EP1749288A1 - Method for determination of turning rates in a traffic network - Google Patents
Method for determination of turning rates in a traffic networkInfo
- Publication number
- EP1749288A1 EP1749288A1 EP06763261A EP06763261A EP1749288A1 EP 1749288 A1 EP1749288 A1 EP 1749288A1 EP 06763261 A EP06763261 A EP 06763261A EP 06763261 A EP06763261 A EP 06763261A EP 1749288 A1 EP1749288 A1 EP 1749288A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- traffic
- determined
- subnetwork
- exit
- turning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
Definitions
- the invention relates to a method for determining turning rates in a road network according to the preamble of claim 1 and applications thereof in various road traffic engineering methods.
- a basic task of traffic control systems in cities is the online determination of the traffic situation in the road network in order to inform about the traffic situation and to optimally control the connected subsystems.
- systems are used for the large-area determination of the traffic situation, but also for the precise determination of the traffic status in subnetworks and for the optimization of associated traffic signal systems.
- An essential task of these methods is the determination of the traffic flows in the road network, whereby the determination of the turn currents at junctions is a central algorithmic question.
- This known method is characterized high demands on the data supply - such as an aggregation of measured data in intervals of two to three seconds - and a complex network modeling from. Partly also very special positions for the measuring cross sections are needed.
- the model equation for the traffic intensities used as the basis for the estimation method either the same measurement interval for the left and right side is used as the time reference or the exit traffic volume for the measurement interval k is calculated from the access traffic intensities of the preceding measurement interval k-1.
- This method suffers from the disadvantage that the estimation result depends strongly on the travel times between the measurement cross sections of the considered subnetwork.
- the invention is therefore based on the object to provide a method of the type mentioned, which is robust in terms of travel times between the measuring cross sections and still works fast and accurate.
- the object is achieved according to the invention by a generic method in which the access traffic volumes of a plurality of preceding measurement intervals are taken into account in the weighted sum for the exit traffic volume of a given measurement interval, with a forward-related turning rate to be determined being the sum of the corresponding turning rates gives the measurement intervals considered in the model equation.
- the generalized time reference for the model equation has shown that the method according to the invention for determining the turning rates is robust with regard to travel times between the measuring cross sections and thus robust with respect to the size of the considered subnetworks. It is fast and has a previously unknown in practice accuracy. Finally, in contrast to previously used methods, the method according to the invention does not require any calibration.
- At least one backward-related partial Network of the road network in which measuring cross sections are taken into account at a driveway and at exits of the subnet, set up a model equation in which the driveway traffic intensity is calculated as the weighted sum of the exit traffic volumes and the weighting factors correspond to the backward-related turning rates indicate in each case the proportion of an exit traffic volume which has flowed through the considered access, the turning rates being calculated from the model equation by means of a mathematical estimation method, wherein in the weighted sum for the inbound traffic strength of a given measurement interval the exit traffic strengths of a plurality of subsequent measurement intervals are taken into account, and wherein a backward-related turning rate to be determined results as the sum of the corresponding turning rates of the measuring intervals considered in the model equation.
- the mathematical estimation method uses an extended, in particular non-linear Kalman filter, since it is a stochastic system with noise effects.
- the stochastic parameters of the extended Kalman filter can be estimated in advance from the statistical analyzes of the data.
- the filter is robust with regard to the parameterization and only requires the current measured values.
- the non-linear Kalman filter is significantly more accurate, requiring less computation time and fewer data series than correlation analysis.
- the proposed filter requires less calibration effort than heuristic methods of Operations Research.
- the estimation method is interrupted when a traffic congestion is detected at a measuring cross section. This ensures the retention of the billing rates estimated before the onset of overload so that misjudgments be prevented due to the dammed vehicles. These are in fact a buffer that destroys the correlation between incoming and outgoing traffic flows.
- forward-related and / or backward-related subnetworks are considered around the node where measurement cross sections in the approach and exit points of the node are taken into account, the turning rates being upwards be determined.
- the turn rates can be estimated with advantage.
- the turning rates for the entrances and exits of the subnetwork are determined according to the above-mentioned method, whereby measuring cross sections are considered only at the edge of the subnetwork but not in its interior, so that the source-destination traffic flows for this subnetwork are calculated from the determined turning rates and the recorded traffic volumes. In doing so, it is ensured that all relevant access measuring sections are included in the model equation for each one exit measuring section; Similarly, in a backward-related subnetwork to an access measuring cross section all relevant exit measuring cross sections are included. In this way, in subnetworks of limited size, a direct dynamic estimation of source-destination streams can be performed.
