EP0902404A2 - Method for transmitting traffic information - Google Patents

Abstract

The method involves using fixed detectors to form local detection cross-sections for the detection of traffic-related measurement values. The values are pre-processed by local computers and standardised to a defined data format, aggregated and wirelessly passed to a superior data processing system. The fixed detectors are positioned at junctions, nodes and similar road points. The density of the fixed detectors in each area depends on the estimated traffic density.

Description

The invention relates to a method for determining Road routes, in particular traffic information related to motorways.

It is known in the prior art at individual measuring points Capture traffic flow information to get direct interference information to derive or forecast traffic development to develop for neighboring sections of the route. They are each only single solutions known.

For example, in EP 0 256 483 A1 a traffic control and Information system which uses stationary beacons and transmission or Receiving units traffic flow information determined. This traffic flow information becomes in particular Disturbance information determined to switch control signals.

DE-P 44 08 547 describes a method for traffic detection and traffic situation detection on highways, preferably Motorways, known. For the formation of so-called Cross sections are set up for track-related measuring points, with traffic sensors, such as induction loops, for vehicle detection and with a traffic data processing device are provided. There will be regular traffic data such as vehicle speed, traffic volume and traffic density determined and from this determined traffic parameters in one Traffic data processing formed. Each form two neighboring measuring points a measuring section with a certain Track length. From the traffic data of two such measuring points traffic parameters are formed. These are one Speed density difference, calculated from local traffic data medium speed and traffic density, a trend factor, determined over a certain period of time the ratio of the traffic volumes of both measuring points as well a traffic intensity trend. Using a Fuzzy logic the probability of a critical traffic situation derived. When a probability threshold is reached can then be a control signal for a Variable message signs are generated.

In the prior art, detectors are also known, which Presence and the speed of a moving object can capture. For example, such detectors work According to a passive infrared process, which can also be used with other methods can be combined. In the state of the art No method is known to date, traffic information covering the entire area to record and evaluate. Especially no methods are known to determine the traffic information route section variable, event-oriented if necessary and enable with little data transfer effort.

A low data transmission effort is on the one hand Implementation of an energy-saving process required on the other hand, as transparent and easy to maintain as possible Generate databases.

Starting from this prior art, the present invention is based on the object of providing a comprehensive traffic data acquisition which, with simple sensors and low data transmission effort, provides reliable and sufficiently meaningful data bases for different traffic information services.

To solve this problem technically, the invention proposes a method for determining traffic information related to road routes, in particular motorways, wherein local detection cross-sections are formed by means of fixed detectors, traffic-related measured values are recorded, preprocessed by means of local computers and standardized, aggregated and standardized to a predetermined data protocol be transmitted by radio to a higher-level data processing system.

The invention enables the implementation of a step organized acquisition and processing system. Thereby can different traffic models to different Levels are applied, some of which are local, some of which are central. The advantages are that short term results can be achieved by expanding into the individual Levels are consolidated and refined. Through the dissolution there is a high level in individual subtasks or levels Degree of flexibility and reliability through the Formation of fallback levels. Through the local pre-analysis of the Traffic gives rise to extremely energy-saving, event-oriented data transmission to the parent Data processing systems or centers.

The invention proposes that fixed detectors positioned at junctions, nodes and the like become. It is also proposed that the Arrangement density of the fixed detectors depending is determined by traffic expectation estimates. So leave through the arrangement of many local detection systems Build comprehensive networks. It is also with the invention possible to organize an overall network structure. On traffic technology local detectors and critical positions Preprocessing computer arranged, preferably via radio in digital technology the data to higher-level data processing systems forward or control centers. There you can then other traffic models are applied to the data.

The local evaluation results in the possibility of local status detection. By linking the data Adjacent local detection cross sections can be a so-called route-related level of service in a higher-level Data processing system or one of the entire network assigned headquarters are determined.

