CN111667086B

CN111667086B - Vehicle ride-sharing path optimizing method and system

Info

Publication number: CN111667086B
Application number: CN201910176782.7A
Authority: CN
Inventors: 邹难; 陈爽; 杨坤鸿
Original assignee: Shandong University
Current assignee: Shandong University
Priority date: 2019-03-08
Filing date: 2019-03-08
Publication date: 2023-07-28
Anticipated expiration: 2039-03-08
Also published as: CN111667086A

Abstract

The invention provides a vehicle ride-by-ride path optimizing method and system, which are used for collecting real-time data of vehicles, roads and passengers, screening and removing data outside a regional range, temporarily storing the data in the regional range, taking the shortest driving distance of a driver, the shortest idle time of the vehicle and the highest satisfaction of the passengers as optimizing targets, establishing a multi-vehicle ride-by-ride matching and path optimizing model with constraint conditions, solving the multi-vehicle ride-by-ride matching and path optimizing model by utilizing an improved genetic algorithm and a simulated annealing algorithm according to the real-time data of the vehicles, roads and passengers temporarily stored in a server module, obtaining optimal target function values, establishing the connection between the passengers, the passengers and the vehicles, and the vehicles, effectively realizing the ride-by-ride matching between the passengers and the vehicles, balancing the contradiction between the passengers and the driver, saving the vehicle resources and reducing the energy waste.

Description

Vehicle ride-sharing path optimizing method and system

Technical Field

The disclosure relates to a vehicle ride-sharing path optimizing method and system.

Background

The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.

At present, motor vehicles are kept in quantity and continuously climb, the traffic travel demand is rapidly increased, and then a series of traffic problems such as traffic jam, energy consumption, environmental pollution and the like are caused, and travelers are also frequently bothered by the problems such as difficult driving and difficult parking. However, with the rapid development of technologies such as global positioning system, mobile internet and social network, the co-riding trip becomes a new trip mode.

The vehicle ride is beneficial to reducing the travel expense of the travelers, can relieve traffic jam and reduce traffic pollution, and is not a common traffic mode although the vehicle ride has a plurality of advantages, because an efficient and reasonable method for coordinating the travel routes and travel time of different travelers is lacking.

Accordingly, the present inventors have now urgently solved the following problems: (1) No effective method is available for planning the route and travel time of the traveler; (2) The current ride sharing system cannot meet the personalized riding requirements of passengers, the ride sharing satisfaction is low, and the ride sharing wish is not strong; (3) The vehicle is often empty, or only carries passengers for a long distance, and resources are seriously wasted; (4) The waste of vehicle resources brings greater tail gas emission problem, and aggravates environmental pollution; (5) The contradiction between people and vehicles is easy to occur in rush hours of going up and down, the traffic burden and the energy consumption are greatly increased, and meanwhile, the travel time of passengers is wasted.

Disclosure of Invention

In order to solve the defects of the prior art, the present disclosure provides a vehicle ride-sharing path optimizing method and system, which establishes the connection between passengers, between passengers and vehicles, and between vehicles and vehicles, effectively realizes ride-sharing matching between passengers and vehicles, efficiently realizes path planning, improves economic income of drivers, improves traveling efficiency of passengers, balances contradiction between passengers and drivers, saves vehicle resources, reduces energy waste, objectively reduces exhaust emission, and reduces environmental pollution.

In order to achieve the above purpose, the present disclosure adopts the following technical scheme:

in a first aspect, the present disclosure provides a vehicle ride-sharing path optimizing method;

a vehicle ride-sharing path optimizing method comprises the following steps:

101, establishing a data acquisition module, a data preprocessing module and a server module, wherein the data acquisition module is used for acquiring real-time data of vehicles, roads and passengers riding, the data preprocessing module is used for eliminating data outside the range of the area acquired by the data acquisition module according to a preset area, and the server module is used for summarizing and temporarily storing the real-time data processed by the data preprocessing module;

102, taking the highest income of a driver, the shortest driving distance of the vehicle, the least idle time of the vehicle and the highest satisfaction of passengers as optimization targets, and respectively aiming at four passenger types of economic preference type, speed preference type, social preference type and safety preference type, establishing a multi-vehicle ride-on matching and path optimization model with constraint conditions;

103 is further provided with a processor module, and the processor module solves the multi-vehicle simultaneous taking matching and path optimizing model by utilizing an improved genetic algorithm and a simulated annealing algorithm according to the real-time riding data of the vehicles, the roads and the passengers temporarily stored in the server module to obtain an optimal objective function value, and performs matching and path planning on the vehicles and the people.

As some possible implementations, the area may be divided according to the regional scope of the administrative division, or may be divided according to the scope of a certain region to be studied, so as to reject data outside the area.

As some possible implementations, in step 101, the passenger satisfaction includes non-ride-sharing travel satisfaction and ride-sharing travel satisfaction,

the travel satisfaction degree of the non-ride-sharing mode is as follows:

the travel satisfaction degree of the co-riding mode is as follows:

Wherein, when 1 is represented as a co-multiplication mode, when 2 is represented as a single-multiplication mode;indicating the travel utility of passenger s in mode 1, < >>Indicating the travel utility of passenger s in mode 2.

As some possible implementations, in the step 101, the constraint condition includes a node constraint, a pair constraint, a response constraint, a neighbor constraint, a time constraint, an order constraint, a capacity constraint, and a value range constraint.

As some possible implementations, in step 101, the multiple-vehicle co-product matching and path optimization model is as follows:

where k is the number of the vehicle, s is the number of the passenger, M is the set of all the vehicles providing service, n is the total number of reserved passengers, W is the set of all the road network nodes,travel expense for co-riding->For non-ride travel cost, μ is the cost per distance of vehicle transport, +.>Representing the vehicle k passing through the road network node i to the road network node j, ">Representing that vehicle k does not pass through road network node i to road network node j, d _ij Represents the distance of road network node i to road network node j,/->For the passenger s to ride in a satisfactory manner,and is not ride satisfaction for passenger s.

As some possible implementations, in step 103, the improved genetic algorithm includes a coding design of the chromosome, the coding design of the chromosome is encoded according to the road network node number, that is, the transportation route is taken as the chromosome, and the road network node is taken as a gene on the chromosome, and the generation of the initial population and the genetic operator improvement design are performed.

