http://www.abdullahshabarek.com

A Smart Application for Predicting

Network-wide Congestion Hot Spots under

Adverse Weather Conditions

Presenter: Abdullah Shabarek

New Jersey Institute of Technology

NJDOT Annual Research Showcase

October 23rd, 2019

Agenda

Introduction1

Objective2

Methodology3

Application4

Conclusions5

2

INTRODUCTION

• Most of the studies are concerned about monitoring traffic conditions

under adverse weather conditions

• Previous studies predict traffic speed under normal conditions using

deep learning using:

- Deep Neural Networks

- Recursive Neural Networks

• There are some research papers that use deterministic models to

predict traffic speed under weather conditions, but they lack the

ability to consider various weather variables

3

Why to predict traffic congestion due

to adverse weather conditions?

• Predict congestion hot spots due to adverse weather conditions for

congestion mitigation plans

• Allocate larger resources to higher congestion hot spot segments at

certain times depending on the output of the application

4

BACKGROUND

• Adverse weather conditions that affect traffic speed can be categorized into:

- Rain Conditions

- Fog Conditions

- Snow Conditions

Weather Conditions Freeway Average Speed Reduction*

Light Rain/Snow 3% - 13%

Heavy Rain 3% - 16%

Heavy Snow 5% - 40%

Fog 10% - 12%

* FHWA. (2018, September 17). How Do Weather Events Impact Roads? Retrieved October 8, 2019, from https://ops.fhwa.dot.gov/weather/q1_roadimpact.htm.

5

BACKGROUND (CONT.)

Increases vehicles headways and decreases

traffic speed.

Fog Conditions

Can cause Capacity reduction (10% - 30%)

depending on the rain intensity.

Rain Conditions

Snow accumulation impedes the traffic reducing

the traffic speed.

Snow Conditions

Wind conditions can reduce drivers’ visibility

when combined with rain or snow conditions.

Wind Conditions

Weather Conditions

6

OBJECTIVE

• The objective of this study is to propose a smart application that predicts

freeway congestion hot spots due to adverse weather conditions.

• This study provides a system that covers the New Jersey freeway

network and can capture the effect of three different weather conditions:

- Rain Conditions

- Snow Conditions

- Fog Conditions

7

SYSTEM FRAMEWORK

Database DevelopmentBig data analysis is

conducted on traffic speed

and weather conditions data

Predictions of traffic speed under adverse weather conditions

Based on the output from traffic speed prediction under normal

conditions and weather data

Predictions of traffic speed under normal conditions

Based on the traffic speed from previous time stamps

Smart ApplicationBased on real-time feed from the

databases. The application show a

network-wide prediction of hot spot

congestions

Database

Development

Prediction of traffic

speed under normal

conditions

Prediction of traffic

speed under adverse

weather conditions

Smart

Application

8

Probe Vehicle

Data

Captures Traffic

Speed

New Jersey

Congestion

Management Systems

Estimates Traffic

Volume

New Jersey

Straight Line

DiagramRelates All the

Databases in terms

of Geographical

Locations

NOAA

Provides Weather

Information

DATABASE DEVELOPMENT

9

DISTRIBUTION OF THE ADVERSE WEATHER CONDITIONS IN TERMS OF (MILES-HOURS)

Rain 57%Fog 32%

Snow 11%

*The data is based on weather conditions from 2014 until 2019 in all New Jersey Freeway Network

10

RECURRENT NEURAL NETWORKS

11

𝑥1 𝑥2 𝑥3 𝑥𝑛

𝑦1 𝑦2 𝑦3 𝑦𝑚

Model Inputs

Hidden Layers

Model Outputs

PREDICTING TRAFFIC SPEED UNDER NORMAL CONDITIONS

Prediction of Traffic Speed

under Normal Conditions

for the next 24 hours

Traffic Speed of previous 2 days Recurrent Deep Learning model

through two hidden layers

12

Model Inputs Model Outputs

PREDICTING TRAFFIC SPEED UNDER ADVERSE WEATHER CONDITIONS

Volume/Capacity

VisibilityPredicted Traffic Speed

under Normal Conditions

Wind Speed

Traffic Speed

under Adverse

Weather

Conditions

Wind Direction

Rain/Snow Intensity 13

The Model uses recurrent neural networks

through four hidden layers to account for

queuing congestions over time

Weather Condition Type

APPLICATION

• Heavy Rain occurred in July 22nd , 2019

• The rain starts around 4:00 PM at some New Jersey areas and extends

until 10:00 PM

• The analysis is conducted July 22nd , 2019 at 1:00 PM (3 hours prior

to the prediction starting time)

• Interstate-78 (Eastbound direction) is selected for further illustrations

• Hot spot congestion is considered when traffic speed is below 25 mph

14

© 2019 by Abdullah Shabarek from: http://abdullahshabarek.com/res/NJ-DOT-2019.mp4

15

15

NORMAL CONDITIONS VS. PREDICTED CONDITIONS

0

10

20

30

40

50

60

16:00:00 17:00:00 18:00:00 19:00:00 20:00:00 21:00:00 22:00:00

Mil

es o

f C

onges

ted H

ot

Spots

Time

I-78

Garden State Parkway

I-95

I-80

I-287

0

10

20

30

40

50

60

16:00:00 17:00:00 18:00:00 19:00:00 20:00:00 21:00:00 22:00:00

Mil

es o

f C

onges

ted H

ot

Spots

Time

I-78

Garden State Parkway

I-95

I-80

I-287

16

RAIN INTENSITY AND PREDICTED NORMAL CONDITIONS(I-78 EASTBOUND)

5:30 7:00 8:30 10:00 Time

20

40

60

Milepost

0.00 0.15 >0.30

Precipitation Intensity (in/hour)

5:30 7:00 8:30 10:00

20

40

60

Milepost

0.00 30.0 >65.0

Traffic Speed (mph)

ACTUAL TRAFFIC SPEED VS. PREDICTED TRAFFIC SPEED(I-78 EASTBOUND)

Time

Milepost

5:30 7:00 8:30 10:00 Time

20

40

60

Milepost

0.00 30.0 >65.0

Traffic Speed (mph)18

5:30 7:00 8:30 10:00

20

40

60

MAPE VS. RMSE(I-78 EASTBOUND)

5:30 7:00 8:30 10:00 Time

20

40

60

Milepost

7.0 >14.0

RMSE (mph)

0.00

19

5:30 7:00 8:30 10:00

20

40

60

Milepost

100

MAPE (%)

0

CONCLUSIONS

• This model provides a smart application to predict hot spot

congestion on a network level due to adverse weather conditions

• Transportation agencies can use this application for congestion

mitigation plans when adverse weather conditions are forecasted.

• The application can be used to optimize the resources when assigned

to a network-wide locations depending on the predicted level of

congestion.

20

RESEARCH OPPORTUNITIES

Opportunity #1

Opportunity #2

Database Coverage to

Enhance the Application

Performance

Incorporate the Application

with New Jersey Best

Practices

Include Mobility as a Service

aspects to improve traveler

decision choice

Opportunity #3

21

Thank YouAbdullah Shabarek

New Jersey Institute of Technology

NJDOT Annual Research Showcase

October 23rd, 2019

http://www.abdullahshabarek.com

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