CN112669603A - Urban traffic cooperation method and device based on big data - Google Patents
Urban traffic cooperation method and device based on big data Download PDFInfo
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Abstract
The embodiment of the application discloses a big data-based urban traffic coordination method and a big data-based urban traffic coordination device, wherein the method comprises the steps of obtaining historical traffic data of different time periods, and calculating traffic missing data respectively corresponding to any place in different time periods according to the historical traffic data; and processing according to the historical traffic data and the traffic missing data to obtain traffic coordination data of any place in different time periods. According to the technical scheme, the traffic missing condition is obtained through historical traffic data of different time periods, the urban public transport is cooperatively processed according to the degree of mismatching between the traffic demand and the public transport supply quantity corresponding to the traffic missing condition, so that the degree of matching between the traffic demand and the public transport supply quantity is met, the intelligent management of the urban transport is realized, and the matched traffic cooperation scheme is intelligently output according to the traffic conditions of different time periods.
Description
Technical Field
The embodiment of the application relates to the technical field of urban traffic management, in particular to a big data-based urban traffic coordination method and device.
Background
With the increase of urban population and motor vehicle reserves, urban traffic often has the situations of congestion, slow running and the like, the urban traffic operation efficiency is low, and the trip efficiency is reduced.
The intelligent city can not leave the urban traffic management intellectualization, and the public transport has the obvious advantages of low carbon and environmental protection, the 'public transport priority' is one of the basic policies of the urban traffic development in China, and aims to preferentially develop a public transport system to meet the increasing travel demands of residents and relieve the contradiction between the continuous increase of the quantity of motor vehicles kept in the city and the limited traffic resources. At present, the demand of travel is greatly increased, and the current traffic planning can not be intelligently adapted to different instant situations for adjustment, so that the mismatching between the demand of travel and the vehicle storage is caused, the waiting time of passengers is uncertain, and the utilization rate of traffic resources is low.
Disclosure of Invention
The embodiment of the application provides an urban traffic coordination method based on big data, so that intelligent automatic coordination of public traffic according to different time periods is realized, and the utilization rate of public traffic resources is improved.
In a first aspect, an embodiment of the present application provides a method for urban traffic coordination of big data, including:
acquiring historical traffic data of different time periods, wherein the historical traffic data comprises passenger trip data, transfer demand data and public traffic data, and the public traffic data comprises various types of public traffic;
calculating traffic missing data respectively corresponding to any place in different time periods according to the historical traffic data; the traffic loss data is used for representing the degree of mismatch between the traffic demand and the public transportation supply of passengers at any place in different time periods;
and processing according to the historical traffic data and the traffic missing data to obtain traffic coordination data of any place in different time periods.
Further, also comprises
Receiving riding requirements of passengers, wherein the riding requirements comprise destinations, travel time and number of people who travel;
and outputting at least one riding mode according to the riding requirements and the traffic coordination data, wherein each riding mode comprises one or more types of public traffic.
Further, outputting at least one riding mode according to the riding demand and the traffic coordination data, comprising:
obtaining at least one riding mode according to the riding demand and the traffic coordination data, wherein each riding mode comprises riding public traffic, transfer stations, estimated transfer time, estimated trip time and estimated trip amount;
carrying out weighted summation operation on public transport, transfer stations, estimated transfer time, estimated trip time and estimated trip amount in each riding mode according to corresponding preset weight proportion respectively to obtain riding scores of the riding mode;
and sequencing each riding mode according to the riding score, and displaying the sequenced riding mode in the first riding mode to the passengers, or displaying the sequenced riding modes to the passengers in sequence.
Further, the method also comprises the following steps:
acquiring current environment data, and judging whether the weather in the current time period is sunny or rainy according to the current environment data;
the step of outputting at least one riding mode according to the riding requirements and the traffic coordination data comprises the following steps:
obtaining at least one riding mode according to the riding demand and the traffic coordination data, wherein each riding mode comprises riding public traffic, transfer stations, estimated transfer time, estimated trip time and estimated trip amount;
when the weather in the current time period is sunny, carrying out weighted summation operation on public transport, transfer stations, estimated transfer time, estimated trip time and estimated trip amount in each riding mode according to corresponding preset weight proportion respectively to obtain riding scores of the riding mode; sequencing each riding mode according to the riding score, and displaying the sequenced riding mode to the passengers, or displaying the sequenced riding modes to the passengers in sequence;
when the weather in the current time period is rainy, deleting the riding modes corresponding to public transportation which is not suitable for traveling in rainy days, and performing weighted summation operation on the public transportation, transfer stations, estimated transfer time, estimated traveling time and estimated traveling amount in the rest riding modes according to corresponding preset weight proportions to obtain riding scores of the riding modes; and sequencing each riding mode according to the riding score, and displaying the sequenced riding mode in the first riding mode to the passengers, or displaying the sequenced riding modes to the passengers in sequence.
