Intelligent Transportation Traffic Data Management
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1 Intelligent Transportation Traffic Data Management Ugur Demiryurek Asscociate Director, IMSC Viterbi School of Engineering University of Southern California Los Angeles, CA
2 Intelligent Transportation PROBLEM Traffic congestion is a $87.2 billion annual drain on the U.S. economy 1 : 4.2 billion lost hours (one work week for every traveler) billion gallons of wasted fuel (three weeks worth of gas for every traveler) 1 1 Texas Transportation Institute Urban Mobility Report, 2007 data 2
3 PROBLEM Traffic congestion is a $87.2 billion annual drain on the U.S. economy 1 : 4.2 billion lost hours (one work week for every traveler) 1 GOAL Intelligent Transportation 2.8 billion gallons of wasted fuel (three weeks worth of gas for every traveler) 1 1 Texas Transportation Institute Urban Mobility Report, 2007 data To improve the performance of the surface transportation network through: Capturing real-time data from infrastructure and vehicles Developing data-driven solutions to improve mobility by leveraging optimization opportunities (e.g., path planning for commuter groups) 3
4 Intelligent Transportation Facilitating an infrastructure for acquiring, processing, storing and querying real-time and historical transportation datasets 4
5 TransDec: Data-Driven Decision Making in Transportation Systems 5
6 Outline TransDec Accomplishments Projects Stream Data Processing Time-dependent Route Planning Inferring Traffic from Video Feeds Future Plans 6
7 TransDec Team Government LA-MTA: Los Angeles County Metropolitan Transportation Authority USC Annenberg Innovation Lab Price School of Public Policy Integrated Media Systems Center RIITS: Regional Integrated Intelligent Transportation System Industry M\ I 7
8 TransDec Input Traffic Data Data Processing Storage Query Retrieval &Visualization 46 MB/min MB/min TB/Year Highway Arterial Traffic Monitoring Videos Bus & Rail Ramp meter Event CMS Real-time Flow Data Extraction Microsoft StreamInsight BIG DATA! 8
9 Outline TransDec Accomplishments Projects Stream Data Processing Time-dependent Route Planning Inferring Traffic from Video Feeds Future Plans 9
10 Unrivaled research $1.8+ million funding in last 2 years Advanced development Largest repository of SC traffic data (15+TB) End-to-End system Intellectual property Algorithms & Papers (Best paper award) Filed and provisional two patents Technology Transfer Incubator, Amplify.la Accomplishments 10
11 TransDec Accomplishments Projects Outline Stream Data Processing Time-dependent Route Planning Inferring Traffic from Video Feeds Future Plans 11
12 Stream Data Processing Traffic Data Lifecycle: Loop Detectors Loop Detector: most commonly used traffic sensors The data is collected in Detector Cabinet and relayed to the service provider. Provide two data fields: volume (count) and occupancy (% time a vehicle is over the sensor) Loop Detector Detector Cabinet 12
13 Stream Data Processing Data Type Sample XML File Size (in KB) Cycle Duration (in seconds) Minute (in KB) Hourly (in KB) Daily (in KB) Annual (in KB) 3 Years (in KB) Heterogeneous (loop detector, gps, events) bus_mta_inv2.xml , , bus_mta_rt2.xml , , ,882, ,646, cctv_inv.xml , , cms_inv.xml , , cms_rt.xml , , ,183, ,549, event_d7.xml , ,625, ,875, rail_mta_inv.xml , rail_rt.xml , ,204, ,614, rms_inv.xml , , rms_rt.xml , ,423, ,713, ,559,139, signal_inv.xml , , ,294, signal_rt.xml , , ,061, ,847,308, ,541,926, tt_d7_inv.xml , , tt_d7_rt.xml , , ,891, ,673, vds_art_d7_inv.xml , , Continuous vds_art_d7_rt.xml , , ,652, ,956, vds_art_ladot_inv.xml , , ,779, vds_art_ladot_rt.xml , ,395, ,306, ,527,919, vds_fr_d7_inv.xml , ,047, vds_fr_d7_rt.xml , ,039, ,483, ,138,449, Total KB from XML data , , ,057, ,670,968, ,012,906, Large 13
14 Stream Data Processing Microsoft StreamInsight Efficient retrieval of high-rate streaming data On the fly processing and analysis 14
15 TransDec Accomplishments Projects Outline Stream Data Processing Time-dependent Route Planning Inferring Traffic from Video Feeds Future Plans 15
16 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 Speed (m/h) 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 Speed (m/h) Time-dependent Route Planning Traffic patterns varies based on the time of the day, day of the week and season North South 10 0 Time Time 0 North South 16
17 Time-dependent Route Planning Existing FP Techniques Based on the constant edge weights for each edge In Real-world The weight of an edge is a function of time, i.e., time-dependent Arrival-time to an edge determines the travel-time on that edge 8 :30 AM 5:00 PM 17
18 Time-dependent Route Planning Baseline Offerings Constant travel-time Current traffic conditions Unused Variables Historical traffic averages Time of day Day of week Season of year Holidays or events 18
19 Time-dependent Route Planning Technology Transfer for commercialization Moving Forward Collaboration with courier companies 19
20 Time-dependent Route Planning NAVTEQ Launches Enhanced Traffic Patterns 20
21 TransDec Accomplishments Projects Outline Stream Data Processing Time-dependent Route Planning Inferring Traffic from Video Feeds Future Plans 21
22 Inferring Traffic from Video Feeds Today technology: Inductive loop detectors Major drawbacks Expensive to install and maintain Traffic must be interrupted for installation and repair Can not detect slow or stationary vehicle 22
23 Inferring Traffic from Video Feeds Infer traffic flow using Intel Viewmont coprocessor and its SDK Compare traffic flow with loop detector data 23
24 TransDec Input Traffic Data Data Processing Storage Query Retrieval &Visualization 46 MB/min 15 TB/Year Traffic Monitoring Videos Real-time Flow Data Extraction 24
25 TransDec Accomplishments Projects OUTLINE Stream Data Processing Time-dependent Route Planning Inferring Traffic from Video Feeds Future Plans 25
26 Future Plans Develop spatio-temporal analytical and data mining techniques to discover Tipping-points Butterfly Effects Black Swans Approved for Public Release
27 Questions? Ugur Demiryurek 27
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