CN112270480A - Automatic taxi driving scheduling method and system - Google Patents
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Abstract
The invention relates to the field of vehicle dispatching, in particular to a dispatching method and a dispatching system for an automatic driving taxi, which comprises the following steps: acquiring the riding request information; when the time difference between the riding time and the current time exceeds a preset threshold value, sending riding request information to an operating vehicle closest to the position of a passenger; and when the quantity of the reserved riding requests in the current time period exceeds a second preset threshold value, carrying out periodic rolling optimization processing on the passenger request distribution and the recent journey of the vehicle, and when the quantity of the newly appeared reserved riding requests is within the range of the second preset threshold value, determining the operating vehicle meeting the riding requirements according to the insertion cost calculated by each decentralized autonomous computer. The invention provides an automatic driving taxi scheduling method and system, which solve the problems of low efficiency and large empty driving mileage ratio under the condition that the existing scheduling method has timely riding requirements and reserved riding requirements.
Description
Technical Field
The invention relates to the technical field of vehicle dispatching, in particular to a dispatching method and a dispatching system for an automatic driving taxi.
Background
In recent years, the automatic driving technique has been rapidly developed. Although currently autonomous vehicles have not been put into formal operation, numerous studies have shown that the introduction of autonomous vehicles is expected to have a revolutionary impact on the transportation system. Meanwhile, 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 taxi shows wide development prospect.
The existing scheduling method has the problem that the vehicle scheduling problem under the condition that timely requirements and reservation requirements coexist is not considered. The timely ride demands make it impossible for the dispatch system to fully know all future ride demands, and such future incomplete information causes uncertainty in the future position and state of the vehicle, making it difficult to determine whether a service vehicle that is sought for a ride demand whose departure time is far away from the present time is appropriate. The newly-emerging ride demands, which in turn have different desired departure times, need to be distributed taking into account whether the vehicle can complete them within its available time. These increase the difficulty of making passenger pick-up trips. 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. When the empty allocation is performed, whether the task of the empty allocation can be completed in the gap of the passenger is needed to be considered, and whether the effect of the empty allocation can be realized.
Disclosure of Invention
The invention provides an automatic driving taxi scheduling method and system, and aims to solve the problems of low efficiency and large empty driving mileage ratio of the conventional scheduling method under the conditions of timely riding demand and reserved riding demand.
The technical scheme for solving the problems is as follows: the automatic taxi driving dispatching method is characterized by comprising the following steps:
step 1: acquiring 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 time is within a first preset threshold range, and when the time difference exceeds a preset threshold, sending riding request information to an operating vehicle closest to the position of a passenger;
when the time difference value is within a preset threshold value, executing the step 3;
and step 3: judging whether the quantity of the reserved riding demands in the current time period exceeds a second preset threshold, and if the proportion exceeds the second preset threshold, performing periodic rolling optimization processing on the passenger request allocation and the recent travel of the vehicle;
and if the reserved demand quantity of the current time period is within a second preset threshold range, sending the riding request information to the distributed autonomous machines of all the operating vehicles, and determining the operating vehicles meeting the riding demand according to the insertion cost calculated by all the distributed autonomous machines, wherein the insertion cost is the running cost increased by adding the riding request information to a riding receiving and sending stroke.
Preferably, the step 2 of executing the distributed autonomous system operating the vehicle after receiving the riding request information 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, go to step 202;
step 202: sending the riding request information to nearby operating vehicles;
step 203: the nearby operation vehicle repeats the steps 201 and 202 until a vehicle capable of receiving the taking bus request information is found, and if all vehicles cannot receive the taking bus request information, the taking bus request information is refused;
the vehicle receiving and sending travel comprises a vehicle recent travel and a vehicle long-term travel, and the vehicle recent travel is the sum of all riding demands in a preset time period.
Preferably, the step of performing periodic rolling optimization processing on the passenger request information and the recent trip of the vehicle in step 3 specifically includes:
301: receiving riding demand information and preprocessed vehicle information sent by a distributed autonomous system of each operating vehicle, wherein the riding demand information comprises unallocated predicted riding demands, all riding request information in a certain time interval, allocated predicted riding demands and accepted recent riding demands, and the vehicle information comprises available states of all operating vehicles and vehicle recent variable travel;
302: constructing a centralized optimization model according to the vehicle information and the riding information, and performing rolling optimization solution by adopting an algorithm to obtain the optimized recent journey of the vehicle;
303: sending the optimized recent journey of the vehicle to a corresponding decentralized autonomous machine of the operating vehicle;
304: and acquiring the latest states of the vehicle information and the riding demand information at fixed time intervals, and repeating the steps 301 and 303.
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 the vehicle path planning network, and solving by using a network simplex method to obtain the optimized recent vehicle journey.
