Travel appointment strategy optimization method and system considering travel incentive measures

Abstract

The invention discloses a reservation travel strategy optimization method and system considering travel incentive measures, belongs to the field of urban traffic management and control, introduces subsidy incentive and default punishment mechanisms for users, and constructs a multi-objective optimization model based on multiple agents. On one hand, benefits of managers and travelers are comprehensively considered, a multi-objective optimization model is constructed, and a travel incentive scheme oriented to social benefit maximization is optimized and designed. And on the other hand, in a multi-dimensional decision variable environment of individual travel, based on user reservation travel information, road network real-time traffic situation research and judgment, road supply and demand matching status and other multi-source traffic information, a multi-agent-based dynamic traffic simulation model is constructed, heterogeneous behaviors of travelers in a reservation system are explored, and the traffic quality of the travelers is improved. The optimal travel reservation mode implementation scheme is determined, and suggestions can be provided for managers and decision makers to formulate more scientific, effective and perfect travel reservation modes.

Description

Technical Field

[0001] The present invention relates to the field of urban traffic management and control, and in particular to a method and system for optimizing a reservation travel strategy taking travel incentives into consideration. Background Art

[0002] In daily life, the advantage of making appointments instead of offline crowded queues and avoiding inefficient and disorderly queues has been verified in many scenarios such as hospital appointments, restaurant appointments, and parking appointments. With the successful application of appointment travel practice projects for traffic scenarios such as bus appointments, subway appointments, and scenic spot surrounding passages, it is shown that the existing technical conditions are sufficient to support a certain scale of appointment travel. However, compared with the above-mentioned appointment practices, the urban road traffic system is more random and open, and the demands for commuting and going to school in the city are more rigid and easily affected by occasional factors, making it difficult to control individual travel time. In addition, the existing research on appointment travel modes still has the following deficiencies:

[0003] (1) It focuses on static reservation scheme design and strategy effectiveness analysis, and lacks consideration of the synergy of urban multimodal transportation and dynamic traffic information of the road network. Existing research on the evaluation of the effect of reservation travel is mainly based on static traffic allocation models, ignoring the synergy of urban multimodal transportation, resulting in deviations in the comprehensive evaluation of policy effects. In addition, dynamic traffic information of the road network is of great value for in-depth understanding of the service level of various travel modes and insight into users' travel choice habits. Therefore, it is urgent to consider the dynamic traffic information of the road network in the multimodal transportation framework and accurately evaluate the implementation effect of the reservation travel mode.

[0004] (2) The travel choices of multiple types of users are homogenized, and the multidimensional decision variable environment of individual travel is not considered. Heterogeneous travelers have different sensitivities to travel time and cost. Ignoring the heterogeneity of travelers will affect the effectiveness of the strategy evaluation model. In addition, the reservation travel model emphasizes the interaction with travelers. However, existing studies lack comprehensive consideration of the dynamics of the road traffic system, the diversity of users' travel time choices, and multidimensional decision variables such as reservation information feedback.

[0005] (3) The means of travel services are one-sided, lacking coordination with travel incentives, and unable to provide feedback on travel guidance at the macro level. The means of travel services in existing travel reservation model research are one-sided, ignoring the potential impact of supporting travel incentives and punishments on users' travel decisions, making it difficult for users to feel the obvious benefits when making travel decisions, thus affecting the overall effect of the strategy.

[0006] In order to solve the above problems, the present invention aims to provide a method and system for optimizing a reservation travel strategy taking travel incentives into consideration. Summary of the invention

[0007] In order to make up for the deficiencies of the existing technical problems, the purpose of the present invention is to provide a reservation travel strategy optimization method and system that takes travel incentives into consideration. Subsidy incentives and default penalty mechanisms for users are introduced to construct a multi-agent based multi-objective optimization (AMO) model. On the one hand, the interests of managers and travelers are comprehensively considered, a multi-objective optimization model is constructed, and the travel incentive plan for maximizing social benefits is optimized and designed. On the other hand, in the multi-dimensional decision variable environment of individual travel, based on multi-source traffic information such as user reservation travel information, real-time traffic situation analysis of road networks, and the current status of road supply and demand matching, a dynamic traffic simulation model based on multi-agents is constructed to explore the heterogeneous behaviors of travelers in the reservation system and determine the best reservation travel mode implementation plan, which is helpful to provide suggestions for managers and decision makers to formulate more scientific, effective and complete reservation travel modes.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for optimizing a reservation travel strategy considering travel incentives includes the following steps:

[0010] (1) Clarify the implementation mechanism of the reservation travel strategy on frequently congested sections, build a multi-agent multi-objective optimization model by comprehensively considering the social income of the reservation travel mode and the travel cost of users, and output a specific reward and punishment plan;

[0011] (2) Construct a multi-agent-based dynamic traffic simulation model to determine the departure time and travel cost. The dynamic traffic simulation model includes a pre-trip decision model and a travel decision model. In the pre-trip decision model, individual travelers will make decisions on departure time and travel mode based on reward and punishment schemes, personal preferences, time costs, and economic budgets. The travel decision model defines the basic driving rules and related environmental parameters for each vehicle agent in the traffic network, including a following model, an intersection decision model, and traffic signal control parameters.