As some possible implementations, the generating of the initial population includes the following steps:

501 calculating a time continuity judgment matrix TC between all passengers;

502, for m vehicles, generating m+2 0 as the marker bits of the beginning and the end of each vehicle path;

503 allocated l for each vehicle for the last time period _k The reservation passengers do not change the positions of the path nodes and insert the path nodes into corresponding vehicle paths;

504, randomly scrambling and sorting the numbers of NA passengers which are newly and unassigned, wherein the number sequence is NA; randomly scrambling and sorting numbers of the NB passengers which are not allocated and are not willing to ride together, wherein the number sequence is NB;

505, inserting the origin-destination positions of the unassigned passengers willing to ride together into the path by adopting a pair-wise insertion method;

506 inserting the origin-destination positions of the unassigned passengers not willing to ride together into the path by adopting a pair-wise insertion method;

507 returning k=k+1 to step 505 to determine the next vehicle path until all vehicle paths are finished, if the passengers are not allocated finally, indicating that the passengers are rejected, and after the starting point number ln(s) and the end point number en(s) of the passengers are placed in the last 0 zone in sequence, connecting the paths of all vehicles to form an individual;

508 returns to step 504 until M individuals are formed that satisfy the population number.

As some possible implementations, the genetic operator improvement design includes selection, crossover, and mutation of chromosomes, the selection step of chromosomes including:

601, selecting 1/4 individuals with optimal population from the father according to the fitness, and putting the individuals into a set U1;

602, selecting optimal 1/4 individuals from the population after parent hybridization according to fitness, and putting the individuals into a set U2;

603, selecting optimal 1/4 individuals from the population after father variation according to fitness, and putting the individuals into a set U3;

604, selecting optimal 1/4 individuals from the population after the variation of the filial generation according to the fitness, and putting the individuals into a set U4;

605 merging the optimal individuals to obtain a next generation population u=u ₁ ∪U ₂ ∪U ₃ ∪U ₄ 。

As some possible implementations, the crossing step of the chromosomes includes:

701, randomly selecting two chromosomes from a parent population, then randomly selecting a vehicle, and enabling paths of the vehicle corresponding to the two chromosomes to be selected;

702 exchanging the gene segments corresponding to the two parent chromosomes to generate two new chromosomes, wherein the two new chromosomes have the same part and different parts, so that the deletion and repetition of genes occur in the chromosomes;

703, and filling the deleted genes by a forward pair insertion method.

As some possible implementations, the chromosome mutation steps are:

801 randomly selecting two vehicle paths of 1 chromosome, wherein the number of current on-vehicle people of the two selected vehicles is 0;

802, dividing the number sequence of the initial position of the vehicle and the passengers with the scheduled passengers, dividing the passengers with the unoccupied passengers and the passengers with the unoccupied passengers into a group, scrambling the serial numbers of the nodes, and arranging the paths of the next vehicle and the paths of the previous vehicle according to a pairwise insertion method of reverse order.

As some possible implementations, in the step 103, the simulated annealing algorithm includes the following steps:

901 obtain the current solutionSetting a current temperature value T _i ；

902 generating a new solution for current solution adjustment, solving an objective function value A of the new solution, wherein the original objective function value is B, delta f is used for representing increment of the objective function value, namely delta f=A-B, when the increment of the objective function value is larger than 0, the probability of accepting the new solution by the system is 1, otherwise, the probability is used for obtaining the new solution by the systemDiscarding the new solution;

903, cooling according to the decay rate r, and updating the temperature value T _i+1 ＝T _i ·r；

904 determines the termination condition, and if the condition is satisfied, the process goes to step 905; otherwise, go to step 902;

905 outputs the current solution.

In a second aspect, the present disclosure provides a vehicle ride-sharing path optimizing system;

the system comprises a data acquisition module, a data preprocessing module, a server module and a processor module, wherein the data acquisition module comprises a plurality of data acquisition terminals and is used for acquiring real-time riding data of vehicles, roads and passengers;

the data preprocessing module performs real-time screening and removing on the data outside the area range acquired by the data acquisition module according to a preset area; the server module is used for summarizing and temporarily storing the real-time data processed by the data preprocessing module;

the processor module is provided with a plurality of data processing terminals, a multi-vehicle ride-on matching and path optimizing model with constraint conditions is embedded in the data processing terminals, and the data processing terminals solve the multi-vehicle ride-on matching and path optimizing model by utilizing an improved genetic algorithm and a simulated annealing algorithm according to real-time data of vehicles, roads and passengers temporarily stored in the server module, so as to obtain an optimal objective function value.

Compared with the prior art, the beneficial effects of the present disclosure are:

1. the scheme disclosed by the disclosure takes the highest income of a driver, the shortest driving distance of the vehicle, the least idle time of the vehicle and the highest satisfaction of the vehicle as optimization targets, establishes a multi-vehicle simultaneous taking matching and path optimization model with constraint conditions, establishes the connection among passengers, between the passengers and the vehicle and between the vehicle and the vehicle, effectively realizes the simultaneous taking matching among the passengers and the vehicle, improves the income of the driver, improves the travel efficiency of the passengers, balances the contradiction between the passengers and the driver, saves vehicle resources, reduces energy loss, objectively reduces waste gas emission and reduces environmental pollution.

2. According to the scheme, the analysis of the passenger riding satisfaction is effectively realized by establishing the co-riding satisfaction function and the non-co-riding satisfaction function, so that a foundation is provided for achieving the highest passenger satisfaction, the passenger satisfaction can be improved through analysis, efficient vehicle co-riding is further truly realized, the travel efficiency is improved, and the resource waste is reduced.

3. The model disclosed by the disclosure is provided with a plurality of constraint conditions, because the multi-vehicle ride is a complex matching scheduling optimization problem, if all factors are considered during modeling, the research problem is extremely complex and difficult to solve, so that the effective realization of ride path optimization can be satisfied by reasonably setting the constraint conditions, and the quick realization of the solution of the model is realized, thereby realizing quick path planning.

4. The scheme disclosed by the disclosure solves the model through an improved self-adaptive genetic algorithm and a simulated annealing algorithm, the genetic algorithm has high flexibility, can break through the limitation of objective functions and heavy constraints, has strong compatibility, can be fused with various optimization algorithms, has weak local search energy of a single genetic algorithm, is easy to generate premature phenomenon, is simple to operate, is favorable for simplifying complex nonlinear problems, has strong robustness and local search capability, has poor global search capability, can effectively complement the two through fusion of the genetic algorithm and the simulated annealing algorithm, realizes quick solving of the model, and greatly improves the efficiency of the simultaneous multiplication matching and path planning.

5. The method comprises the steps of carrying out certain improvement on a genetic algorithm, wherein the performance of the genetic algorithm is influenced by the change of control parameters to a certain extent, the traditional standard genetic algorithm adopts fixed cross probability and variation probability, the probability of the probability is generally selected by subjective experience, and the probability cannot be well adapted to the complex change.

6. The content of the method comprises the steps of optimizing individual selection, wherein the content comprises a partial optimal population of a parent population, a partial optimal population of a crossed population and a partial optimal population after mutation, so that the operation efficiency is effectively improved, the fact that an optimal solution in the population must enter the next generation is ensured, and the problem of difficult solution caused by complex models is effectively solved.

