CN112270480B - Automatic driving taxi dispatching method and dispatching system - Google Patents

Automatic driving taxi dispatching method and dispatching system Download PDF

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CN112270480B
CN112270480B CN202011199726.4A CN202011199726A CN112270480B CN 112270480 B CN112270480 B CN 112270480B CN 202011199726 A CN202011199726 A CN 202011199726A CN 112270480 B CN112270480 B CN 112270480B
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CN112270480A (en
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魏玉光
段乐毅
张进川
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Beijing Jiaotong University
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Abstract

The invention relates to the field of vehicle dispatching, in particular to an automatic driving taxi dispatching method and dispatching system, which comprise the following steps: acquiring riding request information; when the time difference between the riding time and the current moment exceeds a preset threshold value, transmitting riding request information to an operating vehicle nearest to the passenger position; when the number of the reserved riding requests in the current period exceeds a second preset threshold, the passenger request distribution and the recent travel of the vehicle are subjected to periodic rolling optimization, and when the number of the newly-appearing reserved riding requests is within the second preset threshold, the operation vehicle meeting riding requirements is determined according to the insertion cost calculated by each scattered autonomous machine. The automatic driving taxi dispatching method and the dispatching system solve the problems of low efficiency and large occupied idle driving mileage of the existing dispatching method under the condition of timely taking a bus and reserved taking the bus.

Description

Automatic driving taxi dispatching method and dispatching system
Technical Field
The invention relates to the technical field of vehicle dispatching, in particular to an automatic driving taxi dispatching method and dispatching system.
Background
In recent years, automatic driving technology has rapidly progressed. Although the automatic driving vehicle is not put into formal operation at present, numerous researches show that the introduction of the automatic driving vehicle is expected to have revolutionary influence on a traffic system. Meanwhile, the shared travel service is popular in recent years due to the characteristics of convenience, high accessibility, low travel cost and the like. The combination of the automatic driving technology and the taxis presents a wide development prospect.
The existing scheduling method has the problem that the scheduling problem of the vehicle under the condition that the timely demand and the reservation demand coexist is not considered. Timely ride demands make it impossible for the dispatch system to fully learn all future ride demands, and this future incomplete information creates uncertainty in the future location and status of the vehicle, thus making it difficult to determine whether a service vehicle that is being sought for a ride demand that is farther away from the departure time is appropriate. The newly emerging ride demands, in turn, have different desired departure times, and in assigning the newly emerging ride demands, consideration needs to be given to whether the vehicle can complete them within its available time. These all increase the difficulty in making the passenger pick-up journey. In addition, for the empty allocation journey, not only the possible occurrence area and the possible departure time of the future unknown demand are predicted, but also the possible arrival area and the possible arrival time of the future unknown demand are predicted. Because when empty car allocation is carried out, whether the task of empty car allocation can be completed or not at the gap of receiving passengers or not needs to be considered, and whether the effect of empty car allocation can be achieved or not.
Disclosure of Invention
The invention provides an automatic taxi driving dispatching method and a dispatching system, which are used for solving the problems of low efficiency and large occupied mileage of the existing dispatching method under the condition of timely taking a bus and reserved taking the bus.
The technical scheme for solving the problems is as follows: the automatic taxi dispatching method is characterized by comprising the following steps of:
step 1: obtaining riding request information, wherein the riding request information comprises departure place information, destination information and riding time;
step 2: judging whether the time difference between the riding time and the current moment is within a first preset threshold value range, and sending riding request information to an operating vehicle nearest to the passenger position when the time difference exceeds the preset threshold value;
when the time difference value is within a preset threshold value, executing the step 3;
step 3: judging whether the reserved riding demand quantity in the current period exceeds a second preset threshold value, and if the reserved riding demand quantity in the current period exceeds the second preset threshold value, performing periodic rolling optimization processing on passenger request distribution and the recent travel of the vehicle;
and if the reserved demand quantity in the current period is within a second preset threshold range, sending the riding request information to the scattered autonomous machines of each operation vehicle, and determining the operation vehicle meeting the riding demand according to the insertion cost calculated by each scattered autonomous machine, wherein the insertion cost is the running cost added in the riding receiving and sending journey by adding the riding request information.
Preferably, the step of step 2, after receiving the riding request information, the decentralized autonomous machine for operating the vehicle includes:
step 201: judging whether the riding request information and the vehicle receiving and sending travel information can be linked, if so, adding the riding request information into the vehicle receiving and sending travel; if not, executing step 202;
step 202: transmitting the riding request information to nearby operating vehicles;
step 203: repeating the steps 201 and 202 until a vehicle capable of receiving the riding request information is found, and rejecting the riding request information if all vehicles cannot receive the riding request information;
the vehicle pickup travel comprises a vehicle near travel and a vehicle far travel, wherein the vehicle near travel is the sum of all riding requirements in a preset period.
