Patent Grant
9324232
The present invention relates to the field of vehicular traffic data and information and, more particularly, to a method and system for modeling and processing vehicular traffic data and information, and using the modeled and processed vehicular traffic data and information for providing a variety of vehicular traffic related service applications to end users.
Despite continuing investing in massive amounts of financial and human resources, current road network capacities insufficiently meet the needs dictated by current levels and growth rates of traffic volume. This dilemma relates to current road network capacities, in general, and current road network capacities in urban, suburban, and rural, environments, in particular. Road congestion, or, equivalently, inconveniently high levels or volumes of vehicular road traffic, is a persistent major factor resulting from this dilemma, and needs to be given proper attention and taken into account for efficiently scheduling trips, selecting travel routes, and for attempting to efficiently allocate and exploit time, by individual drivers, as well as by vehicular traffic logistics personnel such as company vehicular fleet managers, responsible for performing such activities. Road congestion and associated traffic data and information also need to be well understood and used by a wide variety of public and private occupations and personnel, such as designers, planners, engineers, coordinators, traffic law makers and enforcers, directly and/or indirectly involved in designing, planning, controlling, engineering, coordinating, and implementing, a wide variety of activities, events, and/or construction projects, which depend upon accurate descriptions of current and future vehicular traffic situations and scenarios. This situation is a main driving force for the on-going development and application of various methods, systems, and devices, for acquiring, analyzing, processing, and applying, vehicular traffic data and information.
There are various prior art techniques for acquiring, analyzing, processing, and applying, vehicular traffic data and information. A few examples of recent prior art in this field are U.S. Pat. No. 6,236,933, issued to Lang, entitled “Instantaneous Traffic Monitoring System”, U.S. Pat. No. 6,012,012, issued to Fleck et al., entitled “Method And System For Determining Dynamic Traffic Information”, U.S. Pat. No. 6,240,364, issued to Kemer et al., entitled “Method And Device For Providing Traffic Information”, and, U.S. Pat. No. 5,845,227, issued to Peterson, entitled “Method And Apparatus For Providing Shortest Elapsed Time Route And Tracking Information To Users”.
Prior art techniques typically include calculating velocities of vehicles, for example, by acquiring series of exact locations of the vehicles located along roads in known time intervals, by measuring vehicular traffic flux along roads, especially, along highways, and/or, by a variety of other means known in the field. There are prior art techniques which are either based on, or, include, the use of networks of fixed or static traffic sensors or electronic devices, such as video cameras, induction boxes, tag readers, traffic detectors, and so on, which are installed and fixed along known locations of main traffic arteries and/or traffic volume. Fixed or static traffic sensors or electronic devices, positioned at known locations, relay crossing times of vehicles to a computerized central traffic data and information handling (gathering, collecting, acquiring, analyzing, processing, communicating, distributing) system that consequently calculates velocities of the vehicles between two such sensors.
Significant limitations of developing and implementing comprehensive, highly accurate and precise, techniques for acquiring, analyzing, processing, and applying, vehicular traffic data and information, primarily based upon a system or network of fixed or static traffic sensors or electronic devices, are the relatively large amounts and expense of the necessary infrastructure and maintenance, especially if such resources are to account for and include vehicular traffic data and information associated with a plethora of minor roads characterized by low volumes of vehicular traffic.
More recent prior art techniques are either based on, or, at least include, the use of mobile sensors or electronic devices physically located in or attached to vehicles, each of which is uniquely or specifically designated or assigned to a particular vehicle, whereby the mobile sensors or electronic devices automatically transmit vehicle locations to the computerized central traffic data and information handling system according to pre-determined time intervals, and, whereby, vehicle velocities are relatively simple to calculate for vehicle locations acquired with sufficient accuracy.
For obtaining dynamic vehicle location and velocity data and information, having varying degrees of accuracy and precision, from uniquely or specifically dedicated in-vehicle mobile sensors or electronic devices, such prior art techniques make use of well known global positioning system (GPS) and/or other types of mobile wireless communication or electronic vehicular tracking technologies, such as cellular telephone or radio types of mobile wireless communications networks or systems, involving the use of corresponding mobile wireless devices such as cellular telephones, laptop computers, personal digital assistants (PDAs), transceivers, and other types of telemetric devices, which are uniquely or specifically designated or assigned to a particular vehicle. Establishing and maintaining various communications of the mobile sensors or electronic devices, the computerized central traffic data and information handling system, and, vehicular end-users, are also performed by mobile wireless communication networks or systems, such as cellular telephone mobile wireless communications networks or systems, for example, involving the Internet.
It is noted, however, that due to the requirement of uniquely or specifically designating or assigning each mobile sensor or electronic device to a particular vehicle during the process of gathering, collecting, or acquiring, the vehicular traffic data and information, the potential number of mobile sensors or electronic devices providing dynamic vehicle location and velocity data and information to the computerized central traffic data and information handling system is limited, in proportion to the number of vehicles featuring the particular mobile wireless communication or electronic vehicular tracking technology. For example, currently, there is a significantly larger potential number of vehicles associated with cellular telephone types of a mobile wireless communication network or system compared to the potential number of vehicles associated with GPS types of a mobile wireless communication network or system.
Various specific techniques for manually and electronically gathering, collecting, or acquiring, vehicular traffic data and information are relatively well developed and taught about in the prior art. Moreover, various specific techniques for electronically communicating, sending, or distributing, analyzed and processed vehicular traffic data and information in vehicular traffic related service applications to end users are also relatively well developed and taught about in the prior art. However, there remains a strong on-going need for developing better, more comprehensive, highly accurate and precise, yet, practicable and implementable techniques for analyzing, modeling, and processing, the acquired, collected, or gathered, vehicular traffic data and information. This last aspect is especially true with regard to using vehicular traffic data and information for comprehensively, yet, accurately and practicably, describing current and predicting future vehicular traffic situations and scenarios, from which vehicular traffic data and information are used for providing a variety of vehicular traffic related service applications to end users.
In the prior art, a critically important aspect requiring new and improved understanding and enabling description for developing better, more comprehensive, highly accurate and precise, yet, practicable and implementable techniques for analyzing, modeling, and processing, the acquired, collected, or gathered, vehicular traffic data and information, relates to the use of a geographical information system (GIS), or, other similarly organized and detailed spatial representation of a network of roads, for a particular local or wide area region, within which the vehicular traffic data and information are acquired, collected, or gathered. In particular, there is a need for properly and efficiently ‘spatially’ modeling a road network, and, properly and efficiently ‘spatially’ modeling, interrelating, and correlating, the vehicular traffic data and information which are acquired, collected, or gathered, among a plurality of sub-regions, sub-areas, or, other designated sub-divisions, within the particular local or wide area region of the spatially modeled road network. Furthermore, there is a particular need for incorporating the factor or dimension of time, for properly and efficiently ‘spatially and temporally’ defining, interrelating, and correlating, the vehicular traffic data and information which are acquired, collected, or gathered, among the plurality of sub-regions, sub-areas, or, other designated sub-divisions, within the particular local or wide area region of the spatially modeled road network.
