A matching optimization method for online-booked buses and subway combined transport

Abstract

The present invention provides a method for optimizing the matching of online-booked buses and subway intermodal transport. The method includes: determining an initial feasible passenger matching set based on passenger selection and vehicle operation interaction mechanism; merging online-booked bus passengers of different routes on the basis of the initial feasible matching set to obtain a secondary carpooling feasible set and corresponding bus merged routes; taking the maximum system benefit that comprehensively considers the road mileage saving benefits and the online-booked bus operation benefits as the optimization goal, constructing an optimized matching model for online-booked buses and subway intermodal transport; solving the online-booked bus and subway intermodal transport optimized matching model through an algorithm to obtain a carpooling matching combination, the location of the online-booked bus line terminal station, the online-booked bus departure time and the estimated arrival time. The online-booked bus intermodal transport subway for passengers of the present invention is a promotion plan to completely transfer taxi users to public transportation, and provides strong support for the design and promotion and development of intermodal transport modes and integrated public transportation plans.

Description

Technical Field

[0001] The present invention relates to the technical field of personnel transportation, and in particular to a method for optimizing the matching of online-booked buses and subway combined transport. Background Art

[0002] With the development of geographic information technology and mobile Internet, online ride-sharing has gradually emerged, providing a new development direction for multi-modal transport in urban transportation. Among them, online ride-sharing can be regarded as a new type of customized public transportation, which is recognized as a green and sustainable mode of transportation.

[0003] For passengers, compared with the traditional bus-subway intermodal transport mode, online-booked buses and subway intermodal transport have many advantages, such as wide connection range, seamless transfer, no need to buy a car, no need to park, and flexibility and convenience. For the entire urban transportation system, online-booked buses and subway intermodal transport can reduce motor vehicle road mileage, reduce the number of small cars, and increase the utilization rate of public transportation, which has a positive effect on alleviating congestion and air pollution. However, the passenger matching method of online-booked buses and subway intermodal transport is still immature, and the accuracy of the model and the reliability of the matching results need to be improved.

[0004] At present, the existing online-booked special bus intermodal transport methods are still unclear about the travel characteristics and sharing motivations of car users. Whether a passenger chooses to share a ride is determined only based on the share fee or time window constraints. There is no comprehensive analysis of the passengers' choices when facing emerging share travel and existing transportation modes. The construction of a matching model based on over-simplified travel utility will lead to large errors in the scale of share passengers and the matching success rate; the use of existing bus stops does not respond flexibly according to demand, and does not reflect the advantages of the on-demand travel service model; the fixed station setting method is often inconsistent with the optimization goal and does not necessarily result in a better matching solution. Summary of the invention

[0005] The present invention provides a method for optimizing the matching of online-booked special buses and subway combined transport, so as to improve the accuracy of the online-booked special bus model and the passenger matching success rate.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme.

[0007] A method for optimizing the matching between online-booked buses and subway combined transport, comprising:

[0008] Determine an initial feasible passenger matching set based on the passenger selection and vehicle operation interaction mechanism;

[0009] In the initial feasible passenger matching set, the online-booked bus passengers of different routes are merged to obtain the secondary carpooling feasible set and the corresponding bus merged routes;

[0010] Based on the feasible set of secondary carpooling and the corresponding bus merged routes, the optimization matching model of online-booked buses and subway combined transport is constructed with the maximum system benefit taking into account the road mileage saving benefit and the online-booked bus operation benefit as the optimization goal;

[0011] The algorithm is used to solve the optimization matching model of the online-booked bus and subway combined transport to obtain the shared ride matching combination, the location of the online-booked bus line terminal station, the online-booked bus departure time and the estimated arrival time.

[0012] Preferably, the determining of an initial feasible passenger matching set based on the passenger selection and vehicle operation interaction mechanism includes:

[0013] Acquire the travel data of taxi users, the travel data including the latitude and longitude information of the starting and ending points of the trip and the time when the user gets on and off the taxi, and also acquire the spatial division information of the subway network and grid map, and set the passenger selection and vehicle operation interaction mechanism based on the travel data of the taxi users and the spatial division information of the subway network and grid map;

[0014] Using the passenger selection and vehicle operation interaction mechanism to search and obtain an initial feasible passenger matching set;

[0015] The passenger selection and vehicle operation interaction mechanism includes:

[0016] (1) Set a minimum passenger capacity constraint and eliminate passenger combinations that do not meet the requirements;

[0017] (2) Establish a candidate set of online-hailing bus stops based on the initial boarding and alighting stops of taxi passengers, remove passengers based on their walking time constraints, and then select bus stops;

[0018] (3) Estimate the alternative set of departure times for online-hailing buses based on the taxi passengers' planned arrival time, calculate the cost of the passengers' planned time deviation, and filter the departure time by eliminating passengers based on the passengers' generalized cost-saving constraints.

