CN113642752A - Method and device for determining simultaneous operation number of urban taxies - Google Patents
Method and device for determining simultaneous operation number of urban taxies Download PDFInfo
- Publication number
- CN113642752A CN113642752A CN202110977637.6A CN202110977637A CN113642752A CN 113642752 A CN113642752 A CN 113642752A CN 202110977637 A CN202110977637 A CN 202110977637A CN 113642752 A CN113642752 A CN 113642752A
- Authority
- CN
- China
- Prior art keywords
- time
- taxi
- task
- taxis
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 230000029305 taxis Effects 0.000 claims abstract description 74
- 238000010276 construction Methods 0.000 claims abstract description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 238000010586 diagram Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 239000002699 waste material Substances 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/02—Reservations, e.g. for tickets, services or events
- G06Q10/025—Coordination of plural reservations, e.g. plural trip segments, transportation combined with accommodation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G06Q50/40—
Abstract
The invention provides a method and a device for determining the number of urban taxis in simultaneous operation. According to the method, the historical operation data and the road network data of the urban taxis are adopted, the simulated scheduling is carried out on the construction and the solution of the vehicle sharing network, the simultaneous operation number of the urban taxis is determined according to the problem of the maximum overlapping number of the solution interval of the optimal scheduling scheme, and the reasonable simultaneous operation number of the urban taxis can be obtained, so that reference is provided for the operation and quantity regulation of the urban taxis.
Description
Technical Field
The invention relates to the technical field of urban taxi operation, in particular to a method and a device for determining the number of urban taxies operating simultaneously.
Background
In recent years, with the development of economy and science and technology in China and the acceleration of urbanization, the requirements of residents on travelling comfort and convenience are higher and higher. The rise of network car booking platforms and automatic driving technologies causes huge influence on the traditional taxi industry, so that the quantity control of taxis becomes more complex, the taxis are used as the components of an urban comprehensive transportation system, long-time high-strength operation is carried out on roads, and the influence on urban problems such as traffic jam is great. Therefore, its quantity control has been a hot topic in the traffic field.
The existing taxi throwing and amplifying method is characterized in that the number of the taxis is determined according to factors such as economic development conditions, population and the like of different cities, or corresponding number adjustment is made during operation according to a market adjusting mechanism, historical operation data of the taxis in the cities are rarely considered, the number of the taxis actually needed in the market can be obtained through optimized simulated scheduling, the traveling efficiency is improved, and the resource waste is reduced.
Disclosure of Invention
In order to simulate and determine the number of taxis actually needed in the market, improve the traveling efficiency and reduce the resource waste, the invention provides a method and a device for determining the number of urban taxis simultaneously operated. The specific technical scheme is as follows:
the method for determining the number of the urban taxies operating simultaneously provided by the embodiment of the invention comprises the following steps:
acquiring historical operation data and road network data of taxis in a target area; the target area is a designated area for determining the number of the taxies participating in simultaneous operation;
determining a time-space constraint condition for vehicle scheduling according to the historical operation data and the road network data; the space-time constraint condition is used for determining a taxi capable of receiving a next trip task;
constructing a vehicle sharing network based on the taxis determined by the space-time constraint condition and the historical operation data and road network data corresponding to the taxis;
simulating an optimal scheduling scheme of the taxi based on the constructed vehicle sharing network, and solving a minimum path coverage problem to obtain the optimal scheduling scheme of the taxi;
obtaining a taxi operation time interval based on the optimal scheduling scheme of the taxi; and solving the maximum overlapping number of the operation interval, and taking the maximum operation number as the number of taxis operating simultaneously in the operation interval.
Further, the historical operation data includes: starting position, terminal position, departure time and arrival time of each trip in the taxi operation data of the target area; the road network data includes: and road vector data of taxis can pass through the target area.
Further, the determining a space-time constraint condition for vehicle scheduling according to the historical operation data and the road network data specifically includes:
obtaining a service flexible constraint condition based on the travel time of the taxi for finishing the current task to reach the starting point of the next trip task, the finishing time of the current trip task, the starting time of the next trip task and a preset delay threshold;
obtaining a time dimension constraint of the travel task acquired within a preset time length based on the preset time length after the starting time of the current travel task;
and obtaining a space dimension constraint condition based on the starting time of the next trip task, the ending time of the current trip task and the idle time threshold value.
