EP0902403A2 - 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 standardized to a defined data format, aggregated and wirelessly passed to a superior data processing system. The locally aggregated data are statistically evaluated and smoothed, set relative to a predefined reference value and only transferred if the results exceeds or lies below a threshold region determined using the reference.

Description

The invention relates to a method for determining Road routes, especially traffic information related to motorways, local detection cross sections by means of fixed detectors formed, traffic-related measured values recorded, preprocessed by means of local computers and to a given one Data protocol standardized, aggregated and sent by radio a higher-level data processing system can be transmitted.

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 seeks to provide a comprehensive traffic data of the generic type improved in such a way so that reliable with single sensors and low data transmission as well as energy consumption and be provided sufficiently precise 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 stationary detectors, traffic-related measured values are recorded, preprocessed by means of local computers and standardized, aggregated and standardized to a predetermined data protocol are transmitted by radio to a higher-level data processing system, in order to improve the fact that the data aggregated by means of local computers is statistically evaluated and smoothed, related to a predetermined reference value and is only transmitted when a threshold value range defined around the reference value is exceeded or fallen below.

The invention enables the implementation of a step organized capture and processing system, being already Short term results can be achieved through Expansion into the individual stages consolidated and refined become. By breaking it down into individual subtasks or levels results in a high degree of flexibility and Reliability through the formation of fallback levels. By The local pre-analysis of traffic gives rise to opportunities for extremely energy-saving, event-oriented data transmission to the higher-level data processing systems or centers.

Fixed detectors are preferably used at connection points, Nodes and the like are positioned. About that In addition, the arrangement density of the fixed detectors in Dependence on traffic expectation estimates determined. Consequently can be arranged by arranging many local detection systems Build comprehensive networks. With the invention it is also possible to organize an overall network structure. Locally critical locations become critical Detectors and preprocessing computers arranged that the data via radio in preferably digital technology forward higher-level data processing systems or centers. Other traffic models can then be accessed there Data are applied.

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 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.

About interval-independent and threshold-oriented sending the ability to implement the individual detectors is a new one Aggregation module required which the existing aggregation module replaced the 1st period in which the transfer based on fixed time intervals. In addition, here too adjustments in the central software are still necessary because the telegram has a new structure and the telegrams must be processed at variable time intervals. Here care was taken to make these adjustments simple and are easy to implement. Everyone is from the adjustment Modules (detector cross sections) affected.

Basically, it must be ensured that the traffic models in the head office with data of an aggregation type (threshold-oriented or fixed). The actuality the traffic-related statements, however, is from that Aggregation interval dependent. The bigger the interval, the statements are all the less up to date.

The following definitions are used for the following descriptions fixed.

Definitions

q

Traffic-related traffic intensity

v

Lane-related average speed

b

Degree of occupancy related to the lane

σ _v

Lane-related standard deviation of speed

t

Aggregation interval

Indices

left, mi, right

Track name

loc

local size

mom

current, route-related size

Vehicle

Vehicles motor vehicles

Car

Vehicles with length <L_Pkw_lkw

truck

Vehicles with length> L_Pkw_lkw

A preliminary analysis of the measurement data per Lanes take place and thus the decision is made whether data of a measuring cross section should be sent. Communication is carried out when on one of the Lanes the changed traffic situation a transmission makes necessary. The decision about communication is determined by the smoothed speed of the car or truck affected.

The detector measured values, which are available in a 10-second grid, are aggregated at 1-minute intervals. The traffic volume for cars, trucks and vehicles, the average speed for cars, trucks, the maximum individual speed, the standard deviation of the speed, the occupancy rate and the errors for length and occupancy are calculated per lane. q Car = ΣPassenger car, q truck = Σ truck, q Vehicle = ΣFz, v Car Truck (t) = i = 1 q v i (t) q Car Truck

Standard deviation of the arithmetic mean σ v = i = 1 n (v i (t) - v (t)) 2nd n - 1

Determination of the degree of occupancy per lane: b i = i = 1 n b i t Aggreg · 100 [%]

Calculation of an error indicator for length and occupancy measurement f = Number of measurements rejected Number of measurements · 100 [%]

In a second step, the speed and traffic volume are smoothed exponentially for each lane and vehicle type. v Car Truck (t) = β v · V Car Truck (t) + (1-β v ) · v Car Truck (t-1) 0≤β v ≤1 q Car Truck (t) = β q · Q Car Truck (t) + (1-β v ) · q Car Truck (t-1) 0≤β q ≤1

Since there was no smoothed value for the previous interval is then the current measured value used. The first measured value for the speed at the start of smoothing or the first smoothed value saved as reference value if the bandwidth is exceeded. Every minute it is decided whether the newly calculated smoothed value within a defined range the reference value. If the bandwidth is exceeded the last smoothed value is transmitted. This represents the new speed level.

