CN1707544A - Method for estimating city road network traffic flow state - Google Patents
Method for estimating city road network traffic flow state Download PDFInfo
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Abstract
The city road net traffic flow state estimating method is based on vehicular GPS data combined with SCATS provided traffic signal state information. The present invention establishes city road net traffic flow state model by adopting unit oriented road sections as targets and through least-square method fitting in the three-dimensional space of distance, time and speed. Through the conversion from curved surface in the three-dimensional space to the curve in the two-dimensional space, the average speeds in various oriented road sections of the city road net in fixed time are obtained, and the current traffic flow jamming state is analyzed and estimated with speed as index. The traffic flow state estimation is completed through establishing the traffic flow state model for each of the unit oriented road sections between two signal lamps and connecting the curve surface models of adjacent equidirectional road sections.
Description
Technical field
The present invention relates to a kind of method for estimating city road network traffic flow state, be used for the estimation of city advanced traffic control system road congestion state, belong to the intelligent transportation research field.
Background technology
Along with socioeconomic fast development, transport need increases greatly on the one hand, and the growth of road progressively is tending towards the limit, makes the contradiction of transport need and supply further intensify; The progress at full speed of infotech is that comprehensive transport solution problem has been brought opportunity on the other hand.Be exactly under this background, advanced traffic information management system (ATIMS) has been subjected to paying close attention to widely prior to the other system of intelligent transportation system (ITS), all obtained development fast in countries in the world, be applied to dynamic route planning, dynamic navigation, road network and coordinate various aspects such as traffic signal system, dynamic traffic scheduling.Wherein, be key components among the ATIMS to the dynamic estimation of real-time road net traffic state and prediction.
Road net traffic state is estimated in real time relevant with the transport information that adopted with prediction, different transport information has determined to estimate and diverse ways and the precision predicted.At present, many correlative studys have been arranged in the world, wherein, that representative is Martin L.Hazclton (" Estimating Vehicle Speed from Count andOccupancy data ", Journal ofData Science 2 (2004), 231-244) research of carrying out according to the vehicle flow and the duty cycle information utilization Markov chain Meng Tekaer theory of Road Detection loop data.Martin L.Hazclton considers effectively and modeling to have handled Road Detection loop data error rate big, the problem that reliability is low, and the result is gratifying, but he is the research of carrying out on the expressway, only be applicable to that traffic flow is the situation of continuous stream, and the traffic flow in city stops between being, the traffic flow modes that is not suitable for city road network is estimated.Utilize the detection ring data that city road network is carried out traffic flow and estimate urban infrastructure is had relatively high expectations, often get less than enough desired datas in a lot of cities, and the high problem of error rate can not get solving effectively.
Summary of the invention
The objective of the invention is at the deficiencies in the prior art, propose a kind of new traffic flow modes method of estimation that is used for city road network, have calculate easy, real-time good, to advantages such as urban infrastructure condition dependence are low.
For realizing such purpose, technical scheme of the present invention is conceived to have the precision height, data volume is big, the vehicle-mounted data that the Global Positioning System (GPS) (GPS) of advantage such as widely distributed provides in the city scope is estimated the congestion in road state in conjunction with the stable traffic signal state information that Sydney self-adaptation traffic control system (SCATS) accordingly provides.The SCATS system is a kind of city traffic signal lamp adaptive control system, and the information such as traffic lights configuration data that comprise vehicle flow, vehicle dutycycle data, crossing can be provided.Wherein, vehicle flow and vehicle dutycycle data still depend on and are embedded in the underground detection ring in crossing, but traffic signal light condition information does not have this dependence, has stable and accurate advantage.
In the method for the present invention, oriented highway section between two signal lamps in the city road network is considered as a processing unit, comprise distance providing, time, the vehicle GPS data point of information such as speed is considered as the sampled point of wagon flow on each oriented highway section, to the sampled point in a period of time section T with distance, time and speed are to carry out the surface fitting modeling on the three dimensions of coordinate axis, obtain the velocity distribution of traffic flow on the time-space domain, on this basis, obtain the speed change curves of a certain moment traffic flow along the highway section direction, thereby obtain the average velocity in this moment highway section, the traffic congestion state in highway section is estimated as index.The average velocity in highway section is divided into five speed class in the road network, corresponding respectively unobstructed, more unobstructed, obstructed, five kinds of congestion in road states block up, seriously block up.Estimation to the traffic flow modes of road network is to carry out on the basis in the oriented highway section of unit.The corresponding traffic flow modes model in the oriented highway section of unit between per two signal lamps altogether to the connection of the surface model in highway section, is realized the traffic flow modes estimation to whole road network by adjacent.
