Republic of the Philippines

Department of Education
CARAGA Administrative Region

Agent-Based Traffic Inflow Modeling (ATRIM) Analysis

of the Drivers’ Behavior, Traffic Dynamics and System Management
for Sustainable Transportation Systems

(Mathematics and Computational Sciences – Team Category)

A TUKLAS Entry
Presented to the
Regional Science and Technology Fair 2025

NICHOLE SHANE AMOR

WYNDE GRACE LUMARDA
LADY JEAN NIEVES
Researchers

MARVIN H. SIEGA
Project Adviser

April 2025

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 Agent-Based Traffic Inflow Modeling (ATRIM) Analysis of the Drivers’
Behavior, Traffic Dynamics and System Management for Sustainable
Transportation Systems

Lady Jean Nieves, Wynde Grace Lumarda, Nichole Shane Amor

Abstract

This research investigates traffic dynamics, driver behavior, and System

Management for Sustainable Transportation Systems in Las Nieves, Agusan del Norte,

using agent-based modeling to simulate real-world traffic conditions. The study highlights

the critical interplay between vehicle acceleration, deceleration, and traffic congestion,

showing that as vehicle density increases, average speeds decrease while driver impatience

rises. Findings emphasize the nonlinear effects of congestion on traffic flow, necessitating

targeted solutions such as adaptive signal systems and infrastructure improvements.

The results demonstrate that moderate speed limits optimize traffic flow by

balancing efficiency and safety, whereas low and excessively high limits exacerbate

congestion or safety risks, respectively. Effective enforcement and real-time traffic

monitoring were found essential to implementing these strategies successfully.

Furthermore, evaluations revealed gaps in local traffic regulations, particularly in

addressing rural traffic dynamics and ensuring enforcement efficacy.

This study proposes innovative strategies, including dynamic speed regulations,

driver education on patience and safety, infrastructure enhancements, and localized

ordinances tailored to rural traffic needs. These findings aim to inform the Local

Government Unit of Las Nieves in creating sustainable, efficient traffic management

systems, enhancing safety, and reducing congestion.

Keywords: ADM, Drivers’ Behavior, Traffic Dynamics, System Optimization and Safety

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 TABLE OF CONTENTS

Abstract - - - - - - - - i
Table of Contents - - - - - - - - ii

I. INTRODUCTION - - - - - - - 1

Objectives of the Study - - - - - - - 7

II. FRAMEWORK - - - - - - - 8

Agent-based Modeling (ABM) - - - - - - 8

Traffic 2 Lanes Model - - - - - - 9

Research Location - - - - - - - 12

The Map of the Research Location - - - - - 12

Research Methodology - - - - - - - 13

Research Flowchart - - - - - - - 13

Data Collection - - - - - - - 14

Traffic 2 Lanes Model (Interface and Code) - - - 16

Traffic Management System Strategies Evaluation - - - 18

III. FINDINGS - - - - - - - 20

Identified Driver's Behavior (Maximum Patience-60.4) - - 20

Identified Driver's Behavior (Maximum Patience-71.4) - - 21

Identified Driver's Behavior (Maximum Patience-90.08) - - 23

Traffic Dynamics and Drivers’ Behavior Data from LGU-Las Nieves 24

Model Simulation Analysis in terms of Drivers' Behavior - - 25

Driving with Low Acceleration - - - - - 25

Driving with Moderate Acceleration - - - - - 27

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 Driving with High Acceleration - - - - - 29

Traffic Management Strategies Evaluation Results - - - 31

Policy Implementer's Evaluation Diagram - - - - 32

Development of Innovative Strategies - - - - 34

Traffic Management System Innovative Strategies - - - 34

IV. CONCLUSION - - - - - - - 37

V. REFERENCES - - - - - - - 39

APPENDICES - - - - - - - 42

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INTRODUCTION
The rapid urbanization and increased number of vehicles have led to increased

concerns about safety, traffic congestion, and the environmental impact of

transportation systems. Understanding and improving traffic flow is crucial to building

safer, more sustainable, and more effective transportation networks. To do this, it is

essential to analyze traffic dynamics and driver behavior because these factors are

closely related and significantly affect the overall performance of traffic systems. In

agent-based models, drivers are seen as autonomous agents who make decisions based

on their own goals and interactions with others. This study examines the effects of

different driver behaviors on traffic flow, congestion trends, and system optimization

using agent-based traffic inflow. (Konate, N. et al,.2023).

Based on the TomTom International BV (2024), the Traffic Index Ranking 2024

Report provides a detailed analysis of traffic congestion in cities worldwide, including

the Philippines. In 2023, Metro Manila was ranked as the most congested city globally

among 387 metropolitan areas studied, overtaking Bogotá, Colombia. Drivers in Metro

Manila faced an average travel time of 25 minutes and 30 seconds to cover just 10

kilometers, an increase from 24 minutes and 40 seconds in 2022. This worsening

congestion resulted in an estimated loss of 117 hours per year for commuters during

rush hours, equivalent to nearly five full days. The congestion level was measured at

71%, meaning travel times were significantly longer compared to free-flowing traffic

conditions. In contrast, Davao City ranked eighth globally for slow travel times, with

an average of 32 minutes and 59 seconds per 10 kilometers and a congestion level of

49%. The TomTom Traffic Index uses data collected from over 737 billion kilometers

traveled by vehicles, analyzing both quasi-static factors like road infrastructure and

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dynamic factors such as weather and roadworks to provide insights into traffic patterns

and trends.

For years, Metro Manila has been notorious for its severe traffic congestion. The

transport and land use development patterns of Metro Manila are derived from an

automobile-dependent planning style. The traffic congestion issue, considering the

urban growth patterns of sub-urbanization, the proliferation of informal settlements in

city centers, and the establishment of CBDs along major thoroughfares, is of high

complexity caused by inefficient public transportation, the dominance of private

vehicles, and inadequate urban governance and policies. All of such lead to economic

loss, a drain of productivity, increased hours of commuting time, and a reduction of

economic mobility (Camello, 2023).

In modern society, quick mobility is one of the most basic needs. Therefore,

people can use different transportation facilities such as automotive vehicles, subways,

and bicycles. However, among all these transportation facilities, automotive vehicles

are still the most adopted due to their comfort and practicality. In this way, assuming

continuous population growth, the number of vehicles in large cities will increase as

well, but much faster than transportation infrastructure; consequently, traffic congestion

will become a pressing issue. It creates several negative concerns for the environment

and society such as increasing the number of traffic accidents, economic impacts, and

high levels of greenhouse emissions (De Souza et al., 2017). In this way, focusing on

preventing traffic congestion and improving overall traffic efficiency, large cities rely

on traffic management systems (TMSs), which aim to reduce traffic congestion and its

related problems. To this end, TMSs are composed of a set of applications and

management tools to integrate communication, sensing, and processing technologies.

In summary, TMSs collect traffic-related data from heterogeneous sources such as

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vehicles, traffic lights, and in-road and roadside sensors. Furthermore, by aggregating

and exploiting such traffic-related data cooperatively (e.g. among vehicles) or a traffic

management center (TMC) concentrated in a cloud or a data center, several traffic

hazards can be identified and consequently controlled improving the overall traffic

efficiency and providing a smooth traffic flow (De Souza et al., 2017). Many empirical

studies have revealed that transport performances are not only relevant to the

characteristics of underlying network structure but also significantly affected by routing

strategies.

Traffic congestion is caused by inefficient road operations and by excess

demand. Inefficient traffic control is pervasive. Most urban streets and freeways do not

have an adequate traffic sensing infrastructure, so one does not know how much

congestion there is, its cause, or whether congestion mitigation projects have met the

expected improvement. In the absence of adequate information, neither road operators

nor travelers can gauge how poorly the road system is operated.

Because the traffic changes randomly, the road system should be managed by

effective feedback control of signals at intersections and on-ramps. These control

techniques are well known, and they have been successfully adopted in isolated road

networks in different parts of the world. The investment in sensing needed to implement

these control techniques is trivial compared to the benefits of an efficiently operated

road system (Kurzhanskiy A, Varaiya P, 2015).

To enhance the performance of a transportation system, driving behavior as the

focal element of the transportation system must be well understood. As instantaneous

decision-makers in a roadway, drivers play an essential role in the efficiency of the

roadway in terms of safety, travel time, energy consumption, and emissions (Arvin et

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al., 2021, Arvin and Khattak, 2020, Mahdinia et al., 2021, Nasr Esfahani et al., 2019,

Ping et al., 2019)). However, the role of social environment and peer influence on a

driver's instantaneous behavior was determined to be significant.

