Method for recognizing disruptions in road traffic

• US 5,684,475 A
• Filed: 04/29/1996
• Issued: 11/04/1997
• Est. Priority Date: 04/28/1995
• Status: Expired due to Fees

• Current Assignee: Inform Institut Fur Operations Research und Management GmbH
• Original Assignee: Inform Institut Fur Operations Research und Management GmbH
• Inventors: Krause, Bernhard, Pozybill, Martin
• Primary Examiner(s): Hofsass, Jeffery
• Assistant Examiner(s): Woods, Davetta
• Application Number:
  US08/639,957
• Time in Patent Office: 554 Days
• Field of Search: 340/934, 340/933, 340/936, 340/917, 340/916, 364/436
• US Class Current: 340/934
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions
• CPC Class Codes: G08G 1/065 by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count

Abstract

In a method for recognizing disruptions in road traffic within a road sector that is to be monitored at respective measurement cross-sections at the beginning and at the end of the sector, the number and the speed of the vehicles passing through the measurement cross-sections are continuously acquired as measured data, which are collected and compiled cyclically during finite measurement intervals to provide average values of the traffic flow and the speed, and are then evaluated. Each measurement cross-section thereby encompasses all lanes of traffic that can be used in one direction of travel. In order to recognize disruptions in road traffic, independent of the traffic condition and with the smallest possible loss in time and low investment in data processing, a prognosis value of the traffic flow for the vehicles passing through the end of the road sector is calculated cyclically from the average values determined for the beginning of the road sector, taking into consideration the length of the road sector and an assumption about the driving behavior of the detected vehicles. Describable uncertainties arising in determining the prognosis value are taken into consideration by fuzzy modeling. The prognosis value is compared with the average value of the traffic flow at the end of the road sector determined from the measured data acquired at the measurement cross-section at the end of the road sector and the respective difference in traffic flow is determined cyclically. A cycle-spanning summation of the values of the difference in traffic flow is carried out and the number of the additional vehicles remaining in the road sector to be monitored is continuously determined.

Claims

1. A method for recognizing a disruption in traffic of vehicles travelling in a road sector of a road that has a certain road sector length, wherein a beginning traffic sensor is arranged at a beginning measurement cross-section at a beginning of said sector and an end traffic sensor is arranged at an end measurement cross-section at an end of said sector,

said method comprising, in a plurality of successive finite measurement time intervals:

(a) using a signal provided by said beginning traffic sensor, determining an average beginning vehicle speed and an average beginning traffic flow of vehicles passing said beginning measurement cross-section, averaged over a respective present one of said time intervals;

(b) calculating a prognosis value of an expected end traffic flow of vehicles passing said end measurement cross-section during said respective present time interval, from said average beginning vehicle speed, said average beginning traffic flow, said road sector length, an assumed time distribution of vehicles in said respective present time interval, and an assumed progression of speed of vehicles while travelling through said road sector, comprising;

(b1) determining an expected transit time, from said road sector length, said average beginning vehicle speed, and said assumed progression of speed,

(b2) from said expected transit time, identifying particular one or ones of said successive measurement time intervals during which vehicles sensed by said beginning traffic sensor during said respective present time interval will pass said end measurement cross-section,

(b3) determining proportion factors for apportioning said average beginning traffic flow to said particular one or ones of said time intervals identified in said step (b2), and

(b4) calculating said prognosis value for said respective present time interval by multiplying said proportion factors with corresponding ones of said average beginning traffic flow for said particular one or ones of said time intervals identified in said step (b2), to provide expected traffic flow products, and then summing said expected traffic flow products over said respective present time interval and all previous ones of said time intervals;

(c) using a signal provided by said end traffic sensor, determining an average end traffic flow of vehicles passing said end measurement cross-section, averaged over said respective present time interval;

(d) determining a respective traffic flow difference value by comparing said prognosis value and said average end traffic flow;

(e) summing said respective traffic flow difference value as determined for said respective present time interval and for all previous ones of said time intervals to determine an excess number of vehicles remaining in said road sector;

(f) triggering a traffic disruption message if said excess number of vehicles remaining in said road sector exceeds a threshold value; and

(g) repeating said steps (a) to (f) for each of said successive time intervals.