The inventive method mainly comprises following step:
1, gps data is carried out the data pre-service:
Providing the vehicle GPS data point that comprises distance, time, velocity information to be considered as the sampled point of wagon flow on each oriented highway section.It is 0 vehicle sampled point that the pre-service of gps data is primarily aimed at speed on the highway section.These points comprise two parts: speed is 0 point and because signal lamp red light and speed is 0 point owing to seriously block up.The former is the key component during the highway section congestion status is analyzed, and the latter is an interference sections.According to the signal lamp status information that the SCATS system provides each sampled point is judged, see whether this sampled point is that speed is 0 point, if, see again whether its moment corresponding belongs to the red light cycle of the corresponding signal lamp of SCATS characterized systematically, if all satisfy, then this sampled point is got rid of from the data point set for the treatment of match.
2, the oriented road traffic delay modeling of unit:
City road network is formed by connecting by crossing and highway section, main crossing is provided with traffic control light, these signal lamps are isolated the highway section, isolated unit highway section is made up of two oriented highway sections of uplink and downlink again, the oriented highway section of unit is considered as a processing unit, is basic research object modeling.
With the gps data sampled point that is in the time period T on the oriented highway section of unit is object, with the polynomial function space is the basic model space, at first select the number of times of corresponding multinomial model (to treat that with several number of match point is a threshold value according to the number of effectively treating the data point of match, when greater than this threshold value, select for use the bicubic polynomial expression to handle, when less than this threshold value, adopt its degenerated form), then to these sampled points in distance, time, utilize the least square fitting modeling on the speed three dimensions, obtain this unit velocity distribution surface model of oriented highway section on space-time in the T time period.
3, city road network traffic flow modeling:
Two highway sections interconnect at the crossing, because the effect of traffic lights, the traffic behavior at crossing is very complicated, and the traffic in adjacent highway section interacts.So,, carry out will increasing the boundary condition of describing place, crossing traffic when traffic flow modes is estimated, to consider the influence in its adjacent highway section from whole road network traffic flow.Go on foot on the basis of the unit oriented highway section velocity distribution surface model that obtains second, order is the highway section maximum length apart from variable, obtains this highway section and is adjacent the speed change curves in time period T to the junction, highway section altogether.On this curve, get a limited number of surely point, these points are participated in together to the boundary condition of the velocity distribution surface model of oriented highway section correspondence and gps data sampled point altogether as this oriented highway section adjacent it is adjacent altogether to the match of highway section velocity distribution surface model.
To be connected to each other to unit oriented highway section velocity distribution curved surface altogether in twos, finally obtain the velocity distribution of entire city road network on space-time.Wherein, the velocity distribution surface model on the corresponding separately space-time in each oriented highway section in the city road network.
4, calculate road-section average speed:
For the oriented highway section of unit, on the basis of the mathematic(al) representation of its velocity distribution surface model, make that time variable is a normal value t among the time period T
0, obtain t constantly
0The oriented highway section of this unit is along the velocity distribution curve on the road direction.This velocity distribution curve in the road direction upper integral, is obtained t
0The average velocity of the moment this unit oriented highway section road direction.The calculating of road-section average speed is carried out in each oriented highway section in the road network one by one, obtained t
0The average velocity of each oriented highway section road direction in the moment city road network.
5, by average velocity predicted link congestion status
With t
0The average velocity of each oriented highway section road direction is that index is carried out the congestion in road state estimation in the city road network constantly.The average velocity in highway section in the city road network is divided into five speed class, corresponding respectively unobstructed, more unobstructed, obstructed, five kinds of congestion in road states block up, seriously block up.Judge the congestion status in each oriented highway section according to the residing speed class of average velocity of each oriented highway section correspondence.
The present invention has overcome the dependence of general traffic flow method of estimation to the city hardware facility effectively, and it is perfect inadequately to have avoided general urban traffic flow monitoring facilities, the problem that reliability is low, have calculate easy, fast operation, reliability advantages of higher.
Description of drawings
Fig. 1 is the FB(flow block) of this method for estimating city road network traffic flow state.
Fig. 2 is for to carry out data pre-service synoptic diagram to gps data.
Fig. 3 is model form conversion synoptic diagram.