Preferably, the step 301 of preprocessing the vehicle information by the distributed autonomous system specifically comprises the steps of:
screening a vehicle recent variable journey, wherein the vehicle recent variable journey is a passenger pick-up journey exceeding ten minutes;
estimating the available state of the operating vehicle;
multiple consecutive ride demands are combined.
Preferably, the step 303, executed by the distributed autonomous machine after receiving the optimized recent trip of the vehicle, includes:
adjusting the optimized recent journey of the vehicle;
calculating the travel calculation cost according to the recent travel of the vehicle, the empty vehicle allocation travel and the road condition information, and establishing a specific driving route for the vehicle by using a Dijkstra algorithm.
Preferably, the step of adjusting the recent trip of the vehicle specifically includes:
3011: judging whether the optimized near-term travel of the vehicle can be connected with the long-term travel of the vehicle, if so, executing the optimized near-term travel of the vehicle; if not, go to step 3012;
3012: incompatible riding demands in the long-term travel of the vehicle are transmitted to a decentralized autonomous machine of a nearby operating vehicle from near to far until the riding demands can be accepted by a certain vehicle. If the riding demand cannot be accepted by any vehicle, the riding demand is left in the long-term travel of the original vehicle;
3013: judging whether the recent journey and the long journey of the vehicle processed in the step 3012 can be connected, if so, executing the processed recent journey of the vehicle, otherwise, executing the step 3014;
3014: removing incompatible riding demands in the recent journey of the vehicle according to the riding demand mode, and removing the prediction demands when the riding demand mode is the allocated prediction demands; when the riding demand mode is riding demand information, refusing the riding demand information, and when the riding demand mode is the received recent riding demand, returning to the original distribution vehicle;
305: and repeating the steps 3011 and 3014 until the connection between the recent journey and the long journey of the vehicle is feasible.
In addition, the invention also provides an automatic taxi-driving dispatching system, which is characterized by comprising
The centralized scheduling platform is used for performing centralized optimization on recent vehicle travel of the operating vehicles;
and the decentralized autonomous machine is carried on the automatic driving taxi and is used for receiving the optimization result sent by the centralized dispatching platform, detecting and adjusting the optimization result, and planning the running route of the operating vehicle and the maintenance of 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 unknown riding demands in the near term in real time;
the centralized optimization module is used for optimizing the distribution of the recent journey and the riding request of the vehicle.
Preferably, the autonomous disperser includes
A preprocessing module: the system is used for preprocessing the vehicle information;
a sending module; the system is used for sending the recent journey of the vehicle to the centralized dispatching platform;
a receiving module: the system is used for receiving the optimized recent vehicle journey sent by the centralized scheduling platform;
an autonomous module: the system is used for checking whether the optimized passenger receiving and delivering travel and the empty vehicle dispatching travel are feasible or not, and if not, adjusting the optimized passenger receiving and delivering travel and the empty vehicle dispatching travel until the optimized passenger receiving and delivering travel and the empty vehicle dispatching travel are feasible;
a driving route planning module: for planning a driving route of the vehicle;
the riding demand distribution module: for distributing the ride request.
Compared with the prior art, the invention has the beneficial effects that:
1) by adopting a rolling optimization mode, the passenger receiving and delivering travel and the empty vehicle dispatching travel of the vehicle can be adjusted in real time according to the dynamic change of the riding demand information, and the empty driving mileage of the vehicle is effectively reduced.
2) The long-term passenger receiving and delivering travel of the vehicle is maintained by the vehicle decentralized 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 scheduling platform optimizes the recent passenger pick-up and delivery travel and the empty vehicle allocation travel, the potential conflict with the long-term passenger pick-up and delivery travel does not need to be considered, and the calculation efficiency of the centralized scheduling platform is further improved.
4) The centralized dispatching platform is used for collaborative optimization of the recent vehicle journey and the empty vehicle dispatching journey, mutual influence of the recent vehicle journey and the empty vehicle dispatching journey can be fully considered, and the quality of the recent vehicle journey and the empty vehicle dispatching journey is improved.
5) The long-term passenger pick-up trips of the vehicle do not need to be performed immediately, and are converted into short-term passenger pick-up trips as time goes by, so that the centralized dispatching platform is optimized from a global perspective, and therefore, the long-term passenger pick-up trips are handed over to the decentralized autonomous machine of the vehicle for maintenance, and the performance of a dispatching system is not reduced.
Drawings
FIG. 1 is a flow chart of bus 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 explanation of the network simplex method.