[0019] Preferably, the method of using the passenger selection and vehicle operation interaction mechanism to search for an initial feasible passenger matching set includes:

[0020] (1) Search and extract the central grid pairs with large passenger flow, and select the number of passengers N (S) with time similarity in the grid neighborhood pairs. O ,U t ,S D ) is greater than the minimum passenger capacity N of the online-booked bus b The grid pair, S O Indicates the grid search area where the starting point of the trip is located, S D Indicates the grid search neighborhood where the endpoint is located; U t represents the temporal similarity threshold;

[0021] (2) The starting and ending points of the passenger trips within the grid neighborhood are taken as the set of candidate bus stop locations. Based on the location of any stop, the walking time T of each passenger in the neighborhood to the stop is calculated. w , if the passenger's walking time to the station meets the upper threshold of walking time U w , then keep the passenger, otherwise remove the passenger and re-judge whether the minimum passenger capacity constraint is met;

[0022] (3) Based on the planned arrival time, walking time to the destination, and bus travel time of any passenger in the grid neighborhood, the latest arrival time and the latest departure time of the bus are estimated, and the departure and arrival time deviation of each passenger in the grid neighborhood is calculated based on the latest departure time;

[0023] (4) Calculate the generalized travel cost of passengers who share a ride on a special bus:

[0024] C vp (o i ,d i )=γ(T w (o i ,st s )+T w (d i ,st e ))+ρT vp (st s ,st e )+ζD vp (st s ,st e )+C td (o i ,d i )

[0025] In the formula, C vp (o i ,d i ) represents the total generalized cost of passenger i taking the online-booked bus; T w (o i ,st s ) represents the time it takes passenger i to walk from the starting point of the bus segment to the bus departure station; T w (d i ,st e ) represents the time it takes passenger i to walk from the bus terminal to the end of the bus segment; T vp (st s ,st e ) indicates the time the passenger is on board; D vp (st s ,st e) represents the travel distance of the online-booked special bus; γ and ρ represent the unit time value parameters of walking and time in the car, respectively; ζ represents the unit mileage cost of the online-booked special bus; C td (o i ,d i ) represents the time deviation cost;

[0026]

[0027]

[0028]

[0029] in and They represent the departure time deviation and arrival time deviation of passenger i, and represent the planned departure and arrival times, respectively. and represents the actual departure and arrival time, parameter η + , η - , + and - The unit time costs corresponding to early departure, delayed departure, early arrival and delayed arrival respectively;

[0030] If the sum of the generalized travel costs of passengers on the online-booked bus is not greater than the sum of the original travel costs, that is N represents the sum of the number of passengers on a special online-booked bus. This combination is considered as a matchable grid neighborhood passenger combination. Otherwise, go to (2), where C t / r (o i ,d i ) represents the original travel cost of passenger i, including the travel time T t / r (o i , d i ) and fees F t / r (o i , d i ). C t / r (o i ,d i )=α t / r T t / r (o i ,d i )+F t / r (o i ,d i ), α is the unit time value parameter; the subscripts “t” and “r” represent cruising taxis and online-hailing taxis, respectively.

[0031] Preferably, the initial feasible passenger matching set merges the online-booked bus passengers of different routes to obtain a secondary carpooling feasible set and corresponding bus merged routes, including:

[0032] The spatiotemporal similar routes in the combined grid pairs of the initial feasible passenger matching set are merged. When the two routes A are calculated and compared, i With A j (i≠j), the total cost savings of the planned merged routes and the penalty cost of stop loss θ=2N b ×χτ, if Then proceed to the next step of route similarity judgment, otherwise, do not consider the route combination;

