CN112632374A - Resident travel mode selection analysis method considering customized bus - Google Patents
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Abstract
The invention discloses a resident travel mode selection analysis method considering customized buses, belonging to the fields of traffic economy, traffic management and control and comprising the following steps: (1) determining a travel mode selection set; (2) analyzing the influence factors of the travel mode selection: determining influence factors selected by a resident trip mode, wherein the influence factors are divided into three categories, namely resident personal attributes, trip behavior attributes and trip scheme attributes; (3) data collection and preprocessing: collecting resident personal attribute data, travel behavior attribute data and travel scheme attribute data, and screening the collected data; (4) and establishing a travel mode selection model considering the customized bus, and fitting the data by using a maximum likelihood estimation method to obtain utility models of different travel modes. The method can effectively simulate the traffic mode division process, obtain different trip mode sharing rates and provide an important basis for the use and subsequent development of the customized bus.
Description
Technical Field
The invention belongs to the fields of traffic economy, traffic management and control, and particularly relates to a resident travel mode selection analysis method considering customized buses.
Background
The contradiction between the increasing travel demand and the limited road resources and the contradiction between the environmental protection and the environmental pollution caused by a large number of trips are two problems which are urgently needed to be solved in the development of current traffic planning and management. The public transport has large passenger capacity, occupies less road resources, has low energy consumption for completing the transportation task of the same order of magnitude, and has important positions in the aspects of balancing the road resources and the road capacity, improving the resource utilization rate, reducing the emission of greenhouse gases and the like. Improving the public trip proportion is a great target of the current traffic industry development.
The service level of the traffic mode is an important reason for influencing the division ratio of the urban trip traffic mode, the quantitative influence of different influencing factors on the utility of the trip mode is mastered, and the traffic mode sharing rate adjusting method has an important guiding function on adjusting a traffic management scheme to change the trip mode sharing rate. A common research method for resident travel mode selection analysis is to establish a discrete selection model, substitute survey data for fitting, obtain travel utility functions of various traffic travel modes, and obtain travel mode sharing rate. However, in the traditional survey, the existing transportation modes are combined into a selection set, and the survey result often only reflects the selection behavior of residents on the existing transportation modes. The concept of the customized bus is explained in a questionnaire by considering the selection and analysis of the resident travel mode of the novel traffic mode, characteristics corresponding to different travel schemes under different travel situations are estimated in the scene setting in the SP (State preference) survey, and the selection behaviors made by residents facing the novel traffic mode of the customized bus and the existing traffic mode are obtained. The analysis result of the resident travel mode selection model considering the novel traffic mode has important reference significance for inputting the scale of the customized bus and the scheme adjustment in the operation strategy.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems, the invention provides a resident travel mode selection analysis method considering the customized public transport, which is used for quantitatively analyzing factors influencing the selection of the resident travel modes under the traffic environment with the existing new and old travel modes. The method comprehensively extracts personal attributes and trip scheme attributes, and utilizes a method (also called a hybrid Model or a Mix Model) combining a General Logit Model and a Conditional Logit Model to carry out maximum likelihood estimation so as to obtain the quantitative influence degree of influence factors on traffic mode division.
The technical scheme is as follows: in order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows: a resident travel mode selection analysis method considering customized buses comprises the following steps:
(1) determining a travel mode selection set: selecting a taxi according to an operation strategy of a customized bus, a pricing mode of the customized bus and the type of a service passenger, and selecting concentrated travel mode options by using a conventional bus and the customized bus as travel schemes;
(2) analyzing the influence factors of the travel mode selection: determining influence factors selected by a resident trip mode, wherein the influence factors are divided into three categories, namely resident personal attributes, trip behavior attributes and trip scheme attributes;
(3) data collection and preprocessing: collecting resident personal attribute data, travel behavior attribute data and travel scheme attribute data, and screening the collected data;
(4) selecting and modeling in consideration of travel modes of the customized bus: the independent variable is expressed as an expression (1), the coefficient vector is defined as an expression (2), the trip mode utility function containing the personal attribute and the trip plan attribute of the resident is expressed as an expression (3), and the selection probability of the resident on the trip mode is calculated by an expression (4);
x in the formula (1)ijRefers to the influence factors influencing the selection of the travel mode j by the resident i,
referring to the kth individual attribute variable of the resident i,
referring to the h attribute variable of the travel plan j; in the formula (2) BijThe coefficient of the influence factors influencing the selection of the travel mode j by the resident i in the utility function corresponding to the travel mode j is shown, wherein
A variable coefficient referring to a kth individual attribute variable of the resident i,
variable coefficient, ASC, referring to the h-th attribute variable of travel plan jijA constant term influencing the selection of the travel mode j by the resident i, and Xij1 in (1) corresponds; u in formula (3)ijThe utility obtained by selecting the travel mode j by the resident i is referred to; p in formula (4)ijRefers to the probability that the resident i selects the travel mode j.
Further, the step (3) collects resident personal attribute data: including gender, age, whether or not to have a driving license, whether or not to have a bus/subway IC card, actual driving experience, occupation, academic calendar, personal monthly income, family type, amount of owned family private cars, and average monthly income of family; collecting travel behavior attribute data: the method comprises two attributes of trip purpose and trip distance; collecting travel scheme attribute data: including whether there is a seat, whether a transfer is required, waiting time before boarding, walking distance to a boarding point, arrival time deviation, travel cost, and travel time.
Further, the step (3) performs SP survey on residents, performs situation configuration by adopting an orthogonal design method, and performs screening processing on the collected data, wherein the method comprises the following steps:
1) rejecting data with the filling time of the questionnaire less than a certain value;
2) removing data with the same scene selection, namely the same intended travel mode;
3) eliminating data that the personal income, the family income and the vehicle ownership do not accord with each other;
4) and removing data which do not accord with common traffic modes, driving experiences and vehicle ownership.
Has the advantages that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
according to the resident trip mode selection analysis method considering the customized bus, the new and old trip modes are used as a trip selection set, control factors needing to be considered in the process of putting the novel traffic mode customized bus into use are researched, and the individual attributes and the trip mode attributes of residents influencing the resident trip modes are researched. Compared with the traditional trip mode selection modeling, the set trip mode selection set not only comprises taxis and net appointment buses in the existing trip modes, but also comprises customized buses with different service levels, and the quantitative influence effect of influence factors influencing the utility of the trip modes in three trip modes with similar service levels is explored through the scenario problem design. The research result has important significance for building a customized bus trip system. The invention adopts a mixed Model (Mix Model) combining a General Logit Model and a Conditional Logit Model, can fully reflect the common influence result of the personal attributes and the travel mode attributes of residents on the traffic mode division, and describe the influence effect of factors and factors considered when travelers make decisions such as commuting, entertainment, short distance, long distance travel and the like.
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FIG. 1 is a schematic flow diagram of the process of the present invention.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The resident trip mode selection and analysis method considering the customized bus comprises the following steps of:
(1) determining a travel mode selection set: according to the operation strategy of the customized bus, the pricing mode of the customized bus and the type of service passengers, a taxi is selected, and the conventional bus and the customized bus are used as travel mode options in a concentrated travel scheme selection. The customized bus sets two types of customized buses with different service levels and prices according to attributes: a customized bus-1 and a customized bus-2.
(2) Analyzing the influence factors of the travel mode selection: determining influence factors selected by the resident trip mode, wherein the influence factors are divided into three categories, namely resident personal attributes, trip behavior attributes and trip scheme attributes.
(3) Data collection and preprocessing: in the embodiment, through relevant literature research, factors influencing resident travel mode selection are summarized and summarized, and problems in questionnaires are designed according to types of the influencing factors. And carrying out SP survey on residents, carrying out situation configuration by adopting an orthogonal design method, reducing the number of situations under multi-factor and multi-level conditions, and selecting the most appropriate situation combination for questionnaire survey.