Detailed Description
In order to make 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 described clearly and completely with reference to the accompanying drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention. Thus, the following detailed description of the embodiments of the present invention, 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-driving scheduling method comprises the following steps:
step 1: acquiring 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 time is within a first preset threshold range or not, and sending riding request information to an operating vehicle closest to the position of a passenger when the time difference exceeds a preset threshold;
when the time difference value is within a preset threshold value, executing the step 3;
and step 3: judging whether the quantity of the reserved riding demands in the current time period exceeds a second preset threshold, and if the proportion exceeds the second preset threshold, performing periodic rolling optimization processing on the passenger request allocation and the recent travel of the vehicle;
and if the reserved riding demand quantity in the current time period is within a second preset threshold range, sending riding request information to the distributed autonomous machines of all the operating vehicles, and determining the operating vehicles meeting the riding demand according to the insertion cost calculated by all the distributed autonomous machines, wherein the insertion cost is the running cost increased by adding the riding request information to the riding receiving and sending travel.
As a preferred embodiment of the present invention: the step 2 of executing the distributed autonomous system operating the vehicle after receiving the riding request information comprises the following steps:
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, go to step 202;
step 202: sending the riding request information to nearby operating vehicles;
step 203: the nearby operation vehicles repeat the steps 201 and 202 until vehicles capable of receiving the taking bus request information are found, and if all the vehicles cannot receive the taking bus request information, the taking bus request information is refused;
the vehicle receiving and sending travel comprises a vehicle recent travel and a vehicle long-term travel, and the vehicle recent travel is the sum of all riding demands in a preset time 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 journey of the vehicle specifically comprises the following steps:
301: receiving riding demand information and preprocessed vehicle information sent by a distributed autonomous system of each operating vehicle, wherein the riding demand information comprises unallocated predicted riding demands, all riding request information in a certain time interval, allocated predicted riding demands and accepted recent riding demands, and the vehicle information comprises available states of all operating vehicles and recent variable travel of the vehicles;
302: constructing a centralized optimization model according to the vehicle information and the riding information, and performing rolling optimization solution by adopting an algorithm to obtain the optimized recent journey of the vehicle;
303: sending the optimized recent journey of the vehicle to a corresponding decentralized autonomous machine of the operating vehicle;
304: and acquiring the latest states of the vehicle information and the riding demand information at fixed time intervals, and repeating the steps 301 and 303.
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 the vehicle path planning network, and solving by using 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 distributed autonomous system in the step 301 are as follows:
screening the recent variable journey of the vehicle, wherein the recent variable journey of the vehicle is a passenger pick-up and delivery journey of more than ten minutes;
estimating the available state of the operating vehicle;
multiple consecutive ride demands are combined.
As a preferred embodiment of the present invention: the steps executed by the autonomous dispersion machine after receiving the optimized recent journey of the vehicle in step 303 include:
adjusting the optimized recent journey of the vehicle;
and according to the recent journey of the vehicle, the dispatching journey of the empty vehicle and road condition information, a specific driving route is made for the vehicle by using a Dijkstra algorithm.
As a preferred embodiment of the present invention: the step of adjusting the recent journey of the vehicle specifically comprises the following steps:
3011: judging whether the optimized near-term travel of the vehicle can be connected with the long-term travel of the vehicle, if so, executing the optimized near-term travel of the vehicle; if not, go to step 3012;
3012: incompatible riding demands in the long-term travel of the vehicle are transmitted to a decentralized autonomous machine of a nearby operating vehicle from near to far until the riding demands can be accepted by a certain vehicle. If the riding demand cannot be accepted by any vehicle, the riding demand is left in the long-term travel of the original vehicle;
3013: judging whether the recent journey and the long journey of the vehicle processed in the step 3012 can be connected, if so, executing the processed recent journey of the vehicle, otherwise, executing the step 3014;
3014: removing incompatible riding demands in the recent journey of the vehicle according to the riding demand mode, and removing the prediction demands when the riding demand mode is the allocated prediction demands; when the riding demand mode is a new riding demand, rejecting the riding demand, and when the riding demand mode is a received recent riding demand, returning to the original distribution vehicle;
305: and repeating the steps 3011 and 3014 until the connection between the recent journey and the long journey of the vehicle is feasible.
An automatic taxi dispatching system comprises
The centralized scheduling platform is used for performing centralized optimization on the recent vehicle journey of the operating vehicle;
and the decentralized autonomous machine is carried on the automatic driving taxi and is used for receiving the optimization result sent by the centralized dispatching platform, detecting and adjusting the optimization result, and planning the running route of the operating vehicle and the maintenance of the long-term travel of the vehicle.
As a preferred embodiment of the present invention: the centralized dispatching platform comprises a riding demand prediction module and a centralized optimization module;
the bus demand prediction module is used for predicting and updating unknown bus demands in the near term in real time;
the centralized optimization module is used for optimizing the distribution of the recent journey and the riding request of the vehicle.
