16th TRB National Transportation Planning Applications Conference APPLICATION OF HIGH-RESOLUTION DATA TO DETERMINE THE IMPACT OF LOS ON DRIVING BEHAVIOR ON ARTERIALS Nabaruna Karmakar Celeste Chavis, Ph.D. Mansoureh Jeihani, Ph.D. Zohreh Rashidi Moghaddam Behzad Aghdashi, Ph.D. May 17, 2017

Outline Introduction and Motivation Data collection Methodology Preliminary Results and Observations Conclusions and Future work 5/17/2017 http://itre.ncsu.edu

Introduction and Motivation 5/17/2017 http://itre.ncsu.edu

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. Conventional freeway analysis has often times focused on mobility improvements. Not much study has been done on analysis of impacts of LOS on arterial streets. This is an ongoing project between ITRE and Morgan State University, funded through UMD-NTC. 5/17/2017 http://itre.ncsu.edu

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 Driver Behavior (Aggressiveness, etc.) User Delay Cost Fuel Cost and Emissions; Safety Concerns Economic Competitiveness Traffic management and planning activities are focused on improving mobility performance measures, aimed at improving LOS. The efficacy of these activities are usually gauged using Travel Time delay. Worsening LOS can also give rise to safety concerns on arterials, resulting from driver frustration and aggressiveness. 5/17/2017 http://itre.ncsu.edu

Data Collection 5/17/2017 http://itre.ncsu.edu

Underlying Technology Vehicle sensor systems Speed RPM Throttle Level Engine Temperature Gear Position There are various sensor systems in our vehicles that can help measure variables that can help us get an idea of a person’s driving style. They can be measured from the car’s OBD port. On-Board Diagnostic (OBD) Port 5/17/2017 http://itre.ncsu.edu

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 5/17/2017 http://itre.ncsu.edu

i2D Technology Integration Process Web Access Vehicles Sensors Cloud Database OBD Port i2D Device Additional Sensors Cellular Network Memory Processor 5/17/2017 http://itre.ncsu.edu

Summary of the Collected Data About 40 volunteer drivers Gathered over 39 million seconds of data over 3 years (2014-2016) for 46,828 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 5/17/2017 http://itre.ncsu.edu

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) 5/17/2017 http://itre.ncsu.edu

methodology 5/17/2017 http://itre.ncsu.edu

Methodology Site Selection Data Cleaning and Integration 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

Preliminary results and observations 5/17/2017 http://itre.ncsu.edu

Selected Site Western Blvd WB near NC State University Western Blvd TMC - 125+14768 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 5/17/2017 http://itre.ncsu.edu

Number of Trips and Drivers - Western Blvd WB Year Number of Drivers Number of Trips 2014 15 121 2015 18 205 2016 12 50 376 total trips 5/17/2017 http://itre.ncsu.edu

Distribution of Trips and LOS observed over Time of Day 5/17/2017 http://itre.ncsu.edu

Sample Size of data over different LOS 5/17/2017 http://itre.ncsu.edu

Number of Zero Acceleration Seconds over different LOS 5/17/2017 http://itre.ncsu.edu

Average and Standard Deviation of Acceleration and Deceleration 5/17/2017 http://itre.ncsu.edu

Maximum Acceleration and Deceleration by trips over different LOS 5/17/2017 http://itre.ncsu.edu

Lateral Acceleration over different LOS 5/17/2017 http://itre.ncsu.edu

3D combined Acceleration over different LOS Average Standard Deviation 5/17/2017 http://itre.ncsu.edu

Conclusions & Future Work 5/17/2017 http://itre.ncsu.edu

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 5/17/2017 http://itre.ncsu.edu

5/17/2017 http://itre.ncsu.edu

Appendix 5/17/2017 http://itre.ncsu.edu

DEFINING LEVEL OF SERVICE HCM Chapter 16 LOS Based on Speed Limit of 45 mph A >36 B >30 C >25 D >20 E >15 F <=15 5/17/2017 http://itre.ncsu.edu

Future Work K-Means Clustering Visualization Site A 5/17/2017 http://itre.ncsu.edu

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. 5/17/2017 http://itre.ncsu.edu

Driver Simulator - LOS Network The developed arterial in the driving simulator 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 A screenshot of the developed network 5/17/2017 http://itre.ncsu.edu

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 5/17/2017 http://itre.ncsu.edu

Number of Trips and Day of Week 5/17/2017 http://itre.ncsu.edu