EP1659550A2 - Procédé pour détecter des données de traffic - Google Patents

Procédé pour détecter des données de traffic Download PDF

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Publication number
EP1659550A2
EP1659550A2 EP05022489A EP05022489A EP1659550A2 EP 1659550 A2 EP1659550 A2 EP 1659550A2 EP 05022489 A EP05022489 A EP 05022489A EP 05022489 A EP05022489 A EP 05022489A EP 1659550 A2 EP1659550 A2 EP 1659550A2
Authority
EP
European Patent Office
Prior art keywords
data
section
survey
traffic
signatures
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
Application number
EP05022489A
Other languages
German (de)
English (en)
Other versions
EP1659550A3 (fr
EP1659550B1 (fr
Inventor
Marc-André Funk
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Mercedes Benz Group AG
Original Assignee
DaimlerChrysler AG
Daimler AG
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by DaimlerChrysler AG, Daimler AG filed Critical DaimlerChrysler AG
Publication of EP1659550A2 publication Critical patent/EP1659550A2/fr
Publication of EP1659550A3 publication Critical patent/EP1659550A3/fr
Application granted granted Critical
Publication of EP1659550B1 publication Critical patent/EP1659550B1/fr
Anticipated expiration legal-status Critical
Expired - Lifetime legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07B—TICKET-ISSUING APPARATUS; FARE-REGISTERING APPARATUS; FRANKING APPARATUS
    • G07B15/00—Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points
    • G07B15/06—Arrangements for road pricing or congestion charging of vehicles or vehicle users, e.g. automatic toll systems
    • G07B15/063—Arrangements for road pricing or congestion charging of vehicles or vehicle users, e.g. automatic toll systems using wireless information transmission between the vehicle and a fixed station
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station
    • 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 generally to traffic data collection systems. Specifically, the invention relates to the methods used in such systems for the evaluation of collected traffic data, in particular for the collection of tolls.
  • Vehicle autonomous toll collection systems are increasingly being used. Due to the financial background, the requirements for precision for reliably avoiding bad bookings are of particular importance for such systems. Therefore, precautionary measures must be taken in the evaluation of incoming data in order to detect irregularities - for example caused by technical disturbances in the transmission path - quickly and reliably.
  • Modern evaluation methods are therefore based on modeling data, which are used as a reference for the plausibility check of the currently collected data. As modeling data, node-edge models of the traffic route network are usually used. The efficiency of such toll collection systems is thus crucially dependent on the quality of the modeling data. For previous methods, the modeling data with measurement errors are collected and quality assured. This procedure is relatively complex, offers no time resolution and has a low statistical significance.
  • the present invention has for its object to develop a method for traffic data acquisition, which overcomes the known disadvantages and has other advantages and is particularly suitable for autonomous toll collection systems. This object is achieved by the method for detecting traffic data with the features of claim 1. Further details and advantageous embodiments of the invention are the subject of the dependent claims.
  • journeys traveled by motor vehicles are compiled on the basis of transmitted data from individual parts of predetermined motorway sections of a traffic network model. Corrections are made on plausibility considerations (coherent route, no double motorway sections, completion of missing pieces, logical time sequence, etc.). By taking place at predetermined time intervals statistical analysis of the data collected error sources are detected in the traffic data collection system. This allows timely quality monitoring of the entire system.
  • the information about the motorway sections (“motorway section”) and the node (“node”) can be used to determine the "FromNach” relation.
  • the updating of the data model is carried out by updating the basic data.
  • the individual road sections raised to a vehicle may be denoted by i, j, k. If, following the relation "node", the highway section j can follow immediately after i, the next survey j with k can be considered.
  • the data constellation is also considered correct if the highway section j is allowed to follow the motorway section i immediately following the relation "node optimization" and i carries an exit identifier and j carries an entry identifier.
  • the potential event (gaps with input / exit identifier) before an entry in the event table does not cross checked against any manual entry to become. This analysis must also be carried out for survey data with the value 0 € (vehicle with vehicle unit has been manually checked in).
  • Event lists are preferably generated for more analysis with more universal tools, including other cuts are used to amplify signals of interest and to set them apart from the background (noise).
  • the duration of the program is sufficient as the observation period, but it is also possible to choose any period of observation (for example, hourly or daily) (see section 4.3).
  • the survey data are provided by the EDP in the form of summary data pots (SDT). These are first to be broken down into individual surveys. Care must be taken to ensure that information about the sum data pot as well as the position in the sum data pot is not lost. The original data can then be discarded.
  • SDT summary data pots