7. The traditional intersection operation comprises single-point intersection and double-point intersection, but the access sequence of the nodes on the running path of each vehicle in the model disclosed by the disclosure is successively divided, and the start and stop points of the same passenger are required to be accessed by the same vehicle at the same time, if a traditional intersection mode is adopted, the sequence of solutions is disordered, a large number of infeasible solutions are formed, and the novel excellent feasibility calculation efficiency in the intersection process is ensured by adopting the intersection operator based on the vehicle path segmentation, so that the problem of disordered solutions brought by the traditional intersection method is effectively solved.

8. The content disclosed by the disclosure has stronger depth searching capability through a simulated annealing algorithm, overcomes the defect of poor local searching capability of a genetic algorithm, and greatly improves the accuracy of the obtained objective function value.

Drawings

Fig. 1 is a flowchart of a data preprocessing module according to embodiment 1 of the present disclosure.

Fig. 2 is a flowchart of a co-product path optimizing process according to embodiment 1 of the present disclosure.

FIG. 3 is a flowchart of a genetic algorithm for fusion simulated annealing according to example 1 of the present disclosure.

Detailed Description

It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used in this example have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments in accordance with the present disclosure. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.

Example 1:

as shown in fig. 1-3, embodiment 1 of the present disclosure provides a vehicle ride-sharing path optimizing method, wherein vehicle ride-sharing is a novel travel mode, and the mode not only can alleviate the problem of difficulty in riding by travelers in peak hours, but also can improve the load carrying capacity of the vehicle, reduce the cost of single person travel, improve traffic jams, reduce traffic pollution, achieve multiple purposes, has significant application value, and comprehensively analyzes economy, timeliness, comfort, responsiveness and safety in passenger riding experience, and constructs a passenger riding satisfaction function; meanwhile, various constraint conditions such as passenger co-taking willingness and luggage carrying are considered, the lowest vehicle carrying cost and the highest passenger satisfaction are taken as optimization targets, and vehicle matching and path optimization modeling are respectively carried out for on-vehicle passengers, reserved and distributed non-boarding passengers and reserved and non-distributed passengers.

The multi-vehicle ride-sharing matching problem studied in the embodiment is an optimization problem aiming at multiple vehicles, multiple passengers and multiple-to-multiple with time windows, namely, on one hand, the passengers release the required information such as the places of getting on and off the vehicles, expected time, ride-sharing will, luggage carrying and the like to a vehicle dispatching center in a reservation mode such as a telephone or a taxi taking platform before traveling, on the other hand, a certain number of vehicles travel in a limited travel range and send the positions and states of the vehicles to the dispatching center in real time, and at the moment, the passengers possibly exist on the vehicles or the passengers with reservation success are not on the vehicles temporarily, and meanwhile, a plurality of new reserved passengers wait for distribution; therefore, the dispatching center determines the optimal matching scheme of the vehicle and the passengers according to the position and the state of the current vehicle, the demand information of the passengers and the externally connected social network information under the condition of meeting various constraints, and plans the vehicle path, so that the carrying efficiency of the vehicle is improved.

The method for optimizing the common multiplication path comprises the following steps:

the method comprises the steps of establishing a data acquisition module, a data preprocessing module and a server module, wherein the data acquisition module is used for acquiring real-time data of vehicles, roads and passengers riding, the data preprocessing module is used for eliminating data outside the range of the area acquired by the data acquisition module according to a preset area, and the server module is used for summarizing and temporarily storing the real-time data processed by the data preprocessing module;

Taking the highest income of a driver, the shortest driving distance of a vehicle, the least idle time of the vehicle and the highest satisfaction of passengers as optimization targets, and respectively aiming at three passenger types of on-vehicle passengers, reserved and allocated non-on-vehicle passengers and reserved and unallocated passengers, establishing a multi-vehicle simultaneous taking matching and path optimization model with constraint conditions;

the system is also provided with a processor module, and the processor module solves a multi-vehicle simultaneous taking matching and path optimizing model by utilizing an improved genetic algorithm and a simulated annealing algorithm according to the real-time riding data of the vehicles, roads and passengers temporarily stored in the server module to obtain an optimal objective function value.

The multi-vehicle ride is a complex matching scheduling optimization problem, if all factors are considered during modeling, the research problem is extremely complex and difficult to solve, so that a model is simplified through a plurality of basic assumptions on the premise of ensuring reasonable, and the assumptions of the model are as follows:

(1) Assuming that the current position of the vehicle and the boarding and disembarking nodes of passengers are randomly distributed in a bounded road network;

(2) Neglecting the influence of road conditions, and assuming that the shortest distance between two nodes in a road network is the straight line distance between the two points;

(3) Neglecting the influence of traffic conditions during the running of the vehicle, assuming that the vehicle is running at a constant speed;

(4) Assuming that all vehicles are of the same type, i.e. the maximum passenger capacity is the same, the average running speed is the same;

(5) Assuming that a passenger's riding request is randomly generated, the passenger request includes the time, location, co-riding willingness, etc. of riding;

(6) Neglecting the time of getting on and off the passengers, and not considering the operation time of dispatching system state updating;

(7) To achieve system optimization, allowing a passenger's ride request to be denied;

(8) The system is updated with a fixed time and once the passenger's reservation request is allocated, the passenger must be allocated vehicle services, no further changes are allowed, and for an unallocated passenger may wait for the next reallocation within an acceptable time frame.

Parameters related to the vehicle co-riding path optimization model mainly comprise information variables such as vehicles, passengers, riding requests, request responses and the like, and the specific model parameters are described as follows:

symbol description

O ₁ On-board passenger collection

O ₂ Reserved passenger set allocated but not on board

O ₃ Unassigned set of reservation passengers

W set of all road network nodes

M all serviced vehicle collections

m total number of vehicles

n total number of reservation passengers

Nodes in i, j-path network

s passenger numbering

Number of k vehicles

c _k Maximum passenger capacity of vehicle k, excluding the driver

f _k Maximum load capacity of vehicle k, mainly large luggage

v _k Average speed of vehicle k

Cost per distance of mu vehicle transport

h _s h _s =1, passengers s are willing to ride together

h _s =0, passengers s are reluctant to ride together

g _s Number of large pieces of luggage carried by passenger s

k(s) the passenger s has been assigned to vehicle k at the beginning of the current assignment of k(s)

b (k) current vehicle position

e(s) starting point coordinates of reserved passenger s

End point coordinates of l(s) reservation passenger s

Earliest boarding time acceptable to passenger s at origin

The night time that passenger s can accept at the start

Earliest time of departure that passenger s can accept at end point

Latest time of departure acceptable to passenger s at terminal point

Time of arrival of vehicle k at road network node i

Waiting time for vehicle k to arrive at road network node i

d _ij Actual shortest distance between node i and node j

t _ij Travel time of vehicle from node i to node j

Head count on vehicle k when it leaves node i

G is a positive infinite number

Passenger s-participated ride share

Non-ride-sharing road section with passenger s participation

Time when passenger makes reservation request

Time when passenger's ride request is allocated

/>Vehicle k gets on with someone at node i

Vehicle k gets off with someone at node i

/>Passenger s is assigned to vehicle k

Passengers s not assigned to vehicle k

/>Vehicle k passes through road network node i to road network node j

Vehicle k does not pass through road network node i to road network node j

/>Passenger s carrying vehicleVehicle k, from road network node i to road network node j