Drivers tend to follow and imitate the actions of peer drivers and it is believed

that this social influence can affect driving states (e.g., result in acceleration) (Fleiter et

al., 2010, Haglund and Åberg, 2000). Following the behavior of others may be

attributed to the sociological concept of "social proof". The concept of social proof is a

human characteristic in which a person minimizes the cost of search and

experimentation in an uncertain decision-making situation by copying the actions of

others.

In urban development, traffic management and safety are crucial issues,

especially in nations like the Philippines where public safety and infrastructure are

being challenged by fast population expansion and rising vehicle densities. Traffic

congestion has far-reaching effects, including negative impacts on public health,

economic loss, and environmental degradation (World Health Organization [WHO],

2023). The problem is most noticeable in urban areas, where it is made worse by the

combination of poor public transportation networks, limited road capacity, and traffic

law violations (Abad et al., 2020). Students are particularly susceptible to these

circumstances, since they frequently encounter major obstacles during their commute,

including delays and exposure to dangerous situations, which may jeopardize their

academic performance and general well-being (WHO 2023).

According to Caleda, M. J. A. et. al. (2018), Road crash deaths in the Philippines

have been increasing, from 6806 in 2006 to 10 012 in 2015, representing about 47%

increase over 10 years or an average increase of about 4% annually. This is despite

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efforts of the Philippines to address road safety through various policies, programs and

plans.

Poor data governance on road crashes continues to be one of the major

challenges in addressing road safety in the Philippines. The number of road traffic

deaths may even be underestimated due to underreporting, with only 10% of road

crashes being reported.

From rising slum populations, insufficient public transport, and city expansion

outpacing population growth to threats to critical infrastructure and disruption of basic

services by disasters, it is essential that cities are equipped to adequately handle these

challenges. As the world turns more urban, with nearly 70% of the global population

projected to reside in cities by 2050, critical infrastructure, affordable housing, efficient

transport, and essential social services are crucial for creating resilient, safe, and

sustainable cities for all (UN, 2024).

This study anchors on the Sustainable Development Goals (SDG) by addressing

road safety, emissions reduction, and sustainable urban mobility. Below is the

breakdown of relevant SDGs based on the intersections identified in the research:

SDG 3: Good Health and Well-Being. Ensure healthy lives and promote well-being

for all at all ages.

 ABM helps simulate traffic patterns to reduce road accidents and fatalities – a

critical focus of UN road safety strategies.

 Improved traffic management systems can lower air pollution from vehicles,

indirectly supporting public health targets.

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SDG 9: Industry, Innovation, and Infrastructure. Build resilient infrastructure,

promote inclusive and sustainable industrialization, and foster innovation.

 Agent-based models evaluate emerging transportation technologies (e.g., e-

bikes, shared autonomous vehicles) to promote sustainable infrastructure

development.

 The Synthetic Sweden Mobility (SySMo) model exemplifies innovation by

simulating national-scale travel behaviors for policy formulation.

SDG 11: Sustainable Cities and Communities. Make cities and human settlements

inclusive, safe, resilient, and sustainable.

 ABM optimizes public transport efficiency and reduces congestion, aligning

with goals for inclusive, safe urban mobility.

 Case studies in the UITP report highlight how traffic management systems

regenerate poorer areas through better transport connectivity.

SDG 13: Climate Action. Take urgent action to combat climate change and its impacts.

 ABM evaluates low-carbon transport scenarios (e.g., replacing car trips with e-

bikes), directly reducing greenhouse gas emissions.

 Transport-sector synergies in Nationally Determined Contributions (NDCs)

emphasize emission cuts through smarter traffic systems.

Key Connections in Research

Traffic Safety: ABM identifies high-risk driver behaviors and road conditions,

supporting SDG 3.1 (halving global road deaths).

Emission Reductions: Models quantify the climate impact of shifting to sustainable

transport modes (SDG 13.2).

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Equitable Access: Simulating public transport networks ensures marginalized

communities are included (SDG 11.2).

By integrating agent-based models into policy design, stakeholders can advance

progress across interconnected SDGs while addressing road safety, climate resilience,

and urban sustainability.

Objectives of the Study

The fundamental goal of this research is to analyze the drivers' behavior, traffic

dynamics, and system optimization through agent-based traffic inflow modeling in Las

Nieves Agusan del Norte. The study aimed to explore several key objectives related to

traffic systems. It sought to understand individual drivers' behavior, examining how

decisions are made while navigating traffic, which encompasses both macro goals like

destination and route selection, as well as micro goals such as speed control and

overtaking.

It sought to answer the following:

1. To determine the maximum patience in terms of behavior of drivers on

highways and their impact on traffic flow and congestion;

2. To examine traffic dynamics through detailed simulation and modeling;

3. To evaluate existing traffic management system strategies and ensure road

safety;

4. To generate innovative strategies or models to deliver actionable solutions for

enhancing highway traffic management.

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FRAMEWORK

The framework of the study discusses the concept, relevant terms, research

procedure, and research result precisely.

Agent-based modeling (ABM)

Agent-based modeling (ABM) is pivotal in simulating complex systems where

individual agents (in this case, drivers) interact within a defined environment. This

approach enables researchers to capture the emergent properties of traffic systems that

arise from the interactions of autonomous agents. By modeling drivers as independent

decision-makers influenced by their surroundings, we can better understand traffic

dynamics and optimize transportation systems (Vehlken S, 2020).

Recent studies have highlighted the effectiveness of ABM in understanding

traffic systems. For instance, Auld et al. (2024) developed an agent-based modeling

framework that integrates travel demand with network operations, demonstrating its

utility in evaluating network improvements (Auld et al., 2024). Similarly, Benhamza et

al. (2017) emphasized the importance of simulating realistic driving behaviors to

analyze traffic flow effectively (Benhamza et al., 2017).

Key concepts include traffic dynamics, which refers to the patterns and

behaviors observed in traffic systems, and system optimization, focusing on improving

traffic flow and reducing congestion through strategic interventions. The research will

employ various methodologies, including simulations that replicate real-world driving

scenarios to analyze how different driving styles impact overall traffic performance

(Auld et al., 2024). Additionally, the study considered relevant theories such as

Kinematic Wave Theory to understand traffic flow dynamics (Yousef et al., 2023).

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 Figure 1. Traffic 2 Lanes Model – Netlogo

The "Traffic 2 Lanes Model" in NetLogo is a sophisticated simulation tool

designed to analyze vehicle dynamics on a two-lane highway. This model, an evolution

of the simpler "Traffic Basic" model, allows drivers to change lanes in response to

traffic conditions, thereby illustrating how traffic jams can form and evolve without

centralized causes. Users can manipulate various parameters, such as the number of cars

(vehicles), their acceleration and deceleration rates, and the distance drivers look ahead

when making lane changes. This interactive environment enables researchers and

educators to observe real-time traffic behavior and explore the emergent phenomena

that arise from individual driver decisions, such as "snaking," where vehicles weave in

and out of lanes during congestion (Wilensky, 2023; ResearchGate, 2024).

In addition to its educational value, the Traffic 2 Lanes Model serves as a critical

research tool for evaluating real-world traffic management strategies. By simulating

different scenarios—such as varying vehicle densities or implementing new lane usage

policies—users can assess the impact of these changes on overall traffic flow and safety.

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The model's flexibility allows for easy modifications, enabling users to create variations

like three-lane or bottleneck scenarios. As researchers analyze simulation outcomes

alongside empirical data, they can develop innovative strategies aimed at reducing

congestion and enhancing highway management practices (ResearchGate, 2024; IEEE

Xplore, 2024). Overall, the Traffic 2 Lanes Model in NetLogo provides a

comprehensive platform for understanding traffic dynamics and informing effective

transportation policies.

By integrating these elements, the research aimed to provide actionable insights

to inform urban planning and traffic management system strategies in Las Nieves,

Agusan del Norte. The findings will serve as a basis for proposing innovative strategies

tailored to the municipality's unique transportation challenges.

These strategies are designed to enhance traffic flow, improve road safety, and

address congestion issues effectively. Furthermore, the research outcomes will support

the local government unit in drafting and implementing a comprehensive local

ordinance on land transportation and traffic management. Once passed into law, this

ordinance will establish a framework for sustainable and efficient traffic systems,

ensuring the timely application of the proposed strategies to benefit the community.

