CN111739287A - Intelligent scheduling system for intelligent station with cooperative vehicle and road - Google Patents
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Abstract
The invention discloses an intelligent scheduling system of a vehicle-road cooperative intelligent platform, which comprises a mobile phone signaling data acquisition module, a data characteristic multidimensional analysis module, a cellular wireless communication network module, a vehicle-road cooperative communication network module, a traffic environment information acquisition module, a simulation line module, a prediction modeling module, a scheduling center and a big data module; the invention collects the human-vehicle-road information acquired by the road coordination technology through the road coordination communication network module, realizes data collection, fault report, position display, data query and statistical analysis, designs a dispatching scheme dynamic planning method through a dispatching center, realizes the optimal matching of the types, the quantities and the lines of the buses, improves the operation efficiency of the public transportation system in the complex urban traffic environment, calculates the efficient dispatching operation line by adopting a particle swarm algorithm, comprehensively analyzes the operation data of the bus line and the operation characteristics of the buses in the station, and reasonably matches and meets the maximum bus type of the utilization degree of the buses according to the passenger flow demand.
Description
Technical Field
The invention belongs to the technical field of intelligent bus dispatching, and particularly relates to an intelligent bus-road cooperative intelligent platform dispatching system.
Background
At present, the expansion and population increase of cities are the main development trend of cities, and the public transportation demand is becoming increasingly huge. Public transport trip is as an important land transportation trip mode, receives the traffic influence of crowding day by day, and it is comfortable to take, the aspects such as accurate point arrival and energy saving and emission reduction can not satisfy society and environmental demand. In view of the above problems, optimization and operation mode innovation of the public transportation service system have become an effective scheme recognized in the industry, and are necessary means for improving the service capability of the public transportation system. However, the travel service system for automatically driving the bus is limited by the current situation that the service system and the operation management mode are immature, and the research, development and demonstration operation of the travel service system for automatically driving the bus are urgent to solve the following problems: travel service, scheduling system and operation and test;
the existing dispatching system fails to fully combine traffic big data to carry out passenger flow demand analysis, a static off-line dispatching scheme is made mainly by the experience and intuition of managers in the aspect of dispatching decision, the best matching of the types, the number and the lines of the buses cannot be realized, the operation efficiency of the traffic environment public transportation system is low, the reasonable dynamic dispatching cannot be carried out by integrating traffic environment information such as urgent road speed limit under the condition of traffic jam and real-time passenger flow information such as the degree of pedestrian aggregation and movement tracks, and the problem of improving the total waiting time of passengers is solved.
Disclosure of Invention
The invention aims to provide an intelligent dispatching system for a bus-road cooperative intelligent platform, which mainly solves the problems that the existing dispatching system in the background art cannot fully combine traffic big data to carry out passenger flow demand analysis, a static off-line dispatching scheme is made mainly by the experience and intuition of managers in the aspect of dispatching decision, the best matching of the type, the number and the route of buses cannot be realized, the operation efficiency of a traffic environment public transportation system is low, the reasonable dynamic dispatching can not be carried out by integrating traffic environment information such as urgent road speed limit under the condition of traffic jam and the like, and real-time passenger flow information such as the degree of people flow gathering, the movement track and the like, and the overall waiting time of passengers is prolonged.
The purpose of the invention can be realized by the following technical scheme:
a vehicle-road cooperative intelligent platform intelligent scheduling system comprises a mobile phone signaling data acquisition module, a data characteristic multidimensional analysis module, a cellular wireless communication network module, a vehicle-road cooperative communication network module, a traffic environment information acquisition module, a simulation line module, a prediction modeling module, a scheduling center and a big data module;
the system comprises a mobile phone signaling data acquisition module, a data characteristic multidimensional analysis module, a bus route cooperative communication network module and a software design module, wherein the mobile phone signaling data acquisition module is used for extracting, cleaning and loading original signaling data to obtain high-quality mobile phone signaling data meeting basic conditions; the traffic environment information acquisition module is used for providing traffic environment basic information for intelligent bus scheduling, the simulation line module is used for simulating and establishing a scheduling optimal line according to the traffic environment basic information, the prediction modeling module is used for establishing a scheduling demand prediction model, the scheduling center designs a scheduling scheme dynamic plan according to the scheduling demand prediction model to realize the optimal matching of bus types, number and lines, and the big data module is used for providing a database for information acquisition of the traffic environment basic information, urban traffic and passenger flow to realize data statistics, dynamic road condition monitoring, automatic platform diagnosis and active line management.
