Method of sensing traffic and detecting traffic situations on roads, preferably freeways

• US 5,696,502 A
• Filed: 09/12/1996
• Issued: 12/09/1997
• Est. Priority Date: 03/14/1994
• Status: Expired due to Fees

• Current Assignee: Siemens AG
• Original Assignee: Siemens AG
• Inventors: Busch, Fritz, Konrad, Johannes, Ghio, Andrea
• Primary Examiner(s): Hofsass, Jeffery
• Assistant Examiner(s): Edwards, Jr., Timothy
• Application Number:
  US08/704,728
• Time in Patent Office: 453 Days
• Field of Search: 340/905, 340/933, 340/934, 340/936, 340/928, 340/910, 340/919, 364/436, 364/437, 364/438
• US Class Current: 340/905
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions

Abstract

A method of sensing traffic and detecting traffic situations on roads (AS), preferably freeways. With measuring points (measuring cross sections MQ1, MQ2, . . . ) set up for the purpose for vehicle detection using traffic sensors (VS) and with a traffic data processing arrangement (VDVE) for traffic control, at regular intervals traffic data (VD), such as vehicle speed (v), traffic intensity (Q) and traffic density (K), are determined and traffic parameters determined therefrom are formed in a traffic data processing system (VDA). Two adjacent measuring points. (MQi, MQ(i+1) form a measuring section (MA) of a given length (l). The following traffic parameters are formed from the traffic data (VD) of two such measuring points:

a) the speed density difference (vk-D), which is calculated from the local traffic data of average speed (v) and traffic density (K);

b) a trend factor (FT), which is formed continually from the ratio between the traffic intensities (Qi/Q(i+1)) of the first and second measuring points (MQi, MQ(i+1)), but determined during a given period (t) in the minute range;

c) the traffic intensity trend (QTi, QT(i+1)) of the respective measuring point (MQi, MQ(i+1)), the trend being derived on the basis of the function of the traffic intensity (Q) over the time (curve Q (t)) from the increase of the tangent to the curve. The probability of a critical traffic situation (WG) is derived therefrom in a fuzzy logic (FUB).

Claims

1. A method for sensing traffic and detecting traffic situations on roads having measuring points set up as measuring cross sections for vehicle detection using traffic sensors and a traffic data processing arrangement for traffic control, comprising the steps of:

determining at regular intervals traffic data, said traffic data have at least vehicle speed, traffic intensity, which is the number of vehicles at a measuring cross section based on a unit of time, and traffic density which is to say the number of vehicles based on a specific section of road, at the measuring points;

forming in a traffic data processing system traffic parameters determined from the traffic data and the traffic density, first and second adjacent measuring points furthermore forming a measuring section of a given length;

forming a speed density difference according to the following relationship;

##EQU4## where vfi, vf(i+1) are adjustable maximum values of which speed at the first and second measuring points, respectively;

kmaxi, kmax(i+1) are adjustable maximum values of traffic density at the first and second measuring points, respectively;

ki is traffic density after the first measuring point;

k(i+1) is traffic density before the second measuring point;

vi, v(i+1) are average speeds at the first and second measuring points, respectively;

vk-D is the speed density difference;

the speed density difference, the trend factor and the traffic intensity trends being traffic parameters;

forming a trend factor continually from a ratio between traffic intensities of the first and second measuring points, but determined during a given time period in a minute range;

the forming a traffic intensity trend of a respective measuring point of the first and second measuring points, the trend being derived by a function of the traffic intensity over time from an increase of tangent to a curve of the function, traffic parameters with fuzzy logic for processing a direction of a critical traffic situation in the measuring section and feeding the traffic parameters as probability variables to a downstream result assessment arrangement, in which control signals for variable message signs are formed in dependence on adjustable threshold values.

2. The method as claimed in claim 1, wherein the traffic parameters of speed density difference and trend factor are dynamically calibrated in dependence on their past values, calibration factor for the speed density difference and a calibration factor for the trend factor being formed from the traffic data, and wherein in a calibration arrangement, arranged between the traffic data processing system and the fuzzy processing, a current speed density difference is divided by the speed density difference calibration factor and the current trend factor is divided by the trend factor calibration factor.

3. The method as claimed in claim 2, wherein, for calibration of the speed density difference, the speed density difference (latter) is assessed, a value of the calibration factor for the speed density difference being a threshold value for the speed density difference from which there is a high probability of a critical traffic situation.

4. The method as claimed in claim 2, wherein, for calibration of the trend factor, a characteristic value of the trend factor estimated to be "small" is defined such that characteristic value comprises a set of all values of the trend factor whose relative cumulative frequency lies below a threshold value, a frequency table being formed with a plurality of classes with defined ranges of values of the trend factor, and the current trend factor being assigned to a class in order to determine the calibration factor.

5. The method as claimed in claim 1, wherein a disruption is determined and displayed as a critical traffic situation, the trend factor and the traffic intensity trend of the first measuring point being used to detect bunching and to form a bunching probability variable, a relationship of which with the traffic intensity trend of the second measuring point is established in order to derive a disruption criterion, the trend factor and the speed density difference and the disruption criterion also being used to detect a disruption and to form a disruption probability variable.