CN112309122A - Intelligent bus grading decision-making system based on multi-system cooperation - Google Patents
Intelligent bus grading decision-making system based on multi-system cooperation Download PDFInfo
- Publication number
- CN112309122A CN112309122A CN202011296551.9A CN202011296551A CN112309122A CN 112309122 A CN112309122 A CN 112309122A CN 202011296551 A CN202011296551 A CN 202011296551A CN 112309122 A CN112309122 A CN 112309122A
- Authority
- CN
- China
- Prior art keywords
- data
- vehicle
- intelligent
- decision
- bus
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000013439 planning Methods 0.000 claims abstract description 28
- 238000005457 optimization Methods 0.000 claims abstract description 23
- 230000006698 induction Effects 0.000 claims description 23
- 238000004422 calculation algorithm Methods 0.000 claims description 20
- 230000006870 function Effects 0.000 claims description 18
- 238000000034 method Methods 0.000 claims description 14
- 238000012544 monitoring process Methods 0.000 claims description 14
- 230000008569 process Effects 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 11
- 238000007405 data analysis Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000004891 communication Methods 0.000 claims description 8
- 238000013473 artificial intelligence Methods 0.000 claims description 6
- 238000013524 data verification Methods 0.000 claims description 3
- 230000003993 interaction Effects 0.000 claims description 3
- 230000007246 mechanism Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 13
- 230000004927 fusion Effects 0.000 abstract description 7
- 230000006399 behavior Effects 0.000 description 7
- 238000007726 management method Methods 0.000 description 5
- 241000283070 Equus zebra Species 0.000 description 4
- 238000004134 energy conservation Methods 0.000 description 4
- 238000005265 energy consumption Methods 0.000 description 4
- 238000007499 fusion processing Methods 0.000 description 4
- 230000011664 signaling Effects 0.000 description 4
- 230000001133 acceleration Effects 0.000 description 3
- 230000008447 perception Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000002068 genetic effect Effects 0.000 description 2
- 230000001939 inductive effect Effects 0.000 description 2
- 238000004080 punching Methods 0.000 description 2
- 238000012351 Integrated analysis Methods 0.000 description 1
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000001035 drying Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000004089 microcirculation Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000009469 supplementation Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000012384 transportation and delivery Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Landscapes
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Analytical Chemistry (AREA)
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses an intelligent bus grading decision-making system based on multi-system cooperation. According to the intelligent traffic platform, technologies such as intelligent network connection control vehicle, edge computing and data cloud platform are adopted, and through fusion of multi-source data, information island data can be led into the same platform, multi-data source fusion is achieved, the relation among various information is fully mined, passenger flow travel is accurately predicted, and effective information input of the intelligent traffic platform is improved; the intelligent promotion is carried out on different levels of business systems, and the prediction, planning, adjustment and execution of a scientific system are realized. And the system can guide the comprehensive planning of urban traffic, realize intelligent scheduling optimization, real-time control of vehicle driving and the like from the perspective of urban global, can effectively combine various systems, and provides assistance for bus decision-making.
Description
Technical Field
The invention relates to a bus grading decision-making system, in particular to an intelligent bus grading decision-making system based on multi-system cooperation.
Background
At present, an integrated command platform and a big data research center are basically built in a domestic public transport system, the centralized control of each big system, the centralized display and the comprehensive statistical query of data are realized, and the flattened command scheduling based on a map can be realized. However, due to the fact that the public transportation system is rich, the number of related departments and subordinate units is large, most of the information systems are developed by different software companies at different periods, data standards and formats are different, the systems operate independently and cannot be interconnected and communicated, massive data in each business system cannot be shared and used, an information island phenomenon exists, traffic data are fragmented and distributed, the information utilization rate is low, the fusion degree is poor, effective information communication and sharing among all the departments are lacked, meanwhile, the sensing and collecting capacity of the traffic information is limited, the potential value of the data is not effectively mined, the due value of the data is not exerted, and the functions of traffic monitoring, travel service, traffic command, emergency disposal and the like cannot fully play the decision of pre-prediction, intelligent management and post-evaluation.
With the information construction of public transport, various technologies exist in each field of public transport. In the aspect of intelligent perception, laser radar, sensing technology and the like are broken through continuously, and information around the bus can be obtained in time; in the aspect of communication technology, 4G networks are popularized, and the transmission standards of mobile signals between vehicle-mounted Ethernet and vehicle cloud reach the practical level of intelligent traffic. Meanwhile, the arrival of the 5G technology is more suitable for the real-time transmission of signals under the condition of high-speed running of the vehicle; in the aspect of platform technology, the progress of cloud computing and artificial intelligence algorithms enables a large amount of vehicle running information to be processed in a short time. But at present, each technology has not yet formed an effective, perfect, unified system.