[0012] (3) The simulated annealing algorithm is used to solve the multi-objective optimization model based on multi-agent, and the Sioux Falls network is used as the study area to verify the effectiveness of the reservation travel mode.

[0013] The implementation mechanism of the travel reservation strategy in the present invention on frequently congested sections:

[0014] Capacity thresholds are set for frequently congested sections of road. During the implementation of the reservation travel strategy, motor vehicles entering the reservation sections must make a reservation in advance. After a successful reservation, the reservation system reserves the time slot for the traveler to use the reserved road. For users who participate in the reservation but do not obtain the right of way, the reservation system provides them with alternative travel routes, and encourages them to choose public transportation and provides them with corresponding discounts. Administrative penalties are imposed on travelers who breach the contract, and fines are used to establish a special subsidy fund for public transportation travel, providing additional subsidies for travelers who fail to make a reservation and choose public transportation.

[0015] The multi-objective optimization model of multiple agents in the present invention is:

[0016] max Z=λ1Z1-λ2Z2 (3a)

[0017] 0≤S m ≤S (3b)

[0018] 0≤f≤f (3c)

[0019] Among them, λ1 is the weight of the revenue target; λ2 is the weight of the cost target; S and F represent the upper limits of subsidies and fines;

[0020] Total revenue of the transport system Z1:

[0021]

[0022] in, represents the system revenue when the nth traveler chooses transportation mode m; represents the subsidy for the nth traveler to choose transportation mode m;

[0023] Traveler's travel cost Z2:

[0024]

[0025] Among them, v t represents the time value of the nth traveler in transit; t n is the travel time of the nth traveler; It represents the travel cost of the nth traveler who chooses transportation mode m, including fuel cost and public transportation cost.

[0026] In the present invention, the travel modes include three single travel modes of private car, bus and subway, and two combined transfer modes of private car changing to bus and private car changing to subway.

[0027] In the present invention, the pre-trip decision model has the following decision steps:

[0028] ① Calculate the generalized cost of each travel mode without considering travel subsidies and determine the initial traffic state of the road network

[0029] The generalized cost of each travel mode without considering travel subsidies is calculated in monetary terms. The generalized cost without considering travel subsidies includes the time cost of arriving early or late, the time cost in transit, and travel expenses.

[0030] Travelers choose the appropriate travel mode based on the original travel time and the generalized cost of each travel mode without considering travel subsidies. Travelers who choose buses and subways follow fixed routes, while travelers who choose private cars participate in the travel decision-making model in the road network. The initial travel route and travel mode selection of each traveler are counted, and the average travel time of the road section at each time interval is counted as the initial traffic state of the road network at that moment.

[0031] ②Users’ travel decisions after implementing the reservation travel mode

[0032] First, the travel decision-making model is used to determine whether the traveler's initial path and shortest path pass through the reserved section. If they do not pass through the reserved section, the reservation decision is not considered. If they pass through the reserved section, the travel time of the section in the initial state is used to calculate the time when the traveler arrives at the entrance of the reserved section, and this time t1 is recorded. It is determined whether there are any remaining reservation places at time t1. If so, the right of way for this time period is directly reserved; if not, whether there is any spare capacity in the time periods before and after t1 is searched in turn; if there is spare capacity, the generalized travel cost of each mode of transportation after travel subsidies is reconsidered, and the departure time and travel mode are re-determined.

[0033] In the present invention, in the pre-trip decision model, the calculation formula of the generalized cost without considering the travel subsidy is as follows:

[0034]

[0035] in, represents the generalized cost of the nth traveler choosing travel mode m at time t; t w The working hours specified by the commuter unit; t is the time in transit; v e 、v l and v t They represent the early arrival time value, late arrival time value and in-transit time value respectively; η is a 0-1 variable. When t w -(t+t t )≥0, that is, the traveler arrives at the destination on time or ahead of schedule, η=1, when t w -(t+t t )<0, that is, when the traveler is late, η=0; m is the travel expenses; n (m) is the probability that the nth traveler chooses travel mode m; θ1 is the dispersion coefficient of transportation mode selection.