Further, the building of a vehicle sharing network based on the taxis determined based on the space-time constraint condition and the historical operation data and road network data corresponding to the taxis specifically includes:
acquiring a starting point position and an end point position of a travel task in historical operation data, and matching the travel task to a road network according to road vector data corresponding to the travel task;
dividing the travel time of the road sections in the road network into different time intervals;
screening out a travel task pair meeting a space-time constraint condition according to a preset delay threshold, a preset duration threshold and a vacant driving time threshold in different operation time intervals, and constructing a vehicle sharing network according to the travel task pair; the travel task pair comprises two sets of travel tasks which can complete tasks through a shared vehicle.
Further, the vehicle sharing network is a directed acyclic graph which comprises trip task points for representing trip tasks and trip task edges for representing whether a vehicle can be shared between the two trip tasks to complete scheduling;
the method for simulating the optimal scheduling scheme of the taxi based on the constructed vehicle sharing network to obtain the optimal scheduling scheme of the taxi comprises the following steps:
modeling the optimal scheduling of the taxi by adopting a minimum path coverage model to obtain an optimal scheduling model; the minimum path coverage model covers all travel task points in the vehicle sharing network by using a minimum number of travel task edges;
and constructing the vehicle sharing network into a bipartite graph by adopting a point splitting method, and solving the minimum path coverage model by using a bipartite graph matching algorithm to obtain an optimal scheduling scheme.
Further, the operation time interval is a time period during which the taxi can be operated;
obtaining a taxi operation time interval based on the optimal scheduling scheme of the taxi; solving the maximum overlapping number of the operation time interval, and taking the maximum operation number as the number of taxis simultaneously operated in the operation time interval, specifically comprising the following steps:
acquiring a time interval of each taxi participating in operation in the optimal scheduling scheme in the target area; the time interval is an interval consisting of the departure time and the arrival time of the travel task of each path of the optimal scheduling scheme;
dividing the operation time into a plurality of time periods, and marking the departure time and the arrival time of each taxi in the operation time interval of the divided time periods;
and respectively calculating the maximum overlapping number of the time intervals in each operation time period, and taking the overlapping number as the simultaneous operation number of the taxis.
The second aspect of the present invention provides a method for determining the number of urban taxies operating simultaneously, including:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring historical operation data and road network data of taxis in a target area; the target area is a designated area for determining the number of the taxies participating in simultaneous operation;
the space-time constraint condition determining module is used for determining the space-time constraint condition of vehicle scheduling according to the historical operation data and the road network data; the space-time constraint condition is used for determining a taxi capable of receiving a next trip task;
the vehicle sharing network construction module is used for constructing a vehicle sharing network based on the taxis determined by the space-time constraint condition and the historical operation data and road network data corresponding to the taxis;
the optimal scheduling scheme solving module is used for simulating the optimal scheduling scheme of the taxi based on the constructed vehicle sharing network, and solving the problem of minimum path coverage to obtain the optimal scheduling scheme of the taxi;
the number determining module for simultaneous operation is used for obtaining taxi operation time intervals based on the optimal scheduling scheme of the taxies; and solving the maximum overlapping number of the operation interval, and taking the maximum operation number as the number of taxis operating simultaneously in the operation interval.
Further, the historical operation data includes: starting position, terminal position, departure time and arrival time of each trip in the taxi operation data of the target area; the road network data includes: and road vector data of taxis can pass through the target area.
A third aspect of the present invention provides a computer-readable storage medium, having a computer program stored thereon, where the computer program, when executed by a processor, causes the processor to process any one of the above-mentioned methods for determining the number of urban taxis simultaneously operated.
A fourth aspect of the present invention provides an electronic apparatus, comprising:
a processor; and the number of the first and second groups,
a memory arranged to store computer executable instructions that when executed cause the processor to perform any of the above described city taxi simultaneous operation number determination methods.
According to the method and the device for determining the simultaneous operation number of the urban taxis, the time-space constraint condition of vehicle scheduling is determined according to the historical operation data and the road network data of the urban taxis in the target area, then the vehicle sharing network is constructed according to the time-space constraint condition of the vehicle scheduling, then the optimal scheduling of the urban taxis is simulated according to the solving of the minimum path coverage problem, the reasonable scheduling scheme of the historical operation data of the urban taxis is obtained, and finally the simultaneous operation number of the urban taxis is determined according to the solving of the maximum overlap number problem of the interval. According to the method and the device for determining the simultaneous operation number of the urban taxis, the simulated scheduling is carried out through the construction and the solution of the vehicle sharing network according to the historical operation data and the road network data of the urban taxis, and the simultaneous operation number of the urban taxis is determined according to the problem of solving the maximum overlapping number of the intervals according to the reasonable scheduling scheme, so that the more reasonable simultaneous operation number of the urban taxis can be obtained, and reference is provided for the operation and quantity regulation of the urban taxis.