In the prior art, another critically important aspect requiring new and improved understanding and enabling description relates to the modeling and processing of vehicular traffic data and information which are acquired, collected, or gathered, using techniques based on cellular telephone types of mobile wireless communications networks or systems, which to date, feature relatively low accuracy and precision of vehicle locations compared to the less widely used, but significantly more highly accurate and precise, GPS types of mobile wireless communication or electronic vehicular tracking technologies.
In prior art, another critically important aspect requiring new and improved understanding and enabling description relates to the modeling and processing of vehicular traffic data and information which are acquired, collected, or gathered, from an ‘arbitrary’, non-pre-determined or non-designated, population or group of vehicles each including a uniquely or specifically designated or assigned mobile sensor or electronic device, therefore, resulting in a potentially large number of mobile sensors or electronic devices providing dynamic vehicle location and velocity data and information to the computerized central traffic data and information handling system.
In the prior art, another critically important aspect requiring new and improved understanding and enabling description relates to the proper and efficient combining or fusing of a variety of vehicular traffic data and information which are acquired, collected, or gathered, using a combination of a various techniques based on networks of fixed or static traffic sensors or electronic devices, GPS and/or cellular telephone types of mobile wireless communications networks or systems, and, various other manual and electronic types of vehicular traffic data and information such as historical and/or event related vehicular traffic data and information.
In the prior art, another important aspect requiring understanding and enabling description relates to techniques for protecting the privacy of individuals associated with or hosting the sources, that is, the mobile sensors or electronic devices, of vehicular traffic data and information which are acquired, collected, or gathered, using techniques based on GPS and/or cellular telephone types of mobile wireless communications networks or systems. The inventors are unaware of any prior art teaching for performing this in the field of vehicular traffic data and information.
There is thus a strong need for, and it would be highly advantageous to have a method and system for modeling and processing vehicular traffic data and information, and using the modeled and processed vehicular traffic data and information for providing a variety of vehicular traffic related service applications to end users. Moreover, there is a particular need for such a generally applicable method and system with regard to using vehicular traffic data and information for comprehensively, yet, accurately and practicably, describing current and predicting future vehicular traffic situations and scenarios, from which vehicular traffic data and information are used for providing the variety of vehicular traffic related service applications to the end users.
The present invention relates to a method and system for modeling and processing vehicular traffic data and information, and using the modeled and processed vehicular traffic data and information for providing a variety of vehicular traffic related service applications to end users. The present invention especially includes features for using vehicular traffic data and information for comprehensively, yet, accurately and practicably, describing current and predicting future vehicular traffic situations and scenarios, from which vehicular traffic data and information are used for providing the variety of vehicular traffic related service applications to the end users.
Thus, according to the present invention, there is provided a method and a 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.
The present invention successfully overcomes all the previously described shortcomings and limitations of presently known techniques for analyzing, modeling, and processing, the acquired, collected, or gathered, vehicular traffic data and information. Especially with regard to using vehicular traffic data and information for comprehensively, yet, accurately and practicably, describing current and predicting future vehicular traffic situations and scenarios, from which vehicular traffic data and information are used for providing a variety of vehicular traffic related service applications to end users. Another important benefit of the present invention is that it is generally applicable and complementary to various different ‘upstream’ prior art techniques of gathering, collecting, or acquiring, vehicular traffic data and information, and, generally applicable and complementary to various different ‘downstream’ prior art techniques of electronically communicating, sending, or distributing, the analyzed, modeled, and processed, vehicular traffic data and information in vehicular traffic related service applications to end users.
Implementation of the method and system for modeling and processing vehicular traffic data and information, and using the modeled and processed vehicular traffic data and information for providing a variety of vehicular traffic related service applications to end users, according to the present invention, involves performing or completing selected tasks or steps manually, automatically, or a combination thereof. Moreover, according to actual instrumentation and/or equipment used for implementing a particular preferred embodiment of the disclosed invention, several selected steps of the present invention could be performed by hardware, by software on any operating system of any firmware, or a combination thereof. In particular, as hardware, selected steps of the invention could be performed by a computerized network, a computer, a computer chip, an electronic circuit, hard-wired circuitry, or a combination thereof, involving any number of digital and/or analog, electrical and/or electronic, components, operations, and protocols. Additionally, or alternatively, as software, selected steps of the invention could be performed by a data processor, such as a computing platform, executing a plurality of computer program types of software instructions or protocols using any suitable computer operating system.
The invention is herein described, by way of example only, with reference to the accompanying drawings, wherein:
The present invention takes priority from U.S. Provisional Patent Application No. 60/227,905, filed Aug. 28, 2000, entitled “Dynamic Traffic Flow Forecasting, Using Large Volumes Of Privacy Protected Location Data”, the teachings of which are incorporated by reference as if fully set forth herein.
The present invention relates to a method and system for modeling and processing vehicular traffic data and information, and using the modeled and processed vehicular traffic data and information for providing a variety of vehicular traffic related service applications to end users. The present invention especially includes features for using vehicular traffic data and information for comprehensively, yet, accurately and practicably, describing current and predicting future vehicular traffic situations and scenarios, from which vehicular traffic data and information are used for providing the variety of vehicular traffic related service applications to the end users.
The present invention features several aspects of novelty and inventive step over the prior art, for developing better, more comprehensive, highly accurate and precise, yet, practicable and implementable techniques for analyzing, modeling, and processing, the acquired, collected, or gathered, vehicular traffic data and information. Several main aspects of the present invention are briefly described herein. These and additional aspects of the present invention are described in more detail thereafter.
One main aspect of the present invention relates to the adaptation and modeling of a geographical information system (GIS), or, other similarly organized and detailed spatial representation of a network of roads, for a particular local or wide area region, within which the vehicular traffic data and information are acquired, collected, or gathered. In particular, there is efficiently ‘spatially’ modeling the road network, in order to accommodate and fit the variety of vehicular traffic data and information, while limiting the amount of data and information needed to be stored and handled. By way of this adaptation and modeling of a road network, especially a GIS type of road network, there is also properly and efficiently ‘spatially’ modeling, interrelating, and correlating, the vehicular traffic data and information which are acquired, collected, or gathered, among a plurality of sub-regions, sub-areas, or, other designated sub-divisions, within the particular local or wide area region of the spatially defined road network. Furthermore, there is incorporating the factor or dimension of time, for properly and efficiently ‘spatially and temporally’ defining, interrelating, and correlating, the vehicular traffic data and information which are acquired, collected, or gathered, among the plurality of sub-regions, sub-areas, or, other designated sub-divisions, within the particular local or wide area region of the spatially defined road network.