The following table shows, based on a series of measurements from a BAB, the effect of the smoothing parameter b and the bandwidth D for the traffic volume and the average speed on the communication between the aggregation module and the control center. Transmission interval Aggregation interval [min] Traffic parameters Smoothing parameter β Bandwidth Δ [+/-%] Communication / day error constant 5 min 1 v, q 288 variable 1 q 0.1 10th 116 variable 1 q 0.1 20th 46 variable 1 q 0.1 30th 22 variable 1 q 0.2 10th 285 variable 1 q 0.2 20th 77 variable 1 q 0.2 30th 37 variable 1 q 0.3 10th 465 variable 1 q 0.3 20th 130 variable 1 q 0.3 30th 57 variable 1 v 0.1 10th 108 variable 1 v 0.1 20th 45 variable 1 v 0.1 30th 25th variable 1 v 0.2 10th 129 variable 1 v 0.2 20th 69 variable 1 v 0.2 30th 31 variable 1 v 0.3 10th 145 variable 1 v 0.3 20th 79 variable 1 v 0.3 30th 48

Are suggested when deciding on the speed deviation ab of 0.4 and a bandwidth D of +/- 10% or above the deviation in traffic intensity a b of 0.2 and a bandwidth D of +/- 15%.

In order to be able to take different routes with different traffic situations into account, the parameters for b and the bandwidth for each MQ (per direction of travel) must be defined separately. Therefore, the following values must be provided in a new runtime file for each MQ. The parameters apply to all lanes in one direction. v: β Car , Δ Car , β truck , Δ truck q: β Car , Δ Car , β truck , Δ truck

As a result of the threshold-oriented transmission of up to 30 minutes cannot be recorded individual minute intervals can be saved and transmitted. In these cases, an n-min interval must be formed become.

For statistical purposes, the exact numbers for class-related traffic and average speed should be calculated for each lane. For this purpose, the current number of vehicles per vehicle class (cars, trucks) and per lane is added up every minute and the associated average speed is calculated. This summation continues until a new telegram is sent. Then the counters are set to zero and a new aggregation is started. Traffic intensity per lane q t Cars, trucks, vehicles = q t-1 Cars, trucks, vehicles + q current Cars, trucks, vehicles [Vehicle / interval]

Average local speed per lane: v t Cars, trucks, loc = i = left, mi, right (q t-1 Car Truck · v t-1 Car Truck + q current Car Truck · v current Car Truck ) q t Car Truck [km / h]

The duration of the aggregation interval must also be added for which these values apply.

The standard deviation per lane is calculated every minute from the current standard deviation of the last minute s and the aggregated standard deviation S of the current aggregation interval σ v = 1 i = 1 2nd q i Vehicle - 1 · i = 1, n 2nd (σ 2nd i (q i Vehicle - 1) + ( v i Vehicle - V Vehicle Loc ) Q i Vehicle ) [km / h]

The degree of occupancy per lane must be weighted with the number n of the aggregations already aggregated: b t = b t-1 · N + b current n + 1 [%]

The error length and error assignment are in the aggregation interval summed up.

To determine whether the detector or module is still functional and the values for the traffic volume are not To get too big, it must be guaranteed that in solid Intervals of telegrams are sent independently from the traffic situation. This interval length should be 30 minutes from the last broadcast. Around to be able to initiate the transmission from the head office, communication must be established every 5 minutes. Then can be requested from the central office, e.g. on a restart the central software, new data can be queried.

