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Multi-Agent Behavioral Modeling of Traffic Applied to E-Vehicles

A. K. T. Ferma, W. F. Infante, R. M. Mojica, J. R. B. Polidario, C. K. B. Tan

Department of Electronics, Computer and Communications Engineering

Ateneo de Manila University

Loyola Heights, Quezon City

February 18, 2013

Abstract— The study aims to promote a feasible

implementation of an E-Vehicle system as an alternate

means of public utility in the Philippine setting. The study

will use the tools of Agent-Based Simulations as a means to

emulate real world traffic policies and gather drive cycle

data of E-Vehicles simulated in that environment. MATLAB

will then be used to analyse the drive-cycle data particular to

these set-ups in order to realize the traffic policy that can

bring the most optimized fuel consumption in the E-Vehicles.

I. INTRODUCTION

A. Objectives

The study aims to achieve the following

objectives: (1) To be able to obtain empirical data

from authorities such as NCTS and MMDA, and

gather measurements of various factors relevant to

the study, (2) Data gathering from the field, (3) To

design several traffic scenarios and policies suitable

for an E-Jeepneys transportation system, and (4) To

be able to see the degradations or improvements to

the E-Jeepney fuel consumption and overall

throughput given each of the simulated traffic

scenario.

II. REVIEW OF RELATED LITERATURE

A. Multi-Agent Based Modeling

Mathematicians, engineers, and various other

scientists have tried making solutions to problems

regarding complex systems, and as such, the most

probable method to simplify these systems to a

certain degree is to represent the individual agents

that make up the system and represent them in a

higher level. The capability of being able to model

the simplistic behaviour or these individual elements

and allowing them to interact with each other in a

simulated environment is the concept of what is

known as Multi-Agent Based modelling.

It should be noted that in Multi-Agent modelling,

the behaviour of the agents, the scale of the explicit

space in which the interactions of agents are

occurring, and the duration of their interactions

should be taken into consideration [1].

B. Motorist Behavior

The aggressive nature of Filipino traffic is heavily

attributed to what is referred to as Aggressive Lateral

Driving. This is most observable at jeepney stops

where the chances of swerving or changing lane are

greater. This behaviour affects lane utilization and

results in frequent unavoidable breaking.

Furthermore, video footages near jeepney stops show

a significant non-observance of lanes. As a part of

his study, Palmiano mentions that there is a high

frequency of lane non-observance, especially in 3-

lane to 4-lane roads. Table 1 below shows the

frequency of this non-observance in detail. Notice

that the average value of non-observance is 14.3%

while the peak is at 22% in a 5-minute interval [2].

Table 1: Frequency of Lane Observance.

C. Hybrid Electric Vehicles

An Electric Vehicle is a vehicle whose prime

mover is electric power, as opposed to conventional

vehicles which uses an internal combustion or diesel

engine. A hybrid vehicle is simply a vehicle that has

two movers. Popular implementations of hybrid

systems include the Parallel Hybrid and Series

Hybrid variants.

Parallel Hybrid vehicles are designed such that

an electric motor and/or the internal combustion

engine can be used as a prime mover. For this set-up,

the system itself, depending on the situation and the

power demand, uses either the electric motor or

combustion engine as prime mover. The set-up can

either use both engines as prime movers, where fuel

efficiency is the determining factor in which of the

two engines will be used. The internal combustion

engine can also charge the electric motor if needed.

These systems are designed with redundancy in mind

as a fail-safe option in case one of the engines break

down, the other can be used as a prime mover

instead.

The Series Hybrid model is made such that the

electric motor becomes the prime mover of the

system, while the internal combustion engine is

optimized as a driver for the battery banks, which

acts as a power supply for the electric motor. It is a

relatively simple set-up; however, the battery banks

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must be sufficiently large in order to have an

optimized system.

Figure 1: Simple Schematic of a Typical Hybrid Vehicle Design

D. NetLogo

The simulation software of choice for multi-agent

based modelling used in the study is NetLogo, which

allows easy entry level programs and accommodate

high level agent-based modelling such as in the

researcher’s case. The end result would be a traffic

system whose tendencies and trends can be observed

as data that can be easily exported for further

analyses.

Figure 2: Predator-Prey Model Simulated in NetLogo.

E. Traffic Simulations

If a particular traffic scenario is modelled

correctly, it can provide the means to produce a drive

cycle output that reflects the trend and behaviour of

traffic. Inputting the drive cycle output as an input to

a Quasi-Static Simulation Toolbox-generated Series

Hybrid vehicle in MATLAB produces efficiency

plots of the vehicles when subjected to certain

conditions and policies. This provided the

groundwork for further study involving the use of

Agent-Based modelling as a tool for traffic

engineering.

