Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – Traffic analysis or control of surface vehicle
Reexamination Certificate
2001-05-10
2002-10-22
Cuchlinski, Jr., William A. (Department: 3663)
Data processing: vehicles, navigation, and relative location
Vehicle control, guidance, operation, or indication
Traffic analysis or control of surface vehicle
C701S200000, C701S118000, C701S119000, C701S216000, C340S905000, C340S934000, C340S995190, C340S988000, C340S989000, C340S990000, C455S456500, C180S065800, C320S107000
Reexamination Certificate
active
06470262
ABSTRACT:
This application claims the priority of German patent document 100 22 812.7, filed May 10, 2000, the disclosure of which is expressly incorporated by reference herein.
The invention relates to a method for evaluating a traffic situation for a traffic network with traffic-controlled network nodes and roadway sections connecting them, based on traffic data obtained by reporting vehicles moving in the traffic.
Many methods are known for determining the actual traffic situation and for predicting the traffic situation to be expected in the future, in particular for road traffic networks. Such methods are becoming increasingly important due to the continuous increase in the amount of traffic. Conventional traffic prediction methods can be subdivided roughly into two types, namely historical progress line predictions and dynamic traffic predictions. The former are based on previously obtained traffic situation data from which an archive of so-called progress lines is formed; based on the latter a so-called matching process (in which a best matching progress line is selected) is then used to deduce the future development of the traffic situation from current traffic situation data. Dynamic traffic prediction, on the other hand, is based on identification of objects in the traffic and traffic states (such as free-flowing traffic, synchronized traffic and jams) from current traffic measurements, and dynamic tracking of these individualized traffic states.
These two prediction methods may also be combined. Such historical and dynamic traffic predictions are described, for example, in German Patent Documents DE 195 26 148 C2, DE 196 47 127 A1 and DE 197 53 034 A1, and German Patent Application 198 35 979.9. A necessary precondition for any traffic prediction method is to determine the actual traffic situation at the time of the prediction and, possibly, at earlier times.
Most conventional methods for traffic situation determination are applied to traffic networks in which the dynamics of the traffic flow are themselves governed essentially by the traffic interactions on the various roadway sections (the route connections between each pair of network nodes); that is, such dynamics are governed by the dynamics of the various identifiable traffic objects and phased transitions between them. Such interactions are applicable, for example, to high-speed roads.
On the other hand, different interactions occur in traffic networks in highly populated areas. There, the traffic flow is generally governed by the traffic control measures at the network nodes (for example, traffic lights at crossings), and scarcely at all by the traffic dynamic effects on the frequently relatively short roadway sections between the nodes. It is known that queuing theory can be used in these cases, in which the length of the queue before a particular traffic-controlled network node, the durations of the free phases during which the traffic is released at the relevant network node and interruption phases during which the traffic is stationary at the network node, the speed of the vehicles outside the typical queues before the network nodes, the inlet flows to the queue and the length of the roadway sections are of importance for the traffic dynamics. See, for example, S. Miyata et al., “STREAM”, Proc. of the 2nd World Congress on Intelligent Transport Systems, Yokohama, Volume 1, Page 289, 1995 and B. Ran and D. Boyce, “Modeling Dynamic Transportation Networks”, Springer-Verlag, Berlin, 1996.
German Patent Application 199 40 957.9 (not prior art) discloses a traffic prediction method which is particularly suitable for traffic networks in highly populated areas. This traffic prediction method is based on detection of actual traffic state parameters, which are formed in discrete time intervals by the free phases and interruption phases at the traffic-controlled network nodes, such as the actual vehicle outlet flow from a queue, the actual vehicle inlet flow into the queue and the actual number of vehicles in the queue. The actual traffic state parameters at discrete time intervals are used to determine effective continuous traffic state parameters, including at least one effective continuous vehicle outlet flow from a queue and/or one effective continuous vehicle inlet flow into the queue. From the latter, one or more traffic parameters is or are predicted on the basis of dynamic macroscopic modeling of the traffic. These include, for example, expected travel time at a prediction time for a specific roadway section and/or the expected traffic situation to be expected, at least with regard to the number of vehicles waiting in queues or traveling outside queues, and/or the predicted length of the respective queue. The contents of this prior Application with regard to the explanatory notes and definitions that can be found there of terminology and physical variables are also relevant here.
A parallel German Patent Application from the applicant discloses a method for obtaining traffic data by means of reporting vehicles moving in the traffic. This system is used to obtain what is referred to as FCD (floating car data), which is likewise especially suitable for traffic networks in highly populated areas (that is, for traffic networks in which the traffic is dominated by traffic controls at the network nodes). This method specifically includes obtaining FCD from dynamic individual or reporting vehicles, with such data including time stamp information denoting a reporting time which is not earlier than the time of leaving the relevant roadway section and is not later than the time at which the reporting vehicle reaches a next traveled roadway section before a next network node to be considered. Such time stamp information allows the routes traveled by the reporting or FCD vehicles to be tracked, and the travel times to be expected for the respective roadway section to be determined, possibly individually for each of a number of direction lane sets in this section. The term “direction lane set” in this case denotes the number of different direction lanes in a roadway section, which may each comprise one or more lanes and are defined in such a way that the one or more lanes in a respective direction lane set can be used equally well by the vehicles in order to pass the network node to continue in one or more associated destination directions. This FCD traffic data acquisition method can be to determine travel times for each respective roadway section for the present traffic situation determination method, as used above.
One object of the invention is to provide an improved method of the type mentioned above, for determining one or more traffic parameters indicative of the traffic situation, using FCD information, particularly for traffic networks in highly populated areas as well.
This and other objects and advantages are achieved by the method according to the invention, in which traffic data indicative of the travel times on the roadway sections (that is, FCD suitable for travel time determination), are obtained by means of reporting vehicles moving in the traffic, and the travel for the roadway sections are determined from such traffic data. The roadway-section-specific travel times which have been determined are then used to obtain one or more traffic situation parameters. More precisely, these include the mean number of vehicles in a queue at a particular roadway section before a traffic-controlled network node, the mean number of vehicles in total on the roadway section, the mean vehicle speed on the roadway section before any queue (between the start of the roadway section and the upstream end of the queue), the mean waiting time in the particular queue and/or the mean vehicle density on the roadway section before the queue.
This method makes it possible to obtain FCD suitable for determining the actual traffic situation with sufficient accuracy, especially for traffic networks in highly populated areas where traffic dynamics are dominated by the traffic control measures at the network nodes, using the FCD for reconstructi
Kerner Boris
Rehborn Hubert
Crowell & Moring LLP
Cuchlinski Jr. William A.
Daimler-Chrysler AG
Mancho Ronnie
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