WO2017097848A1 - Procédé et systeme de prévisions de trafic dans des zones maritimes délimitées - Google Patents

Procédé et systeme de prévisions de trafic dans des zones maritimes délimitées Download PDF

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Publication number
WO2017097848A1
WO2017097848A1 PCT/EP2016/080117 EP2016080117W WO2017097848A1 WO 2017097848 A1 WO2017097848 A1 WO 2017097848A1 EP 2016080117 W EP2016080117 W EP 2016080117W WO 2017097848 A1 WO2017097848 A1 WO 2017097848A1
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Prior art keywords
traffic
area
sea
maritime
cross
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PCT/EP2016/080117
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German (de)
English (en)
Inventor
Carsten HILGENFELD
Nina VOJDANI
Manfred AHN
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Hochschule Wismar
Universität Rostock
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Priority to EP16816605.6A priority Critical patent/EP3387633B1/fr
Publication of WO2017097848A1 publication Critical patent/WO2017097848A1/fr

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G3/00—Traffic control systems for marine craft
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G3/00—Traffic control systems for marine craft
    • G08G3/02—Anti-collision systems

Definitions

  • the invention relates to a method and a system for forecasting and / or controlling shipping traffic in the area of a cross-sectional restricted, in particular international, sea area.
  • the present invention has the object to improve the prediction and / or control of shipping traffic in cross-sectional sea areas.
  • This object is achieved by a method for forecasting and / or controlling shipping traffic in the area of a cross-sectional, in particular international, sea area, wherein a) a fixed measuring area is defined or which comprises a cross-sectionally limited part of the sea area, b) via a first AIS - ("Automatic Identification System") - data from maritime vehicles passing through the site, including their current position, speed and direction of travel, and from the AIS data collected, an empirical model of vessel traffic in the site c) wherein after the empirical model has been generated, a traffic prediction and / or traffic control operation is carried out for a current shipping traffic, in which c1) first, based on AIS data from maritime vehicles, which are located in the measurement area and / or from maritime vehicles approaching the measurement area, a temporal evolution of at least one input parameter of the maritime traffic in the measurement area is calculated, in particular extrapolated, and then c2) from the empirical model, to which at least one calculated input parameter of the shipping traffic is supplied as an input variable, at least one output parameter of the
  • the inventive method is based metrologically on the detection of AIS data, so "Automatic Identification System” - data, which include in an internationally standardized data format, inter alia, information about location, course and speed and other ship data. These data have been legally transmitted by seagoing vessels since 2008 and can be received by other vessels in the same sea area.
  • the AIS signals have a range of about 20 to 25 nautical miles.
  • the AIS has the advantage over the radar that it quickly transmits additional information about other ships as well as their speed and heading changes. However, smaller marine and military vehicles often do not send AlS signals.
  • the data sent can also be patchy and / or erroneous. This depends on the quality of the ship's systems and the up-to-dateness of the data in the individual ship systems.
  • the AIS data has been used to dynamically display the current traffic situation through navigation systems. Such navigation systems are, for example, ARPA systems ("Automatic Radar Plotting Aid") or the electric chart ECDIS ("Electronic Chart and Information Display").
  • a model obtained empirically from longer-term observations of the traffic in the cross-section-limited sea area is used to make a prediction of the future traffic behavior from current circumstances. From the AIS data of the vessels approaching the canalized or cross-sectional restricted part of the sea area, it is quite safe to predict when they will arrive in the survey area. General statements about the composition of maritime traffic in the cross-sectional restricted part of the sea area are then sufficient in order to be able to make forecasts from the empirical model about further characteristics of traffic, for example about an average speed. A consideration and consideration of interactions between vessels is not required, so that the forecast period is significantly increased and can be extended to 30 to 60 minutes.
  • the calculation of interactions between vessels may be used for a shorter-term adaptation of the forecast or for proposed course corrections. However, these hardly affect the general traffic situation in 30 to 60 minutes.
  • An empirical model in the context of the present invention is an approximation model, which is formed on the basis of the historical AIS data and observed traffic conditions. Basis of the empirical The model may be a one- or multi-dimensional approximation function, a look-up table (LUT) or the like.
  • the model response is advantageously communicated to a maritime vehicle or its operator as a prediction of a future arriving value of the output parameter. This gives the skipper the opportunity to adjust the voyage of his vessel before arriving in the cross-section restricted passage, thus saving fuel, among other things.
  • the vessel or its operator uses the predicted value of the output parameter to adapt an operational state of the vessel, in particular a speed or heading before, during or after entry into the cross-sectional restricted part of the sea area, in particular for adaptation to a predicted average speed in the cross-sectional restricted part of the sea sea area.
