EP3432286B1 - Distributions d'estimation de temps de trajet sur des voies artérielles signalisées - Google Patents

Distributions d'estimation de temps de trajet sur des voies artérielles signalisées Download PDF

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Publication number
EP3432286B1
EP3432286B1 EP18191898.8A EP18191898A EP3432286B1 EP 3432286 B1 EP3432286 B1 EP 3432286B1 EP 18191898 A EP18191898 A EP 18191898A EP 3432286 B1 EP3432286 B1 EP 3432286B1
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EP
European Patent Office
Prior art keywords
signalized
travel data
devices
arterial
time
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EP18191898.8A
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German (de)
English (en)
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EP3432286A1 (fr
Inventor
J.D. Margulici
Kevin ADDA
Andre Gueziec
Edgar Rojas
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Allied Security Trust
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Allied Security Trust
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Priority claimed from PCT/US2013/023505 external-priority patent/WO2013113029A1/fr
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    • 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
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • 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
    • 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
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • 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

Definitions

  • the present invention generally concerns traffic management. More specifically, the present invention concerns estimating time travel distributions on signalized arterials and thoroughfares.
  • Highways carry a majority of all vehicle-miles travelled on roads and are instrumented with traffic detectors. Notably, highways lack traffic signals (i.e., they are not "signalized"). Estimating traffic conditions on signalized streets represents a far greater challenge for two main reasons. First, traffic flows are interrupted because vehicles must stop at signalized intersections. These interruptions generate complex traffic patterns. Second, instrumentation amongst signalized arterials is sparse because the low traffic volumes make such instrumentation difficult to justify economically.
  • GPS global positioning system
  • Published European Patent Application EP 1235 195 A2 relates to a method of presuming traffic conditions for implementing a forecast and a presumption of traffic jam situation in an area where probe cars are not travelling currently.
  • the probe cars send "floating car data" (i.e. times and positions of travelled areas) to a central facility.
  • the central facility accumulates the floating car data in a floating car data database by traffic conditions presumption means and also presumes forecast traffic jam information in the forward areas of the probe cars and presumed traffic jam information in the backward areas thereof by using the current floating car data and the floating car data database accumulated from the past to the present.
  • the present invention provides a method as defined in the appended claims.
  • a system for estimating time travel distributions on signalized arterials includes a processor, memory, and an application stored in memory.
  • the application is executable by the processor to receive data regarding travel times on a signalized arterial, estimate a present distribution of the travel times, estimate a prior distribution based on one or more travel time observations, and calibrate the present distribution based on the prior distribution.
  • FIGURE 1 is a block diagram of a system for estimating time travel distributions on signalized arterials.
  • the system of FIGURE 1 includes a client computer 110, network 120, and a server 130.
  • Client computer 110 and server 130 may communicate with one another over network 120.
  • Client computer 110 may be implemented as a desktop, laptop, work station, notebook, tablet computer, smart phones, mobile device or other computing device.
  • Network 120 may be implemented as one or more of a private network, public network, WAN, LAN, an intranet, the Internet, a cellular network or a combination of these networks.
  • Client computer 110 may implement all or a portion of the functionality described herein, including receive traffic data and other data or and information from devices using re-identification technologies. Such technologies may be based on magnetic signatures, toll tags, license plates, or embedded devices.
  • Server 130 may receive probe data from GPS-connected mobile devices. Server 130 may communicate data directly with such data collection devices. Server 130 may also communicate, such as by sending and receiving data, with a third-party server, such as the one maintained by Sensys Networks, Inc. of Berkeley and accessible through the Internet at www. sensysresearch. com.
  • Server computer 130 may communicate with client computer 110 over network 120.
  • Server computer may perform all or a portion of the functionality discussed herein, which may alternatively be distributed between client computer 110 and server 130, or may be provided by server 130 as a network service for client 110.
  • Each of client 110 and server computer 130 are listed as a single block, but it is envisioned that either be implemented using one or more actual or logical machines.
  • the system may utilize Bayesian Inference principles to update a prior belief based on new data.
  • the system may determine the distribution of travel times y on a given signalized arterial at the present time T.
  • the prior beliefs may include the shape of the travel time distribution and the range of its possible parameters ⁇ T (e.g., mean and standard deviation) that are typical of a given time of day, such that y follows a probability function p(y
  • the prior distribution may comprise its own set of parameters ⁇ , which are referred to as hyper-parameters.
  • the system may estimate the current parameters using a recent travel time observation of the arterial of interest.
  • the system may also account for observations on neighboring streets.
  • the system may consider contextual evidence such as local weather, incidents, and special events such as sporting events, one off road closures, or other intermittent traffic diversions.
  • y* may designate the current travel time observations.
  • the system may determine the likeliest ⁇ T using a known y* and ⁇ .
  • the system 100 may account for one or more travel time variability components.
  • System 100 may account for other time travel variability components.
  • the system 100 may employ standard Traffic Message Channel (TMC) location codes as base units of space, and fifteen-minute periods as base units of time. In such an embodiment, the system approximates that traffic conditions remain homogeneous across a given TMC location code over each fifteen-minute period.
