CN103310651A - Bus arrival prediction method based on real-time traffic status information - Google Patents
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Abstract
The invention discloses a bus arrival prediction method based on real-time traffic status information, belonging to an intelligent traffic technology. The bus arrival prediction method comprises the following steps of: preprocessing bus route data; matching bus GPS (global positioning system) points collected in real time onto a bus route according to the preprocessed bus route data; judging travelling behaviors of buses on the route; and carrying out bus arrival prediction based on the real-time traffic status information according to a judgment result. With the adoption of the bus arrival prediction method provided by the invention, on the basis of GPS rapid route matching of a sampling point, the complexity of matching a GPS positioning point and the route is lowered, the arrival distance processing speed is improved, and a simple and effective manner is provided for real-time matching of large-scale GPS data. With the adoption of a bus arrival prediction model based on the real-time traffic status information, dynamic bus prediction is carried out according to the traffic conditions changed in real time; and the bus arrival prediction method has better instantaneity and accuracy.
Description
Technical field
The invention belongs to the intelligent transport technology field, particularly a kind of public transport based on real-time road condition information Forecasting Methodology of arriving at a station.
Background technology
Along with the continuous intensification of urbanization, modernization, motorization degree, vehicle guaranteeding organic quantity increases fast, and the citizens' activities demand presents the feature of variation, personalization, and the urban traffic pressure increases day by day.Both at home and abroad development experience in big city shows, solve urban transport problems, and the traffic problems of especially super-huge international city must be given full play to the vital role of public transport.The service of public transport arrival time is to improve one of key service of public's traffic attractive force.By the real-time vehicle information of arriving at a station is provided, can make the user better arrange own public transport stroke, minimizing stand-by period.Therefore, real-time public transport arrival time forecasting techniques has become the focus of research at intelligent transportation field.Yet because traffic conditions is complicated and changeable and road conditions are unstable, the prediction of arriving at a station of public transport accurately in real time remains a difficult point.Arrive at a station in public transport both at home and abroad at present and carried out many researchs aspect the prediction, mainly contain statistical analysis method, Kalman filtering method, machine learning method, historical data Similarity Model etc.
Statistical analysis method mainly comprises regretional analysis and time series forecasting.Regretional analysis is to carry out forecast method by cause-effect relationship and the influence degree analyzed between the things, as analyze the correlativity of the factor such as number, weather of public transport arrival time and line construction, passenger getting on/off, intermediate stations, make up regression equation, the dependent variable bus running time is predicted in the variation of prediction period according to these independents variable.It is independently that this method requires these influence factors, and this strictness has limited the application of regression analysis.Time series forecasting is the method for inferring its following value by the Changing Pattern of finding the research object past, and its prediction accuracy depends on the journey time Changing Pattern predicted and the matching degree of historical law, has certain limitation.
Kalman filtering is a kind of high efficiency regressive filter, can be from one group of observation sequence to object space that comprises noise, to current location and in the future the position estimate.Kalman filtering is a kind of method of prediction Short-Term Traffic Flow, has robustness preferably, can make effective reaction to external environment condition.But it is only predicting that be effectively the working time in following one or two time period, and when predicting that distance or time integral body are very long, precision of prediction is lower.
Machine learning techniques all is widely used in a lot of fields, is applied to the technology such as artificial neural network and support vector machine that mainly contain that public transport is arrived at a station and predicted at present.This method mainly obtains forecast model by mass historical data is carried out training study, can reach certain precision of prediction.Yet this method needs training and testing widely, thereby finds out correct model structure and parameter, and implementation complexity is higher, is difficult to realize training and the performance prediction of real-time online simultaneously.
Be to be based upon under the prerequisite of regularity that traffic circulation has circulation change, by historical data is analyzed, with the current journey time of journey time mean prediction of historical phase same time bus based on the Forecasting Methodology of historical data Similarity Model.The electronic stop plate in present most of cities shows that arrival time is based on that this naive model predicts.On this basis, the further city bus arrival time forecasting mechanism that proposes based on the road conditions similarity of research is arranged also, proposed polynary group of traffic information, and polynary group of similarity calculation method of traffic information, predict according to similar historical road conditions.This method is not considered the transport information of real-time change fully based on historical data, and real-time and accuracy are relatively poor.
Summary of the invention
In view of this, the object of the present invention is to provide a kind of public transport based on real-time road condition information Forecasting Methodology of arriving at a station, be used for improving prediction accuracy and real-time, reduce complexity.