[0036] In the present invention, the calculation method of the generalized travel cost of each mode of transportation after travel subsidy is as follows:

[0037] (a) Cost of booking a car trip

[0038]

[0039] u re_car =h*u r (5d)

[0040]

[0041] in, is the generalized cost of the nth traveler choosing to make a reservation at time t; is the original departure time; when the traveler departs h time windows earlier or later, the new departure time is t t is the travel time, i.e. the sum of the travel time of each road section; u re_car Subsidize users in the reservation travel mode, u r is the subsidy for changing a travel time window; P n (t) is the probability that the nth traveler chooses departure time t; θ2 is the travel time selection dispersion coefficient;

[0042] (b) Cost of unreserved car travel

[0043]

[0044] Users who do not participate in the reservation will travel according to the original departure time, so the departure time is t t，k is the travel time of path k that does not pass through the reserved road section; non-reserved travel cars do not participate in the subsidy and are therefore not included in the calculation;

[0045] (c) Public transport travel costs

[0046]

[0047] Q bus ≤60 (7c)

[0048] For users who cannot travel as planned due to full reservations and need to switch to public transportation, the departure time will not be adjusted, so the departure time remains The bus travel time is divided into segment travel time and waiting time t i ;Q bus Limit the number of passengers per bus u bus Subsidize public transportation;

[0049] (d) Subway travel costs

[0050]

[0051] Q metro ≤600 (8c)

[0052] Similar to buses, travelers who choose to travel by subway do not adjust their departure time; the time in transit is divided into segment travel time and waiting time t i ;Q metro The number of passengers per subway car is limited; metro Subsidize subway travel;

[0053] (e) Travel costs under two combined transfer modes: private car to bus and private car to subway

[0054] The generalized travel cost of switching from a private car to a bus or from a private car to a subway is the combination of the generalized costs of the above corresponding travel modes; in addition, when a car switches, there is a parking cost P a 、Parking subsidy P+R 、Parking lot capacity limit Q park ≤500;

[0055] The probability P that the nth traveler chooses path k under transportation mode m is n (k|m) is:

[0056]

[0057] Among them, θ3 is the path selection dispersion coefficient;

[0058] In addition, the probability that the nth traveler chooses travel mode m is P n (m):

[0059]

[0060] in, The expected minimum cost for the nth traveler using transportation mode m.

[0061] In the present invention, the following model in the travel decision model adopts the General Motor model to describe the interaction between vehicles.

[0062] In the present invention, the intersection decision model determines the next driving direction based on the currently acquired real-time but incomplete information.

[0063] The probability that a traveler chooses a certain road segment is closely related to the total cost of the remaining routes from that road segment to the destination, including the shortest travel distance of the new route, the waiting time of vehicles at the intersection due to traffic flow or signal control, and the additional cost of the specific route; the probability that a vehicle chooses to travel from intersection i to intersection j is as follows:

[0064]

[0065] Where: C ij(t) is the total cost from intersection i to j; d ij is the distance from intersection i to j; d iJ is the shortest travel distance from intersection j to destination J after selecting the new route; y ij(t) is the average speed of the distance; w ij(t) is the waiting time of vehicles in the intersection; f is the fine for non-reservation users who illegally enter the reserved section.

[0066] A travel reservation system, characterized by comprising a computing system, a reservation system and a management system;

[0067] The computing system determines the road sections and time periods of road network congestion based on historical traffic trends, considers the time-space supply and demand balance of each road section at different time periods, determines the capacity threshold of each reservation time period, and outputs a travel plan for the user;

[0068] The reservation system is responsible for the management of reservation requests. The reservation system displays a reservation travel timetable to travelers, which shows the remaining capacity of the road section that needs to be reserved within a specific time period. Travelers make reservations through the reservation system before traveling. After the reservation is successful, the reservation system reserves the time slot for the traveler to use the reserved road. For users who participate in the reservation but fail to obtain the right of way, the reservation system provides them with alternative travel routes, encourages them to choose public transportation, and provides them with corresponding discounts. Administrative penalties are imposed on travelers who violate the contract, and fines are used to establish a special subsidy fund for public transportation travel, providing additional subsidies for travelers who fail to make reservations and choose public transportation;

[0069] The management system is responsible for maintaining the order of reserved travel and coordinating traffic services; transmitting road traffic control information by setting corresponding traffic signs and markings in the upstream area of ​​the reserved section; using vehicle license plate recognition technology to verify the performance of reserved vehicles and implement a corresponding reward and punishment system.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] 1. This invention introduces a subsidy incentive and default penalty mechanism for users, and constructs a multi-objective optimization model based on multi-agents. On the one hand, the interests of managers and travelers are comprehensively considered, and a multi-objective optimization model is constructed to optimize the design of travel incentive schemes for maximizing social benefits.

[0072] 2. In the multi-dimensional decision variable environment of individual travel, based on multi-source traffic information such as heterogeneous user portraits, user travel reservation information, real-time traffic situation analysis of the road network, and the current status of road supply and demand matching, a multi-agent-based multi-modal dynamic traffic simulation model was constructed to simulate the heterogeneous behaviors of travelers in the reservation system.