Another main aspect of the present invention relates to the modeling and processing of vehicular traffic data and information which are acquired, collected, or gathered, using techniques based on mobile wireless communications networks or systems, such as cellular telephone types of networks and systems, which to date, feature relatively low accuracy and precision of vehicle locations compared to the less widely used, but significantly more highly accurate and precise, GPS types of mobile wireless communication or electronic vehicular tracking technologies.
Another main aspect of the present invention relates to the modeling and processing of vehicular traffic data and information which are acquired, collected, or gathered, from an ‘arbitrary’, non-pre-determined or non-designated, population or group of vehicles each including a uniquely or specifically designated or assigned mobile sensor or electronic device, therefore, resulting in a potentially large number of mobile sensors or electronic devices providing dynamic vehicle location and velocity data and information to the computerized central traffic data and information handling system.
Another main aspect of the present invention relates to efficient combining or fusing of a variety of vehicular traffic data and information which are acquired, collected, or gathered, using a combination of various techniques based on networks of fixed or static traffic sensors or electronic devices, mobile wireless communications networks or systems such as GPS and/or cellular telephone networks or systems, and, various other manual and electronic types of vehicular traffic data and information such as traffic reports, incorporating in the fusion process the historical and/or event related vehicular traffic data and information that is accumulated, analyzed, and processed, from previously accumulated vehicular traffic data and information in the same system.
Another main aspect of the present invention relates to techniques for protecting the privacy of individuals associated with or hosting the sources, that is, the mobile sensors or electronic devices, of vehicular traffic data and information which are acquired, collected, or gathered, using techniques based on GPS and/or cellular telephone types of mobile wireless communications networks or systems. The inventors are unaware of any prior art teaching for performing this in the field of vehicular traffic data and information.
It is to be understood that the present invention is not limited in its application to the details of the order or sequence of steps of operation or implementation of the method, or, to the details of construction and arrangement of the various devices and components of the system, set forth in the following description and drawings. For example, the following description refers to a cellular telephone type of mobile wireless communication network or system as the primary source of electronically acquired vehicular data and information, in order to illustrate implementation of the present invention. As indicated below, additionally, or, alternatively, other types of mobile wireless communication networks or systems function as sources of electronically acquired vehicular data and information. Accordingly, the invention is capable of other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
Steps, components, operation, and implementation of a method and system for modeling and processing vehicular traffic data and information, and using the modeled and processed vehicular traffic data and information for providing a variety of vehicular traffic related service applications to end users, according to the present invention are better understood with reference to the following description and accompanying drawings. Throughout the following description and accompanying drawings, like reference numbers refer to like elements.
Referring now to the drawings,
In Step (a) of the method of the present invention, there is 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.
Specifically, there is transforming a spatial representation of a road network 12 into a network of spatially interdependent and interrelated oriented road sections, for forming an oriented road section network 14. Preferably, road network 12 is a geographical information system (GIS) type of road network 12, which is well known in the art of vehicular road traffic data and information. Typically, a GIS type of road network 12 is a detailed spatial representation of a road network encompassing a particular local or wide area region, within which are a plurality of sub-regions, sub-areas, or, other designated sub-divisions, such as roads, turns, junctions, and, areas or regions of variably populated streets and roads. Associated with road network 12, and consequently, oriented road section network 14, are the vehicular traffic data and information which are acquired, collected, or gathered, among the plurality of sub-regions, sub-areas, or, other designated sub-divisions. Step (a) corresponds to an initialization step when method/system 10 is applied to a new geographical region or road network 12. Vehicular traffic data and information are subsequently added, by various manual and/or electronic means, to the oriented road section network 14 model of a GIS type of road network 12.
The approach of the present invention to the field of vehicular traffic data and information, in general, and to the problematic situation of vehicular traffic congestion, in particular, is from a ‘holistic’ point of view, emphasizing the importance of an entire road network 12 encompassing a particular local or wide area region, and the interdependence and interrelation of the plurality of sub-regions, sub-areas, or, other designated sub-divisions of road network 12. This is accomplished by adapting and transforming a spatial representation of road network 12 into a network of spatially interdependent and interrelated oriented road sections, for forming oriented road section network 14. This process is conceptually analogous to transforming road network 12 into an intertwined weave of oriented road sections, for forming oriented road section network 14.
Accordingly, in the upper part of
Two additional examples, relating to the two directions of a bi-directional street, of a road section, are road section 36 and road section 38, each representing a single consecutive road segment 40, within the road network (12 in
Herein, the term ‘oriented road section’ represents a road section having a single vehicular traffic continuation option located at the head end road junction. Herein, a single vehicular traffic continuation option refers to one of the various vehicular traffic flow options a vehicle may take, such as optionally continuing to travel straight, optionally taking a right turn, or, optionally taking a left turn, from a particular road segment joined or linked to the head end road junction. Accordingly, a vehicular traffic continuation option is selected from the group consisting of continuing to travel straight, taking a right turn, and, taking a left turn, from a particular road segment joined or linked to the head end road junction.
Accordingly, in the lower part of
An equally alternative way of defining the term oriented road section is that, a road section having a head end road junction with a particular plurality of vehicular traffic continuation options is split or divided into that particular plurality of oriented road sections. Accordingly, in
When special lanes in the various road segments within the oriented road section network are assigned to turning traffic, the respective oriented road sections may yield different vehicular traffic data and information, such as different values relating to road congestion or heavy vehicular traffic volume. This representation of a road network enables the incorporation of interdependence and interrelation among the plurality of road segments, the plurality of road sections, and, the plurality of oriented road sections. In particular, the oriented road section network model of a road network accounts for the significant influence of road junctions on vehicular traffic flow and associated travel time delays. Moreover, there is strong interdependence and interrelation between any given particular oriented road section characterized by a particular traffic situation or scenario and other oriented road sections in the same vicinity, either crossing or parallel to the particular oriented road section.
In Step (b), there is acquiring a variety of vehicular traffic data and information associated with the oriented road section network, from a variety of sources.
An important aspect of the present invention is the ability to model and process a wide variety of vehicular traffic data and information, in particular, which are used for generating current and future vehicular traffic situation pictures. Referring to method/system 10 of
Systems of fixed sensors and traffic reports of historical and/or event related vehicular traffic data and information are presently the most common sources of vehicular traffic data and information, and are well known in the art of vehicular traffic data and information. However, the present invention features mobile sensors as most advantageous for acquiring vehicular traffic data and information, even though they have lower confidence levels. A growing number of mobile wireless communication devices are increasingly being installed or carried in vehicles, and are capable of transmitting vehicular locations to a computerized central data and information receiving and processing system. Such mobile wireless communication devices are telemetric devices with GPS capabilities, anti-theft devices, and, driver-carried devices such as computer laptops and cellular phones. The respective computerized central data and information receiving and processing systems have capabilities of locating these devices, and act as sources of vehicular traffic data and information for assessing traffic situations. The variability of these devices and the spreading popularity of some of them leads to an abundant and widespread population of mobile sensors. However, some of these systems, such as a cellular telephone wireless communication networks, have poor location accuracy, and therefore require elaborate and unique processing in order to extract useful vehicular traffic data and information.
