This may be the author s version of a work that was submitted/accepted for publication in the following source:

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This may be the author’s version of a work that was submitted/accepted for publication in the following source:

Afghari, Amir Pooyan, Washington, Simon, & Haque, Md. Mazharul (Shimul)

(2017)

Effects of roadway geometrics and traffic characteristics on drivers speed-ing behaviour. In

International Conference on Road Safety and Simulation (RSS), 2017-10-17 - 202017-10-17-10-19, Netherlands.

This file was downloaded from: https://eprints.qut.edu.au/117397/

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2018 The Author(s)

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E

FFECTS OF

R

OADWAY

G

EOMETRICS AND

T

RAFFIC

C

HARACTERISTICS ON

S

PEEDING

B

EHAVIOUR

Amir Pooyan Afghari1_{, Simon Washington}1_, MD. Mazharul Haque2

1_{School of Civil Engineering}

Faculty of Engineering, Architecture, and Information Technology The University of Queensland

2_{School of Civil Engineering, and Built Environment}

Science and Engineering Faculty Queensland University of Technology

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O

UTLINE

:

 Background

 Research Gaps and Research Objectives

 Empirical Data

 Analysis Methodology

 Results and Discussion

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(5)

B

ACKGROUND

:

Factors that have been shown to contribute to speed selection include:

Driver

Vehicle

Road

Driving above the posted speed limit is a common behaviour on highways Elevated crash risk and severity

(6)

B

ACKGROUND

:

Driver

Vehicle

Age: Drivers younger than 60 are impatient and speed more

Gender: Male drivers are less concerned with violating speed limit Purpose of trip: Drivers speed on business related trips

Perception: Drivers estimate of speed far from actual speed

Size: Large vehicles are lower than other vehicles Type: Vehicles pulling trailer select lower speed

(7)

B

ACKGROUND

:

Road

Mostly focus on horizontal alignment: radius and curvature Drivers decrease their speed along curves

(8)

B

ACKGROUND

:

(9)

R

ESEARCH

G

APS AND

S

TUDY

O

BJECTIVES

:

Prior studies have:

 Considered a limited set of geometric factors, mostly road curvature

 Have not identified conditions for driving under and over the speed limit

 Have omitted the influence of roadway geometric and traffic factors on speeding

behaviour of drivers

 Have not examined the magnitude of speeding as a function of numerous factors

Research Objective:

 To investigate the effects of roadway geometric and traffic factors on speeding

behaviour of drivers

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B

ACKGROUND

:

Hypothesis:

Roadway geometric factors exert their influence on speeding

behaviour of drivers

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(12)

E

MPIRICAL

D

ATA

:

Major arterials and highways in Queensland, Australia

521 Road Segments – 1477 Kilometres – Four years of observations: 2010 – 2013

 Speeding data:

3765 speed cameras, format: number of speeding tickets issued to drivers

 Three categories: Minor (<13 Km/h), Moderate (13 Km/h – 20 Km/h), Major (>20 Km/h)

 Roadway geometric and traffic characteristics:

 AADT, length, median, shoulder, level of service, pavement rutting, posted speed limit, curvature, etc.

 Spatial features of surrounding environment:

 Micro climate factors, intensity of schools, animal habitat, etc.

 Behavioural factors

– Proxy Variables:

 Proportion of run-off road, lane-change and late-night crashes  Ratio of covert/overt speed cameras

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P

RELIMINARY

A

NALYSIS

–

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D

ATA

E

XPLORATION

:

Speeding Frequency Crash Frequency χ2 Degree of

freedom p-value Minor Total 1120.1 1120 0.493 Moderate Total 838.52 1155 1 Major Total 202.46 525 1 Minor Fatal 29.123 64 0.999 Moderate Fatal 37.759 66 0.998 Major Fatal 18.133 30 0.956 Minor Injury 1044.1 960 0.03* Moderate Injury 797.25 990 1 Major Injury 205.01 450 1 Minor PDO 538.14 544 0.563 Moderate PDO 359.76 561 1 Major PDO 67.226 255 1

(15)

D

ATA

E

XPLORATION

:

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

0.45 Low Speed Limit

Medium Speed Limit

High Speed Limit

Fatal Crashes Injury Crashes PDO Crashes

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D

ATA

E

XPLORATION

:

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 _0.62 0.32 0.06 0.56 0.36 0.08 0.51 0.40 0.09 Average Speeding vs Radius of Curves

Minor Speeding

●

Less than 1000 (m)

●

1000 – 20,000 (m)

●

More than 1000 (m)