[0033] Filter and satisfy line A i The starting point and route A j The distance between the starting points O ij 、Line A i The end point of line A j The distance between the end points D ij Both are less than 5 km, and the detour distance is less than that of route A. i and line A j Driving distance OD i With OD j , and line A i The travel time is similar to that of Route A j There are similar time-space routes with overlapping travel times;

[0034] Compare i D j , O j D i OD i With OD j , if the shortest distance is O i D j , then the shortest path is O j →O i →D j →D; if the shortest distance is O j D i , then the shortest path is O i →O j →D i →D j ; If the shortest distance is O i D i , then the shortest path is O j →O i →D i →D j ; If the shortest distance is O j D j , then the shortest path is Oi →O j →D j →D i ; Calculate the departure time and arrival time of the intermediate station and the location of the intermediate station according to the departure time of the first station and the path distance;

[0035] Based on the intermediate station locations and arrival times of the secondary carpooling routes, the two routes A are recalculated according to the generalized travel cost calculation method in the specific search process of the initial feasible passenger matching set. i With A j The generalized travel costs of the two groups of second-time carpooling passengers are The cost savings for the two groups of passengers at this time and The calculation is in Based on the calculation method, the penalty costs for the stop losses of the two groups of passengers need to be deducted.

[0036] C vp (o i ,d i )=γ(T w (o i ,st s )+T w (d i ,st e ))+ρT vp (st s ,st e )+ζD vp (st s ,st e )+C td (o i ,d i )

[0037] The second carpooling fare enjoys a certain discount. If the travel cost is saved, that is, It is considered that the two routes without intermediate stations can be merged into one route. The initial feasible grid is traversed for all routes, and the above-mentioned judgment method is used to obtain the final quadratic carpooling feasible set and the corresponding bus merged route.

[0038] Preferably, based on the secondary carpooling feasible set and the corresponding bus merged routes, the optimization goal is to maximize the system benefit by comprehensively considering the road mileage saving benefit and the online-booked bus operation benefit, and to construct an optimization matching model for online-booked buses and subway combined transport, including:

[0039] Based on the feasible set of secondary carpooling and the corresponding bus merged routes, the maximum system benefit that comprehensively considers the road mileage savings and the operating benefits of the online-booked special bus is taken as the optimization goal, and an optimization matching model of online-booked special bus and subway intermodal transport is constructed. The objective function of the model is expressed as:

[0040]

[0041] The first term of the objective function is the road mileage saving benefit, which is calculated as the product of the road mileage saving and the benefit coefficient. Δc is the weight of road mileage saving benefit; (p,b) represents the generalized travel cost saving between nodes p and b, κ t ,κ vp are the revenue coefficients of taxis and online-booked buses, respectively. B represents the set of buses in feasible matching. D vp (st s ,st e ) represents the travel distance of the online-booked bus; P represents the set of passengers in the feasible matching; β vp represents the unit mileage cost of the online-booked bus, unit: yuan / km; parameter ω is the fare profit ratio coefficient, N u is the maximum passenger capacity;

[0042] The second term of the objective function is the operating cost benefit of the online-booked bus, which is calculated as the difference between the fare revenue and the operating cost;

[0043] The constraints of the online bus and subway combined transport optimization matching model include: Ensure that a passenger can only be matched with one online-booked bus;

[0044] x (p,b) ={0,1}, The value of the decision variable is 1, which means the match is successful, and 0, which means the match is unsuccessful.

[0045] Preferably, the algorithm is used to solve the online-booked bus and subway combined transport optimization matching model to obtain the shared ride matching combination, the location of the online-booked bus line terminal station, the online-booked bus departure time and the estimated arrival time, including:

[0046] The objective function of the above-mentioned online-booked bus and subway intermodal optimization matching model is solved by the k-HPCG heuristic algorithm, and the set of matched passengers on each bus that makes the objective function optimal is obtained, as well as the corresponding departure station information and departure time information. The arrival time of the matched passengers is estimated based on the departure time and the average travel time of the matched passengers' cars.

[0047] The k-HPCG heuristic algorithm first determines the upper limit threshold t of the model solution timemax , if k is the passenger combination scale scenario, the matching model solution time t k Greater than t max , it is considered that the passenger combination scale does not meet the real-time computing requirements, and a smaller k value passenger combination scale scenario is considered instead;

[0048] Calculate the solution error of the online-booked bus matching model, use the maximum k value that meets the solution time threshold to solve the matching model and calculate the objective function, and assume that the total system benefit obtained is the maximum benefit B max , and then calculate the system benefit B under different k value scales k The error from the maximum benefit k ; Normalize the solution time and solution error under different k value scales to obtain and Taking into account the solution time and solution error, the model solution efficiency under different k value scales is evaluated. The formula is: The k value corresponding to the solution efficiency is selected as the appropriate combination scale.

