Vibhanshu Singh

Supervisor: Dr. Caleb Ronald M

Driver Behavioral Analysis in Heterogeneous Traffic Conditions

Tathva 2015

Department of Civil Engineering, NIT Calicut

Introduction

Traffic Data

Driver Behavioral Analysis

Conclusion

22

Overview of the presentation

CED 2

3

Introduction

Why traffic simulation?

Core models of a traffic simulator

Traffic simulation

Homogeneous and Heterogeneous traffic

Objectives

Dynamic representation of real world traffic systems by building a

computer model (Drew 1968)

Driver, vehicle, environment infrastructure

44

Traffic simulation

CED 4

Understand the complex dynamic interactions between

the driver, the vehicle, and the environment

Design of traffic systems

Operational quality of the existing facilities

Understand when and how traffic jams emerge?

Road safety

55

Why traffic simulation

CED 5

66

Core models of a traffic simulator

CED 6

Car-following

Lane-changing

77

Homogeneous & Heterogeneous traffic

CED 7

Homogeneous

• Mostly single vehicle

type

• Strict lane discipline

• Well defined surrounding

vehicles to apply the

traditional car-following

and lane-changing

models

Heterogeneous

• Different vehicle types

• No lane discipline –

Irregular position of

vehicles across the

roadway

• Suitability of traditional

car-following and lane-

changing models???

88

Homogeneous & Heterogeneous traffic

CED 8

AMERICA INDIA

When Normal

When Drunk

When Normal

When Drunk

To obtain the trajectory data of vehicles

To understand the longitudinal movement behavior of

drivers

To analyse the lateral movement behavior of drivers

To analyse the non-lane disciplined movements of drivers

99

Objectives

CED 9

10

Traffic Data

Trajectory dataset

Data processing

Site details

Data collected from a 245m long uninterrupted stretch in Chennai,

India

30 minutes data from 2:45 PM - 3:15 PM

Flow of 6010 veh/hr

Data source - Kanagaraj et al. (2015)

http://toledo.net.technion.ac.il/mixed-traffic-trajectory-data/

1111

Site details

CED 11

Trajectories of 3005 vehicles extracted at a resolution of 0.5s generating a

total of 111,629 observations

1212

Trajectory dataset

CED 12

Vehicle Number

Vehicle Type

Time (sec)

Length (m)

Width (m)

Long. Distance

(m)

Long. Speed

(m/sec)

Long. Acc

(m/sec2)

Lat. Distance

(m)

Lat. Speed

(m/sec)

Lat. Acc

(m/sec2)

1 1 12.00 1.80 0.60 116.97 6.87 -0.09 2.81 -0.15 -0.16

2 1 12.00 1.80 0.60 23.46 5.50 0.38 2.49 0.22 -0.35

1 1 12.50 1.80 0.60 123.43 6.79 -0.18 2.59 -0.14 0.16

2 1 12.50 1.80 0.60 28.62 5.47 0.83 2.50 -0.04 -0.12

1 1 13.00 1.80 0.60 129.82 6.01 -1.06 2.55 -0.01 0.11

2 1 13.00 1.80 0.60 34.72 5.45 -2.22 2.41 0.04 0.26

3 1 13.00 1.80 0.60 33.53 10.34 -0.91 4.97 0.04 0.10

1 1 13.50 1.80 0.60 135.17 5.45 -0.48 2.58 0.07 0.07

2 1 13.50 1.80 0.60 38.52 3.81 -0.16 2.59 0.26 0.17

3 1 13.50 1.80 0.60 43.14 8.84 -2.65 5.06 0.05 -0.07

Algorithm for processing the raw data file

startread the trajectory data filefor all entries

get the vehicle number and vehicle typeget the time

sort according to the vehicle number, vehicle type and time

end forfor all vehicle types

read the lateral shift threshold values for all vehicle types

get cumulative differences of lateral coordinatesif cumulative difference > threshold value and >0; count as right movement

if cumulative difference > threshold value and <0; count as left movement

get the corresponding lateral movement durationsend for

end

1313

Data processing

CED 13

14

Driver Behavioral Analysis

Longitudinal movement behavior

Lateral movement behavior

Vehicle composition

Lateral movement duration distribution models

Non-lane discipline behavior

1515

Vehicle composition

CED 15

Vehicle Type Share (%)

Motor Bike 56.4

Auto Rickshaw 12.2

Passenger Car 26.6

Heavy Vehicle 04.8

1616

Longitudinal speed

CED 16

ANOVA Test ResultsP-value: 0.00Null Hypothesis: Reject

1717

Longitudinal Accl. and Decl.

CED 17

ANOVA Test ResultsP-value: 1.62E-12Null Hypothesis: Reject

ANOVA Test ResultsP-value: 5.00E-59Null Hypothesis: Reject

1818

Lateral speed

CED 18

ANOVA Test ResultsP-value: 0.00Null Hypothesis: Reject

1919

Lateral Accl. and Decl.

CED 19

ANOVA Test ResultsP-value: 6.00E-161Null Hypothesis: Reject

ANOVA Test ResultsP-value: 1.50E-60Null Hypothesis: Reject

2020

Frequency of lateral movements

CED 20

2121

Lateral movement duration

CED 21

ANOVA Test ResultsP-value: 4.96E-79Null Hypothesis: Reject

2222

Lateral movement duration models

CED 22

Lognormal (0.55, 0.62)

2323

Lateral movement duration models

CED 23

Lognormal (0.98, 0.70)

2424

Lateral movement duration models

CED 24

Lognormal (0.94, 0.74)

2525

Lateral movement duration models

CED 25

Lognormal (1.25, 0.50)

2626

Non-lane discipline

CED 26

27

Conclusion

Conclusions

Contributions

Summary

Future work

Proposed an algorithm to process the raw trajectory data of

vehicle and to obtain various driver behavioral characteristics

Analysed the longitudinal movement behavior of drivers in

heterogeneous traffic conditions

Analysed the lateral movement behavior of drivers

Developed lateral movement duration distribution models

Analysed the non-lane discipline characteristics of

heterogeneous traffic streams

2828

Summary

CED 28

Significant differences exist in the driving behaviors of

various vehicle type drivers

Motorbike drivers are more aggressive in their behavior

Heavy vehicle drivers are conservative and mostly

homogenous in their behavior

Lane discipline is not observed by the drivers in

heterogeneous traffic conditions

2929

Conclusions

CED 29

Established the fact that ‘vehicle-type’ plays a major role

in simulating the drivers’ behaviors observed in

heterogeneous traffic streams

Established the fact that ‘lane discipline’ is not followed by

the drivers in heterogeneous traffic conditions

Established the need to account for vehicle-type

dependency and non-lane discipline while developing

heterogeneous traffic simulators

3030

Contributions

CED 30

Analysis of the drivers’ motivation and choice making behaviors

during lateral movements

Development of vehicle-type dependent longitudinal movement

driver behavioral models

Development of vehicle-type dependent lateral movement decision

and choice making driver behavioral models

Development of vehicle-type dependent lateral movement

execution models

Proposing a simulation which can handle non-lane based

movements of heterogeneous traffic drivers

3131

Future work

CED 31

32

Thank You !!!