The invention provides a kind of public transport based on real-time road condition information Forecasting Methodology of arriving at a station, comprising:
The public bus network data are carried out pre-service, according to pretreated public bus network data, the bus GPS point of gathering is in real time matched on the public bus network; The behavior of travelling is on the line judged to bus, carries out based on the public transport of the real-time road condition information prediction of arriving at a station according to judged result.
Public transport provided by the invention is arrived at a station Forecasting Methodology at pretreatment stage, generates and serializing is handled by track data, obtains based on sampled point and the public transport basic data form that has nothing to do with map; And by based on the GPS fast path of sampled point coupling, reduced the complexity of GPS anchor point and circuit coupling, improved and arrived at a station apart from processing speed, for the real-time coupling of gps data on a large scale provides a kind of simple and effective way.The present invention adopts based on the public transport of the real-time road condition information forecast model that arrives at a station, and can carry out dynamic public transport prediction according to the traffic conditions of real-time change, has good real-time performance and accuracy.
Description of drawings
The public transport based on real-time road condition information that Fig. 1 provides for the embodiment of the invention Forecasting Methodology process flow diagram that arrives at a station;
Fig. 2 is the principle schematic of circuit serializing in the embodiment of the invention;
Fig. 3 is for projecting to website in the embodiment of the invention principle schematic on the chain of road;
Fig. 4 is for matching the bus GPS point in the embodiment of the invention principle schematic on the public bus network;
Fig. 5 is the principle schematic of calculating current traffic information in the embodiment of the invention;
Fig. 6 is the principle schematic of prediction public transport arrival time in the embodiment of the invention.
Embodiment
For making the purpose, technical solutions and advantages of the present invention clearer, the present invention is described in further detail below in conjunction with accompanying drawing.
The public transport that the embodiment of the invention provides is arrived at a station Forecasting Methodology at pretreatment stage, generates and serializing is handled by track data, obtains based on sampled point and the public transport basic data form that has nothing to do with map; And by based on the GPS fast path of sampled point coupling, reduced the complexity of GPS anchor point and circuit coupling, improved and arrived at a station apart from processing speed, for the real-time coupling of gps data on a large scale provides a kind of simple and effective way.Present embodiment adopts based on the public transport of the real-time road condition information forecast model that arrives at a station, and can carry out dynamic public transport prediction according to the traffic conditions of real-time change, has good real-time performance and accuracy.
The public transport based on real-time road condition information that Fig. 1 provides for the embodiment of the invention Forecasting Methodology process flow diagram that arrives at a station comprises:
The generation of step 1011, track data.
Because the raw data (data that public transit system provides that public transit system inserts, comprise line number, site location, gps data etc.) lack track data, for satisfying the demand of the prediction of arriving at a station, need be on the basis of raw data, according to the existing road base map, in conjunction with public transport operation GPS historical data, describe or add the road chain according to the GPS track, the road chain is divided according to the crossing usually, and all road chains have been formed the running orbit of this circuit, and road first-in-chain(FIC) tail point is made as sampled point.
Consider that there is deviation original site positional information and actual anchor point position, in order further to improve the accuracy rate of prediction, need site location be proofreaied and correct according to the GPS convergence point, site location is adapted to coupling GPS convergence point.
Step 1012, circuit serializing.Need to carry out serializing pre-service work after track data generates, comprise the chain that opens circuit in the reparation, the ordering of road chain, website is added sampled point, increases the step of sampled point, export the public transport basic data based on sampled point at last, as shown in Figure 2.
1, the chain that opens circuit in repairing refers to the circuit that generates check judge whether circuit has the situation of interruption, and the road chain that interrupts is added automatically, forms the complete only existence path of end points from beginning to end.At first, whether check the sampled point quantity that occurs on the circuit once more than two, in this way, represent that then circuit has interruption or bifurcated is arranged; Then, the road chain with tail point headed by these 2 is searched in breakpoint pairing in twos in the line, and disrupted circuit is repaired.Article one, complete circuit is connected to form from beginning to end by a lot of roads chain, and can there be a common tie point in two road chains that link to each other.According to this principle, on a circuit, the sampled point of all road chains (chain two ends, road are sampled point), except the circuit head and the tail, other sampled points should occur twice at least, and only the head and the tail sampled point at circuit just can occur once.If the quantity of sampled point once therefore on a circuit, occurs more than two, illustrate that then circuit has interruption or bifurcated is arranged.
2, road chain ordering refer to pick out vehicle can be initial from circuit be correct direction to all transitable direction of circuit ending, connect all road chain discharges orders with a circuit according to the head and the tail of road chain, so that derive the circuit sampled point.When the road chain sorts, if the situation that the circuit both direction all can't sort illustrates that the road chain direction is wrong in the circuit, need adjust the back rearrangement.
