Abstract:
A method for notifying a user about traffic including the step of determining an origin of the user. A destination of the user is also determined. Traffic conditions between the origin and the destination are determined. The user is notified of the traffic conditions.

Description:
This application claims priority to U.S. Provisional Application Ser. No. 61/385,583, filed Sep. 23, 2010. 
    
    
     BACKGROUND OF THE INVENTION 
     This application relates to trip time computation, and more specifically to a system for computing trip time that includes traffic profiling and road condition-based computation with localized and cooperative assessment. 
     Previous traffic determination systems have estimated traffic using triangulated positioning of cell phones to determine a speed at which a cell phone moves. There are many limitations and drawbacks in the current systems. For example, if a phone moves quite slowly, it may be assumed that a driver carrying the phone is driving in traffic. 
     SUMMARY OF THE INVENTION 
     A method for notifying a user about traffic including the step of determining an origin of the user. A destination of the user is also determined. Traffic conditions between the origin and the destination are determined. The user is notified of the traffic conditions. Many ways of determining a destination of the user are disclosed. Several optional ways for a user to customize traffic alerts are disclosed. 
     In another, optional feature, the destination is determined by monitoring a history of destinations by the user and predicting the destination based upon the history of destinations. This can be done by monitoring destinations on days of the week and times of day. 
     The traffic conditions may be compared to a threshold set by the user and wherein said step d) includes only notifying the user based upon the comparison with the threshold. The threshold may be a length of delay of calculated travel time relative to expected travel time under normal conditions. 
     In another, optional feature, the destination may be determined based upon an analysis of a set of contacts associated with the user. This may be done by comparing the user&#39;s current direction of travel to addresses in the contacts. Alternatively, or additionally, this may be done based upon a calendar event associated with the user. 
     In another, optional feature, the destination may be determined based upon at least one contact among a plurality of user contacts associated with the user. 
     In another, optional feature, the destination may be determined based upon a direction of travel of the user. The step of monitoring traffic conditions includes determining traffic conditions in the user&#39;s direction of travel. 
     The notification may be performed at a set time interval prior to the user leaving the origin. The notification may be performed at a time that is dependent upon the traffic conditions determined. The notification may be performed at a time that is based upon a latest time of arrival received from the user. 
     In another, optional feature, the destination may be determined based upon the user&#39;s current location and based upon a current direction of travel of the user. 
     In another, optional feature, the destination may be determined based upon a comparison of current location and current direction of travel to addresses in contacts associated with the user. 
     In one example embodiment, at least some of said steps are performed on a mobile device. At least some of said steps a)-d) are performed on a server remote from the user. 
     These and other features of the present invention can be best understood from the following specification and drawings, the following of which is a brief description. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  schematically illustrates a traffic profiling system. 
         FIG. 2  schematically illustrates an onboard device for a vehicle in the system of  FIG. 1 . 
         FIG. 3  schematically illustrates an example traffic index. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
       FIG. 1  schematically illustrates a traffic profiling system  10 . A vehicle  12  includes an onboard traffic conditions computer  14  (hereinafter “onboard device”). In one example the onboard device  14  includes some or all of the features in the commercially available iLane® product (see http://www.ilane.ca/), also described in co-pending application U.S. Pat. App. 20090318119, filed Jun. 19, 2009, which is hereby incorporated by reference in its entirety. A server  16  is operable to communicate wirelessly with the onboard device  14  via a wide-area network, such as the Internet  18 , or a private network or channel. Similar onboard devices  14  are installed on numerous vehicles  12  in the same geographic area and also communicate with the server  16 . The server  16  may also receive traffic information from loop sensors  60 , cell phone data  62 , cameras  64  or other known sources of traffic information, which can be fused with information from the onboard devices  14 . 