In principle, method/system 10 of the present invention is applicable to all types of mobile sensor systems, and models and processes the variability of location accuracy, vehicular traffic movement data and information, reading time intervals, and other types of vehicular traffic data and information as further described below. Method/system 10 acquires vehicular traffic data and information from several such sources in parallel and combines or fuses the acquired vehicular traffic data and information into one coherent and complete vehicular traffic situation picture.
Acquiring the vehicular traffic data and information from a mobile sensor system is performed by tracking a sample of mobile sensors 62 (
In Step (c), there is prioritizing, filtering, and controlling, the vehicular traffic data and information acquired from each of the variety of sources.
In this step of implementing method/system 10, consideration is given to various aspects relating to prioritizing, filtering, and controlling, the vehicular traffic data and information acquired from each of the variety of sources. For example, filtering noise corresponding to irrelevant sensors and erroneous data.
With respect to the choice of the sampled mobile sensor devices, sampling policy or prioritizing the variety of vehicular traffic data and information, and controlling the sampling of the vehicular traffic data and information, statistical consideration show that a very small percentage, approximately 1-2%, of moving vehicles provide a sufficient base to obtain the necessary data. The number of moving sensors is, however, huge, and there is need to efficiently choose the sample population, to decide on a sampling policy, and to control its carrying out, so that the data collected will be as relevant as possible for the purpose of vehicular traffic assessment.
The choice of sample units is done by making sure that they are in high probability moving-vehicle-carried. In cellular telephone networks, this is done by identifying phones whose cell-handover rates indicate a relatively fast movement. A parallel procedure is performed for any mobile sensor source. In mobile anti-theft systems, for example, vehicular ignition activation is a valid indicator of ‘movement-suspicion’. This initial choice of sensor population assures, for example, that pedestrians will not be included, and so prevents, along the track, the confusion between walking pedestrians and slowing traffic due to heavy congestion.
Data acquisition from cellular phones is controlled by a server connected to a particular cellular phone network. The operation of cellular phones provokes a large amount of administrative and control messages that flow in the cellular phone network whenever any cellular ‘event’ occurs. For example, initiation of a call, completion of a call, transmission of a short message service (SMS) message, a cell transition or handover, etc. The server of the cellular phone network monitors these messages and intercepts those that indicate ‘movement’ of cellular phone holders, for example, phones whose cell handovers rate can point to a vehicle speed.
Those cellular phones that are suspected as being ‘vehicle-carried’ are immediately tracked for their locations using the handovers themselves as ‘footprints’ or ‘footsteps’, and/or other locating capability of the cellular phone network. Different particular cellular phone networks use different locating techniques and systems, each having a different characteristic locating accuracy. Method/system 10 is independent of specific locating techniques and systems, and is suited to a variability of accuracy and precision levels. For example, a common difficulty concerning locating accuracy and precision of a mobile sensor network is the phenomenon of ‘noise’, such as that caused by reflections, low cell-efficiency management, and/or, even errors. As a result of this, a substantial number of the footprints is erroneous, whereby they do not represent real or actual sensor locations.
The tracking of the ‘moving’ phones is done by polling their locations in known time intervals. This operation is controlled by Sampler software module 1 indicated in
Once the ‘moving’ sensors are identified, method/system 10 includes them in the sample population and controls the flow of the vehicular traffic data and information according to actual features and capabilities of method/system 10, in general, and of Sampler software module 1, in particular, and, according to changing locations of the moving sensors along their respective paths. For example, Sampler software module 1 obtains the vehicular traffic data and information by ‘push’ or ‘pull’ procedures, that is, where ‘push’ is a mode of receiving the vehicular traffic data and information that is initiated by source 62 of the mobile sensors, and, where ‘pull’ is a mode of receiving the vehicular traffic data and information when the initiator is Sampler software module 1. This tracking operation is controlled by method/system 10 according to a predefined and updated policy. For instance, according to a policy of ‘not to track too many vehicles in a same region’, ‘focus the tracking on a certain problematic region’, ‘stop tracking a vehicle that stopped for a predetermined time interval’, and, ‘collect vehicular traffic data and information within a certain limited capacity of the cellular phone network’.
As previously indicated, a main aspect of the present invention relates to techniques for protecting the privacy of individuals associated with, or hosting, the sources 62 (
Moreover, these processing steps take place in a server that is connected to the mobile sensor source network. When more than one mobile sensor source provides vehicular traffic data and information to method/system 10, such a server is in communication with each of the data providers. Tracked vehicles are not identified by using phone numbers. Even within the short tracking times, mobile sensor identities are kept within the location of the cellular phone network, so that during the whole process, privacy of cellular phone users is protected.
In Step (d), there is calculating a mean normalized travel time (NTT) value for each oriented road section of the 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.
Prioritized, filtered, and controlled, vehicular traffic data and information associated with each source are transmitted from Sampler software module 1 to NTT calculator software module 2, as indicated in
Data from fixed sensors are usually received in terms of velocity or NTT values. Transition of these values for processing according to the model of method/system 10 of the present invention is straightforward to one of ordinary skill in the art, and is not further described herein. Vehicular traffic data and information obtained from traffic reports are handled manually and used for determining specific NTT values on oriented road sections or the specific patterns to be used. Vehicular traffic data and information from mobile sensor sources is processed in a more elaborate way. As previously indicated, above, in this description of the preferred embodiments, source 62 of the plurality of mobile sensors is represented as a cellular phone network, being the most complex one, but is generally applicable to other mobile wireless communication networks or systems operating with a plurality of mobile sensors or electronic devices.
Specifically applicable to vehicular traffic data and information associated with the mobile cellular phone network source, there is identifying a path taken by each vehicle and calculating a mean normalized travel time (NTT) value for each oriented road section of oriented road section network 14 (
Location readings acquired from mobile sensor networks are the ‘footprints’ of the sensor's track. When footprints are inaccurate, they are represented by geometrical areas, whose form and size depend on the particular features of the locating method. In cellular phone networks, footprints can be the cells themselves, with or without time-advance data, and their graphic representation is by segments of circle sectors whose coverage can reach dimensions between tens and thousands of meters. Another type of footprint associated with cellular phone networks is a cell-handover, whose graphic representation is dictated by the relative position of the two cells involved in the ‘handover’.