  • Events with local signatures can be displayed in the format shown in Table 1 plus the "Signature” column and stored in individual case lists. For events with non-local signatures, however, specific data formats (which must also contain the "Signature” column) must be defined.
  • the fields of stored events are also referred to as event variables.
  • the plausibility of survey data is initially checked locally.
  • the data fields contained in the survey data are to be tested against a range of values.
  • the range of values results from this model.
  • upper and lower bounds or a discrete set should be specified as hard cuts .
  • the attribute "safe” is used in the sense of "unique”. Thus, for example, even with signatures of length 2 (see FIG. 2) gaps can be recognized, although here it is not certain that exactly one section was not or was raised incorrectly.
  • the data stream contains three sections p, q and r that do not form a contiguous path. There is no need for another hard cut parameter.
  • Figure 3 shows some non-local 4-length signatures without considering the entrance and exit identifiers. It is clear that due to the strong combinatorial dependence on length, the number of cases to be considered increases strongly. The relatively low profits of the information discernible by these signatures, at the present time, do not appear to make the effort associated with recognition as proportionate. All basic functions of the survey can be checked quantitatively and qualitatively well with the signatures shown in FIG. 1 and FIG.
  • the events of the individual signatures are to be statistically evaluated according to the invention. This is done technically by classifying and counting events (absolute frequency) and normalizing to a corresponding population to obtain relative frequencies. In this case, for independent events, the error (the standard deviation) of the absolute frequency by the root is to be given (estimated) from this frequency. This means that the true value of the underlying random variable is 67% probability in the interval N ⁇ ⁇ N. The relative error of a measurand therefore falls with 1 / ⁇ N.
  • a (one-dimensional) histogram is a set of counters (channels) in which the frequency of occurrence of a value of an event variable is recorded.
  • a channel is also called bin , the assignment of value ranges of the histogrammed variables to the channels is called binning.
  • the histogram should always be kept on the number of individual surveys per STD.
  • two- or multi-dimensional histograms result.
  • a two-dimensional histogram would be the number of bumps above the EHK ID and over the leg.
  • Multidimensional histograms can be reduced in their dimensionality by means of projections (adding all entries over a dimension to be eliminated). By increasing the absolute number of events, the relative error per bin is reduced.
  • All statistically collected data should preferably be issued in the form of database tables regularly (ie once per observation period), ie made available to subsequent programs. After a period of observation, all counter contents are to be deleted. Counters should not be persistent, ie they are initialized to zero at program start. For each output, a header table is to be output, which contains the period of observation as well as the values used for signature detection hardcuts and possibly further options and parameters that are important for the interpretation of the data.
  • event lists contain, among other things, data describing individual events (see Section 3). These lists can serve as data base for further analysis tools (data warehouses, etc.).
  • Filter conditions are thus general logical expressions that are evaluated by accessing the data fields of an event. To allow the user to formulate their own filter conditions, a parser for these expressions can be implemented. However, this is not absolutely necessary if filter conditions are first anchored in the program code.
  • each filter In an expanded embodiment of the method according to the invention, several filters can be operated simultaneously. In this case, each filter generates its own output list, in which data can be contained redundantly.
  • Kirchhoff's rules Freedom from sources here means that the observed objects can not spontaneously arise from a source or disappear in a sink.
  • a source-free flow can be assumed, and thus divergences in the flow can be regarded as an indication of error effects).
  • Kirchhoff's rule in the present case is that the sum of all flows at a node must be zero.
  • the survey efficiency is the quotient of sections correctly identified as individual surveys and the number of sections of the actual vehicle on the actual journey. For a majority of the sections from the topological signatures of length 3 depicted in Section 3.2, using the absolute frequencies shown in Section 4.1, this collection efficiency thus defined can be approximately estimated as the quotient of the sum of the absolute frequencies of the events with the signatures "Safe On - Section Gaps "and” Safe One-Section Misidentification "and the number of individual surveys plus the number of events with the" Secure One-Section Gaps "signature.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Business, Economics & Management (AREA)
  • Finance (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Complex Calculations (AREA)
  • Traffic Control Systems (AREA)
  • Devices For Checking Fares Or Tickets At Control Points (AREA)
EP05022489A 2004-11-19 2005-10-14 Procédé pour détecter des données de traffic Expired - Lifetime EP1659550B1 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
DE102004055992A DE102004055992A1 (de) 2004-11-19 2004-11-19 Verfahren zur Erfassung von Verkehrsdaten

Publications (3)