  • the reporting of the prediction calculated from the empirical model to the ships or their operators, for example captain, first officer, watch officer, navigator or to a navigation system of the ship thus not only gives the surgeon the possibility of early reliable information about travel times through the cross-section limited In addition to gaining part of the sea area, but also to be able to make early measures for course and speed correction to adapt to the expected traffic. Such an early rate or speed change will allow you to save on fuel and CO2 emissions and make the course more predictable.
  • the at least one input parameter is a predicted maritime traffic density and / or a predicted maritime traffic volume and the at least one output parameter averaged average speed of shipping traffic in the measurement area, based on a functional dependence of the average speed of maritime traffic density and / or maritime traffic intensity on the empirical model calculated from the AIS data collected during the first measurement period.
  • the functional dependence of the throughput of traffic density on the one hand and / or traffic volume on the other hand are fundamental concepts and dependencies in traffic systems.
  • the traffic density is defined as the number of vehicles or maritime vehicles on a specified area
  • the traffic volume is the number of vehicles that pass a specific location or a cross section along the measurement section per unit of time.
  • the traffic volume is usually calculated over a moving time unit, for example a sliding hour.
  • the traffic density would be 1.2 ships per nautical mile. If these 12 ships also accidentally retracted within 60 minutes in the measuring section, the traffic for these ships is then 1 2 ships per hour.
  • the traffic volume can also be higher or lower, depending on how many ships have passed the reference point within the currently running hour. However, the sliding measurement period does not have to be 60 minutes, but can be longer or shorter.
  • the first measurement period in which the statistics for the empirical model are compiled, is advantageously several months. nate, in particular 3 months. This is generally dependent on traffic volume, as the statistical reliability of the empirical model increases with the number of recorded vessel movements. In the exemplary case of the Kadettrinne, the 3-month period has proven to be sufficient. Any chosen 3-month periods give consistent results for the empirical model.
  • a limit traffic density and / or a limit traffic strength is determined, from which the vessels reduce their speed. This measure is based on the recognition that below a border traffic density and / or border traffic strength the average speed does not depend substantially on the traffic density or traffic intensity, whereas when this border point is exceeded an initially so-called partially bound traffic flow and finally a bound traffic flow occurs.
  • the determination of the border traffic density and / or strength leads to a simplification of the empirical model in this area of the free traffic flow below the border point, since in this area the average speed is essentially constant, because sea vehicles in the free traffic flow are usually not mutually exclusive also hamper cross-section designated track.
  • an advantageous further development provides that the empirical model is updated on the basis of newly added AIS data continuously or at intervals, in particular older AIS data from fall out of the empirical model or, with less weight, feed into the empirical model than newer AIS data.
  • longer-term developments in traffic behavior can be incorporated.
  • These may be seasonal effects or the effect that a method according to the invention results in improved collective behavior in the navigation of the vessels, thereby increasing throughput and speed in the cross-sectional restricted sea area. For this purpose, even a relatively small percentage of maritime vehicles controlled by the method according to the invention can be sufficient.
  • one or more correlations of the output parameter (s) are parameterized with one or more environmental factors, in particular season, time of day, ice drift, ice warnings, wind force and / or wind direction and / or visibility conditions or the data in the model are related to the respective current conditions be filtered.
  • the latter filtering means that the empirical model is approximated only to the historical AIS data that substantially matches the current conditions. This allows an improvement in prediction accuracy.
  • a further improvement of the method according to the invention results if, on the basis of the acquired AIS data, additionally a traffic flow composition is predicted and in particular is communicated. This provides a skipper with additional information that can be used to make decisions about a course or speed change at an early stage. It may be necessary to make more adjustments in the cross-section restricted sea area in the presence of heavy or downhole disabled tankers or large container ships, such as Ultra Large Container Ships (ULCS) than in the presence of smaller vessels.
  • ULCS Ultra Large Container Ships
  • a pulse formation is predicted and in particular communicated by determining distances and speed of sea vehicles which travel in the same direction.
  • Pulk Strukturen are generally unwanted collective phenomena at sea, which should be avoided.
  • An early prediction then makes it possible to make course changes or speed changes to avoid pulping or connecting to a pulp. This then also leads to a significant reduction of the latent accident risk, since a aufku on a Pul k vessel lack of Bremsmögl ichkeit would perform hectic maneuvers, which can now be avoided.
  • relevant courses of maritime vehicles for minimizing GPS interference are advantageously extrapolated from a sequence of AIS data in method step c1), in particular with the help of modified Bezier curves. This avoids or minimizes mispredictions due to incomplete or incorrect GPS and / or AIS data.
