CN106710218A - Method for predicting arrival time of bus - Google Patents
Method for predicting arrival time of bus Download PDFInfo
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- CN106710218A CN106710218A CN201710137914.6A CN201710137914A CN106710218A CN 106710218 A CN106710218 A CN 106710218A CN 201710137914 A CN201710137914 A CN 201710137914A CN 106710218 A CN106710218 A CN 106710218A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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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
Abstract
The invention discloses a method for predicting arrival time of a bus. The method comprises the following steps: acquiring vehicle route and station basic data, vehicle GPS data and vehicle GPS real-time data; extracting a vehicle route track according to the vehicle route and station basic data, and matching the vehicle GPS data and the vehicle route track with a route of a vehicle; determining the real-time position of the vehicle according to the vehicle GPS real-time data; and judging a driving behavior of the vehicle on the route, and predicting the arrival time of the vehicle according to a judgment result. By virtue of the method, the route track and the route matching are extracted by virtue of the positioning of the vehicle, the vehicle speed and an arrival distance are calculated by virtue of an arrival prediction algorithm, the time for the vehicle to arrive a next station is calculated, the dynamic information of the vehicle is practically sent to a passenger information service platform in real time, and arrival time prediction data is provided, so that the prediction accuracy and instantaneity of the arrival time of the vehicle are improved.
Description
Technical field
It is to be related to a kind of bus arrival time prediction side in particular the present invention relates to technical field of intelligent traffic
Method.
Background technology
With deepening continuously for domestic intelligent transportation system research, the bus of GPS device is installed increasing
Realized in city, these vehicles for being configured with GPS device can provide all logs of public transit vehicle, including vehicle location,
Travel speed, switch door state, even state out of the station, information of on-board and off-board etc..And because the journey time of public transport receives road
The influence of the factors such as road traffic flow, integrative design intersection and upper and lower passenger flow, the journey time between standing and standing is a difficulty
With the underrange predicted.
There are static prediction and dynamic prediction both of which for the prediction of bus arrival time at present, static prediction is logical
The regression analysis Pharaoh for crossing length and intersection number between Travel Time for Public Transport Vehicles and station estimates public transport journey time, so that root
According to the public transport arrival time that bus departure time reckoning is respectively stood；Dynamic prediction is passed through according to the real-time GPS data of public transit vehicle
The method of fitting of a polynomial estimates the traffic behavior in section, so as to predict the journey time of public transport, and determines car according to GPS
Position, calculate public transit vehicle arrival time.But static prediction method is difficult to adapt to road traffic state complicated and changeable；
And the defect of dynamic prediction method is position and velocity information merely with the GPS of public transit vehicle calculates during public transit vehicle stroke
Between and arrival time, and not according to the situation real-time adjustment vehicle to the arrival time of downstream station that arrives at a station of public transit vehicle, prediction
Precision by GPS accuracy and send interval influenceed very big, the degree of accuracy of prediction and real-time are poor.
The content of the invention
In view of this, it is static in the prior art to solve the invention provides a kind of bus arrival time Forecasting Methodology
Forecasting Methodology cannot adapt to the situation of complex road condition, and dynamic prediction method is adjusted in real time not according to the situation of arriving at a station of public transit vehicle
Vehicle arrives the arrival time of downstream station, the degree of accuracy of prediction and the poor problem of real-time.
To achieve the above object, the present invention provides following technical scheme：
A kind of vehicle arrival time Forecasting Methodology, including：
Obtain vehicle line and website basic data, vehicle GPS data and vehicle GPS real time data；
Vehicle line track is extracted according to the vehicle line and website basic data；
By the vehicle GPS data and circuit where vehicle described in the vehicle line path matching；
Real time position determination is carried out to the vehicle according to the vehicle GPS real time data；
Vehicle traveling behavior on the line is judged, it is pre- according to the arrival time that judged result carries out the vehicle
Survey, the traveling behavior to vehicle on the line carries out judgement and specifically includes：Up-downgoing judges and judgement of turning back.
Preferably, counted in real time in the acquisition vehicle line and website basic data, vehicle GPS data and vehicle GPS
According to before, also include：
Vehicle line data are pre-processed.