Passenger s carries vehicle k from road network node i to road network node j

The vehicle ride-through path optimizing method of the embodiment specifically includes the following aspects:

1. passenger travel satisfaction analysis

1. Travel mode selection influence factor analysis

The travel mode studied in this embodiment is mainly aimed at a non-co-riding mode and a co-riding mode, when a general passenger selects the travel mode, the general passenger is influenced by various factors, and most of the existing vehicle co-riding path optimization problems only concern the influence of the vehicle co-riding bypass distance and the riding expense, and the embodiment comprehensively considers five factors of economy, timeliness, comfort, responsiveness and safety of the co-riding service.

(1) Economical efficiency

Ride cost is an important motive force for stimulating the passenger to ride the selection together, the passenger often needs to be forced to detour, and usually a driver can charge 70% of the display cost of the taximeter for the ride-sharing road section in the taxi charge rule of the south-order city based on certain economic compensation of the passenger. In order to simplify the model, in the aspect of charging, the embodiment only considers transportation charging, and according to the charging standard of taxis in Jinan city, the starting is 3 km 9 yuan, the more 1.5 yuan per kilometer, the more the passengers hope to save the cost, the better.

Expression (1-1) represents a charging standard for riding in a normal non-ride-sharing mode, and expression (1-2) represents a charging pricing method in a ride-sharing mode.

(2) Timeliness of

In riding experience, passengers want to spend time as short as possible, and the time consumption of the passengers is mainly represented by waiting time and riding time, and the sum of the time and the riding time is equal to the time difference between the waiting time of the passengers at the riding point and the getting-off time of the passengers. While riding, a typical passenger has a desired time window for the time of ridingThe earliest entering time which is acceptable>Latest arrival time->The earliest boarding time of the passenger in this embodiment is the waiting time of the passenger at the boarding point, and expressions (1-3) and (1-4) are the same and respectively represent the time consumption of the passenger in the non-co-ride mode.

Wherein, the liquid crystal display device comprises a liquid crystal display device,represents average human GDP, h _work The annual working time of people is represented; according to the investigation of the GDP of 2016 in the south of the Chinese, the GDP of the year is 36394 yuan/person, and the working time of the year is 2200h, because the value theta of the unit time is 16.54 yuan.

(3) Social property

Enjoying social contact is an inherent cause of people participating in a collective activity, people can learn new friends through riding together and gain fun in talking with people, and social contact of the study of the present disclosure means that the more common topics of people of the same age are in a relatively closed carriage, the more comfortable the riding together is, and the comfort level in the present embodiment is mainly related to the minimum age difference of passengers in the riding together road section.

Route(s){i，j…f} (1-6)

Wherein (1-7) the node sequence set traversed by the vehicle in the travel path of passenger s, assuming that the travel path of passenger s is divided into lm segments, the number of passengers in each segment is different,representing the level of the age difference with the passenger s in the co-taking section l, phi represents the conversion rate of the average age difference level with the riding cost, 0.3 is taken here, and expressions (1-8) and (1-9) represent social benefits of the passenger in the co-taking mode and the non-co-taking mode, respectively.

(4) Safety of

The safety concern of passengers over the ride mode comes mainly from the distrust with strangers. Thus, to reduce the impact of such distrust, the present disclosure improves the fit by evaluating the affinity level from passenger to passenger and the credit level of the passenger, and studies have shown that sharing with friends and relatives can increase the willingness and tolerance of the passenger. Assuming that the system can obtain affinity level data and credit level data from person to person by connecting to data of other social networks or by analyzing bus records before passengers, if social relations between people are classified into five levels, the social relations are-2, -1,0,1,2. The level of immediate relatives is defined as 2, the level of common friends is defined as 1, and the level of strangers is defined as 0 the lower the level the greater the likelihood of passenger unsafe. The higher the credit rating of the personnel, the higher the security, assuming that the credit rating of the person is classified into five ratings of-2, -1,0,1,2. Likewise, passengers may also influence the passenger compartment's affinity and credit rating based on feedback of the sharing experience.

Wherein, the liquid crystal display device comprises a liquid crystal display device,a intimacy class coefficient indicating a person of the passenger who is most intimate with himself in the section l; />The credit factor of the passenger with the highest credit rating in the road section l is indicated. L (L) _l Representing the path length of the road section l; phi represents the conversion rate of the average safety level and the riding cost, and is 0.2; expressions (1-10) and (1-11) represent social benefits of the passenger in the co-riding mode and the non-co-riding mode, respectively.

Comprehensively analyzing influence factors in riding experience of passengers, and respectively defining travel utilities of the passengers in a co-riding mode and a non-co-riding mode as (1-12) and (1-13) as shown in the following steps:

wherein, the liquid crystal display device comprises a liquid crystal display device,as determined by the individual preferences of the passenger.

2. Individual preference demand index matrix

Because of the heterogeneity of passengers, different passengers have different preferences, such as female passengers mostly pay more attention to riding safety and riding expense, while male passengers prefer to pursue speed and the like; it is difficult to meet the needs of all passengers with the same set of metrics, so in order to meet the personalized needs of more passengers, the present disclosure adjusts the ride-through strategy by building an individual preference demand matrix.

For n unreserved passengers, the demand degree of each passenger on economic, time-efficiency, social and safety indexes of riding is different, so that the passenger individual preference demand index matrix is as follows:

Each row in the X individual preference requirement matrix represents the preference degree of each passenger for four indexes of economy, timeliness, social property and safety, and the calculation of the travel utility weights in (1-12) and (1-13) is determined by the individual preference of the passenger, namely,where the matrix can be autonomously assessed by the passenger at the ride reservation, but requires x _ij ∈[0,1]At the same time->

When x is _i1 ＝max(x _i1 ,x _i2 ,x _i3 ,x _i4 ) When the passenger preference is defined as an economy preference type;

when x is _i2 ＝max(x _i1 ,x _i2 ,x _i3 ,x _i4 ) When, defining the passenger preference as a speed preference type;

when x is _i3 ＝max(x _i1 ,x _i2 ,x _i3 ,x _i4 ) When the passenger preference is defined as a social preference type;

when x is _i4 ＝max(x _i1 ,x _i2 ,x _i3 ,x _i4 ) When the passenger preference is defined as a security preference type.

