1. ITS Lab Members 1 Welcome, Dr. Levinson! PSU ITS Lab: Bertini Group

2. ITS Lab Members  3 rd year Ph.D. Student in Computer Science  Areas of Interest  Data Stream Management Systems  Intelligent Transportation Systems  Thesis Topic: Inter-operator feedback, bounded execution guarantees 2 Rafael J. Fernández-Moctezuma

3. ITS Lab Members Deal efficiently with high- volumes of incoming data Traffic Data  CS theory –Inter-Operator Feedback –Guarantees on Bounded Stream Query Execution Work with Prof. Maier, and Prof. Tufte 3 Rafael J. Fernández-Moctezuma Data Stream Management Systems (a) Centralized Adaptation (b) Localized Adaptation DUPLICATE σCσC σ¬Cσ¬C IMPUTE PACE

4. ITS Lab Members Big picture: Adapt to incoming data characteristics to perform near real- time imputation Looked at diverse strategies, not all amicable for low latency processing Spatial and Temporal models, some heuristic, some statistical. Work with Prof. Bertini, Prof. Maier, and Prof. Tufte 4 Rafael J. Fernández-Moctezuma On-Line Imputation Strategies

5. ITS Lab Members Work toward automatic bottleneck detection “Living history” of Portland Bottlenecks Can process one year of data per corridor in one day (commodity PC) Work with Prof. Bertini, Jerzy Wieczorek, Huan Li Bottleneck Identification 5 Rafael J. Fernández-Moctezuma

6. ITS Lab Members Where do we position loop detectors to better operate the freeway infrastructure? Challenges: What’s “better”? Optimal ramp metering? Better travel time estimations? Early bottleneck detection? Recently focused on Linear Programming approach for early bottleneck detection Work with Prof. Figliozzi, Prof. Bertini 6 Rafael J. Fernández-Moctezuma Optimal Sensor Placement

7. ITS Lab Members  1 st year Graduate Student in Transportation Engineering  Current Research Topics in the ITS Lab  Impacts of Sensor Spacing on Accurate Freeway Travel Time Estimation for Traveler Information  Dynamic Bi-level Programming Models for Distribution Centers Location 7 Wei Feng

8. ITS Lab Members Compute VHT errors of different travel time estimation methods where transition happens 8 Wei Feng Work with Porf. Bertini Travel Time Estimation/Sensor Spacing Calculate relationship between all types of errors and sensor spacing for each method

9. ITS Lab Members Minimize the combined cost of VHT error cost and sensor construction cost. Express optimal sensor spacing with parameters: speed, flow and cost coefficients. Sensitivity analysis of parameters to the optimal sensor spacing. 9 Wei Feng Work with Porf. Bertini Travel Time Estimation/Sensor Spacing

10. ITS Lab Members Travel Time Estimation/Sensor Spacing 10 Wei Feng Work with Porf. Bertini Convert absolute VHT error or percentage VHT error into money, and how to set the conversion coefficient? What would be the reasonable constraints of absolute VHT error and percentage VHT error when applying optimization method?

11. ITS Lab Members Upper level: Minimize total cost (system minimization) Lower level: Minimize distribution cost (customer minimization) –Radial distribution –Multi TSP distribution –Multi VRP distribution 11 Wei Feng Work with Porf. Figliozzi Dynamic Bi-level Model for DC Location Solution Algorithm: Cluster and Approximation

12. ITS Lab Members  Ph.D Student in Civil Engineering  Areas of Interest  Freeway Management and Operation  Transit Operation  Intelligent Transportation System  Climate Change 12 Huan Li

13. ITS Lab Members Assess optimal stop spacing considering access cost and riding & stopping cost 13 Huan Li Transit Service Evaluation On- Board Computer Radio Doors Lift APC (Automatic Passenger Counter) Overhead Signs Odometer Signal Priority Emitters Stop Annunciation Memory Card Radio System Garage PC’s Radio Antenna GPS Antenna Navstar GPS Satellites Control Head Use high resolution archived stop-level data –One year’s worth of data –Referring all routes in Portland Metropolitan region every trip every bus stop event

14. ITS Lab Members 14 Huan Li Transit Service Evaluation

15. ITS Lab Members Analyze lane changing effect on speed using lane by lane oblique curve “Historical data” Automatically identify HOV lane merging and diverging features –Indicator: piece wised linear regression for curve fitting –Endogenous Model vs. Extraneous Model Traffic Flow Features on HOV lane 15 Huan Li

16. ITS Lab Members Next step: compose oblique method with threshold based identification method Other applications: incident detection, bottleneck identification…. 16 Huan Li Traffic Flow Features on HOV lane