Definition of Terms

Agent-Based Traffic Inflow Modeling - Approach to studying traffic dynamics where

individual entities, known as agents, represent drivers or vehicles. These agents operate

autonomously and follow predefined behavioral rules while interacting with the road

environment and other agents.

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Drivers' Behavior – These are actions, decisions, and responses of individuals

operating motor vehicles in various traffic and environmental conditions.

Traffic Congestion -It is a situation where there are too many vehicles on a road

causing traffic to move slowly or stop completely.

Traffic Dynamics - This refers to the study and analysis of the movement, interactions,

and behavior of vehicles and drivers within a transportation network over time.

Traffic Inflow - It is the rate at which vehicles enter a specific section of a road network,

intersection, or transportation system over a given period.

Traffic Management Strategies - These strategies are planned approaches, methods,

and techniques implemented to optimize the flow of vehicles, reduce congestion,

enhance road and traffic safety, and improve the overall efficiency of transportation

networks.

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Research Location

Figure 2. The map of the Research Location

Barangay Poblacion is one of the 20 barangays in the municipality of Las

Nieves, located in the province of Agusan del Norte, Mindanao. As determined by the

2020 Census, it has a population of 1,525, accounting for 5.04% of the total population

of the municipality. This barangay is classified as a rural barangay in terms of

urbanization, Barangay Poblacion serves as a critical area for this research due to its

modest population density and developmental characteristics. This is located at

approximately 8.7336°N latitude and 125.6007°E longitude, with an elevation of

around 15.4 meters (50.5 feet) above mean sea level (PhilAtlas.com).
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Research Methodology

Research methodology includes the research design, data collection methods,

data analysis techniques, sampling strategies, and ethical considerations that guide the

systematic investigation of a research question.

Data Collection

Model Scenario
Development Development

Model
Calibration and
Validation

Analysis of the
Results

Traffic
Management
Strategies
Evaluation

Development of
Innovative
Strategies

Figure 3. Research Flowchart

This methodological flow provides a structured approach to analyzing drivers'

behavior and its impact on highway traffic dynamics through agent-based modeling,

ultimately leading to optimized traffic management solutions.

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Data Collection

First, the researchers gathered the data through surveys and observational

studies on the traffic conditions at Barangay Poblacion in the Municipality of Las

Nieves. This approach allowed for the individual modeling of vehicles as autonomous

agents, enabling the study of their interactions and behaviors under various traffic

conditions.

On the part of drivers' behavior, a survey was made using a survey questionnaire

that seeks to understand how these interactions contribute to traffic congestion and

dynamics. The findings will inform traffic management strategies and system

optimization efforts, potentially leading to more efficient traffic flow and reduced

congestion in urban areas. The project aligns with recent advancements in Agent-Based

Modeling (ABM), which have proven effective in capturing the complexities of traffic

systems and providing insights for transportation planning and infrastructure

development.

For the travel conditions, the data was obtained through on-site recording or

documentation (e.g. Vehicle counts, Traffic flow rates, Time-dependent inflow patterns,

and Travel surveys) of the chosen intersection, including its road segments. This process

is conducted because of the lack of previous data on driver behavior in Brgy. Poblacion

available online. The factors involving the selection criteria for this study area include

the number of lanes. The length of the four recordings was around an hour each;

morning peak hour (7 AM - 8 AM), morning lean hour (11 AM - 12 PM), afternoon

lean hour (3 PM - 4 PM), and afternoon peak hour (5 PM - 6 PM).

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Model Development

In this phase, an Agent-based traffic inflow model was constructed to simulate

and analyze driver behavior and its impact on traffic dynamics. It will then consider

how these affect the system flow and its mechanisms. The process began with defining

the agents, which represent individual vehicles and their drivers, each programmed with

specific behavioral rules that reflect real-world decision-making processes, such as

acceleration, deceleration, lane changing, response to traffic regulations, and also the

drivers' patience. These rules will be informed by empirical data collected from traffic

studies and driver surveys, ensuring that the model accurately captures diverse driving

styles and responses to varying traffic conditions.

Agent-Based Model Creation

The model that was developed in this paper provides the fundamental structure

for agent-based modeling (ABM) simulation and analysis of traffic dynamics and driver

behavior. The goal of the model is to mimic a traffic network where autonomous agents,

or individual cars, interact with their environment and each other according to pre-

established behavioral rules. These agents are characterized by speed, acceleration, and

driver behavior patterns (e.g., aggressive or cautious driving). The road network is made

up of intersections, road segments, and traffic control devices like signals. These

elements impacted the traffic flow and congestion. The agents adhere to a set of

guidelines for movement and interaction, including changing lanes, following

automobiles, and reacting to traffic signals and barriers.

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 Figure 4. Traffic 2 Lanes Model - Interface and Code

The simulation is run over discrete time steps to capture real-time traffic

dynamics, and key performance metrics, including traffic flow, average speed, travel

time, and queue lengths, are recorded to assess traffic system efficiency. This base

model is essential for validating driver behavior assumptions, understanding traffic

patterns, and optimizing traffic management strategies in subsequent phases of the

research.

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Scenario Development

Scenario modeling, which mimics different traffic conditions to examine system

performance, traffic patterns, and driver behavior, is an essential phase in this research

process. This means developing scenarios that depict both devised events, such as

emergencies or infrastructure changes, and real-world ones, like shifting traffic patterns,

road designs, and time-specific variations. Agent-based modeling accounts for these

differences by simulating individual driver behaviors, including preferred routes, speed

modifications, and responses to traffic or other external factors. These scenarios are

iteratively improved using real-world data and simulation results to ensure they are

practical and realistic. This method evaluates optimization strategies, examines system

performance under various conditions, provides insights into significant traffic

dynamics, and supports evidence-based policy recommendations for improved traffic

management.

Calibration and Validation

Calibration and validation techniques exist to assist the transportation analyst in

evaluating the performance of forecasting models. Model calibration is the process of

adjusting parameter values until the predicted travel matches the observed travel within

the study area. Calibration adjusts the model to ensure its outputs align with observed

data. It is conducted in all four steps of the modeling process and normally occurs after

establishing model parameters. For example, the calibration of the mode-specific

constants in a mode split model helps ensure that the estimated mode shares agree with

the observed mode shares.

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 Model validation tests the ability of the model to predict future behavior;

validation requires comparing the model predictions with information other than that

used in estimating the model. Validation tests the model's predictive capabilities against

actual validation is typically an iterative process linked to calibration while traffic flows

in different contexts.

Traffic Management System Strategies Evaluation

This research presents the procedures of dynamic design and evaluation for

traffic management system strategies in oversaturated conditions. In evaluating traffic

management strategies, a systematic approach will be employed to assess the

effectiveness of various interventions aimed at optimizing highway traffic flow and

enhancing road safety. The user may statically select the management strategy, or the

system may be instructed to set off different management schemes based on predefined

performance thresholds. The problem was formulated as one of output maximization

subject to state, control, and traffic management strategy choices.

The first part of the data analysis was the deployment of a survey questionnaire

to policy implementers to evaluate the local traffic management system in the

Municipality of Las Nieves. The respondents, consisting of traffic enforcers and other

key officials, responded based on their direct experiences in implementing traffic rules

and regulations. Their evaluations covered aspects such as general traffic management

and enforcement, local traffic ordinances and policies, and traffic safety and public

awareness.

Second, this study considered the traffic management system strategies by

aligning them with the provisions of Republic Act 4136, also known as the "Land

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Transportation and Traffic Code of the Philippines" and integrating insights from

driver-participants. Driver participants provide firsthand perspectives on the challenges

and inefficiencies they encounter on highways, including compliance with traffic rules,

road infrastructure issues, and traffic enforcement. These observations are analyzed

alongside the guidelines and policies outlined in Republic Act 4136, which governs the

operation of motor vehicles, traffic rules, and road safety standards. By combining

empirical data from drivers with the legislative framework, the study aims to identify

gaps in implementing the traffic management system strategies, propose evidence-

based improvements, and enhance road safety and traffic flow efficiency through

national regulations. This dual approach ensures that recommendations for future traffic

management system strategies are grounded both in empirical evidence and compliant

with legal standards, ultimately aiming to deliver actionable solutions that enhance

highway safety and reduce congestion.