- the number of measurement intervals considered is increased with increasing size of the considered subnetwork. If the measurement cross sections are close to each other, it is sufficient to consider a smaller number of preceding or subsequent measurement intervals. If, as the size of the considered subnetwork increases, the travel times between If the access and exit measurement cross sections grow, a larger number of measurement intervals must also be taken into account.
- the measurement intervals considered are extended as the size of the considered subnetwork increases. Increasing the aggregation intervals to, for example, five minutes reduces noise disturbances in the estimation process.
- a method for determining the traffic intensity at a road cross section of a road network are for a subnetwork of the road network, an entrance or exit the road cross section and its other entrances and / or exits measuring cross sections, according to the above-mentioned method provided determined turning rates and calculated from the provided bends and the detected at the measurement cross sections traffic levels of the other entrances and / or exits the traffic volume at the road cross section of an entrance or exit.
- known turning rates for a subnetwork traffic volumes in its entrances or exits can be determined therefrom where no measured value is present.
- the traffic intensity determined for the roadway cross section is used as a substitute value for a faulty or failed measuring cross section.
- the traffic intensity is detected at a measuring cross section and at the second end point no measuring cross section is arranged, the traffic intensity at the second end point according to the above-described method and from this, the number of vehicles in the carriageway section is determined by temporal integration of the difference between the traffic volume flowing into the roadway section and the traffic volume flowing therefrom.
- a homogeneous system of equations is first set up from the vehicle preservation of the actual forward and reverse-related turning rates for the correction factors to be determined, then off the optimization of the homogeneous system of equations, together with a constraint which excludes the trivial solution, whereby the correction factors result as a solution to the optimization problem.
- the determined correction factors are divided by their median value. It is assumed that in a subnet less than half of all measurement cross sections count too many vehicles and fewer than half count too few vehicles, so that the median value of the list of specific correction factors can be used as the reference value. Due to the mentioned correction division this is then at the value one.
- a considered subnetwork of the road network is divided into island networks, each island network comprising only measuring cross sections at its network edge, and correction factors for the island grids are determined.
- a parameter is calculated in the solution of the optimization problem whose value is used as a measure of the estimated quality of the turning rates.
- this parameter is ideally close to zero, if the turning rates are accurately estimated and between the measuring cross sections neither vehicle losses nor vehicle increases occur and all measurement errors are proportional.
- an error message is output if the value of the parameter exceeds a predefinable limit.
- This is an indicator of inaccurately estimated turning rates, unrecorded access or exit traffic levels, or measurement errors of a non-proportional nature, which may arise, for example, if not all relevant access and exit traffic levels in the subnetwork are measured.
- two correction factors are determined for each measuring cross section, which is shared by two adjoining island networks, the correction factors of each island network being scaled such that the correction factors of common measuring cross sections are equal to one another.
- the ratio of the correction factors within each island network remains unchanged, but common estimation errors between the island networks are adjusted.
- the traffic intensities recorded at the measuring cross sections and the turning rates determined by estimation are calibrated by means of the determined correction factors.
- FIG. 1 subnetworks of a network section with a node
- FIG. 2 the forward-related subnetwork of FIG. 1 with turn-off connections
- FIG. 1 subnetworks of a network section with a node
- FIG. 3 shows the backward-related subnetwork from FIG. 1 with turn-related connections, FIG.
- FIG. 4 shows an island network around a node
- FIG. 5 shows the time course of estimated turning rates of the island network according to FIG. 4
- FIG. 1 shows a network section of a road network having a node, for example a city road network in which the turning rates of the traffic flows are to be determined for the purpose of traffic control.
- the roadway from driveway 1 to exit 3 does not include any measuring cross-sections in the exemplary embodiment described. At all other entrances and exits of the junction are measuring cross sections with detectors for detecting entrance traffic volumes q "(n) and exit traffic volumes q ° ut (n) in predetermined measuring intervals n.
- Basic element of the inventive method for dynamic estimation of turning rates 1 shows a first subnetwork fw whose network edge is shown as a dashed-dotted line and which measuring cross-sections in exit 1 as well as in the relevant access roads 3 and 4.
- the access roads 3 and 4 are relevant, because on them partial traffic flows into the subnetwork fw, which flow through the exit 1 from the subnetwork fw.
- FIG. 2 shows the subnetwork fw from FIG. 1 with the associated turn-off relationships.
- the turning rate m ⁇ "(k) indicates the proportion of traffic volume q j n (k) measured on access 3, which flows out of subnetwork fw through exit 1 and thus contributes to the measured traffic q o ut (k)
- a - naloges applies to the turning rate mf "(k) in relation to the entrance traffic volume q ⁇ fk).