[0073] 3. Through case analysis, this paper reveals the potential impact of the reward and punishment mechanism on travelers' behavioral decisions, provides new ideas and methods for urban traffic congestion management, and promotes the sustainable development of urban transportation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 The present invention is a flow chart of the method.

[0075] Figure 2 This is a schematic diagram of the travel reservation implementation mechanism of the present invention.

[0076] Figure 3 This is a schematic diagram of the road network structure in the research area of ​​the present invention.

[0077] Figure 4 A schematic diagram of a public transportation route is studied for the present invention.

[0078] Figure 5 This is a schematic diagram of the saturation change of a road section in scenario 1 according to the present invention.

[0079] Figure 6 This is a schematic diagram of the saturation change of a road section in scenario 2 according to the present invention.

[0080] Figure 7 This is a schematic diagram of the saturation change of a road section in scenario 3 according to the present invention.

[0081] Figure 8 It is a schematic diagram of the change of travel mode of heterogeneous users in different scenarios of the present invention.

[0082] Fig. 9 Schematic diagram of decision changes for booking user travel time under scenarios 2 and 3 of the present invention. DETAILED DESCRIPTION

[0083] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0084] according to Figure 1-9 The present invention is described in detail.

[0085] (I) The system for booking travel taking travel incentives into consideration according to the present invention comprises at least three subsystems: a computing system, a booking system and a management system.

[0086] The calculation system is responsible for formulating reasonable travel plans. The system determines the congested sections and time periods of the road network based on historical traffic trends, considers the spatial and temporal supply and demand balance of each section at different times, determines the capacity threshold of each reservation period, and outputs scientific and reasonable travel plans for users.

[0087] The reservation system is responsible for the management of reservation requests. The reservation system shows travelers a reservation travel schedule, which shows the remaining capacity of the road section that needs to be reserved within a specific time period, so that travelers can reasonably plan their travel time and travel route. Travelers make reservations through devices such as smartphones and the Internet before traveling. After the reservation is successful, the reservation system reserves the time slot for travelers to use the reserved road. For users who participate in the reservation but do not obtain the right of way, the reservation system provides them with alternative travel routes, encourages them to choose public transportation, and provides them with corresponding discounts. In the end, this group will have three travel modes: public transportation, detours along alternative routes, and illegal passage through the reserved section. The reward and punishment mechanism of the tradable right of way strategy is used to regulate the benefits of travelers, impose administrative penalties on travelers who violate the contract, use fines to establish a special subsidy fund for public transportation, and provide additional subsidies for travelers who choose public transportation because of failed reservations, so as to enhance the attractiveness of public transportation.

[40] , guiding users who failed to make reservations to transfer their travel needs to public transportation.

[0088] The management system is responsible for maintaining the order of scheduled travel and coordinating traffic services. It transmits road traffic control information by setting up corresponding traffic signs and markings in the upstream area of ​​the scheduled road section. It uses vehicle license plate recognition technology to verify the performance of the scheduled vehicles and implement a corresponding reward and punishment system.

[0089] (II) The present invention is a method for optimizing a reservation travel strategy taking travel incentives into consideration, and the flow chart is as follows: Figure 1 As shown, the specific steps include:

[0090] Step 1: According to the implementation steps and mechanisms of the appointment travel strategy (such as Figure 2 As shown in the figure, during the implementation of the reservation travel strategy, motor vehicles entering the reserved road section must make reservations in advance. Travelers who are inclined to make reservations will make reservations in advance on the corresponding platform before traveling, and can only travel on the reserved road after the reservation is successful. If the reserved road section has reached the total number of reservations, the traveler must choose an alternative route or take public transportation. In other words, reserved vehicles can travel on the reserved road section while ordinary vehicles cannot travel on the reserved road.

[0091] The multi-objective optimization model outputs a specific reward and punishment plan by comprehensively considering the social income of the reservation travel mode and the travel cost of users. Among them, social income takes into account multiple factors such as public transportation fare revenue, default fines and strategic subsidies, while travel costs mainly focus on users' travel expenses and travel time.

[0092] (1) Total revenue of the transportation system

[0093]

[0094] in, Represents the system revenue when the nth traveler chooses transportation mode m, that is, public transportation fares or violation fines. Represents the subsidy for the nth traveler to choose transportation mode m.

[0095] (2) Travel costs of travelers

[0096]

[0097] Among them, v t represents the time value of the nth traveler in transit; t n is the travel time of the nth traveler; It represents the travel cost of the nth traveler who chooses transportation mode m, including fuel cost and public transportation cost.