[0049] It can be seen from the technical solutions provided by the above-mentioned embodiments of the present invention that the method of the present invention for passengers to use online-booked special buses for intermodal transportation with subways is a promotion plan to completely transfer taxi users to public transportation, and provides strong support for the design and promotion and development of intermodal modes and integrated public transportation solutions.

[0050] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0052] Figure 1 A processing flow chart of a method for optimizing the matching of online-booked buses and subway combined transport provided in an embodiment of the present invention.

[0053] Figure 2 This is an initial feasible passenger matching set search flow chart of the present invention.

[0054] Figure 3 The present invention is a flow chart of a secondary carpooling route merging method based on route similarity. DETAILED DESCRIPTION

[0055] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0056] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0057] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0058] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0059] The processing flow chart of a method for optimizing the matching of online-booked buses and subway combined transport provided by an embodiment of the present invention is as follows: Figure 1 As shown, the following steps are included:

[0060] Step S1: For cities that plan to introduce online-booked buses and subways, the original taxi users (including cruising taxis and online-booked taxis) who use the subways as potential users of online-booked buses. The travel data of taxi users is obtained, which includes the latitude and longitude information of the starting and ending points of the trip and the time when the user gets on and off the bus, as well as the spatial division information of the subway network and grid map.

[0061] Step S2: Determine an initial feasible passenger matching set based on passenger selection and vehicle operation interaction mechanism.

[0062] The interaction mechanism between passenger selection and vehicle operation mainly refers to the mutual influence between the bus minimum passenger capacity constraint and passenger cost constraint and vehicle operation information, which is manifested as follows:

[0063] (1) Set a minimum passenger capacity constraint and eliminate passenger combinations that do not meet the requirements;

[0064] (2) Establish a candidate set of online-hailing bus stops based on the initial boarding and alighting stops of taxi passengers, remove passengers based on their walking time constraints, and then select bus stops;

[0065] (3) Estimate the alternative set of departure times for online-hailing buses based on the taxi passengers' planned arrival time, calculate the cost of the passengers' planned time deviation, and filter the departure time by eliminating passengers based on the passengers' generalized cost-saving constraints.

[0066] Step S3: Based on the initial feasible matching set, the passengers of online-booked buses on different routes are merged to obtain a secondary carpooling feasible set and the corresponding bus merged routes.

[0067] The time-space similar routes in the combined grid pairs of the initial feasible passenger matching set are merged, and only the routes with similar time and space are considered for secondary merging to reduce the setting of intermediate stations of the online-booked special bus routes.

[0068] Step S4: Based on the feasible set of secondary carpooling and the corresponding bus merged routes, the maximum system benefit that comprehensively considers the road mileage savings and the operating benefits of the online-booked special bus is taken as the optimization goal, and an optimization matching model of online-booked special bus and subway intermodal transport is constructed. The objective function of the model is expressed as:

[0069]

[0070] The first term of the objective function is the road mileage saving benefit, which is calculated as the product of the road mileage saving and the benefit coefficient. Δc is the weight of road mileage saving benefit; (p,b) represents the generalized travel cost saving between nodes p and b, κ t ,κ vp are the revenue coefficients of taxis and online-booked buses, respectively. B represents the set of buses in feasible matching. D vp (st s ,st e ) represents the travel distance of the online-booked bus; P represents the set of passengers in the feasible matching; β vp represents the unit mileage cost of the online-booked bus, unit: yuan / km; parameter ω is the fare profit ratio coefficient, N u The maximum passenger capacity.

[0071] The second term of the objective function is the operating cost benefit of the online-booked bus, which is calculated as the difference between fare revenue and operating cost.

[0072] Constraints Ensure that a passenger can only be matched with one online-booked bus, constraint x (p,b) ={0,1}, The value of the decision variable is 1, which means the match is successful, and 0, which means the match is unsuccessful.