Drawings
FIG. 1 is a schematic flow chart of a method for determining the number of urban taxies operating simultaneously according to the present invention;
FIG. 2 is a schematic diagram of a dispatch path in an actual road network;
FIG. 3 is a schematic diagram of a vehicle sharing network constructed by constructing travel tasks as points and schedulable relationships as edges;
FIG. 4 is a schematic diagram of the structure of the line optimization adjusting device;
table 1 shows the obtained operation data of some urban taxis;
table 2 shows the results of the average traveling speeds of different road sections;
table 3 shows the minimum path coverage problem solution result corresponding to the vehicle sharing network.
Detailed Description
The present invention is described below with reference to the accompanying drawings, but the present invention is not limited thereto.
Fig. 1 is a schematic flow chart of a method for determining the number of urban taxies in simultaneous operation, which includes:
s1: acquiring historical operation data and road network data of taxis in a target area; the target area refers to a designated area determined by the number of the taxies participating in simultaneous operation.
The historical operation data is data formed in the operation process of the taxi; the road network data is a road vector formed by the taxi in the operation process.
The historical operation data comprises a starting point position, an end point position, a departure time and an arrival time of each trip in the taxi operation data.
S2: determining a time-space constraint condition for vehicle scheduling according to the historical operation data and the road network data; the space-time constraint condition is used for determining a taxi capable of receiving a next trip task.
In consideration of service flexibility, time dimension, space dimension and the like, the invention constructs 3 space-time constraint conditions which are respectively a service flexibility constraint condition, a time dimension constraint condition and a space dimension constraint condition.
(1) Service flexibility constraints: and obtaining a service flexible constraint condition based on the travel time of the taxi for finishing the current task to reach the starting point of the next trip task, the finishing time of the current trip task, the starting time of the next trip task and a preset delay threshold. Specifically, the invention is expressed by the following formula:
assuming that the current travel task i is finished, the time for reaching the starting point of the next travel task j cannot exceed the sum of the original starting point time of the travel task j and the allowed maximum acceptable delay.
Wherein, tijThe travel time required from the end point of the travel task i to the start point of the travel task j,
as the end time of the travel task i,
and delta is a delay threshold value for the departure time of the trip task j.
(2) And (3) time dimension constraint: and obtaining a time dimension constraint for obtaining the travel task within a preset time length based on the preset time length after the starting time of the current travel task.
And at most, the rest travel task information within T time after the starting time of the current travel task i can be acquired.
Wherein T is a predicted duration.
(3) And (3) space dimension constraint: and obtaining a space dimension constraint condition based on the starting time of the next trip task, the ending time of the current trip task and the idle time threshold value.
The idle travel distance caused by the scheduling among different travel tasks is not suitable to be too large, otherwise, urban taxi drivers can be led to go around long routes to pick up remote passengers. When the travel time of different road segments in the road network is determined, the spatial dimension constraint can be expressed in the form of a corresponding time constraint.
Wherein δ is the empty time threshold.
S3: and constructing a vehicle sharing network based on the taxis determined by the space-time constraint condition and the historical operation data and road network data corresponding to the taxis.
In the embodiment, the trip tasks can be matched to the road network according to the urban taxi operation data of the target area and the geographic spatial position information contained in the road network data, and then the travel time of the road sections in the road network is estimated in different time intervals. For example, the problem may be modeled as an optimization problem that is solved using an intelligent optimization algorithm.
In this embodiment, since the construction of the vehicle sharing network directly affects the result of the simulated scheduling, determining a reasonable delay threshold, a predicted duration and a free-driving time threshold, and setting a suitable space-time constraint condition are more critical steps. In this embodiment, more reasonable parameter values can be selected according to the experimental results under the condition of multiple sets of parameters.
In an optional implementation manner of the embodiment of the present invention, the constructing the vehicle sharing network specifically includes the following steps:
s31: acquiring a starting point position and an end point position of a travel task in historical operation data, and matching the travel task to a road network according to road vector data corresponding to the travel task;
s32: dividing the travel time of the road sections in the road network into different time intervals;
s33: screening out a travel task pair meeting a space-time constraint condition according to a preset delay threshold, a preset duration threshold and a vacant driving time threshold in different operation time intervals, and constructing a vehicle sharing network according to the travel task pair; the travel task pair comprises two sets of travel tasks which can complete tasks through a shared vehicle.