  • the object on which the invention is based is also achieved by a system for forecasting and / or controlling shipping traffic in the area of a cross-border-restricted, in particular international, sea area, wherein the system comprises at least one central data processing system and at least one AIS data reception device, which is used to receive AIS data from maritime vehicles in the area of the cross-sectional restricted sea area as well as for the transmission of received AIS data to the central data processing system is embodied and configured, in which the central data processing system is designed and set up to carry out a method according to the invention described above.
  • the central component of the system is a central data processing system connected to an AIS data receiver, ie a server.
  • This central data processing system is designed so that it is set up by means of a computer program such that it generates an empirical model from passing ships of method A according to the method according to the invention, processes the current AIS data according to method step c1) and according to method step c2). then calculate an appropriate model response as a prediction.
  • AIS data receiving stations may be positioned at the entrance, along and exit of the cross-sectional restricted part of the sea area in order to maximize coverage.
  • a receiving device arranged in a maritime vehicle is designed and set up to receive data from the central data processing system, in particular from a data transmission device connected to the central data processing system, and to display it to an operator of the maritime vehicle.
  • the receiving device is preferably a navigation device, an ARPA system, an electronic nautical chart (ECDIS) or a mobile phone connected to or connectable to the Internet of the operator of the maritime vehicle.
  • the latter can be provided, for example, with an application ("app") which is designed to display the data and forecasts transmitted by the central data processing system, possibly including recommendations for changes in course and / or speed.
  • the object of the invention is also based on the use of AIS data from maritime vessels traversing a measurement area comprising a cross-sectional restricted part of an international, in particular international, sea area for forecasting and / or controlling vessel traffic in the cross-sectional restricted part of the sea area dissolved in a method according to the invention described above.
  • AIS data both for long-term modeling and for extrapolation into the future between, for example, 1 0 to 60 minutes for the extraction of a model response as a prediction value solves the problem that there were no fixed measurement sites as in the case of land-based roads, and so far No statements about expected traffic conditions could be made in 10 to 60 minutes.
  • Fig. 1 a traffic density map of the Kadettrinne
  • Fig. 2 a definition of a measuring area for the Kadettrinne
  • Fig. 3 a map of the Kadettrinne with a covered Messareal
  • Fig. 4 is an illustration of a display prediction app
  • Fig. 5 is a schematic flowchart
  • Fig. 6 a table with ship types
  • Fig. 1 shows a map of a sea chart 8 of the Baltic Sea between the Danish Gedser in the northwest (top left) and the German Ostseebad Ahrenshoop in the southeast (bottom right). Between these land areas, the Baltic Sea is except for a central channel, the Kadettrinne K, very shallow and is bordered by the Gedser Reef R from the north. The Kadettrinne K is therefore channeled for international ocean shipping. Superimposed on the map are visualized vessel movements based on AIS data from March 201 0 0. The gray scales provide information on the frequency of ship movements at specific locations, where white means a few to a few dates for a particular location and black indicates all points are at least 50% of the maximum occurring frequency.
  • Fig. 2 shows a definition for a measurement area 10 defined for a part of the Kadettrinne K. Nördl me and south it is limited by traffic intensity points or lines 1 2, 14. Traffic density area 1 6 covers the entire area of the measuring area 10 including deep water area 1 8. Measurements are made separately for north and southbound vessels.
  • Fig. 3 is the measuring area 10 of the Kadettrinne K superimposed darge- represents, along with some northbound ships 20 and marker tons T, which delimit the tracks from each other.
  • the measuring area 1 0 are at the time shown three ships 20, two of which are located in the central deep water area 1 8.
  • exemplary measuring points are recorded in a diagram in which a traffic density is shown on the horizontal axis and a speed V in kn on the vertical axis.
  • the measurement is carried out in such a way that traffic density is determined every minute for the presence of data for northbound and southbound vessels.
  • Each stationary state is detected only once. As soon as the stationary state changes, for example the number of ships changes, a new measured value is recorded.
  • the diagram on the right in FIG. 3 comes about when three ships with 20 kn, 1 5 kn and 1 0 kn one after another enter the area 1 0.
  • the front ship enters with 20 kn, so that a measuring point 20kn / 1 is registered.
  • the last and slowest ship with 1 0 kn comes in, the first two ships have already left the measurement area 1 0, so that a data point 1 0kn / 1 is added.
  • a navigation display 30 of an exemplary system according to the invention is shown with a speed forecasting tool.
  • This can be, for example, a mobile app or a program on a bridge PC.
  • the nautical chart 8 shown previously with the Kadettrinne K with currently existing ships 20 and their proji- illustrated cures.
  • an expected traffic flow composition divided into north and south going and according to ship classes A to D for times in 10, 20 and 30 minutes is shown, in the lower part 34 the respective expected speed.