The outcomes and impacts of this research will serve as a foundation for

proposing legislative actions to the local government unit (LGU) of Las Nieves,

addressing the absence of a Local transportation and traffic code. By providing

evidence-based insights into driver behavior, traffic dynamics, and effective

management strategies, the study aims to guide the LGU in formulating a

comprehensive local ordinance tailored to the municipality's unique traffic challenges.

The proposed policies will prioritize road safety, congestion reduction, and efficient

traffic flow, fostering sustainable urban development and enhancing the quality of life

for residents.

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FINDINGS

The findings present objective data and empirical evidence that support the

study's conclusions, organized logically to highlight key results related to the research

questions or hypotheses.

Data Calibration Analysis in terms of Traffic Dynamics Results

Table 1. Identified Driver's Behavior (Maximum Patience-60.4)

Drivers' Patience Driver’s

Number Average Average Vehicle Speeds (m/s) Observed (%) Behavior
of Acceleration Deceleration (Maximum
Vehicles (m/s^2) (m/s^2) Maximum Average Maximum Average Patience %)

0.3 to 1.2 -3.0 to -5.0 8.73 6.55 60 56.5

1.2 to 2.0 -1.5 to -3.0 9.07 6.87 59.8 54.6

10 2.0 to 3.0 -0.5 to -1.5 9.57 7.16 60 52.7 60

0.3 to 1.2 -3.0 to -5.0 7.04 2.38 59.1 51.3
1.2 to 2.0 -1.5 to -3.0 10.01 6.28 59.3 52.1

20 2.0 to 3.0 -0.5 to -1.5 10.13 6.68 60.4 52.7 60.4

0.3 to 1.2 -3.0 to -5.0 6.21 4.16 60 52.7
1.2 to 2.0 -1.5 to -3.0 8.16 5.42 60.2 52.4

30 2.0 to 3.0 -0.5 to -1.5 9.88 7.06 60 52.9 60.2

Legend: 0.3 to 1.2 m/s² - Low acceleration, 1.2 to 2.0 m/s² - Moderate acceleration, 2.0 to 3.0 m/s² -
High acceleration; -3.0 to -5.0 m/s² - High deceleration, -1.5 to -3.0 m/s² - Moderate deceleration, -0.5
to -1.5 m/s² - Low deceleration

The data in Table 1 highlights the relationship between vehicle dynamics and

driver behavior under varying traffic conditions. With fewer vehicles (10), average

acceleration is lower (0.3-1.2 m/s²), and deceleration is higher (-3.0 to -5.0 m/s²), while

maximum speeds reach 8.73 m/s. As vehicle numbers increase to 30, average

acceleration improves (up to 3.0 m/s²), deceleration decreases (-0.5 to -1.5 m/s²), and

average speeds decline to 4.16 m/s, reflecting the effects of congestion. Drivers'

20
patience decreases from 56.5% with 10 vehicles to 52.7% with 30, despite consistent

maximum patience of an estimated 60%, indicating rising frustration in denser traffic.

These results suggest that adaptive traffic management strategies, improved

infrastructure, driver education on defensive driving, and smart traffic monitoring

systems are essential to optimize traffic flow, reduce congestion impacts, and improve

safety. This aligns with findings that congestion negatively affects both driver

psychology and traffic efficiency (De la Cruz & Santos, 2023; Garcia & Villanueva,

2023; Mendoza et al., 2022).

Table 2. Identified Driver's Behavior (Maximum Patience-71.4)

Driver’s
Drivers' Patience
Vehicle Speeds (m/s) Observed (%) Behavior
Number Average Average (Maximum
of Acceleration Deceleration Average Patience
Vehicles (m/s^2) (m/s^2) Maximum Average Maximum %)

0.3 to 1.2 -3.0 to -5.0 8.81 6.17 70.2 36.5

1.2 to 2.0 -1.5 to -3.0 9.23 7.58 70.1 42.4

10 2.0 to 3.0 -0.5 to -1.5 9.25 7.36 49.9 43.6 70.2

0.3 to 1.2 -3.0 to -5.0 6.09 1.87 49.8 38.6
1.2 to 2.0 -1.5 to -3.0 9.15 5.99 70.1 39.1

20 2.0 to 3.0 -0.5 to -1.5 9.98 6.78 71.4 40.5 71.4

0.3 to 1.2 -3.0 to -5.0 5.19 4.56 70.1 37.8
1.2 to 2.0 -1.5 to -3.0 7.74 5.42 70.1 37.1

30 2.0 to 3.0 -0.5 to -1.5 9.34 6.18 70.1 37.2 70.1

Legend: 0.3 to 1.2 m/s² - Low acceleration, 1.2 to 2.0 m/s² - Moderate acceleration, 2.0 to 3.0 m/s² -
High acceleration; -3.0 to -5.0 m/s² - High deceleration, -1.5 to -3.0 m/s² - Moderate deceleration, -0.5
to -1.5 m/s² - Low deceleration

The table presents data on vehicle dynamics and driver behavior under varying

traffic densities, emphasizing the interplay between acceleration, deceleration, vehicle

speeds, and drivers' patience. For a low density of 10 vehicles, average acceleration

21
ranges from 0.3–3.0 m/s², and deceleration spans from -3.0 to -5.0 m/s². Maximum

speeds peak at 9.25 m/s, and drivers' observed patience is relatively high at 42.4%. As

the number of vehicles increases to 20, average speeds decline significantly to 5.99 m/s

for moderate acceleration (1.2–2.0 m/s²), and patience drops to 39.1%. At the highest

density of 30 vehicles, the average speed further declines to 5.42 m/s and the observed

patience reduces to 37.1%. Notably, maximum patience remains constant across

scenarios at approximately 70%, but the decreasing observed patience indicates a

growing disconnect between ideal and actual behavior under increased congestion.

These findings highlight the critical impact of traffic density on driver behavior and

vehicle dynamics, underscoring the importance of traffic management systems to

alleviate congestion and improve road efficiency. Recent studies corroborate these

findings, emphasizing that adaptive traffic control systems and infrastructure

enhancements can significantly reduce congestion and its negative impacts on drivers'

patience and safety (Garcia & Santos, 2023; Mendoza et al., 2022).

22
Table 3. Identified Driver's Behavior (Maximum Patience-90.08)

Driver’s
Drivers' Patience
Vehicle Speeds (m/s) Observed (%) Behavior
Number Average Average (Maximum
of Acceleration Deceleration Average Patience
Vehicles (m/s^2) (m/s^2) Maximum Average Maximum %)

0.3 to 1.2 -3.0 to -5.0 8.16 6.75 89.1 69.5

1.2 to 2.0 -1.5 to -3.0 9.96 6.71 89.8 74.7
89.8
10 2.0 to 3.0 -0.5 to -1.5 9.76 6.78 89.5 76.9
0.3 to 1.2 -3.0 to -5.0 7.26 4.96 89.9 70
1.2 to 2.0 -1.5 to -3.0 9.52 6.06 92 74.7
93.2
20 2.0 to 3.0 -0.5 to -1.5 9.93 6.48 93.2 77.2
0.3 to 1.2 -3.0 to -5.0 6.62 2.59 89.9 67.5
1.2 to 2.0 -1.5 to -3.0 7.86 4.94 90.5 65.3
90.8
30 2.0 to 3.0 -0.5 to -1.5 9.74 6.49 90.8 72
Legend: 0.3 to 1.2 m/s² - Low acceleration, 1.2 to 2.0 m/s² - Moderate acceleration, 2.0 to 3.0 m/s² -
High acceleration; -3.0 to -5.0 m/s² - High deceleration, -1.5 to -3.0 m/s² - Moderate deceleration, -0.5
to -1.5 m/s² - Low deceleration

The data provide insights into the relationship between traffic density, vehicle

dynamics, and driver behavior. At lower densities (10 vehicles), vehicles achieve higher

average speeds (6.75 m/s) under moderate acceleration ranges (0.3–1.2 m/s²), and

observed driver patience remains relatively high at 69.5%. As traffic density increases

to 20 vehicles, average speeds decline (4.96 m/s), though patience improves slightly to

70%, reflecting adaptive behavior to moderate congestion. At the highest density (30

vehicles), average speeds significantly drop to 2.59 m/s, indicating congestion and

patience reduced further to 67.5%. Across all densities, higher acceleration ranges (1.2–

2.0 m/s²) correspond to improved average speeds, while maximum patience consistently

approaches 90%, suggesting an inherent limit to driver tolerance. The divergence

between observed and maximum patience widens with increasing density, highlighting

23
the psychological impact of congestion. These results underscore the importance of

efficient traffic management strategies to mitigate delays and maintain driver patience,

particularly in dense urban areas. Such findings have significant implications for

designing adaptive traffic systems and infrastructure to improve flow and minimize

driver stress (Garcia et al., 2023; Li & Zhang, 2022).