The linking of this data, possibly in combination with the data the local acquisition cross sections enables the calculation an advanced situation detection. Here dynamic State estimates are made to provide an improved state estimate in critical sections by connecting a customized system for advanced situation detection to get. The results are detailed route-related data and more detailed situation classifications. In addition, information from a achieve any certainty of the respective estimate. A Correction regarding noisy data due to poor data transmission, at larger time intervals or only sporadic data is provided with the invention.

It is proposed with particular advantage that local Pre-processing of the data and its plausibility based on model comparisons is checked, mean value calculations are carried out, determined trend factors from the change in the measured values, and that from the determined data clockwise status codes be determined. At least vehicle speed, Traffic volume and cross-sectional Occupancy recorded.

After a detector, for example a passive infrared detector, Measured data are delivered, they will be preprocessed, for example by calculating mean values, Plausibility checks and trend factor determinations carried out become. From the changes in the data or the data state codes themselves are then determined, for example in the form of a numerical value for conditions such as free traffic flow, Traffic jam, stop and go, traffic jam or standstill etc. Evaluation cycles can, for example, every 1 to 5 minutes to get voted. However, the evaluation cycle can be variable be determined, for example depending on the status codes or the traffic conditions. The same applies the data transfer rate, which depends, for example of the determined status code is used, for example one transmission every 30 minutes when traffic is free with averaging every 5 minutes. Depending on the fault condition the transmission density can be increased. In doing so the data transfer rates of adjacent acquisition cross sections matched to each other.

The measured values can be recorded in relation to the lane, but what is not absolutely necessary, other detection cross sections can also be used To be defined. It is also fundamental possible, vehicle type differentiation values, for example Detect trucks, cars and the like.

The invention proposes that superordinate data processing systems at least for those grouped together neighboring detection cross sections can be assigned. A control center can act as a higher-level data processing system for all detection cross sections of a network or for several higher-level data processing systems can be assigned.

The network organization can be done in whatever stages flexibility and data security are affected. Here economic parameters can be used as a boundary condition become.

With the invention it is further proposed that calculated Data series through model comparisons with given models checked or corrected that central data evaluations carried out for accident detection and that central data evaluations performed for dynamic state estimation become.

It also further suggests that source-to-target relationships by analyzing the data of all acquisition cross sections of a network determines that the data for route search, evaluated for the output of traffic management information, subjected to statistical analysis for clarification and that the data for making traffic development forecasts be evaluated.

The invention provides methods for different Types and qualities of traffic information data to provide. The main task is to provide such data to prepare for the motor vehicle driver and this provide appropriate information. It can be for example, travel time displays, route displays, traffic forecast, Act traffic jams and the like. In the individual vehicles, for example, information displays arranged on which the motor vehicle driver their planned routes and travel time information are displayed to get. You can then, for example, under different Alternatives choose the fastest route. Additionally or alternatively, indications of traffic jam developments, Probabilities related to further development on the upcoming route section and the like are displayed. The range of applications is extensive.

The invention provides an extremely flexible method, with which by linking different traffic models an almost network-wide, nationwide Traffic information system is buildable, which data for provides a wide variety of information purposes. It can be conventional and already known models and processes are used and be combined. Forecasts can be curve-based Forecasts at measuring points, model-based forecasts for Sections and meshes and additions of immeasurable effects using artificial intelligence. For the calculation Standard formulas of average values are used. Trend factors can, for example, use the extrapolation method based on the quadratic extrapolation of Lewandowski (1974).

Figur 1

ein Diagramm, das die Verkehrsstärke über die Zeit aufgetragen zeigt;

Figur 2

ein Diagramm, das die Darstellung der Geschwindigkeit über die Zeit zeigt;

Figur 3

ein Diagramm, welches die Darstellung der Verkehrsdichte über die Zeit zeigt;

Figur 4

ein Diagramm, welches den linearen Trend über die Zeit zeigt;

Figur 5

ein Diagramm, welches die Veränderung des linearen Trends über die Zeit zeigt;

Figur 6

ein Diagramm, in dem die Geschwindigkeit über der Verkehrsstärke aufgetragen ist;