Publication Number Publication Date
EP1659550A2 true EP1659550A2 (fr) 2006-05-24
EP1659550A3 EP1659550A3 (fr) 2007-05-23
EP1659550B1 EP1659550B1 (fr) 2009-01-21

Family

ID=35427273

Family Applications (1)

Application Number Title Priority Date Filing Date
EP05022489A Expired - Lifetime EP1659550B1 (fr) 2004-11-19 2005-10-14 Procédé pour détecter des données de traffic

Country Status (3)

Country Link
EP (1) EP1659550B1 (fr)
AT (1) ATE421743T1 (fr)
DE (2) DE102004055992A1 (fr)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102081846A (zh) * 2011-02-22 2011-06-01 交通运输部公路科学研究所 基于高速公路收费数据轨迹匹配的交通状态识别方法
EP2811468A2 (fr) 2013-06-04 2014-12-10 Toll Collect GmbH Procédé et dispositif de reconnaissance d'erreurs dans un système de péage
CN104318772A (zh) * 2014-10-31 2015-01-28 重庆大学 高速公路交通流数据质量检测方法
EP2922031A1 (fr) 2014-12-09 2015-09-23 Toll Collect GmbH Procédé et dispositifs de détection des erreurs dans un système de péage
CN106056912A (zh) * 2016-07-29 2016-10-26 浙江银江研究院有限公司 一种卡口运行状态量化评估方法及系统
EP3300030A1 (fr) * 2016-09-22 2018-03-28 Toll Collect GmbH Dispositif de traitement de données et procédé de réduction de la complexité d'un réseau de tronçons de route

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5164904A (en) * 1990-07-26 1992-11-17 Farradyne Systems, Inc. In-vehicle traffic congestion information system
DE19755875A1 (de) * 1996-12-09 1998-06-10 Mannesmann Ag Verfahren zur Übertragung von Ortsdaten und Meßdaten von einem Endgerät, insbesondere Telematikendgerät an eine Verkehrszentrale
DE10241538B3 (de) * 2002-09-05 2004-04-22 Deutsches Zentrum für Luft- und Raumfahrt e.V. Routingverfahren und ein Routingsystem mit einer Verkehrsprognose

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102081846B (zh) * 2011-02-22 2013-06-05 交通运输部公路科学研究所 基于高速公路收费数据轨迹匹配的交通状态识别方法
CN102081846A (zh) * 2011-02-22 2011-06-01 交通运输部公路科学研究所 基于高速公路收费数据轨迹匹配的交通状态识别方法
EP2811468A3 (fr) * 2013-06-04 2015-04-29 Toll Collect GmbH Procédé et dispositif de reconnaissance d'erreurs dans un système de péage
EP2811468A2 (fr) 2013-06-04 2014-12-10 Toll Collect GmbH Procédé et dispositif de reconnaissance d'erreurs dans un système de péage
EP2811467A2 (fr) 2013-06-04 2014-12-10 Toll Collect GmbH Procédé et dispositifs de détection des erreurs dans un système de péage
EP2811466A2 (fr) 2013-06-04 2014-12-10 Toll Collect GmbH Procédé et dispositifs de détection des erreurs dans un système de péage
EP2811467A3 (fr) * 2013-06-04 2014-12-31 Toll Collect GmbH Procédé et dispositifs de détection des erreurs dans un système de péage
EP2811466A3 (fr) * 2013-06-04 2015-04-15 Toll Collect GmbH Procédé et dispositifs de détection des erreurs dans un système de péage
CN104318772A (zh) * 2014-10-31 2015-01-28 重庆大学 高速公路交通流数据质量检测方法
EP2922031A1 (fr) 2014-12-09 2015-09-23 Toll Collect GmbH Procédé et dispositifs de détection des erreurs dans un système de péage
CN106056912A (zh) * 2016-07-29 2016-10-26 浙江银江研究院有限公司 一种卡口运行状态量化评估方法及系统
CN106056912B (zh) * 2016-07-29 2018-10-09 浙江银江研究院有限公司 一种卡口运行状态量化评估方法及系统
EP3300030A1 (fr) * 2016-09-22 2018-03-28 Toll Collect GmbH Dispositif de traitement de données et procédé de réduction de la complexité d'un réseau de tronçons de route

Also Published As

Publication number Publication date
EP1659550A3 (fr) 2007-05-23
ATE421743T1 (de) 2009-02-15
DE102004055992A1 (de) 2006-06-14
EP1659550B1 (fr) 2009-01-21
DE502005006523D1 (de) 2009-03-12

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