Dynamic evolution analysis method for researching traffic mode selection in carpooling travel system

Abstract

The invention discloses a dynamic evolution analysis method for researching traffic mode selection in a carpooling travel system, which comprises the following steps: acquiring a travel mode set of travelers, the travel mode set comprising travel modes selectable by travelers with vehicles and travelers without vehicles; calculating the travel cost of different travel modes according to the travel mode set; according to the travel cost, generalized travel cost is calculated, and the generalized travel cost considers the constraint relation between the number of the carpooling passengers and the number of the carpooling drivers; according to the generalized travel cost, obtaining selection probabilities of different travel traffic modes of the traveler; and constructing a co-riding travel day-by-day dynamic evolution model, and obtaining a dynamic evolution process of the traveler day-by-day travel traffic mode selection behavior through the co-riding travel day-by-day dynamic evolution model according to the selection probability. According to the invention, the fluctuation of the traffic flow of different daily travel modes can be simulated, and technical support is provided for traffic planning and management departments to make management decisions related to the carpooling travel.

Description

Technical Field

[0001] The present invention belongs to the technical field of dynamic analysis of carpooling traffic, and in particular to a dynamic evolution analysis method for studying traffic mode selection in a carpooling travel system. Background Art

[0002] As an emerging urban transportation mode, carpooling is favored for its flexibility, low cost, traffic congestion relief and no increase in the number of motor vehicles. It provides urban residents with a new choice besides driving a private car alone or taking public transportation, changes the choice behavior of travelers and the spatial distribution of traffic flow, and poses new challenges and opportunities to the development of urban transportation.

[0003] Although existing studies have established equilibrium models for carpooling users, these models mainly focus on the static equilibrium state of travel mode choice and fail to capture the daily dynamic evolution of travelers' transportation mode choice behavior. In particular, existing models usually assume that a carpooling driver can only carry one passenger at a time, which ignores the actual passenger capacity of the carpooling vehicle and fails to accurately reflect the daily changes in traffic flow of different travel modes. This simplified assumption is inconsistent with the actual situation and limits the application of the model in describing and predicting the dynamics of actual traffic flow. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a dynamic evolution analysis method for studying the choice of transportation mode in a carpooling system, so as to solve the problems existing in the above prior art.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a dynamic evolution analysis method for studying the traffic mode selection in a carpooling system, comprising:

[0006] Acquire a travel mode set of travelers, wherein the travel mode set includes travel modes selectable by travelers with cars and travelers without cars;

[0007] Calculating travel costs of different travel modes according to the travel mode set;

[0008] Calculating a generalized travel cost based on the travel cost, wherein the generalized travel cost takes into account the constraint relationship between the number of passengers in the carpooling trip and the number of drivers in the carpooling trip;

[0009] According to the generalized travel cost, the probability of travelers choosing different travel modes is obtained;

[0010] A daily dynamic evolution model of carpooling is constructed, and the dynamic evolution process of the traveler's daily travel mode selection behavior is obtained through the daily dynamic evolution model of carpooling according to the selection probability.

[0011] Preferably, calculating the travel costs of different travel modes includes:

[0012] Calculate the time and fuel costs of private car travel;

[0013] Calculate the inconvenience cost of ride-sharing and the driver’s compensation income;

[0014] Calculate the running time cost, average waiting time cost, and bus fare for taking the bus.

[0015] Preferably, obtaining the selection probabilities of different travel modes for travelers includes:

[0016] Based on the travel mode of the Logit model, the probability of travelers choosing different travel modes is obtained; wherein the selection probability takes into account the random error term between the travel cost perceived by the traveler and the actual travel cost.

[0017] Preferably, the daily dynamic evolution model of carpooling is a model constructed based on discrete time, and the model allows a carpooling driver to carry multiple carpooling passengers in one trip.

[0018] Preferably, the dynamic evolution analysis method further includes:

[0019] The perceived travel cost in the travel cost is adjusted by a weighted average learning model; wherein, in the weighted average learning model, the perceived travel cost of each travel mode is a linear combination of the actual travel cost of the previous day and the perceived travel cost of the current day.

