Jeremiah Dunn. Overview Introduction Mobile Millenium Goal Complexity of the Problem Gathering Data Data Fusion Modeling the Flow of Traffic Mobile Century.

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Presentation transcript:

Jeremiah Dunn

Overview Introduction Mobile Millenium Goal Complexity of the Problem Gathering Data Data Fusion Modeling the Flow of Traffic Mobile Century Conclusion

Introduction Fixation of humanity on futuristic cars & autonomous travel has drastically changed the modern car. While not quite going in that direction, cheap sensors and network availability are essentially boosting the “brainpower” of our driving environment. Between road-side sensors, dashboard GPS, and Smart phones many companies are provided with traffic data collection.

Mobile Millenium Started in 2007 One of the first large-scale projects for traffic monitoring Run by Nokia, NAVTEQ, and UC Berkeley Only able to be conceived and work thanks to the rise of the “smart-phone” thanks to embedded GPS

Goal Merge road-side sensor networks with smartphone GPS feedback to generate a real-time traffic monitoring situation.

Complexity of the Solution

Gathering Data VTLs (Virtual Trip Lines): to prevent constant packet transfer, the phone will only upload statistics when it crosses a “checkpoint” along the VTLs.

Data Fusion Incoming data from many sources GPS Buses Taxis Cars Static Sensors Loop Detectors RFID tag readers GPS may have faulty Data Walkers/Parked/etc

Modeling the Flow of Traffic Obvious way to think about modeling traffic is by individual cars Designed a new set of algorithms based on fluid mechanics

Mobile Century All this data collection culminated in 2008 in a test 100 Cars mixed in a 10-mile stretch in Norther California 10 Hours and accounted for 2-5% of the cars on the highway Mobile Millinum vs Google Maps w/ Traffic Noticed a sudden red blotch appeared on the test stretch, but it took several minutes to appear on Google’s system.

Conclusion Was able to detect and report a slow-down in under a minute Proved that only a few cars were needed to get the system to run efficiently (2-5%) Successful test has led to the concept and technology demonstrated to become widespread into Google’s mobile Maps app. Proved that the mobile scene was better performed than any static detector system.