Wherein, it is described that pre-treatment step is carried out to vehicle line data, specifically include：
On the basis of the initial data that Vehicular system is accessed, according to existing road map, GPS history numbers are run with reference to vehicle
According to, to be described according to GPS track or addition road chain, the road chain is divided according to crossing, and all road chains constitute the fortune of this circuit
Row track, sampled point is set to by the head and the tail end points of the road chain；
Site location is corrected according to GPS accumulation points, site location is adapted to the matching GPS focus points.
Wherein, it is described by the vehicle GPS data and circuit where vehicle described in the vehicle line path matching, bag
Include：
The line number of the vehicle GPS point according to Real-time Collection and circuit up-downgoing judgement, obtain all samplings of route
Press the list after longitude size is ranked up；
All sampled points of the predeterminable range before and after Current vehicle GPS point longitude are found out from the list；
The distance that the sampled point to acquiring carries out point-to-point is calculated, and calculates vehicle GPS point to the sampled point
Distance, it is matching sampled point to take out closest sampled point；
According to the projector distance of vehicle GPS point, judge vehicle GPS point in the position of the sampled point for being matched.
Wherein, the up-downgoing judges to be specially：
Default T2It is the timestamp of current bus GPS point, T1For the same time of car GPS point that the last time receives
Stamp, only works as T2>T1When, it is considered as normal GPS information；
Default SnowIt is in uplink, according to the sampled point that current GPS point is matched, SpreBe in uplink,
According to the sampled point that last GPS point is matched；XnowIt is in downgoing line, according to adopting that current bus GPS point is matched
Sampling point, XpreIt is in downgoing line, according to the sampled point that last GPS point is matched；
When meeting SpreSequence number be less than Snow, and XpreSequence number be more than or equal to Xnow, then for up；When meeting Xpre
Sequence number be less than Xnow, and SpreSequence number be more than or equal to Snow, then for descending.
Wherein, the arrival time prediction for carrying out the vehicle according to judged result is specifically included：
Judged according to the up-downgoing and described turning back judges the road chain length for determining vehicle；
According to the current traffic information of vehicle GPS data acquisition, including the Current vehicle speed of service and road chain speed, according to
The road chain speed and road chain length prediction vehicle arrival time.
Wherein, it is described to be specifically included according to the current traffic information of vehicle GPS data acquisition：
After one gps data of every reception, by the sampling Point matching, the sampled point that will match to puts into vehicle
Lateral velocity queue；
When the Current vehicle speed of service is calculated, sampled point in the queue is taken out, and remove invalid sampled point, for institute
The effective sampling points in queue are stated, the operating range d of its process is calculatedis, and elapsed time Te-Ts, wherein, TsIt is first
The time of individual effective sampling points, TeIt it is the time of last effective sampling points, then now the current speed of service of vehicle i is：vi
=dis/(Te-Ts)；
For circuit Shang Meitiao roads chain sets up a longitudinal velocity queue；
When the Current vehicle speed of service is obtained, the road chain list of vehicle process is extracted, and Current vehicle is run
Speed is added in the longitudinal velocity queue of each road chain of vehicle process；
After certain the Current vehicle speed of service of car is calculated, the velocity amplitude is added to the longitudinal velocity team of road chain
In row；
Assuming that store n in the longitudinal velocity queue of road chain recently by the car speed of the road chain, then road chain speed
For：
Wherein, it is described that vehicle arrival time step is predicted according to the road chain speed and the road chain length, specifically include：
The GPS point of vehicle to be predicted is projected into sampled point, obtain current sampling point apart from the distance of the next stop and between
Every road chain information；
Assuming that middle by n road chain, each road chain length is Li(i=1 ... n), and Current vehicle GPS point positional distance
The distance of road last-of-chain is Lnow, then distance of the Current vehicle GPS point apart from the next stop be：
The road chain speed of each road chain is vi(i=1 ... n), VnowThe speed of road chain is currently located for vehicle, then to the next stop
Time prediction be：
Understood via above-mentioned technical scheme, compared with prior art, the invention discloses a kind of bus arrival time
Forecasting Methodology, obtains vehicle line and website basic data, vehicle GPS data and vehicle GPS real time data；According to the car
Circuit and website basic data extract vehicle line track；By the vehicle GPS data and the vehicle line path matching
Circuit where the vehicle；Real time position determination is carried out to the vehicle according to the vehicle GPS real time data；It is online to vehicle
Traveling behavior on road is judged, is predicted according to the arrival time that judged result carries out the vehicle.The present invention passes through vehicle
Positioning extraction tracks and line matching, and by predicting that the algorithm that arrives at a station calculates car speed and arrive at a station distance and then calculating
Vehicle reaches the time of the next stop, Real-time by vehicle Dynamic Information Publishing to Customer information service platform, there is provided arrive at a station
Time prediction data, improve the degree of accuracy and the real-time of vehicle arrival time prediction.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
The accompanying drawing to be used needed for having technology description is briefly described, it should be apparent that, drawings in the following description are only this
Inventive embodiment, for those of ordinary skill in the art, on the premise of not paying creative work, can also basis
The accompanying drawing of offer obtains other accompanying drawings.
