[go: up one dir, main page]

WO2005088493A1 - Procede et systeme d'optimisation de travaux de transport - Google Patents

Procede et systeme d'optimisation de travaux de transport Download PDF

Info

Publication number
WO2005088493A1
WO2005088493A1 PCT/DE2005/000426 DE2005000426W WO2005088493A1 WO 2005088493 A1 WO2005088493 A1 WO 2005088493A1 DE 2005000426 W DE2005000426 W DE 2005000426W WO 2005088493 A1 WO2005088493 A1 WO 2005088493A1
Authority
WO
WIPO (PCT)
Prior art keywords
optimizing
dependent
tasks according
transportation
probabilities
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.)
Ceased
Application number
PCT/DE2005/000426
Other languages
German (de)
English (en)
Inventor
Ingo Morgenstern
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.)
Individual
Original Assignee
Individual
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 Individual filed Critical Individual
Priority to EP05715088A priority Critical patent/EP1741052A1/fr
Publication of WO2005088493A1 publication Critical patent/WO2005088493A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • 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
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
    • G08G1/096833—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route
    • G08G1/096844—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route where the complete route is dynamically recomputed based on new data

Definitions

  • the invention relates to methods and systems for optimizing transport tasks, such as, for example, the transportation of goods by forwarding agents or the forward movement of conveyor belts in the manufacture of products.
  • the invention relates to methods and systems for optimizing location- and time-dependent transport and also methods and systems for optimal utilization of traffic networks and for reducing costs in transport tasks and production line production.
  • the information such as traffic information
  • the information does not really solve the problem, even if it is transmitted correctly so that an actual traffic jam can be avoided.
  • Bypassing a traffic jam can resolve it faster, but those who bypass the reported traffic jam often create a new traffic jam on the bypass route, so that the individual user of the traffic information is not really helped with the latter. He wants to avoid a traffic jam and ends up in the next traffic jam.
  • the invention provides a method for optimizing transportation tasks, wherein at least one location-dependent transportation parameter is recorded, wherein probabilities are determined for the at least one location-dependent transportation parameter that are used as a basis for the optimization of the transportation task.
  • the optimization of the transport task is taken as a basis.
  • the probabilities of the at least one location-dependent transportation parameter are used as the basis for the transportation task in the form of an error bar, and / or that the probabilities or possibly error bars are continuously updated or improved with inclusion of the at least one location-dependent transportation parameter ,
  • a preferred development consists in that a temporal variation or dependency of the at least one location-dependent transport parameter is recorded, and for this purpose time-dependent probabilities are determined which are instead of the at least one location and time-dependent given transport parameter or in addition to the at least one location and time-dependent given transportation parameters of the transportation task.
  • a probability distribution is determined from a plurality of probabilities, which is used as the basis for the transport task instead of the location-specific and time-dependent given transport parameter or in addition to the location-specific and time-dependent given transport parameter.
  • the location-dependent transportation time is recorded as the at least one location-dependent transportation parameter.
  • a preferred embodiment of the invention consists in that the ascertained probabilities or, if applicable, the ascertained probability distribution are used as a basis for route optimization.
  • This can be further developed by using time-dependent probabilities for the travel times, which are constantly adapted to the current traffic situations, with preference being given to adapting the time-dependent probabilities to the current traffic situation via satellite-based systems, and / or preferably adapting the time-dependent ones Probabilities of the current traffic situation contains a damping factor that prevents chaotic behavior.
  • tellite-based data from road toll systems are used, and / or that inaccurate, noisy GPS data are used to create the time-dependent probabilities, and / or that a cost function is expanded for optimization by a risk term that reflects the time-dependent probability distributions for the travel times, and / or that to optimize the probability distributions for the total travel times Total costs are calculated.
  • a further preferred embodiment includes that physical, stochastic, genetic algorithms and / or the savings algorithm are used for optimization. Alternatively or additionally, an analog optimization with probability distributions can be used.
  • a bouncing method is used for optimization.
  • Simulated Annealing, Threshold Accepting, Great Deluge (deluge) genetic algorithms, savings algorithms with or without subsequent bouncing are used.
  • a preferred further development of these variants consists in that a bombshell method is used for the optimization, wherein in the context of the bombshell method in particular random coordinates are generated with a random generator, a radius around each coordinate point is determined or determined or generated , in particular generated by the random generator, and only the sections of the transport route or possibly the tour lying within these radii will be optimized with the bouncing method. This can be further developed by the random generator constantly changing new random coordinates and / or radii
  • a risk assessment is included, the risk assessment in particular being carried out using the "value-at-risk” method.
  • the method for optimizing transportation tasks can also provide for the Search Space Smoothing method to be optimized with or without threshold and / or low-temperature simulated annealing processes.
  • a small threshold can be used when using threshold acceptance or a threshold method.