In summary, the invention relates to an intelligent bus grading decision-making system based on multi-system cooperation.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a multi-system cooperation-based intelligent bus grading decision-making system, which adopts the technologies of intelligent network connection and control, edge calculation, a data cloud platform and the like, can guide information isolated island data into the same platform through the fusion of multi-source data, realizes the fusion of multiple data sources, fully excavates the connection among various information, accurately predicts passenger flow travel and promotes the effective information input of a smart traffic platform; the intelligent promotion is carried out on different levels of business systems, and the prediction, planning, adjustment and execution of a scientific system are realized. And the system can guide the comprehensive planning of urban traffic, realize intelligent scheduling optimization, real-time control of vehicle driving and the like from the perspective of urban global, can effectively combine various systems, and provides assistance for bus decision-making.
In order to achieve the purpose, the invention is realized by the following technical scheme: a multi-system cooperation based intelligent bus grading decision system comprises a cloud platform, an edge cloud and a vehicle-mounted intelligent terminal, wherein the cloud platform is a computing core of an intelligent public traffic system and mainly comprises a data analysis module, an intelligent scheduling module, a bus release decision module, a line optimization module, an intelligent scheduling module, a monitoring early warning module and a data query module; the edge cloud is a real-time computing processing system of the intelligent public transport system, and performs data interaction with the vehicles and the road side equipment; the vehicle-mounted intelligent terminal is terminal equipment arranged on a vehicle in an intelligent public transport system.
The main functions of the edge cloud comprise vehicle path planning, network connection automatic driving, multi-dimensional space-time service and vehicle data forwarding. The vehicle path planning reasonably plans a vehicle running path by collecting travel reservation requests of passengers and using an intelligent path planning algorithm for the travel flow of passenger groups so as to improve the operation efficiency of the bus; the internet automatic driving establishes interconnection and intercommunication between vehicles and the environment in an intelligent internet mode, and can effectively improve the driving safety of the vehicles; the multidimensional space-time service mainly comprises high-precision information such as time, space, environment and the like related to vehicle running, solves the problems of information bottleneck, calculation bottleneck and the like existing in single-vehicle intelligence, and provides comprehensive and complete multidimensional information perception service for the vehicle quantity by utilizing the capabilities of low delay and high calculation power of edge clouds. In addition, in order to realize the functions, the edge cloud has the function of processing various data of vehicles and environments in real time, and further submits information to the cloud platform through the data forwarding module, so that data support is provided for various big data analysis of the cloud platform.
The main functions of the vehicle-mounted intelligent terminal comprise cloud communication, data acquisition, OTA, intelligent control and the like. The data acquisition is to acquire the vehicle can data, video monitoring data, radar point cloud data and the like of the vehicle through various sensors and perform primary processing and arrangement on the data; the intelligent control is to analyze and judge various data through an artificial intelligence algorithm on the vehicle-mounted intelligent terminal, and issue a control instruction to the vehicle to complete the automatic driving function of the vehicle; the cloud communication is to send various collected data to the edge cloud end through a special protocol, and ensure the safety and correctness of the data through mechanisms such as data encryption, data verification, data reissue and the like; OTA is that intelligent terminal software can long-range upgrading, improves the maintainability of system.
The decision making process of the invention comprises the following steps: the method comprises the following steps of route optimization decision, bus release decision, scheduling and scheduling decision, path planning decision, safety and energy-saving induction decision and automatic driving control decision.
The system classification principle of the invention is as follows: taking the time-ductility and the regionality of the decision as the division principle of the system. Meanwhile, data is used as a link for fusing each system, so that each system can organically fuse together to form a uniform and complete intelligent bus system while finishing respective decision through grading and layering.
The time-ductility of the decision refers to the time delay that exists between the decision making and the execution. It can be divided into milliseconds, seconds, minutes, hours and non-real time; regional decision making means how large range of vehicles can be affected by the decision. He can be classified into meter, hundred meter, kilometer and global. Therefore, when the multiple systems make decisions for the intelligent bus, the timeliness and the regionality of the decisions made by the systems need to be effectively distinguished, and grading decisions are made. And when each system makes a decision, the decision needs to be guaranteed to have priority, so that all levels of decisions can be effectively matched, and conflicts are reduced.
The invention has the beneficial effects that: according to the intelligent traffic platform, technologies such as intelligent network connection control vehicle, edge computing and data cloud platform are adopted, and through fusion of multi-source data, information island data can be led into the same platform, multi-data source fusion is achieved, the relation among various information is fully mined, passenger flow travel is accurately predicted, and effective information input of the intelligent traffic platform is improved; the intelligent promotion is carried out on different levels of business systems, and the prediction, planning, adjustment and execution of a scientific system are realized. And the system can guide the comprehensive planning of urban traffic, realize intelligent scheduling optimization, real-time control of vehicle driving and the like from the perspective of urban global, can effectively combine various systems, and provides assistance for bus decision-making.