3. Travel satisfaction function construction

Satisfaction of passengers riding is affected by various random factors such as weather conditions, personal moods and the like, in addition to important indexes such as economy, timeliness, social contact performance, safety and the like in riding experience. None of these random factors can be quantified, so the present disclosure uses a random utility theory to estimate passenger satisfaction, equating the probability of theoretically selecting a ride-sharing mode to the passenger satisfaction level.

Wherein, v represents a taxi taking mode, and is represented as a co-taking mode when being 1, and is represented as a single-taking mode when being 2; Indicating the travel utility of passenger s in mode v,/->The maximum travel utility of the two travel modes is represented. The passenger satisfaction in the simultaneous multiplication and non-simultaneous multiplication modes can be found according to the formulas (3-15), as shown in the formulas (1-16) and (1-17).

2. Model construction

1. Optimization objective analysis

(1) Income of operation

The operation income refers to the direct income obtained by all drivers to pick up passengers, and is the support for maintaining the dispatch operation of the system and the key for attracting more drivers to join the platform. The operating revenues consist of the expenditure of passengers in all co-ride and non-co-ride modes.

(2) Cost of transportation

The transportation cost of the vehicle mainly refers to the fuel cost generated by fuel consumption in the driving process of the vehicle, and generally, the farther the driving distance is, the more the generated fuel cost is, so the transportation cost is proportional to the driving distance of the vehicle, and can be expressed as:

(3) Cost of idling

The earliest boarding time acceptable by passengers can be reached in advance, so that the vehicles wait for idle, and the waiting time is equal to the period that resources are not fully utilized, and a certain loss is generated. More ride demands can be serviced if latency is utilized. The idle cost is as follows:

(4) Satisfactory reduction of loss costs

The service satisfaction degree of passengers on the system is reduced, which causes loss of the passengers, and is unfavorable for the development of a platform in long-term benefit, and the average satisfaction degree of the passengers on the system is expressed as:

In summary, the analysis balances the benefits of both the driver and the passenger of the vehicle and simplifies the model to convert the multi-objective problem into a single objective.

2. Constraint analysis

(1) Node balancing constraints

Node equalization is that the traffic flow of each node is equal. Wherein the expressions (1-23) ensure that all vehicles start from the current initial position, (1-24) (1-25) ensure that passengers who have been reserved successfully before are always served to carry vehicles from the reserved starting point to the corresponding end point, and (1-26) and (1-27) indicate that the traffic of the nodes is conserved, and the traffic of any node is equal.

/>

(2) Paired constraint

Paired restraint means that any passenger's pick-up and drop-off points need to be serviced in pairs by the same vehicle.

(3) Response constraints

The response indicates that a reservation request for a passenger is at most serviced by a vehicle response, i.e., without limitation, that a reservation requiring all passengers must be satisfied, allowing the vehicle to reject the passenger.

(4) Adjacent constraint

The adjacent constraint represents a condition that any two nodes can be continuously accessed by the same vehicle, and firstly, the earliest boarding time and the latest arrival time which can be accepted by a departure point and a destination in reservation information of passengers are obtained, and the latest boarding time and the earliest arrival time which meet the conditions are calculated as formulas (1-/30) and (1-31). If, in the most ideal case, the vehicle does not have any detour delay in the middle from the earliest arrival time of the previous node, it still fails to arrive before the latest arrival time acceptable for the next node, indicating that the two nodes cannot be adjacent, denoted (1-32).

(5) Time constraint

The time constraint indicates that the vehicle should serve the passenger for a time period acceptable for the passenger's reservation, and the expression (1-33) (1-34) indicates that the vehicle reaches the start of the passenger getting on and the end of getting off within the time window.

(7) Order constraint

Order constraint refers to the fact that the access order of the same vehicle to the boarding location of the same passenger should precede the alighting location;

(8) Capacity constraint

Capacity constraints, mainly passenger and object carrying constraints. Wherein the passenger carrying constraint is classified into a passenger carrying constraint which is willing to take passengers together and a passenger carrying constraint which is unwilling to take passengers together, and the passenger carrying constraints are respectively indicated as (1-36), (1-37) and (1-38) which indicate that the number of the vehicle carried by the vehicle cannot exceed the maximum carried number of the vehicle at any time.

(9) Value range constraint

The decision variables are 0 and 1, and are expressed as (1-39).

3. Algorithm optimization design

The genetic algorithm has high flexibility, can break through the limitation of objective functions and heavy constraints, has stronger compatibility, and can be fused with various optimization algorithms. Although the genetic algorithm has strong global searching capability, the local searching capability is weak, the early ripening phenomenon is easy to occur, the simulated annealing algorithm is simple to operate, the complicated nonlinear problem is facilitated to be simplified, and the genetic algorithm has strong robustness and local searching capability, but the global searching capability is poor. The genetic algorithm and the simulated annealing algorithm are fused to effectively complement each other, but aiming at the problem of dynamic vehicle ride sharing, the algorithm needs to be improved aiming at the specific problem.

1. Genetic algorithm element design

To accommodate the high update frequency of the co-multiplication information state, the present embodiment selects a genetic algorithm with a parallel computing scheme. Although genetic algorithms find wide application in optimization problems, traditional genetic algorithms, due to their relatively fixed genetic strategies, have difficulty achieving desirable results in complex situations. In order to further improve the performance of the algorithm, the genetic algorithm needs to be further improved aiming at the problem of multi-vehicle ride sharing.

2. Chromosome coding and decoding design

Because the vehicle ride-sharing route scheme is a problem of having a plurality of passenger carrying lines of a plurality of vehicles, and the starting position of each vehicle is different, the departure point, destination, accepted service time range, ride-sharing intention and luggage carrying quantity of each passenger are also different. Aiming at the characteristics of the model, the embodiment adopts the coding according to the road network node number, namely the conveying route is taken as a chromosome, and the road network node is taken as a gene on the chromosome for coding.

The coding rules are implemented by adding "zeros", each time "zero" representing the addition of a vehicle driver. For a route, the road network nodes are arranged in a certain order, and a plurality of zeros are added in the road network nodes to divide the whole route into a plurality of route segments, and each route segment is responsible for delivery by a driver. The boarding point and the alighting point of any other passengers, except for the on-board passengers in the initial state, must be in the path of the same vehicle; in addition to 0, the number of each node contains a set of attribute values such as position coordinate information, time window information, passenger loading number at the node, and willingness to ride. The order of the different nodes in a single vehicle path characterizes the different paths, and a change in the order of the nodes within a single path causes a change in the solution. When the order of the path nodes of the single vehicle is unchanged, the arrangement order of the vehicles is changed, and the value of the solution is not affected.