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D

ATA

E

XPLORATION

:

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.58 0.36 0.07 0.53 0.38 0.09

Average Speeding vs Functional

Classification of Roads

●

Urban Roads

●

Rural Roads

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D

ATA

E

XPLORATION

:

(19)

D

ATA

E

XPLORATION

:

(20)

M

ODELLING

M

ETHODOLOGY

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M

ETHODOLOGY

:





J j

j

Utility

EXP

J

Utility

EXP

J

1

)]

:

(

[

)]

:

(

[

)

Pr(

Discrete Outcome Models – Logit Model

(22)

M

ETHODOLOGY

:

j j j j

X

U













J j j j j j

X

EXP

X

EXP

j

P

1

]

[

]

[

)

(



Vector of contributing factors to speeding in category j

Probability of selecting jth _{category of speeding}



 



J j J j j j j j j

X

EXP

X

EXP

L

1 1

]

[

]

[





_{Multiple categories may}

occur at the same time with different proportions

(23)

(24)

R

ESULTS AND DISCUSSION

:

Roadway geometric factors influencing speeding behaviour of drivers (95% Significance Level)

Percentage of Heavy Vehicle Traffic along Segments

with Divided Median

Radius of

Horizontal Curves

Ratio of (Covert/Overt)

Speed Cameras

Posted Speed Limit –

More than 100 Km/hr

Functional

Classification

Positive Intercept for

Minor Speeding

Negative Intercept for

Major Speeding

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R

:

Proportion of Minor Speed Violations Proportion of Moderate Speed Violations Proportion of Major Speed Violations Radius of Horizontal Curves -1.245 1.654 1.332

Ratio of Covert/Overt Speed Cameras 2.491 -3.307 -2.663

Posted Speed Limit – More than 100 Km/hr -1.423 2.756 -1.332

Percentage of HV Traffic along Segments with

Divided Median -1.067 -1.102 13.315 Functional Classification – Rural Road -0.712 -0.827 9.321

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R

:

Radius of

Horizontal Curves

Covert Speed

Cameras

- Increasing effect on moderate and major speeding

- Drivers’ speeding tendency is likely to increase as driving complexity reduces - The curvature of horizontal curves decreases as the radius increases

- Decreasing effect on moderate and major speeding

- Drivers not being aware of exact camera locations are perhaps more careful before committing any major speed violations

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R

:

Posted Speed

Limit – more than

100 Km/hr

- Decreasing effect on minor and major speeding

- Increasing effect on moderate speeding

(Censored nature of speeding behaviour with respect to speed limit)

- High speed segments are usually designed to higher standards, which may facilitate drivers to speed at higher speeds

- They are most capable of handling high speed violations

- Speeding thresholds are measured on a km/hr basis and not speeding percentage basis

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R

:

Functional

Classification of

Road

- Increasing effect on major speeding

- Slow moving heavy vehicles encourage drivers to overtake them

- The desire to not driving alongside heavy vehicles for very long due to fear of not being seen and/or feeling vulnerable

- This behaviour is exacerbated with the presence of divided median

- Increasing effect on major speeding

- Drivers tend to speed at higher levels along rural roads - Traffic volume is usually less along rural highways.

- Traffic stream is less impeded by signals, stop signs and congestion. - The enforcement is less in rural areas compared to urban roads

Heavy Vehicle

Traffic – Divided

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R

:

Positive Intercept

for Minor Speeding

- Baseline: Speeding between 13-20 Km/hr over the posted speed limit

- The likelihood of speed violations less than 13 Km/hr over the speed limit is higher

- The likelihood of speed violations more than 20 Km/hr over the speed limit is lower

- This finding is in the absence of any causal factors

- May reflect the unintentional lack of compliance among drivers on major arterials and highways

- Needs more exploration

Negative Intercept

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C

ONCLUSIONS

:

1. Roadway geometrics –although are static– have influence on drivers speeding behaviour

2. This influence is homogeneous across segments (tested the heterogeneity and was insignificant)

3. The tendency among drivers in committing lower speeding levels is more than higher speeding

levels –the need to test more behavioural factors

4. Radius of curves: larger radius curves are associated with higher speeding levels

5. Heavy Vehicle Traffic: heavy vehicle traffic along roads with divided median is associated with

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C

ONCLUSIONS

:

6. Speeding Thresholds: need to measure speeding thresholds based on relative percentages

7. Speed Enforcement: covert speed cameras are effective in decreasing high speeding levels

8. Rural Roads: although not designed to high standards, drivers are more likely to speed at higher

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