Drawings
The invention is described in detail below with reference to the drawings and the detailed description;
FIG. 1 is a system architecture diagram of the present invention;
FIG. 2 is a schematic diagram of the system of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
Referring to fig. 1-2, the following technical solutions are adopted in the present embodiment: the utility model provides a hierarchical decision-making system of wisdom public transit based on multisystem cooperation, includes cloud platform, marginal cloud and on-vehicle intelligent terminal, the cloud platform be intelligent public transport system's calculation core, mainly include, data analysis module, intelligent scheduling module, bus put in decision-making module, circuit optimization module, intelligent scheduling module, control early warning module, data query module. The data analysis module can establish a mathematical model of the driving behavior of the driver by analyzing the collected vehicle driving data, and evaluate the excellent driving habits of the driver. The running state of the important component can be analyzed according to the historical data of the vehicle running, and possible faults of the vehicle can be predicted in advance. The running oil consumption of the vehicle can be analyzed, and a basis is provided for vehicle oil saving measures; the intelligent scheduling module can automatically complete the running scheduling of the vehicle according to the running condition of the vehicle in the line, the running road condition and other factors. The vehicle speed planning can be dynamically issued according to the running distance of the vehicles in the whole line, and the running efficiency of the whole line is adjusted; the intelligent induction module continuously optimizes and induces the vehicle driving safety and energy conservation through an automatic intelligent algorithm, so that the vehicle driving safety and energy conservation and the driving feeling of a driver are balanced; the route planning module can optimize the existing route according to the characteristics of urban population trip and the historical operation data analysis of the public transport vehicles, and improves the passenger transporting efficiency of the route. A regional development model can be established to predict the possible change of population travel and provide a basis for new route planning; the data query module provides a uniform data query interface and can acquire vehicle data through various conditions such as vehicles, routes, time and the like; the intelligent shift arrangement automatically finishes the shift operation planning of the bus operation line through an intelligent optimization algorithm, dynamically optimizes the shift arrangement according to the actual operation condition of the bus, and improves the operation efficiency of the bus.
The edge cloud is a real-time computing processing system of the intelligent public transportation system, and data interaction between the edge cloud and the vehicles and road side equipment has the characteristics of high stability and low delay. The main functions comprise vehicle path planning, network connection automatic driving, multi-dimensional space-time service and vehicle data forwarding. The vehicle path planning reasonably plans a vehicle running path by collecting travel reservation requests of passengers and using an intelligent path planning algorithm for the travel flow of passenger groups so as to improve the operation efficiency of the bus; the internet automatic driving establishes interconnection and intercommunication between vehicles and the environment in an intelligent internet mode, and can effectively improve the driving safety of the vehicles; the multidimensional space-time service mainly comprises high-precision information such as time, space, environment and the like related to vehicle running, solves the problems of information bottleneck, calculation bottleneck and the like existing in single-vehicle intelligence, and provides comprehensive and complete multidimensional information perception service for the vehicle quantity by utilizing the capabilities of low delay and high calculation power of edge clouds. In addition, in order to realize the functions, the edge cloud has the function of processing various data of vehicles and environments in real time, and further submits information to the cloud platform through the data forwarding module, so that data support is provided for various big data analysis of the cloud platform.
The vehicle-mounted intelligent terminal is a terminal device installed on a vehicle in an intelligent public transport system, and has the characteristics of low power consumption and high stability. The main functions comprise cloud communication, data acquisition, OTA, intelligent control and the like. The data acquisition is to acquire the vehicle can data, video monitoring data, radar point cloud data and the like of the vehicle through various sensors and perform primary processing and arrangement on the data; the intelligent control is to analyze and judge various data through an artificial intelligence algorithm on the vehicle-mounted intelligent terminal, and issue a control instruction to the vehicle to complete the automatic driving function of the vehicle; the cloud communication is to send various collected data to the edge cloud end through a special protocol, and ensure the safety and correctness of the data through mechanisms such as data encryption, data verification, data reissue and the like; OTA is that intelligent terminal software can long-range upgrading, improves the maintainability of system.
The decision making process of the present embodiment includes:
1. and (3) line optimization decision: by additionally installing vehicle-mounted intelligent equipment, roadside equipment, fusing various data sources (including vehicle-mounted cameras, signaling data, operation data such as card swiping data, monitoring data, road data and the like) of an external system and the like, integrating and processing the data sources by the system to conclude travel rules of routes and crowds; evaluating the efficiency of the existing route; and performing simulation on the results of the line optimization and adjustment.
On the premise of guaranteeing the travel demands of residents on buses, in order to improve the operation efficiency of the existing lines of the buses, the system utilizes an artificial intelligent genetic algorithm to establish a road network optimization model and provide optimization transformation and planning suggestions for the existing bus network, so that the bus lines can obtain better operation efficiency under various requirements of travel time of passengers, the repeatability coefficient of the bus lines, network density, economic benefits of bus enterprises and the like.