Fig. 1 is a kind of vehicle arrival time Forecasting Methodology schematic flow sheet provided in an embodiment of the present invention；
Fig. 2 is the embodiment of the present invention by the principle schematic on spot projection to road chain；
Fig. 3 is the embodiment of the present invention by the principle schematic on vehicle GPS Point matching to circuit；
Fig. 4 is the principle schematic that the embodiment of the present invention calculates current traffic information；
Fig. 5 is the principle schematic that the embodiment of the present invention predicts public transport arrival time.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
Accompanying drawing 1 is referred to, Fig. 1 illustrates for a kind of vehicle arrival time Forecasting Methodology flow provided in an embodiment of the present invention
Figure.As shown in figure 1, the embodiment of the invention discloses a kind of vehicle arrival time Forecasting Methodology, the method specific steps are included such as
Under：
S101, acquisition vehicle line and website basic data, vehicle GPS data and vehicle GPS real time data.
For Vehicular system access basic data including line number, site location, gps data etc., because it lacks line
Circuit-switched data, in order to meet the demand of prediction of arriving at a station, it is necessary on the basis of basic data, according to existing road map, with reference to car
Operation GPS historical datas, describe or addition road chain according to GPS track, and road chain is divided generally according to crossing, all road chains composition
Road chain two ends are set to sampled point by the running orbit of this circuit.
S102, vehicle line track is extracted according to vehicle line and website basic data.
S103, by circuit where vehicle GPS data and vehicle line path matching vehicle.
Specifically, the step includes：
The line number of the vehicle GPS point according to Real-time Collection and circuit up-downgoing judgement, obtain all samplings of route
Press the list after longitude size is ranked up；
All sampled points of the predeterminable range before and after Current vehicle GPS point longitude are found out from the list；
The distance that the sampled point to acquiring carries out point-to-point is calculated, and calculates vehicle GPS point to the sampled point
Distance, it is matching sampled point to take out closest sampled point；
According to the projector distance of vehicle GPS point, judge vehicle GPS point in the position of the sampled point for being matched.
It should be noted that first, the line number of the bus GPS point according to Real-time Collection and circuit up-downgoing are obtained
The all sampled points of the circuit be ranked up by longitude size after list；Secondly, using binary chop, found out from list away from
100 meters of all sampled points before and after current bus GPS point longitude；Finally, point-to-point is carried out again to the sampled point found out
Distance is calculated, and calculates bus GPS point to the distance of sampled point, and it is matching sampled point, such as Fig. 3 to take out closest sampled point
Shown (wherein, × be sampled point).
After the sampled point that bus GPS point is matched is found out, not can determine that its be before sampled point or after, entering
, it is necessary to judge GPS point before and after website correspondence sampled point when the row next stop judges.As shown in Fig. 2 S2For GPS point is matched
Sampled point, pick out S2Former and later two sampled points S1And S3Composition line segment S1S2And S2S3, GPS point is projected to and is asked on two line segments
Beeline, judges that GPS is before sampled point or rear.
S104, foundation vehicle GPS real time data carry out real time position determination to vehicle.
S105, the traveling behavior to vehicle on the line judge, the arrival time of vehicle is carried out according to judged result
Prediction, carries out judgement and specifically includes to vehicle traveling behavior on the line：Up-downgoing judges and judgement of turning back.