As a preferred embodiment of the present invention: the autonomous disperser comprises
A preprocessing module: the system is used for preprocessing the vehicle information;
a sending module; the central dispatching platform is used for sending the recent travel to the central dispatching platform;
a receiving module: the system is used for receiving the optimized recent vehicle journey sent by the centralized scheduling platform;
an autonomous module: the system is used for checking whether the optimized passenger receiving and delivering travel and the empty vehicle dispatching travel are feasible or not, and if not, adjusting the optimized passenger receiving and delivering travel and the empty vehicle dispatching travel until the optimized passenger receiving and delivering travel and the empty vehicle dispatching travel are feasible;
a driving route planning module: for planning a driving route of the vehicle;
the riding demand distribution module: for distributing the ride request.
Example 1: the automatic taxi-driving scheduling method comprises a riding request information distribution method and a vehicle recent journey optimization method.
The distribution method of the riding request information comprises the following steps:
and judging whether the time difference between the riding time and the current time is within a first preset threshold range, wherein the riding request information can be captured by the nearest vehicle distributed autonomous system when the time difference exceeds the preset threshold. The vehicle decentralized autonomous machine detects whether the taking bus request can be added into the passenger receiving and sending journey, if so, the vehicle is allocated to the vehicle long-term journey, otherwise, the vehicle transmits the taking bus request information to nearby vehicles. The nearby vehicle determines again, and if the riding request can be received, the riding request is received by the vehicle, and the information transmission is finished. Otherwise, the vehicle transmits the riding request to nearby vehicles until finding a vehicle capable of receiving the riding request, or all the vehicles are checked and found not to meet the riding request, and the riding request is rejected;
when the time difference is within a preset threshold value, judging whether the quantity of the reserved riding demands in the current time period exceeds a second preset threshold value, and when the reserved riding demands are less, distributing the riding requests and optimizing the recent journey of the vehicle are separately carried out so as to shorten the time for the passenger to wait for the response of the scheduling system and the time for the vehicle to reach the position of the passenger as much as possible;
when the reserved riding demands are more, the distribution of the new-to-recent riding demands and the recent journey of the vehicle are optimized in a coordinated mode, the riding requests can be more appropriately served by the aid of the coordinated optimization, and running cost of the vehicle is saved.
When the reserved riding demand is less, the allocation of the riding request is carried out independently, and the allocation method of the riding request comprises the following steps:
s1, when a passenger sends a riding demand, all vehicles receive the information of the riding demand. The distributed autonomous system of each vehicle calculates an insertion cost of the riding request, that is, an increase in travel cost due to the passenger pick-up trip for adding the riding request to the vehicle. If the riding demand conflicts with the time of the passenger receiving and sending journey of the vehicle, the insertion cost is M (a great positive number). And after the calculation is finished, the decentralized autonomous machine transmits the insertion cost to the centralized scheduling platform.
S2, the centralized scheduling platform picks out the minimum insertion cost, if the minimum insertion cost is M, all vehicles cannot accept the riding request, namely, the centralized scheduling platform sends a notice to the passenger, and cannot provide service for the passenger. Otherwise, the ride request is distributed to the vehicle with the minimum intervening fee and the passenger is notified that the ride request has been accepted.
When the reserved riding demands are more, the distribution of the riding requests and the recent travel of the vehicle are optimized in a coordinated mode. The recent journey of the vehicle is optimized in a rolling optimization mode at regular intervals, and the riding requests are distributed at the same time intervals.
The recent journey of the vehicle comprises the passenger pick-up journey, the empty vehicle allocation journey and the riding demand information in the recent time period. Recent journey in vk→…→ra→rbForm of → …, where ra、rbThe riding demand can be actual riding demand or predicted riding demand. If the passenger demand is predicted, the vehicle is driven to the starting area of the passenger demand in an empty mode to wait for the passenger demand which may happen in the future. For each ride demand r, the information includes a starting point orEnd point drExpected departure time tperLatest departure time tplrPredicted arrival time tdlrIncome f brought to the operating companyr. If r is the actual riding demand, frFor the riding demandDeducting the driving cost caused by the riding demand; if r is the predicted ride demand, frPotential benefits are brought to the completion of the empty allocation task.
Optimizing the recent journey of the vehicle at certain time intervals, wherein each optimization comprises the following steps:
s1, preprocessing vehicle information by each vehicle decentralized autonomous machine, including division of recent travel, prediction of available state and demand combination, and then transmitting the available state of the vehicle and the recent variable travel of the vehicle to a centralized dispatching platform.