- the subnetwork fw (forward) models the turn-related relationships forward in time.
- the exit traffic volume q ° ut (k) can be modeled as a weighted sum of the entrance traffic volumes q j n (k) and q ⁇ fk) for a given measurement interval k, the weighting factors corresponding to the corresponding turning rate m ⁇ "(k) and mf" (k).
- r can be used at r for a j exit accesses relevant:
- the turning rates m i "(k) can be determined by means of a mathematical estimation method.
- the estimation method used here a generalized time reference.
- n k-1 , k-2, ...
- the inventive method is robust in terms of travel times between the measuring cross sections, with high accuracy and sufficient speed.
- FIG. 1 shows such a subnetwork bw whose network edge is shown as a dashed line and which comprises measuring cross sections in the access road 3 and in the relevant exits 1, 2 and 4.
- the exits 1, 2 and 4 are relevant because flow on them partial traffic flows from the sub-network bw, which have flowed through the driveway 3 in the sub-network bw.
- FIG. 3 shows this subnetwork bw with the associated turn-off relationships.
- the turning rate m ⁇ fk) indicates the proportion of traffic volume q ⁇ Ck measured in exit 2, which has flowed into the subnetwork bw via driveway 3 and thus contributes to the traffic strength q j n (k) measured there.
- the subnetwork bw thus models the turn-related relationships backwards in time. In general, at s for departures j relevant exits i taking into account z measuring intervals n again the weighted sum can be applied:
- ⁇ M ⁇ ⁇ ⁇ k + i - D • q ° ut ( k + 1-D
- an extended Kalman filter for estimating the turning rates is used as the mathematical estimation method.
- the measurement cross sections are close to each other, for example one to two intervening traffic signal systems, a smaller number of measurement intervals to be taken into account is sufficient, according to experience three or four.
- the model equation can also be applied in larger subnetworks whose detectors are evaluated only at the edge of the network and not inside the network. It merely has to be ensured that in the forward-related case to an exit measuring cross-section of a sub-network all relevant access measuring cross-sections are included, or that in the backward-related case to an access measuring cross section all relevant exit measuring cross sections are included.
- the estimation process is interrupted if an overload is detected on one of the measuring cross sections used for a subnetwork.
- an accounting approach is used to determine traffic jams in access roads to traffic lights. This determines the number of vehicles, ie the traffic jam length, in one of the driveways by integrating the traffic volumes at the end points of the roadway section over time. If, as is usually the case in practice, only one measuring cross-section is located at one of the two end points, the traffic volume at the other end point can be estimated via turning rates determined according to the invention.
- a further advantage of an application of the method according to the invention is described below for the case where a measuring cross section is located at the entrance to a roadway section, while the traffic volume at the exit from the roadway section is estimated via turning rates, since there is no measuring cross section at this roadway cross section.
- the above model equations are characterized by the fact that the turn rates of the entrances and exits are estimated in relation to the exit or access traffic volume. If the measured values of the traffic intensities have proportional errors, these are compensated in the turn rates. The calculated access traffic volumes are consistent with the measured exit traffic. In this way, the quality of balances is significantly increased without the need for special calibrations for each balanced road section.
- q out M fw • q in
- q in and q out are column vectors whose components represent the access traffic volumes q ⁇ n and exit traffic volumes q ° ut of all measurement cross sections in the entrances and exits of the node arms i, while M fw represents a (nxn)
- Matrix means whose elements are the turn rates mf ".
- the column vectors q in and q out comprise in this case all the measuring cross sections in the same sequence, even if there is no driving relationship between components q ⁇ the right side and q, the left side of the equation.
- ItI 1 - 0.
- detectors of the measuring cross-sections capture the actual traffic volumes only with a certain accuracy. This may result from their location, e.g. Passing through vehicles of two lanes, but also through a faulty internal calibration process, in which the measured values drift, as it often occurs in practice over time.
- n in access and out n exit measurement cross sections results in an over-determined homogeneous system of equations for the U 1 with n in + n out ⁇ n equations.
- the simplest case is that of a single traffic signal system in which all relevant entrances and exits are recorded.
- Wi and W2 represent selectable weights.
- the formulated optimization problem can be formally applied to a network as a whole.
- this has two disadvantages: On the one hand, the computational effort for the optimization increases disproportionately with the number of measurement cross sections. On the other hand, the fact that many measuring cross sections in the network have further measuring cross sections in both directions leads to leveling effects of the estimated correction factor
- the method can be refined by properly decomposing the network.
- the application to subnetworks has proven to be favorable, which are defined such that from any point within such a network only those measuring points are part of this subnetwork which can be reached directly via network edges.
