CN102081786A - Vehicle scheduling method and system - Google Patents
Vehicle scheduling method and system Download PDFInfo
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- CN102081786A CN102081786A CN2011100330347A CN201110033034A CN102081786A CN 102081786 A CN102081786 A CN 102081786A CN 2011100330347 A CN2011100330347 A CN 2011100330347A CN 201110033034 A CN201110033034 A CN 201110033034A CN 102081786 A CN102081786 A CN 102081786A
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Abstract
The invention discloses a vehicle scheduling method and system. The method comprises the following steps: S1, calculating an estimated vehicle driving time and a driving path in the case of shortest driving distance of the vehicle in a static road network according to the user order data; S2, in combination with the historical adjustment and optimization parameter of the system, performing further adjustment and optimization on the estimated vehicle driving time and driving path obtained under the conditions of the static road network, and generating a scheduling scheme; S3, storing the user order data and the distance information of the vehicle into a system database; and S4, calculating to generate a new historical adjustment and optimization parameter by the system in an offline manner. According to the invention, single order optimization in the static road network is combined with the system optimization, and the existing uncontrollable factors are completely considered, thus the vehicle scheduling efficiency can be improved and the vehicle unloaded rate can be reduced; and moreover, according to the invention, sudden events can be preferentially disposed in real time, and bad influence of the sudden events on vehicle scheduling is remarkably reduced.
Description
Technical field
The present invention relates to a kind of method and system of vehicle scheduling, belong to the vehicle management service field.
Background technology
The problem that general taxi and the optimization of Car Rental dispatching system are solved is exactly how when each order arrives, and with reference to parameter as much as possible, car of the scheduling of " optimum " satisfies service.Because limitation of traditional techniques, the data weak foundation of this kind dispatching system, mostly be to carry out according to decision-maker's subjective judgment, the parameter of this kind scheduling hypothesis institute reference is limited, does not consider rider, driver, existing uncontrollable factor such as road conditions, again can be in practical action " anticipation " optimal way and adjustment behavior, further weaken the effect of former optimized Algorithm, thereby the rate of empty ride of vehicle is higher, causes the waste of ample resources.
Example 1: former Hangzhou taxi Optimization Dispatching algorithm is to transfer a car nearest and that be ready to pass by, middle owing to Qian Jiangyi bridge railway grade separation is closed a road to traffic temporarily, cause showing on the map that the actual deadhead kilometres of nearest car increase severely, consequence is that nearest driver is reluctant to accept the order from one day square, causes complaining increasing severely.To be the prompting taxi driver wear this but not Hilton at Jiang Jing to actual solution lies prone to live and just can address this problem.
Example 2: Beijing's traffic congestion, assign 8-20 point restricted driving measure, wish to solve the peak period problem, consequence is that the part driver advances to the 6-7 point and gos out, and the result is that the peak period is extended to 6 beginnings, and the city is absorbed in new round misery.
More than the explanation of two examples no matter be to narrow down to the scope optimization of single order and simply all can not well reach Expected Results from overall angle regulation and control because driver/passenger/road conditions variable is too many, and does mutually and have mercy on.
Summary of the invention
The objective of the invention is to, provide a kind of vehicle to order the method and system of sending, it can reduce the rate of empty ride of vehicle, reduces the wasting of resources, improves the accuracy rate of vehicle scheduling.
For solving the problems of the technologies described above, the present invention adopts following technical scheme: a kind of method of vehicle scheduling may further comprise the steps:
S1, according to user's order data, the vehicle when calculating vehicle ' short line in the static road network is estimated running time and driving path;
S2, the historical tuning parameter of coupling system is estimated running time and driving path to the vehicle that obtains under the static road network situation and is further adjusted to optimize and obtain new vehicle and estimate running time and driving path, generates scheduling scheme;
S3 deposits the range of driving information of user's order data and vehicle in system database;
S4, system off-line is calculated and is generated new historical tuning parameter.
The method of aforesaid a kind of vehicle scheduling, among the described step S4, system off-line is calculated the new historical tuning parameter of generation and is comprised:
According to the real-time road condition information in user's range of driving information, upgrade each day part road conditions historical information storehouse, highway section;
According to user's History Order data, recomputate each place day part new order probability;
According to being beneficial to order range of driving information, recomputate each highway section day part active path parameter.
Described order range of driving information comprises: riding time, pick-up point, destination, rider, rider's telephone number, number of passengers, the vehicle rank of selection.