17. ITS Lab Members Civil engineering undergraduate, senior (focus on transportation) Areas of Interest –Transp. system sustainability –Modeling transp. emissions and diffusion Honor Program – Thesis topic: Carbon Sponsoring for Personal Travel 17 Alex Bigazzi

18. ITS Lab Members Sustainability performance measures for the transportation data archive at PSU –Emissions: currently MOBILE 6.2, will use MOVES –Fuel Consumption –Cost of Delay –Personal Mobility (PHT, PHD, PMT) 18 Alex Bigazzi ‘Greening’ PORTAL

19. ITS Lab Members Errors from temporal aggregation Data source: disaggregate speeds from loop data Event: car passes over loop Error 1: Time resolution –Shock speed 19 Alex Bigazzi ITS Data Aggregation Effects

20. ITS Lab Members Error 2: Parameter distribution –Speed distribution narrows Underestimate emissions, delay –Travel time errors from using time mean speed Underestimate delay Corrected using harmonic mean Can be estimated w/ variance ITS Data Aggregation Effects 20 Alex Bigazzi

21. ITS Lab Members Framework for individuals to seek direct, voluntary carbon offsets for personal travel Targets carbon reductions outside of current monetary-based offset programs Project objectives: –Establish calculation methods for carbon outputs –Develop effective and simple online interface –Analyze initial feedback from pilot users 21 Alex Bigazzi CarbonSponsor.org

22. ITS Lab Members  1 st year M.S. Student in Civil Engineering  Areas of Interest  Traffic Flow Theory  Intelligent Transportation Systems  Possible Thesis Topics: Uncertainty Propagation in Traffic Flow Models or Bottleneck Identification using FOTO and ASDA Models 22 Meead Saberi K.

23. ITS Lab Members Dealing with two large databases of traffic data and weather data (PORTAL) Traffic and weather data fusion and quality 23 Meead Saberi K. Effects of Weather on Traffic Flow on Freeways

24. ITS Lab Members Effects of precipitation, visibility and wind speed on: speed and flow (average, standard deviation, and statistical significance) Probabilistic Approach: using cumulative distribution function 24 Meead Saberi K. Effects of Weather on Traffic Flow on Freeways

25. ITS Lab Members 25 Meead Saberi K. Segment Level Analysis of Travel Time Reliability Breaking the overall I-5 NB freeway into shorter segments; this study shows how travel time reliability can vary across freeway segments using different reliability measures.

26. ITS Lab Members 26 Meead Saberi K. Segment Level Analysis of Travel Time Reliability Segment ranking based on travel time reliability Reliability of corridor vs. segments

27. ITS Lab Members  Master Student in Civil engineering at the ENTPE  5 month internship at the ITS Lab  Areas of Interest:  Traffic flow theory  Transportation Economics 27 Helene Siri

28. ITS Lab Members  ENTPE : Civil engineering school (Lyon)  Structure, environment, urbanism and transportation  Transportation department at the ENTPE  LET (CNRS - University of Lyon II – ENTPE)  LICIT (INRETS – ENTPE) 28 Helene Siri About the ENTPE

29. ITS Lab Members  Loop Detector Data from lane 3 northbound station 20 on the I-880 freeway  Aggregation of data in different sampling periods 29 Helene Siri A practice study for ITS data aggregation

30. ITS Lab Members  Net speed on a urban grid with different densities of intersections and different legal posted speed  Developing a program using Matlab to estimate Net speed 30 Helene Siri Net Speed Calculator

31. ITS Lab Members  ITS Data Aggregation using NGSIM Data 31 Helene Siri Next step:

32. ITS Lab Members  2 nd year M.S. Student in Statistics  ITS Research:  Historical and real-time bottleneck identification  Statistics Research:  Minimum Kolmogorov-Smirnov Estimation (MSKE) with censored data 32 Jerzy Wieczorek

33. ITS Lab Members Using speed data to track historical congestion and rank bottlenecks by cost Incorporating historical information into model to predict real-time bottleneck behavior Work with Prof. Bertini, Huan Li, Rafael J. Fernández-Moctezuma Bottleneck Identification 33 Jerzy Wieczorek

34. ITS Lab Members Expanding model to use volume data Validating against ground truth from Bertini-Cassidy method Work with Prof. Bertini, Huan Li, Rafael J. Fernández-Moctezuma Bottleneck Identification 34 Jerzy Wieczorek

35. ITS Lab Members Choose distribution and parameter estimates that minimize the K-S statistic (max. vertical difference in CDFs) Work with Prof. Kim 35 Jerzy Wieczorek Minimum K-S Estimation θ = 96.1 θ = 72.2

36. ITS Lab Members Extend to censored data Evaluate in comparison with MLEs (standard) Create R library if worthwhile Work with Prof. Kim 36 Jerzy Wieczorek Minimum K-S Estimation