The chromosomes 1-10 represent the current location nodes of the vehicle, and the path number of the vehicle 2 in this chromosome is 21-13-14-22, where 2 is the current location of the vehicle, 13 represents the boarding location of the passenger 2, and 14 represents the alighting location of the passenger 2. The next to last 0 indicates a passenger riding node to which the current cycle is temporarily not allocated to wait for allocation of the next cycle, and the last 0-th node indicates a passenger riding node to which the reservation request is denied.

Parent 1:0 1 11 12 0 21 2 13 14 22 … 0 35 36 0 17 18

3. Generation of initial population

In genetic algorithms, usually the initial population is randomly generated, but in the problem of temporary co-riding of vehicles, the passengers get on and off the vehicle nodes in strict sequence, and the vehicles need to meet the pair constraint, the time window constraint, the capacity constraint and the riding intent constraint. If a random generation method is adopted, a large number of invalid solutions are generated, and the operation efficiency of the algorithm is greatly reduced. Therefore, the embodiment adopts a method of inserting the passenger origin-destination pairs on the basis of randomly sequencing the passenger numbers, generates a plurality of row feasible solutions and constructs an initial population; the specific generation steps of the population are as follows:

The first step: calculating a time continuity judgment matrix TC between all passengers;

and a second step of: for m vehicles, generating m+2 0 as the marker bits of the start and the end of each vehicle path;

and a third step of: assigned l for each vehicle for the last time period _k With reservation passengers (including already-delivered destinationPassengers, passengers on board and passengers assigned but not on board) without changing the path node position. Inserting it into a corresponding vehicle path;

fourth step: randomly scrambling and sorting the numbers of NA passengers which are newly and unassigned, wherein the number sequence is NA; randomly scrambling and sorting numbers of the NB passengers which are not allocated and are not willing to ride together, wherein the number sequence is NB;

fifth step: inserting origin-destination positions of non-assigned passengers willing to ride together into a path by pair insertion

I. Starting from the path of the first vehicle, the current vehicle number k=1, and selecting the start number en(s) and the end number ln(s) of the passenger s in the NA number order;

II, trying to insert en(s) into the initial path of the vehicle k after the current position of the vehicle (namely, the node k), and firstly judging whether the node has a sharing willingness. If not, moving backwards by 2 bits, and retrying insertion; if so, it is determined whether the point satisfies the time constraint according to the value of the time continuity determination matrix TC. If yes, the point is temporarily placed at the position, and the next step is carried out; if not, sequentially moving backwards by 1 bit until the vehicle is inserted to the tail of the vehicle path, and if the vehicle is not successfully inserted to the tail, repeating the step to try to insert the en (s+1) of the passenger selected next;

Attempting to insert ln(s) into the path of vehicle k from the next bit of en(s), first determining if the node has a co-riding intent. If not, moving backwards by 2 bits, and retrying insertion; if yes, judging whether the inserted point meets the time constraint, if yes, temporarily placing the point at the position, returning to the step II, and selecting the getting-on and getting-off node of the next passenger to insert; if not, sequentially moving backward by 1 bit until the vehicle is inserted to the end of the vehicle path, and if the insertion is not successful until the end, moving ln(s) and en(s) of the passenger together from the vehicle path R _k Returning to the step II, and selecting the getting-on and getting-off node of the next passenger to insert;

after traversing all passengers in NA, judging the capacity constraint of the generated path, if any one of en(s) and ln(s) is not satisfied, then the passenger is determined to be the passengerIn (c) and en(s) together from the vehicle path R _k To determine passenger set B for vehicle k service _k ；

V. will gather B _k The passenger number in (a) is deleted from the sequence N, and NA is updated.

Sixth step: inserting origin-destination positions of non-assigned passengers not willing to ride together into paths by paired insertion

I. Starting from the path of the first vehicle, the current vehicle number k=1, and selecting the start number en(s) and the end number ln(s) of the passenger s in NB number order;

II, trying to insert en(s) into an initial path of the vehicle k from the current position (namely a node k) of the vehicle, firstly judging whether the number of passengers in the current node is 0, if not, moving backwards by 1 bit to try to insert again; if the node is 0, judging whether the node and en(s) meet the time constraint according to the value of the time continuity judgment matrix TC, if so, judging whether ln(s) can be adjacent to the next node of the node in time, if the node and en(s) meet the time constraint in time, inserting the en(s) and the ln(s) into the node in pairs adjacently, then selecting the upper node and the lower node of the next passenger in the NB to try to insert, and repeating the steps. If not, moving 1 bit backwards to try to insert again, if the insertion is not successful until the vehicle is moved to the tail end, selecting the upper and lower nodes of the next passenger in the NB to try to insert, and repeating the steps;

and III, after traversing all passengers in the NB once, the successfully inserted passenger s numbers are deleted from the NB, and the NB is updated.

Seventh step: returning k=k+1 to the step five, and determining the next vehicle path until all vehicle paths are finished. If the passenger is not allocated finally, the passenger is rejected, and after ln(s) and en(s) of the passenger are placed in the last 0 zone bit in sequence, the paths of all vehicles are connected to form a single body.

Eighth step: returning to the fourth step until M individuals meeting the population number are formed.

4. Calculation of fitness value

The fitness value of a genetic algorithm is used to characterize the fitness of an individual to an environment, the value of the fitness value of an individual directly affects the individual's chance of survival, and the probability that a gene is selected for inheritance to the next generation. According to the multi-vehicle path optimization model, the objective function value of the model is selected as a fitness value, and the larger the objective function value in the model is, the larger the overall income is, the better the route optimization effect is, the larger the fitness value is, and the larger the probability that the gene of the individual inherits to the next generation is.

5. Genetic operator improved design

Genetic manipulation mainly involves selection, crossover and mutation of chromosomes.