Collecting personal attribute data of residents: including gender, age, whether or not to have a driving license, whether or not to have a bus/subway IC card, actual driving experience, occupation, academic calendar, personal monthly income, family type, amount of owned family private cars, and average monthly income of family; wherein the age range is 18-25 years old, 26-35 years old, 36-50 years old, more than 50 years old; occupation is classified into 5 categories: students, full-time work, free occupation, no industry or retirement, and part-time work; the academic calendar is classified into 5 types: high school and below, major, this family, major, and more than major; the average monthly income of an individual is divided into 5 grades: 3000 yuan below, 3000-6000 yuan, 6000-1 ten thousand yuan, 1 ten thousand-1.5 ten thousand yuan above and 1.5 ten thousand yuan above; the average monthly income of families is divided into 5 grades: below 5000 yuan, 5000-1 ten thousand yuan, 1-1.5 ten thousand yuan, 1.5 ten thousand-2 ten thousand yuan, more than 2 ten thousand yuan; the possession of the family private car is classified into 3 grades: the owned quantity is 0, own 1, own 2 or more.
Collecting travel behavior attribute data: the travel distance and the travel purpose (such as office study and leisure shopping) are included; the travel distances are divided into short-haul travel (below 5km), medium-haul travel (5-15km), and long-haul travel (above 15 km). Collecting travel scheme attribute data: including whether there is a seat, whether a transfer is needed, waiting time before getting on, walking distance to a riding point, arrival time deviation, trip cost, and travel time; the transfer scheme is divided into: the transfer is not needed, 1 time and 2 times are needed; the waiting time range before getting on the bus is divided into: 5-10 min; 10-15 min; 10-15 min; more than 15 min; walking distance to riding spot: walking for 500-1km, walking for more than 1 km; the arrival time deviation is divided into: as early as 2 minutes, as early as 5 minutes, as early as 6 minutes, as early as 8 minutes, as early as 10 minutes.
And (3) screening the collected data by the following method:
1) rejecting data with the filling time of the questionnaire less than a certain value;
2) removing data with the same scene selection, namely the same intended travel mode;
3) eliminating data that the personal income, the family income and the vehicle ownership do not accord with each other;
4) and removing data which do not accord with common traffic modes, driving experiences and vehicle ownership.
Examination of reliability and validity of questionnaires: before questionnaire data analysis is carried out, reliability and effectiveness analysis is carried out on questionnaire collected data, reliability is measured by using a clone Bach reliability coefficient (Cronbach alpha) method, the alpha coefficient is 0.719, so that the reliability is high, and questionnaire survey results can be used for follow-up research. The questionnaire data were subjected to validity test to obtain KMO values of 0.631 and greater than 0.6, which means that the data were valid by Bartlett sphere test. In conclusion, the conclusion obtained based on the questionnaire data is real and reliable in data and convincing.
(4) Selecting and modeling in consideration of travel modes of the customized bus: in the multi-item Logit Model, if the independent variables are all attributes related to the decision maker, the corresponding multi-item Logit Model is called a Generalized Logit Model, and if the independent variables are all attributes related to the scheme, the corresponding multi-item Logit Model is called a Conditional Logit Model. In the travel mode selection modeling process considering the customized bus, the decision process is influenced by the resident correlation attribute and the scheme correlation attribute, so a Mixed Model (Mixed Model) combining the Generalized Logit Model and the Conditional Logit Model is used in the invention.
Induction factors influencing an intended travel mode are induced, personal factors and secondary travel scheme attribute factors are expressed as an expression (1), an influence coefficient vector corresponding to the induction factors is defined as an expression (2), a travel mode utility function containing resident personal attributes and travel scheme attributes is expressed as an expression (3), and the selection probability of residents on the travel mode is calculated by an expression (4);
x in the formula (1)ijRefers to the influence factors influencing the selection of the travel mode j by the resident i,
referring to the kth individual attribute variable of the resident i,
referring to the h attribute variable of the travel plan j; in the formula (2) BijThe coefficient of the influence factors influencing the selection of the travel mode j by the resident i in the utility function corresponding to the travel mode j is shown, wherein
A variable coefficient referring to a kth individual attribute variable of the resident i,
variable coefficient, ASC, referring to the h-th attribute variable of travel plan jijA constant term influencing the selection of the travel mode j by the resident i, and Xij1 in (1) corresponds; u in formula (3)ijThe utility obtained by selecting the travel mode j by the resident i is referred to; p in formula (4)ijRefers to the probability that the resident i selects the travel mode j.
