historical traffic trip demandsiMaximum Q that the total amount of travel demand can reach in future development0(ii) a Where the subscript i represents the year of the data.
Further, in the step (2), firstly, an economic self-evolution model, a population self-evolution model and a traffic self-evolution model are respectively constructed, and then a traffic system co-evolution model is constructed according to the three self-evolution models;
the construction of the economic self-evolution model comprises the following steps:
wherein f and g represent consumption level data of residents and city GDP data of a certain year, t represents time, alpha11、α12、θ1、β1Is a parameter to be calibrated;
the population self-evolution model is as follows:
wherein n and p represent the number of urban households and urban population in a certain year, t represents time, alpha21、α22、θ2、β2Is a parameter to be calibrated;
the traffic self-evolution model is as follows:
wherein c and q represent urban road area and total amount of travel demand in a year, t represents time, alpha31、α32、θ3、β3Is a parameter to be calibrated;
the traffic system co-evolution model comprises the following steps:
wherein, mu12、μ13、μ21、μ23、μ31、μ32Is the parameter to be calibrated.
Further, in the step (3), a regression analysis method is adopted to calibrate the building of the economic self-evolution model, the population self-evolution model, the traffic self-evolution model and the traffic system co-evolution model.
Further, in step (4), the total amount of urban traffic demand q for the j-th yearjForecasting by adopting a method of forecasting year by year, and the specific process is as follows:
(401) searching for the largest year k less than j to satisfy gk、pk、qkAre all known data;
(402) bringing the data of the k year into a traffic system co-evolution model with calibrated parameters, and predicting to obtain data g of the k +1 yeark+1、pk+1、qk+1And the step is circulated until the total urban traffic demand q of the j year is obtainedj。
Adopt the beneficial effect that above-mentioned technical scheme brought:
the method comprehensively considers various factors influencing urban traffic travel demands, constructs a traffic system co-evolution model, can iteratively predict the total urban traffic travel demand through the model, is suitable for cities with any structural characteristics, and provides basis and support for urban traffic planning, traffic development policy formulation and traffic network design.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
The invention designs a method for predicting the total demand of urban transportation travel, which comprises the following steps as shown in figure 1:
step 1: collecting relevant data of urban traffic travel demands;
step 2: constructing a traffic system co-evolution model;
and step 3: calibrating parameters in the traffic system co-evolution model by using the data acquired in the step 1;
and 4, step 4: and substituting the calibrated parameters into the traffic system co-evolution model to predict the total urban traffic travel demand.
Preferably, in step 1, the data related to urban transportation travel demand includes historical consumption level data f of residentsiMaximum value F achievable by future development of the consumption level of the residents0Historical city GDP data giMaximum G that urban GDP can reach in future development0Number n of urban households over the yearsiMaximum value N that can be reached in the future of the number of family members0Number of urban population p of the yeariMaximum P that the city population can reach in future development0Urban road area c of the yeariThe maximum value that the urban road area can reach in future development and the total quantity q of the historical traffic trip demandsiMaximum Q that the total amount of travel demand can reach in future development0(ii) a Where the subscript i represents the year of the data.
Preferably, in the step 2, an economic self-evolution model, a population self-evolution model and a traffic self-evolution model are respectively constructed, and then a traffic system co-evolution model is constructed according to the three self-evolution models;
the construction of the economic self-evolution model comprises the following steps:
wherein f and g represent consumption level data of residents and city GDP data of a certain year, t represents time, alpha11、α12、θ1、β1Is a parameter to be calibrated;
the population self-evolution model is as follows:
wherein n and p represent the number of urban households and urban population in a year, and t representsTime, alpha21、α22、θ2、β2Is a parameter to be calibrated;
the traffic self-evolution model is as follows:
wherein c and q represent urban road area and total amount of travel demand in a year, t represents time, alpha31、α32、θ3、β3Is a parameter to be calibrated;
the traffic system co-evolution model comprises the following steps:
wherein, mu12、μ13、μ21、μ23、μ31、μ32Is the parameter to be calibrated.
Preferably, in step 3, a regression analysis method is adopted to calibrate the building of the economic self-evolution model, the population self-evolution model, the traffic self-evolution model and the traffic system co-evolution model.