[0098] Therefore, the multi-objective optimization model is:

[0099] max Z=λ1Z1-λ2Z2 (3a)

[0100] 0≤S m ≤S (3b)

[0101] 0≤f≤F (3c)

[0102] Among them, λ1 is the weight of the income target; λ2 is the weight of the cost target; S m is the user subsidy (yuan); f is the penalty for defaulting users (yuan); S and F represent the upper limits of subsidies and penalties. is the weight of the income target; λ2 is the weight of the cost target; S and F represent the upper limits of subsidies and penalties

[0103] Step 2: In the pre-trip decision model, individual travelers will make decisions on departure time and travel mode based on factors such as reward and punishment schemes, personal preferences, time costs, and economic budgets. These decisions are affected by individual heterogeneity, and different travelers will have different preferences and constraints, resulting in complex decision-making behaviors. The decision-making steps are as follows:

[0104] ① Calculate the generalized cost of each travel mode and determine the initial traffic status of the road network.

[0105] The road network at this stage includes three single travel modes (private car, bus and subway) and two combined transfer modes (private car to bus (C&B) and private car to subway (C&M)). The morning rush hour has strict commuting time requirements for commuters, that is, they must arrive at the destination before the time specified by the unit. Therefore, whether arriving early or late, a certain time cost will be incurred. The present invention measures the generalized cost of each travel mode in monetary terms, covering the time cost of arriving early or late, the time cost in transit and the travel fee, as shown in Formula 4.

[0106]

[0107]

[0108] in, represents the generalized cost of the nth traveler choosing travel mode m at time t; t w The working hours specified by the commuter unit; t is the time in transit; v e 、v l and v t They represent the early arrival time value, late arrival time value and in-transit time value respectively; η is a 0-1 variable. When t w -(t+t t )≥0, that is, the traveler arrives at the destination on time or ahead of schedule, η=1, when t w -(t+t t )<0, that is, when the traveler is late, η=0. m is the travel cost; l is the fuel cost per unit mileage; d a is the length of section a; f bus is the bus fare; f metro is the subway fare; P a is the parking fee in the “P+R” mode; P n (m) is the probability that the nth traveler chooses travel mode m; θ1 is the dispersion coefficient of transportation mode selection.

[0109] Travelers choose the appropriate travel mode based on the original travel time and the generalized cost of each mode. Travelers who choose buses and subways follow fixed routes, while travelers who choose private cars participate in the travel decision-making model in the road network (detailed introduction later), and count the initial travel routes and travel mode choices of each traveler. With a time interval of 10 minutes, the average travel time of the road section in each time interval is counted as the initial traffic state of the road network at that time.

[0110] ②Users’ travel decisions after implementing the reservation travel mode

[0111] After the implementation of the reservation travel mode, the user's travel decision-making process is relatively complicated. This process involves the user's adjustment of travel time and how to use the reservation system. The specific steps are as follows:

[0112] Set the total number of reservations for a section: The core concept of the reservation travel mode is to improve the matching efficiency of traffic supply and demand by adjusting the travel time of individuals. Setting a reasonable total number of reservations for a section can maximize the total number of traffic on the section while ensuring smooth traffic on the road, avoiding wasting road resources. Set the peak hour traffic capacity of the section as the total number of reservations for the section per hour. At the same time, with every 10 minutes as a scheduled time window, the total number of reservations for the section is evenly distributed to each time window.

[0113] Reservation decision time limit: The present invention assumes that for the nth traveler, if his original travel time is t, then the traveler needs to make a reservation decision at least 30 minutes before time t. For example, if the original travel time of traveler n=1 is 07:12, then he needs to decide whether to make a reservation at 06:42.

[0114] Determine and decide whether a reservation is needed: First, determine whether the traveler's initial path and shortest path pass through the reservation section. If not, the reservation decision is not considered. If it passes through the reservation section, use the travel time of the section in the initial state to calculate the time when the traveler arrives at the entrance of the reservation section, and record this time t1. Determine whether there are any remaining reservation places at time t1. If so, directly reserve the right of passage for this time period without adjusting the original departure time. Accordingly, the number of reservation places for this time period is reduced by 1.

[0115] Adjust travel time: If there are no available reservations at time t1, the traveler needs to consider changing the travel time (i.e., traveling earlier or later). Since the traveler expects the departure adjustment time to be as small as possible, the search is conducted in sequence to see if there is any available capacity in the time periods before and after t1. If there is available capacity, consider re-determining the departure time and travel mode under the reservation subsidy mechanism.

[0116] After taking travel subsidies into account, the generalized travel costs of each mode of transportation are as follows:

[0117] (a) Cost of booking a car trip

[0118]

[0119] u re_car =h*u r (5d)

[0120]

[0121] in, is the generalized cost of the nth traveler choosing to make a reservation at time t; is the original departure time; when the traveler departs h time windows earlier or later, the new departure time is t t is the travel time, i.e. the sum of the travel time of each road section; t a is the travel time of road section a; u re_car Subsidize users in the reservation travel mode, u r is the subsidy for changing a travel time window; P n (t) is the probability that the nth traveler chooses departure time t; θ2 is the dispersion coefficient of travel time selection.

