Institute for Transportation Research and Education – N.C. State University High Resolution In Vehicle Sensing Nagui M. Rouphail Director, ITRE Professor of Civil Engineering NC State University 1 DriveSense14 October 30-31, 2014

The challenge 2 1 billion highway vehicles SAFETY  1.2 million traffic fatalities per year ENERGY  30% of world Energy EMISSIONS  25% of world CO 2 Emissions TRAFFIC  1.5 hours per day on a vehicle

Outline Description of in-vehicle sensor Data description and demonstration Research questions and hypotheses Planned capabilities (VIV) 3

In-Vehicle Sensor: Background Partnership with TUL(Technical University of Lisbon) and ITds (software company in Lisbon) Funded collaboration through NSF international supplement for a just concluded NSF award Sensor developed in Portugal by ITds and TUL through an Innovation co-fund award Initial prototype was to provide feedback to driver on fuel use and emissions via a secure website Ongoing prototype testing through funding from the University of Maryland National UTC 4

The In-Vehicle Sensor 5 i2D INTELLIGENCE TO DRIVE

How it Works 6 GPRS/GSM

The Data Levels (min 1Hz Resolution) 7 1 st Level Raw Data All available PIDs from OBD as: speed (odometer), rpm, engine temperature, accelerator position, error codes, VIN… (most of them on a 1 Hz basis) From additional sensors: location (GPS), 3 axis accelerometer (up to 50 Hz local), altitude (barometer) … 2 nd Level Processed Data fuel consumption (i2D algorithms), CO2 and other pollutant emissions, engine cold temperature points, slope, distances, driving periods, driving events (Stops, predefined alerts over speed, rpm, accelerations…), average speed, energy efficiency for each trip… trip mapping and reconstruction, benchmarking, driving indicators, driving learning support, Driving Profiling, why and where are you spending fuel, …

Raw Data: OBD Speed vs. Acceleration 8

Raw Data: GPS vs. OBD Speed 9

Raw Data: Lateral Acceleration Distribution 10

Database at NC State ~2 million records of data seconds are collected each month, each having 40 data fields from about vehicles driven by student/ staff volunteers – About ~3,500 miles of travel (low use) – Consumes ~200MB of memory (xlsx format) Available to NC State in a SINGLE table format – Hard to perform queries, changes, etc. NCSU broke down the table into several tables connected to each other in a SINGLE database – More efficient query and search – Is needed to perform faster visualization 11

Other Databases Individual users and fleet managers access website – Basic configuration of unit, vehicle, password – User friendly reports, visualizations, etc Research Website – Simple web access for researchers to download raw data i2D public website – Shows the overall performance of drivers and vehicles anonymously NCSU Website under construction – Based on SQL database – Performs faster search and visualization 12

Private Driver Website View (1) 13 Events…

Private Driver Website View (2) 14 Trip Summaries, benchmarking and fuel waste reports

SAFETY Applications Queue Warning “Event / Exception Trigger Based” alert; each generating “n” customized messages that are automatically delivered by the system to identified vehicles Preventing accidents and traffic jams 15 i2D Dual Communication System (M2M) for VIV M2M communication establishes an IP connection 2 independent, parallel, communication channels are created 1 st Level communication Channel DATA M2M Data may be associated with  Fleet Mangmt.  Individual usage  UBI (insurance) 2 nd Level communication Channel DATA M2M Data just for VIV purposes:  SAFETY Applications  TRAFFIC Applications a Random ID is generated for each trip –> No Privacy issues PLANNED CAPABILITY –December 2014 VIV – Vehicle to Infrastructure to Vehicle Priority Real Time

Research Questions / Hypotheses Generating driving profiles from Hi Res data Developing micro-scale vehicle interaction models based on driver profiles (car-following, lane changing, gap acceptance) Testing hypotheses of micro-scale driver behavior vs. long term safety record Distinguishing contributing factors to crashes (driver behavior, road/ traffic control effects) Testing impact of feedback on eco-driving perform. Long term driving trends vs. economic factors 16

Research Questions / Hypotheses Real time data quality checks and imputations Testing regional and national travel demand model route choice assumptions (UE vs. SE vs. SO) Testing assumptions about traffic signal timing Testing the value of and compliance with travel information to calibrate/ validate ATIS models Feasibility of PHYD or PAYD tolling schemes Privacy issues… 17

Questions Thank you ! 18