Described active path calculation of parameter: by pick-up point and destination, can calculate user's driving path, according to the data of the History Order data of user's driving path and present real-time road, draw the active path parameter again.
In the method for aforesaid a kind of vehicle scheduling, described historical tuning parameter comprises: historical road conditions, new order probability and active path parameter.
The method of aforesaid a kind of vehicle scheduling also comprises, accident is promoted priority quasi real time handle.
The method of aforesaid a kind of vehicle scheduling among the described step S1, is used car information according to the user, and path and required time when calculating vehicle ' short line in the static road network comprise:
S11 utilizes A* (A-Star) algorithm, and in conjunction with the vehicle initial position, that calculates vehicle estimates running time and driving path as the ServiceStart value;
S12, according to the running time of estimating of vehicle, the binding purpose place, that calculates vehicle once more estimates running time and driving path as the ServiceEnd value.
Described destination comprises by way of the place.
The method of aforesaid a kind of vehicle scheduling, among the described step S2, the historical tuning parameter of coupling system is further adjusted to optimize to the vehicle running path that obtains under the static road network situation and required time and is comprised:
S21 according to the historical tuning parameter of system, carries out secondary likelihood estimation to ServiceStart value and ServiceEnd value, obtains normalization ServiceDelta value;
S22 according to ServiceDelta value result of calculation, draws the driving path of planning and estimates running time.
Realize the system of a kind of vehicle scheduling of preceding method, comprise background server, database server, subscription client, vehicle portable terminal and Call Center Server;
Also comprise and being located on the background server:
Order is estimated module, is used for the order data according to the user, and the vehicle when calculating vehicle ' short line in the static road network is estimated running time and driving path;
The system optimization module is used for the historical tuning parameter of coupling system, the vehicle that obtains under the static road network situation is estimated running time and driving path further adjust to optimize and obtain new vehicle and estimate running time and driving path;
The parameter generation module is used for calculated off-line and generates new historical tuning parameter;
Also comprise the information storage module of being located on the database server, be used to store the range of driving information of user's order data and vehicle;
Wherein, subscription client, vehicle portable terminal and Call Center Server are connected with background server respectively, and background server connects database server; Order is estimated module connected system optimal module.
The system of aforesaid a kind of vehicle scheduling also comprises the real-time road module of being located at background server, is used for the real-time road condition information according to user's range of driving information, upgrades each day part road conditions historical information storehouse, highway section;
Order probability module is used for according to user's History Order data, recomputates each place day part new order probability;
The active path module is used for recomputating each highway section day part active path parameter according to being beneficial to order range of driving information.
The system of aforesaid a kind of vehicle scheduling also comprises the emergency response module of being located at background server, is used for that accident is promoted priority and quasi real time handles.
Compared with prior art, the vehicle of the present invention when calculating earlier vehicle ' short line under the static road network situation estimated running time and driving path, the historical tuning parameter of coupling system then, the vehicle that obtains under the static road network situation is estimated running time and driving path further adjust to optimize and obtain new vehicle and estimate running time and driving path, generate scheduling scheme; The present invention combines the single order optimization in the static road network with system optimization, considered existing uncontrollable factors such as rider, driver, road conditions fully, thereby can improve the efficient of vehicle scheduling, reduces the rate of empty ride of vehicle, has reduced the wasting of resources; Secondly, of the present invention can the processing in real time accident is preferential significantly reduced the harmful effect that accident causes vehicle scheduling, improves the efficient of vehicle scheduling; In addition, the generation of historical tuning parameter of the present invention has been considered real-time road condition information, user's History Order data and has been beneficial to order range of driving information; Consider to influence the practical factor of vehicle scheduling fully, improved the accuracy rate of vehicle scheduling; In the actual operation, used method of the present invention after, the driver just can receive the another one order in a last user's destination in very short time after finishing an order, the rate of empty ride of vehicle has reduced by 60%.
Description of drawings
Fig. 1 is the system schematic of a kind of embodiment of the present invention;
Fig. 2 is the workflow diagram of a kind of embodiment of the present invention.
Reference numeral: 1-background server, 2-database server, 3-subscription client, 4-vehicle portable terminal, the 5-Call Center Server, the 6-order is estimated module, 7-system optimization module, 8-parameter generation module, the 9-information storage module, 10-real-time road module, 11-order probability module, 12-active path module, 13-emergency response module.
The present invention is further illustrated below in conjunction with the drawings and specific embodiments.