2. And (3) bus release decision: by additionally arranging equipment such as a vehicle front-view/side-view camera, an intelligent automatic driving controller, a high-precision positioning terminal and a vehicle-mounted high-definition video terminal and fusing various data sources such as an external system (comprising the vehicle-mounted camera, signaling data and operation data such as card swiping data', monitoring data, road data and the like), the travel rule of people in a parcel is summarized through the fusion processing of the system; the transportation capacity is supplemented in the hot spot area, and the net contracting type supplementation is carried out on the empty spot covered by public transportation; real-time delivery rules are realized for travel rules of the crowd; realize the flexible public trip mode of 'quick-drying branch and little'.
In order to make up blind areas existing in the existing public transport network and solve the problem that the traveling demands of the residents in part of work and life cannot be met, the system is better connected with other public transport, the system identifies the blind areas of the public transport network by using a cluster analysis algorithm and gives a proposal for releasing routes of buses around the network, so that the traveling of the residents is more convenient.
Utilize and remove signaling data, public transit operation data (passenger data of punching a card), and road data (public transit road network data) integrated analysis resident's trip demand, think the trip demand that obtains according to removing signaling data among the analytic process for the whole trip demands of resident, remove the trip demand that the public transit covered on this basis (obtain according to public transit data analysis of punching a card), to the trip demand that remaining public transit does not cover, put in minibus and utilize modes such as microcirculation to satisfy.
3. Scheduling and scheduling decision: by additionally arranging equipment such as a vehicle front-view/side-view camera, an intelligent induction controller, a roadside camera/radar, a vehicle-mounted high-definition video terminal and the like, and fusing various data sources such as an external system (comprising whole vehicle CAN data 'GPS, vehicle speed', vehicle-mounted camera 'front vehicle distance, crowd', road test data 'line speed', operation data 'card swiping data', monitoring data 'pedestrian' and road data and the like), the travel rule of the route crowd is summarized through the fusion processing of the system; an optimal scheduling schedule is given according to trips in different time intervals by combining operation principles (reasonable space and reasonable real load); and according to the crowd and road parameters, giving a quasi-real-time scheduling mode.
In order to improve indexes such as road section average load rate, passenger average waiting time, average inter-vehicle distance, vehicle utilization rate and the like of the current operation shift of the bus and improve the operation efficiency of the bus, the system provides an optimization suggestion for the current scheduling by using an artificial intelligent genetic algorithm.
(2) Implementing content
The intelligent scheduling optimization means that under the current bus scheduling arrangement, the actual operation data of the bus is analyzed, and 1) the bus operation condition under the current scheduling condition is displayed in a reasonable form; 2) aiming at the current travel passenger flow demand, an intelligent simulation algorithm is applied to automatically simulate the bus running conditions of different lines after the shift adjustment; 3) and (3) automatically calculating the optimal scheduling conditions of different lines by using an intelligent algorithm, and finally providing the optimal scheduling conditions as scheduling optimization suggestions to clients.
4. And (3) path planning decision: by additionally arranging equipment such as an automatic driving controller, a vehicle front-view/side-view camera, a high-precision positioning terminal, a road side camera/radar, a vehicle-mounted high-definition video terminal and the like, and fusing various data sources such as an external system and the like (comprising whole vehicle CAN data ('GPS, course angle, vehicle speed', vehicle-mounted camera 'front vehicle distance, crowd', road side data 'line vehicle speed', operation data 'passenger getting-on and getting-off position data', road data 'high-precision map', monitoring data 'person in vehicle' and the like), network reduction data are obtained through fusion processing of the system, and are dispatched and delivered in real time, optimal vehicle path planning is realized by judging road conditions according to various data sources, and scheduling rules of different periods among regions are realized.
In order to improve the waiting experience of the passengers, the system adopts the thinking of the Internet +, so that the passengers can make real-time bus appointment on the mobile phone terminal app, and the passengers can better plan the own travel time according to the bus arrival time prompted by the mobile phone terminal app. Meanwhile, the system also realizes the path planning and reasonable scheduling of the vehicle by using an ant colony algorithm according to the car appointment condition of the passengers.
The net bus combines 'a peak main line and a flat peak tour', meets public travel demands of the public within 5 kilometers, can realize connection of BRT stations and subway stations with key parks and commercial districts, and is beneficial supplement of traffic modes such as public transport, BRT and subway.
The network contract microbar service sells tickets in an APP reservation mode, and three service scenes are realized:
the peak main line (commute contract) is characterized by that according to the peak passenger flow corridor on duty and off duty the virtual tour main line can be made, in the peak period the vehicle can make tour in the preset main line, and can preferentially respond to the contract call of main line, and the platform can be used for sending passenger OD information to driver. During the morning and evening peak period, a dispatcher can preset part of fixed commuting times in advance through a management dispatching system, appoint a route of micro-bus walking, and meet the fixed traveling demands of going on and off duty.