And S101, dividing a recent journey, namely dividing a part of the recent journey, which is sent by passengers in the next ten minutes, into a recent fixed journey by the distributed autonomous system, and dividing the rest into a recent variable journey by the distributed autonomous system. The recent fixed stroke is not changeable and the recent variable stroke can be optimized.
S102, estimating available state, namely estimating the time ta when the vehicle finishes the near-term fixed travelvAnd position lavAnd the vehicle is used as the available state of the vehicle. The available state of the vehicle is the initial state of the vehicle at the time of recent trip optimization.
And S103, merging the demands, namely merging a plurality of continuous riding demands into one demand according to the following rule so as to reduce the calculated amount of the centralized dispatching platform. For two adjacent riding demands r in recent variable journeya、rbIf the two riding requirements are both actual riding requirements or predicted riding requirements and meet the following inequalities (1) to (3), the travel for servicing the two riding requirements is suitable for being completed by the same vehicle, and the two riding requirements are combined into a new requirement rcAs shown in formula (4).
In the formula, delta, theta and epsilon are parameters for controlling the merging of the requirements and respectively represent the allowed maximum joining distance, the maximum joining time and the maximum merging mileage; dist (a, b) represents the distance from a ground to b ground; tc (a, b) represents the travel cost from a place to b place. After the demands are combined, the decentralized autonomous machines transmit the available state of the vehicle and the recent variable journey to the centralized dispatching platform.
And S2, the centralized scheduling platform predicts the unknown riding demands in the recent period in real time by using a big data technology, and the predicted riding demands are input to a centralized optimization module of the centralized scheduling platform before the recent travel optimization of the vehicle is carried out. And if the distribution of the taking requests and the recent journey of the vehicle are optimized in a coordinated mode, the taking requests collected in the time interval are transmitted to a centralized optimization module of the centralized scheduling platform.
S3, receiving vehicle and riding demand information by the centralized dispatching platform, wherein the vehicle information comprises the available state of the vehicle and the recent variable travel of the vehicle; the riding demand information comprises the undistributed predicted riding demand RPNNewly emerging recent ride demand RSNAllocated predicted ride demand R for recent variable trips of the vehiclePAAnd an accepted future ride demand R for a recently variable journey of the vehicleSA. Based on the information, the centralized scheduling platform constructs a vehicle path planning network, as shown in fig. 2. And then, constructing a minimum cost flow model on the basis of the network, and solving by using a network simplex method to obtain the optimized recent journey of the vehicle.
And S301, constructing a vehicle path planning network. The network comprises four types of points, the available location points la of the vehiclevE is V; starting point o of demandrE.g. O and end point drE is as for D; a virtual end point S indicating the end of the recent route. The network includes five types of arcs: pick-up arc (la)v，or)∈EVOIndicating that the vehicle is approaching a desired origin from an available location; travel arc (o)r，dr)∈EODIndicating that the vehicle takes the passenger from the departure place to the destination; transferred arc
Indicates that the vehicle is moving the passenger raDelivered to its destination and then transferred to passenger rbA starting point of (a); waiting arc (la)v，S)∈EVSIndicating that the vehicle has remained stationary in place from the time of availability to the end of the recent period; ending arc (d)r，S)∈EDSIndicating that the estimated time for the vehicle to deliver the passenger r to the destination has exceeded the recent period of time or that the passenger r is standing still in place after delivery to the destination until the recent period of time has ended.
Due to the limitation of the departure time window of the passenger, the vehicle needs to arrive at the passenger location before the latest departure time of the passenger, otherwise the passenger can select other transportation modes or change a travel mode. Thus, the vehicle pick-up arc (la)v，or) Is shown in inequality (5), when the vehicle v and the demand r satisfy inequality (5), which indicates that the vehicle can arrive at its departure place before the latest departure time of the passenger r, an arc (la) is establishedv，or). Similarly, the establishment condition of the transferred arc is shown as inequality (6). If ra and rb satisfy inequality (6) indicating that both can be continuously serviced by the same vehicle, a transferred arc is established
tav+tt(lav，or)≤lplr (5)
The cost of the arc is shown as the formula (7), and the cost of the pick-up arc and the transfer arc is the idle running cost of the vehicle; for recent demand RSNAnd forecast demand RPN∪RPAThe corresponding travel arc, the expense of which is the opposite number of the income brought by the operator for the travel demand; for recent demand R that has been acceptedSAThey must be guaranteed to be serviced by the vehicle, so the cost on their corresponding travel arc is set to a very small negative number (-M); the costs on waiting for and ending arcs are zero.