  • the stated goal is also achieved with a system for optimizing transport tasks, wherein detection devices are provided for recording at least one location-dependent transport parameter and information devices which are designed to provide users with inclusion of the at least one location-dependent transport parameter via an optimization possibility of the transport task inform, and furthermore calculation devices are provided which are designed to determine probabilities for the at least one location-dependent transport parameter, and wherein the information devices are designed to inform users, including the probabilities, of an optimization possibility of the transport task.
  • 1 shows by way of example an explanatory and illustrative traffic flow forecast for Kunststoff at 10:00 p.m.
  • 2 shows by way of example an explanatory and illustrative traffic flow forecast for Kunststoff at 9:00 a.m.
  • FIG. 4 shows, by way of example, a probability distribution for the travel time of the route according to FIG. 3 at a specific time, but different with respect to the representation in FIG. 3,
  • 11 shows an example of a risk component calculated as a value-at-risk in the total cost function
  • FIG. 13 shows a schematic representation to represent the installation of various components at different stations of an assembly line
  • time-dependent probability distributions are used in the exemplary embodiment dealt with here, which are constantly adapted to the current traffic situation with the aid of, in particular, satellite-based systems.
  • the exemplary embodiment of the method and system for optimizing transport tasks which is considered in more detail here, circumvents this difficulty.
  • the adjustment takes place automatically.
  • the procedure in the present exemplary embodiment is as follows:
  • Satellite data is a kind of feedback of the system or an actual-target comparison.
  • the system regulates itself and approaches the optimum, ie the system and thus the process is capable of learning or self-learning.
  • This regulation is the fundamental advantage of the new procedure.
  • a damping factor can prevent tendencies towards chaotic behavior or vibrational behavior.
  • the damping factor determines the degree to which the old satellite data are replaced by new ones.
  • the measurement accuracy is of less importance than in legally usable data structures such as toll systems. This means that the current GPS data can be used with the known accuracy because, as mentioned, the creation of probabilities has a certain fault tolerance.
  • an essential component of this exemplary embodiment of the system and method for optimizing transport tasks is the influence of the distributions on the driving behavior of the users. Based on the assumption that truck traffic is an essential factor in the flow of traffic, truck tour planning is of the utmost importance.
  • the previous route planning and optimization relates to purely deterministic data, e.g. B. from A to B exactly 60 minutes. It is therefore not possible to include traffic congestion.
  • the method and system for optimizing transport tasks in accordance with the exemplary embodiment concerned in the present case includes route optimization which is based on the time-dependent probability distributions for the travel times from A to B mentioned.
  • the distribution for an entire tour can be calculated for this in physical, stochastic or genetic algorithms. This procedure is time-consuming and must be replaced in the case of short response times or short-term required responses by treating the moments of the distribution.
  • the simplest method is the use of the average travel time and the standard deviation, i. H. the error bar. On this
  • system can also be implemented on other modes of transport or their network.
  • a generalization of the exemplary embodiment dealt with above consists in the application of the probability-oriented optimization to production processes.
  • a sub-process no longer takes 10 minutes deterministically, but the corresponding probability distribution becomes the basis of the optimization. This results in an application of the new method to almost all cases of deterministic optimization.
  • a further exemplary embodiment is now discussed, which relates to a method and system for optimizing transport tasks with a learning ability.
  • This new method and system is based on inventive findings in algorithm generation, which make it possible to include time-dependent probabilities in route optimization. These time-dependent probabilities are based on measurements of the traffic flow. The ability to learn is based on a target-actual comparison with the measured data.
  • Truck traffic is influenced in such a way that the main reasons for traffic jams are eliminated.
  • the overall system is of great economic benefit, which will only increase if the most extensive possible introduction is achieved. For individual users, such as freight forwarding companies, there is an improvement in the business situation even with isolated introduction, since they can adapt to the traffic situation optimally. Some of the required traffic data is already available.
  • Route planning can be relevant to two criteria in particular: the expected journey time and the travel costs. By balancing these two factors, each weighted according to his personal needs, he will ultimately make the decision for a specific mode of transport and a specific route.
  • a freight forwarding company that has to send its trucks to certain destinations in order to fulfill customer orders and wants to plan the trips of its trucks for this purpose, faces a much more difficult problem. As far as the loading capacities of the trucks allow, several destinations should be approached in one trip for cost reasons, ie the trucks are usually on longer trips. In addition, the company can provide additional tours for a truck after returning from the first tour. The more you want to reduce downtimes at the fleet location, for example in order to achieve a good utilization of the vehicles, the more important it is to calculate the time required for each individual tour, which is made up of the travel times and length of stay at the customer. This scheduling becomes even more critical when you consider that most jobs need to be completed within one or more time slots.
  • the freight forwarding company is therefore faced with the task of distributing the individual customer orders in a suitable manner to the trucks in their own fleet and combining them into tours, and ensuring that the destinations are in the best order for each tour. So it has to draw up an extensive tour schedule for the near future. In practice, other restrictions are sometimes to be observed, for example because not every truck is suitable for every job.