As a further scheme of the invention: the cellular wireless communication network module is used for acquiring urban traffic and passenger flow and executing the following steps:
s1, carrying out feature research on urban traffic and passenger flow, constructing a high-efficiency and accurate travel feature extraction model by adopting a data mining algorithm, analyzing track point space-time features of the mobile phone signaling data, and finally obtaining user travel feature information;
s2, according to the space-time distribution characteristics of the track points of the mobile phone signaling data, designing an urban traffic and passenger flow travel identification algorithm, identifying channel passenger flow travel information, and distinguishing urban area travel characteristics;
s3, analyzing travel characteristic connotations according to the channel passenger flow travel information in the step S2, and extracting various channel passenger flow travel characteristic index formulas;
and S4, analyzing sample expansion influence factors of the mobile phone signaling data by combining the urban area travel characteristics and the travel identification algorithm in the step S2, and performing result sample expansion.
As a further scheme of the invention: the passage passenger flow travel information mainly comprises the in-out time and the residence time of the track points, and the urban regional travel characteristics are travel and residence states.
As a further scheme of the invention: the channel passenger flow travel characteristic index formula is mainly judged by a travel user, travel times, commuting travel amount, travel space-time distribution and a travel mode.
As a further scheme of the invention: the design framework comprises real-time vehicle state management, operation data storage and data statistical analysis and is used for collecting human-vehicle-road information acquired by a road cooperation technology and realizing data collection, fault reporting, position display, data query and statistical analysis.
As a further scheme of the invention: the traffic environment basic information mainly comprises a traffic jam condition and a road speed limit condition.
The invention has the beneficial effects that:
(1) in the invention, the mobile phone signaling data is subjected to space-time distribution characteristic analysis through a data characteristic multidimensional analysis module to obtain urban traffic and passenger flow, historical traffic passenger flow data is known through a road cooperative communication network module according to a database provided by a big data module, a smart bus operation management system is designed, and a modular software design framework is established, wherein the design framework comprises real-time vehicle state management, operation data storage and data statistical analysis and is used for collecting human-vehicle-road information obtained by a road cooperative technology to realize data collection, fault report, position display, data query and statistical analysis, and a dynamic scheduling scheme planning method is designed through a scheduling center to realize bus category, bus passenger flow and traffic flow, The optimal matching of the number and the lines improves the operation efficiency of the public transportation system in the complex urban traffic environment;
(2) the invention can realize wireless connection of a plurality of groups of buses by matching a cellular wireless communication network module with a built-in bus networking module of the bus, and the traffic environment information acquisition module can provide basic traffic environment information for intelligent bus scheduling by a built-in GPS positioning module of the bus, wherein the basic traffic environment information mainly comprises traffic jam conditions and road speed limit conditions, then an optimal scheduling route is simulated and established by a simulation line module according to the basic traffic environment information, an efficient scheduling operation route is calculated by adopting a particle swarm algorithm, bus route operation data and in-station bus operation characteristics are comprehensively analyzed, the maximum bus type of the utilization degree of the bus is reasonably matched and satisfied according to passenger flow requirements, the maximum utilization of the bus is realized, and the total waiting time of passengers is reduced.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of the operation of the present invention;
fig. 2 is a framework of urban traffic and passenger flow calculation based on a cellular wireless communication network according to the present invention.