Embodiment
Embodiments of the invention: a kind of method of vehicle scheduling may further comprise the steps:
S1, according to user's order data, the vehicle when calculating vehicle ' short line in the static road network is estimated running time and driving path;
S2, the historical tuning parameter of coupling system is estimated running time and driving path to the vehicle that obtains under the static road network situation and is further adjusted to optimize and obtain new vehicle and estimate running time and driving path, generates scheduling scheme;
S3 deposits the range of driving information of user's order data and vehicle in system database;
S4, system off-line is calculated and is generated new historical tuning parameter.
Among the described step S4, system off-line is calculated the new historical tuning parameter of generation and is comprised:
According to the real-time road condition information in user's range of driving information, upgrade each day part road conditions historical information storehouse, highway section;
According to user's History Order data, recomputate each place day part new order probability;
According to being beneficial to order range of driving information, recomputate each highway section day part active path parameter.
Described order range of driving information comprises: riding time, pick-up point, destination, rider, rider's telephone number, number of passengers, the vehicle rank of selection.
Described active path calculation of parameter: by pick-up point and destination, can calculate user's driving path, according to the data of the History Order data of user's driving path and present real-time road, draw the active path parameter again.
Described historical tuning parameter comprises: historical road conditions, new order probability and active path parameter.
Method also comprises, accident is promoted priority quasi real time handle.
Among the described step S1, use car information according to the user, path and required time when calculating vehicle ' short line in the static road network comprise:
S11 utilizes A* (A-Star) algorithm, and in conjunction with in conjunction with the vehicle initial position, that calculates vehicle estimates running time and driving path as the ServiceStart value;
S12, according to the running time of estimating of vehicle, the binding purpose place, that calculates vehicle once more estimates running time and driving path as the ServiceEnd value.
Described destination comprises by way of the place.
Among the described step S2, the historical tuning parameter of coupling system is further adjusted to optimize to the vehicle running path that obtains under the static road network situation and required time and is comprised:
S21 according to the historical tuning parameter of system, carries out secondary likelihood estimation to ServiceStart value and ServiceEnd value, obtains normalization ServiceDelta value;
S22 according to ServiceDelta value result of calculation, draws the driving path of planning and estimates running time.
Realize the system of a kind of vehicle scheduling of preceding method, its system schematic as shown in Figure 1; Comprise background server 1, database server 2, subscription client 3, vehicle portable terminal 4 and Call Center Server 5;
Also comprise and being located on the background server 1:
Order is estimated module 6, is used for the order data according to the user, and the vehicle when calculating vehicle ' short line in the static road network is estimated running time and driving path;
Also comprise the information storage module of being located on the database server 29, be used to store the range of driving information of user's order data and vehicle;
Wherein, subscription client 3, vehicle portable terminal 4 and Call Center Server 5 are connected with background server 1 respectively, and background server 1 connects database server 2; Order is estimated module 6 connected system optimal module 7.
System also comprises the real-time road module 10 of being located at background server 1, is used for the real-time road condition information according to user's range of driving information, upgrades each day part road conditions historical information storehouse, highway section;
Order probability module 11 is used for according to user's History Order data, recomputates each place day part new order probability;
Active path module 12 is used for recomputating each highway section day part active path parameter according to being beneficial to order range of driving information.
Described order range of driving information comprises: riding time, pick-up point, destination, rider, rider's telephone number, number of passengers, the vehicle rank of selection.
Described active path calculation of parameter: by pick-up point and destination, can calculate user's driving path, according to the data of the History Order data of user's driving path and present real-time road, draw the active path parameter again.The real-time road data can obtain according to information of vehicles.
System also comprises the emergency response module of being located on the background server 1 13, is used for that accident is promoted priority and quasi real time handles.
The workflow of a kind of embodiment of the present invention: (as shown in Figure 2)
S10, background server be according to user's order data, and the vehicle when calculating vehicle ' short line in the static road network is estimated running time and driving path;
S20, the historical tuning parameter of background server coupling system is estimated running time and driving path to the vehicle that obtains under the static road network situation and is further adjusted to optimize and obtain new vehicle and estimate running time and driving path, generates scheduling scheme;
S30 deposits the range of driving information of user's order data and vehicle in database server;
S40, the background server calculated off-line generates new historical tuning parameter.
The example explanation
For example: the user places an order in the afternoon that 5 o'clock drove to Home Base from international trade.
The path of systems organization from international trade to Home Base, and calculate mileage, according to running time=mileage number/standard speed per hour, calculate running time, obtain basic data.