     The onboard device  14  is schematically illustrated in greater detail in  FIG. 2 . The onboard device  14  includes a road database  44  and a speed limit database  46  indicating speed limits on the road segments in the road database  44 . The road database  44  and speed limit database  46  may be pre-stored on the onboard device  14  or downloaded and/or updated from the server  16 . If the road database  44  and speed limit database  46  are downloaded and/or updated from the server  16 , they may be downloaded and/or updated only for roads in the vicinity of the vehicle  12 . The vicinity may be defined as an area around the vehicle  12  which is set to be dependent on density of roads and density of populations (e.g., the higher the density the smaller is the area). 
     The onboard device  14  includes a processor  52  and storage for storing the data and programs to perform the functions described herein. The onboard device  14  may include a GPS receiver  48 , or may receive GPS location from a cell phone or other mobile device  22  ( FIG. 1 ). The onboard device  14  includes an OBD port  50  for receiving on-board diagnostic information from an OBD port (or OBD-II or any other protocol) on the vehicle  12 . A mobile device communication module  40  provides wireless (or alternatively, wired) communication with the mobile device  22  to provide communication to the server  16  and to obtain information from the mobile device  22  itself (contacts, email, GPS location information, etc). The onboard device  14  may also include one or more wireless transceivers  54  to communicate directly with cell towers to access the Internet  18  and/or with wireless transceivers  54  on other vehicles  12 . The onboard device  14  further includes a microphone  56  for receiving voice commands from a user and a speaker  58  for giving audible information to the user. The speaker  58  could alternatively be part of the vehicle  12  audio system. The onboard device  14  preferably communicates with the user primarily via voice, although a display output module  38  for sending information to a display  20  could also be provided. Thus, the onboard device  14  includes a speech recognition module  34  and a text-to-speech module  36 . 
     Although the vehicle  12  is illustrated as being an automobile, it is understood that the onboard device  14  could be applied to other vehicles too, such as motorcycles, bicycles, etc. 
     Since the onboard device  14  may be used by a vehicle operator (e.g. a driver), by a vehicle passenger (e.g. limousine passenger), or by another party, the term “user” will be used to refer to a person interacting with the onboard device  14 . 
     Localized Assessment 
     The system  10  determines its location relative to the database of roads  44  based upon (for example) the GPS location information and then obtains the current speed limit of the current road segment from the speed limit database  46 . The onboard device  14  determines its current speed based upon information from the GPS receiver  48  and/or from the speed information available on the OBD  50  and/or from an accelerometer on the onboard device  14 . The onboard device  14  compares the current speed limit with the current estimated speed of the vehicle  12 , and computes a traffic condition index based on the comparison of speed with the speed limit, and indexed to position, as shown in  FIG. 3 . The index is one of a number of traffic condition classes (see, e.g.,  FIG. 3 ). If at the time the traffic index matches some traffic conditions criteria, a spatio-temporal profile of the traffic index is transmitted to the server  16 . For example, if the index indicated the presence of traffic congestion, then a message is sent to the server  16  indicating a traffic congestion event along with the profile. The message includes the time, road segment, location and current speed. 
     Thus, the onboard device  14  is operable to perform a “localized assessment” on the vehicle  12  of traffic (e.g., comparing a speed limit to a current vehicle speed). 
     Cooperative Assessment 
     The onboard device  14  is responsive to voice commands via speech recognition module  34  (see  FIG. 2 ). In one example, a user who recognizes a traffic congestion event can choose to send a traffic profile report alert to the server  16  by using a voice command to tell the onboard device  14  to send a traffic report alert to the server  16  in the form of a natural language sentence such as “very heavy road congestion,” “congestion due to an accident,” “congestion due to slippery conditions,” etc. The onboard device  14  will send the sentence along with a time and a location of the vehicle  12 . 
     In this example, the server  16  parses the sentences it receives to estimate the traffic condition in and around the reported location of the report. An algorithm at the server  16  is used to process the parsed sentences to compute a traffic conditions profile throughout the road network and to determine and eliminate outlier reports or incorrect reports. A similar algorithm may be used to process the traffic condition indices in the “Localized Assessment” section above. 