Specifically, travel tasks are matched to the road network according to the historical operation data and the geographic spatial position information in the road network data, and the travel time of road sections in the road network is estimated in different time periods; setting different delay threshold values, predicted duration and idle running time threshold values according to the time-space constraint condition of vehicle scheduling, screening out a travel task pair meeting the time-space constraint condition under each group of parameters, constructing a vehicle sharing network, wherein the travel task not meeting the time-space constraint does not participate in the construction of the vehicle sharing network, and an urban taxi needs to be allocated independently to complete the travel task; the vehicle sharing network is a directed acyclic graph, points in the vehicle sharing network represent travel tasks, edges represent whether space-time constraints are met between the two travel tasks (whether the shared vehicle can complete scheduling or not), if one edge exists between the two travel task points, the two tasks are schedulable, taxis in the same city can continue to select to complete the schedulable subsequent task after completing the previous travel task, and otherwise, if no edge exists between the two travel task points, the two travel tasks cannot be completed by the same vehicle in tandem.
In the above S33, in different operation time intervals, a travel task pair satisfying the space-time constraint condition is screened out according to a preset delay threshold, a preset duration threshold and a preset empty travel time threshold, and can be obtained by the following steps:
s331: and matching the trip task to the road network according to the urban taxi operation data of the target area and the geographic spatial position information contained in the road network data. In this embodiment, the starting point position and the ending point position of each trip are matched to the nearest intersection in the road network, and the distance is calculated by using a Haversine formula.
Wherein haversin (theta) ═ sin2(theta/2) — (1-cos (theta))/2, R is the earth radius,
and
the latitude of two points is shown, the delta lambda represents the difference of the longitudes of the two points, and d is the distance to be obtained.
S332: and estimating the travel time of the road section in the road network in different time periods according to the departure time and the arrival time in the urban taxi operation data of the target area. In this embodiment, a model is established with the goal of minimizing the average absolute error between the estimated travel time and the actual travel time of the used travel task, the decision variable is the average traveling speed of each road segment, and a differential evolution algorithm is used for solving. Since the length of the road segment is known, the estimated average travel speed allows the travel time of the road segment to be calculated.
Li＝ShortestPath(Oi,Di,t)
Wherein f isiEstimated travel time, y, for travel task iiThe actual travel time of the travel task i is N, and the total number of the travel tasks participating in estimation is N; l isiSet of road segments, s, of the route under the current road network time impedance for the trip task ilIs the length of the section l, vlThe current estimated speed of the road section l is estimated and obtained from the urban taxi operation data by combining with practical experience; o isiAnd DiRespectively as the starting point and the end point of the trip task i, t is the time impedance of the current road network, and the shortestPath function represents that O is obtained according to the time impedance of the current road networkiTo DiThe set of shortest road segments.
S333: and correcting the arrival time of each travel task according to the estimated road section travel time of the target area and the shortest path algorithm, setting multiple groups of delay thresholds, predicted time and idle travel time thresholds, and screening out travel task pairs meeting space-time constraint conditions under each group of parameters.
In the embodiment, urban taxi operation data and road network data 1 hour and 15 minutes before 1 month and 1 day of new york manhattan 2014 are adopted, wherein the urban taxi operation data are shown in the following table 1.
TABLE 1
In this embodiment, the road segments are divided into six types for average traveling speed estimation, the model is solved through a differential evolution algorithm, the speed estimation results of the six types of road segments are shown in table 2 below, and the length of each road segment is divided by the corresponding estimated speed to obtain the estimated travel time of each road segment.
TABLE 2
In the above S33, the vehicle sharing network is constructed according to the travel task pair, and is specifically obtained through the following steps:
as shown in FIG. 2, assume that there are 6 travel tasks in the target area, TA、TB、TC、TD、TE、TFAnd a plurality of pairs of travel tasks exist among the taxi service system and the taxi service system, and the travel tasks meet space-time constraint conditions, namely, the taxi in the same city can continuously select to complete the schedulable subsequent task after completing the previous travel task. The travel task is constructed as a point and a schedulable relation is constructed as an edge, and then a vehicle sharing network can be constructed, as shown in fig. 3.
In this embodiment, a pair of travel tasks satisfying the time-space constraint condition is screened out corresponding to each set of parameters, and is constructed as a vehicle sharing network.
S4: and simulating the optimal scheduling scheme of the taxi based on the constructed vehicle sharing network, and solving the problem of minimum path coverage to obtain the optimal scheduling scheme of the taxi.