Fig. 3 with two adjacent be example to the highway section altogether, expressed in the city road network traffic flow state estimation procedure data mode and spreaded over a whole area from one point, face is to the overall process of line.Wherein, Fig. 3 (a) is a gps data sampled point synoptic diagram on the oriented highway section of unit; Fig. 3 (b) is a unit oriented highway section velocity distribution surface model synoptic diagram; Fig. 3 (c) is two adjacent highway section velocity distribution surface model connection diagrams; The oriented highway section of Fig. 3 (d) unit at fixed time along the speed change curves synoptic diagram on the direction of highway section.
Fig. 4 is that Xuhui District of Shanghai traffic network traffic flow modes is estimated synoptic diagram.
Embodiment
In order to explain technical scheme of the present invention better, be described in further detail below in conjunction with drawings and Examples.
Input data of the presently claimed invention are gps system vehicle satellite location datas, provide to comprise sampling car label, time, position, speed, traffic direction, dynamic traffic detection informations such as vehicle-state.Supplementary is the signal lamp status information that comprises signal lamp phase place and phase transition cycle that SCATS traffic adaptive control system provides.
The present invention adopts city road network traffic flow state estimation scheme shown in Figure 1, and concrete implementation step is as follows:
1, gps data is carried out the data pre-service
Providing the vehicle GPS data point that comprises distance, time, velocity information to be considered as the sampled point of wagon flow on each oriented highway section.It is 0 vehicle sampled point that the pre-service of gps data is primarily aimed at speed on the highway section.These points comprise two parts: speed is 0 point and because signal lamp red light and speed is 0 point owing to seriously block up.The former is the key component during the highway section congestion status is analyzed, and the latter is an interference sections.Generally speaking, the vehicle on the oriented highway section of unit distributes as shown in Figure 2.L is the road trunk, and L is the road length overall.Along the highway section direction, the signal lamp B that is in the highway section front end is main research object.Because signal lamp B is in red light cycle, has formed the fleet that waits red light on the highway section, car speed v=0.These vehicle points are the redundant points that will remove in the data pre-service.
According to the signal lamp status information that the SCATS system provides each sampled point is judged, see whether this sampled point is that speed is 0 point, if, see again whether its moment corresponding belongs to the red light cycle of the corresponding signal lamp of SCATS characterized systematically, if all satisfy, then this sampled point is got rid of from the data point set for the treatment of match.That is: for the vehicle sampled point P on the oriented highway section of unit
i(l
i, t
i, v
i), if v
i=0, and t
i∈ T
Red(T
RedBe the red light cycle time of corresponding signal lamp), sampled point is considered to redundant points, and it is got rid of from the data point set for the treatment of match.
2, the oriented road traffic delay modeling of unit
City road network is formed by connecting by crossing and highway section, main crossing is provided with traffic control light, these signal lamps are isolated the highway section, isolated unit highway section is made up of two oriented highway sections of uplink and downlink again, the oriented highway section of unit is considered as a processing unit, is basic research object modeling.
With the gps data sampled point that is in the time period T on the oriented highway section of unit is that object utilizes the least square fitting modeling on distance, time, speed three dimensions.Fig. 3 (a) has expressed the gps data sampled point that is distributed on the oriented highway section 2, has constituted a sampled point set
Be the basic model space with the polynomial function space during modeling.Select the number of times of corresponding multinomial model according to the number of the data point of effectively treating match.With 20 numbers for the treatment of match point is threshold value, selects for use the bicubic polynomial expression to handle when greater than this threshold value, when less than this threshold value, adopts its degenerated form.That is:
Wherein: a
MnBe parameter to be asked; L is the distance of sampled point apart from the road starting point; T is the residing moment of sampled point; v
LQBe the velocity distribution on the highway section in the time period T; (a is b) for treating the high reps of polynomial fitting model; Mdata is a gps data sampled point number on the oriented highway section, satisfies:
Obtain this unit velocity distribution surface model of oriented highway section on space-time in the T time period.Fig. 3 (b) has expressed fitting result.
3, city road network traffic flow modeling
Each unit is connected to each other to the surface model in highway section altogether, forms road network.Go on foot on the basis of the unit oriented highway section velocity distribution surface model that obtains second, order is the highway section maximum length apart from variable, obtains this highway section and is adjacent the speed change curves in time period T to the junction, highway section altogether.On this curve, get a limited number of surely point, these points are participated in together to the boundary condition of the velocity distribution surface model of oriented highway section correspondence and gps data sampled point altogether as this oriented highway section adjacent it is adjacent altogether to the match of highway section velocity distribution surface model.Fig. 3 (c) expressed the connection of two adjacent highway section velocity distribution surface models, and its mean camber 2 is the fitting result of oriented highway section 2 after having increased the boundary condition that curved surface 1 provides.