     Thus, the onboard device  14  is also operable to perform a “cooperative assessment” because there is some interaction or discourse between the onboard device  12  and the user to assess traffic conditions. 
     Merging of Traffic Data from Multiple Sources 
     Whenever possible, the server  16  may fuse the parsed sentences from many users for the area and reported indices from many vehicles  12  for the area to compute a reliable and explainable traffic condition for a traffic segment, leading to determination of the traffic conditions in the area. Furthermore, this information may be fused with traffic data obtained from other sources, such as loop sensors  60 , cameras  64 , and GSM-mobility data  62 . Such diverse multi-source reports allow for high confidence and more accurate traffic conditions estimation. The server  6  may process parsed sentences (the cooperative assessments) and indices (the localized assessments) collected from multiple vehicles  12  to establish time and contextual statistical traffic record for an area, and to ensure accuracy of traffic data. 
     Road Condition Inquiries from Onboard Device 
     The onboard device  14  can send inquiries about road conditions on a certain road segment to the server  16 . Based on the processed reported sentences and indices received from multiple vehicles  12 , the server  16  can send the inquirer a response indicating the traffic condition of the area. Also, in this case other traffic profiling data from GPRS/GSM and loop sensors may be used to compose a report. If no report or index is available for the area then a message is sent to the onboard device  14  indicating such condition (e.g., a “no incident” or “no data available” response is sent to the onboard device  14 ). The onboard device  14  conveys the information to the operator of the vehicle  12  using voice (using Text to Speech module  36  in  FIG. 2 ) or congestion color code road map on a display  20  (using Display Output module  38  in  FIG. 2 ). Of course, other reporting methods would be possible. This information may also be reported on a web portal for viewing (e.g. on the display  20 ). 
     Selective Transmission of Traffic Alerts 
     The server  16  may receive traffic condition reports from many vehicles  12 , and the server  16  continuously processes those reports to determine traffic alerts. Onboard devices  14  may be used to navigate the user via a calculated route to a destination, such that the current location and destination are easily known. 
     Alternatively, even without navigation, the destination of the vehicle  12  may otherwise be known or may be deduced (e.g. based upon driving patterns, such as driving home after work on weekdays). For example, first, the destination and even (if necessary) the current location can be scheduled or can be deduced. 
     A user can a user-friendly user interface (such as by accessing the server  16  via the internet  18 ) to define a path (or at least an origin and destination) on a map and time of day window of interest (e.g, 4:00 PM and 6:00 PM on weekdays), and trip time tolerance for the path, and Latest time of arrival (LTA). The user can also associate a contact (from mobile device  22 ) that is relevant to the path. 
     Alternatively, the server  16  (and/or onboard device  14  and/or mobile device  22 ) can deduce the user current path and destination based upon driving history. For example, if user tends to be in location A first in the day and in location B later in the day, the system will deduce based on the time window the start of the trip and the end of the trip, and hence can deduce relevant traffic flow. For example, if the user normally commutes from Toronto to Waterloo in the morning and back from Waterloo to Toronto later in the day, the system will report westbound traffic conditions in the morning and eastbound traffic conditions later in the day—depending on the time of day window. The user can always be more specific on a path entry so as to inform the system the start of the path and the end of the path, and as such the system can deduce the relevant traffic flow. 
     The server  16  determines the vehicles  12  that are affected by the alert (based upon their current locations and based upon the known or assumed destinations) and sends the alerts to those affected vehicles. Additionally, or alternatively, where the destination is not known, road segments in the area in the direction that the vehicle  12  is heading are considered relevant. For example, based on destination and location of vehicle  12 , an alert may be sent to the vehicle  12 . Vehicles not affected by the condition are not bothered and the server  16  may choose to not even send the report to those vehicles. 