Preferably, in the step 3, the step of performing the periodic rolling optimization processing on the passenger request information and the recent travel of the vehicle specifically includes:
301: receiving bus demand information and preprocessed vehicle information sent by scattered autonomous machines of each operating vehicle, wherein the bus demand information comprises unassigned predicted bus demands, all bus request information within a certain time interval, assigned predicted bus demands and accepted recent bus demands, and the vehicle information comprises available states of all operating vehicles and recent variable strokes of the vehicles;
302: constructing a centralized optimization model according to the vehicle information and the riding information, and adopting an algorithm to perform rolling optimization solution to obtain an optimized recent vehicle journey;
303: sending the optimized recent travel of the vehicle to a distributed autonomous machine of a corresponding operation vehicle;
304: the latest states of the vehicle information and the riding demand information are acquired every fixed time, and steps 301 to 303 are repeated.
Preferably, the step 302 specifically includes:
constructing a vehicle path planning network according to the vehicle information and the riding information;
and constructing a minimum cost flow model based on a vehicle path planning network, and solving by a network simplex method to obtain the optimized recent vehicle journey.
Preferably, the specific steps of preprocessing the vehicle information by the distributed autonomous machine in the step 301 are:
screening a recent variable course of the vehicle, which is a passenger pickup course exceeding ten minutes;
estimating the available state of the operation vehicle;
a plurality of consecutive ride demands are consolidated.
Preferably, the step of the decentralized autonomous machine in step 303 after receiving the optimized recent travel of the vehicle includes:
adjusting the optimized recent travel of the vehicle;
calculating the calculation cost of the travel route according to the recent travel of the vehicle, the allocation travel of the empty vehicle and the road condition information, and making a specific travel route for the vehicle by using a Dijkstra algorithm.
Preferably, the step of adjusting the recent travel of the vehicle specifically includes:
3011: judging whether the optimized recent travel of the vehicle is connected with the vehicle long-term travel, if so, executing the optimized recent travel of the vehicle; if not, executing step 3012;
3012: and transmitting incompatible riding demands in the long-term travel of the vehicle to a scattered autonomous machine of a nearby operating vehicle from the near to the far until the riding demands can be accepted by a certain vehicle. If the ride request is not acceptable to either vehicle, the ride request remains on the original vehicle long range;
3013: judging whether the vehicle near-term travel and the vehicle far-term travel processed in the step 3012 can be linked, if so, executing the processed vehicle near-term travel, otherwise, executing the step 3014;
3014: rejecting incompatible riding demands in the recent travel of the vehicle according to the riding demand mode, and rejecting the predicted demands when the riding demand mode is the allocated predicted demands; rejecting the ride request information when the ride request mode is the ride request information, and returning to the original distribution vehicle when the ride request mode is the received recent ride request;
305: steps 3011-3014 are repeated until the engagement of the near travel of the vehicle and the far travel of the vehicle is feasible.
In addition, the invention also provides an automatic taxi dispatching system, which is characterized by comprising
The centralized dispatching platform is used for performing centralized optimization on the recent vehicle journey of the operation vehicle;
the scattered autonomous machines are mounted on the automatic driving taxis and used for receiving the optimization results sent by the centralized dispatching platform, detecting and adjusting the optimization results, planning the driving route of the operation vehicle and maintaining the long-term travel of the vehicle.
Preferably, the centralized scheduling platform comprises a riding demand prediction module and a centralized optimization module;
the riding demand prediction module is used for predicting and updating the recently unknown riding demand in real time;
the centralized optimization module is used for optimizing the distribution of the recent travel and the riding request of the vehicle.
Preferably, the autonomous dispersing machine comprises
And a pretreatment module: the method comprises the steps of preprocessing vehicle information;
a transmitting module; the vehicle near-term travel system is used for sending the vehicle near-term travel to the centralized dispatching platform;
and a receiving module: the vehicle near-term travel optimization system is used for receiving the optimized vehicle near-term travel sent by the centralized dispatching platform;
an autonomic module: the method comprises the steps of checking whether an optimized passenger pick-up stroke and an empty allocation stroke are feasible or not, and if not, adjusting the passenger pick-up stroke and the empty allocation stroke until the passenger pick-up stroke and the empty allocation stroke are feasible;
and a driving route planning module: for planning a travel route of the vehicle;
riding demand distribution module: for distributing ride requests.
Compared with the prior art, the invention has the beneficial effects that:
1) By adopting a rolling optimization mode, the receiving and sending travel of passengers and the allocation travel of empty vehicles can be adjusted in real time according to the dynamic change of the riding demand information, and the empty driving mileage of the vehicles is effectively reduced.
2) The remote passenger pick-up journey of the vehicle is maintained by the vehicle scattered autonomous machine, the intelligence of the automatic driving vehicle is fully utilized, the calculated amount of the centralized dispatching platform is greatly reduced, and the calculation efficiency of the centralized dispatching platform is improved.
3) When the centralized dispatching platform optimizes the near-term passenger pickup travel and the empty vehicle dispatching travel, potential conflict with the long-term passenger pickup travel does not need to be considered, and the computing efficiency of the centralized dispatching platform is further improved.