The vehicle sharing network is a directed acyclic graph which comprises trip task points used for representing trip tasks and trip task edges used for representing whether a vehicle can be shared between the two trip tasks to complete scheduling;
the implementation manner of the optimal scheduling scheme specifically includes the following steps:
modeling the optimal scheduling of the taxi by adopting a minimum path coverage model to obtain an optimal scheduling model; the minimum path coverage model covers all travel task points in the vehicle sharing network by using a minimum number of travel task edges;
and constructing the vehicle sharing network into a bipartite graph by adopting a point splitting method, and solving the minimum path coverage model by using a bipartite graph matching algorithm to obtain an optimal scheduling scheme.
S5: obtaining a taxi operation time interval based on the optimal scheduling scheme of the taxi; and solving the maximum overlapping number of the operation interval, and taking the maximum operation number as the number of taxis operating simultaneously in the operation interval.
In this embodiment, the simultaneous operation number of the urban taxis may be determined according to the solution of the problem of the maximum overlap number of the intervals. Acquiring a time interval of each urban taxi participating in operation in the scheduling scheme of the target area, namely a time interval consisting of the starting time of the starting trip task and the arrival time of the ending trip task of each path in the path covering scheme; and determining and modeling the simultaneous operation number of the urban taxis as an interval maximum overlapping number problem to solve, wherein the interval maximum overlapping number problem is the number of the maximum time interval overlapping obtained in a plurality of time intervals, and is equivalent to the simultaneous operation number of the urban taxis.
Next, the above step S5 will be described with reference to specific examples.
And solving the minimum path coverage problem corresponding to the vehicle sharing network by adopting a Hopcroft-Karp algorithm. In this example, the delay threshold is determined to be 0 minutes, the predicted duration and the empty time threshold are all 15 minutes, and the solution results are shown in table 3 below:
TABLE 3
Wherein x is1For schedulable trip task logarithm, x2Number of travel tasks, x, which can be scheduled3Is the maximum match of the bipartite graph, x4Number of travel tasks, x, to participate in the matching5For vehicles sharing a minimum number of coverage paths, x, of the network6Number of paths to participate in matching (schedulable points do not necessarily participate in scheduling at all), x7To match the total empty time, x, of the trip task8And the quotient of the total idle running time of the matched trip task and the number of the matched nodes is obtained.
As shown in fig. 4, assuming that there are 5 urban taxis to complete different dispatching paths, the maximum simultaneous operation number can be calculated by solving the problem of the maximum number of overlaps between the intervals. Sequencing time points (starting points and end points) of all travel scheduling paths, traversing the end points, if the starting points are met, adding 1, and if the end points are met, subtracting 1, so that the section overlapping number of any section can be obtained, namely the number of operating vehicles in different sections is obtained, for example, the number of operating vehicles in the sections is 3.
And determining that the number of the urban taxis operated simultaneously is 4337 according to the solution of the problem of the maximum overlap number of the interval, wherein in the embodiment of the invention, the number of the urban taxis operated simultaneously is 4337 under the condition of reasonable scheduling of the urban taxis 1 hour and 15 minutes before 1 month and 1 day in Manhattan 2014 in New York.
According to the technical scheme, the method for simultaneously operating the number of urban taxis, provided by the embodiment of the invention, comprises the steps of obtaining historical operation data and road network data of the urban taxis, determining space-time constraint conditions of vehicle scheduling, constructing a vehicle sharing network according to the space-time constraint conditions, converting the space-time constraint conditions into a minimum path coverage problem solution to simulate the optimal scheduling of the urban taxis, obtaining a reasonable scheduling scheme of the historical operation data of the urban taxis, and introducing an interval maximum overlap number problem to determine the simultaneously operating number of the urban taxis. According to the embodiment of the invention, the simulated optimal scheduling can be carried out based on the historical data, and the more reasonable simultaneous operation number of the urban taxies can be obtained through efficient and accurate calculation, so that reference is provided for operation and quantity regulation of the urban taxies, the travel efficiency is improved, and the resource waste is reduced.