  • the traffic density is quite low in the northbound lane, so that a largely constant speed is to be expected.
  • the number of ships changes in 1 0, 20 and 30 minutes from first 7 to only 5 and then 8.
  • the composition changes so that more class C and D ships come in, leaving one Significant reduction in speed is forecasted from 1 1, 9 kn over 1 1, 6 kn to 1 0.8 kn in 30 minutes. Based on this information, southbound ships 20 can adjust their speed even before entering the Kadettrinne K and therefore save energy.
  • Fig. 5 schematically illustrates the sequence of the method according to the invention.
  • the ships 20 approach from the left of a not yet cross-section limited part a cross-section limited part 42 of the sea area 40, for example, the Kadettrinne K.
  • the ships 20 have under defencel iche Courses and speeds.
  • the cross-sectional restricted area 42 which in this case is an international cross-section limited sea-waterway, all ships 20 broadcast their AlS signals 50.
  • These are received by one or more local base stations or a central service (box 52) and forwarded to a central computer, which, for example, predicts the amount of traffic in 10, 20 or 30 minutes to hours (box 54). From the predicted traffic volume, an expected average speed in the respective driving direction is determined on the basis of an empirical traffic flow model (box 56). This empirical model is going out Base data from previous months, which are determined in a sub-module 58.
  • step 56 The average speed determined in step 56 or other or other output parameters, such as a traffic composition, are sent in step 60 or made available online.
  • a unit present on the ship 20 receives or retrieves the provided data in step 62.
  • the speed of the ship 20 is reduced if the airspeed is higher than the expected speed. This saves the ship 20 fuel, reduces its CO2 emissions, since it does not need to slow down, and makes the traffic in the track of the cross-sectional restricted portion 42 even.
  • Fig. 6 shows by way of example the grouping of the types of ships in categories A to D on the basis of their type (cargo ships and the like, passenger ships and tankers and special ships) as well as their dimensions. These are the categories that in the example in FIG. 4 were used to indicate the traffic compositions.
  • Fig. 7a), 7b) and 7c) schematically show fundamental diagrams for shipping traffic in cross-section-restricted waterways.
  • Fig. 7a shows the maritime traffic density D (curve 70) in relation to the speed in the unit ships per nautical mile (Nm).
  • the traffic flow is free (reference numeral 72).
  • the traffic is partially bound (reference numeral 74) to designate a bounded traffic flow 76 from a second limit value 75.
  • the speed does not decrease in the free traffic flow 72, but in the partially bound traffic flow 74 it does not decrease much, but decreases sharply in the bound traffic flow 76.
  • the dashed line means that such high traffic densities have not yet been observed. Theoretically, however, the speed can reach the minimum speed of 4 knots, below which ships become non-maneuverable due to shipping-related reasons, because control movements do not build up sufficient pressure in the flow to turn the ship.
  • Fig. 7b the maritime traffic intensity fundamental diagram Q in the unit ship crossings per hour is shown as curve 80.
  • the first and second thresholds 73 'and 75' are different from those shown in FIG. 7a).
  • the dependence of the speed of the traffic intensity Q is less pronounced in the free and partially bounded area than that of the traffic D. Since the traffic intensity Q but at least with the traffic density D is correlated with even higher traffic levels Q but also with a sharp drop in To calculate speed.
  • Fig. 7c schematically shows the correlation 90 of maritime traffic intensity Q to the maritime traffic density D, parameterized over the average speed, superimposed with the limits 73 and 75, corresponding to 73 'and 75', which the curve 90 into the traffic flow sections 72, 74 and 76 for free , partial and bound traffic flow. From such a diagram, the theoretical capacity limit can be calculated with sufficient data. This is not reached in the case of Kadettrinne. Also, the correlation is not as clear as it appears because only averages are linked. A temporary high level of Traffic density D does not necessarily result in a high traffic volume Q in the moving hour, since the traffic flow in the cadet channel is highly inhomogeneous.
  • FIG. 8a) and FIG. 8b) are measured values of an evaluation of traffic densities D and traffic volumes Q over 1 766 days from 201 0 to 2014 in the measurement area 10 of FIG. 2 shown in the Kadettrinne K, with ice days were removed.
  • the measurements in FIG. 8a) extend up to 1 7 ships simultaneously in one direction of travel in the measuring area 1 0.
  • the individual data points which are summarized here in bars, have come about as described above for the diagram in FIG. 3 described on the right.
  • the boxes and bars in this case denote no error bars, but quantiles.
  • the data bars have three curves drawn to illustrate the functional dependency, which use different methods to indicate the course of the average speed, with outliers being ignored.
  • the uppermost line forms an arithmetic mean 1 00 of the values
  • the lower line corresponds to a fit of a polynomial by the median 1 04 of the values
  • the middle line corresponds to an average value 1 02 of the arithmetic mean 1 00 and the median 1 04. This applies correspondingly to the polynomials 1 1 0, 1 12 and 1 14 in FIG. 8b), which are defined as well as in FIG. 8a).
  • the polynomial is relatively weakly limited due to the relatively smaller number of values at traffic levels Q of 1 3 or more towards high values of Q, so that the curves 1 1 0, 1 1 2 and 1 14 to higher values relatively diverge from Q. Nevertheless, there is a clear trend in the data-driven area.