  • the travel time for any journey from A to B can only be calculated as a first approximation from the route length, the type of road (highway, federal road, country road, etc.), the (legally permitted) speed of the vehicle and other factors that are constant over time. Your own progress in traffic is often hampered by the presence of other road users. If a particularly large number of road users are on the road, there may even be a standstill, i.e. a traffic jam. As a result, the travel time can be predicted more precisely if one includes data on the expected traffic volume includes his planning. In heavy traffic, so there is a risk of traffic jams, the average speed of the vehicles will probably have to be corrected slightly downwards.
  • the travel costs are initially the same as the travel times - at least if you assume operating costs per time.
  • additional costs which are proportional to the route length and depending on vehicle characteristics.
  • traffic control is also possible via it. For example, one could abolish the Sunday driving ban and charge increased tolls to compensate for these days.
  • control the traffic according to the time of day by trying to relieve the highways in favor of car traffic at higher times and at the same time create an incentive for driving at night with low fees. Perhaps this could lead to more balanced utilization of road capacities.
  • the travel time for each road connection should therefore be given in the form of a time-dependent distribution.
  • Such distributions are obtained by statistical analysis of measured traffic data. The greater the fluctuations in the measured travel times, the broader the distribution for the travel time. The average of this distribution is the average journey time. Their width can be viewed as a measure of the blurring or inaccuracy of the travel time and should also be taken into account when planning the tour.
  • the shipping company can decide to what extent it wants to reduce the risk and accept higher costs.
  • an independent tour planning program for implementing the method according to the invention and as part of a system according to the invention for optimizing transport tasks can be brought onto the market, which competes with the established programs, but can Invention can also be incorporated into already established route planning programs, systems and procedures.
  • traffic forecasts that have already been determined in the invention.
  • a connection to existing digital map material is also possible.
  • existing, in particular comprehensive, reliable recordings of traffic flow information can be included.
  • Such data and information, regardless of whether they already exist or are yet to be obtained, are preferably from several different sources, such as.
  • truck traffic on highways in Germany is used.
  • this assumed exemplary situation can also be generalized for all roads and also for only or with the inclusion of cars.
  • a toll system such as, for. B. Toll-Collect in Germany
  • GPS is used here, the basics of which are assumed to be generally known and therefore are no longer explained separately here
  • data is collected in a central computer about the time when a certain truck drives on a certain section of the motorway. The travel time for the section is then calculated from this.
  • a histogram is created for different times of the day, days of the week, different seasons, etc. In turn, these histograms are used to create probability distributions, which are entered into a traffic forecast program and compared with reality. From this one obtains probability information for the "truth content" and thus a probability distribution. For example, route plans can be created based on such probability distributions.
  • Route planning - definition A tour planning exists if a forwarding agency specifies driving parameters with only one order (only one truck), up to the case that a forwarding agency with a certain number of trucks that have a certain number of orders have to carry out the entire fleet and all orders in plans, organizes and / or manages in an optimizing manner.
  • tours therefore include a single truck that travels from Frankfurt to Kunststoff, for example, but also a complex fleet, such as for fuel oil export.
  • the inventive step in the present exemplary embodiment again consists in the fact that instead of a deterministic statement about a travel time from A to B, a probability statement is made according to the invention as before.
  • Optimization methods are also implemented, such as physical methods, e.g. Simulated annealing, threshold acceptance, Great Deluge (deluge), genetic algorithms, savings algorithms with or without subsequent bouncing ("bouncing" method) (one takes the solution, for example, of the savings algorithm as a starting configuration for the bouncing method, which later is described in more detail).
  • Savings algorithms alone provide rather inadequate solutions, so that an improvement with bouncing based on a physical algorithm (simulated annealing, etc.) is preferably provided and achieved, which means optimization with probabilities.
  • the result is a probability distribution for the total costs, for which an illustrative example is shown in FIG. 5.
  • a risk assessment is preferably carried out with
  • Such situations can be calculated using the probability distributions created as above.
  • the relationship between traffic density on the one hand and travel times or speeds on the other hand is used, as is already known as a procedure.
  • Other design options are to change the (time-dependent) toll depending on the effect achieved, such as: B. Gradually increasing slowly, etc.
  • the system and method according to the invention enables a evasion of predicted problem cases (traffic jams, etc., high traffic volume, etc.), that is to say a greater deviation from the normal case.
  • the toll can preferably be designed in such a way that trucks avoid these vulnerable points.
  • Control can take place, for example, by e.g. B. lower toll if a certain section is not traveled at a certain time, or a higher toll for vulnerable areas.
  • the greatest advantage of the method and system according to the invention for optimizing transport tasks is learning ability. This is very much supported when trucks are based on predicted probabilities.
  • online data real-time data