A second aspect of the present invention provides an apparatus for determining the number of urban taxies operating simultaneously, including:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring historical operation data and road network data of taxis in a target area; the target area is a designated area for determining the number of the taxies participating in simultaneous operation;
the space-time constraint condition determining module is used for determining the space-time constraint condition of vehicle scheduling according to the historical operation data and the road network data; the space-time constraint condition is used for determining a taxi capable of receiving a next trip task;
the vehicle sharing network construction module is used for constructing a vehicle sharing network based on the taxis determined by the space-time constraint condition and the historical operation data and road network data corresponding to the taxis;
the optimal scheduling scheme solving module is used for simulating the optimal scheduling scheme of the taxi based on the constructed vehicle sharing network, and solving the problem of minimum path coverage to obtain the optimal scheduling scheme of the taxi;
the number determining module for simultaneous operation is used for obtaining taxi operation time intervals based on the optimal scheduling scheme of the taxies; and solving the maximum overlapping number of the operation interval, and taking the maximum operation number as the number of taxis operating simultaneously in the operation interval.
Further, the historical operation data includes: starting position, terminal position, departure time and arrival time of each trip in the taxi operation data of the target area; the road network data includes: and road vector data of taxis can pass through the target area.
A third aspect of the present invention provides a computer-readable storage medium, having a computer program stored thereon, where the computer program, when executed by a processor, causes the processor to process any one of the above-mentioned methods for determining the number of urban taxis simultaneously operated.
A fourth aspect of the present invention provides an electronic apparatus, comprising:
a processor; and the number of the first and second groups,
a memory arranged to store computer executable instructions that when executed cause the processor to perform any of the above described city taxi simultaneous operation number determination methods.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (10)
1. A method for determining the number of urban taxies in simultaneous operation is characterized by comprising the following steps:
acquiring historical operation data and road network data of taxis in a target area; the target area is a designated area for determining the number of the taxies participating in simultaneous operation;
determining a time-space constraint condition for vehicle scheduling according to the historical operation data and the road network data; the space-time constraint condition is used for determining a taxi capable of receiving a next trip task;
constructing a vehicle sharing network based on the taxis determined by the space-time constraint condition and the historical operation data and road network data corresponding to the taxis;
simulating an optimal scheduling scheme of the taxi based on the constructed vehicle sharing network, and solving a minimum path coverage problem to obtain the optimal scheduling scheme of the taxi;
obtaining a taxi operation time interval based on the optimal scheduling scheme of the taxi; and solving the maximum overlapping number of the operation interval, and taking the maximum operation number as the number of taxis operating simultaneously in the operation interval.
2. The method for determining the number of urban taxies simultaneously operated according to claim 1, wherein the historical operation data includes: starting position, terminal position, departure time and arrival time of each trip in the taxi operation data of the target area; the road network data includes: and road vector data of taxis can pass through the target area.
3. The method for determining the number of urban taxies operating simultaneously according to claim 2, wherein the determining the space-time constraint condition of vehicle scheduling according to the historical operating data and the road network data specifically comprises:
obtaining a service flexible constraint condition based on the travel time of the taxi for finishing the current task to reach the starting point of the next trip task, the finishing time of the current trip task, the starting time of the next trip task and a preset delay threshold;
obtaining a time dimension constraint of the travel task acquired within a preset time length based on the preset time length after the starting time of the current travel task;
and obtaining a space dimension constraint condition based on the starting time of the next trip task, the ending time of the current trip task and the idle time threshold value.
4. The method for determining the number of urban taxies simultaneously operated according to claim 2, wherein the step of constructing a vehicle sharing network based on the taxies determined by the space-time constraint condition and the historical operation data and road network data corresponding to the taxies comprises the following specific steps:
acquiring a starting point position and an end point position of a travel task in historical operation data, and matching the travel task to a road network according to road vector data corresponding to the travel task;
dividing the travel time of the road sections in the road network into different time intervals;
screening out a travel task pair meeting a space-time constraint condition according to a preset delay threshold, a preset duration threshold and a vacant driving time threshold in different operation time intervals, and constructing a vehicle sharing network according to the travel task pair; the travel task pair comprises two sets of travel tasks which can complete tasks through a shared vehicle.
5. The method for determining the number of urban taxies simultaneously operated according to claim 2, wherein the vehicle sharing network is a directed acyclic graph including trip task points for representing trip tasks and trip task edges for representing whether a vehicle can be shared between two trip tasks to complete scheduling;
the method for simulating the optimal scheduling scheme of the taxi based on the constructed vehicle sharing network to obtain the optimal scheduling scheme of the taxi comprises the following steps:
modeling the optimal scheduling of the taxi by adopting a minimum path coverage model to obtain an optimal scheduling model; the minimum path coverage model covers all travel task points in the vehicle sharing network by using a minimum number of travel task edges;
and constructing the vehicle sharing network into a bipartite graph by adopting a point splitting method, and solving the minimum path coverage model by using a bipartite graph matching algorithm to obtain an optimal scheduling scheme.