III. METHODOLOGY

A. General Overview

Figure 3: Methodological Framework

The researchers are to follow the following

methodology: (1) Data gathering from authorities, (2)

Field data gathering, (3) Simulation input

approximation, (4) Traffic policy simulations, and (5)

Software calculation.

The researchers would begin by gathering

traffic data in the form of recent Annual Average

Daily Traffic (AADT) records of major highways

from sources such as the Metropolitan Manila

Development Authority (MMDA) or academic

institutions like the National Center for

Transportation Studies (NCTS) in University of the

Philippines-Diliman.

This is followed by Actual data gathering,

both from a field location and within a vehicle. The

first part would involve taking 10-15 minute (or

longer depending on use) video samples of traffic.

The second part would involve the measurement of

vehicle-specific data such as cruising and U-turn

speeds.

These data are combined with physical data to

simulate as close as possible to real scenario.

NetLogo will be used as the simulation platform of

choice.

The drive cycle output from the NetLogo

simulations would then be used as an input to the

series hybrid electric vehicle simulated in MATLAB.

This model will then calculate and produce

efficiency plots based on the drive-cycle behaviour

generated by the NetLogo simulations.

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Figure 4: Traffic and Vehicle Simulation Set-Up.

B. Data Gathering and Results

Compare

Against

Random Stops Fixed Stops 4-Lane

w/Fixed

stops

4-Lane

w/Fixed

Stops

and

Exclusiv

e Lane

(Low

jeep

count)

Fixed

Stops

Fuel: 14.92%

decrease. Passengers:

148.78%

increase.

4-Lane

w/Fixed

stops

Fuel: 20.18% decrease.

Passengers:

724.05% increase.

Fuel: 6.18% decrease.

Passengers:

231.44% increase.

4-Lane

w/Fixed

Stops

and

Exclusive

Lane

(Low

jeep

count)

Fuel: 33.41%

decrease.

Passengers:

851.67% increase.

Fuel: 21.73%

decrease.

Passengers:

282.54% increase.

Fuel:

16.58%

decrease.

Passengers: 15.49%

increase.

4-Lane

w/Fixed

Stops

and

Exclusive

Lane

(High

jeep

count)

Fuel: 108.09% increase.

Passengers:

898.44% increase.

Fuel: 144.58%

increase.

Passengers: 301.34%

increase.

Fuel: 160.69%

increase.

Passengers: 21.16%

increase.

Fuel: 202.49%

increase.

Passengers: 4.92%

increase.

Table 4: Average percentage difference summary of best sub

cases. Compare Against Fuel Consumption

(L/100KM)

Passenger

Throughput

Random Stops 34.99 449

Fixed Stops 29.77 1117

4-Lane w/Fixed stops 27.93 3700

4-Lane w/Fixed Stops

and Exclusive Lane

(Low jeep count)

23.3 4273

4-Lane w/Fixed Stops

and Exclusive Lane

(High jeep count)

72.81 4483

Table 4: Fuel Consumption and Passenger throughput of each

best sub case.

IV. CONCLUSION

The study points to the aggressive nature of

Metro Manila traffic as the main reason of the un-

viability of E-vehicles as an alternative mode of

public utility. The constant stop and go traffic, as

well as, aggressive lateral movement of vehicles

in the Philippines are the main causes of traffic

congestion and fuel-inefficiency. These

behaviours have an adverse effect on the engine,

as well as, the loading and unloading of

passengers. However, there have been promising

results by gradually eliminating these problems

with implementing the four main cases discussed.

Lastly, although the system is far from being

perfect, it is a step in the right direction. It only

proves that there are two sides in E-vehicle

implementation one is making an efficient engine,

and another is engineering a better traffic system

for it.

REFERENCES

[1] Mitchell, Melanie. "Dynamics, Chaos and

Prediction" Complexity - A Guided Tour. New

York: Oxford UP, 2009. 14-27. Print.

[2] Palmiano, Hilario Sean, Ueda Shimpei, Tetsuo

Yai, and Daisuke Fukuda. Development Of A

Simulation System For Jeepney Stop Vicinity

Located In Front Of A Shopping Center. Thesis.

Tokyo Institute of Technology, 2003. Print.

[3] Teodoro, Rene Val R., Dr. Eng. Empirical

Analysis on the Relationship Between Air

Pollution And Traffic Flow Parameters. Thesis.

University of the Philippines Diliman, 1996.

Quezon City: 1996. Print.

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