4) The centralized dispatching platform is used for carrying out cooperative optimization on the recent vehicle travel and the empty vehicle dispatching travel, so that the mutual influence of the recent vehicle travel and the empty vehicle dispatching travel can be fully considered, and the quality of the recent vehicle travel and the empty vehicle dispatching travel is improved.
5) The long-term passenger pickup strokes of the vehicle do not need to be performed immediately, and over time they may be converted into near-term passenger pickup strokes, resulting in global optimization of the centralized dispatch platform, so that the delivery of the long-term passenger pickup strokes to the vehicle decentralized autonomous maintenance does not degrade the performance of the dispatch system.
Drawings
FIG. 1 is a flow chart of a ride request allocation and recent vehicle travel collaborative optimization;
FIG. 2 is an exemplary diagram of a vehicle path planning network;
fig. 3 is a diagram illustrating an initial solution of the network simplex method.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, based on the embodiments of the invention, which are apparent to those of ordinary skill in the art without inventive faculty, are intended to be within the scope of the invention. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention.
The automatic taxi dispatching method comprises the following steps:
step 1: obtaining riding request information, wherein the riding request information comprises departure place information, destination information and riding time;
step 2: judging whether the time difference between the riding time and the current moment is within a first preset threshold value range or not, and sending riding request information to an operating vehicle nearest to the passenger position when the time difference exceeds the preset threshold value;
when the time difference value is within a preset threshold value, executing the step 3;
step 3: judging whether the reserved riding demand quantity in the current period exceeds a second preset threshold value, and if the reserved riding demand quantity in the current period exceeds the second preset threshold value, performing periodic rolling optimization processing on passenger request distribution and the recent travel of the vehicle;
and if the reserved riding demand quantity in the current period is within a second preset threshold range, transmitting riding request information to the scattered autonomous machines of each operation vehicle, and determining the operation vehicle meeting the riding demand according to the insertion cost calculated by each scattered autonomous machine, wherein the insertion cost is the running cost added in the riding receiving and sending journey by adding the riding request information.
As a preferred embodiment of the present invention: the steps executed by the scattered autonomous machines of the running vehicles after receiving the riding request information in the step 2 include:
step 201: judging whether the riding request information and the vehicle receiving and sending travel information can be linked, if so, adding the riding request information into the vehicle receiving and sending travel; if not, executing step 202;
step 202: transmitting riding request information to nearby operating vehicles;
step 203: the nearby operating vehicles repeat the steps 201 and 202 until a vehicle capable of receiving the riding request information is found, and if all vehicles cannot receive the riding request information, the riding request information is refused;
the vehicle pickup travel comprises a vehicle near travel and a vehicle far travel, wherein the vehicle near travel is the sum of all riding requirements in a preset period.
As a preferred embodiment of the present invention: the step 3 of performing periodic rolling optimization processing on the passenger request information and the recent travel of the vehicle specifically comprises the following steps:
301: receiving riding requirement information and preprocessed vehicle information sent by a scattered autonomous machine of each operating vehicle, wherein the riding requirement information comprises unassigned predicted riding requirements, all riding request information within a certain time interval, assigned predicted riding requirements and accepted recent riding requirements, and the vehicle information comprises available states of all operating vehicles and recent variable strokes of the vehicles;
302: constructing a centralized optimization model according to the vehicle information and the riding information, and adopting an algorithm to perform rolling optimization solution to obtain an optimized recent vehicle journey;
303: sending the optimized recent travel of the vehicle to a distributed autonomous machine of a corresponding operation vehicle;
304: the latest states of the vehicle information and the riding demand information are acquired every fixed time, and steps 301 to 303 are repeated.
As a preferred embodiment of the present invention: step 302 specifically includes:
constructing a vehicle path planning network according to the vehicle information and the riding information;
and constructing a minimum cost flow model based on a vehicle path planning network, and solving by a network simplex method to obtain the optimized recent vehicle journey.
As a preferred embodiment of the present invention: the specific steps of preprocessing the vehicle information by the scattered autonomous machine in step 301 are as follows:
screening a recent variable travel of the vehicle, wherein the recent variable travel of the vehicle is a passenger pickup travel exceeding ten minutes;
estimating the available state of the operation vehicle;
a plurality of consecutive ride demands are consolidated.
As a preferred embodiment of the present invention: the steps performed by the decentralized autonomous machine after receiving the optimized recent travel of the vehicle in step 303 include:
adjusting the optimized recent travel of the vehicle;
and according to the recent travel of the vehicle, the allocation travel of the empty vehicle and the road condition information, a specific driving route is formulated for the vehicle by using a Dijkstra algorithm.