Specifically, the up-downgoing judges to be specially：
Default T2It is the timestamp of current bus GPS point, T1For the same time of car GPS point that the last time receives
Stamp, only works as T2>T1When, it is considered as normal GPS information；
Default SnowIt is in uplink, according to the sampled point that current GPS point is matched, SpreBe in uplink,
According to the sampled point that last GPS point is matched；XnowIt is in downgoing line, according to adopting that current bus GPS point is matched
Sampling point, XpreIt is in downgoing line, according to the sampled point that last GPS point is matched；
When meeting SpreSequence number be less than Snow, and XpreSequence number be more than or equal to Xnow, then for up；When meeting Xpre
Sequence number be less than Xnow, and SpreSequence number be more than or equal to Snow, then for descending.
Specifically, the judgement of turning back is specially：
Because operating line has one direction, with the situation of the traveling of chain reciprocating operation all the way, it is necessary to calculation Chinese module pair of turning back
Vehicle-state treatment, makes prediction more accurate.
Major function has：Turn back area identification：By line name and direction, to turning back, region carries out specific identifier；Turn back
Treatment：According to turning back, logic realizes accurately prediction to vehicle.
Specifically, being counted in real time in the acquisition vehicle line and website basic data, vehicle GPS data and vehicle GPS
According to before, also including step：
Vehicle line data are pre-processed.
Specifically, carry out pretreatment to vehicle line data comprising the following steps：
On the basis of the initial data that Vehicular system is accessed, according to existing road map, GPS history numbers are run with reference to vehicle
According to, to be described according to GPS track or addition road chain, the road chain is divided according to crossing, and all road chains constitute the fortune of this circuit
Row track, sampled point is set to by the head and the tail end points of the road chain.
Site location is corrected according to GPS accumulation points, site location is adapted to the matching GPS focus points.
There is deviation in view of original site positional information and actual stop position, in order to further improve the accurate of prediction
Site location is adapted to matching GPS focus points, it is necessary to according to GPS accumulation points, be corrected to site location by rate.
As shown in figure 3, ABCD is three road chains on a circuit, S is website position, S1And S2It is website not
The projection gone the same way on chain, d1And d2It is website to the distance of road chain.When sampled point is added, d is compared1And d2, select from road chain compared with
Near subpoint is added in the chain of road as sampled point, such as Fig. 3, d1<d2, then S is added1As sampled point.
Because part road chain sampled point is rare, can cause have relatively large deviation during matching, in order to improve the essence of GPS point matching
Degree, then need to add sampled point on the chain of road.Adding the principle of sampled point is：Distance is big between have two sampled points on a road chain
In 15m, then added a little between two sampled points.
Assuming that A, B are sampled point adjacent on the chain of road, and apart from dAB>15, then need addition n=[d in point-to-point transmissionAB/15]
Individual sampled point S1, S2…Sn, and meet
, it is necessary to calculate sampled point to road first-in-chain(FIC) back range with a distance from the next stop, as sampling after increase sampled point
The attribute storage of point.Finally export by circuit and station data, sample point data, road chain data, website and affiliated road chain data
The public transport basic data of composition.
Specifically, the arrival time prediction for carrying out the vehicle according to judged result is specifically included：
Judged according to the up-downgoing and described turning back judges the road chain length for determining vehicle；
According to the current traffic information of vehicle GPS data acquisition, including the Current vehicle speed of service and road chain speed, according to
The road chain speed and road chain length prediction vehicle arrival time.
Specifically, described specifically include according to the current traffic information of vehicle GPS data acquisition：
After one gps data of every reception, by the sampling Point matching, the sampled point that will match to puts into vehicle
Lateral velocity queue.
As shown in figure 4, when the Current vehicle speed of service is calculated, taking out sampled point in the queue, and remove invalid adopting
Sampling point, for the effective sampling points in the queue, calculates the operating range d of its processis, and elapsed time Te-Ts, its
In, TsIt is first time of effective sampling points, TeBe the time of last effective sampling points, then the now current fortune of vehicle i
Scanning frequency degree is：vi=dis/(Te-Ts)。
The Current vehicle speed of service is defined as the current 3 minutes vehicle average speed of (current time pushes away forward 3 minutes).Such as
Shown in Fig. 4, after a gps data is received, matched through oversampled points, the sampled point that will match to is put into vehicle lateral speed
Queue (queue length is 9, about 3 minutes).When vehicle present speed is calculated, sampled point in queue is taken out first, remove nothing
Effect sampled point (less than the sampled point sequence number that previous gps time, latter GPS are matched less than previous GPS sample by such as latter gps time
The sampled points such as point sequence number), the sampled point 1~2 in such as Fig. 4 (a) in lateral velocity queue is invalid sampled point, and calculating is not included.