Further, the method also comprises the following steps:
obtaining historical riding requirements of a plurality of groups of passengers, historical environment data corresponding to the historical riding requirements and historical riding modes selected by users, and constructing riding models corresponding to the passengers according to the historical riding requirements, the historical environment data and the historical riding modes;
acquiring a plurality of groups of historical travel time according to a plurality of groups of historical riding requirements of passengers, acquiring the earliest historical travel time, and recording the earliest historical travel time as target travel time;
acquiring current time and environmental data, inputting the time and environmental data into a riding model, and outputting an optimal riding mode;
and displaying the optimal riding mode to the user before the target travel time is reached.
Further, the traffic loss data comprises gap amount or surplus amount of different types of public transportation, the gap amount is used for indicating that the traffic demand of passengers at any place in different time periods is larger than the supply amount of the public transportation, and the surplus amount is used for indicating that the traffic demand of passengers at any place in different time periods is smaller than the supply amount of the public transportation;
processing the historical traffic data and the traffic loss data to obtain traffic coordination data of any place in different time periods, wherein the traffic coordination data comprises the following steps:
obtaining the gap amount or surplus amount of different types of public transportation at any place in different time periods according to the traffic loss data;
respectively forming the public traffic corresponding to the gap amount and the public traffic corresponding to the surplus amount into two categories, and sequentially arranging all the public traffic in each category according to the numerical value of the gap amount or the surplus amount from large to small;
selecting any group of public transportation with two types, and scheduling a plurality of public transportation with surplus in the group of public transportation to supplement the public transportation with the corresponding gap amount; wherein, in the group of public transportation, the sequence of one type of public transportation according to the gap amount corresponds to the sequence of the other type of public transportation according to the surplus amount.
Further, the traffic loss data comprises a gap amount or a surplus amount of public transportation, wherein the gap amount is used for indicating that the traffic demand of passengers at any place in different time periods is larger than the supply amount of the public transportation, and the surplus amount is used for indicating that the traffic demand of passengers at any place in different time periods is smaller than the supply amount of the public transportation;
processing the historical traffic data and the traffic loss data to obtain traffic coordination data of any place in different time periods, wherein the traffic coordination data comprises the following steps:
acquiring the gap amount or surplus amount of public transportation of any place in different time periods;
and scheduling a plurality of public traffics of the places corresponding to the surplus amount according to a preset rule to supplement the public traffic of the places corresponding to the gap amount in the time period.
In a second aspect, an embodiment of the present application provides a big data-based urban traffic coordination apparatus, including:
a data acquisition module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring historical traffic data of different time periods, the historical traffic data comprises passenger trip data, transfer demand data and public traffic data, and the public traffic data comprises various types of public traffic;
a data calculation module: the system is used for calculating traffic missing data respectively corresponding to any place in different time periods according to the historical traffic data; the traffic loss data is used for representing the degree of mismatch between the traffic demand and the public transportation supply of passengers at any place in different time periods;
a cooperative processing module: and the traffic coordination data of any place in different time periods are obtained by processing according to the historical traffic data and the traffic missing data.
Further, the method also comprises the following steps:
the riding demand receiving module: the system comprises a passenger receiving system, a passenger receiving system and a passenger sending system, wherein the passenger receiving system is used for receiving riding demands of passengers, and the riding demands comprise destinations, travel time and travel number;
a riding mode output module: and the system is used for outputting at least one riding mode according to the riding requirements and the traffic coordination data, wherein each riding mode comprises one or more types of public traffic.
Further, outputting at least one riding mode according to the riding demand and the traffic coordination data, comprising:
obtaining at least one riding mode according to the riding demand and the traffic coordination data, wherein each riding mode comprises riding public traffic, transfer stations, estimated transfer time, estimated trip time and estimated trip amount;
carrying out weighted summation operation on public transport, transfer stations, estimated transfer time, estimated trip time and estimated trip amount in each riding mode according to corresponding preset weight proportion respectively to obtain riding scores of the riding mode;
and sequencing each riding mode according to the riding score, and displaying the sequenced riding mode in the first riding mode to the passengers, or displaying the sequenced riding modes to the passengers in sequence.
Further, the method also comprises the following steps:
acquiring current environment data, and judging whether the weather in the current time period is sunny or rainy according to the current environment data;
the step of outputting at least one riding mode according to the riding requirements and the traffic coordination data comprises the following steps:
obtaining at least one riding mode according to the riding demand and the traffic coordination data, wherein each riding mode comprises riding public traffic, transfer stations, estimated transfer time, estimated trip time and estimated trip amount;
when the weather in the current time period is sunny, carrying out weighted summation operation on public transport, transfer stations, estimated transfer time, estimated trip time and estimated trip amount in each riding mode according to corresponding preset weight proportion respectively to obtain riding scores of the riding mode; sequencing each riding mode according to the riding score, and displaying the sequenced riding mode to the passengers, or displaying the sequenced riding modes to the passengers in sequence;
when the weather in the current time period is rainy, deleting the riding modes corresponding to public transportation which is not suitable for traveling in rainy days, and performing weighted summation operation on the public transportation, transfer stations, estimated transfer time, estimated traveling time and estimated traveling amount in the rest riding modes according to corresponding preset weight proportions to obtain riding scores of the riding modes; and sequencing each riding mode according to the riding score, and displaying the sequenced riding mode in the first riding mode to the passengers, or displaying the sequenced riding modes to the passengers in sequence.
Further, the method also comprises the following steps:
history demand collection module: the system comprises a passenger selection module, a passenger selection module and a passenger selection module, wherein the passenger selection module is used for acquiring historical riding demands of a plurality of groups of passengers, historical environment data corresponding to the historical riding demands and historical riding modes selected by users, and constructing riding models corresponding to the passengers according to the historical riding demands, the historical environment data and the historical riding modes;
historical trip acquisition module: the system comprises a plurality of groups of passenger riding time acquisition units, a plurality of groups of passenger riding time acquisition units and a plurality of groups of passenger riding time acquisition units, wherein the plurality of groups of passenger riding time acquisition units are used for acquiring the earliest historical;
an input parameter acquisition module: the system comprises a bus model, a bus mode acquisition module, a bus taking module and a bus taking module, wherein the bus model acquisition module is used for acquiring current time and environmental data, inputting the time and environmental data to the bus taking module and outputting an optimal bus taking mode;
the riding mode display module: and the optimal riding mode is displayed to the user before the target travel time is reached.
Further, the traffic loss data comprises gap amount or surplus amount of different types of public transportation, the gap amount is used for indicating that the traffic demand of passengers at any place in different time periods is larger than the supply amount of the public transportation, and the surplus amount is used for indicating that the traffic demand of passengers at any place in different time periods is smaller than the supply amount of the public transportation;
processing the historical traffic data and the traffic loss data to obtain traffic coordination data of any place in different time periods, wherein the traffic coordination data comprises the following steps:
obtaining the gap amount or surplus amount of different types of public transportation at any place in different time periods according to the traffic loss data;
respectively forming the public traffic corresponding to the gap amount and the public traffic corresponding to the surplus amount into two categories, and sequentially arranging all the public traffic in each category according to the numerical value of the gap amount or the surplus amount from large to small;
selecting any group of public transportation with two types, and scheduling a plurality of public transportation with surplus in the group of public transportation to supplement the public transportation with the corresponding gap amount; wherein, in the group of public transportation, the sequence of one type of public transportation according to the gap amount corresponds to the sequence of the other type of public transportation according to the surplus amount.
Further, the traffic loss data comprises a gap amount or a surplus amount of public transportation, wherein the gap amount is used for indicating that the traffic demand of passengers at any place in different time periods is larger than the supply amount of the public transportation, and the surplus amount is used for indicating that the traffic demand of passengers at any place in different time periods is smaller than the supply amount of the public transportation;
processing the historical traffic data and the traffic loss data to obtain traffic coordination data of any place in different time periods, wherein the traffic coordination data comprises the following steps:
acquiring the gap amount or surplus amount of public transportation of any place in different time periods;
and scheduling a plurality of public traffics of the places corresponding to the surplus amount according to a preset rule to supplement the public traffic of the places corresponding to the gap amount in the time period.
In a third aspect, an embodiment of the present application provides a computer device, including: a memory and one or more processors;
the memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the big data-based urban transportation collaboration method as described in the first aspect.
In a fourth aspect, the present application provides a storage medium containing computer-executable instructions for performing the big-data based urban traffic coordination method according to the first aspect when executed by a computer processor.
According to the embodiment of the application, the traffic missing condition is obtained through historical traffic data of different time periods, the urban public transport is cooperatively processed according to the degree of mismatching between the traffic demand and the public transport supply amount corresponding to the traffic missing condition, so that the degree of matching between the traffic demand and the public transport supply amount is met, the intelligent management of the urban transport is realized, and the matched traffic cooperation scheme is intelligently output according to the traffic conditions of different time periods.
Drawings
Fig. 1 is a flowchart of a big data-based urban traffic coordination method according to an embodiment of the present application;
FIG. 2 is a flow chart of another big data-based urban traffic coordination method provided by the embodiment of the application;
FIG. 3 is a flow chart of another big data-based urban traffic coordination method provided by the embodiment of the application;
fig. 4 is a schematic structural diagram of a big data-based urban traffic coordination device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some but not all of the relevant portions of the present application are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
The embodiment of the application provides a big data-based urban traffic coordination method and device, the traffic missing condition is obtained through historical traffic data of different time periods, urban public traffic is cooperatively processed according to the degree of mismatching between traffic demand and public traffic supply corresponding to the traffic missing condition, so that the degree of matching between the traffic demand and the public traffic supply is met, the intelligent management of urban traffic is realized, and the matched traffic coordination scheme is intelligently output according to the traffic conditions of different time periods.
Fig. 1 to fig. 3 are flowcharts provided in three different embodiments of the present application, and the urban traffic coordination method based on big data provided in the embodiments of the present application may be executed by a urban traffic coordination device based on big data, which may be implemented by hardware and/or software and integrated in a computer device.
The embodiment of the application can be applied to a server end and a processor end, and used as a data receiving end for processing and calculating according to the acquired data. The collected data come from various collecting devices related to urban traffic, including intelligent terminals, card reading devices and positioning devices on public transport vehicles, cameras installed on urban streets and the like, and the various collecting devices are used as sending ends. The receiving end and the sending end communicate with each other through a communication network, which can be a wide area network or a local area network. The connection between the receiving end and the sending end can be through wired network or wireless network communication, and can be direct communication or indirect communication. The terminal comprises any intelligent equipment, such as intelligent mobile phones, tablet computers, notebook computers, desktop computers, intelligent watches and other intelligent equipment, and meanwhile, the intelligent terminal can also be a server. The server can be an independent physical server, can also be a server cluster or a distributed system formed by a plurality of physical servers, and can also provide cloud servers of basic cloud computing servers such as a cloud server, a cloud database, cloud computing, cloud communication, a big database, an artificial intelligence platform and the like.
The following description will be given taking as an example that the big data based urban traffic coordination apparatus executes the big data based urban traffic coordination method. Referring to fig. 1, the big data-based urban traffic coordination method includes:
s101: and acquiring historical traffic data of different time periods.
In this embodiment, the historical traffic data includes passenger travel data, transfer demand data, and public transportation data, and the public transportation data includes various types of public transportation. The passenger travel data comprises passenger travel time, departure place, travel destination, travel number, and also comprises intended vehicle and the like. The transfer demand data can be calculated into at least one travel path according to a departure place and a travel destination in the passenger travel data, each travel path may relate to transfer and is used for selecting any one travel path, and when the travel path carries the transfer, the transfer demand data is obtained according to the transfer rule. The public transportation data is used to indicate a plurality of types of public transportation and the number of each type of public transportation, and the number of public transportation includes the same type of public transportation, and has different number records corresponding to different routes, respectively.
S102: and calculating traffic missing data respectively corresponding to any place in different time periods according to the historical traffic data.
In the traffic planning, the planned traffic supply amount, that is, the amount of traffic supply for the same travel route and the same vehicle corresponding to different time periods, is matched to a certain amount, for example, a bus of route a every 10 minutes, or a bus of route B every 15 minutes, or in time period C, 30 shared vehicles are set at the position D, and in time period E, 20 shared vehicles are set at the position D. The traffic loss data of the present embodiment is used to indicate the degree of mismatch between the traffic demand of passengers and the public transportation supply amount at different time periods at any one place.
More preferably, in this embodiment, the traffic loss data includes a gap amount or a surplus amount of different types of public transportation, the gap amount is used to indicate that the traffic demand of the passengers is greater than the supply amount of the public transportation at any one point in different time periods, and the surplus amount is used to indicate that the traffic demand of the passengers is less than the supply amount of the public transportation at any one point in different time periods.
S103: and processing according to the historical traffic data and the traffic missing data to obtain traffic coordination data of any place in different time periods.
In this step, as a preferred embodiment, specifically, according to the traffic loss data, the gap amount or the surplus amount of different types of public transportation at any place in different time periods is obtained; respectively forming the public traffic corresponding to the gap amount and the public traffic corresponding to the surplus amount into two categories, and sequentially arranging all the public traffic in each category according to the numerical value of the gap amount or the surplus amount from large to small; selecting any group of public transportation with two types, and scheduling a plurality of public transportation with surplus in the group of public transportation to supplement the public transportation with the corresponding gap amount; wherein, in the group of public transportation, the sequence of one type of public transportation according to the gap amount corresponds to the sequence of the other type of public transportation according to the surplus amount.
For example, in the time period of 8:00-9:00 in the morning, the number of gaps of buses in the route a is 15, the number of gaps of buses in the route B is 11, the number of gaps of buses in the route C is 8, the surplus number of buses in the route D is 14, the surplus number of buses in the route E is 12, and the number of gaps of buses in the route F is 6. After sorting, the sequence of the buses corresponding to the gap amount category is ABC, the sequence of the buses corresponding to the surplus amount category is DEF, that is, the route A corresponds to the route D, 14 surplus of the route D is supplemented to 15 gaps of the route A, and the rest is done in the same way.
As shown in fig. 2, an embodiment of the present application further provides another urban traffic coordination method based on big data, including:
s201: and acquiring historical traffic data of different time periods. The historical traffic data comprises passenger travel data, transfer demand data and public traffic data, and the public traffic data comprises various types of public traffic.
S202: calculating traffic missing data respectively corresponding to any place in different time periods according to the historical traffic data; the traffic loss data is used to indicate a degree of mismatch between the traffic demand of the passengers and the mass transit supply at any one location during different time periods.
In accordance with the above-described embodiments, the traffic loss data in the present embodiment includes the gap amount or the surplus amount of different types of public transportation, the gap amount is used to indicate that the traffic demand of passengers is larger than the supply amount of public transportation at any one point in different time periods, and the surplus amount is used to indicate that the traffic demand of passengers is smaller than the supply amount of public transportation at any one point in different time periods.
S203: and processing according to the historical traffic data and the traffic missing data to obtain traffic coordination data of any place in different time periods.
S204: the method comprises the steps of receiving riding requirements of passengers, wherein the riding requirements comprise destinations, travel time and number of people who travel.
S205: and outputting at least one riding mode according to the riding requirements and the traffic coordination data, wherein each riding mode comprises one or more types of public traffic.
In this embodiment, the traffic coordination data of any place in different time periods is obtained through processing according to the historical traffic data and the traffic missing data, that is, under the premise that the riding demand of passengers does not exist, the public traffic with surplus and the public traffic with gap amount are matched and output according to the past experience, so that a coordination scheme is obtained. And when the riding requirements of the passengers are received, outputting a riding mode matched with the riding requirements according to the cooperation scheme. That is, the riding mode matched with the riding demand of the passenger is the traffic data after the cooperation is combined.
For example, the amount of the public transportation gap of the route a is 15, the amount of the public transportation gap of the route B is 8, the surplus public transportation of the route B is supplemented to the gap of the route a, and when the passenger riding demand is not in traffic coordination, the public transportation gap of the route a is large, and the waiting time is too long, and the like, the output riding mode is to go out through other public transportation, such as a sharing single vehicle. After the cooperation, the public transportation of the route A is supplemented by the public transportation of the route B of 8 vehicles, so that the gap of good public communication of the route A is reduced, and the riding mode output according to the riding demand of passengers after the cooperation is the public transportation travel through the route A.
As a further preferred embodiment, outputting at least one riding mode according to the riding demand and the traffic coordination data includes: obtaining at least one riding mode according to the riding demand and the traffic coordination data, wherein each riding mode comprises riding public traffic, transfer stations, estimated transfer time, estimated trip time and estimated trip amount; carrying out weighted summation operation on public transport, transfer stations, estimated transfer time, estimated trip time and estimated trip amount in each riding mode according to corresponding preset weight proportion respectively to obtain riding scores of the riding mode; and sequencing each riding mode according to the riding score, and displaying the sequenced riding mode in the first riding mode to the passengers, or displaying the sequenced riding modes to the passengers in sequence.
As shown in fig. 3, the embodiment further provides another urban traffic coordination method based on big data, including:
s301: and acquiring historical traffic data of different time periods. The historical traffic data comprises passenger travel data, transfer demand data and public traffic data, and the public traffic data comprises various types of public traffic.
S302: calculating traffic missing data respectively corresponding to any place in different time periods according to the historical traffic data; the traffic loss data is used to indicate a degree of mismatch between the traffic demand of the passengers and the mass transit supply at any one location during different time periods.
In accordance with the above-described embodiments, the traffic loss data in the present embodiment includes the gap amount or the surplus amount of different types of public transportation, the gap amount is used to indicate that the traffic demand of passengers is larger than the supply amount of public transportation at any one point in different time periods, and the surplus amount is used to indicate that the traffic demand of passengers is smaller than the supply amount of public transportation at any one point in different time periods.
S303: and processing according to the historical traffic data and the traffic missing data to obtain traffic coordination data of any place in different time periods.
In the embodiment, specifically, the gap amount or the surplus amount of public transportation at any place in different time periods is obtained; and scheduling a plurality of public traffics of the places corresponding to the surplus amount according to a preset rule to supplement the public traffic of the places corresponding to the gap amount in the time period.
In other embodiments, the public transportation is correspondingly scheduled according to the ranking order according to the numerical value of the gap amount or the surplus amount of all the public transportation in each category from large to small. In this embodiment, other situations are considered more intelligently. For example, the gap amount of the buses on the route A is 15, the gap amount of the buses on the route B is 11, the gap amount of the buses on the route C is 8, the surplus amount of the buses on the route D is 14, the surplus amount of the buses on the route E is 12, and the gap amount of the buses on the route F is 6, the buses on the route D are supplemented to the gaps of the buses on the route A, meanwhile, the priority of the route A and the priority of the route B are judged, if the priority of the route A is higher than the priority of the route B, as the surplus amount of the route D cannot completely meet the gap amount of the route A, the surplus gaps are supplemented by the surplus amount of the route E, and the surplus amount of the route E is supplemented to the gap amount of the route B. Otherwise, if the priority of the route B is higher than that of the route A, the surplus margin of the route E is used for supplementing the gap amount of the route B, and the surplus margin of the route E is used for supplementing the gap amount of the route A.
S304: the method comprises the steps of receiving riding requirements of passengers, wherein the riding requirements comprise destinations, travel time and number of people who travel. And acquiring current environment data, and judging whether the weather in the current time period is sunny or rainy according to the current environment data.
S305: and outputting at least one riding mode according to the riding requirements and the traffic coordination data, wherein each riding mode comprises one or more types of public traffic.