3, for ease of follow-up prediction, website need be added on the chain of road as sampled point, because website not on the chain of road, therefore need project to website on the nearest road chain.
As shown in Figure 3, ABCD is three road chains on the circuit, and S is the website position, S
1And S
2Be the projection of website on the chain of not going the same way, d
1And d
2Be the distance of website to the road chain.When adding sampled point, comparison d
1And d
2, select to add in the approach chain as sampled point from the nearer subpoint of road chain, as Fig. 3, d
1＜d
2, then add S
1As sampled point.
4, because part road chain sampled point rareness has than large deviation in the time of can causing coupling, in order to improve the precision of GPS point coupling, then need add sampled point at the road chain.The principle of adding sampled point is: have between two sampled points distance then add a little between two sampled points greater than 15m on a road chain.
Suppose that A, B are sampled point adjacent on the chain of road, and apart from d
AB15, then need add n=[d at point-to-point transmission
AB/ 15] individual sampled point S
1, S
2... S
n, and satisfy
,
After increasing sampled point, need calculating sampling put road first-in-chain(FIC) back range from and to the distance of the next stop, as the property store of sampled point.
Export the public transport basic data that is constituted by circuit and station data, sampling number certificate, road chain data, website and affiliated road chain data at last.
At first, according to line number and the circuit up-downgoing of the bus GPS point of real-time collection, obtain the tabulation after all sampled points of this circuit sort by the longitude size; Secondly, adopt binary chop, from tabulation, find out all sampled points of 100 meters of the current bus GPS point longitude of distance front and back; At last, the sampled point of finding out is carried out the distance of point-to-point again and calculate, calculate the distance that bus GPS is put sampled point, take out nearest sampled point and be the coupling sampled point, as shown in Figure 4 (wherein, * be sampled point).
After finding out the sampled point that bus GPS point mates, can not determine its be before sampled point or after, carrying out the next stop when judging, need judge GPS point in the front and back of website correspondence sampled point.As shown in Figure 4, S
2For the sampled point of GPS point coupling, pick out S
2Former and later two sampled points S
1And S
3Form line segment S
1S
2And S
2S
3, the GPS spot projection is asked bee-line to two line segments, judge GPS be before sampled point or after.
1, up-downgoing is judged
Public bus network is divided into uplink and downgoing line, and judge by up-downgoing to access the traffic direction of current vehicle, thereby obtain the GPS match point on correct up-downgoing circuit, and next stop information.
Suppose T
2Be the timestamp of current bus GPS point, T
1For the last time receives the timestamp that same car GPS ordered, has only the T of working as
2T
1The time, be considered as normal GPS information.Suppose S
NowFor in uplink, according to the sampled point that the Current GPS point matches, S
PreFor in uplink, the sampled point that matches according to last GPS point; X
NowFor in downgoing line, according to the sampled point that the Current GPS point matches, X
PreFor in downgoing line, the sampled point that matches according to last GPS point.
Then ought satisfy upstream sampling point sequence number and increase progressively, be i.e. S
PreSequence number less than S
Now, and descending sampled point sequence number successively decreases or constant, i.e. X
PreSequence number more than or equal to X
Now, be considered as up; Increase progressively when satisfying descending sampled point sequence number, i.e. X
PreSequence number less than X
Now, and upstream sampling point sequence number successively decreases or constant, i.e. S
PreSequence number more than or equal to S
Now, be considered as descending.
2, assembling stop judges
When public bus network appearance gathering stops, need to analyze the reason of special data generation, the vehicle that is parked in special areas such as bus terminus, parking lot is no longer arrived at a station predict issue.
At first, analyze according to historical GPS gathering situation unusually, and gathering situations such as bus terminus, parking lot are carried out mark; Secondly, judge the bus GPS point gathered in real time whether in convergence point, and motionless for a long time, if, then being labeled as corresponding gathering and stopping mark, the prediction of no longer arriving at a station is issued.
Step 1041, calculate current traffic information according to bus GPS data, comprise current running velocity and road chain speed.