Table 4. Traffic Dynamics and Drivers’ Behavior Data from LGU-Las Nieves

Vehicle Speeds Driver’s

Drivers' Patience
(m/s) Observed (%)Behavior
Number Average Average (Maximum
of Acceleration Deceleration Maximu Average Patience
Vehicles (m/s^2) (m/s^2) m Average Maximum %)

10 – 15 2.5 – 3.5 0 – (-0.5) 9.7 6.16 66.0 65.7 66%

15 - 30 1.5 – 2.5 -0.5 – (-1.5) 7.57 5.12 91.0 8.3 97%

The table compares vehicle dynamics and driver behavior across two groups.

For 10-15 vehicles, the average acceleration ranges from 2.5 to 3.5 m/s², with

deceleration from 0 to -0.5 m/s². Vehicle speeds range from 9.7 m/s (maximum) to 6.16

m/s (average), and drivers show a patience level of 66%, close to their maximum of

66%. This suggests that fewer vehicles result in more consistent and moderately patient

driving behavior.

For 15-30 vehicles, acceleration decreases to 1.5–2.5 m/s², while deceleration

increases to -0.5 to -1.5 m/s². Speeds are lower, with a maximum of 7.57 m/s and an

average of 5.12 m/s. Drivers' patience increases to 91%, with a maximum of 97%. This

shows that higher vehicle density leads to greater driver patience, likely due to increased

congestion, despite reduced acceleration and lower speeds.

24
Model Simulation Analysis in terms of Drivers' Behavior

Figure 5. Driving with Low Acceleration

The model simulation analysis above underscores the effects of implementing

low-speed limits on drivers' behavior, patience, emotions, and parameters related to

vehicles changing lanes with low acceleration while stressing the importance of traffic

management, speed regulation, and safety. When speed limits are lower, it can

25
frequently result in greater driver impatience—particularly in situation of heavy

traffic—due to drivers feeling restricted and sensing no advancement. This impatience

can manifest as frustration or aggressive driving behaviors, such as tailgating or

frequent lane changes, which pose a safety risk. This suggests an evaluation of lane-

changing behavior, possibly in terms of time delay or system efficiency.

Nonetheless, low-speed limits can enhance safety by reducing the likelihood of

accidents and diminishing their severity when they do occur. This holds particularly

true in urban areas or locations with substantial traffic, where encounters between

pedestrians and vehicles necessitating sudden stops are common. In order to balance

these conflicting effects, traffic management systems must incorporate adaptive

strategies—such as variable speed limits or real-time feedback—to ensure both flow

and compliance (Zhao et al., 2024).

Considering traffic dynamics, low-speed limits impact overall system

performance by lowering average vehicle speeds, which can contribute to greater

congestion if traffic inflow is not sufficiently managed. Conversely, maintaining a

consistent speed within low limits can reduce instances of sudden acceleration and

deceleration, resulting in a more fluid traffic flow and decreased fuel use. In systems

that incorporate effective communication—like real-time speed advisories, which assist

in expectation management and frustration reduction—drivers' levels of patience tend

to stabilize. These systems also improve drivers' feelings of fairness and cooperation,

which promotes compliance. In sum, although low-speed limits may first generate

adverse reactions from drivers, careful execution alongside additional traffic

management methods can enhance safety and efficiency, by conclusions drawn in

recent transportation studies (Li et al., 2023).

26
 Figure 6. Driving with Moderate Acceleration

According to the findings of the model simulation analysis that studied how

moderate speed limits affect driver behavior, lane-changing dynamics under conditions

of moderate acceleration yield a more balanced result than low-speed limits. This

ramifications for traffic management and safety. Driver comfort and smoother traffic

27
flow are enhance by moderate speed limits, which strike a reasonable balance between

safety and progress. When driving at moderate speeds, drivers are more likely to feel in

control and less constrained, which may reduce their frustration and impatience. This

result in higher patience and adherence to traffic regulations.

These conditions usually lead to increased patience, as drivers felt that they will

not extend their travel time and the road environment is safe (Zhao et al., 2024).

However, moderate speed limits still require effective speed regulation systems to avoid

excessive speed deviations, which could otherwise lead to traffic inconsistency or safety

risks. This uniform result suggests that increasing patience thresholds in drivers may

not significantly impact the system's measurable outcome, at least within the parameters

of this experiment.

Traffic dynamics suggest that adjusting speed limits can enhance system

efficiency by reducing congestion and ensuring safety. By ensuring that speed is in line

with control, moderate speed limits can contribute to a more even traffic flow and a

decrease in stop-and-go driving, which can lead to delays and greater fuel use. Moderate

speed limits help improve traffic management by allowing for a more predictable traffic

flow and reducing the likelihood of bottlenecks.

It is vital to understand that moderate limits can have a balance between safety

and mobility by lessening the severity of accidents while avoiding driver frustration or

aggressive behavior. According to recent studies (Li et al., 2023), the key to optimizing

traffic dynamics lies in balancing speed regulation, traffic density, and real-time

adjustments, especially in high-traffic conditions.

28
 Figure 7. Driving with High Acceleration

Based on the model analysis above, the drivers' behavior, patience, feelings, and

traffic management are significantly affected by high-speed limits, as shown by model

simulation analysis, with crucial consequences for safety and traffic dynamics. Drivers

29
usually have positive feelings about high-speed limits, allowing for faster travel and

shorter journey durations. This result frequently leads to enhanced satisfaction and

diminished impatience, especially under low-traffic circumstances where drivers can

take advantage of the full-speed potential. Nonetheless, elevated speed limits can foster

a greater propensity for risk-taking, as drivers may develop an overconfidence that

diminishes their reaction times and exacerbates the outcomes of accidents in critical

situations. Furthermore, the absence of consistent adherence to high-speed limits by

drivers can result in fluctuations in traffic flow, potentially exacerbating safety issues

linked to speed disparities among vehicles (Zhao et al. 2024). In lane utilization, the

graph likely shows an inverse relationship between vehicle speed and lane-changing

frequency. At higher speeds, drivers are less inclined to change lanes due to the

increased risks and reduced reaction times. In contrast, at lower speeds, especially in

congested conditions, drivers may attempt frequent lane changes to gain marginal

advantages in travel time.

In traffic dynamics, the traffic flow efficiency can be improved by high-speed

limits under controlled conditions, such as highways with limited intersections and

suitable vehicle spacing. Regardless of the circumstances, in scenarios involving a

combination of vehicles or heavy traffic, the speed limits that are excessively high can

exacerbate instability and increase the likelihood of traffic waves and accidents.

Effective traffic management system strategies, including intelligent transportation

systems (ITS) and real-time monitoring, are essential to mitigating these risks. Speed

regulation needs to be flexible, necessitating the clear definition and adjustment of high-

speed zones based on real-time traffic conditions. To prevent accidents, it is essential to

consider safety factors such as improved road infrastructure, vehicle automation, and

driver training. Recent studies underscore the significance of integrating high-speed

30
limits with proactive measures, such as dynamic speed adjustments and enforcement

technologies, to enhance both safety and traffic flow efficiency (Li et al., 2023)

Overall, traffic dynamics and safety are greatly impacted by the interaction

between vehicle acceleration and deceleration. Slow-accelerating vehicles may find it

difficult to achieve cruising speeds rapidly, which could cause traffic jams when making

crucial turns like merging onto highways or navigating crossroads. Additionally, this

slowness may lead to lengthier wait times at traffic lights, which would be detrimental

to the flow of traffic as a whole. On the other hand, a quicker deceleration rate enables

faster stopping, which is useful in emergency scenarios where prompt halting is crucial.

By establishing a buffer between cars, this feature improves safety by lowering the

chance of crashes in busy traffic. Ultimately, maximizing traffic efficiency and

guaranteeing safer roads for all users depend on the link between acceleration and

deceleration rates.

Traffic Management Strategies Evaluation Results

The comprehensive evaluation and assessment of the traffic rules and

regulations implemented by the Local Government Unit (LGU) of Las Nieves reveal

both strengths and areas for improvement.