Detailed Description
As shown in fig. 1-2, an intelligent scheduling system for a vehicle-road cooperative intelligent platform includes a mobile phone signaling data acquisition module, a data characteristic multidimensional analysis module, a cellular wireless communication network module, a vehicle-road cooperative communication network module, a traffic environment information acquisition module, an analog line module, a prediction modeling module, a scheduling center, and a big data module;
example 1
The mobile phone signaling data acquisition module is used for extracting, cleaning and loading original signaling data to obtain mobile phone signaling data which meets basic conditions and has high quality, the data characteristic multi-dimensional analysis module is used for carrying out space-time distribution characteristic analysis on the mobile phone signaling data to obtain urban traffic and passenger flow, and the bus route cooperative communication network module is used for designing an intelligent bus operation management system and establishing a modular software design framework; the dispatching center designs a dispatching scheme dynamic plan according to a dispatching demand prediction model, and realizes the optimal matching of the bus type, the bus number and the bus route;
the design framework comprises real-time vehicle state management, operation data storage and data statistical analysis, and is used for acquiring human-vehicle-road information acquired by a road cooperation technology, and realizing data aggregation, fault reporting, position display, data query and statistical analysis; the method comprises the steps of analyzing space-time distribution characteristics of mobile phone signaling data through a data characteristic multi-dimensional analysis module to obtain urban traffic and passenger flow, knowing historical traffic passenger flow data according to a database provided by a big data module through a bus route cooperative communication network module, designing an intelligent bus operation management system, and establishing a modular software design framework, wherein the design framework comprises real-time vehicle state management, operation data storage and data statistical analysis and is used for collecting human-bus-route information acquired by a bus route cooperative technology, realizing data collection, fault reporting, position display, data query and statistical analysis, designing a dynamic scheduling scheme planning method through a scheduling center, realizing the optimal matching of bus types, number and lines, and improving the operation efficiency of a bus system in a complex urban traffic environment.
Example 2
The traffic environment information acquisition module is used for providing basic traffic environment information for intelligent bus scheduling, the simulation line module is used for simulating and establishing a scheduling optimal line according to the basic traffic environment information, the prediction modeling module is used for establishing a scheduling demand prediction model, and the big data module is used for acquiring the basic traffic environment information, urban traffic and passenger flow information and providing a database, so that data statistics, dynamic road condition monitoring, automatic platform diagnosis and active line management are realized.
The cellular wireless communication network module is used for acquiring urban traffic and passenger flow and executing the following steps:
s1, carrying out feature research on urban traffic and passenger flow, constructing a high-efficiency and accurate travel feature extraction model by adopting a data mining algorithm, analyzing track point space-time features of the mobile phone signaling data, and finally obtaining user travel feature information;
s2, according to the space-time distribution characteristics of the track points of the mobile phone signaling data, designing an urban traffic and passenger flow travel identification algorithm, identifying channel passenger flow travel information, and distinguishing urban area travel characteristics;
s3, analyzing travel characteristic connotations according to the channel passenger flow travel information in the step S2, and extracting various channel passenger flow travel characteristic index formulas; the channel passenger flow travel characteristic index formula is mainly judged by a travel user, travel times, commuting travel amount, travel space-time distribution and a travel mode;
and S4, analyzing sample expansion influence factors of the mobile phone signaling data by combining the urban area travel characteristics and the travel identification algorithm in the step S2, and performing result sample expansion.
The passage passenger flow travel information mainly comprises the in-out time and the stay time of track points, and the urban area travel characteristics are travel and stay states; the traffic environment basic information mainly comprises a traffic jam condition and a road speed limit condition; the cellular wireless communication network module is matched with a built-in bus networking module of a bus to realize wireless connection of a plurality of groups of buses, the traffic environment information acquisition module can provide basic traffic environment information for intelligent bus scheduling through a built-in GPS positioning module of the bus, the basic traffic environment information mainly comprises the traffic jam condition and the road speed limit condition, then an optimal scheduling route is established through a simulation route module according to the basic traffic environment information simulation, an efficient scheduling operation route is calculated by adopting a particle swarm algorithm, the bus route operation data and the bus operation characteristics in a station are comprehensively analyzed, the maximum bus type of the bus utilization degree is met according to reasonable matching of passenger flow demands, the maximum utilization of the buses is realized, and the total waiting time of passengers is reduced.