     The system will insert this information from the user information in a stack that is time of day indexed. When the system day timer approaches the start of the time window the system computes a traffic condition report along the path that was introduced by the user. The system will use the current location (from user GPS unit or cell phone location) of the user to deduce traffic flow direction to be relevant to the user (e.g., east bound vs west bound). If the user location information is not available then the system may use historical information on user mobility to deduce user location based on the time. 
     The user can enter to the system a trip time tolerance value. The system will use this time tolerance to compute a threshold for the alert. For example, the user may indicate to the system that his/her trip time tolerance for the path is a certain length of time, e.g. 20 minutes. In this case the system will compute trip time for the path, and compares that to the trip time of the path under normal traffic conditions. If the computed trip time for the path is different from the trip time of the path under normal traffic conditions by more than 20 minutes, then the system will send an alert, otherwise no alert is sent. Alternatively, the time tolerance can be expressed in terms of a percent of normal travel time (e.g. send an alert if travel time will increase more than 10% over the normal travel time). 
     The user can enter the path information via the system web site or by composing a message (such as a properly formatted email, text message, etc) to the server  16 . The system can parse the message to determine the relevant information, such as path start, path end, LTA, LTD, Contact, Tolerance, etc. 
     As an alternative, instead of generating alerts based upon a time frame, the alert session can be initiated as soon as it is determined that the car is on or approaching the path to the destination. Further, if the user does not enter a specific path, the system will deduce paths relevant to the user distinction, based on historical driving behavior of the user around the time frame of travel and or best paths possible. 
     A “path-relevant contact” could be home, office, friend, colleague, a meeting party, etc., that is a contact on the user&#39;s mobile device  22 . The system can deduce a path-relevant contact from the user&#39;s calendar. If there exists a calendar event (e.g. on the mobile device  22 ) that coincides with the estimated time of arrival and/or a calendar event the location of which is in the vicinity of the user destination then all participants in the calendar event (meeting parties, or destination contact) may be sent an alert (e.g. if the user is going to be late) to all or some of the participants in the meeting or the contact person associated with the destination (for example, if the destination address matches a contact in the user&#39;s contacts). 
     The system acquires incident reports on the road network for example by connecting to a road management entity a government road management entity. The system matches reported incidents to the user paths of travel. Once an incident is detected it is continuously monitored to estimate severity and persistence of impact on trip time along the paths. The system will alert the user on the incident and provide linguistic description on the incident such as where, how long is the traffic jam due to the incident, and suggest alternative routing if possible. 
     The user informs the system (e.g. server  16  and/or mobile device  22 ) that the user intends to go to a destination and would like to be at destination by a certain time. The system can also deduce this information from monitoring the user&#39;s travel trends and history. For example, the system observes that the user makes daily trips to arrive at destination around 7 PM. The agent uses 7 PM as a target arrival time and plans the trip in terms of best path and trip starting time so that the user will arrive at the destination at 7 PM. The system uses real-time path planning system to propose trip starting time based on its knowledge of the traffic conditions. This knowledge is derived from historical data, current traffic conditions, and information provided by other agents. The user can use attributes such as (critical arrival time, flexible arrival time, and not-before arrival time, etc) to describe his planned arrival time at the destination. This information is used by the system to perform multi-criteria optimization calculation to balance distance traveled, time traveled, and total idle time. 
     Trip Time Computation 
     If a vehicle  12  operated has programmed with a destination into the onboard device  14  or the server  16 , then the trip time to the destination may be computed based on routing data and traffic conditions on the route. The onboard unit  14  or the server  16  determine a sequence of road segments, which can be computed onboard or can be obtained from a generic routing service provider such as MapQuest. The onboard unit  14  or the server  16  then checks if a road segment is affected by a congestion situation. If a segment is determined to be affected by a traffic congestion event, the travel time for the segment may be recomputed and the trip time to destination may be updated, and the user may be informed of the updated trip time (e.g. via Text to Speech module  36 ). Alternatively, if a segment on the route is determined to be affected by a traffic congestion event, then the route can be recalculated to avoid the congested segment. 