  • TMC Traffic Message Channel
  • the system 100 may also use other spatial or temporal time units depending on the degree of precision desired. For example, the system 100 may normalize travel time data into a unit of pace that is expressed in seconds per mile. The system 100 may also calculate the average pace as a linear combination of individual paces weighted by distance travelled. Such calculations may be more convenient than using speed values.
  • TMC Traffic Message Channel
  • FIGURE 2 is a series of graphs showing distributions of pace on a signalized arterial segment at the same time on over three consecutive days. More specifically, FIGURE 2 shows an exemplary distribution of pace on a 2-km arterial segment in Seattle, Washington for the same fifteen-minute time period on three consecutive days. As suggested in FIGURE 2 , determining an exact distribution shape for a given fifteen minute period on any given day may pose a difficult realistic objective.
  • the presently described system can, however, directly observe three different states of an arterial segment and then calibrate the prior probabilities of being in either state from archived data. The system may also use real-time data to help refine a given belief regarding which of the multiple state applies to the real-time prediction.
  • FIGURE 3 is a graph showing variations in pace throughout different times periods in a day.
  • the presently disclosed system may account for time-of-day variations.
  • the box indicates the 25th, 50th, and 75th percentile value while the dotted lines extend to extreme values.
  • the system may use data regarding regular patterns of increase and decrease in travel times to calibrate prior distributions by time of day.
  • FIGURE 4 is a block diagram of a device 400 for implementing an embodiment of the presently disclosed invention.
  • Device 400 of FIGURE 4 may be implemented in the contexts of the likes of client computer 110 and server computer 130.
  • the device 400 of FIGURE 4 includes one or more processors 410 and memory 420.
  • Main memory 420 may store, in part, instructions and data for execution by processor 410.
  • Main memory can store the executable code when in operation.
  • the device 400 of FIGURE 4 further includes a storage 430, which may include mass storage and portable storage, antenna 440, output devices 450, user input devices 460, a display system 470, and peripheral devices 480.
  • FIGURE 4 The components shown in FIGURE 4 are depicted as being connected via a single bus 490.
  • the components may, however, be connected through one or more means of data transport.
  • processor unit 410 and main memory 420 may be connected via a local microprocessor bus
  • the storage 430, peripheral device (s) 480 and display system 470 may be connected via one or more input/output (I/O) buses.
  • I/O input/output
  • the exemplary computing device of FIGURE 4 should not be considered limiting as to implementation of the presently disclosed invention.
  • Embodiments may utilize one or more of the components illustrated in FIGURE 4 as might be necessary and otherwise understood to one of ordinary skill in the art.
  • Storage device 430 which may include mass storage implemented with a magnetic disk drive or an optical disk drive, may be a non-volatile storage device for storing data and instructions for use by processor unit 410. Storage device 430 can store the system software for implementing embodiments of the present invention for purposes of loading that software into main memory 410.
  • Portable storage device of storage 430 operates in conjunction with a portable non-volatile storage medium, such as a floppy disk, compact disk or Digital video disc, to input and output data and code to and from the device 400 of FIGURE 4 .
  • a portable non-volatile storage medium such as a floppy disk, compact disk or Digital video disc
  • the system software for implementing embodiments of the present invention may be stored on such a portable medium and input to the device 400 via the portable storage device.
  • Antenna 440 may include one or more antennas for communicating wirelessly with another device.
  • Antenna 440 may be used, for example, to communicate wirelessly via Wi-Fi, Bluetooth, with a cellular network, or with other wireless protocols and systems including but not limited to GPS, A-GPS, or other location based service technologies.
  • the one or more antennas may be controlled by a processor 410, which may include a controller, to transmit and receive wireless signals.
  • processor 410 execute programs stored in memory 412 to control antenna 440 transmit a wireless signal to a cellular network and receive a wireless signal from a cellular network.
  • the device 400 as shown in FIGURE 4 includes output devices 450 and input device 460.
  • Examples of suitable output devices include speakers, printers, network interfaces, and monitors.
  • Input devices 460 may include a touch screen, microphone, accelerometers, a camera, and other device.
  • Input devices 460 may include an alpha- numeric keypad, such as a keyboard, for inputting alpha-numeric and other information, or a pointing device, such as a mouse, a trackball, stylus, or cursor direction keys.
  • Display system 470 may include a liquid crystal display (LCD), LED display, or other suitable display device.
  • Display system 470 receives textual and graphical information, and processes the information for output to the display device.
  • Peripherals 480 may include any type of computer support device to add additional functionality to the computer system.
  • peripheral device (s) 480 may include a modem or a router.
  • the components contained in the device 400 of FIGURE 4 are those typically found in computing system, such as but not limited to a desk top computer, lap top computer, notebook computer, net book computer, tablet computer, smart phone, personal data assistant (PDA), or other computer that may be suitable for use with embodiments of the present invention and are intended to represent a broad category of such computer components that are well known in the art.
  • the device 400 of FIGURE 4 can be a personal computer, hand held computing device, telephone, mobile computing device, workstation, server, minicomputer, mainframe computer, or any other computing device.
  • the computer can also include different bus configurations, networked platforms, multi-processor platforms, etc.
  • Various operating systems can be used including Unix, Linux, Windows, Macintosh OS, Palm OS, and other suitable operating systems.
  • One aspect provides a system for estimating time travel distributions on signalized arterials, comprising: a processor; memory; and an application stored in memory and executable by the processor to: receive travel data, estimate a distribution based on the travel data, and calibrate the distribution.