6. The method for determining the number of urban taxies simultaneously operated according to claim 2, wherein the operation time interval is a time period during which a taxi can be operated;
obtaining a taxi operation time interval based on the optimal scheduling scheme of the taxi; solving the maximum overlapping number of the operation time interval, and taking the maximum operation number as the number of taxis simultaneously operated in the operation time interval, specifically comprising the following steps:
acquiring a time interval of each taxi participating in operation in the optimal scheduling scheme in the target area; the time interval is an interval consisting of the departure time and the arrival time of the travel task of each path of the optimal scheduling scheme;
dividing the operation time into a plurality of time periods, and marking the departure time and the arrival time of each taxi in the operation time interval of the divided time periods;
and respectively calculating the maximum overlapping number of the time intervals in each operation time period, and taking the overlapping number as the simultaneous operation number of the taxis.
7. A device for determining the number of urban taxies operating simultaneously is characterized by comprising:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring historical operation data and road network data of taxis in a target area; the target area is a designated area for determining the number of the taxies participating in simultaneous operation;
the space-time constraint condition determining module is used for determining the space-time constraint condition of vehicle scheduling according to the historical operation data and the road network data; the space-time constraint condition is used for determining a taxi capable of receiving a next trip task;
the vehicle sharing network construction module is used for constructing a vehicle sharing network based on the taxis determined by the space-time constraint condition and the historical operation data and road network data corresponding to the taxis;
the optimal scheduling scheme solving module is used for simulating the optimal scheduling scheme of the taxi based on the constructed vehicle sharing network, and solving the problem of minimum path coverage to obtain the optimal scheduling scheme of the taxi;
the number determining module for simultaneous operation is used for obtaining taxi operation time intervals based on the optimal scheduling scheme of the taxies; and solving the maximum overlapping number of the operation interval, and taking the maximum operation number as the number of taxis operating simultaneously in the operation interval.
8. The city taxi simultaneous operation number determination apparatus according to claim 7, wherein the historical operation data includes: starting position, terminal position, departure time and arrival time of each trip in the taxi operation data of the target area; the road network data includes: and road vector data of taxis can pass through the target area.
9. A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when executed by a processor, causes the processor to process the method for determining the number of simultaneous operations of urban taxis according to any one of claims 1 to 6.
10. An electronic device, characterized in that the electronic device comprises:
a processor; and a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the city taxi simultaneous operation number determination method of any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110977637.6A CN113642752A (en) | 2021-08-24 | 2021-08-24 | Method and device for determining simultaneous operation number of urban taxies |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110977637.6A CN113642752A (en) | 2021-08-24 | 2021-08-24 | Method and device for determining simultaneous operation number of urban taxies |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113642752A true CN113642752A (en) | 2021-11-12 |
Family
ID=78423679
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110977637.6A Pending CN113642752A (en) | 2021-08-24 | 2021-08-24 | Method and device for determining simultaneous operation number of urban taxies |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113642752A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101751777A (en) * | 2008-12-02 | 2010-06-23 | 同济大学 | Dynamic urban road network traffic zone partitioning method based on space cluster analysis |
| CN106781511A (en) * | 2017-03-22 | 2017-05-31 | 北京工业大学 | A kind of congestion time forecasting methods based on gps data and traffic accident type |
| US20180204158A1 (en) * | 2017-01-19 | 2018-07-19 | Massachusetts Institue Of Technology | Data-Driven System for Optimal Vehicle Fleet Dimensioning and Real-Time Dispatching Based on Shareability Networks |
| CN109657820A (en) * | 2018-10-23 | 2019-04-19 | 厦门大学 | A kind of taxi matching process reserved |
| CN110119835A (en) * | 2019-03-26 | 2019-08-13 | 杭州电子科技大学 | A kind of public transport dynamic based on interval computation is dispatched a car method for optimizing scheduling |