Performing data conversion on questionnaire data, including processing of dummy variables, obtaining a data fitting result by utilizing maximum likelihood estimation, and obtaining a data fitting degree R2The number of the particles is 0.321, which is between 0.3 and 0.6, so that the fitting degree is good, and the rationality of selection of influencing factors is reflected. Under the condition that other conditions are not changed, if an influence factor changes x, the proportion of the trip mode before and after the influence factor changes is p and p ', respectively, and the relation of p and p' is as shown in the formula (5). In the invention, the x values corresponding to continuous variables in the significant influence factors of the travel modes in the travel mode selection set are shown in the table 1. And the influence of the continuous variable on different travel modes is different. The influence of the change of the dummy variable on the proportion occupied by the travel mode also follows equation (5), except that when the influence of the dummy variable is studied, p in the equation represents the proportion occupied by the travel mode in the reference case.
TABLE 1 continuous variables with significant impact on trip patterns
Table 2 dummy variables having significant influence on each trip mode
The above examples are only preferred embodiments of the present invention, it should be noted that: it will be apparent to those skilled in the art that various modifications and equivalents can be made without departing from the spirit of the invention, and it is intended that all such modifications and equivalents fall within the scope of the invention as defined in the claims.
Claims (3)
1. A resident travel mode selection analysis method considering customized buses is characterized in that: the method comprises the following steps:
(1) determining a travel mode selection set: selecting a taxi according to an operation strategy of a customized bus, a pricing mode of the customized bus and the type of a service passenger, and selecting concentrated travel mode options by using a conventional bus and the customized bus as travel schemes;
(2) analyzing the influence factors of the travel mode selection: determining influence factors selected by a resident trip mode, wherein the influence factors are divided into three categories, namely resident personal attributes, trip behavior attributes and trip scheme attributes;
(3) data collection and preprocessing: collecting resident personal attribute data, travel behavior attribute data and travel scheme attribute data, and screening the collected data;
(4) selecting and modeling in consideration of travel modes of the customized bus: the independent variable is expressed as an expression (1), the coefficient vector is defined as an expression (2), the trip mode utility function containing the personal attribute and the trip plan attribute of the resident is expressed as an expression (3), and the selection probability of the resident on the trip mode is calculated by an expression (4);
x in the formula (1)ijRefers to the influence factors influencing the selection of the travel mode j by the resident i,
referring to the kth individual attribute variable of the resident i,
referring to the h attribute variable of the travel plan j; in the formula (2) BijThe coefficient of the influence factors influencing the selection of the travel mode j by the resident i in the utility function corresponding to the travel mode j is shown, wherein
A variable coefficient referring to a kth individual attribute variable of the resident i,
variable coefficient, ASC, referring to the h-th attribute variable of travel plan jijA constant term influencing the selection of the travel mode j by the resident i, and Xij1 in (1) corresponds; u in formula (3)ijRefers to the result obtained by resident i selecting travel mode jUtility; p in formula (4)ijRefers to the probability that the resident i selects the travel mode j.
2. The resident travel mode selection analysis method considering the customized bus according to claim 1, characterized in that: the step (3) collects the personal attribute data of residents: including gender, age, whether or not to have a driving license, whether or not to have a bus/subway IC card, actual driving experience, occupation, academic calendar, personal monthly income, family type, amount of owned family private cars, and average monthly income of family; collecting travel behavior attribute data: the method comprises two attributes of trip purpose and trip distance; collecting travel scheme attribute data: including whether there is a seat, whether a transfer is required, waiting time before boarding, walking distance to a boarding point, arrival time deviation, travel cost, and travel time.
3. The resident travel mode selection analysis method considering the customized bus according to claim 1 or 2, characterized in that: the step (3) is to carry out SP survey on residents, adopt an orthogonal design method to carry out situation configuration, and carry out screening processing on the collected data, and the method comprises the following steps:
1) rejecting data with the time for filling the questionnaire less than a certain value;
2) removing the data which are always the same in scene selection, namely in the intended travel mode selection;
3) eliminating data that the personal income, the family income and the vehicle ownership do not accord with each other;
4) and removing data which do not accord with common traffic modes, driving experiences and vehicle ownership.
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| CN116628527A (en) * | 2023-07-24 | 2023-08-22 | 浙江大学 | Design method and system for integrated travel strategy |
| CN116628527B (en) * | 2023-07-24 | 2023-11-10 | 浙江大学 | Design method and system for integrated travel strategy |
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