As a preferred embodiment of the present invention: the step of adjusting the recent travel of the vehicle specifically includes:
3011: judging whether the optimized recent travel of the vehicle is connected with the vehicle long-term travel, if so, executing the optimized recent travel of the vehicle; if not, executing step 3012;
3012: and transmitting incompatible riding demands in the long-term travel of the vehicle to a scattered autonomous machine of a nearby operating vehicle from the near to the far until the riding demands can be accepted by a certain vehicle. If the ride request is not acceptable to either vehicle, the ride request remains on the original vehicle long range;
3013: judging whether the vehicle near-term travel and the vehicle far-term travel processed in the step 3012 can be linked, if so, executing the processed vehicle near-term travel, otherwise, executing the step 3014;
3014: rejecting incompatible riding demands in the recent travel of the vehicle according to the riding demand mode, and rejecting the predicted demands when the riding demand mode is the allocated predicted demands; rejecting the ride request when the ride request pattern is a newly occurring ride request, and returning to the original distribution vehicle when the ride request pattern is a recently received ride request;
305: steps 3011-3014 are repeated until the engagement of the near travel of the vehicle and the far travel of the vehicle is feasible.
An autopilot taxi dispatching system comprising
The centralized dispatching platform is used for performing centralized optimization on the recent travel of the vehicles for operating the vehicles;
the scattered autonomous machines are mounted on the automatic driving taxis and used for receiving the optimization results sent by the centralized dispatching platform, detecting and adjusting the optimization results, planning the driving route of the operation vehicle and maintaining the long-term travel of the vehicle.
As a preferred embodiment of the present invention: the centralized scheduling platform comprises a riding demand prediction module and a centralized optimization module;
the riding demand prediction module is used for predicting and updating the recently unknown riding demand in real time;
the centralized optimization module is used for optimizing the distribution of the recent travel and the riding request of the vehicle.
As a preferred embodiment of the present invention: the autonomous dispersing machine comprises
And a pretreatment module: the method comprises the steps of preprocessing vehicle information;
a transmitting module; the system is used for sending the recent travel of the bus to the centralized dispatching platform;
and a receiving module: the vehicle near-term travel optimization system is used for receiving the optimized vehicle near-term travel sent by the centralized dispatching platform;
an autonomic module: the method comprises the steps of checking whether an optimized passenger pick-up stroke and an empty allocation stroke are feasible or not, and if not, adjusting the passenger pick-up stroke and the empty allocation stroke until the passenger pick-up stroke and the empty allocation stroke are feasible;
and a driving route planning module: for planning a travel route of the vehicle;
riding demand distribution module: for distributing ride requests.
Example 1: the automatic driving taxi dispatching method comprises a taxi taking request information distribution method and a vehicle recent journey optimization method.
The method for distributing the riding request information comprises the following steps:
and judging whether the time difference between the riding time and the current moment is within a first preset threshold value range, wherein the time difference exceeds the preset threshold value, and the riding request information is captured by the nearest vehicle scattered autonomous machines. The vehicle decentralized autonomous machine detects whether a ride request can be added to a passenger pickup trip, if so, the vehicle will be assigned to a vehicle forward trip, otherwise the vehicle will communicate the ride request information to nearby vehicles. The nearby vehicle further determines that if the vehicle-taking request can be accepted, the vehicle-taking request is accepted by the vehicle, and the information transmission is completed. Otherwise, the vehicle transmits the riding request to the nearby vehicles until a vehicle capable of receiving the riding requirement is found, or all vehicles are inspected, and the riding requirement cannot be met, and the riding requirement is refused;
when the time difference value is within a preset threshold value, judging whether the reserved riding demand quantity in the current period exceeds a second preset threshold value, and when the reserved riding demand is smaller, carrying out the allocation of riding requests and the optimization of the recent travel of the vehicle separately so as to shorten the time for a passenger waiting and scheduling system to reply to the passenger waiting and the time for the passenger to arrive at the passenger position;
when the reserved riding demands are more, the allocation of the new to the recent riding demands and the recent journey of the vehicle are cooperatively optimized, and the riding demands can be more properly served by the cooperative optimization, so that the running cost of the vehicle is saved.
When the reservation riding demand is small, the riding request distribution is carried out independently, and the riding request distribution method comprises the following steps:
s1, when a passenger gives a riding demand, all vehicles receive the information of the riding demand. The decentralized autonomous machine of each vehicle calculates the insertion cost of the ride request, i.e., the increase in running cost caused by adding the ride request to the passenger pickup travel of the vehicle. If there is a time conflict between the demand and the pickup stroke of the vehicle, the insertion cost is M (a very large positive number). After the calculation is completed, the scattered autonomous machine transmits the insertion cost to the centralized dispatching platform.
S2, the centralized dispatching platform selects the minimum insertion cost, if the minimum insertion cost is M, the fact that all vehicles cannot accept the riding request is indicated, namely, a notification is sent to a passenger, and the passenger cannot be served. Otherwise, the ride request is assigned to the vehicle with the least insertion cost and the passenger is notified that his ride request has been accepted.
When the reservation ride demands are greater, the allocation of ride requests is optimized in coordination with the recent travel of the vehicle. The recent travel of the vehicle is optimized in a rolling optimization mode at regular intervals, and the riding request is distributed at the same time intervals.