Group chartered vehicle (group appointment bus) which adds the function of 'group appointment bar' during peak hours (not accepted during peak hours); when calling, the number of seats per car is prompted for the passengers, and the passengers can make an appointment according to the number of the cars. In response, the platform provides the driver-related information to the passenger and requests that the vehicle arrive 10 minutes in advance.
And (4) releasing call restriction of a tour main line, responding calls in a framed area, summarizing by a platform according to OD information of calling passengers, and automatically planning a navigation path by using an ant colony algorithm according to the number of the passengers and the distance between the passengers and a vehicle to inform a driver of running in time. The intelligent dispatching subsystem in the platform automatically matches orders issued by passengers with the forward-going contract bars or automatically accompanies the orders, combines the orders in the same direction of passenger travel together to generate dispatching shifts, and automatically distributes the dispatching shifts to nearby idle contract bars, so that fewer vehicles are used for saving more paths to transport more passengers.
5. And (4) safe energy-saving induction decision making: by additionally arranging equipment such as a vehicle front-view/side-view camera, an intelligent induction controller, a roadside camera/radar, a vehicle-mounted high-definition video terminal and the like, and fusing various data sources such as an external system and the like (comprising vehicle CAN data, vehicle-mounted camera front-vehicle distance, crowd ', roadside equipment traffic light signals ', road data zebra crossing data ', monitoring data and the like), a vehicle optimal energy consumption model is obtained through the fusion processing of the system; determining the number of passengers in real time according to various data sources; determining accurate zebra crossing, traffic lights, stations, high-risk places and road conditions according to the road data; energy-saving induction is realized; and realizing the safety induction based on the location.
In order to gradually improve the intelligent level in the traditional bus driving operation and solve the partial safety and energy-saving problems existing in the driving process, the system adopts an intelligent induction technology and a multi-objective optimization technology, and in the process of driving a vehicle by a driver, links with safety and energy-saving problems are analyzed by the system to implement intelligent induction. The intelligent induction is a process for gradually optimizing the driving behavior of a driver, and helps the driver to progressively improve the safety and energy-saving effect in the process of driving the vehicle within the range which can be received by the driver.
The intelligent bus induction system is used for carrying out automatic, real-time and dynamic intelligent induction control on vehicle power output by analyzing vehicle Can data, sensor data, vehicle-mounted monitoring data, road test data and road data, and comprises two aspects of intelligent driving induction and intelligent safety induction:
intelligent driving induction: the big data is used for inducing the power output (torque) of the vehicle in real time according to the information of the current bus route, the road surface gradient, the friction coefficient, the vehicle load and the like of the vehicle in the starting or accelerating process of the vehicle, so that the driving intention of a driver is realized with optimal energy consumption, and the optimization of the overall operation energy consumption of the bus is supported. The real bus running control guidance ensures an effective and visible control guidance strategy, the guidance strategy is realized not by voice or interface reminding and the like, and the output power of the bus needs to be continuously optimized for a long time; in addition, the change of the output power cannot be obviously felt by a driver, and the real vehicle running control induction is realized in the real sense. In order to perform iterative optimization on the vehicle control induction execution condition, the vehicle terminal must also be ensured to be capable of performing real-time feedback on the received control strategy execution condition. The specific scheme is as follows:
obtaining complete running data of vehicle by data acquisition device
Obtaining the dynamic characteristic of an engine, the main road characteristic of vehicle operation and the driving style characteristic of a driver through data analysis;
calculating the optimal starting strategy and the optimal accelerating strategy of the vehicle on the route according to the combination of an artificial intelligence algorithm and vehicle simulation software;
the non-optimal driving behavior of the driver is subjected to induced optimization through the vehicle-mounted intelligent terminal, and the purposes of energy conservation, safety and high efficiency are achieved under the condition that normal driving of the driver is not influenced as much as possible;
according to the route characteristics, the shift characteristics and the driver behavior characteristics, more detailed induction services are formulated, and the driving efficiency is optimized on the basis of safety and energy conservation;
intelligent safe induction: when the vehicle passes through a driving safety sensitive zone such as a zebra crossing, a traffic light and the like, the driving of the vehicle is guided without people, and the acceleration/deceleration, the acceleration and deceleration place, the maximum speed, the average speed and the like of the vehicle are intelligently controlled, so that the driving safety of the bus is supported. In addition to the safety induction, the non-safety driving behaviors of the driver with rapid acceleration and zebra crossing overspeed are identified, and information of the occurrence moment of the non-safety driving behaviors is displayed. In order to visually show the quality of the unsafe driving behaviors of each driver, the rank of the unsafe driving of the driver is also shown, and data support is provided for scientific management of bus operation.
The intelligent induction of the public transport vehicle is realized based on artificial intelligence and big data technology, and the realization process is as follows:
repeating the steps
～
The intelligent guidance strategy is continuously optimized and executed, auxiliary management functions such as energy consumption analysis, driver evaluation, driving report and real-time monitoring are provided, a management closed loop is formed, and automation, intellectualization and continuous optimization of bus safety and energy-saving operation are finally realized.
