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Transpo 2012

Transpo 2012. Utilization of ITS Data for off-Line and Real-Time Assessment of Transportation System Performance. Mohammed Hadi, Yan Xiao, Tao Wang Lehman Center for Transportation Research Department of Civil and Environmental Engineering Florida International University Miami, FL

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Transpo 2012

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  1. Transpo 2012 Utilization of ITS Data for off-Line and Real-Time Assessment of Transportation System Performance Mohammed Hadi, Yan Xiao, Tao Wang Lehman Center for Transportation Research Department of Civil and Environmental Engineering Florida International University Miami, FL October 30, 2012

  2. Background ITS data can be used in combination with traffic analysis, simulation modeling, data fusion/data mining and optimization for planning and operation Real-time and Off-line Performance measurements of transportation system Transportation system modeling Benefits and costs assessment of ITS applications Associations of attributes utilizing data mining and visualization methods Decision support systems Predictive travel time and impacts

  3. ITSDCAP Components ITS Data Capture and Analysis of Performance (ITSDCAP) Capture and fusion of data from multiple sources Data mining Performance measurements and visualization ITS evaluation/benefit-cost analysis Modeling and analysis support Decision support systems to be incorporated

  4. Data from Multiple Sources SunGuide data (TSS, TVT data, incident, DMS, etc.) Inrix Statistics office data Central data warehouse Weather data Managed lane dynamic congestion pricing rates Work zones Crash data/CARS 511 data FHP data

  5. Data Grouping Data grouping allow the extraction and archiving of data based on different conditions Time-of-day, working days/holidays, day of the week Specific segments Similarity in traffic patterns Incidents Weather conditions Work zones

  6. Selection based on Traffic Patterns Progress Meeting

  7. Performance Measurements Mobility measures Reliability measures Environmental measures Safety measures

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  9. Mobility and Reliability Mobility measures: speed, density, queue length/location, travel time, delay, vehicle-mile traveled (VMT), and vehicle-hour traveled (VHT) Alternative methods are incorporated to calculate different measures based on point detector data and INRIX data. AVI data being incorporated and studied Reliability measures: standard deviation/variance, Buffer Index, Failure/On-time performance, Planning Time Index based on the 95th or 80th percentile, Skew Statistics, and Misery Index New measures will potentially be incorporated At least one year of data

  10. Safety and Environmental Impacts Safety: Estimate a number of safety measures including crash frequency by crash type, crash frequency by severity, total crash frequency, crash rate by type, crash rate by severity, and total crash rate Energy and Emission: Currently based on Mobile 6. When emission rates based on MOVES are calculated for Florida they will be incorporated

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  12. Travel Time Comparison I-95 NB from South of NW 62nd St. to Golden Glades Interchange (Exit 12A) 9/1/2011 9/22/2011

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  17. IRISDS Web-based real-time information sharing and decision support system Integrated Regional Information Sharing and Decision Support system (IRISDS) Proof of concept of two main components Information sharing (SunGuide C2C, transit AVL, INRIX) Decision support systems

  18. IRISDS

  19. Decision Support System Incident impacts, impact index, and dashboard Real-time simulation of incidents Utilizing bus travel time to estimate corridor travel time Estimate diversion during incidents and potential impacts on alternative routes

  20. Prediction of Incident Impacts The prediction of impacts based on measured incident attributes include: Predict lane blockage duration Predict potential for secondary incidents Predict delays and queue lengths Assigns a severity Index to each incident based on the impacts and user assessment of impact weights

  21. Incident Impacts and Index

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