- Each such subnet constitutes, as it were, an island network with entrances and exits into the It summarizes all forward or backward related subnets that have the same entry and exit measurement cross sections.
- FIG. 4 shows such a stand-alone grid, which generally arises around the traffic signal system at the usual detector equipment at nodes. It has measuring cross-sections only at the network edge, which is illustrated in FIG. 4 with a double-dashed line.
- the parameter P resulting from the solution to the optimization problem for an island grid provides further information about possible detector disturbances: the value P of the solution to the optimization problem for an island grid ideally approaches zero if the turning rates are exactly estimated and between the measuring cross sections no vehicle losses (sinks) or increases (sources) occur, and all measurement errors are proportional. If P results in a value significantly greater than zero, e.g. Two, this is an indication of inaccurately estimated turning rates, missed entrances and exits, or measurement errors other than proportional. Typically, such an error arises when not all traffic flows in the relevant entrances and exits of such a subnetwork are detected or e.g. due to incorrect supply / wiring, measuring sections were assigned incorrectly to the sections.
- substitute values if detectors or entire measuring sections have failed. Such failures are either already detected in the hardware of the devices and reported further or they can be detected via simple plausibility checks. In this case, substitute values can simply be formed based on the measured and calibrated traffic intensities and turning rates of the surrounding measuring cross sections. This represents a considerable leap in quality over known methods, which in the simple case simply replace missing measured values with previously-dependent time-dependent values or-very costly-determine these via pre-recorded, day-type-specific hydrographs and the current time.
- the turn rates estimated by the described method are accurate enough for both traffic estimation and reuse in adaptive network control techniques and far surpassing the estimation quality of conventional metering based methods.
- the estimation of the correction factors U 1 for all measuring cross sections on the one hand enables an online correction of the turning rates and traffic volume counts.
- the thus possible method for network-related replacement value formation works without hydrographs or pre-supplied default count values and also works with several closely spaced detector failures, since in this case substitutions can also be made recursively.
- the consistency check provides valuable information for the maintenance of the detector network.
- Corresponding notification mechanisms allow a city's maintenance service to efficiently and quickly repair defective detectors, with significant cost savings.
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- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
- Road Repair (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102005024953A DE102005024953A1 (en) | 2005-05-31 | 2005-05-31 | Method for determining turning rates in a road network |
PCT/EP2006/062571 WO2006128819A1 (en) | 2005-05-31 | 2006-05-24 | Method for determination of turning rates in a traffic network |
Publications (2)
Publication Number | Publication Date |
---|---|
EP1749288A1 true EP1749288A1 (en) | 2007-02-07 |
EP1749288B1 EP1749288B1 (en) | 2008-02-27 |
Family
ID=36939069
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP06763261A Not-in-force EP1749288B1 (en) | 2005-05-31 | 2006-05-24 | Method for determination of turning rates in a traffic network |
Country Status (8)
Country | Link |
---|---|
US (1) | US7894979B2 (en) |
EP (1) | EP1749288B1 (en) |
CN (1) | CN101006481B (en) |
AT (1) | ATE387695T1 (en) |
DE (2) | DE102005024953A1 (en) |
DK (1) | DK1749288T3 (en) |
NO (1) | NO20065797L (en) |
WO (1) | WO2006128819A1 (en) |
Families Citing this family (6)
Publication number | Priority date | Publication date | Assignee | Title |
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JP4770858B2 (en) * | 2008-03-28 | 2011-09-14 | アイシン・エィ・ダブリュ株式会社 | Signalized intersection information acquisition apparatus, signalized intersection information acquisition method, and signalized intersection information acquisition program |
DE102008022349A1 (en) * | 2008-05-02 | 2009-11-12 | Deutsches Zentrum für Luft- und Raumfahrt e.V. | Method and device for determining tailback lengths at traffic lights |