Example 3
As shown in fig. 2, in embodiment 2, in order to reduce the study complexity, it is assumed that the travel demand of the passenger is known, the passenger travels according to the reservation situation, and only the passenger travel in one direction is considered. The purpose of bus dispatching path planning is to serve more passengers, reduce rejection rate of the system and combine travel demands, so that special passengers need to be given travel priority in the design of a primary objective function, and if the weight of the service demands of the special passengers is greater than that of common passengers, the constructed optimal dispatching path is as follows:
in addition, another objective of the dispatching path optimization is that the vehicle can reduce the detour distance during the driving process and travel in the ideal time period, and the two objectives can be represented by an objective function for minimizing the travel time of the passenger, wherein the objective function is composed of the deviation of the actual vehicle-entering time tp of the passenger from the scheduled vehicle-entering time tr and the driving time td of the passenger in the vehicle, and different weights are respectively given to the two objectives:
in summary, by establishing a route planning model of a variable-route bus with a double-layer target, the upper layer aims at maximizing the number of service passengers, and the lower layer aims at minimizing the travel time of the passengers. The problem constraints mainly include: the method comprises the steps of vehicle path rationality constraint (namely, each station only passes once), passenger riding constraint (each passenger can be served at most once) and vehicle station arrival and departure constraint (and strict adherence to a driving schedule service), wherein scheduling path planning is subdivided into 'passenger allocation' and 'route planning', wherein the passenger allocation (namely, an upper-layer objective function) determines which passengers are specifically served in each shift, the route planning (namely, a lower-layer objective function) determines the specific sequence of each bus for receiving and sending the passengers, and the optimal scheduling path meeting multi-objective and multi-constraint conditions is calculated by adopting a particle swarm algorithm, so that the vehicle shift and departure intervals can be adjusted according to the dynamic passenger requirements of each station, and the total waiting time of the passengers is reduced.
The invention has the following beneficial effects:
(1) the invention obtains urban traffic and passenger flow by analyzing the time-space distribution characteristics of mobile phone signaling data through a data characteristic multidimensional analysis module, learns historical traffic passenger flow data according to a database provided by a big data module through a road cooperative communication network module, designs a smart bus operation management system, establishes a modular software design framework, wherein the design framework comprises real-time vehicle state management, operation data storage and data statistical analysis, is used for collecting human-vehicle-road information obtained by a road cooperative technology, realizes data collection, fault report, position display, data query and statistical analysis, and realizes bus category, bus class and passenger flow through a dispatching center design dispatching scheme dynamic planning method, The optimal matching of the number and the lines improves the operation efficiency of the public transportation system in the complex urban traffic environment;
(2) the invention can realize wireless connection of a plurality of groups of buses by matching a cellular wireless communication network module with a built-in bus networking module of the bus, and the traffic environment information acquisition module can provide basic traffic environment information for intelligent bus scheduling by a built-in GPS positioning module of the bus, wherein the basic traffic environment information mainly comprises traffic jam conditions and road speed limit conditions, then an optimal scheduling route is simulated and established by a simulation line module according to the basic traffic environment information, an efficient scheduling operation route is calculated by adopting a particle swarm algorithm, bus route operation data and in-station bus operation characteristics are comprehensively analyzed, the maximum bus type of the utilization degree of the bus is reasonably matched and satisfied according to passenger flow requirements, the maximum utilization of the bus is realized, and the total waiting time of passengers is reduced.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (6)
