Method and system for modeling and processing vehicular traffic data and information and applying thereof
First Claim
1. A method for modeling and processing vehicular traffic data, comprising the steps of:
- generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections;
acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network;
determining a current vehicular traffic situation based on current vehicular traffic data for a road section, wherein gaps in current vehicular traffic data for a road section are filled by using values that are predicted; and
predicting a future vehicular traffic situation with aid of vehicular traffic behavior patterns and correlation rules, wherein said correlation rules of said road section network are based on historic vehicular traffic data and are time dependent, and said time dependent correlation rules determine correlation of and interrelation between different road sections as a function of time, wherein said behavior patterns of said road section network are time dependent, andwherein said time dependent behavior pattern of a road section describes at least one of;
regular changing of associated normalized travel time (NTT) values as a function of time or regular changing of associated speed values as a function of time.
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Abstract
A method and system for modeling and processing vehicular traffic data and information, comprising: (a) transforming a spatial representation of a road network into a network of spatially interdependent and interrelated oriented road sections, for forming an oriented road section network; (b) acquiring a variety of the vehicular traffic data and information associated with the oriented road section network, from a variety of sources; (c) prioritizing, filtering, and controlling, the vehicular traffic data and information acquired from each of the variety of sources; (d) calculating a mean normalized travel time (NTT) value for each oriented road section of said oriented road section network using the prioritized, filtered, and controlled, vehicular traffic data and information associated with each source, for forming a partial current vehicular traffic situation picture associated with each source; (e) fusing the partial current traffic situation picture associated with each source, for generating a single complete current vehicular traffic situation picture associated with entire oriented road section network; (f) predicting a future complete vehicular traffic situation picture associated with the entire oriented road section network; and (g) using the current vehicular traffic situation picture and the future vehicular traffic situation picture for providing a variety of vehicular traffic related service applications to end users.
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Citations
46 Claims
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1. A method for modeling and processing vehicular traffic data, comprising the steps of:
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generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network; determining a current vehicular traffic situation based on current vehicular traffic data for a road section, wherein gaps in current vehicular traffic data for a road section are filled by using values that are predicted; and predicting a future vehicular traffic situation with aid of vehicular traffic behavior patterns and correlation rules, wherein said correlation rules of said road section network are based on historic vehicular traffic data and are time dependent, and said time dependent correlation rules determine correlation of and interrelation between different road sections as a function of time, wherein said behavior patterns of said road section network are time dependent, and wherein said time dependent behavior pattern of a road section describes at least one of;
regular changing of associated normalized travel time (NTT) values as a function of time or regular changing of associated speed values as a function of time. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35)
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36. A method for modeling and processing vehicular traffic data, comprising the steps of:
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generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; acquiring vehicular traffic data associated with said road network from at least a plurality of sensors and predicting a future traffic situation; and determining a current vehicular traffic situation based on current vehicular traffic data for a road section, wherein predicting a traffic situation comprises predicting normalized travel times over a plurality of road sections based on time dependent vehicle behavior patterns, and wherein the normalized travel times comprise travel times normalized with respect to a pre-determined distance, such that the predicted traffic parameter for a particular road section takes into account the time of passage of that road section.
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37. A method for modeling and processing vehicular traffic data, comprising the steps of:
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generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network and predicting a future traffic situation; and determining a current vehicular traffic situation based on current vehicular traffic data for a road section, wherein predicting a future traffic situation comprises predicting travel times over a plurality of road sections based on time dependent vehicle behavior patterns, the method being further operable to compare detected traffic developments to regular time dependent behavior patterns of said road section network, and to identify discrepancies from said regular time dependent behavior patterns, wherein propagation in time along adjacent and non-adjacent road sections of traffic events identified by discrepancies from said time dependent behavior patterns can be determined using time dependent correlation rules of said road section network and said time dependent correlation rule determines correlation of and interrelation between different road sections as a function of time, wherein said behavior patterns of said road section network are time dependent, and wherein said time dependent behavior pattern of a road section describes at least one of;
regular changing of associated normalized travel time (NTT) values as a function of time or regular changing of associated speed values as a function of time. - View Dependent Claims (38)
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39. A method for modeling and processing vehicular traffic data, comprising the steps of:
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generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network; determining a current vehicular traffic situation based on current vehicular traffic data for a road section, wherein gaps in current vehicular traffic data for a road section are filled by using values that are predicted, and predicting a future vehicular traffic situation by using correlation rules based on historic vehicular traffic data and vehicular traffic behavior patterns, wherein said behavior patterns of said road section network are time dependent, and wherein said time dependent behavior pattern of a road section describes at least one of;
regular changing of associated normalized travel time (NTT) values as a function of time or regular changing of associated speed values as a function of time. - View Dependent Claims (40, 41, 42)
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43. A method for modeling and processing vehicular traffic data, comprising the steps of:
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generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network, wherein acquiring vehicular traffic data comprises selecting a sample comprising vehicle-carried mobile sensors from the plurality of mobile sensors and tracking locations of mobile sensors of said sample for modelling and processing purposes, and wherein locations of said vehicle-carried mobile sensors are obtained from said wireless telecommunication network in known time intervals, such that a path of each said vehicle is determined, said path being a sequence of connected road sections constituting a logical route to take between two points within said represented road network; determining a current vehicular traffic situation based on current vehicular traffic data for a road section, wherein gaps in current vehicular traffic data for a road section are filled by using values that are predicted; and predicting a future vehicular traffic situation with aid of vehicular traffic behavior patterns and correlation rules, wherein said correlation rules of said road section network are based on historic vehicular traffic data and are time dependent, and said time dependent correlation rules determine correlation of and interrelation between different road sections as a function of time, wherein normalized travel times (NTT) of said road sections of said determined path are calculated by using assumptions as to reasonable vehicle behavior and said tracked footprints of said mobile sensors.