[0122] (b) Cost of unreserved car travel

[0123]

[0124] Users who do not participate in the reservation will travel according to the original departure time, so the departure time is t t，k is the travel time of path k that does not pass through the reserved section; t k，a is the travel time of section a in path k. Non-booked cars are not subsidized and are therefore not included in the calculation.

[0125] (c) Public transport travel costs

[0126]

[0127] Q bus ≤60 (7c)

[0128] For users who cannot travel as planned due to full reservations and need to switch to public transportation, the departure time will not be adjusted, so the departure time remains The bus travel time is divided into segment travel time and waiting time t i ;Q bus The number of passengers per bus is limited; bus Subsidize public transportation; bus For bus fare.

[0129] (d) Subway travel costs

[0130]

[0131] Q metro ≤600 (8c)

[0132] Similar to buses, travelers who choose to travel by subway do not adjust their departure time; the time in transit is divided into segment travel time and waiting time t i ;f metro is the subway fare; Q metro The number of passengers per subway car is limited; metro Subsidy for subway travel.

[0133] (e) Travel costs under the “P+R” model

[0134] The generalized travel costs of C&B and C&M are the combination of the generalized costs of the above corresponding travel modes. In addition, when the car transfers, there is a parking cost P a 、Parking subsidy P+R 、Parking lot capacity limit Q park ≤500.

[0135] In summary, the probability P of the nth traveler choosing path k under transportation mode m is n (k|m) is:

[0136]

[0137] Among them, θ3 is the path selection dispersion coefficient;

[0138] In addition, the probability that the nth traveler chooses travel mode m is P n (m)::

[0139]

[0140] in, The expected minimum cost for the nth traveler using transportation mode m.

[0141] Step 3: The on-the-go decision model defines the basic driving rules and related environmental parameters for each vehicle agent in the traffic network, including the following model, intersection decision model, and traffic signal control parameters. Therefore, within the on-the-go decision model, individual travelers will adjust their route decisions based on real-time traffic information and reward and punishment schemes. Due to the dynamic changes in traffic conditions and incomplete information, travelers will pursue real-time maximum utility rather than the global optimal solution.

[0142] (a) Car-following model

[0143] The car-following model is a traffic flow model that describes how the following vehicle reacts to the behavior of the preceding vehicle, and is used to simulate the interaction between vehicles and the driver's decision-making process in real traffic. The present invention uses the General Motor (GM) model to describe the interaction between vehicles.

[0144] (b) Traffic signal control parameters

[0145] Traffic signal control is an important part of traffic simulation, which can have a significant impact on the simulation results, making them closer to the actual traffic situation. Therefore, the present invention simplifies the signal parameters of the intersection: the green light time for going straight and turning left is 60 seconds and 45 seconds respectively, and the yellow light time is 3 seconds.

[0146] (c) Intersection Decision Model

[0147] In a real driving environment, travelers usually rely on smartphones or electronic information boards on the road section to obtain real-time road traffic information, and adjust their travel routes based on this real-time information to pursue maximum utility. Vehicles only decide and adjust their driving direction at intersections. This means that when a vehicle arrives at an intersection, it will decide the next driving direction based on the real-time but incomplete information currently obtained. The probability that a traveler chooses a certain road section is closely related to the total cost of the remaining routes from that road section to the destination, including the shortest travel distance of the new route, the waiting time of the vehicle in queues at the intersection due to traffic flow or signal control, and the additional cost of a specific route. The probability that a vehicle chooses to travel from intersection i to intersection j is as follows:

[0148]

[0149] Where: C ij(t) is the total cost from intersection i to j; d ij is the distance from intersection i to j; d iJ is the shortest travel distance from intersection j to destination J after selecting the new route; y ij(t) is the average speed of the distance; w ij(t) is the waiting time of vehicles in the intersection; f is the fine for non-reservation users who illegally enter the reserved section.

[0150] Step 3: Use the simulated annealing algorithm (SA) to solve the AMO model. The calculation process of the algorithm is shown below.

[0151]

[0152] Step 4: Take the Sioux Falls network as the study area and use the resident travel data based on the Sioux Falls network provided by the MATSim community as the basic data. The road network structure is as follows: Figure 3As shown in the figure. Hypothetical scenario 1: the initial state of the road network before the implementation of the reservation travel mode. Hypothetical scenario 2: select congested sections in the road network to implement the reservation travel mode. Hypothetical scenario 3: implement the reservation travel mode with the introduction of the best reward and punishment scheme based on scenario 2. The travel data during the morning peak period (07:00-08:00) is selected to test the effectiveness of the proposed model, and further quantify the improvement effect of the reservation travel mode on the traffic status of the road network. The saturation changes of the sections under each scenario are shown in the figure. Figure 5-7 The road network performance under different scenarios is shown in Table 1. The changes in user travel modes and travel time under different scenarios are shown in Table 1. Figure 8 and 9 shown.

