CN113611105A - Urban traffic travel demand total quantity prediction method - Google Patents

Urban traffic travel demand total quantity prediction method Download PDF

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CN113611105A
CN113611105A CN202110776910.9A CN202110776910A CN113611105A CN 113611105 A CN113611105 A CN 113611105A CN 202110776910 A CN202110776910 A CN 202110776910A CN 113611105 A CN113611105 A CN 113611105A
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华雪东
王炜
谢文杰
王建
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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

Urban traffic travel demand total quantity prediction method
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:
Figure BDA0003155819920000021
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:
Figure BDA0003155819920000022
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:
Figure BDA0003155819920000031
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:
Figure BDA0003155819920000032
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:
Figure BDA0003155819920000051
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:
Figure BDA0003155819920000052
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:
Figure BDA0003155819920000053
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:
Figure BDA0003155819920000054
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:
Figure BDA0003155819920000071
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:
Figure FDA0003155819910000011
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:
Figure FDA0003155819910000021
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:
Figure FDA0003155819910000022
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:
Figure FDA0003155819910000023
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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CN116227737B (en) * 2023-05-04 2023-08-18 中铁第四勘察设计院集团有限公司 Regional passenger traffic prediction method, system and equipment based on iterative optimization model
CN117808169A (en) * 2024-01-17 2024-04-02 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
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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