[0073] Step S5: Solve the objective function of the above-mentioned online-booked bus and subway intermodal optimization matching model through the k-HPCG (k-Higher Passenger Capacity Group, based on k maximum passenger number combinations) heuristic algorithm, and obtain the set of matched passengers on each bus that makes the objective function optimal, as well as the corresponding departure station information and departure time information. Estimate the arrival time of the matched passengers based on the departure time and the average travel time of the matched passengers' cars.

[0074] Since the number of passengers in a grid travel pair is large, the size of the passenger combination that meets the requirements is even larger, which brings great difficulties to the fast matching calculation in practical applications. Therefore, a heuristic algorithm based on the k largest passenger combinations is proposed here. According to the different values ​​of k, the passenger combinations with the largest number of passengers (k=0), the second largest number (k=1) and the largest number of passengers in the grid travel pair are considered. At the same time, the model solution time and error are balanced, and the solution efficiency index under different k values ​​is established to determine the optimal combination size that is most suitable for the case scenario. The specific steps of the above k-HPCG heuristic algorithm are as follows:

[0075] First, determine the upper limit threshold t of the model solution time max , if k is the passenger combination scale scenario, the matching model solution time t k Greater than t max , it is considered that the passenger combination scale does not meet the real-time computing requirements, and a smaller k value passenger combination scale scenario is considered instead;

[0076] Secondly, the solution error of the online-booked bus matching model is calculated. The maximum k value that meets the solution time threshold is used to solve the matching model and calculate the objective function. It is assumed that the total system benefit obtained is the maximum benefit B max , and then calculate the system benefit B under different k value scales k The error from the maximum benefit k ; Again, the solution time and solution error under different k value scales are normalized to obtain and Finally, considering the solution time and solution error comprehensively, the model solution efficiency under different k value scales is evaluated, and the formula is: The k value corresponding to the solution efficiency is selected as the appropriate combination scale.

[0077] The specific search process for the initial feasible passenger matching set in step S2 is as follows: Figure 2 Specifically including:

[0078] (1) Search and extract the central grid pairs with large passenger flow. The number of passengers with similar time in the grid search neighborhood pair N(S O ,U t ,S D ) is greater than the minimum passenger capacity N of the online-booked bus b The grid pair is the center grid pair. For any grid, all grids adjacent to it constitute the search neighborhood of the grid.

[0079] (2) The starting and ending points of the passenger trips within the grid neighborhood are taken as the set of candidate bus stop locations. Based on any stop location, the walking time T of each passenger in the neighborhood to the stop is calculated. w If the passenger's walking time to the station meets the upper threshold of walking time U w If the minimum passenger capacity constraint is met, the passenger will be retained; otherwise, the passenger will be removed and re-evaluated.

[0080] (3) Based on the planned arrival time, walking time to the destination, and bus travel time of any passenger in the grid neighborhood, the latest bus arrival time and the latest bus departure time are estimated, and the departure and arrival time deviations of each passenger in the grid neighborhood are calculated based on the latest departure time.

[0081] (4) Calculate the generalized travel cost of passengers who share a ride on a special bus:

[0082] C vp (o i ,d i )=γ(T w (o i ,st s )+T w (d i ,st e ))+ρT vp (st s ,st e )+ζD vp (st s ,st e )+C td (o i ,d i )

[0083] In the formula, C vp (o i ,d i) represents the total generalized cost of passenger i taking the online-booked bus; T w (o i ,st s ) represents the time it takes passenger i to walk from the starting point of the bus segment to the bus departure station; T w (d i ,st e ) represents the time it takes passenger i to walk from the bus terminal to the end of the bus segment; T vp (st s ,st e ) indicates the time the passenger is on board; D vp (st s ,st e ) represents the travel distance of the online-booked special bus; γ and ρ represent the unit time value parameters of walking and time in the car, respectively; ζ represents the unit mileage cost of the online-booked special bus; C td (o i ,d i ) represents the time deviation cost.

[0084]

[0085]

[0086]

[0087] in and They represent the departure time deviation and arrival time deviation of passenger i, and represent the planned departure and arrival times, respectively. and represents the actual departure and arrival time, parameter η + , η - , + and - They correspond to the unit time costs of early departure, delayed departure, early arrival and delayed arrival respectively.