Preferably, in step 4, the total amount of urban traffic demand q for the j-th yearjForecasting by adopting a method of forecasting year by year, and the specific process is as follows:
401. searching for the largest year k less than j to satisfy gk、pk、qkAre all known data;
402. bringing the data of the k year into a traffic system co-evolution model with calibrated parameters, and predicting to obtain data g of the k +1 yeark+1、pk+1、qk+1And the step is circulated until the total urban traffic demand q of the j year is obtainedj。
In this embodiment, a city O is selected as a test object, and the total amount of travel demand of the city is quantitatively analyzed and predicted. The GDP, the consumption level of residents, the population of a city, the number of family members, the road area of the city and the total amount of the travel demand data of the city O over the year are as follows 1:
TABLE 1
Chronological order | GDP | Consumption level of residents | Population of human | Number of family | Travel demand | Area of road |
1 | 435.3435 | 5011 | 317.12 | 99.93 | 262510 | 807 |
2 | 536.5443 | 5519 | 321.92 | 102.16 | 293370 | 1147 |
3 | 578.1757 | 6166 | 325.98 | 106.95 | 333030 | 1231 |
4 | 625.3391 | 6808 | 330.29 | 110.84 | 379780 | 1338 |
5 | 669.855 | 7109 | 335.86 | 112.08 | 451640 | 1559 |
6 | 777.5256 | 7498 | 341.52 | 145.23 | 519380 | 2222 |
7 | 1007.366 | 7812 | 439.79 | 152.6 | 663270 | 2941 |
8 | 1144.295 | 8565 | 452.57 | 159.69 | 830000 | 4602 |
9 | 1351.034 | 9472 | 464.54 | 168.31 | 648060 | 4780 |
10 | 1517.656 | 10329 | 482.07 | 174.54 | 806700 | 5320 |
11 | 1923.486 | 11981 | 497.15 | 178.26 | 849810 | 4775 |
12 | 2091.944 | 13527 | 502.71 | 183.3 | 1194690 | 4919 |
13 | 2738.393 | 14447 | 510.15 | 190.14 | 1082710 | 5379 |
14 | 3139.862 | 16260 | 520.86 | 199.88 | 1198610 | 6289 |
15 | 3932.469 | 18968 | 535.15 | 205.37 | 1164000 | 6460 |
16 | 4782.558 | 20759 | 544.8 | 210.62 | 1455150 | 6715 |
17 | 5731.731 | 22583 | 554.2 | 216.55 | 1580060 | 7441 |
18 | 6481.551 | 24508 | 564.9 | 226.01 | 1646410 | 7444 |
19 | 7332.654 | 26413 | 581.6 | 271.28 | 1808530 | 7404 |
20 | 8460.016 | 28600 | 698.14 | 301.59 | 1729500 | 7710 |
Obtaining a calibrated traffic system co-evolution model according to the data:
the embodiments are only for illustrating the technical idea of the present invention, and the technical idea of the present invention is not limited thereto, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the scope of the present invention.
Claims (5)
1. A total demand forecasting method for urban transportation travel is characterized by comprising the following steps:
(1) collecting relevant data of urban traffic travel demands;
(2) constructing a traffic system co-evolution model;
(3) calibrating parameters in the traffic system co-evolution model by using the data acquired in the step (1);
(4) and substituting the calibrated parameters into the traffic system co-evolution model to predict the total urban traffic travel demand.
2. The method for predicting the total urban transportation travel demand according to claim 1, wherein in step (1), the data related to urban transportation travel demand includes historical data f of consumption level of residentsiMaximum value F achievable by future development of the consumption level of the residents0Historical city GDP data giMaximum G that urban GDP can reach in future development0Number n of urban households over the yearsiMaximum value N that can be reached in the future of the number of family members0Number of urban population p of the yeariMaximum P that the city population can reach in future development0Urban road area c of the yeariThe maximum value that the urban road area can reach in future development and the total quantity q of the historical traffic trip demandsiMaximum Q that the total amount of travel demand can reach in future development0(ii) a Where the subscript i represents the year of the data.
3. The urban transportation travel demand total quantity prediction method according to claim 2, characterized in that in step (2), an economic self-evolution model, a population self-evolution model and a traffic self-evolution model are respectively constructed, and then a traffic system co-evolution model is constructed according to the three self-evolution models;
the construction of the economic self-evolution model comprises the following steps:
wherein f and g represent consumption level data of residents and city GDP data of a certain year, t represents time, alpha11、α12、θ1、β1Is a parameter to be calibrated;
the population self-evolution model is as follows:
wherein n and p represent the number of urban households and urban population in a certain year, t represents time, alpha21、α22、θ2、β2Is a parameter to be calibrated;
the traffic self-evolution model is as follows:
wherein c and q represent urban road area and total amount of travel demand in a year, t represents time, alpha31、α32、θ3、β3Is a parameter to be calibrated;
the traffic system co-evolution model comprises the following steps:
wherein, mu12、μ13、μ21、μ23、μ31、μ32Is the parameter to be calibrated.
4. The urban transportation travel demand total quantity prediction method according to claim 3, characterized in that in step (3), a regression analysis method is adopted to calibrate the building of an economic self-evolution model, a population self-evolution model, a traffic self-evolution model and a traffic system co-evolution model.
5. The method for predicting the total urban transportation travel demand according to claim 3, wherein in step (4), the total urban transportation demand q for the j-th yearjForecasting by adopting a method of forecasting year by year, and the specific process is as follows:
(401) searching for the largest year k less than j to satisfy gk、pk、qkAre all known data;
(402) bringing the data of the k year into a traffic system co-evolution model with calibrated parameters, and predicting to obtain data g of the k +1 yeark+1、pk+1、qk+1And the step is circulated until the total urban traffic demand q of the j year is obtainedj。
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CN117808169B (en) * | 2024-01-17 | 2024-05-07 | 中国科学院地理科学与资源研究所 | Urban commute data prediction method, terminal device and storage medium |
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