S302, constructing a recent journey optimization model of the vehicle. In the above vehicle path planning network, each route from the vehicle available position point to the virtual terminal point represents one recent route of the vehicle. The planning problem of the recent route is converted into the minimum cost flow problem, the available position point of each vehicle is a flow source point with the outflow quantity of 1, the virtual terminal point is a flow collection point, and the route through which each flow passes is the recent route of the vehicle. And (4) establishing an optimization model of the recent route of the vehicle as shown in formulas (8) to (12) if the total cost of the network is the total cost generated by all vehicles in the recent period. 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; constraints (9) - (11) are flow balance constraints, wherein the available position points of the vehicle are flow source points (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.
And S303, solving the model by using a network simplex method. In the network simplicity method, each basis solution corresponds to a spanning tree (spanning tree). The solution structure may be defined as (L, T, U), where T holds the spanning tree arcs corresponding to all constituent basis solutions, L holds the arcs with the remaining flows as the lower bound of the arc capacity, and U holds the arcs with the remaining flows as the upper bound of the arc capacity. If there are arcs whose check number is negative, then an arc whose check number (reduced cost) is negative is selected as an entering base arc (entering arc). Then, tracing from two end points of the incoming base arc along the tree until the two end points are intersected, finding a circle formed by the incoming base arc and the tree, increasing the flow along the circle, and selecting the arc with the flow changed into the upper bound or the lower bound of the arc capacity in the circle as the outgoing base arc (leaving arc). And then, updating the tree structure, adding the incoming base arcs into the spanning tree corresponding to the base solution, removing the outgoing base arcs from the tree, and correspondingly updating the potential of each point and the check number of each arc. If all the check numbers are not negative, ending the circulation to obtain the optimal solution, otherwise, searching an arc advancing base with a negative check number, and iterating again until the optimal solution is obtained.
In the design of the initial solution, the virtual terminal point S is used as a root node, and the initial solution is constructed according to the original recent journey of the vehicle. The specific method comprises the following steps: firstly, incorporating arcs contained in an original recent route into a spanning tree T, and setting the flow of the arcs as 1; then the unallocated recent demand (including the new recent actual demand R)SNWith unallocated forecasted demand RPN) The corresponding travel arc hangs under any connected vehicle point and becomes part of the spanning tree T, and the flow of this arc is set to 0. The flow of all the remaining arcs is 0 and added into the set L. In addition, set U is empty. For example, in the case shown in FIG. 2, the vehicle's original recent route is v1→r1→S，v2→S，v3→ S. Then an initial solution can be constructed according to the method described above, such as in fig. 3, where the thick line represents the spanning tree corresponding to the initial solution and the number on the arc represents the arc flow. Arcs that do not appear in the spanning tree are all taken into the set L, and the traffic for these arcs is 0.
And S4, after finishing the optimization of the recent journey of the vehicle, the centralized scheduling platform sends the optimized recent journey of the vehicle to the corresponding vehicle. Since the centralized dispatching platform does not consider the long-term passenger pick-up journey of the vehicle when the short-term journey optimization of the vehicle is carried out, the situation that the optimized short-term journey and the long-term passenger pick-up journey of the vehicle conflict with each other may occur. The decentralized autonomous on board the vehicle, after receiving the optimized recent journey, checks and adjusts it to make the journey of the vehicle feasible. When the forward passenger pick-up stroke is S → rjVehicle v → …kReceiving the optimized recent journey v of the centralized scheduling platformk→…→riTime → S, vkThe distributed autonomous machine is executed according to the following steps:
s401, checking r according to the formula (6)iAnd rjWhether the vehicle can continuously serve or not, if so, the route is feasible without further adjustment.
S402, if the same vehicle serves riAnd rjIf there is a time conflict, the distributed autonomous system will divide r from near to farjThe information (c) is transmitted to the nearby vehicle, and the distributed autonomous system of the nearby vehicle judges whether r can be received or not according to the formulas (5) and (6)j. If there are other vehicles capable of accepting rjThen r is terminatedjTransfer of information, willjReassigned to other vehicles. And detecting whether the optimized near-term journey is linked with the long-term passenger pick-up journey or not again. If the travel is feasible, the travel checking and adjusting are finished; otherwise, sequentially sending the long-term passengers to and from the journey rjUntil the near trip is available to be engaged with the long passenger pick-up trip or the rear demand is unacceptable to other vehicles.
S403. if the connection between the recent journey and the long-term passenger pick-up journey is still not feasible after the step S402 is executed, the distributed autonomy is carried outThe machine will try to reject r in the near tripi. If r isiIs a predicted demand, directly adds riRemoving; if r isiIs a new recent demand toiRefusing; if r isiIs an accepted reservation request, riReturning to its original dispensing vehicle vl，vlAt receiving riThe passenger pick-up travel of the vehicle is then checked and adjusted in the same way to make it feasible. Then the vehicle vkDetecting whether the short-term travel and the long-term passenger pick-up and delivery travel are feasible again, and if so, ending the route check and adjustment; otherwise, sequentially rejecting r in the same manneriUntil the vehicle route is feasible.