[0153] Table 1 Road network performance under different scenarios

[0154]

[0155] What is described above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing travel reservation strategies considering travel incentives, characterized in that: The following steps are involved: (1) Clarify the implementation mechanism of the reservation travel strategy on frequently congested sections, build a multi-agent multi-objective optimization model by comprehensively considering the social income of the reservation travel mode and the travel cost of users, and output a specific reward and punishment plan; (2) Construct a multi-agent-based dynamic traffic simulation model to determine the departure time and travel cost. The dynamic traffic simulation model includes a pre-trip decision model and a travel decision model. In the pre-trip decision model, individual travelers will make decisions on departure time and travel mode based on reward and punishment schemes, personal preferences, time costs, and economic budgets. The travel decision model defines the basic driving rules and related environmental parameters for each vehicle agent in the traffic network, including a following model, an intersection decision model, and traffic signal control parameters. (3) The simulated annealing algorithm is used to solve the multi-objective optimization model based on multi-agent, and the Sioux Falls network is used as the study area to verify the effectiveness of the reservation travel mode.

2. The method for optimizing travel reservation strategies taking travel incentives into consideration according to claim 1, characterized in that: Implementation mechanism of the travel reservation strategy on frequently congested roads: Capacity thresholds are set for frequently congested sections, and motor vehicles entering the reserved sections during the implementation of the reservation travel strategy must make reservations in advance; After a successful reservation, the reservation system reserves the time slot for the traveler to use the reserved road; for users who participate in the reservation but do not obtain the right of way, the reservation system provides them with alternative travel routes, encourages them to choose public transportation, provides them with corresponding discounts, imposes administrative penalties on travelers who breach the contract, uses fines to establish a special subsidy fund for public transportation, and provides additional subsidies for travelers who fail to make a reservation and choose public transportation.

3. The method for optimizing travel reservation strategies taking travel incentives into consideration according to claim 1, characterized in that: The multi-agent multi-objective optimization model is: max Z=λ1Z1-λ2Z2 (3a) 0≤S m ≤S(3b) 0≤f≤F(3c) Among them, λ1 is the weight of the revenue target; λ2 is the weight of the cost target; S and F represent the upper limits of subsidies and fines; S m is the subsidy for users; f is the penalty for users who default; The total revenue of the transportation system z1: in, represents the system revenue when the nth traveler chooses transportation mode m; represents the subsidy for the nth traveler to choose transportation mode m; Traveler's travel cost Z2: Among them, v t represents the time value of the nth traveler in transit; t n is the travel time of the nth traveler; It represents the travel cost of the nth traveler who chooses transportation mode m, including fuel cost and public transportation cost.

4. The method for optimizing travel reservation strategies taking travel incentives into consideration according to claim 1, characterized in that: The travel modes include three single travel modes: private car, bus and subway, as well as two combined transfer modes: private car to bus and private car to subway.

5. The method for optimizing travel reservation strategies taking travel incentives into consideration according to claim 4 is characterized in that: Pre-trip decision model, the decision steps are as follows: ① Calculate the generalized cost of each travel mode without considering travel subsidies and determine the initial traffic state of the road network The generalized cost of each travel mode without considering travel subsidies is calculated in monetary terms. The generalized cost without considering travel subsidies includes the time cost of arriving early or late, the time cost in transit, and travel expenses. Travelers choose the appropriate travel mode based on the original travel time and the generalized cost of each travel mode without considering travel subsidies. Travelers who choose buses and subways follow fixed routes, while travelers who choose private cars participate in the travel decision-making model in the road network. The initial travel route and travel mode selection of each traveler are counted, and the average travel time of the road section at each time interval is counted as the initial traffic state of the road network at that moment. ②Users’ travel decisions after implementing the reservation travel mode First, the decision-making model during travel is used to determine whether the traveler's initial path and shortest path pass through the reserved section. If they do not pass through the reserved section, the reservation decision is not considered. If they pass through the reserved section, the travel time of the section in the initial state is used to calculate the time when the traveler arrives at the entrance of the reserved section, and this time t1 is recorded. It is determined whether there are any remaining reservation quotas at time t1. If so, the right of passage for this time period is directly reserved; if not, the time periods before and after t1 are searched in turn to see if there are any available capacity; If there is spare capacity, the broad travel costs of each mode of transportation after travel subsidies will be reconsidered, and the departure time and travel mode will be re-determined.

