Hossein Naraghi Traffic Records Forum August 7, 2017 Institute for Transportation (InTrans) Observing and modeling drivers’ natural driving behavior in work zones Hossein Naraghi Traffic Records Forum August 7, 2017

Background 11% increase in work zone fatalities (2011 – 2015) Presence of road constructions and maintenance Increase in safety risks Disturbance to traffic flow High cognitive work load for drivers 700-1000 fatalities per year in last 10 years 11% increase in work zone fatalities (2011 – 2015) Slight decrease in non-work zone fatalities Limited details on available crash data Officer interpretation, Witness testimony Unclear driver behavior and underlying causes of work zone crashes Institute for Transportation (InTrans)

Background Speed management effectiveness Numerous countermeasures tends to get drivers attention to reduce speed How drivers react to measures like DMS, merge sign,..? How effective are safety strategies in getting driver attention? The most effective countermeasure cannot be identified because driver behavior is not well understood SHRP2 NDS Time Series speed data collected at high frequency at 10HZ (every 0.1 second) Opportunity for a first hand observation of driver behavior Institute for Transportation (InTrans)

Background Major contributing factors Speeding Inattentive driving Driver The more recent study by NHTSA in 2015 confirms numbers and reveals critical reasons attributed to Drivers 94% Vehicles 2% Environment 2% Unknown 2% Limitation in understanding human factor in available crash data Roadway Vehicle Rumar et al.’ study (1985) NHTSA Traffic safety Facts 2015 (Data Source: NMVCCS 2005–2007) Institute for Transportation (InTrans)

Objectives Utilize SHRP2 NDS time series data and forward video images Observe drivers behavior Determine where drivers starts to react to different work zone features Identify the effectiveness of speed countermeasures Develop models to predict driver reactions to work zone features Institute for Transportation (InTrans)

Primary Participant Count Data collection SHRP 2 Naturalistic Driving Study Data (NDS) 3100 participant drivers Oct 2010 – Nov 2013 6 Sites 5 M trips 20 M data miles 4 M GIGs data Site Primary Participant Count Buffalo, NY 719 Tampa, FL 698 Seattle, WA 676 Durham, NC 504 Bloomington, IN 239 State College, PA 256 VTTI, 2013 Institute for Transportation (InTrans)

Data collection SHRP 2 Roadway Information Database (RID) 25,000miles of roadway data Curve Radius Lane Types Curve Length Shoulder Types Curve Point of curvature All Signs on MUTCD Curve Point of tangency Guardrails/Barriers Curve Direction Intersection Geometry Grade Median Types Cross-slope/Super elevation Presence of Rumble strips Number of Lanes Presence of Lighting Lane Width   A site map shows the RID database in the SHRP2 NDS (Smadi, 2015) Institute for Transportation (InTrans)

Methodology Functional data Analysis (FDA) Institute for Transportation (InTrans)

Data extraction and reduction FDA analyses Major contribution in locating WZ within 511 data in RID Database and matching to WZ identified in SHRP2 NDS data State specific attribute query in ArcGIS with key word search in “Event Description” field Construction, Lane closure, Road work, Maintenance WZ for less than 3 days duration excluded Low possibility of having NDS trip 9290 potential WZ identified Link the WZ to NDS trip density map Potential WZ determined if trip falling within dates indicated in 511 Institute for Transportation (InTrans)

Data extraction and reduction 4800 time series traces along with forward/rear video Roadway characteristics # of lanes, median type, traffic control, speed limit, … WZ characteristics DMS, lane closure, speed feedback sign, WZ speed limit, lane shift, shoulder closure, …. Drive characteristics Age, gender, and other socioeconomic data provided by VTTI Countermeasure distance Calculated based on timestamp and speed Institute for Transportation (InTrans)

Data quality assurance Forward video image quality Excluded traces with very poor image quality Unable to identify the position of vehicle Unable to confirm active WZ Speed time series data issue was missing data Excluded traces with more than 25% missing values Utilize interpolation techniques to calculate missing values Institute for Transportation (InTrans)

Chall enge Institute for Transportation (InTrans)

Challenges of time series data Use of time series data brought many challenges Missing data and noises Outliers Lack of well-established statistical method Institute for Transportation (InTrans)

Functional Data Analysis (FDA) FDA emerged in early 90s and quickly developed more recently in medical field, environmental monitoring and economic research In FDA a series of discrete time series observations converted to functional observations Primary interest is to understand the variation in underlying process over a group of repeated measurements Appropriate for high frequency measurement, smooth process, examine complex process and explain how process changes over time

Functional Data Analysis (FDA) Development of FDA from discrete time series data First step smooth noises Values can evaluated at any points from best fitted function Institute for Transportation (InTrans)

Smoothing splines 4-lane divided Rural Left lane closure Cross-over head to head traffic Best fitted spline to each trace 62 traces Institute for Transportation (InTrans)

Functional Data Analysis (FDA) Best fitted spline to each curve along with discrete observations Institute for Transportation (InTrans)

Functional Data Analysis (FDA) Scree plot to identify number of basis functions Generalized cross validation used to choose optimal smoothing parameters Institute for Transportation (InTrans)

Functional Data Analysis (FDA) Polynomial segments with order of four polynomials 17 spline basis functions defined over the interval (-1.3, 1.0) by 13 interior knots Institute for Transportation (InTrans)

Functional Data Analysis (FDA) 62 best fitted spline functions based on 17 spline basis Institute for Transportation (InTrans)

Functional Data Analysis (FDA) Institute for Transportation (InTrans)

Solid black line is average speed profile Useful technique to summarize average behavior from a group of time series data Institute for Transportation (InTrans)

Institute for Transportation (InTrans) 26 Traces closed lane 26 Traces closed lane 62 Traces All 33 Traces female drivers 36 Traces open lane 29Traces male drivers Institute for Transportation (InTrans)

Institute for Transportation (InTrans)

First derivative First derivative of vehicle speed indicates vehicle acceleration Useful to technique to illustrate how drivers change their speeds when encountering various countermeasures Institute for Transportation (InTrans)

First derivative Institute for Transportation (InTrans)

First derivative Institute for Transportation (InTrans) 62 Traces All 26 Traces closed lane 62 Traces All 33 Traces female drivers 36 Traces open lane 29 Traces male drivers Institute for Transportation (InTrans)

Conclusions Functional Data Analysis (FDA) FDA method very useful and effective in analyzing time series data The typical driver’s behavior could be visualized and calculated with FDA methods The derivative information could provide important insights on time series data It is more important to study the rate of change This method allow us to examine how a group of drivers reacted to different WZ characteristics or speed countermeasures Institute for Transportation (InTrans)

Conclusions Identify which work zone features are more likely to get driver’s attention DMS vs. to static sign DSFS vs. WZ speed limit sign Determine the effectiveness of the locations of the countermeasures DMS located after 1st warning sign DMS after the merge sign Investigate the effectiveness of WZ configurations left lane vs. right lane closure lane shift no shoulder vs. lane closure Institute for Transportation (InTrans)

Future Research Utilize Multiple Changepoint and FDA analysis to study driver behavior Alternative locations of countermeasures WZ configurations Nighttime versus daytime comparison Driver demographics Distractions’ effects Institute for Transportation (InTrans)

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