[0088] If the sum of the generalized travel costs of passengers on the online-booked bus is not greater than the sum of the original travel costs, that is N represents the sum of the number of passengers on a special online-booked bus. This combination is considered as a matchable grid neighborhood passenger combination. Otherwise, go to (2), where C t / r (o i ,d i ) represents the original travel cost of passenger i, including the travel time T t / r (o i , d i ) and fees F t / r (o i , di ). C t / r (o i ,d i )=α t / r T t / r (o i ,d i )+F t / r (o i ,d i ), α is the unit time value parameter; the subscripts “t” and “r” represent cruising taxis and online-booked taxis, respectively. Define an online-booked taxi route A i Total passenger cost savings have

[0089] (5) After searching all the alternative stops and departure times for passengers in the grid domain, the set of matching grid domain pairs A is returned. Finally, the feasible matching set A{(B,P)} is obtained, where P and B represent the set of passengers and the set of buses in the feasible matching, respectively; the set of online-booked buses Including bus terminal location information and running time information ts ,s te ,t dep ,t arr Represents the starting station, terminal station, departure time and arrival time of the online-booked bus route. ; Passenger collection Includes the passenger's initial starting and ending location information and actual travel time information. i ,d i Indicates the starting and ending points of passenger i's trip. o ,t' d represents the actual departure time and actual arrival time of passenger i.

[0090] The process of the secondary carpooling route merging method based on route similarity in step S3 is as follows: Figure 3 First, calculate and compare the two lines A i With A j (i≠j) Total cost savings of planned merged routes and the penalty cost of stop loss θ=2N b ×χτ, if Then proceed to the next step of route similarity judgment, otherwise, do not consider the route combination. and The calculation method of Secondly, filter the lines that satisfy line A i The starting (ending) point and route A j The distance between the starting and ending points is O ij (D ij) are less than 5 km; the detour distance must be less than the driving distance OD of the two lines respectively i With OD j ; Line A i The travel time is similar to that of route A j There are similar time and space routes with overlapping travel time. Again, calculate the shortest path of the planned merged routes and determine the operation information of the intermediate stations.

[0091] Route A i Starting (ending) point and A j Distance between the end (start) point O i D j (O j D i ), and the driving distance OD of the two routes i With OD j , if the shortest distance is O i D j , then the shortest path is O j →O i →D j →D i , if the shortest distance is O j D i , then the shortest path is O i →O j →D i →D j , if the shortest distance is O i D i , then the shortest path is O j →O i →D i →D j , if the shortest distance is O j D j , then the shortest path is O i →O j →D j →D i ; According to the departure time of the first station and the path distance, the departure time and arrival time of the intermediate station are calculated. The location of the intermediate station also follows the principle of interaction between passenger selection and vehicle operation, and is determined based on the actual passenger get-off point through the optimization model.

[0092] Finally, based on the intermediate station locations and arrival times of the secondary carpooling routes given above, the generalized travel cost calculation method in the specific search process of the initial feasible passenger matching set in step S2 is used to recalculate the two routes A i With A j The generalized travel costs of the two groups of second-time carpooling passengers are The cost savings for the two groups of passengers at this time and The calculation is in Based on the calculation method, the penalty costs for the stop losses of the two groups of passengers need to be deducted.

[0093] The second carpooling fare enjoys a certain discount. If the travel cost is saved, that is, Then it is considered that the two routes without intermediate stations can be merged into one route. Traverse the initial feasible grid for all routes and use the above judgment method to obtain the final quadratic carpooling feasible set and the corresponding bus merged route.

[0094] To sum up, the method for optimizing the matching of online-booked buses and subway intermodal transport provided in the embodiment of the present invention proposes an optimized matching mode of online-booked buses and subway intermodal transport based on the principles of on-demand operation of online-booked buses and direct line access, and comprehensively analyzes the travel utility of carpooling passengers considering travel mode selection, which is conducive to improving the accuracy of the model and the reliability of the matching results; the k-HPCG heuristic algorithm proposed in the embodiment of the present invention when solving the optimized matching model of online-booked buses and subway intermodal transport can realize large-scale combined fast matching and efficient solution, providing method support for solving similar matching models.