  • online data real-time data
  • the aspect of the system and method according to the invention specifies probabilities and the online data indicate the reality. So z. For example, it can also be taken into account that a traffic jam is actually at one point, although only a low probability of traffic jam was determined for this point.
  • Truck forwarding agencies in Germany are essentially equipped with route planning systems. Almost all of these are based on savings algorithms, generally at different levels. For example, this is done as follows: First, customers are distributed to trucks. Then the overall situation is optimized. This is followed by a partial splitting off of constraints, theoretically according to the principle: "Devide and loose" (first customers on a truck and then planning a tour). This results in a deviation from the total optimum of z. B. five percent depending on the complexity of the tour planning.
  • the invention is replaced by or supplemented with new algorithms.
  • the consideration of the probability distributions only brings additional improvement in "bouncing", i.e. Savings solution.
  • the probability distributions are preferably taken into account as a function of time. This time dependency naturally also applies to the mean values. This enables a simplified procedure to be obtained: Instead of entire probability distributions, only error bars are used / taken into account, e.g. B. in simulated annealing or analogously in other methods. An exemplary explanatory mathematical representation thereof as a representation of a tour is shown in FIG. 8. Significant progress compared to pure simulated annealing is that the time dependency and the error bars are taken into account.
  • T stands for the virtual temperature temperatures in simulated annealing or threshold, as is common in physical optimization processes.
  • a subspecies of the bouncing process is the so-called bombshell process.
  • Bouncing e.g. For example, to carry out an entire map, "bomb funnels" are set: only what is in it is bounced. That is, e.g. B. generates a random generator random coordinates and a radius around each such coordinate point, d. H. so-called "bomb funnels". Only the sections of the tour that lie within these radii are optimized with the bouncing method. In other words, a change in the tour in optimization processes only takes place within these radii, and thus bouncing, as indicated, only within the funnel. Random generator funnels are always being generated until the desired result is achieved, especially until no further improvements can be obtained.
  • Another suitable optimization method is search space smoothing.
  • the smoothing parameter is changed in the same way as the temperature, ie switching as in bouncing of the improved search space smoothing, and thus the same procedure as in bouncing, only with changes in the smoothing parameters: ever greater change, analogous to bouncing; Combination with bombshell processes means only changing the funnels.
  • An essential aspect of the exemplary embodiments dealt with above and the invention in this context is the inclusion of the error bars or the probability distribution and / or the time dependence in optimization methods. Further advantageous and preferred configurations relate to bouncing, bombshell and improved search space smoothing.
  • the sequence of vehicles on an assembly line is used for an exemplary embodiment.
  • a wide variety of components are installed at different stations, as the schematic illustration in FIG. 13 illustrates.
  • a type j rear-view mirror is installed at installation location i.
  • a cantilever or a probability distribution is obtained. H. you get probability distributions for all installation locations and installation types: distribution for each individual type; Type for everyone
  • Usual changes to the configuration (order) can be based on known methods (simulated annealing, etc., but also genetic algorithms).
  • the introduction of the probabilities and in particular the time-dependent probabilities is also advantageous here.
  • the belt does not only move further by one station if the longest assembly time on a station tion has expired, and the function is not such that the belt is moved intermittently according to the longest time, but the assembly or production belt runs continuously at a suitable average speed.
  • search space smoothing also represents a further improvement in the solutions already created / described, and also in somewhat different methods, so that this is a fundamental aspect of the invention with also independent inventive step.
  • search space smoothing also represents a further improvement in the solutions already created / described, and also in somewhat different methods, so that this is a fundamental aspect of the invention with also independent inventive step.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Analytical Chemistry (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Development Economics (AREA)
  • Mathematical Physics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Human Resources & Organizations (AREA)
  • Chemical & Material Sciences (AREA)
  • Quality & Reliability (AREA)
  • Remote Sensing (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un procédé d'optimisation de travaux de transport consistant à détecter au moins un paramètre de transport défini en fonction de la position, et à déterminer des probabilités pour le ou les paramètres, constituant la base de l'optimisation du travail de transport. L'invention concerne également un système d'optimisation de travaux de transport, comportant des dispositifs de détection d'au moins un paramètre de transport défini en fonction de la position, des dispositifs d'information conçus pour informer des utilisateurs au sujet de la possibilité d'optimisation du travail de transport avec prise en compte du ou des paramètres de transport définis en fonction de la position, et des dispositifs de calcul conçus pour déterminer des probabilités pour le ou les paramètres de transport définis en fonction de la position, les dispositifs d'information étant conçus pour informer des utilisateurs au sujet de la possibilité d'optimisation du travail de transport avec prise en compte des probabilités.
PCT/DE2005/000426 2004-03-10 2005-03-10 Procede et systeme d'optimisation de travaux de transport Ceased WO2005088493A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP05715088A EP1741052A1 (fr) 2004-03-10 2005-03-10 Procede et systeme d'optimisation de travaux de transport