US8798897B2 (en) | 2010-11-01 | 2014-08-05 | International Business Machines Corporation | Real-time traffic analysis through integration of road traffic prediction and traffic microsimulation models |
CA2955961A1 (en) | 2014-07-28 | 2016-02-04 | Econolite Group, Inc. | Self-configuring traffic signal controller |
DE102017208854A1 (en) * | 2017-05-24 | 2018-11-29 | Volkswagen Aktiengesellschaft | A method, apparatus and computer readable storage medium having instructions for determining applicable traffic rules for a motor vehicle |
KR20210144411A (en) * | 2020-05-22 | 2021-11-30 | 현대자동차주식회사 | Apparatus and method for recognizing driving lane of vehicle |
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US3663937A (en) * | 1970-06-08 | 1972-05-16 | Thiokol Chemical Corp | Intersection ingress-egress automatic electronic traffic monitoring equipment |
US3920967A (en) * | 1974-02-22 | 1975-11-18 | Trw Inc | Computerized traffic control apparatus |
DE3870709D1 (en) | 1987-05-27 | 1992-06-11 | Siemens Ag | METHOD FOR MEASURING THE INTENSITY OF ROAD TRAFFIC. |
JP2712844B2 (en) * | 1990-04-27 | 1998-02-16 | 株式会社日立製作所 | Traffic flow measurement device and traffic flow measurement control device |
JP3414843B2 (en) * | 1993-06-22 | 2003-06-09 | 三菱電機株式会社 | Transportation control device |
DE4408547A1 (en) * | 1994-03-14 | 1995-10-12 | Siemens Ag | Process for traffic detection and traffic situation detection on highways, preferably motorways |
DE4415737A1 (en) * | 1994-05-04 | 1995-11-23 | Siemens Ag | Procedure for the ongoing measurement of the current traffic flows at a traffic junction |
JP3435623B2 (en) * | 1996-05-15 | 2003-08-11 | 株式会社日立製作所 | Traffic flow monitoring device |
DE19647127C2 (en) * | 1996-11-14 | 2000-04-20 | Daimler Chrysler Ag | Process for automatic traffic monitoring with dynamic analysis |
US5921701A (en) * | 1997-06-25 | 1999-07-13 | Clayton; Robert F. | Traffic interchange |
DE19729914A1 (en) | 1997-07-04 | 1999-01-07 | Mannesmann Ag | Process for the analysis of a traffic network, traffic analysis, traffic forecast as well as creation of a historical traffic database and traffic analysis and forecasting center |
DE19835979B4 (en) * | 1998-08-08 | 2005-01-05 | Daimlerchrysler Ag | Method for monitoring traffic conditions and vehicle inflow control in a road network |
KR20000025578A (en) * | 1998-10-13 | 2000-05-06 | 윤종용 | Method for modeling traffic road and method for recognizing traffic lane using the same |
DE19944075C2 (en) * | 1999-09-14 | 2002-01-31 | Daimler Chrysler Ag | Traffic condition monitoring method for a traffic network with effective bottlenecks |
DE10036789A1 (en) * | 2000-07-28 | 2002-02-07 | Daimler Chrysler Ag | Method for determining the traffic condition in a traffic network with effective bottlenecks |
DE10136646A1 (en) | 2001-07-20 | 2003-02-06 | Ddg Ges Fuer Verkehrsdaten Mbh | Method for balancing of vehicle flows from input traffic data relating to a particular road stretch, whereby an instantaneous flow can be determined and from it useful traffic data that can be quickly transmitted to drivers |
US20040088392A1 (en) * | 2002-03-18 | 2004-05-06 | The Regents Of The University Of California | Population mobility generator and simulator |
US7135989B2 (en) * | 2004-09-27 | 2006-11-14 | Gregory Fife Parsons | Parallel flow vehicle turn system for traffic intersections |
-
2005
- 2005-05-31 DE DE102005024953A patent/DE102005024953A1/en not_active Withdrawn
-
2006
- 2006-05-24 EP EP06763261A patent/EP1749288B1/en not_active Not-in-force
- 2006-05-24 DE DE502006000385T patent/DE502006000385D1/en active Active
- 2006-05-24 CN CN2006800003588A patent/CN101006481B/en not_active Expired - Fee Related
- 2006-05-24 AT AT06763261T patent/ATE387695T1/en active
- 2006-05-24 DK DK06763261T patent/DK1749288T3/en active
- 2006-05-24 US US11/629,796 patent/US7894979B2/en not_active Expired - Fee Related
- 2006-05-24 WO PCT/EP2006/062571 patent/WO2006128819A1/en active IP Right Grant
- 2006-12-14 NO NO20065797A patent/NO20065797L/en not_active Application Discontinuation
Non-Patent Citations (1)
Title |
---|
See references of WO2006128819A1 * |
Also Published As
Publication number | Publication date |
---|---|
DK1749288T3 (en) | 2008-06-23 |
US7894979B2 (en) | 2011-02-22 |
NO20065797L (en) | 2007-03-05 |
EP1749288B1 (en) | 2008-02-27 |
CN101006481B (en) | 2010-10-06 |
CN101006481A (en) | 2007-07-25 |
ATE387695T1 (en) | 2008-03-15 |
US20080069000A1 (en) | 2008-03-20 |
DE502006000385D1 (en) | 2008-04-10 |
DE102005024953A1 (en) | 2006-12-07 |
WO2006128819A1 (en) | 2006-12-07 |
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