WO2011120194A1 - Procédé, système et nœud pour la mesure d'une durée de trajet dans un réseau routier - Google Patents

Procédé, système et nœud pour la mesure d'une durée de trajet dans un réseau routier Download PDF

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Publication number
WO2011120194A1
WO2011120194A1 PCT/CN2010/000412 CN2010000412W WO2011120194A1 WO 2011120194 A1 WO2011120194 A1 WO 2011120194A1 CN 2010000412 W CN2010000412 W CN 2010000412W WO 2011120194 A1 WO2011120194 A1 WO 2011120194A1
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WO
WIPO (PCT)
Prior art keywords
node
characteristic
neighbor
car sequence
nodes
Prior art date
Application number
PCT/CN2010/000412
Other languages
English (en)
Inventor
Dan Yu
Leiming Xu
Wei Qiu
Michael Sax
Original Assignee
Siemens Aktiengesellschaft
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 Siemens Aktiengesellschaft filed Critical Siemens Aktiengesellschaft
Priority to EP10848656.4A priority Critical patent/EP2553672A4/fr
Priority to PCT/CN2010/000412 priority patent/WO2011120194A1/fr
Publication of WO2011120194A1 publication Critical patent/WO2011120194A1/fr

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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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
    • 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
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
    • 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
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • 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
    • G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
    • 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
    • G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
    • 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
    • G08G1/042—Detecting movement of traffic to be counted or controlled using inductive or magnetic detectors

