Impact of LOS on Driving Behavior on Arterials: High-Resolution Data Analysis

1. 16th TRB National Transportation Planning Applications Conference APPLICATION OF HIGH-RESOLUTION DATA TO DETERMINE THE IMPACT OF LOS ON DRIVING BEHAVIOR ON ARTERIALSNabaruna KarmakarCeleste Chavis, Ph.D.MansourehJeihani, Ph.D.ZohrehRashidiMoghaddamBehzadAghdashi, Ph.D. May 17, 2017

2. Outline • Introduction and Motivation • Data collection • Methodology • Preliminary Results and Observations • Conclusions and Future work http://itre.ncsu.edu

3. Introduction and Motivation http://itre.ncsu.edu

4. Introduction and Motivation • Typically, “user delay costs” is the key factor in characterizing benefits of treatment projects on arterials. • However, improvements in LOS may have impact on driver aggressiveness and, as such, bring safety costs into decision making. • The main goal of this project is to assess the impact of LOS on driver behavior on arterials. • This is an ongoing project between ITRE and Morgan State University, funded through UMD-NTC. http://itre.ncsu.edu

5. Introduction and Motivation • Active Traffic Management (ATM) • Regional Planning • Strategic Development Plans Traffic Condition (LOS = A, B, C, D, E, F) Emerged Travel Time and Delays User Delay Cost Economic Competitiveness Driver Behavior (Aggressiveness, etc.) Fuel Cost and Emissions; Safety Concerns http://itre.ncsu.edu

6. Data Collection http://itre.ncsu.edu

7. Underlying Technology • Vehicle sensor systems • Speed • RPM • Throttle Level • Engine Temperature • Gear Position • On-Board Diagnostic (OBD) Port http://itre.ncsu.edu

8. Introduction of New Technology Through i2D – “Intelligence to Drivers” • Collaboration with partners in Portugal (TUL and ITDS) • Additional Sensors • GPS Coordinates • 3D Acceleration • Barometric altimeter • Enhanced Processing, Storage, and Communications • Simple Processor • 4GB SD Card • Cellular Network http://itre.ncsu.edu

9. i2D Technology Integration Process Web Access Vehicles Sensors Cloud Database OBD Port i2D Device Cellular Network Additional Sensors Memory Processor http://itre.ncsu.edu

10. Summary of the Collected Data • About 40 volunteer drivers • Gathered over 39 million seconds of data over 3 years (2014-2016)for46,828 trips. • Recorded over 360K miles of driving behavior • All data have been archived on an ITRE server (DaTA Lab- Driver and Transportation Analytics) • We are in process of analyzing and interpreting collected data using • ArcGIS • Independent Platforms http://itre.ncsu.edu

11. HERE/ INRIX data • HCM service measure for LOS on arterials is the ratio of travel speed to free flow speed. • Measured speeds on the study segments were used to assess the LOS during the trips. • ITS probe data, provided by HERE and INRIX, • Data collected for three years (2014 - 2016). • All data have been archived on an ITRE server (DaTA Lab- Driver and Transportation Analytics) http://itre.ncsu.edu

12. methodology http://itre.ncsu.edu

13. Methodology • First, trip data was filtered for only the selected road segments, using an internally developed Arc GIS Tool • INRIX and HERE Speed data was downloaded for the years 2014-2016 for the selected TMC segments • Data Integration of these two data sets was done by matching the timestamps of trip data and probe data • Outlier Removal was done using K-means clustering • Exploratory Data Analysis was done using R and Tableau • Sample size – Distribution of number of trips and different drivers • Trends in acceleration across different LOS • Frequently traveled road segments • From a set of 20 sites, we selected one site with good distribution of LOS observations (A through F) Site Selection Frequently traveled road segments with a good distribution of LOS observations Data Cleaning and Integration Data extraction on road segments ArcGIS and Data Integration and formatting using R Data Mining and Visualization Exploratory Data Analysis and visualization using Tableau and R http://itre.ncsu.edu

14. Preliminary results and observations http://itre.ncsu.edu

15. Selected Site Western Blvd WB near NC State University Western Blvd • TMC: 125+14768 • Length of Segment: 0.7 miles • Includes 4 signalized intersections (3 HCM Segments) • Speed Limit: 45 mph • Number of Lanes: 2 • 48,952 rows of data TMC - 125+14768 http://itre.ncsu.edu

16. Number of Trips and Drivers - Western Blvd WB 376 total trips http://itre.ncsu.edu

17. Distribution of Trips and LOS observed over Time of Day http://itre.ncsu.edu

18. Sample Size of data over different LOS http://itre.ncsu.edu

19. Number of Zero Acceleration Seconds over different LOS http://itre.ncsu.edu

20. Average and Standard Deviation of Acceleration and Deceleration http://itre.ncsu.edu

21. Maximum Acceleration and Deceleration by trips over different LOS Acceleration Deceleration http://itre.ncsu.edu

22. Lateral Acceleration over different LOS http://itre.ncsu.edu

23. 3D combined Acceleration over different LOS Average Standard Deviation http://itre.ncsu.edu

24. Conclusions & Future Work http://itre.ncsu.edu

25. Future Work • Characterize the impact of weather and incidents in analysis. • Consider other measures of aggressiveness • Further data mining • Outlier analysis • Predictive modeling of driver behavior • Validate findings from Driver Simulator http://itre.ncsu.edu

26. http://itre.ncsu.edu

27. Appendix http://itre.ncsu.edu

28. DEFINING LEVEL OF SERVICEHCM Chapter 16 http://itre.ncsu.edu

29. Future WorkK-Means Clustering Visualization Site A http://itre.ncsu.edu

30. Driving Simulator Capabilities • The hardware is very similar to a real car and consists of the driver seat, cockpit, steering wheel, acceleration and brake pedals, etc. • A road network can be developed in the software and drivers are able to select their own route between origin and destination. • The software has a capability of generating up to 5000 vehicles per lane on the road, mixes of cars, buses, trucks, and motorcycles • It collects data of the driver’s car every 1/100 seconds including but not limited to geographic positions, speed, lane, distance traveled, offset from road’s shoulder, acceleration, brake, yaw/pitch/roll. • Different scenarios of traffic situations, environment and information provision can be simulated. http://itre.ncsu.edu

31. Driver Simulator - LOS Network • A 4.5-mile arterial in Baltimore, MD is developed in the driving simulator • High congestion level in this network is assured mainly by growing traffic volume and decreasing traffic speed • 50 participants are expected to be recruited to drive • All participants are expected to drive all levels of service The developed arterial in the driving simulator http://itre.ncsu.edu A screenshot of the developed network

32. Contrasting i2D and Smart Phone Sensors • i2D allows connection to vehicle sensors • Improved accuracy of sensor readings (e.g. 3D accelerometer) by installing device in vehicle • Enhanced and organized database in the cloud • Web access to download organized data • i2D personal users web access • Demonstration of driving behavior • Managing fleet members • i2D research platform • Several methods to access and download detailed data • i2D VIV Control Panel http://itre.ncsu.edu

33. Number of Trips and Day of Week http://itre.ncsu.edu