For effective sampled point in queue, calculator pass through apart from dis, and elapsed time Te-Ts, wherein, TsIt is first
The time of effective sampling points, TeIt it is the time of last effective sampling points, so as to calculate average car of the vehicle in 3 minutes
Speed, i.e. vehicle present speed.
Assuming that store n in the longitudinal velocity queue of road chain recently by the car speed of the road chain, then road chain speed
For：
Road chain speed is defined as by an average speed for the nearest n car of road chain.For circuit Shang Meitiao roads chain is set up
One longitudinal velocity queue；When the Current vehicle speed of service is obtained, the road chain list of vehicle process is extracted, and will be current
Running velocity is added in the longitudinal velocity queue of each road chain of vehicle process.As shown in Fig. 4 (b), road chain it is vertical
In to speed queue, speed of nearest 4 cars by the vehicle of this road chain is stored, then chain speed in road is
Specifically, described predict vehicle arrival time step, specific bag according to the road chain speed and the road chain length
Include：
The GPS point of vehicle to be predicted is projected into sampled point, obtain current sampling point apart from the distance of the next stop and between
Every road chain information.
Assuming that middle by n road chain, each road chain length is Li(i=1 ... n), and Current vehicle GPS point positional distance
The distance of road last-of-chain is Lnow, then distance of the Current vehicle GPS point apart from the next stop be：
As shown in figure 5, road last-of-chain road chain 1 and road chain 2 that vehicle car to be predicted is spaced apart from the next stop, then arrive the next stop
Distance is Lnow+L1+L2.The road chain speed of each road chain is, it is known that be vi(i=1 ... n), VnowThe speed of road chain is currently located for vehicle
Spend, then the time prediction to the next stop is：
In sum, the invention discloses a kind of bus arrival time Forecasting Methodology, vehicle line and website base are obtained
Plinth data, vehicle GPS data and vehicle GPS real time data；Vehicle is extracted according to the vehicle line and website basic data
Tracks；By the vehicle GPS data and circuit where vehicle described in the vehicle line path matching；According to the vehicle
GPS real time datas carry out real time position determination to the vehicle；Vehicle traveling behavior on the line is judged, according to sentencing
Disconnected result carries out the arrival time prediction of the vehicle.The present invention extracts tracks and line matching by vehicle location,
And by predicting that the algorithm that arrives at a station calculates car speed and the time of arrive at a station distance and then the calculating vehicle arrival next stop, Real-time is used
By vehicle Dynamic Information Publishing to Customer information service platform, there is provided arrival time prediction data, improve vehicle arrival time
The degree of accuracy of prediction and real-time.
It should be noted that each embodiment in this specification is described by the way of progressive, each embodiment weight
Point explanation is all difference with other embodiment, between each embodiment identical similar part mutually referring to.
Above in association with accompanying drawing to exemplary description proposed by the invention, the explanation of above example is only intended to help and manages
Solve core concept of the invention.For those of ordinary skill in the art, according to thought of the invention, in specific embodiment and
Be will change in range of application, such as front-rear axle has motor to participate in the hybrid power system for driving.In sum, originally
Description should not be construed as limiting the invention.
The foregoing description of the disclosed embodiments, enables professional and technical personnel in the field to realize or uses the present invention.
Various modifications to these embodiments will be apparent for those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, the present invention
The embodiments shown herein is not intended to be limited to, and is to fit to and principles disclosed herein and features of novelty phase one
The scope most wide for causing.
Claims (8)
1. a kind of vehicle arrival time Forecasting Methodology, it is characterised in that including：
Obtain vehicle line and website basic data, vehicle GPS data and vehicle GPS real time data；
Vehicle line track is extracted according to the vehicle line and website basic data；
By the vehicle GPS data and circuit where vehicle described in the vehicle line path matching；
Real time position determination is carried out to the vehicle according to the vehicle GPS real time data；
Vehicle traveling behavior on the line is judged, the arrival time prediction of the vehicle is carried out according to judged result,
The traveling behavior to vehicle on the line carries out judgement and specifically includes：Up-downgoing judges and judgement of turning back.