Definitions

  • This invention relates to traffic information acquisition technique, and more particularly relates to a method, system and node for journey time measurement in a road network.
  • Journey time measurement system is a basic traffic information acquisition system in Information Transfer System (ITS).
  • Journey time of link represents average traffic speed and status of traffic congestion on a road segment (i.e. a link) of a road network.
  • the journey time of link provides foundation of traffic control at a local area. After collecting journey time of every link in the road network, journey time along a route (including a number of links) can be estimated by adding up all link journey times along this route.
  • the journey time along a route provides evidence for route planning and optimization of traffic control in the whole urban area.
  • link coverage and flexibility are the most important problems to be solved.
  • Different methods are provided in the conventional art for journey time measurement.
  • a kind of detectors will be mounted at different positions (measured points) in the road network to detect some information of vehicles, and information from different detectors will be synthesized to estimate the journey time between detectors.
  • information from different detectors will be synthesized to estimate the journey time between detectors.
  • a lot of detectors will be deployed.
  • a big volume of data will be generated from detectors and need to be processed. This results in challenges to communication capacity, computation capacity and cost of the system.
  • Systems like automatic number plate recognition (ANPR) or RFID based JTMS, detect some unique identity of a vehicle and recognize the vehicle by its unique identity.
  • ANPR automatic number plate recognition
  • RFID based JTMS RFID based JTMS
  • cameras are installed at different sites to capture images of vehicles. Texts on license plates of vehicles will be recognized. By matching license plates from different sites, the system can identify a same vehicle at different sites and learn the time difference, i.e. the journey time, between the two sites.
  • Another similar scheme is Dacolian's JTMS system, in which some unique feature (or characteristics) of a vehicle is extracted from images and coded as a signature. By comparing signatures, the system can identify same vehicles and learn the journey time.
  • this method usually involves costly equipments and high computation complexity of algorithm.
  • ANPR based or Dacolian's JTMS cameras of high resolution and high frame rate must be used to make the video clear enough for OCR or other unique feature recognition. This also results in limited coverage area of a single camera.
  • a vehicle group also called as a car sequence or a vehicle sequence
  • vehicle group also called as a car sequence or a vehicle sequence
  • vehicles in the same road segment at the same time will remain a similar speed.
  • two vehicles are close to each other when they arrive at the beginning of the road segment, their relative locations will not change very much at the end of this road segment, especially when the road segment is not so long.
  • a first feature sequence i.e. some features of a first sequence of cars
  • a second feature sequence features of a second sequence of cars
  • the first and second feature sequences are reported to some device and compared by the device to find whether the car sequences at the two places are matched. If the first and second car sequences are matched, the journey time through this road segment can be obtained.
  • features detected in such a system do not have to differentiate this vehicle from others. Any common features, such as vehicle color, shape, weight etc., can be utilized for vehicle group matching. For example, a solution is proposed to use a loop to detect the length of a vehicle and use characteristics in length sequence to recognize a vehicle sequence.
  • a system is adapted to measure only one link (a road segment between 2 intersections) or one route (including consecutive links), wherein a detector is deployed at each end of the link or route. For every link or route to measure, two new detectors are deployed (at two ends respectively). Detectors and data are not shared between these separate JTMSs.
  • TMC Traffic Management Center
  • a new method, system architecture of JTMS i.e. distributed JTMS
  • a node structure in which the system can be constructed automatically and configured flexibly and data processing can be distributed to every node, so that the problems of computation capacity, communication capacity and system flexibility can be solved.
  • a method for journey time measurement in a road network includes:
  • the first node is any node in the road network
  • detecting characteristic of a car sequence sequentially passing through the measured point of the node includes:
  • determining, at a first node, one or more neighbor nodes of the first node includes:
  • the first node broadcasting, by the first node, the characteristic of the car sequence of the first node during a period of time to part of or all the other nodes in the road network; any other node performing characteristic matching between the characteristic of the car sequence detected by itself and the characteristic of the car sequence of the first node, and sending a neighborhood indication to the first node when the two characteristic are found to be matched;
  • determining, at a first node, one or more neighbor nodes of the first node includes:
  • comparing, at the neighbor node, the characteristic of the car sequence of the first node and the characteristic of the car sequence of the neighbor node to find a matching position includes:
  • each of the multiple second segments has the same length as the first segment
  • obtaining, at the neighbor node, a journey time from the first node to the neighbor node according to the matching position includes:
  • a system for journey time measurement in a road network includes:
  • multiple nodes each of which is placed at a measured point and adapted to detect characteristic of a car sequence sequentially passing through the measured point of the node, wherein multiple measured points are designated in the road network;
  • a first node among the multiple nodes is further adapted to determine one or more neighbor nodes of the first node, and report to the neighbor nodes the characteristic of the car sequence of the first node;
  • any of the neighbor nodes is adapted to compare the characteristic of the car sequence of the first node and the characteristic of the car sequence of the neighbor node to find a matching position, and obtain a journey time from the first node to the neighbor node according to the matching position.
  • the first node is adapted to broadcast the characteristic of the car sequence of the first node during a period of time to part of or all the other nodes in the road network;
  • any other node is adapted to perform characteristic matching between the characteristic of the car sequence detected by itself and the characteristic of the car sequence of the first node, and send a neighborhood indication to the first node when the two characteristic are found to be matched;
  • the first node is adapted to record any other node that has sent the neighborhood indication to the first node as a neighbor node.
  • the system further includes:
  • a management center adapted to provide configuration information to the first node for informing the first node of its neighbor nodes, and receive the journey time from the first node to any of its neighbor nodes reported by the neighbor node.
  • a node placed at a measured point in a road network for journey time measurement includes:
  • a detector adapted to detect characteristic of a car sequence sequentially passing through the measured point of its node
  • a communication unit adapted to receive characteristic of a car sequence of another node and provide the characteristic of the car sequence of another node to a computation unit
  • the computation unit adapted to compare me characteristic of the car sequence of another node and the characteristic of the car sequence of its node to find a matching position, and obtain a journey time from another node to its node according to the matching position.
  • the node further includes:
  • a neighborhood management unit adapted to record any other node that has sent a neighborhood indication to its node into a neighbor list, and instruct the communication unit to send the characteristic of the car sequence of its node to neighbor nodes in the neighbor list;
  • the communication unit is further adapted to broadcast the characteristic of the car sequence of its node detected by the detector during a period of time to part of or all the other nodes in the road network, and report the characteristic of the car sequence of its node to the neighbor nodes after receiving the instruction from the neighborhood management unit.
  • the node further includes:
  • a neighborhood management unit adapted to update its neighbor list according to configuration information from a management center, and inform the communication unit to send the characteristic of the car sequence of its node to neighbor nodes in the neighbor list.
  • FIG. 1 is an ANPR based JTMS in the conventional art
  • FIG. 2 is a JTMS using vehicle group matching in the conventional art
  • FIG. 3 is an example of a distributed JTMS provided in an embodiment of the present invention.
  • FIG. 4 is an automatic topology learning procedure in an embodiment of the present invention
  • FIG. 5 is a structure of a node used in a distributed JTMS according to an embodiment of the present invention.
  • a node including a detector (also named as a sensor) will be mounted at every measured point in a road network, and all the nodes will be connected to form a JTMS. It should be noted that multiple measured points are designated in the road network according to characteristics of real roads, to guarantee that the actual road distance of two measured points is not so long.
  • the detector can be any kind of detectors capable of obtaining characteristic of a car sequence, such as obtaining variation of magnetic field caused by each of the cars sequentially passing through the measured point of the node; or obtaining color, shape or voiceprint of each of the cars; or obtaining any combination of variation of magnetic field, color, shape and voiceprint of each of the cars.
  • nodes 1-8 are located at some points (referred to as measured points) in a street map of the road network, wherein the arrow represents a road direction.
  • Nodes 1 and 7 send their data to node 2 since node 2 is their neighbor node.
  • Node 2 finds matches between data detected by itself and data from nodes 1 and 7, and knows journey time of link between nodes 1 and 2 and journey time of link between nodes 7 and 2.
  • node 4 will learn journey time of 3 ⁇ — > 4; node 5 will learn journey time of 4 ⁇ -- 5; node 6 will learn journey time of 3 ⁇ — 6 and 4 6; node 8 will learn journey time of 7 ⁇ --> 8 etc.
  • Step 1 Node 2 selects a first segment from characteristic of the car sequence of node 1 , and selects multiple second segments from characteristic of the car sequence of node 2, wherein each of the multiple second segments has the same length as the first segment.
  • Step 2 Node 2 performs correlation between the first segment and each of the multiple second segments, and determines a matching position according to the maximum correlation coefficient.
  • Step 3 Node 2 determines a first time of the matching position on the characteristic of the car sequence of node 1, determines a second time of the matching position on the characteristic of the car sequence of node 2, and obtains the delay between the second time and the first time as the journey time from node 1 to node 2.
  • every node knows its neighbor nodes, and will send data generated by itself to the neighbor nodes. Also, every node is possible to serve as a neighbor node of other nodes, will receive data from the other nodes and is responsible to calculate the journey times between the other nodes and itself.
  • knowledge of neighborhood can be configured by a TMC according to a map of the road network, provided that the TMC knows location of every node and knows which links' journey times are wanted. That is, the TMC can tell every node to which nodes it should send data. Then, the node will configure one or more other nodes as its neighbor nodes according to the instruction from the TMC, and store the configuration on itself.
  • neighborhood relationship can be learnt by nodes automatically.
  • every node will broadcast its data, i.e. characteristic of a car sequence passing through itself, to other nodes in the system.
  • data i.e. characteristic of a car sequence passing through itself
  • a node can learn which nodes have direct links to it and feed back matching information to the other nodes having direct links.
  • every node will set the other nodes providing matching information to it as its neighbor nodes.
  • all nodes stop broadcasting and only send data to their neighbor nodes.
  • a possible automatic topology; learning procedure can be as illustrated in Figure
  • Node A performs feature detection and broadcasts the feature detected to nodes B-D and nodes U- W. At the same time, nodes B-D and nodes U-W perform feature detection at their own points, respectively.
  • Each of nodes B-D and nodes U-W compares the feature detected by itself and the feature broadcasted by node A for feature sequence matching.
  • nodes B-D send a neighborhood indication to node A.
  • Node A records nodes B-D as its neighbor nodes, and only transmits feature of its car sequence to its neighbor nodes. As to nodes U-W, since no matches have been found, node A will not provide feature of its car sequence to nodes U-W any longer.
  • node B is a neighbor node of node A
  • node A may not necessarily be a neighbor node of node B.
  • node 3 is a neighbor node of node 2
  • node 2 is not a neighbor node of node 3.
  • the node includes: a detector, a communication unit, a computation unit and a neighborhood management unit.
  • the detector is adapted for detecting features of vehicles on road. Any features, which are detectable and can be regarded as unchanged between two detecting points, can be utilized by the detector. Specifically, the features can be such as color, shape parameters, weight, voiceprint and even gray level of image. Of course, identities, such as RFID or license plate number, also can be used here. Detected features may be sent to the computation unit for searching matches with data from other nodes, and/or sent to neighbor nodes through the communication unit.
  • the communication unit is adapted to receive configuration information from a management center, report journey time measurement results to the management center, and receive/send data from/to other nodes.
  • the computation unit is adapted to find matching vehicle groups from data of itself and data of other nodes it received, and calculate journey times. Usually, the computation unit needs a memory to buffer a certain time period of data from other nodes and from itself. In case of some identity used, it will retrogress to individual vehicle matching.
  • the neighborhood management unit is adapted for maintaining a list of neighbor nodes, and informing the communication unit to whom data should be sent.
  • the neighborhood management unit regards all other nodes as its neighbor nodes, and the communication unit will broadcast data to all the other nodes. After neighborhood indications are received by the communication unit, the neighborhood management unit will extract neighborhood information from the communication unit, and update its list.
  • the neighborhood management unit is adapted for updating its neighbor list according to configuration information from the management center.
  • Measured links can be selected easily by configuration, and nodes are not dedicated to any measured link or route.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Traffic Control Systems (AREA)