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102004011604.0 2004-03-10
DE102004011604A DE102004011604A1 (de) 2004-03-10 2004-03-10 Verfahren zur optimalen Auslastung eines Verkehrsnetzes

Publications (1)

Publication Number Publication Date
WO2005088493A1 true WO2005088493A1 (fr) 2005-09-22

Family

ID=34895122

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/DE2005/000426 Ceased WO2005088493A1 (fr) 2004-03-10 2005-03-10 Procede et systeme d'optimisation de travaux de transport

Country Status (3)

Country Link
EP (1) EP1741052A1 (fr)
DE (1) DE102004011604A1 (fr)
WO (1) WO2005088493A1 (fr)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009026238A2 (fr) 2007-08-16 2009-02-26 Breazeale Earl Edward Jr Suivi des soins de santé
WO2009071366A1 (fr) * 2007-12-03 2009-06-11 Robert Bosch Gmbh Procédé de fonctionnement d'un système d'information et système d'information
WO2009116105A3 (fr) * 2008-03-21 2010-01-28 Gianfranco Antonini Procédé d'attribution de trafic pour des réseaux de transport multimodaux
DE102007058093B4 (de) * 2007-12-03 2019-05-16 Robert Bosch Gmbh Verfahren und Vorrichtung zum Ermitteln einer Routenempfehlung aus einer Mehrzahl von Wegstrecken
CN111144808A (zh) * 2019-12-20 2020-05-12 贵州黔岸科技有限公司 用于建筑材料运输的任务统计管理系统及方法