Specific characteristics of the location data obtained from the variety of mobile sensor networks varies with the network, whereby, method/system 10 accounts for them in a generic way, by implementing statistical methods and algorithms that rely on some assumptions as to the reasonable behavior of vehicular drivers. The path of the vehicle is deduced out of several consecutive readings. The deduced path is the sequence of connected oriented road sections that cross the location areas in the order of their appearance, and which join into a logical route to take between two points. In this step, some of the ‘noise’ is discovered, for example, footprints that went beyond bounds of a ‘sensible’ travel route. Additionally, those mobile sensors that are not relevant, for example, those carried in trains, are also identified. Those single footprints or irrelevant sample units are immediately rejected and their tracking is stopped.
The NTTs of the vehicle on every section of the deduced path is calculated using the timings of the footprints. The vehicle's position on the road at the reading's moment is determined by introducing some additional assumptions, like minimal acceleration and minimal velocity. The size of the footprints of a specific source influences the resolution of the oriented road sections that can be valued. Actually, the method emphasizes the more congested roads, because they statistically offer more readings. Those are identified even if they are minor roads, but, with worse accuracy. However, there will always be smaller streets and roads that this system will fail to exactly identify. Those areas of smaller streets, confined and closed within higher level arteries of the road network are referred to as ‘regions’ in the road network. Method/system 10 identifies ‘passage’ of the mobile sensor in such a region, and determines the average NTT value in that region. Indetermination of specific streets within regions of the road network is insignificant for the purpose of implementing method/system 10, because they usually behave in a similar way, otherwise specific streets that are more congested would stand out in the first place.
NTT values on an individual oriented section are calculated using the results obtained from all mobile sensors that passed that oriented road section, hence resulting in statistically determined NTT values for each oriented road section. The unification of the individual NTT values into a determined value per oriented road section is done with consideration of the confidence factor of each of the individual data. This confidence factor is a function of the accuracy, the amount of footprints, the error rate, and so on.
In the transition process from individual to comprehensive NTT values, an additional filtering stage takes place. Irregularities of some sensors may again indicate irrelevancy (stationed or slow moving vehicle, stop-and-go vehicles like trash collectors, etc.) or error. Data from those mobile sensors are rejected and filtered out of the continuation in the processing of the data. The outcome of this step in method/system 10 is an NTT picture of the oriented road sections for a certain time stamp, as calculated and assessed out of the specific network of mobile sensors. Accordingly, there is forming a partial current vehicular traffic situation picture associated with each network source of mobile sensors.
As indicated in
Sectors, for example, sectors 72, 74, and, 76, in
Once a path is identified, the normalized travel times (NTTs) values between the footsteps are calculated, hence, NTT values of the plurality of oriented road sections for an individual vehicle are assessed. At this stage, the determined path of the mobile sensor may be identified as crossing a ‘region’ of streets having low vehicular traffic congestion. Ordinarily, footprint accuracy prevents identification of specific streets in such a region. NTT calculator software module 2 processes such regions as a special type of oriented road section and allocates the region with a calculated NTT value that is a good average indicating crossing time of the region, no matter which one of the inner roads is traveled along by the vehicle.
In the next part of Step (d) of method/system 10, individual NTT values calculated in a given processing cycle for every oriented road section are accumulated and statistically analyzed for providing a mean NTT value for each respective oriented road section. Eventually, more than one NTT (statistical ‘peak’) value is identified, thereby, suggesting the possibility of different lanes having different NTT values. Such a situation occurs, for example, when an oriented road section has several lanes, each with a different NTT value.
The overall data processing of Step (d) forms a current snapshot of NTT values on part of road network 12, and, thus, on part of oriented section network 14 (
In Step (e), there is fusing the partial current traffic situation picture associated with each source, for generating a single complete current vehicular traffic situation picture associated with the entire oriented road section network.
The partial current traffic situation picture associated with each source, for the plurality of sources of vehicular traffic data and information, is fused by a Fusion and current Picture generator module 3, as indicated in
‘Final’ NTT values for each of the oriented road sections is obtained by integrating or fusing NTT values associated with all of the individual sources. NTT values associated with each source are weighed with a confidence factor appropriate for each respective source, where confidence factors are determined by source parameters, such as location accuracy, and, quality and quantity of sensors per source. This current vehicular traffic situation
In the first procedure, gaps are filled in using NTT values that are predicted, for those missing oriented road sections, in previous cycles of the process, that is, in previous time intervals of performing Steps (b)-(e) of method/system 10. In the initial cycles of the process, some default values are determined by the analysis of historical vehicular traffic data and information, and road types. It is to be emphasized, that this stage of the overall process is continuously performed, and therefore, each single complete current vehicular traffic situation picture associated with the entire oriented road section network is based on the combination of predictions and new vehicular traffic data and information.
In the second procedure, gaps in the current partial vehicular traffic situation picture are filled in by using a set of vehicular traffic rules for describing the interdependence, interrelation, and mutual correlation of vehicular traffic parameters among the plurality of oriented road sections in a particular region. Some of the vehicular traffic rules supply default values of the vehicular traffic parameters. The vehicular traffic rules are derived by, and based on, analyzing historical vehicular traffic data and information.
In Step (f), there is predicting a future complete vehicular traffic situation picture associated with the entire oriented road section network.
Predicting a future complete vehicular traffic situation picture associated with the entire oriented road section network is performed by a Predictor software module 4, as indicated in
The concept of the model of the comprehensive oriented road section network 14 (
Vehicular traffic behavior patterns feature behavior rules of individual oriented road sections and correlation rules among the plurality of different oriented road sections, of the entire oriented road section network 14. Accordingly, by using both types of rules, the step of predicting a future complete vehicular traffic situation picture associated with a particular oriented road section in a region within the entire oriented road section network, is influenced by both the current vehicular traffic situation picture associated with that particular oriented road section, and, by the plurality of current vehicular traffic situation pictures associated with other oriented road sections in the same region of the entire oriented road section network.
The continuously updated comprehensive and complete current vehicular traffic situation picture serves as a baseline of a three-dimensional vehicular traffic forecast picture, where the horizontal plane represents the roadmap and the vertical axis represents progression of time. The three dimensional vehicular traffic situation picture is constructed from discreet layers of vehicular traffic situation pictures, in time-intervals of a given processing cycle. The lower layer vehicular traffic situation picture always corresponds to the current time, and higher layers of vehicular traffic situation pictures correspond to future predictions.
Future time layers are produced by operating prediction tools that are products of the analysis of historical vehicular traffic data and information. This analysis is an on-going processing of the incoming vehicular traffic data and information, and the main resulting tools are behavior patterns and correlation rules. A behavior pattern of a specific oriented road section describes the regular changing of the associated NTT values as a function of time, for example, during minute and hourly progression of a day. Every oriented road section has its own behavior pattern, and different behavior patterns describe the behavior in different circumstances, such as different days of the week, holidays, special events, weather conditions, and so on.