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  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Navigation (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Traffic Control Systems (AREA)

Claims (15)

  1. Procédé d'estimation de distribution de temps de parcours sur des artères signalées, le procédé comprenant les étapes consistant à :
    recevoir des données de parcours incluant une observation de temps de parcours récent concernant une artère signalée,
    normaliser les données de parcours en une pluralité de valeurs de cadence individuelles, les valeurs de cadence étant exprimées sous la forme d'un rapport de temps par distance,
    calculer une valeur de cadence moyenne pour l'artère signalée sous la forme d'une combinaison linéaire des valeurs de cadence individuelles pondérées par la distance parcourue à travers l'artère signalée,
    estimer une distribution sur la base de la valeur de cadence moyenne,
    recevoir des données de parcours en temps réel concernant l'artère signalée,
    calibrer la distribution estimée sur la base des données de parcours en temps réel, et
    générer une prédiction en temps réel des conditions de trafic de l'artère signalée sur la base de la distribution calibrée.
  2. Procédé selon la revendication 1, dans lequel les données de parcours concernant une artère signalée sont reçues à partir d'une pluralité de dispositifs de réidentification, des dispositifs de réidentification étant des dispositifs utilisant des technologies de réidentification sur la base d'au moins un parmi des signatures magnétiques, des badges de péage, des plaques d'immatriculation, ou des dispositifs intégrés.
  3. Procédé selon la revendication 2, dans lequel la pluralité de dispositifs de réidentification inclut un dispositif intégré.
  4. Procédé selon la revendication 2 ou 3, dans lequel la pluralité de dispositifs de réidentification inclut un badge de péage.
  5. Procédé selon l'une quelconque des revendications 2 à 4, dans lequel les données de parcours concernant une artère signalée sont collectées en identifiant des signatures magnétiques.
  6. Procédé selon l'une quelconque des revendications 2 à 5, dans lequel les données de parcours concernant une artère signalée sont collectées en identifiant des plaques d'immatriculation.
  7. Procédé selon la revendication 1, dans lequel les données de parcours sont reçues d'un serveur tiers.
  8. Procédé selon l'une quelconque des revendications 1 à 7, dans lequel les artères signalées sont des artères avec des signaux de trafic.
  9. Système (400) d'estimation de distribution de temps de parcours sur des artères signalées, comprenant :
    un processeur (410) ;
    une mémoire (420) ; et
    des instructions stockées dans la mémoire (420) et exécutables par le processeur (410) pour :
    recevoir des données de parcours incluant une observation de temps de parcours récent concernant une artère signalée,
    normaliser les données de parcours en une pluralité de valeurs de cadence individuelles, les valeurs de cadence étant exprimées sous la forme d'un rapport de temps par distance,
    calculer une valeur de cadence moyenne pour l'artère signalée sous la forme d'une combinaison linéaire des valeurs de cadence individuelles pondérées par la distance parcourue à travers l'artère signalée,
    estimer une distribution sur la base de la valeur de cadence moyenne,
    recevoir des données de parcours en temps réel concernant l'artère signalée,
    calibrer la distribution estimée sur la base des données de parcours en temps réel, et
    générer une prédiction en temps réel des conditions de trafic de l'artère signalée sur la base de la distribution calibrée.
  10. Système selon la revendication 9, dans lequel les instructions stockées dans une mémoire incluent des instructions exécutables par le processeur pour recevoir des données de parcours concernant une artère signalée à partir d'une pluralité de dispositifs de réidentification, des dispositifs de réidentification étant des dispositifs utilisant des technologies de réidentification sur la base d'au moins un parmi des signatures magnétiques, des badges de péage, des plaques d'immatriculation, ou des dispositifs intégrés.
  11. Système selon la revendication 10, dans lequel la pluralité de dispositifs de réidentification inclut un badge de péage.
  12. Système selon les revendications 10 ou 11, dans lequel la pluralité de dispositifs de réidentification inclut un dispositif intégré.
  13. Système selon la revendication 9, dans lequel les instructions stockées dans une mémoire incluent des instructions exécutables par le processeur pour recevoir les données de parcours concernant une artère signalée d'un serveur tiers.
  14. Produit programme informatique comprenant des instructions qui, lorsque le programme est exécuté par un ordinateur, amènent l'ordinateur à mettre en œuvre le procédé selon l'une quelconque des revendications 1 à 8.
  15. Support lisible par ordinateur comprenant des instructions qui, lorsqu'elles sont exécutées par un ordinateur, amènent l'ordinateur à mettre en œuvre le procédé selon l'une quelconque des revendications 1 à 8.
EP18191898.8A 2012-01-27 2013-01-28 Distributions d'estimation de temps de trajet sur des voies artérielles signalisées Active EP3432286B1 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US201261591758P 2012-01-27 2012-01-27
EP13740931.4A EP2820631B1 (fr) 2012-01-27 2013-01-28 Estimation des distributions temporelles de parcours sur des artères signalisées
PCT/US2013/023505 WO2013113029A1 (fr) 2012-01-27 2013-01-28 Estimation des distributions temporelles de parcours sur des artères signalisées