1. An intelligent scheduling system of a vehicle-road cooperative intelligent platform is characterized by comprising a mobile phone signaling data acquisition module, a data characteristic multidimensional analysis module, a cellular wireless communication network module, a vehicle-road cooperative communication network module, a traffic environment information acquisition module, a simulation line module, a prediction modeling module, a scheduling center and a big data module;
the system comprises a mobile phone signaling data acquisition module, a data characteristic multidimensional analysis module, a bus route cooperative communication network module and a software design module, wherein the mobile phone signaling data acquisition module is used for extracting, cleaning and loading original signaling data to obtain high-quality mobile phone signaling data meeting basic conditions; the traffic environment information acquisition module is used for providing traffic environment basic information for intelligent bus scheduling, the simulation line module is used for simulating and establishing a scheduling optimal line according to the traffic environment basic information, the prediction modeling module is used for establishing a scheduling demand prediction model, the scheduling center designs a scheduling scheme dynamic plan according to the scheduling demand prediction model to realize the optimal matching of bus types, number and lines, and the big data module is used for providing a database for information acquisition of the traffic environment basic information, urban traffic and passenger flow to realize data statistics, dynamic road condition monitoring, automatic platform diagnosis and active line management.
2. The system of claim 1, wherein the cellular wireless communication network module is configured to obtain urban traffic and passenger flow, and perform the following steps:
s1, carrying out feature research on urban traffic and passenger flow, constructing a high-efficiency and accurate travel feature extraction model by adopting a data mining algorithm, analyzing track point space-time features of the mobile phone signaling data, and finally obtaining user travel feature information;
s2, according to the space-time distribution characteristics of the track points of the mobile phone signaling data, designing an urban traffic and passenger flow travel identification algorithm, identifying channel passenger flow travel information, and distinguishing urban area travel characteristics;
s3, analyzing travel characteristic connotations according to the channel passenger flow travel information in the step S2, and extracting various channel passenger flow travel characteristic index formulas;
and S4, analyzing sample expansion influence factors of the mobile phone signaling data by combining the urban area travel characteristics and the travel identification algorithm in the step S2, and performing result sample expansion.
3. The system of claim 2, wherein the passage passenger flow travel information mainly includes the time of the track point and the time of the track point, and the urban area travel characteristics are travel and stop states.
4. The system of claim 2, wherein the channel passenger flow travel characteristic index formula is mainly determined by a travel user, travel times, commute travel volume, travel space-time distribution and travel mode.
5. The intelligent bus-road cooperative intelligent platform dispatching system as claimed in claim 1, wherein the design framework comprises real-time vehicle state management, operation data storage and data statistical analysis, and is used for collecting human-bus-road information obtained by bus-road cooperative technology, and realizing data aggregation, fault reporting, position display, data query and statistical analysis.
6. The system of claim 1, wherein the traffic environment basic information mainly includes traffic jam and road speed limit.
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| CN113095649A (en) * | 2021-04-01 | 2021-07-09 | 福州物联网开放实验室有限公司 | Intelligent traffic dynamic bus simulation method and storage device |
| CN113160602A (en) * | 2021-04-27 | 2021-07-23 | 华录智达科技股份有限公司 | Intelligent bus dispatching simulation system |
| CN113160602B (en) * | 2021-04-27 | 2022-05-10 | 华录智达科技股份有限公司 | Intelligent bus dispatching simulation system |
| CN113506461A (en) * | 2021-07-07 | 2021-10-15 | 安徽富煌科技股份有限公司 | Traffic scheduling system based on dynamic self-adaptive particle swarm algorithm |
| CN113781822A (en) * | 2021-09-24 | 2021-12-10 | 湖北惠诚共创科技有限公司 | Bus dispatching system based on big data |
| CN115983732A (en) * | 2022-12-23 | 2023-04-18 | 中国民航大学 | Airport land side traffic sand table scheduling system based on vehicle-road cooperation |
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