Abstract

La présente invention porte sur un procédé, un système et un nœud pour la mesure d'une durée de trajet dans un réseau routier. Le procédé comprend : la désignation de multiples points mesurés dans le réseau routier, le placement d'un nœud à chacun des multiples points mesurés, et la détection d'une caractéristique d'une séquence de véhicules passant séquentiellement par le point mesuré du nœud; la détermination, au niveau d'un premier nœud, d'un ou plusieurs nœuds voisins du premier nœud, et le rapport à un nœud voisin, de la caractéristique de la séquence de véhicules du premier nœud, le premier nœud étant n'importe quel nœud dans le réseau routier; la comparaison, au niveau du nœud voisin, de la caractéristique de la séquence de véhicules du premier nœud avec la caractéristique de la séquence de véhicules du nœud voisin pour trouver une position correspondante; l'obtention, au niveau du nœud voisin, d'une durée de trajet du premier nœud au nœud voisin selon la position correspondante.
PCT/CN2010/000412 2010-03-31 2010-03-31 Procédé, système et nœud pour la mesure d'une durée de trajet dans un réseau routier WO2011120194A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP10848656.4A EP2553672A4 (fr) 2010-03-31 2010-03-31 Procédé, système et n ud pour la mesure d'une durée de trajet dans un réseau routier
PCT/CN2010/000412 WO2011120194A1 (fr) 2010-03-31 2010-03-31 Procédé, système et nœud pour la mesure d'une durée de trajet dans un réseau routier

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PCT/CN2010/000412 WO2011120194A1 (fr) 2010-03-31 2010-03-31 Procédé, système et nœud pour la mesure d'une durée de trajet dans un réseau routier

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US9449258B1 (en) 2015-07-02 2016-09-20 Agt International Gmbh Multi-camera vehicle identification system
CN106448187A (zh) * 2016-11-14 2017-02-22 苏州大学 基于磁传感器和超声波传感器融合的车辆检测系统及方法
US11915308B2 (en) 2018-05-10 2024-02-27 Miovision Technologies Incorporated Blockchain data exchange network and methods and systems for submitting data to and transacting data on such a network

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CN107766945B (zh) * 2017-09-06 2021-03-02 北京交通发展研究院 城市路网承载力的计算方法

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Cited By (5)

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US9449258B1 (en) 2015-07-02 2016-09-20 Agt International Gmbh Multi-camera vehicle identification system
WO2017002118A1 (fr) 2015-07-02 2017-01-05 Agt International Gmbh Système d'identification de véhicules par caméras multiples
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CN106448187A (zh) * 2016-11-14 2017-02-22 苏州大学 基于磁传感器和超声波传感器融合的车辆检测系统及方法
US11915308B2 (en) 2018-05-10 2024-02-27 Miovision Technologies Incorporated Blockchain data exchange network and methods and systems for submitting data to and transacting data on such a network

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