The recent travel of the vehicle includes passenger pickup travel, empty allocation travel and riding demand information in the recent period. Short distance v k →…→r a →r b A representation of the form → …, where r a 、r b The actual riding demand may be the predicted riding demand. If it is an actual demand, it indicates that the vehicle will pick up the passenger, and if it is a predicted demand, it indicates that the vehicle will be free to drive to the starting region of the demand to await a demand that may occur in the future. For each of the riding demands r, the information thereof includes a starting point o r Endpoint d r Expected departure time tpe r Latest departure time tpl r Predicted arrival time tdl r Revenue f for operation company r . If r is the actual ride demand, f r Deducting a driving fee caused by completing the riding demand for the price of the riding demand; if r is the predicted ride demand, f r The potential benefits brought by the empty vehicle allocation task are completed.
The near-term travel of the vehicle is optimized once at certain time intervals, and each optimization comprises the following steps:
s1, preprocessing vehicle information by each vehicle scattered autonomous machine, including the division of recent travel, the estimation of available states, the combination of requirements, and then transmitting the available states of the vehicles and the recent variable travel of the vehicles to a centralized dispatching platform.
S101, dividing a near-term travel, dividing a part, which is sent by passengers in the next ten minutes in the near-term travel, into near-term fixed travel by the scattered autonomous machine, and dividing the rest part into near-term variable travel. The near term fixed travel is not changeable and the near term variable travel can be optimized.
S102.Estimating the available state, and estimating the time ta when the vehicle completes the recent fixed travel v And position la v And takes this as the available state of the vehicle. The available state of the vehicle is the starting state of the vehicle at the time of the recent trip optimization.
S103, combining the demands, and combining a plurality of continuous riding demands into one according to the following rule so as to reduce the calculated amount of the centralized dispatching platform. For two adjacent ride demands r in recent variable travel a 、r b If both are actual ride demands, or both are predicted ride demands, and the following inequality (1) - (3) is satisfied, indicating that the journey servicing both ride demands is appropriate to be completed by the same vehicle, then combining both into a new demand r c As in formula (4).
Wherein delta, theta and epsilon are parameters for controlling the combination of requirements and respectively represent the allowable maximum engagement distance, the maximum engagement time and the maximum combination mileage; dist (a, b) represents the distance from a to b; tc (a, b) represents a travel cost from a to b. After the combination of the requirements is completed, the scattered autonomous machines transmit the available states of the vehicles and the recent variable strokes to the centralized dispatching platform.
S2, the centralized dispatching platform predicts unknown riding demands in a recent period in real time by using a big data technology, and before the recent journey optimization of the vehicle is carried out, the predicted riding demands are input into a centralized optimization module of the centralized dispatching platform. If the allocation of the riding requests is optimized in coordination with the recent journey of the vehicle, the riding requests collected at the time interval are transmitted to a centralized optimization module of a centralized dispatching platform.
S3, the centralized dispatching platform receives vehicle and riding demand information, wherein the vehicle information comprises available states of vehicles and recent variable strokes of the vehicles; the ride demand information includes an unassigned predicted ride demand R PN Newly-emerging recent riding demand R SN Assigned predicted ride demand R in recent variable range of vehicle PA And an accepted recent ride demand R in a recent variable range of the vehicle SA . From this information, the centralized scheduling platform builds a vehicle path planning network, as in fig. 2. And then, constructing a minimum cost flow model on the basis of the network, and solving by a network simplex method to obtain the optimized recent travel of the vehicle.
S301, vehicle path planning network construction. The network comprises four types of points, the available location point la of the vehicle v E, V; origin o of demand r E O and endpoint d r E, D; a virtual endpoint S indicating the end of the recent route. The network includes five types of arcs: passenger arc (la) v ，o r )∈E VO A pick-up indicating a start of a request for the vehicle to travel from an available location; travel arc (o) r ，d r )∈E OD Indicating that the vehicle is transmitting the passenger from the departure location to the destination; transfer arcIndicating that the vehicle is taking the passenger r a To passenger r after being sent to its destination b Is a starting point of (2); waiting arc (la) v ，S)∈E VS Indicating that the vehicle remains stationary in place from the time of availability to the end of the near-term period; ending arc (d) r ，S)∈E DS The estimated time for the vehicle to send the passenger r to the destination is indicated to have exceeded the range of the near-term period, or the passenger r is stationary in place after being sent to the destination until the near-term period ends.
Due to the limitation of the departure time window of the passengers, the vehicle needs to be at the most of the passengersThe passenger arrives at the passenger's place before the late departure time, otherwise the passenger will select other traffic modes or change the trip mode. Thus, the vehicle receiving arc (la) v ，o r ) As shown in inequality (5), when the vehicle v and the demand r satisfy inequality (5), indicating that the vehicle is able to reach its departure place before the latest departure time of the passenger r, an arc (la) is established v ，o r ). Similarly, the condition for establishing the transfer arc is as shown in inequality (6). If ra and rb satisfy inequality (6), indicating that both can be continuously serviced with the same vehicle, then a transfer arc is established
ta v +tt(la v ，o r )≤lpl r (5)
The cost of the arc is shown in the formula (7), and the cost on the passenger receiving arc and the transferring arc is the idle running cost of the vehicle; for new recent demand R SN And forecast demand R PN ∪R PA A corresponding travel arc for which the cost is the opposite of the benefit the operator would bring from the travel demand; for the recent demand R that has been accepted SA It must be ensured that they can be serviced by the vehicle, thus setting the cost on their corresponding travel arc to a very small negative (-M); the cost on the waiting and ending arcs is zero.