The traffic flow surface model of oriented highway section 1 correspondence is f
LQ1(l, t), L
1Be the length in oriented highway section 1, Mdata2 is a gps data sampled point number on the oriented highway section 2:
These data points are converted to:
With these data points is boundary condition, participates in the traffic flow surface model match in oriented highway section 2.
To be connected to each other to unit oriented highway section velocity distribution curved surface altogether in twos, finally obtain the velocity distribution of entire city road network on space-time.Wherein, the velocity distribution surface model on the corresponding separately space-time in each oriented highway section in the city road network.
4, calculate road-section average speed
For the oriented highway section of unit, on the basis of the mathematic(al) representation of its velocity distribution surface model, make that time variable is a normal value t among the time period T
0, obtain t constantly
0The oriented highway section of this unit is along the velocity distribution curve on the road direction.This velocity distribution curve in the road direction upper integral, is obtained t
0The average velocity of the moment this unit oriented highway section road direction.
f
LQ2(l t) is the traffic flow surface model of oriented highway section 2 correspondences.f
LQ2(l, t
0) be t
0Constantly along highway section direction traffic flow speed change curves.t
0Oriented highway section highway section, 2 upper edge direction traffic flow speed change curves was shown in Fig. 3 (d) in=3 o'clock.
Average velocity is:
The calculating of road-section average speed is carried out in each oriented highway section in the road network one by one, obtained t
0The average velocity of each oriented highway section road direction in the moment city road network.
5, by average velocity predicted link congestion status
With t
0The average velocity of each oriented highway section road direction is that index is carried out the congestion in road state estimation in the city road network constantly.The average velocity in highway section in the road network is divided into five speed class, corresponding respectively unobstructed, more unobstructed, obstructed, the five kinds of congestion in road states that block up, seriously block up, available different color showing come out (Fig. 4).Judge the congestion status in each highway section according to the residing speed class of average velocity of each oriented highway section correspondence.
Claims (1)
1, a kind of method for estimating city road network traffic flow state is characterized in that comprising the steps:
1) providing the GPS data point that comprises distance, time, velocity information to be considered as the sampled point of wagon flow on each oriented highway section, according to the signal lamp status information that Sydney self-adaptation traffic control system provides each sampled point is judged, see whether this sampled point is that speed is 0 point, if, see again whether its moment corresponding belongs to the red light cycle of the corresponding signal lamp of Sydney self-adaptation traffic control system sign, if all satisfy, this sampled point is got rid of from the data point set for the treatment of match;
2) be object with the GPS data sampled point that is in the time period T on the oriented highway section of unit, with bicubic polynomial expression and degenerated form thereof is the basic model modeling, at first select the number of times of corresponding multinomial model according to the number of effectively treating the data point of match, then these sampled points are utilized the least square fitting modeling on distance, time, speed three dimensions, obtain this unit velocity distribution surface model of oriented highway section on space-time in the T time period;
3) on the basis of unit oriented highway section velocity distribution surface model, order is the highway section maximum length apart from variable, obtain this highway section and be adjacent the speed change curves in time period T to the junction, highway section altogether, on this curve, get a limited number of surely point, these points adjacent as this oriented highway section altogether to the boundary condition of the velocity distribution surface model of oriented highway section correspondence, participate in together with the GPS data sampled point that it is adjacent altogether to the match of highway section velocity distribution surface model, to be connected to each other to unit oriented highway section velocity distribution curved surface altogether in twos, finally obtain the velocity distribution of entire city road network on space-time, wherein, the velocity distribution surface model on the corresponding separately space-time in each oriented highway section in the city road network;
4), on the basis of the mathematic(al) representation of its velocity distribution surface model, make that time variable is a normal value t among the time period T for the oriented highway section of unit
0, obtain t constantly
0The oriented highway section of this unit in the road direction upper integral, obtains t to this velocity distribution curve along the velocity distribution curve on the road direction
0The average velocity of this unit oriented highway section road direction carries out the calculating of road-section average speed one by one to each oriented highway section in the road network constantly, obtains t
0The average velocity of each oriented highway section road direction in the moment city road network;
5) with t
0The average velocity of each oriented highway section road direction is that index is carried out the congestion in road state estimation in the city road network constantly, the average velocity in highway section in the city road network is divided into five speed class, corresponding respectively unobstructed, more unobstructed, obstructed, five kinds of congestion in road states block up, seriously block up, judge the congestion status in each oriented highway section according to the residing speed class of average velocity of each oriented highway section correspondence, obtain the state estimation of entire city road network traffic flow at last.
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