(1) Individual selection

And selecting chromosomes with high fitness in the population to enter the next genetic operation by taking the superior and inferior jigs as selection mechanisms, wherein the chromosomes are key operators for ensuring the continuous improvement of the overall fitness of the population. In the traditional genetic algorithm, only the offspring is selected, because of the complexity of the model, the generation of a feasible solution is difficult, most of the time for the algorithm to run is needed to be consumed, and a new individual, namely a new feasible solution, is generated by each step of genetic operation of intersecting and mutating a parent chromosome. In order to improve the operation efficiency of the algorithm and ensure that the optimal solution in the population must enter the next generation, the composition of the next generation individuals consists of a partial optimal population of the parent population, a partial optimal population of the crossed population and a partial optimal population after mutation. The method comprises the following specific steps:

The first step: selecting 1/4 individuals with optimal population from father according to fitness person, and placing into a set U ₁ ；

And a second step of: selecting optimal 1/4 individuals from the population after male parent hybridization according to fitness, and placing into a set U ₂ ；

And a third step of: selecting optimal 1/4 individuals from the population after father variation according to fitness, and putting into a set U ₃ ；

Fourth step: selecting optimal 1/4 individuals from the population after variation of the hybrid generation according to fitness, and putting into a set U ₄ ；

Combining the optimal individuals to obtain a next generation population U=U ₁ ∪U ₂ ∪U ₃ ∪U ₄ 。

(2) Individual crossing

The purpose of individual crossing is to allow the offspring to inherit as much of the superior genes of the father as possible, forming individuals with higher fitness, which simulates the genetic evolution of an organism. Conventional crossover operations include single-point crossover, double-point crossover, and the like. However, the access sequence of the nodes on the running path of each vehicle in the model is divided successively, and the start and stop points of the same passenger need to be accessed by the same vehicle at the same time, if a traditional crossing mode is adopted, the sequence of solutions is disordered, and a large number of infeasible solutions are formed. Harbaoui et al solve the infeasible problem of solution by modifying the point-to-point sequence after crossing, but this approach not only reduces the speed of operation, but also reduces the genetic efficiency of good properties in offspring because the crossing destroys the structural properties of the parent gene. In order to solve the problem, the present embodiment adopts a crossover operator based on vehicle path segmentation to ensure the calculation efficiency of generating new excellent feasibility in the crossover process. The steps of crossing are as follows:

The first step: randomly selecting two chromosomes from the parent population, then randomly selecting a vehicle, and selecting paths of the vehicle corresponding to the two chromosomes, wherein gray shading font numbers represent the gene segments selected respectively;

parent 1:0 1 16 17 0 21 2 13 14 22 … 0 35 36 0 17 18

Parent 2:0 1 11 13 14 12 0 21 2 16 17 22 … 0 0 17 18

And a second step of: and exchanging gene segments corresponding to the two parent chromosomes to generate two new chromosomes. Since the two gene fragments have the same part and different parts, deletion and duplication of the gene occur on the chromosome, and the initially transformed chromosome is as follows:

offspring 1:0 1 16 17 0 21 2 16 17 22 … 0 35 36 0 17 18

Progeny 2:0 1 11 13 14 12 0 21 2 13 14 22 … 0 0 17 18

And a third step of: deleting repeated genes, wherein the father 2 and the father 1 are different genes (16 17), and for the offspring 1, the repeated genes are deleted, and the repeated gene formation mechanism of the offspring 2 is the same as that of the offspring 1;

offspring 1:0 1 0 21 2 16 17 22 … 0 35 36 0 17 18

Progeny 2:0 1 11 12 0 21 2 13 14 22 … 0 0 17 18

Fourth step: filling the missing genes, wherein the genes (13) of the father 1 and the father 2 are different, and for the offspring 1, the genes belonging to the missing need to be filled, and the formation mechanism of the missing genes of the offspring 2 is the same as that of the offspring 1; aiming at the filling method of the missing genes, a forward pair insertion method is adopted; because the gene coding sequence of the offspring is disordered, the passenger node genes rejected in the parent are likely to be served, so that the gene number after the last 0 marker bit in the parent is reinserted into the chromosome together with the deletion gene.

Offspring 1:0 1 0 21 2 16 17 22 … 0 35 36 0 17 18

Gene 13 14 35 36 to be inserted into progeny 1

Progeny 2:0 1 11 12 0 21 2 13 14 22 … 0 0 17 18

Gene 16 17 required for insertion into progeny 2

By the crossover operation in this way, it is ensured that the chromosomes remain viable solutions after crossover, and as many excellent genes of the parent are inherited as possible.

(3) Individual variation

Mutation operator is to make another filial generation appear different from parent's gene, so as to increase diversity of population, raise local searching capability of algorithm and prevent algorithm from being matured. Common mutation operations include single-point mutation, transposition mutation and the like, but the conventional method is likely to generate infeasible solutions, and the method for integrally exchanging mutation of a single vehicle path is adopted in the embodiment, and comprises the following specific steps:

the first step: randomly selecting two vehicle paths of 1 chromosome, wherein the number of current on-vehicle people of the two selected vehicles is 0;

parent 1:0 1 16 17 0 21 22 2 19 14 13 20 … 0 0 17 18

And a second step of: the method comprises the steps of dividing the initial positions of vehicles and the number sequence of passengers with the initial reservation allocation on the vehicles into a group by using the bus nodes of the passengers without the vehicles and the passengers without the allocation, scrambling the serial numbers of the passengers, and arranging the paths of the next vehicles and the bus paths of the previous vehicles according to a pairwise insertion method of reverse order, wherein the aim is to prevent the service number of the passengers of the vehicles with the previous numbers from being far greater than that of the vehicles with the subsequent numbers.

Offspring 1:0 1 0 21 22 2 19 14 13 20 … 0 0 17 18

It is desirable to insert the gene 16 17 19 14 13 20 in progeny 1.

5. Adaptive control parameter design

The performance of the genetic algorithm is affected by the change of the control parameters to a certain extent, while the traditional standard genetic algorithm adopts fixed crossover probability and variation probability, and the probability size selection is generally determined by subjective experience and cannot well adapt to the problem of complex change. The self-adaptive control method and the self-adaptive control system of the population are capable of connecting individuals in the population with the whole body through self-adaptive adjustment of the cross probability and the variation probability, influencing individual change according to the state change of the whole body of the population, and avoiding damage to a better individual structure or occurrence of premature phenomenon to a greater extent through self-adaptive control parameters of the algorithm.

(1) Adaptive adjustment of crossover probabilities

The magnitude of the crossover probability directly affects the speed of the new individual. The greater the probability, the faster the population changes. However, if the cross probability is too large, the better individual structure may be damaged, and the optimal solution is difficult to obtain; however, if the crossover probability is too small, the progress of the optimization is slowed down. Thus, to obtain a faster convergence speed and a higher quality solution, it is necessary to transform the fixed crossover probability into a value that can adaptively adjust itself according to population changes.

In the iterative optimization process, in order to enable the population to rapidly evolve towards a better direction, individuals with low fitness values need to be subjected to high cross probability; smaller crossover probabilities are taken for individuals with higher fitness values. To represent the changing state of the population in the genetic evolution process. The present disclosure first introduces an inverse coefficient of variation ζ to characterize the degree of variation of the population as a whole from the preferred individuals of each generation.