     Timed Event Functionality 
     If the user programs a timed event (e.g. such as a meeting, can be fetched from calendar on mobile device  22 ), the onboard device  14  may provide a proper warning on the possibility of missing the meeting (e.g. providing a computer generated speech message to the user). The onboard device  14  may offer to call the meeting inviter to allow the user to notify the meeting inviter of a possible delay, or may offer to transmit an email message or a text message to the user to provide the notification. The call, email, or text message may be performed using a mobile device  22  that the onboard device  14  communicates with via Mobile Device Communication module  40 . 
     The onboard device  14  may suggest to the user a superior route to the destination that would exhibit less traffic. Thus, the onboard device  14  may perform a less traffic congestion routing feature. 
     If the user enters a meeting location and time in his mobile device  22  or office computer calendar, the system  10  will continue to monitor traffic conditions that affect the roads between the user&#39;s current location and that where the meeting will take place. If the onboard device  14  or server  16  determine that a difference between the present time and that when the meeting will take place is becoming critically tight for the user to travel to the meeting place, a warning may be sent to the user on his computer or mobile phone  22  to warn him/her that timing is getting tight for them to make it to the meeting. The user can add some safety factors in the form of extra time (e.g., if it takes 2 hours to travel to the meeting place, and the difference between the present time and the meeting starting time is 2 hours, the user may ask the system to allow for 30 minutes extra, and the system  10  may provide the warning 30 minutes before the present time). 
     The system uses the LTA and computed traffic conditions to determine the latest time of departure (LTD) for the user. The user is sent an alert x minutes before the LTD to inform the user that he/she need to leave in x minutes to make it to the destination on time (LTA). If during travel time on the path the system determines that LTA is unachievable a revised ETA (estimated time of arrival) is computed and the user is informed of the revised ETA. The system will offer to connect the user to the path relevant contact so that the user can inform them of travel delay. Alternatively, the system can send an alert to the path relevant contact to inform them of a delay the user is experiencing due to traffic conditions and the revised ETA. Optionally, the system will mark the current location of the user on a map and present the map to the path relevant contact. 
     Information Sharing 
     In addition to uploading a traffic profile report to the server  16 , the system  10  may use short range communication capabilities of the transceiver  54  of the onboard device  14  to broadcast to vehicles in its vicinity the presence of traffic congestion. Thus, in one example, traffic information may be shared directly between onboard devices  14  in vehicles within a predefined proximity to each other. Alternatively, the information could be transmitted via the Internet or even via the server  16  (although, without filtering or fusion with other sources) between other onboard devices  14  within a radius of one another. 
     Information Weighting 
     Since the server  16  receives information about traffic from multiple vehicles  12  and other sensors  60 ,  62 ,  64 , the server  16  may assign weights of evidence to the different sources and combine the information from the different sources and assign a weight of evidence (or confidence factor) on the traffic condition. 
     Abstraction of Traffic Conditions 
     In one example, the system  10  employs multi-level abstraction of traffic conditions of a road segment that ranges from numerical traffic data such as speed (e.g., “Current speed on road segment is 70 km/hour”) to linguistic natural language traffic descriptors (e.g., “Traffic condition on the road segment is very slow”). A Fuzzy Logic Engine  42  (see  FIG. 2 ) may be used to produce linguistic traffic descriptors from speed range measurements. 