[0095] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0096] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0097] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0098] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention 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 by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for optimizing the matching between online-booked buses and subway combined transport, characterized in that: include: Determine an initial feasible passenger matching set based on the passenger selection and vehicle operation interaction mechanism; In the initial feasible passenger matching set, the online-booked bus passengers of different routes are merged to obtain the secondary carpooling feasible set and the corresponding bus merged routes; Based on the feasible set of secondary carpooling and the corresponding bus merged routes, the optimization matching model of online-booked buses and subway combined transport is constructed with the maximum system benefit taking into account the road mileage saving benefit and the online-booked bus operation benefit as the optimization goal; Solve the optimization matching model of online-booked buses and subway combined transport through algorithms to obtain the carpooling matching combination, the location of the online-booked bus line terminal station, the online-booked bus departure time and the estimated arrival time; The method of determining an initial feasible passenger matching set based on the passenger selection and vehicle operation interaction mechanism includes: (1) Search and extract the central grid pairs with large passenger flow, and select the number of passengers N (S) with time similarity in the grid neighborhood pairs. O ,U t ,S D ) is greater than the minimum passenger capacity N of the online-booked bus b The grid pair, S O Indicates the grid search area where the starting point of the trip is located, S D Indicates the grid search neighborhood where the endpoint is located; U t represents the temporal similarity threshold; (2) The starting and ending points of the passenger trips within the grid neighborhood are taken as the set of candidate bus stop locations. Based on the location of any stop, the walking time T of each passenger in the neighborhood to the stop is calculated. w , if the passenger's walking time to the station meets the upper threshold of walking time U w , then keep the passenger, otherwise remove the passenger and re-judge whether the minimum passenger capacity constraint is met; (3) Based on the planned arrival time, walking time to the destination, and bus travel time of any passenger in the grid neighborhood, the latest arrival time and the latest departure time of the bus are estimated, and the departure and arrival time deviation of each passenger in the grid neighborhood is calculated based on the latest departure time; (4) Calculate the generalized travel cost of passengers who share a ride on a special bus: C vp (o i ,d i )=γ(T w (o i ,st s )+T w (d i ,st e ))+ρT vp (st s ,st e )+ζD vp (st s ,st e )+C td (o i ,d i ) In the formula, C vp (o i ,d i ) represents the total generalized cost of passenger i taking the online-booked bus; T w (o i ,st s ) represents the time it takes passenger i to walk from the starting point of the bus segment to the bus departure station; T w (d i ,st e ) represents the time it takes passenger i to walk from the bus terminal to the end of the bus segment; T vp (st s ,st e ) indicates the time the passenger is on board; D vp (st s ,st e ) represents the travel distance of the online-booked special bus; γ and ρ represent the unit time value parameters of walking and time in the car, respectively; ζ represents the unit mileage cost of the online-booked special bus; C td (o i ,d i ) represents the time deviation cost; in and They represent the departure time deviation and arrival time deviation of passenger i, and are the planned departure and arrival times, respectively. and represents the actual departure and arrival time, parameter η + , η - , + and - The unit time costs corresponding to early departure, delayed departure, early arrival and delayed arrival respectively; If the sum of the generalized travel costs of passengers on the online-booked bus is not greater than the sum of the original travel costs, that is N represents the sum of the number of passengers on a special online booking bus. This combination is considered as a matching grid neighborhood passenger combination. Otherwise, go to (2), where C t / r (o i ,d i ) represents the original travel cost of passenger i, including the travel time T t / r (o i ,d i ) and fees F t / r (o i ,d i );C t / r (o i ,d i )=α t / r T t / r (o i ,d i )+F t / r (o i ,d i ), α is the unit time value parameter; the subscripts "t" and "r" represent cruising taxis and online-hailing cars, respectively, and vp represents online-hailing buses; The initial feasible passenger matching set merges the online-booked bus passengers of different routes to obtain a secondary carpooling feasible set and corresponding bus merged routes, including: The spatiotemporal similar routes in the combined grid pairs of the initial feasible passenger matching set are merged. When the two routes A are calculated and compared, i With A j (i≠j), the total cost savings of the planned merged routes and the penalty cost of stop loss θ=2N b ×χτ, if Then proceed to the next step of route similarity judgment, otherwise, do not consider the route combination; Filter and satisfy line A i Starting point and route A j The distance between the starting points O ij 、Line A i The end point of line A j The distance between the end points D ij Both are less than 5 km, and the detour distance is less than that of route A. i and line A j Driving distance