Current running velocity the is defined as current 3 minutes vehicle average velocity of (current time pushed away forward 3 minutes).As shown in Figure 5, after receiving a gps data, through the oversampled points coupling, the sampled point that matches is put into vehicle lateral speed formation (queue length is 9, is about 3 minutes).When calculating the vehicle present speed, at first take out element in the formation, and remove inactive elements (as back one gps time less than the sampled point sequence number of last gps time, back one GPS coupling less than elements such as last GPS sampled point sequence numbers), be inactive elements as the element 1～2 in the transverse velocity formation among Fig. 5 (a), do not include calculating in.For effective sampled point in the formation, calculate its process apart from dis, and elapsed time T
e-T
s(T
sBe the time of first effective sampling points, T
eBe the time of last effective sampling points), thus calculate the average velocity of vehicle in 3 minutes, i.e. vehicle present speed.Then the current travelling speed of vehicle i is at this moment:
Road chain speed is defined as the average velocity through nearest n car of a road chain.For every road chain on the circuit is set up a longitudinal velocity formation; When obtaining current running velocity, extract the road chain tabulation of this vehicle process, and current running velocity is added in the longitudinal velocity formation of each road chain of its process.Shown in Fig. 5 (b), in the longitudinal velocity formation of road chain, stored the speed of the public transit vehicle of nearest 4 these road chains of process.Behind the current running velocity that calculates certain car, this velocity amplitude is joined in the longitudinal velocity formation of road chain.Suppose to have stored in the longitudinal velocity formation of road chain n recently through the car speed of this road chain, then chain speed in road is:
Step 1042, based on real-time road condition information prediction public transport arrival time.Specifically comprise:
The GPS spot projection of bus to be predicted to sampled point, is obtained current sampling point apart from the distance of the next stop and road chain information at interval; By n road chain, each road chain length is L in the middle of supposing
i(i=1...n), and the position of Current GPS point be L apart from the distance of road last-of-chain
Now, then Current GPS point apart from the distance of the next stop is:
As shown in Figure 6, bus to be predicted is road chain 1 and road chain 2 apart from next stop road chain at interval, and then the distance to the next stop is L
Now+ L
1+ L
2The road chain speed of each road chain is known, is v
i(i=1...n), v
NowBe the speed of the current place of bus road chain, then the time prediction to the next stop is:
The present invention's beneficial effect compared with prior art is: at pretreatment stage, generate and serializing is handled by track data, obtain based on sampled point, the public transport basic data form that has nothing to do with map; By based on the GPS fast path of sampled point coupling, reduced the complexity of GPS anchor point and circuit coupling, improved and arrived at a station apart from processing speed, for the real-time coupling of gps data on a large scale provides a kind of simple and effective way.Employing can be carried out dynamic public transport prediction according to the traffic conditions of real-time change based on the public transport of the real-time road condition information forecast model that arrives at a station, and has good real-time performance and accuracy.
In a word, the above is preferred embodiment of the present invention only, is not for limiting protection scope of the present invention.
Claims (12)
1. the public transport based on real-time road condition information Forecasting Methodology of arriving at a station is characterized in that, comprising:
The public bus network data are carried out pre-service, according to pretreated public bus network data, the bus GPS point of gathering is in real time matched on the public bus network; The behavior of travelling is on the line judged to bus, carries out based on the public transport of the real-time road condition information prediction of arriving at a station according to judged result.
2. the public transport based on the real-time road condition information according to claim 1 Forecasting Methodology of arriving at a station is characterized in that described pretreated step specifically comprises generation and the public bus network serializing of public bus network data.
3. the public transport based on the real-time road condition information according to claim 2 Forecasting Methodology of arriving at a station is characterized in that the generation of described public bus network data specifically comprises:
On the raw data basis that public transit system inserts, according to the existing road base map, in conjunction with public transport operation GPS historical data, describe or add the road chain according to the GPS track, described road chain is divided according to the crossing, and all road chains have been formed the running orbit of this circuit, and road first-in-chain(FIC) tail point is made as sampled point.
4. the public transport based on the real-time road condition information according to claim 3 Forecasting Methodology of arriving at a station is characterized in that the generation of described public bus network data also further comprises:
According to the GPS convergence point, site location is proofreaied and correct, site location is adapted to coupling GPS convergence point.
5. the public transport based on the real-time road condition information according to claim 4 Forecasting Methodology of arriving at a station is characterized in that described public bus network serializing specifically comprises:
Whether check the sampled point quantity that occurs on the circuit once more than two, if, then carry out breakpoint pairing in twos, search interpolation in the line with the road chain of tail point headed by these 2, disrupted circuit is repaired;
Connect all road chain discharges orders with a circuit according to the head and the tail of road chain, when the road chain sort, if the situation that the circuit both direction all can't sort, then the road chain direction was wrong in the circuit, adjusted the back and resequenced;
Website is projected on the nearest road chain, thereby website is added on this road chain as sampled point;
Distance is arranged between two sampled points greater than 15m on a road chain, then between two sampled points, add sampled point.