And S5, after determining that the passenger receiving and delivering travel and the empty vehicle dispatching travel of the vehicle are feasible, estimating the travel time of each road section by the vehicle dispersion autonomous machine according to the real-time road condition, taking the travel time as the cost on the road section, and then solving the specific running route of the vehicle by using a Dijkstra algorithm.
Example 2: an automatic taxi dispatching system comprises
And the centralized scheduling platform is responsible for performing centralized optimization on recent trips of all vehicles and comprises a riding demand prediction module and a centralized optimization module.
And 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 lapse of time and the occurrence of the actual demand. And the recent journey centralized optimization module is used for constructing a recent journey optimization model according to the received vehicle and demand information, solving by using an optimization algorithm and optimizing recent journeys of all vehicles in a centralized manner. Additionally, the allocation of ride requests may also be performed in conjunction with the optimization of recent trips.
A plurality of distributed autonomous machines. The system is carried on each vehicle and is responsible for maintaining the long-term passenger pick-up and delivery travel of the vehicle, receiving the decision of the centralized scheduling platform on the short-term time period and adjusting the decision to be feasible, and making a specific vehicle driving route, wherein the specific vehicle driving route comprises a pre-optimization information preprocessing module, an optimized travel autonomous module, a driving route planning module and a riding demand distribution module. And the pre-optimization information preprocessing module is used for preprocessing the information of the vehicle before the recent journey optimization, and comprises recent journey division, vehicle available state prediction and demand combination.
And dividing a part of the near-term journey which is executed nearby into a near-term fixed journey, and taking the rest part as a near-term variable journey. The recent fixed stroke does not allow for changes and the recent variable stroke can be optimized. The time and position of the vehicle when it has completed a recent fixed trip is estimated and used 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 receiving and delivering routes are combined into one route, so that the calculation amount of the centralized dispatching platform is reduced. After vehicle information preprocessing is completed, the available state and the recent variable journey are sent to a centralized dispatching platform to serve as input of centralized optimization.
And the optimized journey self-discipline module checks whether the optimized passenger delivery journey and the empty vehicle dispatching journey are feasible or not after receiving the optimization result of the centralized dispatching platform, and adjusts the optimized passenger delivery journey and the empty vehicle dispatching journey until the optimized passenger delivery journey and the empty vehicle dispatching journey are feasible if the optimized passenger delivery journey and the empty vehicle dispatching journey are not feasible.
And the riding demand distribution module can optimize the distribution of the riding requests and the recent journey of the vehicle by a centralized scheduling platform, and can also solve the distribution of the riding requests by a riding demand distribution module of a vehicle decentralized autonomous machine by a heuristic rule.
And the running route planning module plans a specific running route for the vehicle by utilizing a Dijkstra algorithm according to the passenger receiving and delivering route, the empty vehicle dispatching route and the real-time road condition.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent structures or equivalent flow transformations made by using the contents of the specification and the drawings, or applied directly or indirectly to other related systems, are included in the scope of the present invention.
Claims (10)
1. The automatic taxi driving scheduling method is characterized by comprising the following steps:
step 1: acquiring 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 time is within a first preset threshold range, and when the time difference exceeds a preset threshold, sending riding request information to an operating vehicle closest to the position of a passenger;
when the time difference value is within a preset threshold value, executing the step 3;
and step 3: judging whether the quantity of the reserved riding demands in the current time period exceeds a second preset threshold, and if the proportion exceeds the second preset threshold, performing periodic rolling optimization processing on the passenger request information and the recent travel of the vehicle;
and if the reserved riding demand quantity is within a second preset threshold range, sending the riding request information to the distributed autonomous machines of all the operating vehicles, and determining the operating vehicles meeting the riding requests according to the insertion cost calculated by all the distributed autonomous machines, wherein the insertion cost is the running cost increased by adding the riding request information to the riding receiving and sending travel.
2. The automatic taxi dispatching method according to claim 1, wherein the step performed by the decentralized autonomous machine operating the vehicle in the step 2 after receiving the taking request information comprises:
step 201: judging whether the riding request information can be connected with the vehicle receiving and sending journey, if so, adding the riding request information into the vehicle receiving and sending journey; if not, go to step 202;
step 202: sending the riding request information to nearby operating vehicles;
step 203: the nearby operating vehicles repeat the steps 201 and 202 until the operating vehicles capable of receiving the taking bus request information are found, and if all the operating vehicles cannot receive the taking bus request information, the taking bus request information is refused;
the vehicle receiving and delivering travel comprises a vehicle recent travel and a vehicle long-term travel, the vehicle recent travel is the sum of all riding demands in a preset time period, and the vehicle long-term travel is the sum of all riding demands exceeding the preset time period.