According to basic data, from system, obtain the parameters optimization before in this path, (historical road conditions, new order probability and active path parameter) plans the driving path of international trade to Home Base again according to optimal case.After the order service finishes, after system off-line is analyzed the detailed data of this order and calculate, draw the parameters optimization of this order.
Claims (9)
1. the method for a vehicle scheduling is characterized in that, may further comprise the steps:
S1, according to user's order data, the vehicle when calculating vehicle ' short line in the static road network is estimated running time and driving path;
S2, the historical tuning parameter of coupling system is estimated running time and driving path to the vehicle that obtains under the static road network situation and is further adjusted to optimize and obtain new vehicle and estimate running time and driving path, generates scheduling scheme;
S3 deposits the range of driving information of user's order data and vehicle in system database;
S4, system off-line is calculated and is generated new historical tuning parameter.
2. the method for a kind of vehicle scheduling according to claim 1 is characterized in that: among the described step S4, system off-line is calculated and is generated new historical tuning parameter and comprise:
According to the real-time road condition information in user's range of driving information, upgrade each day part road conditions historical information storehouse, highway section;
According to user's History Order data, recomputate each place day part new order probability;
According to being beneficial to order range of driving information, recomputate each highway section day part active path parameter.
3. the method for a kind of vehicle scheduling according to claim 1, it is characterized in that: described historical tuning parameter comprises: historical road conditions, new order probability and active path parameter.
4. the method for a kind of vehicle scheduling according to claim 1 is characterized in that: also comprise, accident is promoted priority quasi real time handle.
5. the method for a kind of vehicle scheduling according to claim 1 is characterized in that: among the described step S1, use car information according to the user, path and required time when calculating vehicle ' short line in the static road network comprise:
S11 utilizes A* (A-Star) algorithm, and in conjunction with in conjunction with the vehicle initial position, that calculates vehicle estimates running time and driving path as the ServiceStart value;
S12, according to the running time of estimating of vehicle, the binding purpose place, that calculates vehicle once more estimates running time and driving path as the ServiceEnd value.
6. the method for a kind of vehicle scheduling according to claim 5 is characterized in that, among the described step S2, the historical tuning parameter of coupling system is further adjusted to optimize to the vehicle running path that obtains under the static road network situation and required time and comprised:
S21 according to the historical tuning parameter of system, carries out secondary likelihood estimation to ServiceStart value and ServiceEnd value, obtains normalization ServiceDelta value;
S22 according to ServiceDelta value result of calculation, draws the driving path of planning and estimates running time.
7. realize the system of a kind of vehicle scheduling of the described method of claim 1～6, it is characterized in that: comprise background server (1), database server (2), subscription client (3), vehicle portable terminal (4) and Call Center Server (5); Also comprise and being located on the background server (1):
Order is estimated module (6), is used for the order data according to the user, and the vehicle when calculating vehicle ' short line in the static road network is estimated running time and driving path;
System optimization module (7) is used for the historical tuning parameter of coupling system, the vehicle that obtains under the static road network situation is estimated running time and driving path further adjust to optimize and obtain new vehicle and estimate running time and driving path;
Parameter generation module (8) is used for calculated off-line and generates new historical tuning parameter;
Also comprise the information storage module of being located on the database server (2) (9), the range of driving information that is used to store user's order data and vehicle;
Wherein, subscription client (3), vehicle portable terminal (4) and Call Center Server (5) are connected with background server (1) respectively, and background server (1) connects database server (2); Order is estimated module (6) connected system optimal module (7).
8. the system of a kind of vehicle scheduling according to claim 7, it is characterized in that: also comprise the real-time road module (10) of being located at background server (1), be used for real-time road condition information, upgrade each day part road conditions historical information storehouse, highway section according to user's range of driving information;
Order probability module (11) is used for according to user's History Order data, recomputates each place day part new order probability;
Active path module (12) is used for recomputating each highway section day part active path parameter according to being beneficial to order range of driving information.
9. the system of a kind of vehicle scheduling according to claim 7 is characterized in that: also comprise the emergency response module (13) of being located at background server (1), be used for that accident is promoted priority and quasi real time handle.