Wherein N represents the number of individuals in the population, f _max And the maximum fitness value in the ith generation of population individuals is represented. The greater the individual and maximum fitness value difference, the smaller ζ. The value range of the crossover probability is p _c ∈[0.4,0.9]The method comprises the steps of carrying out a first treatment on the surface of the I.e. p _cmax 0.4, p _cmax 0.9. In order to prevent the iteration from being in local optimum after a plurality of times, the upper limit and the lower limit of the crossover probability are changed by adjusting zeta. Denoted as (4-2), the improved crossover probability formula is (4-3).

Wherein, the liquid crystal display device comprises a liquid crystal display device,respectively representing the upper and lower limits of the cross probability after the j-th generation population is regulated; />The crossover probability of chromosome i representing the adjusted j-th generation population; />Fitness value of chromosome i representing the jth generation population,/->The average fitness value of the j-th generation population is represented, and the value of A is 9.803438.

(2) Adaptive adjustment of variation probability

Although the probability of variation is small, it also plays a crucial role. The mutation operation can not only increase the diversity of the population, but also avoid the algorithm from being trapped into local optimum, but if the mutation probability is too large, the better individual structure can be damaged as well; however, if the crossover probability is too small, the genetic algorithm becomes too random and lacks directionality. Thus, the variation probability also needs to be adaptively adjusted to its own value. The adaptive tuning principle is like crossover probability variation.

The variation probability is usually 0.001-0.1, the upper and lower limit values of the crossover probability are changed by adjusting ζ, which is expressed as (4-4), and the modified variation probability formula is expressed as (4-5).

Wherein, the liquid crystal display device comprises a liquid crystal display device,respectively representing the upper and lower limits of the variation probability after the j generation population is regulated; />Expressing the mutation probability of chromosome i of the j-th generation population after adjustment; />Fitness value of chromosome i representing the jth generation population,/->The average fitness value of the j-th generation population is represented, and the value of A is 9.803438.

6. Improved design of simulated annealing algorithm

The simulated annealing algorithm has strong deep searching capability, and can make up the defect of poor local searching capability of the genetic algorithm. In the embodiment, a new individual is obtained through genetic algorithm operation, then an objective function value is calculated on the new individual, and finally new solution acceptance judgment is performed through Metropolis criterion. The simulated annealing algorithm steps are as follows:

The first step: obtaining a current solution, and setting a current temperature value T _i ；

And a second step of: generating a new solution Nesolution for current solution adjustment, solving a new solution objective function value A, wherein the original objective function value is B, delta f is used for representing increment of the objective function value, namely delta f=A-B, the Metropolis criterion represents that when increment of the objective function value is larger than 0, the probability of accepting the new solution by the system is 1, otherwise, the probability is represented by the probabilityThe new solution is discarded.

And a third step of: cooling according to the attenuation rate r, and updating the temperature value T _i+1 ＝T _i ·r；

Fourth step: judging termination conditions, and if the conditions are met, turning to a fifth step; otherwise, turning to the second step;

fifth step: and outputting the current solution.

The embodiment 1 of the disclosure also provides a vehicle ride path optimizing system, which comprises a data acquisition module, a data preprocessing module, a server module and a processor module, wherein the data acquisition module comprises a plurality of data acquisition terminals and is used for acquiring real-time riding data of vehicles, roads and passengers;

The foregoing description of the preferred embodiments of the present disclosure is provided only and not intended to limit the disclosure so that various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. The vehicle ride-sharing path optimizing method is characterized by comprising the following steps of:

101, collecting real-time data of vehicles, roads and passengers riding, presetting a data collection area, screening and eliminating the data outside the collected area according to the preset area, and temporarily storing the data in the area;

102, taking the highest income of a driver, the shortest driving distance of a vehicle, the least idle time of the vehicle and the highest satisfaction of passengers as optimization targets, and respectively aiming at three passenger types of on-vehicle passengers, reserved and allocated non-on-vehicle passengers and reserved and unallocated passengers, establishing a multi-vehicle simultaneous taking matching and path optimization model with constraint conditions;

103, according to the temporarily stored real-time riding data of the vehicles, roads and passengers, solving a multi-vehicle simultaneous riding matching and path optimizing model by utilizing an improved genetic algorithm and a simulated annealing algorithm to obtain an optimal objective function value, and carrying out matching and path planning on the vehicles;

in step 103, the improved genetic algorithm comprises a coding design of a chromosome, generation of an initial population and an improved genetic operator design, wherein the coding design of the chromosome is coded according to the number of road network nodes, namely, a transportation route is taken as the chromosome, and the road network nodes are taken as genes on the chromosome;

the generation of the initial population comprises the following steps:

501 calculating a time continuity judgment matrix TC between all passengers;

2. The vehicle ride-sharing route optimizing method according to claim 1, wherein in the step 101, the passenger satisfaction includes a non-ride-sharing mode travel satisfaction and a ride-sharing mode travel satisfaction, the non-ride-sharing mode travel satisfaction being:

the travel satisfaction degree of the co-riding mode is as follows:

3. The method for optimizing a vehicle ride-sharing path according to claim 1, wherein in the step 101, the multiple-vehicle ride-sharing matching and path optimizing model is as follows:

Where k is the number of the vehicle, s is the number of the passenger, M is the set of all the vehicles providing the service, N is the set of all the reserved passengers, N is the total number of reserved passengers, W is the set of all the road network nodes,travel expense for co-riding->For non-ride travel cost, μ is the cost per distance of vehicle transport, +.>Representing a vehicle k passing through road network node i to road network node j,representing that vehicle k does not pass through road network node i to road network node j, d _ij Representing a road network node iDistance to road network node j, +.>Passenger s ride satisfaction, +.>And is not ride satisfaction for passenger s.

4. The vehicle ride-sharing path optimization method of claim 1, wherein the genetic operator improvement design includes selection, crossover, and mutation of chromosomes, the chromosome selection step comprising:

5. The vehicle ride-sharing path optimizing method of claim 4, wherein the crossing of the chromosomes step comprises:

703, and filling the deleted genes by a forward pair insertion method.

6. The vehicle ride-sharing path optimizing method of claim 4, wherein the chromosome mutation step is:

7. The vehicle ride-sharing path optimizing method according to claim 1, wherein in the step 103, the simulated annealing algorithm is as follows:

901 obtaining a current solution, and setting a current temperature value T _i ；

905 outputs the current solution.

8. A vehicle ride-sharing path optimizing system, characterized in that the path optimizing method according to any one of claims 1-7 is adopted for path optimization, and the system comprises a data acquisition module, a data preprocessing module, a server module and a processor module, wherein the data acquisition module comprises a plurality of data acquisition terminals for acquiring real-time riding data of vehicles, roads and passengers;