[0020] In a second aspect, the present invention further provides a dynamic evolution analysis system for studying the traffic mode selection in a carpooling system, comprising:

[0021] A travel mode acquisition module is used to acquire a travel mode set of travelers, wherein the travel mode set includes travel modes that can be selected by travelers with cars and travelers without cars;

[0022] A first calculation module, used for calculating travel costs of different travel modes according to the travel mode set;

[0023] A second calculation module is used to calculate a generalized travel cost according to the travel cost, wherein the generalized travel cost takes into account the constraint relationship between the number of passengers in the carpooling trip and the number of drivers in the carpooling trip;

[0024] A selection probability calculation module is used to obtain the selection probability of different travel modes of the traveler according to the generalized travel cost;

[0025] The model prediction module is used to construct a daily dynamic evolution model of carpooling travel, and obtain the dynamic evolution process of travelers' daily travel mode selection behavior through the daily dynamic evolution model of carpooling travel according to the selection probability.

[0026] In a third aspect, the present invention further discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the first aspect.

[0027] In a fourth aspect, the present invention further discloses a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described in the first aspect when executed by a processor.

[0028] In a fifth aspect, the present invention further discloses a computer program product, including a computer program, which implements the steps of the method described in the first aspect when executed by a processor.

[0029] Compared with the prior art, the present invention has the following advantages and technical effects:

[0030] The present invention provides a dynamic evolution analysis method for studying the choice of transportation mode in a carpooling travel system, comprising: first, obtaining a set of travel modes of travelers, wherein the travel mode set includes travel modes selectable by travelers with cars and travelers without cars; second, calculating the travel costs of different travel modes based on the travel mode set; then, calculating the generalized travel cost based on the travel cost, wherein the generalized travel cost takes into account the constraint relationship between the number of carpooling passengers and the number of carpooling drivers; further, obtaining the selection probability of different travel transportation modes by travelers based on the generalized travel cost; finally, constructing a daily dynamic evolution model of carpooling travel, and obtaining the dynamic evolution process of the traveler's daily travel transportation mode selection behavior through the daily dynamic evolution model of carpooling travel based on the selection probability.

[0031] The present invention can capture the dynamic changes of travelers' transportation modes over a period of time through the daily dynamic evolution model of carpooling, simulate the fluctuations of traffic flow of different daily travel modes, and provide technical support and theoretical basis for traffic planning and management departments to make management decisions related to carpooling. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0033] Figure 1 This is a schematic diagram of a linear single-center city according to an embodiment of the present invention;

[0034] Figure 2 A daily evolution diagram of the traffic flow of the travel mode of non-car travelers in an embodiment of the present invention;

[0035] Figure 3 A daily evolution diagram of traffic flow of different travel modes for car travelers according to an embodiment of the present invention;

[0036] Figure 4 The figure is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0038] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0039] First, the technical terms involved in the following embodiments are explained.

[0040] Carpooling is a shared travel mode. Usually, the carpooling service provider releases travel information in advance. People who have the same travel route choose to take the carpooling service provider's car to share part of the travel cost or help each other for free. Carpooling has many forms, including public welfare carpooling and mutual aid carpooling. Public welfare carpooling does not charge any fees and is completely based on the principle of mutual assistance; while mutual aid carpooling can appropriately share some expenses according to actual conditions, such as fuel costs, tolls, etc.

[0041] Carpooling, also known as hitchhiking, refers to a shared travel mode where people who have the same travel route choose to take a car provided by a carpooling service provider, share part of the travel cost or provide free mutual assistance. Depending on whether there is a charge, carpooling can be divided into two forms: public welfare carpooling and mutual assistance carpooling. Public welfare carpooling does not charge any fees and is completely based on the principle of mutual assistance; mutual assistance carpooling can appropriately share the cost according to the actual situation.

[0042] Carpooling can be applied to a variety of scenarios, including commuting, returning home during holidays, and traveling. For example, long-term carpooling for commuting can allow colleagues who live close to each other to discuss a ride together; long-distance carpooling for returning home during holidays and traveling can find companions and save a lot of money.

[0043] Carpooling is legal, but it should be noted that carpooling in "illegal cars" is not allowed. In order to ensure your own safety and rights, you must choose a regular carpooling platform or carpool with reliable people. In addition, some cities have set up "carpooling lanes" to encourage and guide people to travel in an intensive manner, ensure priority passage for multi-passenger vehicles and buses, maximize the use of road capacity, alleviate traffic congestion and reduce traffic pollution.

[0044] Dynamic evolution analysis is widely used in many fields, including but not limited to:

[0045] Economic field: Study the changing patterns of economic variables over time, such as price and demand.

[0046] Technology theme analysis: In the dynamic evolution analysis of technology themes, the DPL-BMM model is a labeled hybrid model based on the two-term Dirichlet process, which is used to reveal the context of technology evolution.

[0047] Policy implementation: In the study of the generation and dynamic evolution of the "joint efforts and shared management" strategy, the dynamic changes in the policy implementation process and its impact on the implementation effect are analyzed.

[0048] The specific steps or processes of dynamic evolution analysis include:

[0049] Data collection: Collect time series data on relevant variables.

[0050] Time series analysis: Establish a time series model to analyze the changing trajectory of variables over time.

[0051] Statistical correlation analysis: Conduct statistical correlation analysis to determine the correlation between variables.

[0052] Regression analysis: Regression analysis is used to establish the functional relationship between variables.

[0053] Result interpretation: Explain the analysis results and put forward policy recommendations or optimization plans.

[0054] The advantages of dynamic evolution analysis include:

[0055] Comprehensiveness: Consider the interaction of internal and external factors of the system and provide a comprehensive analytical perspective.

[0056] Dynamics: emphasizes the impact of time changes on the system and is suitable for dynamic studies of complex systems.

[0057] Probabilistic: The impact is considered to be probabilistic, providing a more flexible analysis framework.

[0058] Disadvantages of dynamic evolution analysis include:

[0059] High data requirements: A large amount of high-quality time series data is required, and data acquisition and processing are difficult.

[0060] Calculation complexity: Dynamic evolution analysis involves complex statistical and regression analysis, which has a high computational cost.

[0061] Difficulty of interpretation: Interpretation of results may be complex due to the involvement of multiple factors and probability relationships.

[0062] In summary, the dynamic evolution analysis method reveals the law of system change over time through time series analysis and statistical correlation analysis, and is suitable for research and application in multiple fields.

[0063] Aiming at the urban transportation system with the participation of carpooling, this embodiment proposes a carpooling daily dynamic evolution model based on discrete time by analyzing the traveler's travel mode decision-making process, which is used to describe the daily dynamic evolution process of travelers' travel mode choices in the urban transportation system. In the process of model construction, this embodiment relaxes the assumption that "a carpooling driver can only carry one passenger", allowing carpooling drivers to carry multiple passengers at the same time in a trip, thereby making up for the deficiency of existing dynamic evolution models that ignore the passenger capacity of carpooling vehicles. In addition, the model can capture the dynamic changes of travelers' transportation modes over a period of time, simulate the fluctuations of traffic flow of different travel modes on a daily basis, and provide technical support and theoretical basis for transportation planning and management departments to make management decisions related to carpooling.

[0064] This embodiment provides a complete technical solution, such as Figure 4 As shown, the following steps are included:

[0065] Step 1: Definition of available travel modes and related symbols;

[0066] This example considers a linear single-center city with a ride-sharing service (such as Figure 1 As shown in the figure, there is a road and a bus lane, and travelers travel from their residence to their workplace every day by various modes of transportation. These travelers can be divided into two categories according to their vehicle ownership: car owners and carless travelers. Taking into account the car usage habits of car owners, it is believed that there are three travel modes available for car owners: driving alone, traveling as a carpool driver (that is, participating in the carpool travel service as a driver), and traveling as a carpool passenger (that is, participating in the carpool travel service as a passenger). For carless travelers, there are two travel modes available: traveling as a carpool passenger and traveling as a bus passenger. Therefore, there are four travel roles in the urban transportation system with the participation of carpool travel: single driving drivers, carpool drivers, carpool passengers, and bus passengers.

[0067] In order to distinguish between car-owning and car-free travelers, variables and parameters related to car-owning travelers are marked with superscript 1, while variables and parameters related to car-free travelers are marked with superscript 0. In order to simplify the representation, variables or parameters related to the four travel roles (single driver, carpool driver, carpool passenger, and bus passenger) are represented by subscripts v, d, p, and u. In order to better represent the set of travel roles that can be selected by car-owning and car-free travelers, this embodiment introduces H for representation. Therefore, the set of travel roles that can be selected by car-owning and car-free travelers is represented as H 1 = {v, d, p} and H 0 = {p,u}. The symbol i is introduced to represent the travel role chosen by the traveler. In the city, there are q 1 Car owners and q 0 There are 1,000 people without cars traveling from their place of residence to their place of work. The number of people with cars who choose travel role i is The number of car-free travelers who choose travel role i is For example represents the number of car-owning travelers who choose to share a ride, Represents the number of car-free travelers who choose to share rides.

[0068] Step 2: Calculation of travel costs for different travel modes;

[0069] Step 2-1: Cost composition analysis of different travel modes;

[0070] The first mode of travel, basic cost calculation of private car:

[0071] 1) Travel time by private car

[0072] The vehicles on road a can be divided into two categories: carpooling vehicles and single-driver vehicles. The number of carpooling vehicles is equal to the number of carpooling drivers on road a. The number of single-driver vehicles is equal to the number of single-driver drivers on road a. Therefore, the traffic flow on road a can be calculated as the sum of the traffic flow of carpool drivers and the traffic flow of single drivers. This embodiment uses the travel time function of the Bureau of Public Roads (BPR) of the United States, which is widely used in academia, to calculate the travel time of a private car on road a. The calculation formula is as follows:

[0073]

[0074] in represents the free flow travel time on road a; c aRepresents the traffic capacity of road a; α, β are non-negative parameters in the BPR function.

[0075] By introducing the time value parameter, i.e., the monetary amount corresponding to a unit of time, this embodiment can convert travel time into travel time costs. However, considering that the time value of travelers with cars and those without cars is different, in order to ensure that the travel costs of the two types of travelers can be more accurately reflected, this embodiment introduces two time value parameters η1 and η0, where η1 represents the time value of travelers with cars and η0 represents the time value of travelers without cars. Therefore, this embodiment can obtain the travel time costs of travelers with cars and those without cars when they use private cars to travel:

[0076] <1> Travel time costs for car users

[0077]

[0078] <2> Travel time costs for people without a car

[0079]

[0080] 2) Fuel costs for private cars

[0081] In addition to the travel time cost, private cars also have costs due to fuel consumption. The calculation formula for private car fuel costs is: τ f l, where τ f is the fuel cost per unit distance during driving, and l is the length of road a.

[0082] The second mode of travel, calculation of additional costs for carpooling:

[0083] 1) Inconvenience cost

[0084] Unlike travelers who choose to drive alone or take the bus, travelers who choose to share a car, whether they are carpool passengers or carpool drivers, need to endure the inconvenience of sharing a car with others, such as the discomfort caused by sharing the same space with strangers, the extra detour time caused by picking up and dropping off passengers, etc. This inconvenience is closely related to the average number of passengers per carpool vehicle, and as the number of passengers in the vehicle increases, the inconvenience cost felt by the driver and each passenger will also increase. Therefore, the calculation of the inconvenience cost is: where τ u Indicates the inconvenience fee when sharing a ride with a stranger. represents the average number of passengers in any car on road a, that is, the ratio of the number of carpooling passengers to the number of carpooling drivers. They respectively represent the flow number of people with cars serving as carpool passengers and the flow number of people without cars serving as carpool passengers.

[0085] 2) Compensation income for ride-sharing drivers

[0086] Each carpool passenger needs to pay the carpool driver a fare τ according to the pre-determined pricing rules a , to compensate the carpool driver for providing travel services. Therefore, the compensation income received by the carpool driver is equal to the sum of the fares paid by all the passengers he carries, and the calculation formula is:

[0087] 3) Rideshare driver’s passenger fees

[0088] Each carpool passenger needs to pay the carpool driver a fare τ a , so the fare for the shared passenger is τ a .

[0089] The third mode of travel, the cost calculation of taking the bus:

[0090] 1) Bus operating hours

[0091] Since there is a bus lane between the residence and the workplace, the running time of the bus can be considered to be fixed at t b .

[0092] 2) Average waiting time

[0093] Due to the limited capacity of buses, passengers traveling by bus will have extra waiting time when waiting for the bus at the departure station. The bus runs on the dedicated line b according to the scheduled schedule, and the headway is fixed at h b Assuming that the passenger's departure station is the starting node of the dedicated line b, and that passengers arrive at the station evenly and take the bus in a first-come, first-served order, the average waiting time is a piecewise function of the passenger flow. Using linear approximation technology, the average waiting time can be approximately calculated as: in, represents the number of passengers taking the bus, c b Indicates the capacity of the bus.

[0094] 3) Bus fares

[0095] Passengers riding the bus need to pay a fixed fare τ b .

[0096] Step 2-2: Calculation of travel costs for different travel modes;

[0097] According to the above analysis of the cost composition of different travel modes, this embodiment can calculate the travel costs corresponding to different travel modes selected by car travelers and non-car travelers as follows:

[0098] (1) For a car owner who chooses to drive alone, his travel cost is composed of travel time and fuel cost, namely:

[0099]

[0100] (2) For car owners who choose to be carpool drivers, their travel costs include travel time, fuel costs, and inconvenience costs minus compensation income, that is:

[0101]

[0102] (3) For car owners who choose to travel as a carpool passenger, their travel cost is composed of travel time cost, inconvenience cost and fare, namely:

[0103]

[0104] (4) For those who do not have a car and choose to travel as a carpool passenger, their travel cost is composed of travel time cost, inconvenience cost and fare, namely:

[0105]

[0106] (5) For those who do not have a car and choose to be bus passengers, their travel cost is composed of the bus running time cost, the average waiting time cost and the bus fare, that is:

[0107]

[0108] Step 3: Calculation of generalized travel cost;

[0109] In a transportation system with carpooling services, there is a constraint relationship between the number of carpooling passengers and the number of carpooling drivers:

[0110]

[0111] Where Cap represents the upper limit of the number of carpool passengers that a carpool vehicle can carry. Formula (9) indicates that the number of carpool passengers should be greater than or equal to the number of carpool drivers, and formula (10) indicates that the number of carpool passengers should be less than or equal to the upper limit of the passenger capacity that all carpool drivers can provide.

[0112] Assume that travelers choose their travel mode based on the cost calculated in step 2. As rational individuals, they usually tend to choose the travel mode with the lowest cost. However, in this case, the number of carpool drivers and carpool passengers may not meet the above constraints. This means that in the carpool system, the supply side (the number of seats provided by carpool drivers) is higher than the demand side (the number of carpool passengers), or the demand side is lower than the supply side. Therefore, in order to ensure that the constraint relationship between the number of carpool passengers and the number of carpool drivers is satisfied, it is necessary to consider the impact of these constraints on the cost of different travel modes. This impact can be reflected by introducing Lagrange multipliers associated with the constraints. Let ν + For The associated Lagrange multiplier represents the reduction in fare due to the reduction in the number of carpool passengers. ν - For The related Lagrange multiplier represents the increase in fare due to the increase in carpooling passengers. The conditions satisfied by the two multipliers are as follows:

[0113]

[0114] Therefore, this embodiment can obtain the generalized travel costs corresponding to different travel modes selected by car travelers and non-car travelers, which are calculated as follows:

[0115] (1) For a car owner who chooses to drive alone, his generalized travel cost is:

[0116]

[0117] (2) For car owners who choose to be carpool drivers, their generalized travel cost is:

[0118]

[0119] (3) For car owners who choose to travel as carpool passengers, their generalized travel cost is:

[0120]

[0121] (4) For those who do not have a car and choose to travel as a carpool passenger, their generalized travel cost is:

[0122]

[0123] (5) For those who do not have a car and choose to be bus passengers, their generalized travel cost is:

[0124]

[0125] Step 4: Probability formula for travel mode selection based on the Logit model;

[0126] Due to the limited travel information obtained, travelers can only estimate but cannot accurately predict the actual travel costs of various travel modes. Therefore, there is always a random error term between the travel cost perceived by travelers and the actual travel cost. The Logit model is used to describe the travel mode selection behavior of travelers under the principle of random user equilibrium. Therefore, the probabilities of car travelers and non-car travelers choosing travel mode i are:

[0127]

[0128] Where P i 1 represents the probability that a car traveler chooses transportation mode i, P i 0 represents the probability that a traveler without a car chooses travel mode i, and θ is the perception error parameter.

[0129] Step 5: Construct a daily dynamic evolution model of carpooling;

[0130] Travelers need to choose a mode of transportation every day, so the dynamic system that explores travelers' daily travel mode selection behavior is discrete. In addition, rationality usually drives travelers to choose the mode of transportation with the lowest perceived travel cost. They will use the available traffic information to estimate the cost of various travel modes and update these estimates based on previous travel experience.

[0131] In order to describe the adjustment process of perceived travel cost, this embodiment adopts a widely used weighted average learning model, in which the perceived travel cost of each mode of travel is a linear combination of the actual travel cost of the previous day and the perceived travel cost of the current day. However, in a transportation network with carpooling services, there is a quantity constraint relationship between carpooling passengers and carpooling drivers. Therefore, the flow of various types of travelers in the system on the tth day will be regulated by the carpooling market, and this regulation is achieved through the multiplier ν corresponding to the constraint relationship. + , ν - reflect.