Related Parent Applications (2)

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EP13740931.4A Division-Into EP2820631B1 (fr) 2012-01-27 2013-01-28 Estimation des distributions temporelles de parcours sur des artères signalisées
EP13740931.4A Division EP2820631B1 (fr) 2012-01-27 2013-01-28 Estimation des distributions temporelles de parcours sur des artères signalisées

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EP13740931.4A Active EP2820631B1 (fr) 2012-01-27 2013-01-28 Estimation des distributions temporelles de parcours sur des artères signalisées

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US7221287B2 (en) 2002-03-05 2007-05-22 Triangle Software Llc Three-dimensional traffic report
WO2005013063A2 (fr) 2003-07-25 2005-02-10 Landsonar, Inc. Systeme et procede pour determiner une heure de depart recommandee
US9046924B2 (en) 2009-03-04 2015-06-02 Pelmorex Canada Inc. Gesture based interaction with traffic data
US8619072B2 (en) 2009-03-04 2013-12-31 Triangle Software Llc Controlling a three-dimensional virtual broadcast presentation
CA2839866C (fr) 2011-05-18 2021-04-13 Triangle Software Llc Systeme permettant d'utiliser des donnees de trafic et des donnees d'efficacite de conduite
US8781718B2 (en) 2012-01-27 2014-07-15 Pelmorex Canada Inc. Estimating time travel distributions on signalized arterials
US10223909B2 (en) 2012-10-18 2019-03-05 Uber Technologies, Inc. Estimating time travel distributions on signalized arterials
CN108629982B (zh) * 2018-05-16 2020-12-29 中山大学 一种基于旅行时间分布规律的路段车辆数估计方法

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WO2005013063A2 (fr) * 2003-07-25 2005-02-10 Landsonar, Inc. Systeme et procede pour determiner une heure de depart recommandee
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EP2820631B1 (fr) 2019-01-16
EP3432286A1 (fr) 2019-01-23
EP2820631A1 (fr) 2015-01-07
EP2820631A4 (fr) 2016-05-18

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