2. method according to claim 1, it is characterised in that in acquisition vehicle line and website basic data, the car
Before gps data and vehicle GPS real time data, also include：
Vehicle line data are pre-processed.
3. method according to claim 2, it is characterised in that described that vehicle line data are carried out with pre-treatment step, tool
Body includes：
On the basis of the initial data that Vehicular system is accessed, according to existing road map, GPS historical datas are run with reference to vehicle,
Described according to GPS track or addition road chain, the road chain is divided according to crossing, and all road chains constitute the operation rail of this circuit
Mark, sampled point is set to by the head and the tail end points of the road chain；
Site location is corrected according to GPS accumulation points, site location is adapted to the matching GPS focus points.
4. method according to claim 1, it is characterised in that described by the vehicle GPS data and the vehicle line
Circuit where vehicle described in path matching, including：
The line number of the vehicle GPS point according to Real-time Collection and circuit up-downgoing judge, obtain all sampled points of route and press
Longitude size be ranked up after list；
All sampled points of the predeterminable range before and after Current vehicle GPS point longitude are found out from the list；
The sampled point to acquiring carry out point-to-point distance calculate, calculate vehicle GPS point to the sampled point away from
From it is matching sampled point to take out closest sampled point；
According to the projector distance of vehicle GPS point, judge vehicle GPS point in the position of the sampled point for being matched.
5. method according to claim 1, it is characterised in that the up-downgoing judges to be specially：
Default T2It is the timestamp of current bus GPS point, T1It is the same timestamp of car GPS point that the last time receives, only
Have and work as T2>T1When, it is considered as normal GPS information；
Default SnowIt is in uplink, according to the sampled point that current GPS point is matched, SpreBe in uplink, according to
The sampled point that last GPS point is matched；XnowIt is in downgoing line, according to the sampling that current bus GPS point is matched
Point, XpreIt is in downgoing line, according to the sampled point that last GPS point is matched；
When meeting SpreSequence number be less than Snow, and XpreSequence number be more than or equal to Xnow, then for up；When meeting XpreSequence
Number be less than Xnow, and SpreSequence number be more than or equal to Snow, then for descending.
6. method according to claim 1, it is characterised in that described that when arriving at a station of the vehicle is carried out according to judged result
Between predict and specifically include：
Judged according to the up-downgoing and described turning back judges the road chain length for determining vehicle；
According to the current traffic information of vehicle GPS data acquisition, including the Current vehicle speed of service and road chain speed, according to described
Road chain speed and road chain length prediction vehicle arrival time.
7. method according to claim 6, it is characterised in that described according to the current traffic information of vehicle GPS data acquisition
Specifically include：
After one gps data of every reception, by the sampling Point matching, the sampled point that will match to puts into lateral direction of car
Speed queue；
When the Current vehicle speed of service is calculated, sampled point in the queue is taken out, and remove invalid sampled point, for the team
Effective sampling points in row, calculate the operating range d of its processis, and elapsed time Te-Ts, wherein, TsHave for first
Imitate the time of sampled point, TeIt it is the time of last effective sampling points, then now the current speed of service of vehicle i is：vi=
dis/(Te-Ts)；
For circuit Shang Meitiao roads chain sets up a longitudinal velocity queue；
When the Current vehicle speed of service is obtained, the road chain list of vehicle process is extracted, and by the Current vehicle speed of service
It is added in the longitudinal velocity queue of each road chain of vehicle process；
After certain the Current vehicle speed of service of car is calculated, the velocity amplitude is added to the longitudinal velocity queue of road chain