S302, constructing a vehicle recent travel optimization model. In the vehicle path planning network described above, each route from the vehicle available location point to the virtual destination point represents a recent route of the vehicle. The problem of planning the recent route is converted into the problem of minimum cost flow, wherein the available position point of each vehicle is a flow source point with the outflow quantity of 1, the virtual end point is a flow collection point, and the route through which each flow passes is the recent route of the vehicle. The total cost of the network is the total cost of all vehicles driving in the recent period, and an optimized model of the recent route of the vehicle is built as shown in formulas (8) - (12). The model is a standard minimum cost flow model, and the objective function (8) represents that the total cost of the network flow is minimum, namely the total cost of the vehicle running in the recent period is minimum; the constraints (9) - (11) are flow balance constraints, wherein the available position point of the vehicle is a flow source point (9), the starting point and the end point of the demand are intermediate points (10), and the virtual end point is a flow collection point (11); the constraint (12) is a capacity constraint that is an arc.
S303, solving the model by a network simplex method. In the network simplex method, each base solution corresponds to one spanning tree (spanning tree). The solution structure may be defined as (L, T, U), where T accommodates arcs of the spanning tree for which all constituent basis solutions correspond, L accommodates arcs for which the remaining flow is the lower bound of arc capacity, and U accommodates arcs for which the remaining flow is the upper bound of arc capacity. If there is an arc with a negative checknumber, an arc with a negative checknumber (reduced cost) is selected as the base arc (entering arc). Then tracing from two end points of the base entering arc along the tree until the end points are intersected, thereby finding a circle formed by the base entering arc and the tree, amplifying the flow along the circle, and selecting an arc of which the flow in the circle is equal to the upper limit or the lower limit of the arc capacity as a base exiting arc (leaving arc). Then, updating the tree structure, adding the base-entering arcs into the spanning tree corresponding to the base solution, removing the base-exiting arcs from the tree, and correspondingly updating the potential of each point and the verification number of each arc. If all the test numbers are not negative, ending the loop, and obtaining the optimal solution, otherwise searching an arc base with the negative test number, and iterating again until obtaining the optimal solution.
In the design of the initial solution, the virtual terminal point S is taken as a root node, and the initial solution is built by the original recent journey of the vehicle. The specific method comprises the following steps: firstly, arcs contained in an original recent route are included in a spanning tree T, and the flow of the arcs is set to be 1; then the unallocated recent demand (including the new recent actual demand R SN And unallocated predicted demand R PN ) The corresponding travel arc hangs under any vehicle point to which it is connected, becomes part of the spanning tree T, and sets the flow of that arc to 0. The flow of all the remaining arcs is 0, added to the set L. In addition, set U is empty. For example, in the case shown in FIG. 2, the original recent route of the vehicle is v 1 →r 1 →S，v 2 →S，v 3 S. Then, according to the above method, an initial solution may be constructed as shown in fig. 3, in which the thick line represents the spanning tree corresponding to the initial solution, and the numbers on the arcs represent the flow of the arcs. Arcs that do not occur in the spanning tree are all admitted into the set L, and the traffic for these arcs is 0.
S4, after the optimization of the recent travel of the vehicle is completed, the centralized dispatching platform sends the optimized recent travel of the vehicle to the corresponding vehicle. Since the centralized dispatching platform does not consider the long-term passenger pickup journey of the vehicle when optimizing the short-term journey of the vehicle, the situation that the optimized short-term journey and the vehicle long-term passenger pickup journey conflict can occur. The decentralized autonomous device on the vehicle will therefore check and adjust the optimized recent trip after it has been received to make the vehicle trip viable. When the pickup travel of the long-term passenger is S-r j Vehicle v of [ … ] k Receiving the optimized recent travel v of the centralized dispatching platform k →…→r i At S, v k The decentralized autonomous machine of (2) will perform the following steps:
s401 checking r according to formula (6) i And r j Whether the vehicle can continuously serve the route or not, if so, the route is feasible, and no further adjustment is needed.
S402 if served by the same vehicle r i And r j There is time conflict, the scattered autonomic machine will be r from the near to the far j Is transmitted to the nearby vehicle, and the scattered autonomous device of the nearby vehicle judges whether r can be accepted according to the formulas (5) and (6) j . If other vehicles can accept r j Terminating r j Information transfer, r j Reassigned to other vehicles. And again detecting whether the optimized connection of the near-term travel and the long-term passenger pick-up travel is feasible. If so, ending the travel check and adjustment; otherwise, sequentially taking the long-term passengers in r in the journey j Is redistributed to other vehicles until the near-term trip is feasible with the long-term passenger pickup trip or the rear demand is unacceptable to other vehicles.