OD i With OD j , and line A i The travel time is similar to that of route A j There are similar time-space routes with overlapping travel times; Compare i D j , O j D i OD i With OD j , if the shortest distance is O i D j , then the shortest path is O j →O i →D j →D i ; If the shortest distance is O j D i , then the shortest path is O i →O j →D i →D j ; If the shortest distance is O i D i , then the shortest path is O j →O i →D i →D j ; If the shortest distance is O j D j , then the shortest path is O i →O j →D j →D i ; Calculate the departure time and arrival time of the intermediate station and the location of the intermediate station according to the departure time of the first station and the path distance; Based on the intermediate station locations and arrival times of the secondary carpooling routes, the two routes A are recalculated according to the generalized travel cost calculation method in the specific search process of the initial feasible passenger matching set. i With A j The generalized travel costs of the two groups of second-time carpooling passengers are The cost savings for the two groups of passengers at this time and The calculation is in On the basis of the calculation method, the penalty cost of stop loss of two groups of passengers needs to be subtracted; C vp (o i ,d i )=γ(T w (o i ,st s )+T w (d i ,st e ))+ρT vp (st s ,st e )+ζD vp (st s ,st e )+C td (o i ,d i ) The second carpooling fare enjoys a certain discount. If the travel cost is saved, that is, It is considered that the two routes without intermediate stations can be merged into one route. The initial feasible grid is traversed for all routes, and the above judgment method is used to obtain the final quadratic carpooling feasible set and the corresponding bus merged route; Based on the feasible set of secondary carpooling and the corresponding bus merged routes, the optimization goal is to maximize the system benefit by comprehensively considering the road mileage saving benefits and the operating benefits of the online-booked special bus, and to construct an optimization matching model for the online-booked special bus and subway intermodal transport, including: Based on the feasible set of secondary carpooling and the corresponding bus merged routes, the maximum system benefit that comprehensively considers the road mileage savings and the operating benefits of the online-booked special bus is taken as the optimization goal, and an optimization matching model of online-booked special bus and subway intermodal transport is constructed. The objective function of the model is expressed as: The first term of the objective function is the road mileage saving benefit, which is calculated as the product of the road mileage saving and the benefit coefficient. Δc is the weight of road mileage saving benefit; (p,b) represents the generalized travel cost saving between nodes p and b, κ t ,κ vp are the revenue coefficients of taxis and online-booked buses, respectively. B represents the set of buses in feasible matching. D vp (st s ,st e ) represents the travel distance of the online-booked bus; P represents the set of passengers in the feasible matching; β vp represents the unit mileage cost of the online-booked bus, unit: yuan / km; parameter ω is the fare profit ratio coefficient, N u is the maximum passenger capacity; The second term of the objective function is the operating cost benefit of the online-booked bus, which is calculated as the difference between the fare revenue and the operating cost; The constraints of the online bus and subway combined transport optimization matching model include: Ensure that a passenger can only be matched with one online-booked bus; x (p,b) ={0,1}, is the value of the decision variable, 1 indicates a successful match, and 0 indicates a failed match; The algorithm is used to solve the online-booked bus and subway combined transport optimization matching model to obtain the shared ride matching combination, the location of the online-booked bus line terminal station, the online-booked bus departure time and the estimated arrival time, including: The objective function of the above-mentioned online-booked bus and subway intermodal optimization matching model is solved by the k-HPCG heuristic algorithm, and the set of matched passengers on each bus that makes the objective function optimal is obtained, as well as the corresponding departure station information and departure time information. The arrival time of the matched passengers is estimated based on the departure time and the average travel time of the matched passengers' cars. The k-HPCG heuristic algorithm first determines the upper limit threshold t of the model solution time max , if k is the passenger combination scale scenario, the matching model solution time t k Greater than t max , it is considered that the passenger combination scale does not meet the real-time computing requirements, and a smaller k value passenger combination scale scenario is considered instead; Calculate the solution error of the online-booked bus matching model, use the maximum k value that meets the solution time threshold to solve the matching model and calculate the objective function, and assume that the total system benefit obtained is the maximum benefit B max , and then calculate the system benefit B under different k value scales k The error from the maximum benefit k ; Normalize the solution time and solution error under different k value scales to obtain and Taking into account the solution time and solution error, the model solution efficiency under different k value scales is evaluated. The formula is: The k value corresponding to the solution efficiency is selected as the appropriate combination scale.

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