A correlation rule determines the correlation and interrelation of the vehicular traffic situation picture between different oriented road sections as a function of time. Correlation rules are mostly if-then rules. For example, they represent the fact that ‘if’ a road congestion is being observed on oriented road section A, ‘then’, a similar road congestion is expected to occur on oriented road section B after a certain period of time. These rules are the outcome of a data-mining operation, also known as an advanced database searching procedure, on the historical vehicular traffic data and information.
Oriented road sections tend to behave differently even without the attachment of a certain characteristic to each vehicular traffic behavior pattern. The observance of values of the current vehicular traffic situation picture in respect to optional vehicular traffic behavior patterns, and the operation of vehicular traffic pattern recognition methods determines the vehicular traffic pattern that describes the behavior of the oriented road section at an instant of time.
Furthermore, while regular situations are handled with these tools, unexpected vehicular traffic developments are identified from the current vehicular traffic situation pictures by comparing them to regular vehicular traffic behavior patterns. The set of several recently calculated NTT values on each oriented road section is compared to the pattern that describes the behavior of this oriented road section. Any significant discrepancy from the pattern activates a respective correction of the NTT values predicted for this section in the near future. The corrections are assuming continuous change rates and a ‘back-to-normal’ return after a prediction horizon period, as previously described above and graphically illustrated in
Determination of the future behavior of individual oriented road sections from their respective traffic behavior patterns is integrated with the mutual horizontal influence of adjacent oriented road sections using the previously described correlation rules. These rules can predict, for example, the outcome of traffic events identified by the said discrepancies, and, predict their propagation in time along adjacent, and non-adjacent, oriented road sections.
In Step (g), there is 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.
Method/system 10 (
The unique structure of the three dimensional vehicular traffic situation picture leads to a corresponding method of analyzing it, in order to respond to traffic oriented queries associated with a variety of traffic related service applications. This analysis is performed by a designated software module, Service engine software module 5, for a variety of vehicular traffic related service applications 7, as indicated in
Method/system 10 of the present invention provides vehicular traffic data and information, and, provides a variety of vehicular traffic related service applications to end users, on the basis of existing infrastructure of vehicular traffic data and information acquisition and collection, such as by using a cellular phone mobile communication network, and the construction of centralized dynamic vehicular traffic situation pictures, both current and future, for a geographical area within road network 12 (
The present invention is implemented for acquiring, modeling, and processing vehicular traffic data and information, in order to be compatible with a wide variety of end user types of traffic related service applications. Therefore, it is a main aspect of the present invention that the ‘data providing mobile sensors’ are not limited to being those which are associated with the particular end users of such traffic related service applications. Hence, acquiring the vehicular traffic data and information from the data providing network does not depend at all on a specific group of mobile sensor device carrying end users.
The final result of method/system 10 (
While the invention has been described in conjunction with specific embodiments and examples thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims.
This application is a Continuation of U.S. patent application Ser. No. 10/999,994 filed 1 Dec. 2004, which is a Divisional of U.S. patent application Ser. No. 10/461,478 filed Jun. 16, 2003, now U.S. Pat. No. 6,879,907 which is a Continuation of U.S. patent application Ser. No. 09/939,620 filed Aug. 28, 2001, now U.S. Pat. No. 6,587,781, which claims priority from U.S. Provisional Application No. 60/227,905, filed Aug. 28, 2000, all of which applications are hereby incorporated herein by reference.
| Number | Name | Date | Kind |
|---|---|---|---|
| 4361202 | Minovitch | Nov 1982 | A |
| 4985705 | Stammler | Jan 1991 | A |
| 5122959 | Nathanson et al. | Jun 1992 | A |
| 5131020 | Liebesny et al. | Jul 1992 | A |
| 5182555 | Sumner | Jan 1993 | A |
| 5187810 | Yoneyama et al. | Feb 1993 | A |
| 5272638 | Martin et al. | Dec 1993 | A |
| 5327144 | Stilp et al. | Jul 1994 | A |
| 5343906 | Tibbals, III | Sep 1994 | A |
| 5428545 | Maegawa et al. | Jun 1995 | A |
| 5432842 | Kinoshita et al. | Jul 1995 | A |
| 5438517 | Sennott | Aug 1995 | A |
| 5465088 | Braegas | Nov 1995 | A |
| 5465289 | Kennedy, Jr. | Nov 1995 | A |
| 5523950 | Peterson | Jun 1996 | A |
| 5539645 | Mandhyan et al. | Jul 1996 | A |