6. according to the Forecasting Methodology of arriving at a station of any described public transport based on real-time road condition information in the claim 1 to 5, it is characterized in that the step that the described bus GPS point that will gather in real time matches on the public bus network specifically comprises:
According to line number and the circuit up-downgoing of the bus GPS point of real-time collection, obtain the tabulation after all sampled points of this circuit sort by the longitude size; From described tabulation, find out all sampled points of 100 meters of the current bus GPS point longitude of distance front and back; The sampled point of finding out is carried out the distance of point-to-point again and calculate, calculate the distance that bus GPS is put each sampled point, take out nearest sampled point and be the coupling sampled point;
According to the projector distance of bus GPS point, judge that bus GPS point is after the sampled point that mates preceding still is.
7. the public transport based on the real-time road condition information according to claim 6 Forecasting Methodology of arriving at a station is characterized in that, the described step that the behavior of travelling is on the line judged to bus comprises that specifically up-downgoing judges and assemble to stop and judge.
8. the public transport based on the real-time road condition information according to claim 7 Forecasting Methodology of arriving at a station is characterized in that, described up-downgoing is judged and specifically comprised:
Suppose T
2Be the timestamp of current bus GPS point, T
1Same the timestamp that car GPS is ordered for the last time receives has only the T of working as
2T
1The time, be considered as normal GPS information; Suppose S
NowFor in uplink, according to the sampled point that the Current GPS point matches, S
PreFor in uplink, the sampled point that matches according to last GPS point; X
NowFor in downgoing line, according to the sampled point that current bus GPS point matches, X
PreFor in downgoing line, the sampled point according to last GPS point matches then ought satisfy S
PreSequence number less than S
Now, and X
PreSequence number more than or equal to X
Now, for up; When satisfying X
PreSequence number less than X
Now, and S
PreSequence number more than or equal to S
Now, for descending.
9. the public transport based on the real-time road condition information according to claim 8 Forecasting Methodology of arriving at a station is characterized in that, described gathering stops to be judged and specifically comprise:
Analyze according to historical GPS gathering situation unusually, and the gathering situation in bus terminus, parking lot is carried out mark; Judge the bus GPS point gathered in real time whether in convergence point, and motionless for a long time, if, then being labeled as corresponding gathering and stopping mark, the prediction of no longer arriving at a station is issued.
10. the public transport based on the real-time road condition information according to claim 9 Forecasting Methodology of arriving at a station is characterized in that, described public transport based on the real-time road condition information prediction of arriving at a station specifically comprises:
Obtain current traffic information according to bus GPS data, comprise current running velocity and road chain speed, according to described road chain speed and road chain length prediction public transport arrival time.
The Forecasting Methodology 11. the public transport based on real-time road condition information according to claim 10 is arrived at a station is characterized in that, the described step of obtaining current traffic information according to bus GPS data specifically comprises:
Behind gps data of every reception, through the oversampled points coupling, the sampled point that matches is put into the vehicle lateral speed formation; When calculating current running velocity, take out element in the described formation, and remove inactive elements, for effective sampled point in the formation, calculate its process apart from dis, and elapsed time T
e-T
s, T
sBe the time of first effective sampling points, T
eBe the time of last effective sampling points, then the current running velocity of vehicle i is at this moment:
For every road chain on the circuit is set up a longitudinal velocity formation; When obtaining current running velocity, extract the road chain tabulation of this vehicle process, and current running velocity is added in the longitudinal velocity formation of each road chain of this vehicle process; Behind the current running velocity that calculates certain car, this velocity amplitude is joined in the longitudinal velocity formation of road chain; Suppose to have stored in the longitudinal velocity formation of road chain n recently through the car speed of this road chain, then chain speed in road is:
The Forecasting Methodology 12. the public transport based on real-time road condition information according to claim 11 is arrived at a station is characterized in that, described step according to road chain speed and road chain length prediction public transport arrival time specifically comprises:
The GPS spot projection of bus to be predicted to sampled point, is obtained current sampling point apart from the distance of the next stop and road chain information at interval; By n road chain, each road chain length is L in the middle of supposing
i(i=1...n), and the position of current bus GPS point be L apart from the distance of road last-of-chain
Now, then current bus GPS point apart from the distance of the next stop is:
The road chain speed of each road chain is v
i(i=1...n), v
NowBe the speed of the current place of bus road chain, then the time prediction to the next stop is:
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| CN104064024A (en) * | 2014-06-23 | 2014-09-24 | 银江股份有限公司 | Public transport vehicle arrival time prediction method based on history data |
| CN104252795A (en) * | 2014-09-23 | 2014-12-31 | 深圳市谷米科技有限公司 | Bus arrival time prediction method |
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