Estimating Traffic Crash Counts 

Using Crowdsourced Data 

2018-10-24

Volpe Center

Erika Sudderth, PhD 

Dan Flynn, PhD

Michelle Gilmore

BTS

Pat Hu

Ed Strocko

OST-P

Paul Teicher

David Winter
Slide 2

• Crash data are typically available for certain 

crashes, after several months 

• EDT (Electronic Data Transfer) of police accident 

reports available nightly for nine states

• Waze incident data available where user 

reported, all 50 states and DC, every 2 minutes

• Waze and EDT could provide near-real time, 

granular estimates of crashes to inform safety 

policy and operations

Challenge: Tracking crashes in near real-time
Slide 3

Safety Data Initiative: Waze Pilot Project Overview

Objectives 

• Use crowdsourced data insights to 

improve transportation safety

Questions

• Can we integrate DOT data resources at 

large scales?

• Do Waze data support vision of a rapid 

crash indictor?

Data Integration

EDT, Waze, auxiliary

80% of effort!

Model Training

Random Forests - Machine 

Learning approach

Scale models up

Rapid iterations utilizing 

DOT cloud resources

Vision

Rapid crash trend 

monitoring tool

Model Performance

Validate and test 

estimation models

Pipeline Development

Approach
Slide 4

Analysis: Challenges and Solutions

Observed data

Observed

EDT Crash

Waze event

Challenges

• Waze and EDT coordinates do not all align with FHWA road network

• How do we associate Waze events and EDT reports?

• Need to define zeros (time and places with no accidents)

Solutions

• Spatial aggregation of data to hexagonal grids (1-mile area) 

• Match Waze to EDT on user-selected buffers in space and time

• Define zeros as grid cells and time periods with 1 or more nonaccident Waze events but no EDT reports
Slide 5

Model Performance (April-Sept 2017 in MD)

Figure from Est EDT workbook

Model estimates highly accurate overall; miss some precise patterns
Slide 6

SDI Waze Data Pipeline Development

Waze

• JSON files of 

events

• 2 min 

increments

• 50 States + 

DC

Secure Data Commons

https://portal.securedatacommons.com

• Monthly, state-aggregated, clipped Waze

• Add EDT, weather, census, roadway data

• Machine learning estimation of EDT-level 

crash events

• Hot spot, event sequence, special event 

analysis

Output

• Tabular and 

graphical 

outputs of 

model results

• Interactive 

dashboards

Technology platform

• AWS S3 buckets for curated data and team 

working folders

• AWS Redshift database for derived data

• RStudio + Jupyter on virtual computer

• GitHub integration for collaboration (private)
Slide 7

SDI Waze Data Pipeline Development

0. Waze Data Ingestion and Curation 1. Query, Clip, Reduce 2. Space-Time Match

Amazon 

Redshift

Amazon

S3

Amazon 

EC2

Lambda 

Waze function

API

E

- +

W W

SDC
Slide 8

SDI Waze Data Pipeline Development

3. Grid and Urban Area Overlay 4. Grid Aggregation 5. Weather Overlay

SDC

Adding:

• Urban Areas

• Hexagonal grid tessellations

Adding:

• Raster weather reflectivity
Slide 9

SDI Waze Data Pipeline Development

6. Modeling 7. Visualization and Reporting

ATA ATA + Local

Adding:

• FARS

• HPMS road class

• AADT

• LEHD
Slide 10

Statistical Approach: Supervised Classification

Random Forests

• Machine learning approach which minimizes 

overfitting

• Trained models on 70% of data using EDT reports as 

our labeled さground-truthざ

• Tested model performance using 30% of data to 

compare estimated EDT crashes with observed EDT 

crashes

• Rigorously trained and tested data feature 

combinations (50+ models) 

• Best crash estimation models minimize False 

Positives and False Negatives

Image credit: https://medium.com/@williamkoehrsen/randomforest-simple-explanation-377895a60d2d
Slide 11

Results – what have we learned?

We can integrate DOT data resources at large scales

• Our data integration and analysis pipeline can support rapid crash estimates 

(when/where Waze signal present)

• Successfully integrated transportation data that are not originally intended to track 

traffic safety

Waze data support rapid crash indicator 

• With Waze signal, models produce good overall estimates for multiple states

• Foundation for tool for rapid tracking of traffic safety trajectories
Slide 12

• Potential for Waze data to support 

analysis of roadway incident 

clearance times

• Sequence of event analysis shows 

potential for crash precursor early 

warning

• Waze data can evaluate impact of 

special events using heat maps

• Beginning partnerships with state 

agencies to deliver usable tool

Results – what have we learned?
Slide 13

Results – what have we learned?

• Potential for Waze data to support 

analysis of roadway incident 

clearance times

• Sequence of event analysis shows 

potential for crash precursor early 

warning

• Waze data can evaluate impact of 

special events using heat maps

• Beginning partnerships with state 

agencies to deliver usable tool

Special event

No special event
Slide 14

Next Steps

Potential Applications

Rapid crash trend monitoring tool 

• Flag anomalies

• Short-term intervention assessment 

• Cross-state comparisons

• Effectiveness models

• Incident Duration

• Clearance Times

• Secondary Crashes

• Full year modeling on multiple 

states

• Partnerships with state or local 

DOTs to identify use cases

• Cross-state Waze data assessment 

& dashboard

• Applications of segment-based 

models
Additional Slides
Slide 16

Evaluating Model Performance

Divide data into training and testing subsets

• Training data: Select 70% of observations (random by rows, 

whole days, or whole weeks) 

• Test data: Remaining 30% of observations

Training: fit model parameters with a large set of known EDT 

crashes, associated Waze events and other predictors

Testing: apply fitted model parameters to a new set of Waze 

events and other predictors to generate estimated EDT crashes

Compare estimated EDT crashes to observed EDT crashes in 

the test data set to evaluate model performance

Dataset

Training Test

Training

Data

Test

Data

Fit 

Model

Evaluate

Performance
Slide 17

Waze Data: Distribution in Space and Time

Six months of geolocated Waze data for Maryland (April - September, 2017)
Slide 18

Variable Importance: Waze Accidents (April-Sept)

Mean decrease in Gini 

impurity: 

• Variable is useful in separating 

a node of mixed classes (both 0 

and 1 EDT crashes, in our case) 

into two nodes with pure 

classes (all 0 or all 1 EDT 

crashes).

• Across all nodes in all the trees, 

how much does this variable 

decrease node impurities, 

averaged over all trees? 
Slide 19

Waze Data: Jams and Crash Sequence Analysis

April, 2017 MD

Potential Applications

• Incident Duration

• Clearance Times

• Secondary Crashes