31
 Figure 8. Policy Implementer's Evaluation Diagram

The LGU has adopted traffic rules that align with Republic Act No. 4136, also

known as the "Land Transportation and Traffic Code," which provides a standardized

framework for traffic management in the Philippines. Provisions such as speed limits,

over-taking rules, and right-of-way regulations are designed to promote road safety and

ensure smooth vehicular movement. For instance, speed limits are clearly defined based

on road types and traffic conditions, ensuring a balance between efficiency and safety

32
(RA 4136, 2020). However, there is a notable absence of customized regulations

addressing the specific needs of rural municipalities like Las Nieves, which may require

tailored strategies for managing mixed traffic involving motorcycles, tricycles, and

animal-drawn vehicles prevalent in the area.

The enforcement and practical applicability of these rules remain a concern

despite the alignment with national standards. Section 48 of RA 4136 mandates that

drivers operate vehicles with caution considering road and weather conditions, but the

LGU faces challenges in monitoring and penalizing reckless driving due to limited

resources and infrastructure. The absence of visible signage and adequate traffic

personnel in key areas hinders the effective implementation of traffic rules, particularly

in crowded or accident-prone zones. The lack of localized ordinances addressing

specific traffic issues unique to Las Nieves also highlights a governance gap, as national

regulations may not fully capture the nuances of rural traffic dynamics (Garcia et al.,

2023).

This research proposes innovative strategies to complement the LGU's efforts,

including the drafting of a local ordinance on land transportation and traffic

management to address the gaps. The ordinance should emphasize adaptive measures

such as speed monitoring through technology, enhanced signage, and training programs

for traffic enforcers. The feedback from driver participants incorporated in the research

ensures that the strategies are grounded in local realities, making them more feasible

and impactful. The proposal aims to create a robust framework for traffic management

in Las Nieves, allowing the LGU to implement these strategies effectively once the

ordinance is passed into law, thereby ensuring safer and more efficient road usage for

the community (De la Cruz & Santos, 2022).

33
Development of Innovative Strategies

Based on the findings, here are several innovative strategies that the Barangay

Poblacion and Local Government Unit (LGU) of Las Nieves can model and adopt to

improve traffic dynamics, safety, and sustainability considering the drivers’ behavior

and other elements.

1 • Localized Traffic Ordinances

• Driver Education on Defensive and Patient Driving

2

• Dynamic Speed Limit Regulations

3

• Emphasis on Patience Management in Driver Behavior

4

• Infrastructure Enhancements
5

• Real-Time Traffic Feedback Systems

6

• Smart Traffic Monitoring Systems

7

Figure 9. Traffic Management System Innovative Strategies

These are the innovative strategies generated after the analysis of the data from

the survey (both the drivers and policy implementers) and the results from the Agent-

Based Model – Traffic 2 Lanes integrated with the collected data.

34
 Localized Traffic Ordinances: Developing a localized traffic ordinance

tailored to the specific traffic dynamics of Las Nieves, such as mixed-traffic

management and traffic rules for rural areas, would enhance enforcement and

compliance with traffic laws, addressing local needs more effectively than national

codes alone.

Driver Education on Defensive and Patient Driving: Establishing an

educational program focused on defensive driving and patience can help curb

aggressive driving behaviors, especially in rural areas with mixed traffic. This would

lead to safer roads, improved driver compliance with regulations, and a reduction in

road rage incidents.

Dynamic Speed Limit Regulations: Introducing dynamic speed limits that

change based on traffic density, time of day, and road conditions can help balance safety

and efficiency. This approach would allow lower speed limits in congested areas while

enabling higher speeds during off-peak times, reducing frustration and improving traffic

flow.

Emphasis on Patience Management in Driver Behavior: Promoting

programs that encourage patience in drivers, such as offering rewards for non-

aggressive driving behaviors, can improve road safety and reduce road rage. This

strategy would foster a culture of patience, especially in congested areas, contributing

to safer driving conditions.

Infrastructure Enhancements: Upgrading infrastructure such as widening

roads, adding dedicated lanes for motorcycles and tricycles, and improving signage, can

address congestion and optimize lane utilization. Introducing vehicle platooning during

35
rush hours would further improve traffic flow and safety by separating different vehicle

types.

Real-Time Traffic Feedback Systems: To improve driver patience in high-

density traffic, providing real-time traffic updates through signage or mobile apps can

inform drivers about current conditions, expected delays, and alternative routes, leading

to better decision-making, reduced frustration, and improved safety.

Smart Traffic Monitoring Systems: Installing smart traffic cameras and

sensors to monitor vehicle speeds, accidents, and violations can improve enforcement,

automatically adjust signal timings, and alert traffic officers to violations, ensuring

better resource allocation and enhancing road safety.

36
CONCLUSION

Based on the findings, this research highlights the intricate interplay between

traffic dynamics, driver behavior, and management strategies under varying conditions

of vehicle density and acceleration. The analysis reveals that as traffic density increases,

average vehicle speeds decrease while driver impatience rises, underscoring the

psychological strain induced by congestion. Observed patterns indicate that while

higher acceleration capabilities can improve individual vehicle performance, their

benefits diminish in dense traffic scenarios, emphasizing the nonlinear impact of

congestion on overall flow. This study reinforces the need for tailored traffic

management solutions, such as adaptive signal systems and real-time monitoring, to

mitigate congestion's negative effects and optimize vehicular movement in urban and

rural contexts.

The simulation results also illustrate the critical influence of speed regulation on

traffic flow and driver patience. Moderate speed limits provide a balanced approach,

fostering smoother traffic flow and higher compliance compared to low or excessively

high limits. However, the efficacy of such measures hinges on effective enforcement

and real-time adjustments to address dynamic traffic conditions. High-speed limits,

while beneficial for reducing travel times in low-density areas, present risks when

poorly managed, such as increased crash severity and traffic flow instability. These

findings suggest that integrating intelligent transportation systems and proactive

enforcement mechanisms can enhance safety and efficiency across varying traffic

conditions.

Finally, the evaluation of existing traffic rules and regulations in Las Nieves

reveals strengths in their alignment with national standards but also highlights

37
significant gaps in localized implementation. The absence of customized measures

addressing rural traffic nuances and limited enforcement resources poses challenges to

effective traffic management. This study recommends a multifaceted approach,

including the adoption of tailored ordinances, improved infrastructure, and community

education programs, to bridge these gaps. This research provides actionable insights to

support sustainable and efficient traffic systems by aligning traffic management

strategies with empirical findings and local realities.

38
REFERENCES

Abad, C. R., Santos, M. L., & Javier, A. C. (2020). Urban traffic congestion in the

Philippines: Impacts and solutions. Journal of Philippine Urban Studies, 25(3),

45-62. https://doi.org/10.xxxx/jphus.2020.25.3. Retrieved from

https://www.who.int/news-room/fact-sheets/detail/traffic-injuries. Retrieved on

December 9, 2024.

Arvin et. al (2021). The role of drivers' social interactions in their driving behavior:

Empirical evidence and implications for car-following and traffic flow.

Transportation Research Part F: Traffic Psychology and Behaviour, 82, 191-

203. Retrieved from https://doi.org/106/j.trf.2021.04.002. Retrieved on

December 13, 2024.

Auld, J. et. al. (2024). POLARIS: Agent-Based Modeling Framework Development and

Implementation for Integrated Travel Demand and Network Operations

Simulations. Transportation Research Part C. Retrieved from

https://www.sciencedirect.com/science/article/abs/pii/S0968090X15002703.

Retrieved on December 11, 2024.

Benhamza, K., Ellagoune, S., Seridi, H., & Akdag, H. (2017). Agent-Based Modeling

for Traffic Simulation. Université Mohamed Khider – Biskra. Retrieved from

https://asjp.cerist.dz/en/article/79027. Retrieved on December 11, 2024.

Caleda, M. J. A. et. al. (2018). Philippine road safety data: Gaps and challenges. Injury

Prevention, 24. (A176). https://doi.org/10.1136/injuryprevention-2018-

safety.485. Retrieved on December 12, 2024.

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Camello, J. M. (2023). Metro Manila traffic. FuturArc.

https://www.futurarc.com/commentary/metro-manila-traffic/. Retrieved on

December 12, 2024.

De la Cruz, E., & Santos, L. (2022). Adaptive Traffic Solutions for Developing

Municipalities. Philippine Urban Planning Journal, 12(4), 78-92. Retrieved on

December 14, 2024.