Data packet from aggregation module per transmission

time [wt: hh: mm] End time acquisition interval MQ [-] Cross section no. Track-related values, smoothed: Q_Pkw_exp [Vehicle / min] Traffic volume, cars, smoothed Q_Lkw_exp [Vehicle / min] Traffic volume, truck, smoothed Q_ges_exp [Vehicle / min] Traffic volume, motor vehicles, smoothed V_Pkw_exp [km / h] local speed, car, smoothed V_Lkw_exp [km / h] local speed, truck, smoothed track-related values, per aggregation interval interval [min] Aggregation interval Q_Pkw [Vehicle / interval] Traffic volume, cars Q_ truck [Vehicle / interval] Traffic, truck Q_ges [Vehicle / interval] Traffic volume, motor vehicle V_Pkw [km / h] local speed, car V_ truck [km / h] local speed, truck SV [km / h] Standard deviation V_Kfz B [%] Occupancy rate Error length [-] encrypted error code Error B [-] encrypted error code

The transmitted values for the smoothed traffic volume and speed as well as for standard deviation and occupancy for the individual future minute intervals are accepted in the control center until a new value is transmitted. In the archive, the smoothed values must be marked with a marker that determines whether the archived value is a real smoothed measured value or an updated (assumed) value for the point in time.
A separate archive is to be created for the new total values for statistical evaluations.

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.

Figur 1

ein Flußdiagramm als Beispiel für ein Realisationsbeispiel für den ereignisorientierten Sendevorgang;

Figur 2

Diagramme, die die Darstellung der Verkehrsstärke über die Zeit zeigen, und

Figur 3

Diagramme, welche ebenfalls die Darstellung der Verkehrsstärke über die Zeit in weiteren Zeitintervallen zeigen.

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

Figure 1

a flow chart as an example of a realization example for the event-oriented transmission process;

Figure 2

Diagrams showing the display of traffic volume over time, and

Figure 3

Diagrams, which also show the representation of the traffic volume over time in further time intervals.

At a given measuring cross section, the described Way the various traffic-related values determined. in the shown embodiment according to Figure 1, the values v, q aggregated according to lane. In parallel, the standard deviation calculated. Depending on whether already if there is a smoothed v or not, the exponential takes place Smoothing and bandwidth checking for the values. The send / not send decision is then made.

Figures 2 and 3 show a total of four diagrams of each Six-hour intervals, i.e. the traffic volume values for a whole day, each time in unsmoothed, smoothed as well as sent representation. It can be procedural results in terms of economic Recognize broadcasting decisions.

The illustrations show how with the method according to the invention concrete values related to the measuring point recorded respectively calculated and sent. All recorded, calculated and further processed data and results are now available available to appropriate road users Provide traffic information. For example, Travel times for planned routes or different ones calculate and display alternative routes. Also can be corresponding warnings and forecasts, including forecast probabilities specify.

The examples described are for explanation and are not restrictive.

Claims (18)

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 to a predetermined data protocol, aggregated and transmitted by wireless transmission to a higher-level data processing system, characterized in that the means of local computers aggregated data are statistically analyzed and smoothed, is set to a predetermined reference value with respect and only at above or below a reference value to the predetermined threshold value range transmitted. A method according to claim 1, characterized in that the reference value is determined from the data of a previous time interval. Method according to one of the preceding claims, characterized in that the threshold value range is variably adjustable. Method according to one of the preceding claims, characterized in that, for local preprocessing of the data, its plausibility is checked on the basis of model comparisons. Method according to one of the preceding, characterized in that mean value calculations are carried out for local preprocessing of the measured values. Method according to one of the preceding claims, characterized in that a time interval of a predetermined length is started for each transmission, at the end of which a transmission takes place if it is not restarted by a situation-related transmission. Method according to one of the preceding claims, characterized in that vehicle speed, traffic volume and occupancy are recorded as measured values. Method according to one of the preceding claims, characterized in that the measured values are recorded in relation to the lane. Method according to one of the preceding claims, characterized in that vehicle type differentiation values are recorded as measured values. Method according to one of the preceding claims, characterized in that a data transfer rate additionally determined depending on status codes becomes. Method according to one of the preceding claims, characterized in that the data transmission rates of adjacent acquisition cross sections are compared. Method according to one of the preceding claims, characterized in that a control center for all acquisition cross-sections of a network or for a number of higher-level data processing systems is assigned as the higher-level data processing system. Method according to one of the preceding claims, characterized in that route-related traffic information is calculated in at least one superordinate data processing system by linking the transmitted data of adjacent detection cross sections. Method according to one of the preceding claims, characterized in that the data for the route search are evaluated. Method according to one of the preceding claims, characterized in that the data are evaluated for outputting traffic management information. Method according to one of the preceding claims, characterized in that the data for submitting traffic development forecasts are evaluated. Method according to one of the preceding claims, characterized in that the data for the output of travel time information are evaluated. Method according to one of the preceding claims, characterized in that the data are evaluated for outputting traffic jam information.

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