6. Automatic driving control decision: by additionally arranging equipment such as a vehicle front-view/side-view camera, a road side camera/radar, a vehicle-mounted high-definition video terminal and the like, collecting various data sources (comprising whole vehicle CAN data including GPS, vehicle speed ', front vehicle distance of the vehicle-mounted camera, crowd ', road test data including line speed ', operation data including card swiping data, monitoring data including pedestrian data, road data and the like), fusing and processing by an intelligent large data platform, and summarizing travel rules of route crowd; an optimal scheduling schedule is given according to trips in different time intervals by combining operation principles (reasonable space and reasonable real load); according to the crowd and road parameters, giving a quasi-real-time scheduling mode; and the running speed of the vehicle is automatically controlled according to safe, efficient and energy-saving multi-objective optimization, and the whole-course automatic driving control of scheduling, dispatching and running is realized.
On the basis of intelligent scheduling and scheduling of basic buses, the vehicle automatic driving technology is combined, the global and local optimal vehicle speed algorithm is utilized, the whole-course planning and local optimization are carried out on the running speed of the BRT bus rapid transit, and the operation of the BRT bus rapid transit is enabled to be faster, more comfortable and more energy-saving.
The system gives the overall average speed of the vehicle and the optimal speed sequence between each station on the basis of the given scheduling requirement, and the vehicle can realize the aims of high efficiency, energy saving, safety and the like along with the planned overall optimal speed in real time. Meanwhile, the local optimal vehicle speed algorithm deployed in the edge MEC can calculate a local optimal vehicle speed sequence in real time according to information such as vehicles, road conditions and scheduling targets, and local optimal vehicle driving of the vehicles is completed on the premise that a global vehicle speed planning target is met.
The intelligent optimal speed strategy is based on a bus full-automatic intelligent scheduling system, the real-time speed of the bus is planned in real time, the situation that the distance between the buses is too small to enhance the safety of the bus and passengers is avoided, the waiting time of the passengers is greatly prolonged, and the energy-saving, safe and efficient operation targets of the bus are achieved.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. A multi-system cooperation based intelligent bus grading decision-making system is characterized by comprising a cloud platform, an edge cloud and a vehicle-mounted intelligent terminal, wherein the cloud platform is a computing core of an intelligent public traffic system and mainly comprises a data analysis module, an intelligent scheduling module, a bus release decision-making module, a line optimization module, an intelligent scheduling module, a monitoring and early warning module and a data query module; the edge cloud is a real-time computing processing system of the intelligent public transport system, and performs data interaction with the vehicles and the road side equipment; the vehicle-mounted intelligent terminal is terminal equipment arranged on a vehicle in an intelligent public transport system.
2. The multi-system cooperation-based intelligent bus grading decision system as claimed in claim 1, wherein the main functions of the edge cloud include vehicle path planning, internet automatic driving, multi-dimensional space-time service, and vehicle data forwarding; the vehicle path planning reasonably plans a vehicle running path by collecting travel reservation requests of passengers and using an intelligent path planning algorithm for the travel flow of passenger groups so as to improve the operation efficiency of the bus; the internet automatic driving establishes interconnection and intercommunication between vehicles and the environment in an intelligent internet mode, and can effectively improve the driving safety of the vehicles; the multidimensional space-time service mainly comprises high-precision information such as time, space, environment and the like related to vehicle running, solves the problems of information bottleneck, calculation bottleneck and the like existing in single-vehicle intelligence, and provides comprehensive and complete multidimensional information sensing service for the vehicle quantity by utilizing the capabilities of low delay and high calculation power of edge clouds; in addition, in order to realize the functions, the edge cloud has the function of processing various data of vehicles and environments in real time, and further submits information to the cloud platform through the data forwarding module, so that data support is provided for various big data analysis of the cloud platform.
3. The multi-system cooperation-based intelligent bus grading decision system as claimed in claim 1, wherein the vehicle-mounted intelligent terminal mainly functions include cloud communication, data acquisition, OTA and intelligent control; the data acquisition is to acquire the vehicle can data, video monitoring data, radar point cloud data and the like of the vehicle through various sensors and perform primary processing and arrangement on the data; the intelligent control is to analyze and judge various data through an artificial intelligence algorithm on the vehicle-mounted intelligent terminal, and issue a control instruction to the vehicle to complete the automatic driving function of the vehicle; the cloud communication is to send various collected data to the edge cloud end through a special protocol, and ensure the safety and correctness of the data through mechanisms such as data encryption, data verification, data reissue and the like; OTA is that intelligent terminal software can long-range upgrading, improves the maintainability of system.