| CN111191899A (en) * | 2019-12-23 | 2020-05-22 | 华南理工大学 | Vehicle scheduling method based on region division parallel genetic algorithm |
| CN112949987A (en) * | 2021-02-01 | 2021-06-11 | 湖南大学 | Taxi dispatching and matching method, system, equipment and medium based on prediction |
-
2021
- 2021-08-24 CN CN202110977637.6A patent/CN113642752A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101751777A (en) * | 2008-12-02 | 2010-06-23 | 同济大学 | Dynamic urban road network traffic zone partitioning method based on space cluster analysis |
| US20180204158A1 (en) * | 2017-01-19 | 2018-07-19 | Massachusetts Institue Of Technology | Data-Driven System for Optimal Vehicle Fleet Dimensioning and Real-Time Dispatching Based on Shareability Networks |
| CA3050520A1 (en) * | 2017-01-19 | 2018-07-26 | Massachusetts Institute Of Technology | A data-driven system for optimal vehicle fleet dimensioning, ride-sharing, and real-time dispatching based on shareability networks |
| CN110431574A (en) * | 2017-01-19 | 2019-11-08 | 麻省理工学院 | For being formulated based on the optimal vehicle Fleet size that can share network, taking shared and Real-Time Scheduling data-driven system |
| CN106781511A (en) * | 2017-03-22 | 2017-05-31 | 北京工业大学 | A kind of congestion time forecasting methods based on gps data and traffic accident type |
| CN109657820A (en) * | 2018-10-23 | 2019-04-19 | 厦门大学 | A kind of taxi matching process reserved |
| CN110119835A (en) * | 2019-03-26 | 2019-08-13 | 杭州电子科技大学 | A kind of public transport dynamic based on interval computation is dispatched a car method for optimizing scheduling |
| CN111191899A (en) * | 2019-12-23 | 2020-05-22 | 华南理工大学 | Vehicle scheduling method based on region division parallel genetic algorithm |
| CN112949987A (en) * | 2021-02-01 | 2021-06-11 | 湖南大学 | Taxi dispatching and matching method, system, equipment and medium based on prediction |
Non-Patent Citations (3)
| Title |
|---|
| 姚晓锐;王冠;杨超;: "未来城市自动驾驶共享汽车规模研究：以上海为例", 交通运输系统工程与信息, no. 06, pages 89 - 95 * |
| 胡宝雨;冯树民;: "城际公交车辆跨线调度优化研究", 交通运输系统工程与信息, no. 04, pages 125 - 130 * |
| 邹兵;余志;黄敏;何兆成;陈金邕;: "考虑接驳费用的车辆共享调度算法研究", 交通信息与安全, no. 02, pages 77 - 85 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021248607A1 (en) | Deep reinforcement learning-based taxi dispatching method and system | |
| Daganzo et al. | A general model of demand-responsive transportation services: From taxi to ridesharing to dial-a-ride | |
| CN107036617B (en) | Travel route planning method and system combining taxi and subway | |
| CN107919014B (en) | Taxi running route optimization method for multiple passenger mileage | |
| CN114076606A (en) | Method and computer system for providing a journey route or time required for a journey route from a departure location to a destination location | |
| CN102044149A (en) | City bus operation coordinating method and device based on time variant passenger flows | |
| CN109612488B (en) | Big data micro-service-based mixed travel mode path planning system and method | |
| CN111161560B (en) | Bus corridor operation order management method and device | |
| CN109657820A (en) | A kind of taxi matching process reserved | |
| CN111311002B (en) | Bus trip planning method considering active transfer of passengers in transit | |
| CN113435777A (en) | Planning method and system for electric operating vehicle charging station | |
| CN116798218A (en) | Urban low-carbon traffic big data detection method based on digital twinning | |
| CN115222156A (en) | Automobile charging scheduling method considering user demand response based on time-sharing dual road network | |
| Calabrò et al. | Comparing the performance of demand responsive and schedule-based feeder services of mass rapid transit: an agent-based simulation approach | |
| Liang et al. | An optimization model for vehicle routing of automated taxi trips with dynamic travel times | |
| van Eck et al. | Model complexities and requirements for multimodal transport network design: Assessment of classical, state-of-the-practice, and state-of-the-research models | |
| CN113379159B (en) | Taxi driver passenger searching route recommendation method based on gray model and Markov decision process | |
| CN112949987B (en) | Taxi scheduling and matching method, system, equipment and medium based on prediction | |
| CN113763695A (en) | Dispatching method and system for automatic driving vehicle | |
| CN112106021A (en) | Method and device for providing vehicle navigation simulation environment | |
| CN113739812B (en) | Distribution plan generating method, device, system and computer readable storage medium | |
| CN113642752A (en) | Method and device for determining simultaneous operation number of urban taxies | |
| CN115083198B (en) | Multi-vehicle power resource scheduling method and device | |
| CN116524705A (en) | Inter-city travel vehicle dispatching method and terminal | |
| CN115729106A (en) | Multi-time-constraint running mileage optimized lightweight car pooling scheduling method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |