CN108154698B - Bus arrival and departure accurate time calculation method based on GPS track big data - Google Patents

Bus arrival and departure accurate time calculation method based on GPS track big data Download PDF

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CN108154698B
CN108154698B CN201810013346.3A CN201810013346A CN108154698B CN 108154698 B CN108154698 B CN 108154698B CN 201810013346 A CN201810013346 A CN 201810013346A CN 108154698 B CN108154698 B CN 108154698B
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gps track
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CN108154698A (en
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李明敏
裘炜毅
李晗
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Shanghai Yuanzhuo Information Technology Co ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO

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Abstract

The invention relates to a method for calculating the accurate time of arrival and departure of a bus based on GPS track big data, which comprises the following steps: recording GPS longitude and latitude coordinate data of an operating vehicle on each bus route, superposing bus speed and time information on the acquired corresponding relation data of the route and station coordinate information through a shortest distance method, and finally establishing a matching relation between the GPS longitude and latitude data and the bus station longitude and latitude data so as to determine the station position which the vehicle may pass through. The invention has the advantages that: the method for detecting the arrival and departure time of the bus at the station along the way in a low-cost, all-road-network, less-delay and high-efficiency mode facing to the public transportation system is provided.

Description

Bus arrival and departure accurate time calculation method based on GPS track big data
Technical Field
The invention relates to a method for detecting bus arrival and departure information based on bus GPS track data.
Background
In recent years, along with the departure of cities, the traffic pressure of urban roads is also increased day by day, and in order to promote the sustainable departure of urban traffic, a departure urban public traffic system must be taken as an optimal way for solving the problems of large and medium urban traffic, and the intellectualization of an urban public traffic system is taken as a main direction in the public traffic research field.
At present, the departure of urban public transport still depends on the traditional operation management means, the operation efficiency of the public transport is low, and the service quality of the public transport is directly influenced, so that the proportion of the public transport in the traffic trip sharing rate is reduced.
The existing bus priority policy faces many problems in practical implementation, such as low operation efficiency, insufficient bus punctuality and the like; in order to improve the operation efficiency, improve the service quality and further perfect the urban public transportation operation management, a public transportation system manager can more accurately master the operation rule of public transportation vehicles, and the public transportation arrival time and other technologies must be applied to public transportation.
Disclosure of Invention
The invention aims to provide a method for accurately calculating the arrival and departure time of a bus.
In order to achieve the purpose, the technical scheme of the invention is to provide a method for calculating the accurate time from bus arrival to departure based on GPS track big data, which is characterized by comprising the following steps:
step 1, obtaining historical bus GPS track data of each operating vehicle on each bus route within a certain time length range, wherein each bus GPS track data comprises a vehicle terminal number, a bus route number, a timestamp, longitude information, latitude information, point speed and an azimuth angle;
acquiring bus line and station corresponding relation data of each bus line, wherein the bus line and station corresponding relation data comprises bus line numbers, line direction numbers, station sequence numbers, bus station numbers, station longitude information and station latitude information;
step 2, judging the corresponding line direction according to the azimuth angle of the bus GPS track data, matching the bus line number of the bus GPS track data in the same line direction with the bus line number in the corresponding relation data of the bus line and the station, matching the longitude information and the latitude information of the bus GPS track data in the same line direction and the same bus line number with the station longitude information and the station latitude information of the corresponding relation data of the bus line and the station, and obtaining the station associated with each bus GPS track data, wherein the same station corresponds to a plurality of bus GPS track data;
step 3, screening data from the multiple bus GPS track data corresponding to each station to form a station-entering and station-exiting GPS track data set, and for the nth bus line LnIth station L in uplink directionSniIn other words, if the bus is corresponding to the jth bus GPS track data LjDis (L) of the twoj,LSni)<If D _ BUSSTATIONmin is a preset threshold, the jth bus GPS track data LjBelong to site LSniThe station-entering and station-exiting GPS track data set;
step 4, if longitude information and latitude of the ith bus GPS track data in the in-and-out GPS track data setIf the degree information coincides with the longitude information and the latitude information of the corresponding station, recording the timestamp T of the ith bus GPS track dataiAnd location information LiAnd recording the timestamp T of the (i + 1) th bus GPS track data adjacent to the ith bus GPS track datai+1And position Li+1Will time stamp TiRecording the arrival time of the current bus station of the current operating vehicle in the current bus route and the current route direction, and recording the distance dis (L) between the ith bus GPS track data and the (i + 1) th bus GPS track datai,Li+1)<When D _ BUSSTATIONmin, the timestamp T is seti+1Recording the departure time of the current operation vehicle at the current bus route and the current bus stop in the current route direction;
if the GPS track data set of the station entering and exiting does not have bus GPS track data which is coincident with the longitude information and the latitude information of the corresponding station, the current GPS track data set of the station entering and exiting P is equal to { P { (P) }1,P2,…,Pk,…,PKFinding bus GPS track data P most matched with the current stationkPublic transport GPS track data PkSatisfies the following conditions: vk<Vk-1，Vk<Vk+1And T isk+1-Tk<＝D_signallampmin，Tk-Tk-1<D _ signalampmin, wherein Vk-1、Vk、Vk+1Respectively bus GPS track data Pk-1Bus GPS track data PkAnd bus GPS track data Pk+1The dot velocity, T, recordedk-1、Tk、Tk+1Respectively bus GPS track data Pk-1Bus GPS track data PkAnd bus GPS track data Pk+1And D _ signalampmin is the minimum time threshold value of the bus waiting for the traffic signal lamp, and the arrival time of the current operation vehicle at the current bus stop in the current bus route and the current route direction is TkThe departure time of the current operation vehicle at the current bus stop in the current bus route and the current route direction is Tk+1。
Preferably, in the step 1, drift data in the historical bus GPS track data is removed.
Preferably, after the step 4, the method further comprises:
step 5, clustering the arrival time and departure data of each operation vehicle at each bus stop of each bus line obtained in the step 4 according to the serial numbers of the bus lines and the operation vehicles, and carrying out forward sequencing on each type of data according to the stop sequence, thereby forming bus arrival data sets of different operation vehicles of different bus lines;
and 6, checking data in the bus arrival data set from the following two aspects:
first) whether the same piece of data corresponds to two matched sites
Setting the same GPS dotting position L in the bus arrival data setSnThere are two matched sites, the number of the corresponding site is ANAnd ASAccording to the dotting position L with the GPSSnAdjacent (n-1) th dotting position LSn-1Matched site number AN-1And the (n + 1) th dotting position LSn+1Matched site number AN+1Dotting position L for GPSSnChecking to satisfy ANOr AS<AN+1And A isNOr AS>AN-1；
Second) obtain abnormal site number set CAm
Sequencing data in the bus arrival data set according to the front-back sequence of the station numbers, and setting the Nth station reached by the current operating vehicle as SNThe site number is ANThe N +1 th site is SN+1The site number is AN+1Calculating the difference between the N +1 th site number and the Nth site number
Figure GDA0002669701330000033
Will be provided with
Figure GDA0002669701330000032
The site transformation of the two sites is classified into an abnormal site number set CAm。
The invention has the advantages that: the method for detecting the arrival and departure time of the bus at the station along the way with low cost, whole road network, less time delay, high efficiency and facing to the public transportation system is provided.
Drawings
Fig. 1A to 1C are flowcharts of the present invention.
Detailed Description
In order to make the invention more comprehensible, preferred embodiments are described in detail below with reference to the accompanying drawings.
With reference to fig. 1A to 1C, the invention provides a method for calculating the accurate time from bus arrival to departure based on GPS track big data, comprising the following steps:
step 1, acquiring bus GPS track data and bus route and station corresponding relation data within a certain time length range, and analyzing station information along the bus route.
Step 1.1, obtain public transit GPS orbit DATA (BUS GPS DATA, BGD for short, the same below), BGD's main field includes: vehicle terminal number (BUS _ NO, BN for short, the same below), BUS line number (BL _ NO, BN for short, the same below), timestamp (TIMESTAMP), LONGITUDE information (long, LON1 for short, the same below), LATITUDE information (LATITUDE, LAT1 for short, the same below), point SPEED (SPEED), azimuth (DIRECTION) information. If the acquired BGD is the data of the Shanghai GPS in 5 and 17 months in 2017, the offline BGD is shown in the following table.
Table 1: bus GPS trajectory data
Figure GDA0002669701330000031
Figure GDA0002669701330000041
Acquiring corresponding relation DATA (LINE AND SITE CORRESPONDENCE DATA, LASCD for short, the same below) of a bus line and a bus stop, wherein the main fields of the LASCD comprise: the information such as a bus line number (BN), a line direction number (DIR), a station sequence number (station _ sequential, SEN for short, the same below), a bus station number (BS _ ID), LONGITUDE information (long, LON2 for short, the same below), and LATITUDE information (LATITUDE, LAT2 for short, the same below), and the like, LASCD is as follows:
table 2: bus route and station corresponding relation data
Figure GDA0002669701330000042
And step 1.2, corresponding the line numbers in the GPS data to the line numbers in the line site corresponding table, and performing association analysis on each GPS track point of each line and the line site numbers. Setting the Nth dotting position LSnOccurs on the line LnUpper, correlation generating station location LSijCalculating the position LSnAnd site location LSijDistance dis (L)Sn,LSij) If dis (L)Sn,LSij)<If D _ BUSSTATIONmin is a preset threshold, the position L is reservedSnAnd (3) performing corresponding Nth dotting, wherein all reserved dotting forms a set P, performing data cleaning and filtering according to a threshold, the first layer is BGD, the second layer is LASCD, and a table is established as follows:
table 3: bus GPS track point and line station number association table
Figure GDA0002669701330000051
Step 1.3, screening the table in the step 1.2, counting a data set of the GPS track point and the station longitude and latitude coincidence, and recording the timestamp T of the ith bus GPS track data in the data set as shown in the table 4iAnd location information LiAnd recording the timestamp T of the (i + 1) th bus GPS track data adjacent to the ith bus GPS track datai+1And position Li+1Will time stamp TiRecording the arrival time of the current bus station of the current operating vehicle in the current bus route and the current route direction, and counting the number of the ith bus GPS track data and the (i + 1) th bus GPS trackAccording to the distance dis (L)i,Li+1)<When D _ BUSSTATIONmin, the timestamp T is seti+1And recording the departure time of the current operation vehicle at the current bus route and the current bus stop in the current route direction.
Table 4: bus GPS track point and line station coincidence information table
Figure GDA0002669701330000052
Figure GDA0002669701330000061
Step 1.4, set P ═ { P > from step 1.21,P2,…,Pk,…,PKFinding bus GPS track data P most matched with the current stationkPublic transport GPS track data PkSatisfies the following conditions: vk<Vk-1，Vk<Vk+1And T isk+1-Tk<＝D_signallampmin，Tk-Tk-1<D _ signalampmin, wherein Vk-1、Vk、Vk+1Respectively bus GPS track data Pk-1Bus GPS track data PkAnd bus GPS track data Pk+1The dot velocity, T, recordedk-1、Tk、Tk+1Respectively bus GPS track data Pk-1Bus GPS track data PkAnd bus GPS track data Pk+1And D _ signalampmin is the minimum time threshold value of the bus waiting for the traffic signal lamp, and the arrival time of the current operation vehicle at the current bus stop in the current bus route and the current route direction is TkThe departure time of the current operation vehicle at the current bus stop in the current bus route and the current route direction is Tk+1。
Step 2, clustering the arrival time and departure data of each operation vehicle at each bus stop of each bus line obtained in the step 1 according to the serial numbers of the bus lines and the operation vehicles, and carrying out forward sequencing on each type of data according to the stop sequence, thereby forming bus arrival data sets of different operation vehicles of different bus lines;
and 3, checking the data in the bus arrival data set from the following two aspects:
first) whether the same piece of data corresponds to two matched sites
Setting the same GPS dotting position L in the bus arrival data setSnThere are two matched sites, the number of the corresponding site is ANAnd ASAccording to the dotting position L with the GPSSnAdjacent (n-1) th dotting position LSn-1Matched site number AN-1And the (n + 1) th dotting position LSn+1Matched site number AN+1Dotting position L for GPSSnChecking to satisfy ANOr AS<AN+1And A isNOr AS>AN-1；
Second) obtain abnormal site number set CAm
Sequencing data in the bus arrival data set according to the front-back sequence of the station numbers, and setting the Nth station reached by the current operating vehicle as SNThe site number is ANThe N +1 th site is SN+1The site number is AN+1Calculating the difference between the N +1 th site number and the Nth site number
Figure GDA0002669701330000062
Will be provided with
Figure GDA0002669701330000063
The site transformation of the two sites is classified into an abnormal site number set CAm. Site number set C for occurrence of such exceptionAmThe reason for this may be that the vehicle on the line is in an idle stage, and in an abnormal operation state, the minimum difference T _ idenitieremmin of the same vehicle number on the same line is set to 1, and the corresponding station is the station where the current bus line arrives and departs from the station.
Acquiring bus GPS arrival and departure data of a corresponding date (5/17/2017), and judging whether the sequence number of the stations before and after is a set minimum difference value T _ IDENTIFIERmin which is 1 or not, wherein the data format is shown as the following table:
table 5: bus GPS station screening
Figure GDA0002669701330000071
And acquiring bus GPS to-station departure data of a corresponding date (5 months and 17 days in 2017), wherein the data format is shown in the following table.
Table 6: bus GPS arrival and departure characteristic vector
Figure GDA0002669701330000072
Figure GDA0002669701330000081
Therefore, the information of the arrival and departure time of the public transport can be completely counted, the station matching is carried out on the public transport track data through the longitude and latitude coordinates, the arrival and departure time of the public transport is counted, and the public transport operation service level can be evaluated.

Claims (3)

1. A bus arrival and departure accurate time calculation method based on GPS track big data is characterized by comprising the following steps:
step 1, obtaining historical bus GPS track data of each operating vehicle on each bus route within a certain time length range, wherein each bus GPS track data comprises a vehicle terminal number, a bus route number, a timestamp, longitude information, latitude information, point speed and an azimuth angle;
acquiring bus line and station corresponding relation data of each bus line, wherein the bus line and station corresponding relation data comprises bus line numbers, line direction numbers, station sequence numbers, bus station numbers, station longitude information and station latitude information;
step 2, judging the corresponding line direction according to the azimuth angle of the bus GPS track data, matching the bus line number of the bus GPS track data in the same line direction with the bus line number in the corresponding relation data of the bus line and the station, matching the longitude information and the latitude information of the bus GPS track data in the same line direction and the same bus line number with the station longitude information and the station latitude information of the corresponding relation data of the bus line and the station, and obtaining the station associated with each bus GPS track data, wherein the same station corresponds to a plurality of bus GPS track data;
step 3, screening corresponding data from a plurality of pieces of bus GPS track data corresponding to each station to form a GPS track data set for the station to enter and exit, and for the nth bus line LnIth station L in uplink directionSniIn other words, if the bus is corresponding to the jth bus GPS track data LjDis (L) of the twoj,LSni)<If D _ BUSSTATIONmin is a preset threshold, the jth bus GPS track data LjBelong to site LSniThe station-entering and station-exiting GPS track data set;
step 4, if the longitude information and the latitude information of the ith bus GPS track data in the incoming and outgoing GPS track data set coincide with the longitude information and the latitude information of the corresponding station, recording the timestamp T of the ith bus GPS track dataiAnd location information LiAnd recording the timestamp T of the (i + 1) th bus GPS track data adjacent to the ith bus GPS track datai+1And position Li+1Will time stamp TiRecording the arrival time of the current bus station of the current operating vehicle in the current bus route and the current route direction, and recording the distance dis (L) between the ith bus GPS track data and the (i + 1) th bus GPS track datai,Li+1)<When D _ BUSSTATIONmin, the timestamp T is seti+1Recording the departure time of the current operation vehicle at the current bus route and the current bus stop in the current route direction;
if the GPS track data set of the station entering and exiting does not have bus GPS track data which is coincident with the longitude information and the latitude information of the corresponding station, the current GPS track data set of the station entering and exiting P is equal to { P { (P) }1,P2,…,Pk,…,PKFinding bus GPS track data P most matched with the current stationkPublic transport GPS track data PkSatisfies the following conditions: vk<Vk-1，Vk<Vk+1And T isk+1-Tk<＝D_signallampmin，Tk-Tk-1<D _ signalampmin, wherein Vk-1、Vk、Vk+1Respectively bus GPS track data Pk-1Bus GPS track data PkAnd bus GPS track data Pk+1The dot velocity, T, recordedk-1、Tk、Tk+1Respectively bus GPS track data Pk-1Bus GPS track data PkAnd bus GPS track data Pk+1And D _ signalampmin is the minimum time threshold value of the bus waiting for the traffic signal lamp, and the arrival time of the current operation vehicle at the current bus stop in the current bus route and the current route direction is TkThe departure time of the current operation vehicle at the current bus stop in the current bus route and the current route direction is Tk+1。
2. The method for calculating the accurate time from bus to station departure based on the GPS track big data as claimed in claim 1, wherein in the step 1, the drift data in the historical bus GPS track data is removed.
3. The method for calculating the accurate time from bus to station departure based on the big data of the GPS track as claimed in claim 1, further comprising after said step 4:
step 5, clustering the arrival time and departure data of each operation vehicle at each bus stop of each bus line obtained in the step 4 according to the serial numbers of the bus lines and the operation vehicles, and carrying out forward sequencing on each type of data according to the stop sequence, thereby forming bus arrival data sets of different operation vehicles of different bus lines;
and 6, checking data in the bus arrival data set from the following two aspects:
first) whether the same piece of data corresponds to two matched sites
Setting the same GPS dotting position L in the bus arrival data setSnThere are two matched sites, the number of the corresponding site is ANAnd ASAccording to the dotting position L with the GPSSnAdjacent (n-1) th dotting position LSn-1Matched site number AN-1And the (n + 1) th dotting position LSn+1Matched site number AN+1Dotting position L for GPSSnChecking to satisfy ANOr AS<AN+1And A isNOr AS>AN-1；
Second) obtain abnormal site number set CAm
Sequencing data in the bus arrival data set according to the front-back sequence of the station numbers, and setting the Nth station reached by the current operating vehicle as SNThe site number is ANThe N +1 th site is SN+1The site number is AN+1Calculating the difference between the N +1 th site number and the Nth site number
Figure FDA0002669701320000021
Will be provided with
Figure FDA0002669701320000022
The site transformation of the two sites is classified into an abnormal site number set CAm。
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Families Citing this family (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108802776B (en) * 2018-07-02 2021-03-02 武汉蓝泰源信息技术有限公司 Bus GPS (global positioning system) deviation rectifying method based on abnormal point elimination and track compression algorithm
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CN110428508A (en) * 2019-08-05 2019-11-08 北京交通大学 A kind of bus based on card data is stolen a ride passenger's screening techniques
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CN110706477B (en) * 2019-10-12 2021-07-20 杭州律橙电子科技有限公司 Bus station matching method
CN111125293B (en) * 2019-12-31 2021-07-09 南京市城市与交通规划设计研究院股份有限公司 Automatic generation method and device of public transport network model and electronic equipment
CN112396865A (en) * 2020-11-18 2021-02-23 南京智慧交通信息股份有限公司 Real-time bus arrival prediction method based on line track
CN113763743B (en) * 2021-08-11 2022-08-23 杭州金通科技集团股份有限公司 Bus automatic station reporting method and system based on GPS track acquisition
CN115019538A (en) * 2022-05-31 2022-09-06 重庆京东方智慧科技有限公司 Method, device and equipment for generating traffic route information and storage medium
CN116204576B (en) * 2023-05-04 2023-08-01 北京城建交通设计研究院有限公司 Method and system for generating GTFS format data by public transportation data
CN117516586B (en) * 2024-01-08 2024-04-05 安徽中科中涣信息技术有限公司 Method for automatically counting non-operation mileage of vehicle based on GPS information

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
IL205371A0 (en) * 2010-04-27 2010-12-30 Transspot Ltd A method for accurately timing stations on a public transportation route
CN103295414B (en) * 2013-05-31 2016-01-20 北京建筑大学 A kind of bus arrival time Forecasting Methodology based on magnanimity history GPS track data
CN104064024B (en) * 2014-06-23 2016-04-06 银江股份有限公司 A kind of public transit vehicle arrival time Forecasting Methodology based on historical data
CN106327867B (en) * 2016-08-30 2020-02-11 北京航空航天大学 Bus punctuation prediction method based on GPS data

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