4. The system of claim 1, wherein the decision-making process of the decision-making system comprises: the method comprises the following steps of route optimization decision, bus release decision, scheduling and scheduling decision, path planning decision, safety and energy-saving induction decision and automatic driving control decision.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011296551.9A CN112309122A (en) | 2020-11-19 | 2020-11-19 | Intelligent bus grading decision-making system based on multi-system cooperation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011296551.9A CN112309122A (en) | 2020-11-19 | 2020-11-19 | Intelligent bus grading decision-making system based on multi-system cooperation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112309122A true CN112309122A (en) | 2021-02-02 |
Family
ID=74334840
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011296551.9A Withdrawn CN112309122A (en) | 2020-11-19 | 2020-11-19 | Intelligent bus grading decision-making system based on multi-system cooperation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112309122A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113096393A (en) * | 2021-03-29 | 2021-07-09 | 中移智行网络科技有限公司 | Road condition early warning method and device and edge cloud equipment |
| CN113470415A (en) * | 2021-08-05 | 2021-10-01 | 安徽富煌科技股份有限公司 | BRT vehicle road cooperative control system based on big data |
| CN113965568A (en) * | 2021-10-19 | 2022-01-21 | 南京莱斯网信技术研究院有限公司 | Edge computing system for urban road C-V2X network |
| CN114550477A (en) * | 2021-12-30 | 2022-05-27 | 广州市公共交通集团有限公司 | Bus driving safety early warning system and method |
| CN114648870A (en) * | 2022-02-11 | 2022-06-21 | 行云新能科技（深圳）有限公司 | Edge calculation system, edge calculation decision prediction method, and computer-readable storage medium |
| WO2022227105A1 (en) * | 2021-04-30 | 2022-11-03 | 广州中国科学院软件应用技术研究所 | Cooperative control system and method based on device-cloud fusion |
| CN115285148A (en) * | 2022-09-01 | 2022-11-04 | 清华大学 | Automatic driving speed planning method and device, electronic equipment and storage medium |
| CN115440047A (en) * | 2022-09-14 | 2022-12-06 | 甘肃新视能科技有限公司 | Intelligent traffic system based on cloud side end fusion technology |
| CN115662166A (en) * | 2022-09-19 | 2023-01-31 | 长安大学 | Automatic driving data processing method and automatic driving traffic system |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105574798A (en) * | 2015-12-14 | 2016-05-11 | 天津智行远创信息科技有限公司 | Intelligent bus electronic system |
| KR20170047143A (en) * | 2015-10-22 | 2017-05-04 | 성균관대학교산학협력단 | Warning method for collision between pedestrian and vehicle based on road-side unit |
| CN108447291A (en) * | 2018-04-03 | 2018-08-24 | 南京锦和佳鑫信息科技有限公司 | A kind of Intelligent road facility system and control method |
| CN110705747A (en) * | 2019-08-27 | 2020-01-17 | 广州交通信息化建设投资营运有限公司 | Intelligent public transport cloud brain system based on big data |
| CN110930747A (en) * | 2018-09-20 | 2020-03-27 | 南京锦和佳鑫信息科技有限公司 | Intelligent internet traffic service system based on cloud computing technology |
| CN111210618A (en) * | 2018-11-22 | 2020-05-29 | 南京锦和佳鑫信息科技有限公司 | Automatic internet public traffic road system |
| CN111367292A (en) * | 2020-03-20 | 2020-07-03 | 特路（北京）科技有限公司 | Intelligent road system for automatically driving automobile |
| CN111583699A (en) * | 2020-04-27 | 2020-08-25 | 深圳众维轨道交通科技发展有限公司 | Intelligent bus monitoring system |
| CN111798665A (en) * | 2020-09-10 | 2020-10-20 | 深圳市城市交通规划设计研究中心股份有限公司 | Road system |
-
2020
- 2020-11-19 CN CN202011296551.9A patent/CN112309122A/en not_active Withdrawn
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20170047143A (en) * | 2015-10-22 | 2017-05-04 | 성균관대학교산학협력단 | Warning method for collision between pedestrian and vehicle based on road-side unit |
| CN105574798A (en) * | 2015-12-14 | 2016-05-11 | 天津智行远创信息科技有限公司 | Intelligent bus electronic system |
| CN108447291A (en) * | 2018-04-03 | 2018-08-24 | 南京锦和佳鑫信息科技有限公司 | A kind of Intelligent road facility system and control method |
| CN110930747A (en) * | 2018-09-20 | 2020-03-27 | 南京锦和佳鑫信息科技有限公司 | Intelligent internet traffic service system based on cloud computing technology |
| CN111210618A (en) * | 2018-11-22 | 2020-05-29 | 南京锦和佳鑫信息科技有限公司 | Automatic internet public traffic road system |
| CN110705747A (en) * | 2019-08-27 | 2020-01-17 | 广州交通信息化建设投资营运有限公司 | Intelligent public transport cloud brain system based on big data |
| CN111367292A (en) * | 2020-03-20 | 2020-07-03 | 特路（北京）科技有限公司 | Intelligent road system for automatically driving automobile |
| CN111583699A (en) * | 2020-04-27 | 2020-08-25 | 深圳众维轨道交通科技发展有限公司 | Intelligent bus monitoring system |