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6209026B1 (en) * 1997-03-07 2001-03-27 Bin Ran Central processing and combined central and local processing of personalized real-time traveler information over internet/intranet
WO2001069488A1 (fr) * 2000-03-10 2001-09-20 Jones Charles P Systeme de planification pour vehicule
WO2002019046A1 (fr) * 2000-08-31 2002-03-07 Cosite.Com, Inc. Systeme et procede centralises destines a acheminer et a suivre des articles de maniere optimale
EP1221666A1 (fr) * 2001-01-05 2002-07-10 BRITISH TELECOMMUNICATIONS public limited company Méthode d'évaluation du comportement
US20020147541A1 (en) * 2001-04-09 2002-10-10 Koninklijke Philips Electronics N.V. System and method for disseminating traffic information
US6650948B1 (en) * 2000-11-28 2003-11-18 Applied Generics Limited Traffic flow monitoring
US20030225668A1 (en) * 2002-03-01 2003-12-04 Mitsubishi Denki Kabushiki Kaisha System and method of acquiring traffic data
US20030236613A1 (en) * 2002-03-26 2003-12-25 Hiroyuki Satoh Traffic-information distribution method and on-vehicle navigation apparatus
FR2843474A1 (fr) * 2002-11-29 2004-02-13 France Telecom Procede, equipement et systeme de production d'informations de trafic routier ainsi que systeme de navigation routiere et equipement de navigation routiere

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6209026B1 (en) * 1997-03-07 2001-03-27 Bin Ran Central processing and combined central and local processing of personalized real-time traveler information over internet/intranet
WO2001069488A1 (fr) * 2000-03-10 2001-09-20 Jones Charles P Systeme de planification pour vehicule
WO2002019046A1 (fr) * 2000-08-31 2002-03-07 Cosite.Com, Inc. Systeme et procede centralises destines a acheminer et a suivre des articles de maniere optimale
US6650948B1 (en) * 2000-11-28 2003-11-18 Applied Generics Limited Traffic flow monitoring
EP1221666A1 (fr) * 2001-01-05 2002-07-10 BRITISH TELECOMMUNICATIONS public limited company Méthode d'évaluation du comportement
US20020147541A1 (en) * 2001-04-09 2002-10-10 Koninklijke Philips Electronics N.V. System and method for disseminating traffic information
US20030225668A1 (en) * 2002-03-01 2003-12-04 Mitsubishi Denki Kabushiki Kaisha System and method of acquiring traffic data
US20030236613A1 (en) * 2002-03-26 2003-12-25 Hiroyuki Satoh Traffic-information distribution method and on-vehicle navigation apparatus
FR2843474A1 (fr) * 2002-11-29 2004-02-13 France Telecom Procede, equipement et systeme de production d'informations de trafic routier ainsi que systeme de navigation routiere et equipement de navigation routiere

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009026238A2 (fr) 2007-08-16 2009-02-26 Breazeale Earl Edward Jr Suivi des soins de santé
EP2191435A4 (fr) * 2007-08-16 2012-10-10 Earl Edward Breazeale Jr Suivi des soins de santé
US9740823B2 (en) 2007-08-16 2017-08-22 Earl Edward Breazeale, JR. Healthcare tracking
US10860686B2 (en) 2007-08-16 2020-12-08 Rsv Qozb Ltss, Inc. Healthcare tracking
WO2009071366A1 (fr) * 2007-12-03 2009-06-11 Robert Bosch Gmbh Procédé de fonctionnement d'un système d'information et système d'information
DE102007058093B4 (de) * 2007-12-03 2019-05-16 Robert Bosch Gmbh Verfahren und Vorrichtung zum Ermitteln einer Routenempfehlung aus einer Mehrzahl von Wegstrecken
WO2009116105A3 (fr) * 2008-03-21 2010-01-28 Gianfranco Antonini Procédé d'attribution de trafic pour des réseaux de transport multimodaux
CN111144808A (zh) * 2019-12-20 2020-05-12 贵州黔岸科技有限公司 用于建筑材料运输的任务统计管理系统及方法