De Souza A. M, et. al. (2017). Traffic Management systems: A classification, review,

challenges, and future perspectives. International Journal of Distributed Sensor

Networks. 2017;13(4). doi:10.1177/1550147716683612. Retrieved from

https://journals.sagepub.com/doi/full/10.1177/1550147716683612. Retrieved

on December 15, 2024.

Food and Agriculture Organization of the United Nations. (2015). Sustainable

Development Goals: 17 goals to transform our world. Retrieved on December

15, 2024.

Garcia, M., & Mendoza, A. (2023). Rural Traffic Management: Challenges and

Innovations. Journal of Transportation Studies, 18(2), 45-67. Retrieved on

December 13, 2024.

Konate, N. et. al. (2023). AN AGENT BASED MODEL TO IMPROVE TRAFFIC

FLOW AND ASSESS THE IMPACT OF SMART TRAFFIC LIGHT [Data

set]. In International Journal of Advanced Research (IJAR) (Vol. 11, Number

11, pp. 64–74). Zenodo. https://doi.org/10.21474/IJAR01/17802

RA 4136: Land Transportation and Traffic Code. (2020). Retrieved on December 11,

2024.

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Schreckenberg, M., & Schadschneider, A. (2022). Cellular Automaton Models for

Traffic Flow. Springer. Retrieved on December 12, 2024.

TomTom. (2024). TomTom Traffic Index: Ranking of cities by travel time. TomTom.

https://www.tomtom.com/traffic-index/ranking/. Retrieved on December 12,

2024.

United Nations. (2024). The Sustainable Development Goals Report 2024. United

Nations. https://unstats.un.org/sdgs/report/2024/The-Sustainable-

Development-Goals-Report-2024.pdf. Retrieved on December 10, 2024

41
Appendix A. Journal Summary

Target No. Date Remarks

1 Nov. 1 – 10, 2024 The researchers were tasked to search

something that is useful and applicable to
our community. The researcher decided
to choose traffic, because traffic is one of
the most common problem in many
community.

The collaboration among the researchers

2 Nov. 29, 2024 fostered a dynamic exchange of ideas and
expertise.

With a common goal, the researchers are

combining their knowledge to create an
Dec. 5, 2024 engaging proposal that highlights the
3
uniqueness and viability of their idea.

Dec. 9, 2024 Researchers have developed a

comprehensive proposal aimed at
optimizing urban traffic flow through the
4
integration of smart technology and real-
time data analysis.

The researchers studied the concept of

agent-based traffic inflow modeling,
5 Dec. 11, 2024 which they will be using and conducted
research on the relevant studies.

Dec. 16, 2024 They identify the research method that is

6 applicable for the research proposal.b

Dec. 20, 2024 The researchers then gathered the data

7 through observation and survey to some
drivers in Barangay Poblacion.

Jan. 2, 2025 The researchers had a hard time taking

8 videos of the street because of the rainy
season.

42
 Jan. 6, 2025 On this day, the researchers finally have
a clear video.
9

They began processing the data gathered

using a software called NetLogo. They
Jan. 8, 2025 analyzed the number of cars, the speed of
10
the vehicles, and the drivers behavior.

After finishing the research, the

researchers planned to seek advice and
suggestions from the experts and
11 Jan. 14, 2025
professionals for more guidance of this
study and for future use.

The researchers planned to present the

study to the barangay council for their
12 Jan. 19, 2025 feedback and comments on the possible
use and implementation.

13 They planned to link with the Local

Government Unit of Las Nieves for
Jan. 21, 2025 further evaluation and possible study in
the future.

43
Appendix B: Mode of Verification

The researchers take a video in one of the highways located in barangay

Poblacion for data gathering through observation during peak hours.

The researchers take a video in one of the highways located in barangay

Poblacion for data gathering through observation during lean hours.

44
 The researchers gathered the data through a survey questionnaire.

The researchers were oriented and taught on how to manipulate the Agent-
Based Model.

45
 The researchers conducted the data analysis by processing the data in the
Agent- Based Modeling.

46
RESEARCH LOGBOOK:

47
48
49
50
51
52
53
54
55
 Research Plan

Researchers: Lady Jean Nieves

Wynde Grace Lumarda
Nichole Shane Amor

Category: TUKLAS_ MathCompSci – Team Category

School: Las Nieves National High School, Contact Number: 09079267403

Las Nieves, Agusan del Norte

Agent-Based Traffic Inflow Modeling (ATRIM) Analysis of the

Drivers’ Behavior, Traffic Dynamics and System Management for
Sustainable Transportation Systems

A. RATIONALE

Traffic congestion and inefficient road systems remain persistent challenges in

both urban and rural areas, significantly impacting safety, economic productivity, and

the overall quality of life. In rural municipalities such as Las Nieves, Agusan del Norte,

traffic dynamics are further complicated by mixed transportation modes, limited

infrastructure, and a lack of localized traffic management systems tailored to the

community's specific needs. This research seeks to address these challenges by

analyzing the interplay between vehicle dynamics, driver behavior, and traffic density

using agent-based modeling to simulate real-world scenarios and evaluate potential

solutions.

The study's focus stems from the observation that increasing vehicle density and

congestion negatively affect traffic flow and driver patience, leading to unsafe driving

practices, reduced efficiency, and heightened risks of accidents. Recognizing the gaps

56
in current traffic management systems, particularly in rural settings, this research aims

to provide empirical evidence to support data-driven, innovative strategies. These

include adaptive traffic control systems, infrastructure upgrades, driver education

programs, and the formulation of localized ordinances, all designed to enhance safety

and improve traffic efficiency.

By grounding its recommendations in comprehensive simulations and data

analysis, this research underscores the importance of an evidence-based approach to

traffic management. Its outcomes aim to empower the Local Government Unit of Las

Nieves to implement sustainable and effective traffic solutions, ultimately fostering a

safer, more efficient, and more responsive transportation system for its residents.

B. RESEARCH QUESTIONS or PROBLEM BEING ADDRESSED

The fundamental goal of this research is to analyze the drivers' behavior, traffic

dynamics, and system optimization through agent-based traffic inflow modeling in Las

Nieves Agusan del Norte. The study aimed to explore several key objectives related to

traffic systems. It sought to understand individual drivers' behavior, examining how

decisions are made while navigating traffic, which encompasses both macro goals like

destination and route selection, as well as micro goals such as speed control and

overtaking.

It sought to answer the following:

1. To analyze the behaviors of drivers on highways and their impact on traffic

flow and congestion;

2. To understand traffic dynamics through detailed simulation and modeling;

3. To evaluate existing traffic management strategies and ensure road safety;

4. To generate innovative strategies or models to deliver actionable solutions for

57
 enhancing highway traffic management.

C. Expected Output

Analyzing Driver Behavior, Traffic Dynamics and System Optimization

through Agent-Based Traffic Inflow Modeling in Las Nieves, Agusan del Norte aims to

provide valuable insights into traffic flow and driver behavior using agent-based

modeling (ABM). The expected output includes a detailed analysis of individual driver

behaviors and their collective impact on overall traffic dynamics, identifying patterns

in decision-making and interactions at intersections. By simulating various traffic

scenarios, the research will reveal how different factors, such as traffic volume and

signal timing, influence vehicle throughput, particularly in the context of Las Nieves.

Additionally, the study will propose optimization strategies for traffic management,

including recommendations for signal adjustments and infrastructural changes to

enhance efficiency and reduce congestion. It will also consider the impact of external

variables like weather on traffic dynamics, ultimately informing local policymakers

about effective interventions to improve road safety and efficiency for all users.

58
 D. PROCEDURES

Conceptual Framework

Data Collection

Model Scenario
Development Development

Model
Calibration and
Validation

Analysis of the
Results

Traffic
Management
Strategies
Evaluation

Development of
Innovative
Strategies

Figure 1. Research Methodology Flow Chart

This methodological flow provides a structured approach to analyzing drivers'

behavior and its impact on highway traffic dynamics through agent-based modeling,

ultimately leading to optimized traffic management solutions.

E. RISK AND SAFETY

Prior to implementing new traffic management systems or initiative, the

researchers will develop standard operating procedures or guidelines and integrate

safety assessments into the planning process. Researchers will seek the guidance of a

qualified traffic expert for the design and testing of traffic signals and coding for

59
intelligence transportation system.

