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UCF EEL 6788 - Accurate, Energy-Aware Road Traffic Delay Estimation using Mobile Phones

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VTrack: Accurate, Energy-Aware Road Traffic Delay Estimation using Mobile PhonesAgendaIntroductionWhat is VTrack?Key ApplicationsKey Applications ContinuedVTrack ArchitectureServer DiagramChallenge # 1Challenge # 2GPS vs. WiFiOvercoming the ChallengesAlgorithmsHMMAlgorithms ContinuedMap Matching ProcessSlide 17Travel Time EstimationTravel Time Estimation ErrorsEvaluationEvaluation-Data and Method UsedEvaluation-Data and Method UsedSlide 23Map of Evaluation DrivesEvaluation-Route PlanningSlide 26Slide 27Evaluation-Hotspot DetectionSlide 29Slide 30Evaluation-Energy vs. AccuracySlide 32Related WorkConclusionFuture WorkReferencesQuestions1VTrack: Accurate, Energy-Aware Road Traffic Delay Estimation using Mobile PhonesBy: Michael Glus, MSEEEEL 678812Agenda•Introduction•Challenges•Overview of algorithms used•Travel time estimation•Evaluation of results•Related work•Conclusion•References3Introduction•Traffic congestion is a serious, growing problem in today's society with over 4 billion hours spent in traffic in 2007•As the world gets “smarter” so should the roadways•The idea of using vehicles as data collection points is not a new idea, but since cell phones emerged we can get much better and more accurate data4What is VTrack?•VTrack is a system for travel time estimation using sensor data (GPS or WiFi)•This idea was developed using four National Science Foundation grants by students at MIT CSAIL•We want to mitigate long traffic delays using this data and inform customers of any potential traffic issues5Key Applications•Two key applications to support1. Detecting and visualizing “hotspots”•A hotspot is a road segment which has a observed travel time that far exceeds its normal travel time•Goal is to display these hotspots to the user via a web browser •User can select their geographic area and see all the traffic spots •Must minimize false hotspots and also missed hotspots6Key Applications Continued2. Real-time Route planning•Users are most concerned about end-to-end time spent in a commute•Route planning can use past and current data to give the user the fastest possible route to their destination•Since the planning is in real-time, the application can update the user to alter their driving path if a hotspot arises suddenly7VTrack Architecture•Users run applications from their cell that reports to server•Server runs algorithm to estimate travel time8Server Diagram9Challenge # 1•The first challenge for estimating travel delays is the energy consumption of the device that is transmitting the data•Cell phones that trasmit frequently can drain a battery quickly•Can not force users to keep phones plugged in all the time while obtaining data10Challenge # 2•The second challenge is sensor unreliability•Will users always have their phone in data collection mode?•How will we know where the users are (ie. Accuracy of the sensor)?•This leads into the debate of GPS and WiFi11GPS vs. WiFi•GPS–GPS not available on all phones–Power hungry (up to 20x vs. WiFi)–Outages in tunnels or users pockets–High resolution•WiFi–Less resolution (only to 50-100m)–Consumes less power–Needs more processing to determine user location12Overcoming the Challenges•Algorithm use–Process streams of time-stamped position samples using a Hidden Markov Model (HMM) to model vehicle trajectory over a map•Map Matching–Map matching is used to associate each position sample with the most likely point on the map and then produces travel time estimates within seconds13Algorithms•HMM is not a new idea, but VTrack is using it in a slightly different way•VTrack uses HMM to evaluate time estimates that come from noisy and sparsely sampled locations•The estimates from these locations are especially important in energy conscious settings14HMM•HMM is a process that uses different states (roads) and observations about those states (data samples) to obtain its output•The sequence of roads traveled is unknown, so the HMM uses probabilities to determine state transition (road usage)–VTrack doesn’t know when a user will turn so it uses these transition probabilities to determine the most likely sequence of roads used15Algorithms Continued•Viterbi decoding is used on top of HMM–This is a programming technique that finds the maximum likelihood sequence of states (roads)•Using HMM and Viterbi together produces a robust method for determining route estimation16Map Matching Process•Prior to HMM, data is processed to eliminate bad points and outages•Outages are dealt with by inserting interpolated points in the regions where an outage occurs–This assumes constant speed on the line, but it works well for map matching accuracy•The output of map matching is the most likely road segment that each point in the raw trajectory came from.17Map Matching Process18Travel Time Estimation•Tleft(S) is the time between the unobserved entry point S and the first observed point in S•Tright(S) is the time between the last point in S and the unobserved exit point from S•The Time estimation is equal to the time interval between the first position point in segment S and the last point in the segment preceeding S (Sprev) divide it equally between Tleft(S) and Tright(S)•This must be done for each road segment, S19Travel Time Estimation Errors•Main source of error is inaccuracy in the map matched output which can occur for two reasons:1. Outages during transition times-If a car is moving from one segment to another during a transition time without observed samples, we don’t know if some delay occurred during that time2. Noisy position samples-A car location could be just entering a segment, but with WiFi, the sample could estimate the car is near the end of the segment; this would lead to an extremely inaccurate delay estimate•It was found that determining travel times for small segments (with lengths near the order of magnitude of noise in that location) were nearly impossible to calculate.20Evaluation•VTrack was evaluated on a large data set (GPS and WiFi) of location estimates from actual drives completed. This info was obtained from CarTel (the other presentation of the afternoon)•Evaluation is based on:–Data and Method of obtaining the data–Route Planning–Hotspot Detection–Energy vs. Accuracy21Evaluation-Data and Method Used•To obtain a “ground truth” is the most challenging part of delay estimation because there


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