| 5543789 | Behr et al. | Aug 1996 | A |
| 5543802 | Villevieille et al. | Aug 1996 | A |
| 5559864 | Kennedy, Jr. | Sep 1996 | A |
| 5613205 | Dufour | Mar 1997 | A |
| 5689252 | Ayanoglu et al. | Nov 1997 | A |
| 5724243 | Westerlage | Mar 1998 | A |
| 5732383 | Foladare et al. | Mar 1998 | A |
| 5740166 | Ekemark et al. | Apr 1998 | A |
| 5745865 | Rostoker et al. | Apr 1998 | A |
| 5751245 | Janky et al. | May 1998 | A |
| 5774827 | Smith et al. | Jun 1998 | A |
| 5801943 | Nasburg | Sep 1998 | A |
| 5839086 | Hirano | Nov 1998 | A |
| 5845227 | Peterson | Dec 1998 | A |
| 5880958 | Helms et al. | Mar 1999 | A |
| 5933100 | Golding | Aug 1999 | A |
| 5948042 | Heimann et al. | Sep 1999 | A |
| 5959568 | Woolley | Sep 1999 | A |
| 6012012 | Fleck et al. | Jan 2000 | A |
| 6038444 | Schipper et al. | Mar 2000 | A |
| 6061625 | Fastenrath | May 2000 | A |
| 6091362 | Stilp et al. | Jul 2000 | A |
| 6092020 | Fastenrath et al. | Jul 2000 | A |
| 6098016 | Ishihara | Aug 2000 | A |
| 6128571 | Ito et al. | Oct 2000 | A |
| 6161071 | Shuman et al. | Dec 2000 | A |
| 6178374 | Mohlenkamp et al. | Jan 2001 | B1 |
| 6219793 | Li et al. | Apr 2001 | B1 |
| 6230011 | Guenther et al. | May 2001 | B1 |
| 6236932 | Fastenrath | May 2001 | B1 |
| 6236933 | Lang | May 2001 | B1 |
| 6240364 | Kerner et al. | May 2001 | B1 |
| 6256577 | Graunke | Jul 2001 | B1 |
| 6263205 | Yamaura et al. | Jul 2001 | B1 |
| 6288676 | Maloney | Sep 2001 | B1 |
| 6314360 | Becker | Nov 2001 | B1 |
| 6317686 | Ran | Nov 2001 | B1 |
| 6341255 | Lapidot | Jan 2002 | B1 |
| 6401027 | Xu et al. | Jun 2002 | B1 |
| 6401037 | Muller et al. | Jun 2002 | B1 |
| 6411897 | Gaspard, II | Jun 2002 | B1 |
| 6424838 | Stobbe et al. | Jul 2002 | B1 |
| 6430496 | Smith et al. | Aug 2002 | B1 |
| 6438561 | Israni et al. | Aug 2002 | B1 |
| 6466862 | Dekock et al. | Oct 2002 | B1 |
| 6480783 | Myr | Nov 2002 | B1 |
| 6490519 | Lapidot et al. | Dec 2002 | B1 |
| 6532414 | Mintz | Mar 2003 | B2 |
| 6545637 | Krull | Apr 2003 | B1 |
| 6587781 | Feldman et al. | Jul 2003 | B2 |
| 6594577 | Nakajima et al. | Jul 2003 | B2 |
| 6606494 | Arpee et al. | Aug 2003 | B1 |
| 6618650 | Nakai et al. | Sep 2003 | B1 |
| 6708036 | Proctor et al. | Mar 2004 | B2 |
| 6711404 | Arpee et al. | Mar 2004 | B1 |
| 6718425 | Pajakowski et al. | Apr 2004 | B1 |
| 6792263 | Kite | Sep 2004 | B1 |
| 6799046 | Tang | Sep 2004 | B1 |
| 6842620 | Smith et al. | Jan 2005 | B2 |
| 6882930 | Trayford et al. | Apr 2005 | B2 |
| 6911918 | Chen | Jun 2005 | B2 |
| 6922629 | Yoshikawa et al. | Jul 2005 | B2 |
| 6931309 | Phelan et al. | Aug 2005 | B2 |
| 6947833 | Kita et al. | Sep 2005 | B2 |
| 6952643 | Matsuoka et al. | Oct 2005 | B2 |
| 6989765 | Gueziec | Jan 2006 | B2 |
| 7031983 | Israni et al. | Apr 2006 | B2 |
| 7062379 | Videtich | Jun 2006 | B2 |
| 7107038 | Fitch et al. | Sep 2006 | B2 |
| 7155376 | Yang et al. | Dec 2006 | B2 |
| 7269507 | Cayford | Sep 2007 | B2 |
| 7620402 | Feldman et al. | Nov 2009 | B2 |
| 7986954 | Steer | Jul 2011 | B1 |
| 20010018628 | Jenkins et al. | Aug 2001 | A1 |
| 20010029425 | Myr | Oct 2001 | A1 |
| 20010034577 | Grounds et al. | Oct 2001 | A1 |
| 20010037174 | Dickerson | Nov 2001 | A1 |
| 20020009184 | Shnier | Jan 2002 | A1 |
| 20020026278 | Feldman et al. | Feb 2002 | A1 |
| 20020030698 | Baur et al. | Mar 2002 | A1 |
| 20020049630 | Furuta et al. | Apr 2002 | A1 |
| 20020055818 | Gaspard | May 2002 | A1 |
| 20020120390 | Bullock | Aug 2002 | A1 |
| 20020198694 | Yang et al. | Dec 2002 | A1 |
| 20030003918 | Proctor et al. | Jan 2003 | A1 |
| 20030040944 | Hileman | Feb 2003 | A1 |
| 20030069683 | Lapidot et al. | Apr 2003 | A1 |
| 20030096620 | Ozturk et al. | May 2003 | A1 |
| 20030135304 | Sroub et al. | Jul 2003 | A1 |
| 20030219024 | Purnadi et al. | Nov 2003 | A1 |
| 20030236818 | Bruner et al. | Dec 2003 | A1 |
| 20040034464 | Yoshikawa et al. | Feb 2004 | A1 |
| 20040076279 | Taschereau | Apr 2004 | A1 |
| 20040082312 | O'Neill et al. | Apr 2004 | A1 |
| 20040111214 | Buecher et al. | Jun 2004 | A1 |
| 20040243285 | Grounder | Dec 2004 | A1 |
| 20050026619 | Jha | Feb 2005 | A1 |
| 20050065682 | Kapadia et al. | Mar 2005 | A1 |
| 20050131641 | Beesley et al. | Jun 2005 | A1 |
| 20050192031 | Vare | Sep 2005 | A1 |
| 20060009885 | Raines et al. | Jan 2006 | A1 |
| 20060025158 | Leblanc et al. | Feb 2006 | A1 |
| 20070060108 | East et al. | Mar 2007 | A1 |
| 20070106465 | Adam et al. | May 2007 | A1 |
| 20080214192 | Soliman | Sep 2008 | A1 |
| 20090036148 | Yach | Feb 2009 | A1 |
| 20100010739 | Tsushima | Jan 2010 | A1 |
| Number | Date | Country |
|---|---|---|
| 2 434 707 | Mar 2004 | CA |
| 33 46 548 | Jul 1985 | DE |
| 40 05 803 | Aug 1990 | DE |
| 42 41 408 | Jun 1994 | DE |
| 196 38 798 | Sep 1996 | DE |
| 195 25 291 | Dec 1996 | DE |
| 196 51 146 | Jun 1998 | DE |
| 197 55 875 | Jun 1998 | DE |
| 199 17 154 | Oct 2000 | DE |
| 199 33 639 | Jan 2001 | DE |
| 199 48 416 | Apr 2001 | DE |
| 100 63 588 | Jul 2001 | DE |
| 100 37 827 | Feb 2002 | DE |
| 0 365 097 | Apr 1990 | EP |
| 0 372 840 | Jun 1990 | EP |
| 0 715 285 | Jun 1996 | EP |
| 0 715 286 | Jun 1996 | EP |
| 0 715 288 | Jun 1996 | EP |
| 0 715 291 | Jun 1996 | EP |
| 0 763 807 | Mar 1997 | EP |
| 0 838 663 | Apr 1998 | EP |
| 0 838 797 | Apr 1998 | EP |
| 0951187 | Apr 1998 | EP |
| 0 936 590 | Aug 1999 | EP |