In this step, specifically, the method includes: obtaining at least one riding mode according to the riding demand and the traffic coordination data, wherein each riding mode comprises riding public traffic, transfer stations, estimated transfer time, estimated trip time and estimated trip amount; when the weather in the current time period is sunny, carrying out weighted summation operation on public transport, transfer stations, estimated transfer time, estimated trip time and estimated trip amount in each riding mode according to corresponding preset weight proportion respectively to obtain riding scores of the riding mode; sequencing each riding mode according to the riding score, and displaying the sequenced riding mode to the passengers, or displaying the sequenced riding modes to the passengers in sequence; when the weather in the current time period is rainy, deleting the riding modes corresponding to public transportation which is not suitable for traveling in rainy days, and performing weighted summation operation on the public transportation, transfer stations, estimated transfer time, estimated traveling time and estimated traveling amount in the rest riding modes according to corresponding preset weight proportions to obtain riding scores of the riding modes; and sequencing each riding mode according to the riding score, and displaying the sequenced riding mode in the first riding mode to the passengers, or displaying the sequenced riding modes to the passengers in sequence.
S306: the method comprises the steps of obtaining historical riding demands of a plurality of groups of passengers, historical environment data corresponding to the historical riding demands and historical riding modes selected by users, and constructing riding models corresponding to the passengers according to the historical riding demands, the historical environment data and the historical riding modes.
S307: the method comprises the steps of obtaining a plurality of groups of historical travel time according to a plurality of groups of historical riding demands of passengers, obtaining the earliest historical travel time, and recording the earliest historical travel time as target travel time.
S308: and acquiring current time and environment data, inputting the time and environment data into a riding model, and outputting an optimal riding mode.
S309: and displaying the optimal riding mode to the user before the target travel time is reached.
The embodiment also considers the passenger independently, and builds a riding model for the passenger according to the riding requirement of the passenger, so that the current time and environment data are automatically collected, and the optimal riding mode is intelligently output and displayed to the user.
The embodiment also provides a city traffic cooperative apparatus based on big data, as shown in fig. 4, including a data obtaining module 41, a data calculating module 42 and a cooperative processing module 43.
The data obtaining module 41 is configured to obtain historical traffic data of different time periods, where the historical traffic data includes passenger trip data, transfer demand data, and public traffic data, and the public traffic data includes various types of public traffic. The data calculation module 42 is configured to calculate traffic missing data corresponding to any one of the locations at different time periods according to the historical traffic data; the traffic loss data is used to indicate a degree of mismatch between the traffic demand of the passengers and the mass transit supply at any one location during different time periods. The cooperative processing module 43 is configured to process the historical traffic data and the traffic loss data to obtain traffic cooperative data at any location in different time periods.
Preferably, the method further comprises the following steps: the riding demand receiving module: the system comprises a passenger receiving system, a passenger receiving system and a passenger sending system, wherein the passenger receiving system is used for receiving riding demands of passengers, and the riding demands comprise destinations, travel time and travel number; a riding mode output module: and the system is used for outputting at least one riding mode according to the riding requirements and the traffic coordination data, wherein each riding mode comprises one or more types of public traffic.
As a further preferred embodiment, outputting at least one riding mode according to the riding demand and the traffic coordination data includes: obtaining at least one riding mode according to the riding demand and the traffic coordination data, wherein each riding mode comprises riding public traffic, transfer stations, estimated transfer time, estimated trip time and estimated trip amount; carrying out weighted summation operation on public transport, transfer stations, estimated transfer time, estimated trip time and estimated trip amount in each riding mode according to corresponding preset weight proportion respectively to obtain riding scores of the riding mode; and sequencing each riding mode according to the riding score, and displaying the sequenced riding mode in the first riding mode to the passengers, or displaying the sequenced riding modes to the passengers in sequence.
As an optional implementation manner of this embodiment, the method further includes:
acquiring current environment data, and judging whether the weather in the current time period is sunny or rainy according to the current environment data; the step of outputting at least one riding mode according to the riding requirements and the traffic coordination data comprises the following steps: obtaining at least one riding mode according to the riding demand and the traffic coordination data, wherein each riding mode comprises riding public traffic, transfer stations, estimated transfer time, estimated trip time and estimated trip amount; when the weather in the current time period is sunny, carrying out weighted summation operation on public transport, transfer stations, estimated transfer time, estimated trip time and estimated trip amount in each riding mode according to corresponding preset weight proportion respectively to obtain riding scores of the riding mode; sequencing each riding mode according to the riding score, and displaying the sequenced riding mode to the passengers, or displaying the sequenced riding modes to the passengers in sequence; when the weather in the current time period is rainy, deleting the riding modes corresponding to public transportation which is not suitable for traveling in rainy days, and performing weighted summation operation on the public transportation, transfer stations, estimated transfer time, estimated traveling time and estimated traveling amount in the rest riding modes according to corresponding preset weight proportions to obtain riding scores of the riding modes; and sequencing each riding mode according to the riding score, and displaying the sequenced riding mode in the first riding mode to the passengers, or displaying the sequenced riding modes to the passengers in sequence.