2. The method of claim 1, wherein each said measurement cross-section encompasses all lanes of traffic that are available for travel in one direction in said road sector.

3. The method of claim 1, wherein said road sector has plural lanes of traffic for one direction of travel, said step (b) of calculating said prognosis value is carried out separately for each one of said plural lanes of traffic to provide a plurality of single-lane prognosis values, and said step (d) further comprises summing all of said single-lane prognosis values for said one direction of travel to provide a total prognosis value, which is then compared with said average end traffic flow to determine said traffic flow difference value.

4. The method of claim 3, wherein said step (f) comprises evaluating said excess number of vehicles and at least one value describing a traffic condition as respective inputs in a fuzzy logic circuit, and releasing an output signal from said fuzzy logic circuit to trigger said traffic disruption message.

5. The method of claim 1, wherein said step (f) comprises evaluating said excess number of vehicles and at least one value describing a traffic condition as respective inputs in a fuzzy logic circuit, and releasing an output signal from said fuzzy logic circuit to trigger said traffic disruption message.

6. The method of claim 5, wherein at least one of said sensors makes an erroneous detection at at least one of said measurement cross-sections, and wherein said fuzzy logic circuit takes into account describable uncertainties regarding said erroneous detection.

7. The method of claim 6, wherein said step (f) comprises triggering a selected one of a plurality of disruption messages corresponding to a plurality of different disruption situations, and wherein said output signal from said fuzzy logic circuit particularly triggers said selected disruption message.

8. The method of claim 5, wherein said step (f) comprises triggering a selected one of a plurality of disruption messages corresponding to a plurality of different disruption situations, and wherein said output signal from said fuzzy logic circuit particularly triggers said selected disruption message.

9. The method of claim 8, wherein said assumed progression of speed of vehicles is determined by means of fuzzy logic.

10. The method of claim 9, wherein said vehicles include trucks and other vehicles, further comprising a step of determining an average total traffic flow and an average truck traffic flow using a signal provided by said beginning traffic sensor, and calculating a truck proportion as said truck traffic flow divided by said total traffic flow, and wherein as long as said truck proportion exceeds a threshold value, said average beginning vehicle speed is an average beginning speed of only said trucks among said vehicles and said average beginning traffic flow is an average beginning traffic flow of only said trucks among said vehicles.

11. The method of claim 10, wherein said road includes a plurality of said road sectors arranged successively adjacent one another, wherein said end traffic sensor of a first one of said road sectors is simultaneously used as said beginning traffic sensor of a second one of said road sectors adjacently following said first road sector, and wherein said signal provided by said end traffic sensor of said first sector is simultaneously used as said signal provided by said beginning traffic sensor of said second sector.

12. The method of claim 1, wherein said road includes a plurality of said road sectors arranged successively adjacent one another, wherein said end traffic sensor of a first one of said road sectors is simultaneously used as said beginning traffic sensor of a second one of said road sectors adjacently following said first road sector, and wherein said signal provided by said end traffic sensor of said first sector is simultaneously used as said signal provided by said beginning traffic sensor of said second sector.

13. The method of claim 1, wherein said vehicles include trucks and other vehicles, further comprising a step of determining an average total traffic flow and an average truck traffic flow using a signal provided by said beginning traffic sensor, and calculating a truck proportion as said truck traffic flow divided by said total traffic flow, and wherein as long as said truck proportion exceeds a threshold value, said average beginning vehicle speed is an average beginning speed of only said trucks among said vehicles and said average beginning traffic flow is an average beginning traffic flow of only said trucks among said vehicles.

14. The method of claim 1, wherein said assumed progression of speed of vehicles is determined by means of fuzzy logic.