Figur 7

ein Diagramm, in dem die Geschwindigkeit an einem zweiten lokalen Meßquerschnitt über der Verkehrsstärke aufgetragen ist;

Figur 8

ein Diagramm, welches die Darstellung von Verkehrszustandswerten über die Zeit am Meßquerschnitt gemäß Figur 6 zeigt;

Figur 9

ein Diagramm, welches die Darstellung von Verkehrszustandswerten über die Zeit am Meßquerschnitt gemäß Figur 7 zeigt;

Figur 10

ein Diagramm, welches die Darstellung einer Verkehrszustands-Ganglinie bezogen auf eine Strecke zwischen zwei Meßquerschnitten zeigt;

Figur 11

ein Diagramm, welches die Darstellung einer Verkehrszustands-Ganglinie bezogen auf eine Strecke zwischen zwei Meßquerschnitten zeigt;

Figur 12

ein Diagramm, welches die Darstellung einer Verkehrszustands-Ganglinie bezogen auf eine Strecke zwischen zwei Meßquerschnitten zeigt;

Figur 13

ein Diagramm, welches die Darstellung einer Verkehrszustands-Ganglinie bezogen auf eine Strecke zwischen zwei Meßquerschnitten zeigt, und

Figur 14

ein Diagramm, welches die Darstellung einer Verkehrszustands-Ganglinie bezogen auf eine Strecke zwischen zwei Meßquerschnitten zeigt.

Further advantages and features of the invention result from the following description with reference to the figures. Show:

Figure 1

a diagram showing the traffic volume over time;

Figure 2

a diagram showing the representation of the speed over time;

Figure 3

a diagram showing the representation of traffic density over time;

Figure 4

a graph showing the linear trend over time;

Figure 5

a graph showing the change in the linear trend over time;

Figure 6

a diagram in which the speed is plotted against the traffic volume;

Figure 7

a diagram in which the speed is plotted on a second local measurement cross-section over the traffic volume;

Figure 8

a diagram showing the representation of traffic condition values over time on the measurement cross section according to FIG. 6;

Figure 9

a diagram showing the representation of traffic condition values over time on the measurement cross section according to FIG. 7;

Figure 10

is a diagram showing the representation of a traffic condition-curve related to a distance between two measurement cross-sections;

Figure 11

is a diagram showing the representation of a traffic condition-curve related to a distance between two measurement cross-sections;

Figure 12

is a diagram showing the representation of a traffic condition-curve related to a distance between two measurement cross-sections;

Figure 13

a diagram showing the representation of a traffic condition-curve with respect to a distance between two measurement cross sections, and

Figure 14

is a diagram showing the representation of a traffic condition-curve based on a distance between two measurement cross-sections.

On a given measurement cross section, as shown in FIG. 1, the traffic volume is determined over time. It results the so-called traffic strength curve, which is found in vehicles per hour from 1-minute measurements. The Figure shows the unsmoothed and the exponentially smoothed Values for a two-lane motorway cross-section. The Figure shows the effect of smoothing on the fluctuation range of the measured values.

As shown in Figure 2, the same measurement cross section at the same time the speeds in the same way Kilometers / hour, calculated from 1-minute measurements, recorded and both unsmoothed and exponentially smoothed for the same motorway cross-section shown.

Traffic density can be determined from the two correlated variables of speed and traffic volume over time by means of the formula k = traffic volume q / speed v calculate and also display smoothed or unsmoothed. Here too, the smoothing is done exponentially.

Figures 1 to 3 show very clearly that at the beginning of a strongly increasing traffic volume a sharp drop in speed takes place and to a very high traffic density leads, which begins to normalize again over time. Figures 4 and 5 show the course of a linear Trend factor of a trend calculation over the day. Here shows that in particular with inhomogeneous traffic flow, for example At night or in the event of faults, the trend factor is one has high fluctuation range. This is also confirmed according to Figure 5, in which the change in linear trend is listed.