The embodiment further comprises a history demand acquisition module: the system comprises a passenger selection module, a passenger selection module and a passenger selection module, wherein the passenger selection module is used for acquiring historical riding demands of a plurality of groups of passengers, historical environment data corresponding to the historical riding demands and historical riding modes selected by users, and constructing riding models corresponding to the passengers according to the historical riding demands, the historical environment data and the historical riding modes; historical trip acquisition module: the system comprises a plurality of groups of passenger riding time acquisition units, a plurality of groups of passenger riding time acquisition units and a plurality of groups of passenger riding time acquisition units, wherein the plurality of groups of passenger riding time acquisition units are used for acquiring the earliest historical; an input parameter acquisition module: the system comprises a bus model, a bus mode acquisition module, a bus taking module and a bus taking module, wherein the bus model acquisition module is used for acquiring current time and environmental data, inputting the time and environmental data to the bus taking module and outputting an optimal bus taking mode; the riding mode display module: and the optimal riding mode is displayed to the user before the target travel time is reached.
In the embodiment of the application, the traffic loss data comprises gap amount or surplus amount of different types of public transportation, the gap amount is used for indicating that the traffic demand of passengers at any place in different time periods is larger than the supply amount of the public transportation, and the surplus amount is used for indicating that the traffic demand of the passengers at any place in different time periods is smaller than the supply amount of the public transportation.
Processing the historical traffic data and the traffic loss data to obtain traffic coordination data of any place in different time periods, wherein the traffic coordination data comprises the following steps: obtaining the gap amount or surplus amount of different types of public transportation at any place in different time periods according to the traffic loss data; respectively forming the public traffic corresponding to the gap amount and the public traffic corresponding to the surplus amount into two categories, and sequentially arranging all the public traffic in each category according to the numerical value of the gap amount or the surplus amount from large to small; selecting any group of public transportation with two types, and scheduling a plurality of public transportation with surplus in the group of public transportation to supplement the public transportation with the corresponding gap amount; wherein, in the group of public transportation, the sequence of one type of public transportation according to the gap amount corresponds to the sequence of the other type of public transportation according to the surplus amount.
As another embodiment, processing the historical traffic data and the traffic loss data to obtain traffic coordination data of any place in different time periods includes: acquiring the gap amount or surplus amount of public transportation of any place in different time periods; and scheduling a plurality of public traffics of the places corresponding to the surplus amount according to a preset rule to supplement the public traffic of the places corresponding to the gap amount in the time period.
An embodiment of the present application provides a computer device, including: a memory and one or more processors; the memory for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement the big data-based urban transportation collaboration method as described in the first aspect.
The present application also provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform the big data based urban traffic coordination method provided in the above embodiments, where the big data based urban traffic coordination method includes: acquiring historical traffic data of different time periods, wherein the historical traffic data comprises passenger trip data, transfer demand data and public traffic data, and the public traffic data comprises various types of public traffic; calculating traffic missing data respectively corresponding to any place in different time periods according to the historical traffic data; the traffic loss data is used for representing the degree of mismatch between the traffic demand and the public transportation supply of passengers at any place in different time periods; and processing according to the historical traffic data and the traffic missing data to obtain traffic coordination data of any place in different time periods.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system connected to the first computer system through a network (such as the internet). The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations, such as in different computer systems that are connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided in the embodiments of the present application contains computer-executable instructions, and the computer-executable instructions are not limited to the big data based urban traffic coordination method described above, and may also perform related operations in the big data based urban traffic coordination method provided in any embodiments of the present application.
The urban traffic coordination device, the equipment and the storage medium based on big data provided in the above embodiments may execute the urban traffic coordination method based on big data provided in any embodiment of the present application, and reference may be made to the urban traffic coordination method based on big data provided in any embodiment of the present application without detailed technical details described in the above embodiments.
The foregoing is considered as illustrative of the preferred embodiments of the invention and the technical principles employed. The present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the claims.
Claims (10)
1. The urban traffic cooperative method based on big data is characterized by comprising the following steps:
acquiring historical traffic data of different time periods, wherein the historical traffic data comprises passenger trip data, transfer demand data and public traffic data, and the public traffic data comprises various types of public traffic;
calculating traffic missing data respectively corresponding to any place in different time periods according to the historical traffic data; the traffic loss data is used for representing the degree of mismatch between the traffic demand and the public transportation supply of passengers at any place in different time periods;
and processing according to the historical traffic data and the traffic missing data to obtain traffic coordination data of any place in different time periods.
2. The urban traffic coordination method according to claim 1, further comprising:
receiving riding requirements of passengers, wherein the riding requirements comprise destinations, travel time and number of people who travel;
and outputting at least one riding mode according to the riding requirements and the traffic coordination data, wherein each riding mode comprises one or more types of public traffic.
3. The urban traffic coordination method according to claim 2, wherein outputting at least one riding mode according to the riding demand and the traffic coordination data comprises:
obtaining at least one riding mode according to the riding demand and the traffic coordination data, wherein each riding mode comprises riding public traffic, transfer stations, estimated transfer time, estimated trip time and estimated trip amount;
carrying out weighted summation operation on public transport, transfer stations, estimated transfer time, estimated trip time and estimated trip amount in each riding mode according to corresponding preset weight proportion respectively to obtain riding scores of the riding mode;
and sequencing each riding mode according to the riding score, and displaying the sequenced riding mode in the first riding mode to the passengers, or displaying the sequenced riding modes to the passengers in sequence.