S403, if the connection of the near trip and the far-end passenger pickup trip is not feasible after the step S402 is executed, the scattered autonomous device will try to reject r in the near trip i . If r i Is the predicted demand, directly let r i Removing; if r i Is a new recent demand, r i Rejecting; if r i Is an accepted reservation requirement, will r i Refund to its original dispensing vehicle v l ，v l At receiver r i Then, the passenger pick-up travel of the vehicle is checked and adjusted in the same manner so as to be feasible. Then vehicle v k Detecting whether the near-term travel and the long-term passenger pickup travel are feasible or not again, and ending the route checking and adjusting if the near-term travel and the long-term passenger pickup travel are feasible; otherwise, r is removed in turn in the same way i Until the vehicle route is feasible.
S5, after the fact that the passenger pick-up travel and the empty vehicle allocation travel of the vehicle are feasible is determined, the vehicle scattered autonomous machine estimates travel time of each road section according to real-time road conditions, the travel time is used as cost on the road section, and then a Dijkstra algorithm is used for solving a specific route of the vehicle.
Example 2: an autopilot taxi dispatching system comprising
And the centralized dispatching platform is responsible for centralized optimization of the recent strokes of all vehicles and comprises a riding demand prediction module and a centralized optimization module.
The riding demand prediction module predicts the unknown riding demand in the recent period by utilizing a big data technology, and updates the predicted demand in real time according to the time lapse and the occurrence of the actual demand. And the near-term journey centralized optimization module is used for constructing a near-term journey optimization model according to the received vehicle and demand information, solving the near-term journey model according to an optimization algorithm and intensively optimizing the near-term journey of all vehicles. In addition, the allocation of the ride request may also be performed along with the optimization of the recent trip.
A plurality of decentralized autonomous machines. The system is mounted on each vehicle and is responsible for maintaining the pickup journey of long-term passengers of the vehicle, receiving the decision of the centralized dispatching platform for the recent period and adjusting the decision to enable the centralized dispatching platform to be feasible, and making a specific vehicle driving route, wherein the specific vehicle driving route comprises a pre-optimization information preprocessing module, a post-optimization journey autonomous module, a driving route planning module and a riding demand distribution module. The pre-optimization information preprocessing module is used for preprocessing the information of the vehicle before the recent journey optimization, and comprises the steps of recent journey division, vehicle available state estimation and demand combination.
A portion of the near-future runs that is executed is divided into near-future fixed runs, and the remaining portion is taken as a near-future variable run. The near term fixed travel does not allow for variation and the near term variable travel can be optimized. The time and the position of the vehicle when the recent fixed travel is completed are estimated, and the time and the position are taken as the available state of the vehicle. In addition, when the calculation scale is large, a plurality of routes suitable for being completed by the same vehicle in the passenger pick-up route are combined into one so as to reduce the calculation amount of the centralized dispatching platform. After the pretreatment of the vehicle information is completed, the available state and the recent variable travel are sent to a centralized dispatching platform to be used as input of centralized optimization.
And after receiving the optimization result of the centralized dispatching platform, the optimized journey self-discipline module checks whether the optimized passenger pickup journey and the empty dispatching journey are feasible or not, and if not, adjusts the optimized passenger pickup journey and the empty dispatching journey until the optimized passenger pickup journey and the empty dispatching journey are feasible.
The riding demand distribution module can optimize the distribution of riding requests together with the recent travel of the vehicle by the centralized dispatching platform, and can solve the distribution of riding demands of the vehicle by the distributed autonomous machine according to heuristic rules.
And the driving route planning module is used for planning a specific driving route for the vehicle by using a Dijkstra algorithm according to the passenger pick-up travel, the empty vehicle allocation travel and the real-time road condition.
The foregoing description is only exemplary embodiments of the present invention, and is not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes using the descriptions and the drawings of the present invention, or direct or indirect application in other related system fields are included in the scope of the present invention.