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| CN103164950A (en) * | 2011-12-12 | 2013-06-19 | 国民技术股份有限公司 | Intelligent vehicle management system and method |
| CN103593712A (en) * | 2013-11-01 | 2014-02-19 | 中国电子科技集团公司第十五研究所 | Resource optimization dispatching system and dispatching method |
| CN104517200A (en) * | 2013-09-29 | 2015-04-15 | 日电（中国）有限公司 | Fuel consumption calculation method, distribution plan acquisition method and distribution plan acquisition device for logistics distribution |
| CN104574934A (en) * | 2013-10-17 | 2015-04-29 | 上海遥薇(集团)有限公司 | Automatic vehicle scheduling method and system |
| CN105373840A (en) * | 2015-10-14 | 2016-03-02 | 深圳市天行家科技有限公司 | Designated-driving order predicting method and designated-driving transport capacity scheduling method |
| WO2016141710A1 (en) * | 2015-03-10 | 2016-09-15 | 北京东方车云信息技术有限公司 | Network vehicle using system for realizing two-way selection of passenger and driver |
| CN106408234A (en) * | 2016-09-09 | 2017-02-15 | 国家电网公司 | Power material dispensing method |
| CN106650984A (en) * | 2016-09-12 | 2017-05-10 | 上海斐讯数据通信技术有限公司 | Vehicle dispatch method and system |
| CN106934490A (en) * | 2017-02-22 | 2017-07-07 | 广州视源电子科技股份有限公司 | A kind of AGV calls Forecasting Methodology and device |
| CN107331188A (en) * | 2017-07-19 | 2017-11-07 | 合肥盛东信息科技有限公司 | A kind of vehicle scheduling management system based on audio call |
| US10740702B2 (en) | 2016-01-08 | 2020-08-11 | Oracle International Corporation | Method, system, and non-transitory computer-readable medium for reducing computation time in one-to-many path searching using heuristics and limited boundary adjustment |
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| CN103164950A (en) * | 2011-12-12 | 2013-06-19 | 国民技术股份有限公司 | Intelligent vehicle management system and method |
| CN104517200A (en) * | 2013-09-29 | 2015-04-15 | 日电（中国）有限公司 | Fuel consumption calculation method, distribution plan acquisition method and distribution plan acquisition device for logistics distribution |
| CN104517200B (en) * | 2013-09-29 | 2018-12-04 | 日电（中国）有限公司 | Calculation Method of Energy Consumption, distribution project acquisition methods and device for logistics distribution |
| CN104574934A (en) * | 2013-10-17 | 2015-04-29 | 上海遥薇(集团)有限公司 | Automatic vehicle scheduling method and system |
| CN103593712B (en) * | 2013-11-01 | 2017-07-21 | 中国电子科技集团公司第十五研究所 | A kind of Resource Scheduling System and dispatching method |
| CN103593712A (en) * | 2013-11-01 | 2014-02-19 | 中国电子科技集团公司第十五研究所 | Resource optimization dispatching system and dispatching method |
| WO2016141710A1 (en) * | 2015-03-10 | 2016-09-15 | 北京东方车云信息技术有限公司 | Network vehicle using system for realizing two-way selection of passenger and driver |
| CN105373840B (en) * | 2015-10-14 | 2018-12-11 | 深圳市天行家科技有限公司 | In generation, drives order forecast method and generation drives Transport capacity dispatching method |
| CN105373840A (en) * | 2015-10-14 | 2016-03-02 | 深圳市天行家科技有限公司 | Designated-driving order predicting method and designated-driving transport capacity scheduling method |
| US10740702B2 (en) | 2016-01-08 | 2020-08-11 | Oracle International Corporation | Method, system, and non-transitory computer-readable medium for reducing computation time in one-to-many path searching using heuristics and limited boundary adjustment |
| CN106408234A (en) * | 2016-09-09 | 2017-02-15 | 国家电网公司 | Power material dispensing method |
| CN106650984A (en) * | 2016-09-12 | 2017-05-10 | 上海斐讯数据通信技术有限公司 | Vehicle dispatch method and system |
| CN106934490A (en) * | 2017-02-22 | 2017-07-07 | 广州视源电子科技股份有限公司 | A kind of AGV calls Forecasting Methodology and device |
| CN107331188A (en) * | 2017-07-19 | 2017-11-07 | 合肥盛东信息科技有限公司 | A kind of vehicle scheduling management system based on audio call |
| CN112313698A (en) * | 2018-06-18 | 2021-02-02 | 日产自动车株式会社 | Commercial vehicle operation system |
| CN112313698B (en) * | 2018-06-18 | 2024-04-26 | 日产自动车株式会社 | Commercial vehicle operation system |
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Application publication date: 20110601 |