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44. A method for modeling and processing vehicular traffic data, comprising the steps of:
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generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network, wherein acquiring vehicular traffic data comprises selecting a sample comprising vehicle-carried mobile sensors from the plurality of mobile sensors and tracking locations of mobile sensors of said sample for modelling and processing purposes, and wherein locations of said vehicle-carried mobile sensors are obtained from said wireless telecommunication network in known time intervals, such that a path of each said vehicle is determined, said path being a sequence of connected road sections constituting a logical route to take between two points within said represented road network; determining a current vehicular traffic situation based on current vehicular traffic data for a road section, wherein gaps in current vehicular traffic data for a road section are filled by using values that are predicted; and predicting a future vehicular traffic situation with aid of vehicular traffic behavior patterns and correlation rules, wherein said correlation rules of said road section network are based on historic vehicular traffic data and are time dependent, and said time dependent correlation rules determine correlation of and interrelation between different road sections as a function of time, wherein normalized travel times (NTT) of said road sections of said determined path are calculated by using assumptions as to reasonable vehicle behavior and said tracked footprints of said mobile sensors, and wherein a mean normalized travel time (NTT) for a road section is calculated by statistically processing data from said mobile sensors traveling on said road section during a time period of an assessment cycle to indicate the possibility of different velocities on different lanes of said road section.
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45. A method for modeling and processing vehicular traffic data, comprising the steps of:
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generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network, wherein acquiring vehicular traffic data comprises selecting a sample comprising vehicle-carried mobile sensors from the plurality of mobile sensors and tracking locations of mobile sensors of said sample for modelling and processing purposes, and wherein locations of said vehicle-carried mobile sensors are obtained from said wireless telecommunication network in known time intervals, such that a path of each said vehicle is determined, said path being a sequence of connected road sections constituting a logical route to take between two points within said represented road network; determining a current vehicular traffic situation based on current vehicular traffic data for a road section, wherein gaps in current vehicular traffic data for a road section are filled by using values that are predicted; and predicting a future vehicular traffic situation with aid of vehicular traffic behavior patterns and correlation rules, wherein said correlation rules of said road section network are based on historic vehicular traffic data and are time dependent, and said time dependent correlation rules determine correlation of and interrelation between different road sections as a function of time, wherein normalized travel times (NTT) of said road sections of said determined path are calculated by using assumptions as to reasonable vehicle behavior and said tracked footprints of said mobile sensors, wherein a mean normalized travel time (NTT) for a road section is calculated by statistically processing data from said mobile sensors traveling on said road section during a time period of an assessment cycle to indicate the possibility of different velocities on different lanes of said road section, and wherein said mean normalized travel time (NTT) values are calculated using a confidence factor.
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46. A method for modeling and processing vehicular traffic data, comprising the steps of:
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generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network, wherein acquiring vehicular traffic data comprises selecting a sample comprising vehicle-carried mobile sensors from the plurality of mobile sensors and tracking locations of mobile sensors of said sample for modelling and processing purposes, and wherein locations of said vehicle-carried mobile sensors are obtained from said wireless telecommunication network in known time intervals, such that a path of each said vehicle is determined, said path being a sequence of connected road sections constituting a logical route to take between two points within said represented road network; determining a current vehicular traffic situation based on current vehicular traffic data for a road section, wherein gaps in current vehicular traffic data for a road section are filled by using values that are predicted; and predicting a future vehicular traffic situation with aid of vehicular traffic behavior patterns and correlation rules, wherein said correlation rules of said road section network are based on historic vehicular traffic data and are time dependent, and said time dependent correlation rules determine correlation of and interrelation between different road sections as a function of time, wherein normalized travel times (NTT) of said road sections of said determined path are calculated by using assumptions as to reasonable vehicle behavior and said tracked footprints of said mobile sensors, wherein a mean normalized travel time (NTT) for a road section is calculated by statistically processing data from said mobile sensors traveling on said road section during a time period of an assessment cycle to indicate the possibility of different velocities on different lanes of said road section, wherein said mean normalized travel time (NTT) values are calculated using a confidence factor, and wherein the confidence factor is a function of one or more of;
accuracy of said footprints;
amount of said footprints; and
, error rate of said footprints.
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Specification