Figures 6 and 7 represent so-called fundamental diagrams for two different cross-sections. In fundamental diagrams the speed v over the traffic volume q applied. The letters A to F are six different ones Quality levels are shown, where A is a free Traffic corresponds to, F a traffic jam. It happens at D already Stop-and-Go or viscous traffic signs that as are to be viewed critically.

For the cross section shown in FIG. 6, this results in a diagram a quality level progression over time. One speaks for quality levels also from the level of service (LOS). In the Figures 8 and 9 are in place of the capital letters A, B, etc. the numbers 1, 2 etc. are set. One can see that the LOS temporarily overlay and from one state to another tilt.

If you calculate the different LOS for entire sections of the route, this results in the representations in FIGS. 10 to 14, in which for different sections of the route the LOS is plotted over time between measuring points Q1 to Q9 are. This shows very clearly how initially in Slowly building up faults in the shape of the route, lead to long-lasting congestion in the middle area, that slowly dissolves in the rear section.

The illustrations show how with the method according to the invention concrete values related to the measuring point recorded respectively calculated and related to cross-section or route Condition values or quality levels processed further become. All recorded, calculated and processed Data and results are now available to the To provide road users with relevant traffic information. For example, travel times for planned Calculate routes or different alternative routes and represent. Corresponding warnings and Provide forecasts including forecast probabilities.

The examples described are for explanation and are not restrictive.

Claims (25)

Procedure for determining road sections, especially motorways, related traffic information, using local detectors Acquisition cross sections formed, traffic-related Measured values recorded, preprocessed using a local computer and standardized to a given data protocol, aggregated and sent wirelessly to a higher-level data processing system are transmitted. A method according to claim 1, characterized in that fixed detectors at connection points, Nodes and the like are positioned. Method according to one of the preceding claims, characterized in that the arrangement density of the fixed detectors depending on Traffic expectations estimates are determined. Method according to one of the preceding claims, characterized in that the local Preprocessing the data based on its plausibility is checked by model comparisons. Method according to one of the preceding claims, characterized in that the local Pre-processing of the measured values, mean value calculations be performed. Method according to one of the preceding claims, characterized in that from the change in Measured values trend factors can be determined. Method according to one of the preceding claims, characterized in that from the determined data cyclical status codes are determined. Method according to one of the preceding claims, characterized in that as measured values Vehicle speed, traffic volume and occupancy be recorded. Method according to one of the preceding claims, characterized in that the measured values lane-related can be recorded. Method according to one of the preceding claims, characterized in that as measured values Vehicle type differentiation values are recorded. Method according to one of the preceding claims, characterized in that the Data evaluation cycle is set variably. Method according to one of the preceding claims, characterized in that the data transfer rate depending on the status codes becomes. Method according to one of the preceding claims, characterized in that the Data transfer rates of neighboring Acquisition cross-sections are compared. Method according to one of the preceding claims, characterized in that parent Data processing systems at least for groups summarized adjacent detection cross sections be assigned. Method according to one of the preceding claims, characterized in that as a parent Data processing system a center for everyone Acquisition cross sections of a network or for several higher-level data processing systems assigned becomes. Method according to one of the preceding claims, characterized in that in at least one higher-level data processing system route-related traffic information Linking the transmitted data of neighboring Acquisition cross sections can be calculated. Method according to one of the preceding claims, characterized in that calculated data series through model comparisons with predefined models be checked or corrected. Method according to one of the preceding claims, characterized in that central Data evaluations for accident detection carried out become. Method according to one of the preceding claims, characterized in that source-target relationships by analyzing everyone's data Acquisition cross sections of a network can be determined. Method according to one of the preceding claims, characterized in that the data for route search be evaluated. Method according to one of the preceding claims, characterized in that the data for the output of Traffic management information can be evaluated. Method according to one of the preceding claims, characterized in that the data for the delivery of Traffic development forecasts can be evaluated. Method according to one of the preceding claims, characterized in that the data for the output of Travel time information can be evaluated. Method according to one of the preceding claims, characterized in that the data for the output of Congestion information can be evaluated. Method according to one of the preceding claims, characterized in that the data for Subjected to precision statistical analysis become.

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