6. The method for optimizing travel reservation strategies taking travel incentives into consideration according to claim 5 is characterized in that: In the pre-trip decision model, the calculation formula for the generalized cost without considering travel subsidies is as follows: in, represents the generalized cost of the nth traveler choosing travel mode m at time t; t w The working hours specified by the commuter unit; t is the time in transit; v e 、v l and v t They represent the early arrival time value, late arrival time value and in-transit time value respectively; η is a 0-1 variable. When t w -(t+t t )≥0, that is, the traveler arrives at the destination on time or ahead of schedule, η=1, when t w -(t+t t )<0, that is, when the traveler is late, η=0; m is the travel expenses; n (m) is the probability that the nth traveler chooses travel mode m; θ1 is the dispersion coefficient of transportation mode selection.

7. The method for optimizing travel reservation strategies taking travel incentives into consideration according to claim 5, characterized in that: The calculation method of the generalized travel cost of each mode of transportation after travel subsidies is as follows: (a) Cost of booking a car trip in re_car =h*u r (5d) in, is the generalized cost of the nth traveler choosing to make a reservation at time t; is the original departure time; when the traveler departs h time windows earlier or later, the new departure time is t t is the travel time, i.e. the sum of the travel time of each road section; u re_car Subsidize users in the reservation travel mode, u r is the subsidy for changing a travel time window; P n (t) is the probability that the nth traveler chooses departure time t; θ2 is the travel time selection dispersion coefficient; (b) Cost of non-reserved car travel Users who do not participate in the reservation will travel according to the original departure time, so the departure time is tt,k is the travel time of path k that does not pass through the reserved section; non-reserved travel cars do not participate in the subsidy and are therefore not included in the calculation; (c) Public transport travel costs Q bus ≤60 (7c) For users who cannot travel as planned due to full reservations and need to switch to public transportation, the departure time will not be adjusted, so the departure time remains The bus travel time is divided into segment travel time and waiting time t i ;Q bus The number of passengers per bus is limited; bus Subsidize public transportation; (d) Subway travel costs metro ≤600 (8c) Similar to buses, travelers who choose to travel by subway do not adjust their departure time; the time in transit is divided into segment travel time and waiting time t i ;Q metro The number of passengers per subway car is limited; metro Subsidize subway travel; (e) Travel costs under two combined transfer modes: private car to bus and private car to subway The generalized travel cost of switching from a private car to a bus or from a private car to a subway is the combination of the generalized costs of the above corresponding travel modes; in addition, when a car switches, there is a parking cost P a 、Parking subsidy P+R 、Parking lot capacity limit Q park ≤500; The probability P that the nth traveler chooses path k under transportation mode m is n (k|m) is: Among them, θ3 is the path selection dispersion coefficient; In addition, the probability that the nth traveler chooses travel mode m is P n (m): in, The expected minimum cost for the nth traveler using transportation mode m.

8. The method for optimizing travel reservation strategies taking travel incentives into consideration according to claim 1, characterized in that: The following model in the travel decision-making model uses the General Motor model to describe the interaction between vehicles.

9. The method for optimizing travel reservation strategies taking travel incentives into consideration according to claim 1, characterized in that: The intersection decision model determines the next driving direction based on the real-time but incomplete information currently obtained. The probability that a traveler chooses a certain road segment is closely related to the total cost of the remaining routes from that road segment to the destination, including the shortest travel distance of the new route, the waiting time of vehicles at the intersection due to traffic flow or signal control, and the additional cost of the specific route; the probability that a vehicle chooses to travel from intersection i to intersection j is as follows: Where: C ij(t) is the total cost from intersection i to j; d ij is the distance from intersection i to j; d iJ is the shortest travel distance from intersection j to destination J after selecting the new route; y ij(t) is the average speed of the distance; w ij(t) is the waiting time of vehicles in the intersection; f is the fine for non-reservation users who illegally enter the reserved section.

10. A travel reservation system for implementing the method according to any one of claims 1 to 9, characterized in that: Including calculation system, reservation system and management system; The computing system determines the road sections and time periods of road network congestion based on historical traffic trends, considers the time-space supply and demand balance of each road section at different time periods, determines the capacity threshold of each reservation time period, and outputs a travel plan for the user; The reservation system is responsible for the management of reservation requests. The reservation system displays a reservation travel timetable to travelers, which shows the remaining capacity of the road section that needs to be reserved within a specific time period. Travelers make reservations through the reservation system before traveling. After the reservation is successful, the reservation system reserves the time slot for the traveler to use the reserved road. For users who participate in the reservation but fail to obtain the right of way, the reservation system provides them with alternative travel routes, encourages them to choose public transportation, and provides them with corresponding discounts. Administrative penalties are imposed on travelers who violate the contract, and fines are used to establish a special subsidy fund for public transportation travel, providing additional subsidies for travelers who fail to make reservations and choose public transportation; The management system is responsible for maintaining the order of reserved travel and coordinating traffic services; transmitting road traffic control information by setting corresponding traffic signs and markings in the upstream area of ​​the reserved section; using vehicle license plate recognition technology to verify the performance of reserved vehicles and implement a corresponding reward and punishment system.

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