In；
Assuming that storing the n car speed for passing through the road chain recently in the longitudinal velocity queue of road chain, then chain speed in road is：
8. method according to claim 6, it is characterised in that described pre- according to the road chain speed and the road chain length
Measuring car arrival time step, specifically includes：
The GPS point of vehicle to be predicted is projected into sampled point, distance and interval of the current sampling point apart from the next stop is obtained
Road chain information；
Assuming that middle by n road chain, each road chain length is Li(i=1 ... n), and Current vehicle GPS point positional distance road chain
The distance of tail is Lnow, then distance of the Current vehicle GPS point apart from the next stop be：
The road chain speed of each road chain is vi(i=1 ... n), VnowThe speed of road chain is currently located for vehicle, then to the time of the next stop
It is predicted as：
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| CN109166337A (en) * | 2018-09-04 | 2019-01-08 | 深圳市东部公共交通有限公司 | Public transport arrival time generation method, device and bus passenger travelling OD acquisition methods |
| CN109920248A (en) * | 2019-03-05 | 2019-06-21 | 南通大学 | A kind of public transport arrival time prediction technique based on GRU neural network |
| CN110702136A (en) * | 2019-10-29 | 2020-01-17 | 北京百度网讯科技有限公司 | Route planning method and device for vehicle, electronic equipment and readable storage medium |
| CN111007544A (en) * | 2019-12-18 | 2020-04-14 | 南京智慧交通信息有限公司 | Method for measuring and calculating distance between front bus and rear bus based on line track |
| CN111667689A (en) * | 2020-05-06 | 2020-09-15 | 浙江师范大学 | Method, device and computer device for predicting vehicle travel time |
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| WO2021000594A1 (en) * | 2019-07-03 | 2021-01-07 | 北京京东振世信息技术有限公司 | Address information collection method and apparatus |
| 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 |
| CN112991722A (en) * | 2021-02-03 | 2021-06-18 | 浙江浙大中控信息技术有限公司 | Method and system for predicting real-time intersection of bus at high-frequency gps point |
| CN113077648A (en) * | 2021-02-03 | 2021-07-06 | 浙江浙大中控信息技术有限公司 | Method and system for predicting real-time station of public transport vehicle |
| CN113096429A (en) * | 2021-03-09 | 2021-07-09 | 东南大学 | Elastic bus area flexibility line generation method based on bus dispatching station distribution |
| CN115311871A (en) * | 2022-08-12 | 2022-11-08 | 深圳市能信安科技股份有限公司 | Vehicle driving direction determination method, device, system, equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101388143A (en) * | 2007-09-14 | 2009-03-18 | 同济大学 | Bus arriving time prediction method and system based on floating data of the bus |
| CN103310651A (en) * | 2013-05-24 | 2013-09-18 | 北京市交通信息中心 | Bus arrival prediction method based on real-time traffic status information |
| CN103400507A (en) * | 2013-07-08 | 2013-11-20 | 青岛海信网络科技股份有限公司 | Bus operation method and bus operation system supporting line section dynamic regulation |
| CN103440768A (en) * | 2013-09-12 | 2013-12-11 | 重庆大学 | Dynamic-correction-based real-time bus arrival time predicting method |
| CN105632222A (en) * | 2015-10-14 | 2016-06-01 | 上海博协软件有限公司 | Method and system for predicting station arrival time |
-
2017
- 2017-03-09 CN CN201710137914.6A patent/CN106710218A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101388143A (en) * | 2007-09-14 | 2009-03-18 | 同济大学 | Bus arriving time prediction method and system based on floating data of the bus |
| CN103310651A (en) * | 2013-05-24 | 2013-09-18 | 北京市交通信息中心 | Bus arrival prediction method based on real-time traffic status information |
| CN103400507A (en) * | 2013-07-08 | 2013-11-20 | 青岛海信网络科技股份有限公司 | Bus operation method and bus operation system supporting line section dynamic regulation |
| CN103440768A (en) * | 2013-09-12 | 2013-12-11 | 重庆大学 | Dynamic-correction-based real-time bus arrival time predicting method |
| CN105632222A (en) * | 2015-10-14 | 2016-06-01 | 上海博协软件有限公司 | Method and system for predicting station arrival time |
Non-Patent Citations (3)
| Title |
|---|
| 张玮: "基于GPS浮动车的路径行程时间估计系统关键技术研究", 《中国博士学位论文全文数据库·工程科技Ⅱ辑》 * |
| 过秀成 等: "《地面公共交通运行可靠性分析与调度控制》", 30 June 2013, 东南大学出版社 * |
| 郑丽娟 等: "基于运行图的快速公交系统运营组织研究", 《交通标准化》 * |
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