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  • Engineering & Computer Science (AREA)
  • Ocean & Marine Engineering (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un procédé et un système de prédiction et/ou de direction du trafic de navires dans une zone maritime (40) délimitée, notamment internationale, et une utilisation. Selon l'invention, on définit une zone de mesure fixe (10) qui comporte une partie délimitée (42) de la zone maritime (40). On détecte des données AIS de navires (20), qui traversent la zone de mesure (10), sur une première période de mesure et on établit, à partir des données AIS détectées, un modèle empirique du trafic de navires dans la zone de mesure (10). Après avoir établi le modèle empirique, on réalise un mode de direction de trafic et/ou de prévision de trafic pour un trafic de navires actuel dans lequel, sur la base de données AIS de navires (20), situés dans la zone de mesure (10), et/ou de navires (20) s'approchant de la zone de mesure (10), on calcule, notamment on extrapole, tout d'abord une évolution temporelle d'au moins un paramètre d'entrée du trafic de navire dans la zone de mesure (10) puis, du modèle empirique auquel on fournit comme grandeur d'entrée l'au moins un paramètre d'entrée calculé, on extrait comme réponse du modèle au moins un paramètre de sortie futur du trafic de navires dans la zone de mesure. La réponse du modèle est communiquée en tant que prédiction d'une future valeur entrante du paramètre de sortie à un navire (20) ou à son opérateur.
PCT/EP2016/080117 2015-12-10 2016-12-07 Procédé et systeme de prévisions de trafic dans des zones maritimes délimitées WO2017097848A1 (fr)

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DE102015224830.5 2015-12-10
DE102015224830.5A DE102015224830A1 (de) 2015-12-10 2015-12-10 Verfahren und System zur Verkehrsvorhersage in querschnittsbeschränkten Seegebieten

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CN108364502A (zh) * 2018-02-08 2018-08-03 上海迈利船舶科技有限公司 一种航运告警方法及系统
CN109936815A (zh) * 2019-03-25 2019-06-25 江苏航运职业技术学院 一种基于无线传感器网络的水下搜救区域预测方法
CN115641750A (zh) * 2022-12-09 2023-01-24 交通运输部水运科学研究所 一种基于北斗的船舶通航调度方法和系统

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