4. The urban traffic coordination method according to claim 2, further comprising:
acquiring current environment data, and judging whether the weather in the current time period is sunny or rainy according to the current environment data;
the step of outputting at least one riding mode according to the riding requirements and the traffic coordination data comprises the following steps:
obtaining at least one riding mode according to the riding demand and the traffic coordination data, wherein each riding mode comprises riding public traffic, transfer stations, estimated transfer time, estimated trip time and estimated trip amount;
when the weather in the current time period is sunny, carrying out weighted summation operation on public transport, transfer stations, estimated transfer time, estimated trip time and estimated trip amount in each riding mode according to corresponding preset weight proportion respectively to obtain riding scores of the riding mode; sequencing each riding mode according to the riding score, and displaying the sequenced riding mode to the passengers, or displaying the sequenced riding modes to the passengers in sequence;
when the weather in the current time period is rainy, deleting the riding modes corresponding to public transportation which is not suitable for traveling in rainy days, and performing weighted summation operation on the public transportation, transfer stations, estimated transfer time, estimated traveling time and estimated traveling amount in the rest riding modes according to corresponding preset weight proportions to obtain riding scores of the riding modes; and sequencing each riding mode according to the riding score, and displaying the sequenced riding mode in the first riding mode to the passengers, or displaying the sequenced riding modes to the passengers in sequence.
5. The urban traffic coordination method according to claim 4, further comprising:
obtaining historical riding requirements of a plurality of groups of passengers, historical environment data corresponding to the historical riding requirements and historical riding modes selected by users, and constructing riding models corresponding to the passengers according to the historical riding requirements, the historical environment data and the historical riding modes;
acquiring a plurality of groups of historical travel time according to a plurality of groups of historical riding requirements of passengers, acquiring the earliest historical travel time, and recording the earliest historical travel time as target travel time;
acquiring current time and environmental data, inputting the time and environmental data into a riding model, and outputting an optimal riding mode;
and displaying the optimal riding mode to the user before the target travel time is reached.
6. The city traffic cooperative method according to claim 1, wherein the traffic shortage data includes a gap amount or a surplus amount of different types of public transportation, the gap amount being used to indicate that the traffic demand of the passengers is greater than the supply amount of the public transportation at any one point during different time periods, and the surplus amount being used to indicate that the traffic demand of the passengers is less than the supply amount of the public transportation at any one point during different time periods;
processing the historical traffic data and the traffic loss data to obtain traffic coordination data of any place in different time periods, wherein the traffic coordination data comprises the following steps:
obtaining the gap amount or surplus amount of different types of public transportation at any place in different time periods according to the traffic loss data;
respectively forming the public traffic corresponding to the gap amount and the public traffic corresponding to the surplus amount into two categories, and sequentially arranging all the public traffic in each category according to the numerical value of the gap amount or the surplus amount from large to small;
selecting any group of public transportation with two types, and scheduling a plurality of public transportation with surplus in the group of public transportation to supplement the public transportation with the corresponding gap amount; wherein, in the group of public transportation, the sequence of one type of public transportation according to the gap amount corresponds to the sequence of the other type of public transportation according to the surplus amount.
7. The city traffic cooperative method according to claim 1, wherein the traffic loss data includes a gap amount or a surplus amount of public traffic, the gap amount representing that the traffic demand of passengers is greater than the supply amount of public traffic at any one point during different time periods, and the surplus amount representing that the traffic demand of passengers is less than the supply amount of public traffic at any one point during different time periods;
processing the historical traffic data and the traffic loss data to obtain traffic coordination data of any place in different time periods, wherein the traffic coordination data comprises the following steps:
acquiring the gap amount or surplus amount of public transportation of any place in different time periods;
and scheduling a plurality of public traffics of the places corresponding to the surplus amount according to a preset rule to supplement the public traffic of the places corresponding to the gap amount in the time period.
8. The city traffic cooperative device based on big data is characterized by comprising:
a data acquisition module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring historical traffic data of different time periods, the historical traffic data comprises passenger trip data, transfer demand data and public traffic data, and the public traffic data comprises various types of public traffic;
a data calculation module: the system is used for calculating traffic missing data respectively corresponding to any place in different time periods according to the historical traffic data; the traffic loss data is used for representing the degree of mismatch between the traffic demand and the public transportation supply of passengers at any place in different time periods;
a cooperative processing module: and the traffic coordination data of any place in different time periods are obtained by processing according to the historical traffic data and the traffic missing data.
9. A computer device, comprising: a memory and one or more processors;
the memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the big data based urban transportation collaboration method of any of claims 1-7.
10. A storage medium containing computer-executable instructions for performing the big data based urban traffic coordination method according to any one of claims 1 to 7 when executed by a computer processor.
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