     The Fuzzy Engine  42  allows the user to discourse with the onboard device  14  inquire about the traffic conditions. For example, the user can ask questions such as traffic conditions on current road on which the vehicle is being driven. The system  10  will scan the road and report using natural language traffic conditions at high level (e.g. “traffic is slow,” or “somehow slow,” or “very slow,” or “smooth on a road segment”). The user can ask questions to the onboard device  14  (e.g., “Tell me traffic conditions on east bound,” “Tell me traffic conditions on north bound,” etc.). The onboard device  14  can take the name of a road uttered via voice by the user to a segment on the road or the whole road. For example, the system can determine based on vehicle location the interpretation of east bound relative to the vehicle location. That is, the system  10  can use the location and/or direction of vehicle  12  movement to determine relevant segment of the road that the user is interested in. The user can ask the system to provide more detailed information (e.g. by asking “How slow?”). Where the system  10  provides a current speed range on the segment (e.g., “Traffic is moving with speed between 40 to 50 km an hour”), the user can ask a question in response (e.g. “How bad is traffic on the segment?). The system  10  can answer with a speed range and possible a duration for which that speed range has been experienced by other users. The system can also say speed is starting to pick up. The user can set an alert flag, such that the system  10  will monitor traffic on the trip path and report emerging deteriorating/improving traffic conditions. 
     Based on the computed traffic assessment for a given user, the system composes a sequence of report to the user. 
     If the system determines that the traffic conditions are normal then a “normal condition” alert is sent to the user. No more alerts are sent until the traffic condition state has changed. The amount of change to trigger a report subsequent to the first “normal condition” is computed based on the persisting nature traffic flow variation. For example a traffic flow change in a small 100 meters segment on the road is ignored unless it persists in way such that its extends on the path to cover more 500 meters. Only then the system will send an alert to the user to inform him/her that traffic starts to worsen on his/her entered path. The system will use street/road names and land marks to localize the traffic alert to the user. For example, the system will send an alert such as “very slow traffic on HW 401, between Exit x and Exit y”. It can also say “very slow traffic on King street just after Fairway Shopping Mall, Or exit into to Fairway shopping mall”. 
     The system uses a fuzzy engine to translate traffic conditions data to linguistic traffic conditions indicators. Furthermore, the system uses an Artificial Neural Network to map current and historical traffic patterns to predicted traffic conditions along the path. These predictions can be used by the system to be more proactive in its alert announcement and traffic condition persistence profiling which is used to compute an alert threshold. 
     Multi-Agent Software Architecture 
     A multi-agent software architecture may be used to implement the system where by each user is assigned a software agent, such as in the mobile device  22  (alternatively, on the onboard device  14  or even on the server  16 ). The agent is the one that monitors on behalf of the user the traffic conditions that could potentially affect his/her trip time. The agent also performs the alert regime above. Furthermore, user can choose to enlist in a group of commuters, for example, daily Toronto-Waterloo commuters, weekly Waterloo-Toronto commuters, etc. The agents assigned to these users cooperate to exchange information about traffic and incidents. An agent that discovers a traffic incident such as an accident or congestion will broadcast information about the incident to agents in the group according to the rules set by the user to the broadcasting agent (e.g., inform agents within a certain range of the incident, agents of commuters who are approaching the incident, agents of commuters who happen to be in a certain geographical area, and/or agent who have reported travel paths that are affected by the incident). The recipient agent uses a set of rules personalized by the user to decide whether to inform its user or not (e.g. only if a certain number of agents reported the incident, only if its user is in the car, only if its user enabled the agent-to-agent traffic information exchange.) In this manner, the system could be run solely on the mobile devices  22 , with little or no collection or analysis of data by the server  16 . 
     User agents fuse information to compute statistical information on travel time for traveled paths. These statistics include, but not limited to, max travel time, min travel time, average travel time, probability of max time, probability of min travel time, variance. Agents are given weights based on how many paths they have processed. An agent that has monitored a road segment or travel path more frequently is given more weigh in the calculation that an agent who monitored the road segment or travel path less frequent. 
     Although a preferred embodiment of this invention has been disclosed, a worker of ordinary skill in this art would recognize that certain modifications would come within the scope of this invention. For that reason, the following claims should be studied to determine the true scope and content of this invention.