Also Published As

Publication number Publication date
DE102004011604A1 (de) 2005-09-29
EP1741052A1 (fr) 2007-01-10

Similar Documents

Publication Publication Date Title
DE112011103424B4 (de) System und Verfahren zum Auswählen von Routen
EP1026649B1 (fr) Procédé et dispositif de préparation d'information de circulation
DE69329119T2 (de) Vorhersageverfahren für strassenverkehrparameter
EP3611709A1 (fr) Simulateur de flux de la circulation
DE102011085893A1 (de) Systeme und Verfahren zum Planen von Fahrzeugrouten auf Grundlage von Sicherheitsfaktoren
DE202013012418U1 (de) Reiseplanung im öffentlichen Personenverkehr
EP0941534A1 (fr) Procede de determination de donnees d'itineraires
DE102019132735A1 (de) Opportunistisches betanken für autonome fahrzeuge
DE102012212065A1 (de) Verfahren zum Betreiben eines Fahrerassistenzsystems für ein Fahrzeug und Fahrerassistenzsystem für ein Fahrzeug
WO2015036416A2 (fr) Dispositif et procédé pour la gestion de la mobilité
DE102021112418A1 (de) Verfahren zum Betreiben einer Energiemarktplattform für einen Energiehandel für zumindest einen Aggregator mittels einer elektronischen Recheneinrichtung, Computerprogrammprodukt sowie Energiemarktplattform
EP1293948A2 (fr) Procédé et dispositif d'optimisation du plan d'itinéraire sur les réseaux de transport en ligne
EP3073423A1 (fr) Dispositif, procede et programme d'ordinateur destines a generer une sequence temporelle d'activites d'un utilisateur
DE202015103648U1 (de) Verarbeitungssystem zur Transportanalyse
DE102019130494A1 (de) Verfahren und vorrichtung zum adaptiven einsatz von temporären verkehrsregelungsmassnahmen und navigationsreaktion
EP1614996B1 (fr) Procédé et système de planification dynamique d'itinéraire
DE19636379C1 (de) Verfahren zur Steuerung des Transport- und Reiseverkehrs
DE102012215447A1 (de) Zentralisierte Routenbestimmung
EP1741052A1 (fr) Procede et systeme d'optimisation de travaux de transport
WO2015149884A1 (fr) Procédé et produit logiciel d'analyse d'ensembles de données de masse de billets de passagers aériens
DE102017008667A1 (de) Verfahren zur dynamischen Routenplanung
DE102018009790A1 (de) Verfahren zur dynamischen Routenplanung
DE102023201878A1 (de) Verfahren zum Überprüfen einer Beförderungsfahrt eines Nutzers, sowie elektronisches Beförderungsinformationssystem und Computerprogramm
DE102021129801A1 (de) Verfahren, Computerprogramm und Vorrichtung zum Steuern einer Route
DE10055000A1 (de) Verkehrsdichtenabschätzung

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A1

Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BW BY BZ CA CH CN CO CR CU CZ DK DM DZ EC EE EG ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NA NI NO NZ OM PG PH PL PT RO SC SD SE SG SK SL SM SY TJ TM TN TR TT TZ UA UG US UZ VC VN YU ZA ZM ZW

AL Designated countries for regional patents

Kind code of ref document: A1

Designated state(s): BW GH GM KE LS MW MZ NA SD SL SZ TZ UG ZM ZW AM AZ BY KG KZ MD RU TJ TM AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HU IE IS IT LT LU MC NL PL PT RO SE SI SK TR BF BJ CF CG CI CM GA GN GQ GW ML MR NE SN TD TG

121 Ep: the epo has been informed by wipo that ep was designated in this application
WWE Wipo information: entry into national phase

Ref document number: 2005715088

Country of ref document: EP

WWP Wipo information: published in national office

Ref document number: 2005715088

Country of ref document: EP