F. DATA ANALYSIS
The researchers will gathered the data through surveys and observational

studies on the traffic conditions at Barangay Poblacion in the Municipality of

Las Nieves. On the part of drivers' behavior, a survey will distribute using a

survey questionnaire that seeks to understand how these interactions contribute

to traffic congestion and dynamics.

The project aligns with recent advancements in Agent-Based Modeling

(ABM), which have proven effective in capturing the complexities of traffic

systems and providing insights for transportation planning and infrastructure

development.

G. BIBLIOGRAPHY

Abad, C. R., Santos, M. L., & Javier, A. C. (2020). Urban traffic congestion in

the Philippines: Impacts and solutions. Journal of Philippine Urban

Studies, 25(3), 45-62. https://doi.org/10.xxxx/jphus.2020.25.3.

Retrieved from https://www.who.int/news-room/fact-

sheets/detail/traffic-injuries. Retrieved on December 9, 2024.

Arvin et. al (2021). The role of drivers' social interactions in their driving

behavior: Empirical evidence and implications for car-following and

traffic flow. Transportation Research Part F: Traffic Psychology and

Behaviour, 82, 191-203. Retrieved from

https://doi.org/106/j.trf.2021.04.002. Retrieved on December 13, 2024.

Auld, J. et. al. (2024). POLARIS: Agent-Based Modeling Framework

Development and Implementation for Integrated Travel Demand and

Network Operations Simulations. Transportation Research Part C.

60
 Retrieved from

https://www.sciencedirect.com/science/article/abs/pii/S0968090X1500

2703. Retrieved on December 11, 2024.

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Modeling for Traffic Simulation. Université Mohamed Khider – Biskra.

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December 11, 2024.

Caleda, M. J. A. et. al. (2018). Philippine road safety data: Gaps and challenges.

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https://www.futurarc.com/commentary/metro-manila-traffic/. Retrieved

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December 10, 2024

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FORMS:

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 AGENT-BASED TRAFFIC INFLOW MODELING (ATRIM) ANALYSIS OF
DRIVERS’ BEHAVIOR, TRAFFIC DYNAMICS, AND SYSTEM
MANAGEMENT FOR SUSTAINABLE TRANSPORTATION SYSTEMS

RESEARCH SURVEY QUESTIONNAIRE

Dear Participant,

We are conducting a research study titled "Agent-Based Traffic Inflow Modeling

(ATRIM) Analysis of Drivers’ Behavior, Traffic Dynamics, and System
Management for Sustainable Transportation Systems." This study aims to analyze
driver behavior on highways, understand traffic dynamics, and evaluate the
effectiveness of current traffic management strategies.

Your participation in this survey is voluntary, and all responses will be kept
confidential. The information collected will be used solely for academic purposes and
to improve traffic management strategies. This survey will take approximately 5-10
minutes to complete.

Thank you for your time and valuable insights.

Instructions: Please select the most appropriate answer for each question.

I. DEMOGRAPHIC INFORMATION

1. What type of vehicle do you primarily drive?

o Car
o Motorcycle
o Truck/Van
o Public Transport (as a driver)
o Other (please specify): ___________
2. How many years of driving experience do you have?
o Less than 1 year
o 1-3 years
o 4-6 years
o More than 6 years

II. DRIVER BEHAVIOR ANALYSIS

3. How often do you feel that traffic conditions push you to drive more
aggressively than you normally would?
o Always
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 o Sometimes
o Rarely
o Never
4. Do you tend to change lanes frequently during traffic congestion?
o Yes, to try to move faster
o No, I stay in my lane
5. How do you feel about drivers who cut into lanes without signaling?
o Very annoyed
o Slightly annoyed
o Neutral
o Supportive
6. How safe do you feel when driving on highways with high traffic volumes?
o Very safe
o Somewhat safe, but concerned about aggressive driving
o Neutral, I’m not sure
o Somewhat unsafe
o Very unsafe
7. What do you think are the biggest safety risks when driving on highways?
(Select all that apply)
o Speeding and aggressive driving
o Tailgating or following too closely
o Lane weaving or changing lanes without signaling
o Poor visibility or weather conditions
o Poor road conditions (e.g., potholes, uneven surfaces)
o Distracted driving (e.g., phone use, eating)
o Other (please specify): ___________

III. TRAFFIC DYNAMICS AND CONGESTION FACTORS

8. How often do you encounter traffic congestion?

o Rarely
o Sometimes
o Frequently
9. What factors contribute to traffic congestion in your area? (Select all that
apply)
o Road construction/repairs
o Poor road design
o Accidents

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 o Lack of public transport options
o High vehicle volume
o Other (please specify): ___________
10. What type of traffic issue frustrates you the most?

 Slow-moving traffic
 Aggressive drivers
 Lack of traffic signals/signage
 Poorly maintained roads
 Other (please specify): ___________

11. What are the effects of road infrastructure on traffic dynamics?

 Improves traffic flow and safety

 Causes delays due to poor design
 Leads to congestion during peak hours
 No significant effect
 Other (please specify): ___________

IV. TRAFFIC MANAGEMENT STRATEGIES

12. How effective do you think current traffic management strategies (e.g.,
traffic lights, road signs, lane markings) are in reducing congestion?

 Very effective
 Somewhat effective
 Neutral
 Somewhat ineffective
 Very ineffective

13. What traffic management strategies do you believe should be prioritized

to improve traffic flow? (Select all that apply)

 Intelligent traffic signals (e.g., adaptive signal control)

 Dedicated bus or bike lanes
 Congestion pricing (e.g., charging for entry to congested areas)
 Improved traffic surveillance and enforcement
 Better road design (e.g., more lanes, wider roads)
 Public transportation incentives (e.g., more buses, reduced fares)

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  Pedestrian-friendly infrastructure (e.g., wider sidewalks, crossings)
 Improved signage and traffic alerts
 Expansion of carpool lanes or HOV (High Occupancy Vehicle) lanes
 Road user education programs
 Other (please specify): ___________

14. Do you believe the current traffic management strategies (e.g., signal
timings, lane design) are effective in reducing congestion?

 Yes
 No
 Not Sure

15. What role do local government units play in enforcing traffic regulations
effectively?

 Strict enforcement of traffic laws

 Implementing better road infrastructure
 Educating the public about traffic safety
 Ineffective or lack of enforcement
 Other (please specify): ___________

V. ADDITIONAL COMMENTS

16. Do you have any suggestions on how traffic congestion and management
strategies can be improved?





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Curriculum Vitae

Nichole Shane Amor

P 3 San Isidro, Las Nieves Agusan del Norte
+63908219825
Nicholeshaneamor6@gmail.com

PERSONAL PROFILE

Age: 16

Date of birth: December 17, 2008

Place of birth: Las Nieves

Civil status: Single

Citizenship: Filipino

Gender: Female

Religion: Roman Catholic

Father’s Name: N/A

Mother’s Name: Meddie A. Amor

EDUCATIONAL ATTAINMENT

PRIMARY:

Agyabao Elementary School

San Isidro, Las Nieves Agusan del Norte

SECONDARY:

Las Nieves National High School

Poblacion, Las Nieves Agusan del Norte

80
 Wynde Grace T. Lumarda
Mat-i, Las Nieves Agusan del Norte
+639380158361
Lumardagrace6@gmail.com

PERSONAL PROFILE

Age: 16

Date of birth: May 19, 2008

Place of birth: Mat-I, Las Nieves

Civil status: Single

Citizenship: Filipino

Gender: Female

Religion: Roman Catholic

Father’s Name: Edbryan C. Lumarda

Mother’s Name: Honeylyn T. Tinapay

EDUCATIONAL ATTAINMENT

PRIMARY:

Mat-I Central Elementary School

Mat-I, Las Nieves Agusan del Norte

SECONDARY:

Las Nieves National High School

Poblacion, Las Nieves Agusan del Norte

81
 Lady Jean C. Nieves
Florida, Butuan City
+639517036565
nievesladyjean4@gmail.com

PERSONAL PROFILE

Age: 16

Date of Birth: March 16, 2008

Place of Birth: Florida Butuan City

Civil Status: Single

Citizenship: Filipino

Gender: Female

Religion: Catholic

Father’s Name: Teodoro C. Nieves

Mother’s Name: Luzminda C. Nieves

EDUCATIONAL ATTAINMENT

PRIMARY:

Florencio R. Sibayan Central Elementary School

SECONDARY:

Florida National High School

Las Nieves National High School

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