[0132] As an additional implementation method, the multiplier introduced by the relationship between the number of drivers and passengers is unique, and the established daily dynamic evolution model of carpooling can be expressed by the following equation, which can be represented by the adjustment process of perceived travel costs.

[0133]

[0134] Among them, x (t) is the matrix composed of the flow of various types of travelers on day t, is the matrix of the perceived travel costs of various modes of transportation for travelers on day t, δ is the weight factor required by the learning model, which is used to indicate the influence of previous travel experience on the current cost estimate. The smaller its value, the more the traveler relies on traffic information when making decisions; and the larger its value, the more habitual the traveler's choice behavior is. C(x (t-1) ) is the matrix of actual travel costs corresponding to various modes of transportation on day t-1, q is the matrix consisting of the demand of car travelers and non-car travelers, is a matrix consisting of the probabilities of choosing various modes of transportation. ν (t) is a matrix of multipliers corresponding to the quantity constraints between carpool passengers and carpool drivers, v (t) =(v + , v - );Φ(ν (t) ) is the matrix of functions that the multipliers need to satisfy,

[0135] As an additional implementation, it can be shown mathematically that the multiplier ν + , ν - Uniqueness in the dynamic evolution model. The nonlinear complementarity problem in formula (22) 0≤ν (t) ⊥Φ(ν (t) )≥0 can be equivalent to the variational inequality problem Φ(ν (t) *)(ν (t) -ν (t) *)≥0, where ν (t) * indicates ν at equilibrium (t) Since it can be mathematically proved that Φ(ν (t) ) is about ν (t) A monotonic function of It can be determined by 0≤ν (t) ⊥Φ(ν (t) )≥0 and Get the only ν (t) =(v + ,ν - ) and x (t) Therefore, the established dynamic evolution model can ensure that the multiplier ν (t) =(v + ,ν - ) and x (t)This ensures that the established daily evolution model of carpooling can simulate the dynamic evolution of travelers’ daily travel mode selection behavior and simulate the fluctuations of daily traffic flow of different travel modes.

[0136] Step 6: Programming solution;

[0137] MATLAB (R2023a) is used to program the contents involved in the above steps, and GAMS (version win6425.1.3) is used to program the flow distribution part (formulas (1)-(21)). After completing the respective programming of MATLAB and GAMS, interactive programming between the two is performed to achieve the purpose of solving the problem. Specifically, first, in MATLAB, this embodiment will set the demand number of car travelers and non-car travelers, the parameters required by the model of this embodiment, and the feasible initial flow (i.e., the initial number of people who choose each type of travel mode) that satisfies the constraints (9)-(10). Then, MATLAB will solve the perceived travel cost of each type of travel mode. Subsequently, through the interaction between GAMS and MATLAB, these data (feasible initial flow and perceived travel cost of each type of travel mode) will be automatically passed to GAMS. In GAMS, by using the built-in solver PATH, this embodiment can solve the flow distribution part and obtain the actual number of people who choose each type of travel mode (i.e., the actual flow corresponding to each type of travel mode) and the multiplier. After the solution is completed, the solution results of GAMS (the actual flow and multipliers corresponding to various modes of travel) will be automatically returned to MATLAB for the next calculation of the perceived cost of various modes of travel (i.e. the perceived travel cost of various modes of travel within the next day). Through this interaction, the calculated perceived travel cost data of various modes of travel will be automatically passed to GAMS to solve the actual flow corresponding to various modes of travel within the next day. This process will continue to cycle until the actual flow of various modes of travel on all days is solved. Finally, MATLAB will draw a curve of the evolution of the actual flow corresponding to various modes of travel over time.

[0138] This embodiment provides an example based on a linear single-center city (such as Figure 1 As shown in Figure 1, there is a corridor between the residence and the workplace. The corridor includes a bus lane road b and a parallel road a. The capacity and free flow time of the two roads are: a =1.5×10^5Veh / h,c b =3.5×10^4Veh / h, The headway of the bus is: h b= 5min. The daily dynamic evolution model proposed in the present embodiment is used to explore the travel mode selection behavior of travelers in the city within one month (30 days).