3. The automatic taxi dispatching method of claim 1, wherein the step of performing periodic rolling optimization processing on the passenger request information and the recent journey of the vehicle in step 3 specifically comprises:
301: receiving riding demand information and preprocessed vehicle information sent by a distributed autonomous system of each operating vehicle, wherein the riding demand information comprises unallocated predicted riding demands, all riding request information in a certain time interval, allocated predicted riding demands and accepted recent riding demands, and the vehicle information comprises available states of all operating vehicles and vehicle recent variable travel;
302: constructing a centralized optimization model according to the vehicle information and the riding information, and performing rolling optimization solution by adopting an algorithm to obtain the optimized recent journey of the vehicle;
303: sending the optimized recent journey of the vehicle to a corresponding decentralized autonomous machine of the operating vehicle;
304: and acquiring the latest states of the vehicle information and the riding demand information at fixed time intervals, and repeating the steps 301 and 303.
4. The automatic taxi driving dispatching method according to claim 3, wherein the step 302 specifically comprises:
constructing a vehicle path planning network according to the vehicle information and the riding information;
and constructing a minimum cost flow model based on the vehicle path planning network, and solving by using a network simplex method to obtain the optimized recent vehicle journey.
5. The automatic taxi dispatching method of claim 3, wherein the step 301 of preprocessing the vehicle information by the autonomous system specifically comprises the steps of:
screening a vehicle recent variable journey, wherein the vehicle recent variable journey is a passenger pick-up journey exceeding ten minutes;
estimating the available state of the operating vehicle;
multiple consecutive ride demands are combined.
6. The automated taxi dispatching method of claim 3,
the step 303, executed by the distributed autonomous machine after receiving the optimized recent journey of the vehicle, includes:
adjusting the optimized recent journey of the vehicle;
calculating the travel calculation cost according to the recent travel of the vehicle, the empty vehicle allocation travel and the road condition information, and establishing a specific driving route for the vehicle by using a Dijkstra algorithm.
7. The automated taxi dispatching method of claim 6,
the step of adjusting the recent journey of the vehicle specifically comprises:
3011: judging whether the optimized near-term travel of the vehicle can be connected with the long-term travel of the vehicle, if so, executing the optimized near-term travel of the vehicle; if not, go to step 3012;
3012: the incompatible riding requirements in the long-term journey of the vehicle are transmitted to a decentralized autonomous machine of a nearby operation vehicle from near to far until the riding requirements can be accepted by a certain vehicle, and if the riding requirements cannot be accepted by any operation vehicle, the riding requirements are left in the long-term journey of the original vehicle;
3013: judging whether the recent journey and the long journey of the vehicle processed in the step 3012 can be connected, if so, executing the processed recent journey of the vehicle, otherwise, executing the step 3014;
3014: removing incompatible riding demands in the recent journey of the vehicle according to the riding demand mode, and removing the prediction demands when the riding demand mode is the allocated prediction demands; when the riding demand mode is riding demand information, refusing the riding demand information, and when the riding demand mode is the received recent riding demand, returning to the original distributed operating vehicle;
305: and repeating the steps 3011 and 3014 until the connection between the recent journey and the long journey of the vehicle is feasible.
8. The automatic taxi dispatching system is characterized by comprising
The centralized scheduling platform is used for receiving and distributing riding requests of passengers and performing centralized optimization on recent vehicle travels of the operating vehicle;
and the decentralized autonomous machine is carried on the automatic driving taxi and is used for receiving the optimization result sent by the centralized dispatching platform, detecting and adjusting the optimization result, and planning the running route of the operating vehicle and the maintenance of the long-term travel of the vehicle.
9. The automated driving taxi dispatching system of claim 8, 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 unknown riding demands in the near term in real time;
the centralized optimization module is used for optimizing the distribution of the recent journey and the riding request of the vehicle.
10. The autonomous-driving taxi dispatching system of claim 8, wherein the autonomous dispenser comprises
A preprocessing module: the system is used for preprocessing the vehicle information;
a sending module; the centralized dispatching platform is used for sending vehicle information to the centralized dispatching platform;
a receiving module: the system is used for receiving the optimized recent vehicle journey sent by the centralized scheduling platform;
an autonomous module: the system is used for checking whether the optimized passenger receiving and delivering travel and the empty vehicle dispatching travel are feasible or not, and if not, adjusting the optimized passenger receiving and delivering travel and the empty vehicle dispatching travel until the optimized passenger receiving and delivering travel and the empty vehicle dispatching travel are feasible;
a driving route planning module: for planning a driving route of the vehicle;
the riding demand distribution module: for distributing the ride request.
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