Claims (5)

1. The automatic taxi dispatching method is characterized by comprising the following steps of:
step 1: obtaining riding request information, wherein the riding request information comprises departure place information, destination information and riding time;
step 2: judging whether the time difference between the riding time and the current moment is within a first preset threshold value range, and sending riding request information to an operating vehicle nearest to the passenger position when the time difference exceeds the preset threshold value;
when the time difference value is within a preset threshold value, executing the step 3;
step 3: judging whether the reserved riding demand quantity in the current period exceeds a second preset threshold value, and if so, carrying out periodic rolling optimization processing on passenger request information and the recent travel of the vehicle:
301: screening a recent variable course of the vehicle, which is a passenger pickup course exceeding ten minutes; estimating the available state of the operation vehicle; merging a plurality of consecutive ride demands; receiving bus demand information and preprocessed vehicle information sent by scattered autonomous machines of each operating vehicle, wherein the bus demand information comprises unassigned predicted bus demands, all bus request information within a certain time interval, assigned predicted bus demands and accepted recent bus demands, and the vehicle information comprises available states of all operating vehicles and recent variable journey of the vehicles:
302: constructing a centralized optimization model according to the vehicle information and the riding information, and adopting an algorithm to perform rolling optimization solution to obtain the optimized recent travel of the vehicle:
constructing a vehicle path planning network according to the vehicle information and the riding information;
constructing a minimum cost flow model based on a vehicle path planning network, and solving by a network simplex method to obtain an optimized recent vehicle journey;
303: sending the optimized recent travel of the vehicle to a distributed autonomous machine of a corresponding operation vehicle; the scattered autonomous machine adjusts the optimized recent travel of the vehicle; calculating the calculation cost of the travel route according to the recent travel of the vehicle, the allocation travel of the empty vehicle and the road condition information, and making a specific travel route for the vehicle by using a Dijkstra algorithm:
3011: judging whether the optimized recent travel of the vehicle is connected with the vehicle long-term travel, if so, executing the optimized recent travel of the vehicle; if not, executing step 3012;
3012: transmitting incompatible riding demands in a long-term journey of the vehicle to a scattered autonomous machine of a nearby operating vehicle from the near to the far until the riding demands can be accepted by a certain vehicle; if the ride request is not acceptable to any of the operating vehicles, the ride request remains on the original vehicle long-range;
3013: judging whether the vehicle near-term travel and the vehicle far-term travel processed in the step 3012 can be linked, if so, executing the processed vehicle near-term travel, otherwise, executing the step 3014;
3014: rejecting incompatible riding demands in the recent travel of the vehicle according to the riding demand mode, and rejecting the predicted demands when the riding demand mode is the allocated predicted demands; rejecting the riding request information when the riding demand mode is riding request information, and returning to the original distribution operation vehicle when the riding demand mode is the received recent riding demand;
3015: repeating steps 3011-3014 until the joint of the near-term travel of the vehicle and the long-term travel of the vehicle is feasible;
304: acquiring the latest states of the vehicle information and the riding demand information at fixed time intervals, and repeating the steps 301-303;
and if the reserved riding demand quantity is within a second preset threshold value range, transmitting riding request information to the scattered autonomous machines of each operation vehicle, and determining the operation vehicle meeting the riding request according to the insertion cost calculated by each scattered autonomous machine, wherein the insertion cost is the running cost added in the riding receiving and sending journey by adding the riding request information.
2. The method for dispatching the automatic taxi according to claim 1, wherein the step of the scattered autonomous machines of the operating vehicle in the step 2 after receiving the riding request information comprises the steps of:
step 201: judging whether the riding request information is connected with a vehicle receiving and sending travel or not, if so, adding the riding request information into the vehicle receiving and sending travel; if not, executing step 202;
step 202: transmitting the riding request information to nearby operating vehicles;
step 203: the nearby operation vehicles repeat the steps 201 and 202 until an operation vehicle capable of receiving the riding request information is found, and if all operation vehicles cannot receive the riding request information, the riding request information is refused;
the vehicle pickup travel comprises a vehicle near travel and a vehicle far travel, wherein the vehicle near travel is the sum of all riding demands in a preset period, and the vehicle far travel is the sum of all riding demands exceeding the preset period.
3. An automatic driving taxi dispatching system, characterized in that the automatic driving taxi dispatching method of claim 1 or 2 is adopted, and the automatic driving taxi dispatching system comprises:
the centralized dispatching platform is used for receiving and distributing the riding requests of passengers and optimizing the recent vehicle journey of the operating vehicle;
the scattered autonomous machines are mounted on the automatic driving taxis and used for receiving the optimization results sent by the centralized dispatching platform, detecting and adjusting the optimization results, planning the driving route of the operation vehicle and maintaining the long-term travel of the vehicle.
4. The automated driving taxi dispatching system of claim 3, wherein the centralized dispatching platform comprises a ride demand prediction module and a centralized optimization module;
the riding demand prediction module is used for predicting and updating the recently unknown riding demand in real time;
the centralized optimization module is used for optimizing the distribution of the recent travel and the riding request of the vehicle.
5. The automated driving taxi dispatching system of claim 4, wherein the decentralized autonomous machine comprises
And a pretreatment module: the method comprises the steps of preprocessing vehicle information;
a transmitting module; the vehicle information sending platform is used for sending vehicle information to the centralized dispatching platform;
and a receiving module: the vehicle near-term travel optimization system is used for receiving the optimized vehicle near-term travel sent by the centralized dispatching platform;
an autonomic module: the method comprises the steps of checking whether an optimized passenger pick-up stroke and an empty allocation stroke are feasible or not, and if not, adjusting the passenger pick-up stroke and the empty allocation stroke until the passenger pick-up stroke and the empty allocation stroke are feasible;
and a driving route planning module: for planning a travel route of the vehicle;
riding demand distribution module: for distributing ride requests.
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