[0139] Other parameters required by the model are detailed in Table 1. The demand of car travelers and non-car travelers are: q 1 =1.5×10^5 peopleq 0 =1.5×10^5 people. Assume that the initial flow of various types of travelers is That is, the number of car-owning travelers who choose to be single drivers, carpool drivers, and carpool passengers is 50*10^3; the number of car-free travelers who choose to be carpool passengers and take the bus are 1×10^5 and 5×10^4 respectively.

[0140] Table 1

[0141] parameter Value α,β 0.15，4 <![CDATA[η1,η0]]> 40$ / h,20$ / h <![CDATA[τ f ]]> 0.1$ / km l 0.1$ / km <![CDATA[τ u ]]> $2 τ $8 <![CDATA[τ b ]]> $2 θ 0.1 δ 0.2

[0142] According to the above steps, GAMS and MATLAB interactive programming are performed to obtain the actual traffic flow evolution over time corresponding to the various travel modes chosen by non-car travelers in the urban transportation system. Figure 2 The actual traffic flow corresponding to various travel modes chosen by car travelers evolves over time. Figure 3 . Figure 2 and Figure 3 It not only shows the actual daily traffic flow of various modes of travel, but also clearly reflects the evolution of the actual daily traffic flow of various modes of travel over time. In addition, the evolution diagram can also capture the stable state that the system eventually reaches.

[0143] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A dynamic evolution analysis method for studying the choice of transportation mode in a carpooling system, characterized in that: The following steps are involved: Acquire a travel mode set of travelers, wherein the travel mode set includes travel modes selectable by travelers with cars and travelers without cars; Calculating travel costs of different travel modes according to the travel mode set; Calculating a generalized travel cost based on the travel cost, wherein the generalized travel cost takes into account the constraint relationship between the number of passengers in the carpooling trip and the number of drivers in the carpooling trip; According to the generalized travel cost, the probability of travelers choosing different travel modes is obtained; A daily dynamic evolution model of carpooling is constructed, and the dynamic evolution process of the traveler's daily travel mode selection behavior is obtained through the daily dynamic evolution model of carpooling according to the selection probability.

2. The dynamic evolution analysis method for studying the choice of transportation mode in a carpooling system according to claim 1 is characterized in that: Calculating travel costs for different travel modes includes: Calculate the time and fuel costs of private car travel; Calculate the inconvenience cost of ride-sharing and the driver’s compensation income; Calculate the running time cost, average waiting time cost, and bus fare for taking the bus.

3. The dynamic evolution analysis method for studying the choice of transportation mode in a carpooling system according to claim 1 is characterized in that: Obtaining the probability of travelers choosing different travel modes includes: Based on the travel mode of the Logit model, the probability of travelers choosing different travel modes is obtained; wherein the selection probability takes into account the random error term between the travel cost perceived by the traveler and the actual travel cost.

4. The dynamic evolution analysis method for studying the choice of transportation mode in a carpooling system according to claim 1 is characterized in that: The daily dynamic evolution model of carpooling is a model constructed based on discrete time, and the model allows a carpooling driver to carry multiple carpooling passengers in one trip.

5. The dynamic evolution analysis method for studying the choice of transportation mode in a carpooling system according to claim 1 is characterized in that: Also includes: The perceived travel cost in the travel cost is adjusted by a weighted average learning model; wherein, in the weighted average learning model, the perceived travel cost of each travel mode is a linear combination of the actual travel cost of the previous day and the perceived travel cost of the current day.

6. A dynamic evolution analysis system for studying the choice of transportation mode in a carpooling system, characterized in that: include: A travel mode acquisition module is used to acquire a travel mode set of travelers, wherein the travel mode set includes travel modes that can be selected by travelers with cars and travelers without cars; A first calculation module, used for calculating travel costs of different travel modes according to the travel mode set; A second calculation module is used to calculate a generalized travel cost according to the travel cost, wherein the generalized travel cost takes into account the constraint relationship between the number of passengers in the carpooling trip and the number of drivers in the carpooling trip; A selection probability calculation module is used to obtain the selection probability of different travel modes of the traveler according to the generalized travel cost; The model prediction module is used to construct a daily dynamic evolution model of carpooling travel, and obtain the dynamic evolution process of travelers' daily travel mode selection behavior through the daily dynamic evolution model of carpooling travel according to the selection probability.

7. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to claims 1 to 5 are implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to claims 1 to 5 are implemented.

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