| CN111798665A (en) * | 2020-09-10 | 2020-10-20 | 深圳市城市交通规划设计研究中心股份有限公司 | Road system |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113096393A (en) * | 2021-03-29 | 2021-07-09 | 中移智行网络科技有限公司 | Road condition early warning method and device and edge cloud equipment |
| WO2022227105A1 (en) * | 2021-04-30 | 2022-11-03 | 广州中国科学院软件应用技术研究所 | Cooperative control system and method based on device-cloud fusion |
| CN113470415A (en) * | 2021-08-05 | 2021-10-01 | 安徽富煌科技股份有限公司 | BRT vehicle road cooperative control system based on big data |
| CN113965568A (en) * | 2021-10-19 | 2022-01-21 | 南京莱斯网信技术研究院有限公司 | Edge computing system for urban road C-V2X network |
| CN113965568B (en) * | 2021-10-19 | 2023-07-04 | 南京莱斯网信技术研究院有限公司 | Edge computing system for urban road C-V2X network |
| CN114550477A (en) * | 2021-12-30 | 2022-05-27 | 广州市公共交通集团有限公司 | Bus driving safety early warning system and method |
| CN114648870A (en) * | 2022-02-11 | 2022-06-21 | 行云新能科技（深圳）有限公司 | Edge calculation system, edge calculation decision prediction method, and computer-readable storage medium |
| CN114648870B (en) * | 2022-02-11 | 2023-07-28 | 行云新能科技（深圳）有限公司 | Edge computing system, edge computing decision prediction method, and computer-readable storage medium |
| CN115285148A (en) * | 2022-09-01 | 2022-11-04 | 清华大学 | Automatic driving speed planning method and device, electronic equipment and storage medium |
| CN115440047A (en) * | 2022-09-14 | 2022-12-06 | 甘肃新视能科技有限公司 | Intelligent traffic system based on cloud side end fusion technology |
| CN115662166A (en) * | 2022-09-19 | 2023-01-31 | 长安大学 | Automatic driving data processing method and automatic driving traffic system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112309122A (en) | Intelligent bus grading decision-making system based on multi-system cooperation | |
| Luo et al. | A new framework of intelligent public transportation system based on the internet of things | |
| CN104021667B (en) | Integrate subscription services and the taxi dispatching system called a taxi in real time and scheduling method | |
| CN102081859B (en) | Control method of bus arrival time prediction model | |
| CN107392336B (en) | Reservation-based distributed electric vehicle charging scheduling method in intelligent transportation | |
| CN112634606A (en) | Truck formation transportation scheduling method, scheduling system, medium and data processing terminal | |
| CN109285373A (en) | A kind of intelligent network connection traffic system towards whole road network | |
| CN111371904A (en) | Cloud-side-end-coordinated highway cloud control system and control method | |
| CN111815189A (en) | Modular bus dispatching system | |
| Lu et al. | Optimal scheduling of a taxi fleet with mixed electric and gasoline vehicles to service advance reservations | |
| Vreeswijk et al. | Energy efficient traffic management and control-the eCoMove approach and expected benefits | |
| CN110458456B (en) | Demand response type public transportation system scheduling method and system based on artificial intelligence | |
| CN101799981A (en) | Multi-mode public transport region scheduling control method | |
| CN113628473B (en) | Intelligent bus response type stop plan and dynamic scheduling system | |
| CN103366224B (en) | Passenger demand prediction system and method based on public transport network | |
| CN105913653A (en) | Public transport intelligent vehicle planning and scheduling system and public transport intelligent vehicle planning and scheduling method under cloud platform environment | |
| CN114202101A (en) | Networked train operation diagram intelligent compilation and evaluation optimization system | |
| CN110688982A (en) | Intelligent rail transit time control method based on target detection technology and ACO-BP algorithm | |
| CN102779406A (en) | Cloud computing intelligent transportation scheduling platform based on Beidou time service technology | |
| CN115860594A (en) | Simulation system and method applied to intelligent bus scheduling | |
| CN115936330A (en) | Demand response type BRT vehicle scheduling algorithm based on Lagrangian principle | |
| CN113298390B (en) | Method and device for constructing multi-mode collaborative evacuation scheme set for sudden heterogeneous passenger flow | |
| CN114971136A (en) | Bus and tour bus scheduling method | |
| Liu et al. | Modelling and simulation of cooperative control for bus rapid transit vehicle platoon in a connected vehicle environment | |
| CN111785015A (en) | Public transport real-time regulation and control information system and scheduling method based on genetic algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20210202 |
|
| WW01 | Invention patent application withdrawn after publication |