| 1 162 560 | Dec 2001 | EP |
| 0 834 840 | Jun 2002 | EP |
| 1 320 075 | Jun 2003 | EP |
| 1 387 333 | Feb 2004 | EP |
| 0 921 509 | Apr 2004 | EP |
| 1591980 | Feb 2005 | EP |
| 1 515 122 | Mar 2005 | EP |
| 1600910 | Nov 2005 | EP |
| 0 763 807 | Aug 2006 | EP |
| 1550842 | Jul 2007 | EP |
| 1-137778 | May 1989 | JP |
| 4-290098 | Oct 1992 | JP |
| 5-46086 | Feb 1993 | JP |
| 0546086 | Feb 1993 | JP |
| 5233996 | Jul 1993 | JP |
| 5-233996 | Sep 1993 | JP |
| 6-12593 | Jan 1994 | JP |
| 7-83685 | Mar 1995 | JP |
| 8-129697 | May 1996 | JP |
| 9-14986 | Jan 1997 | JP |
| 914986 | Jan 1997 | JP |
| 9113290 | May 1997 | JP |
| 10300495 | Nov 1998 | JP |
| 11-25389 | Jan 1999 | JP |
| 2927277 | Jul 1999 | JP |
| 11328571 | Nov 1999 | JP |
| 3052405 | Jun 2000 | JP |
| 2001-124569 | May 2001 | JP |
| 3171031 | May 2001 | JP |
| 2002122437 | Apr 2002 | JP |
| 2002-206395 | Jul 2002 | JP |
| 3353656 | Dec 2002 | JP |
| 3367514 | Jan 2003 | JP |
| 3566503 | Sep 2004 | JP |
| WO 9411839 | May 1994 | WO |
| 9502307 | Jan 1995 | WO |
| 9514292 | May 1995 | WO |
| WO 9514292 | May 1995 | WO |
| WO 9610807 | Apr 1996 | WO |
| WO 9625830 | Aug 1996 | WO |
| WO 9629688 | Sep 1996 | WO |
| WO 9642179 | Dec 1996 | WO |
| 9729471 | Aug 1997 | WO |
| WO 9729470 | Aug 1997 | WO |
| WO 9729471 | Aug 1997 | WO |
| WO 9731241 | Aug 1997 | WO |
| 9737318 | Oct 1997 | WO |
| WO 9736148 | Oct 1997 | WO |
| WO 9740606 | Oct 1997 | WO |
| WO 9815149 | Apr 1998 | WO |
| WO 9815935 | Apr 1998 | WO |
| WO 9823115 | May 1998 | WO |
| WO 9826395 | Jun 1998 | WO |
| WO 9829758 | Jul 1998 | WO |
| WO9829758 | Jul 1998 | WO |
| WO 9836397 | Aug 1998 | WO |
| 9842179 | Oct 1998 | WO |
| WO 9854682 | Dec 1998 | WO |
| WO9854682 | Dec 1998 | WO |
| WO 9963499 | Dec 1999 | WO |
| WO 0101367 | Jan 2001 | WO |
| 0123835 | Apr 2001 | WO |
| WO0123835 | Apr 2001 | WO |
| WO 0135370 | May 2001 | WO |
| WO 0148725 | Jul 2001 | WO |
| WO 0201158 | Jan 2002 | WO |
| WO 0201532 | Jan 2002 | WO |
| WO 0203350 | Jan 2002 | WO |
| WO 0243026 | May 2002 | WO |
| WO 03041030 | May 2003 | WO |
| WO 03073048 | Sep 2003 | WO |
| 2004021305 | Mar 2004 | WO |
| WO 2004086799 | Oct 2004 | WO |
| 2005098780 | Oct 2005 | WO |
| WO 2008114369 | Sep 2008 | WO |
| Entry |
|---|
| Wang, et al., “An Unified Vehicle Supervising and Traffic Information System”, National Chiao Tung University, Taiwan, 1996 IEEE, pp. 968-972. |
| Wang, et al., An Unified Vehicle Supervising and Traffic Information System, Personal, Indoor and Mobile Radio Communications, 1996, PIMRC'96, Seventh IEEE International Symposium on, publication date Oct. 15-18, 1996, vol. 3, pp. 968-972, IEEE, Taipei, Taiwan. |
| United States Office Action issued in U.S. Appl. No. 10/999,994, mailed Sep. 9, 2010. |
| US Office Action issued in U.S. Appl. No. 11/269,908, dated Jun. 9, 2011. |
| United State Office Action issued in U.S. Appl. No. 10/999,994 dated Mar. 21, 2011. |
| United States Office Action issued in U.S. Appl. No. 11/269,908, mailed Jul. 12, 2010. |
| United States Office Action issued in U.S. Appl. No. 11/269,908 dated Dec. 27, 2010. |
| United States Office Action, issued in U.S. Appl. No. 10/999,994, dated Dec. 7, 2011. |
| R. Sankar et al., “Intelligent Traffic Monitoring System Using Wireless Cellular Communications,” © 1997 IEEE. |
| K. Abe et al., “A Planning Method Combining Rule-Bases and Optimization Algorithms for Transportation Network,” © 1992 IEEE. |
| “Data Fusion for Dynamic Route Guidance Systems,” International Federation of Automatic Control (IFAC), 2361 Laxenburg, Schlossplatz 12, 1997. |
| N. Shamanesh, “Getting Drivers Out of a Jam,” Automotive Engineer, Oct. 1999. |
| Fastenrath, Floating Car Data on a Larger Scale, Oct. 24, 1997. |
| J.J. Caffery et al., “Overview of Radiolocation in CDMA Cellular Systems,” published 1998; See Chapter “Accuracy Requirement” pp. 7-8; <http://sss-mag.com/pdf/radioloc.pdf>. |
| International Search Report issued in International Patent Application No. PCT/GB2005/002637 dated Sep. 21, 2005. |
| Written Opinion of the International Searching Authority issued in International Patent Application No. PCT/GB2005/002637 dated Sep. 21, 2005. |
| M. Westerman et al., “Integration of Probe Vehicle and Induction Loop Data—Estimation of Travel Times and Automatic Incident Detection,” California PATH Research Report, Institute of Transportation Studies, Jan. 1, 1996. |
| “Grubbs Test for Outliers,” Engineering Statistics Handbook, 2000. |
| United States Office Action issued in U.S. Appl. No. 11/269,908 dated Jan. 4, 2013. |
| Kerner, et al., “Traffic State Detection with Floating Car Data in Road Networks”, Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems Vienna, Austria, Sep. 13-16, 2005. |
| United States Office Action issued in U.S. Appl. No. 11/269,908 dated Jun. 21, 2013. |
| United States Office Action issued in U.S. Appl. No. 11/269,908 dated Jun. 22, 2012. |
| Number | Date | Country | |
|---|---|---|---|
| 20060069496 A1 | Mar 2006 | US |
| Number | Date | Country | |
|---|---|---|---|
| 60227905 | Aug 2000 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 10461478 | Jun 2003 | US |
| Child | 10999994 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 10999994 | Dec 2004 | US |
| Child | 11280833 | US | |
| Parent | 09939620 | Aug 2001 | US |
| Child | 10461478 | US |
