Средства моделирования дорожного движения / Traffic Flow Factor Framework тема диссертации и автореферата по ВАК РФ 00.00.00, кандидат наук Хабиншути Франсуа Ксавье

• Хабиншути Франсуа Ксавье
• кандидат наук
• 2022

Оглавление диссертации кандидат наук Хабиншути Франсуа Ксавье

TABLE OF CONTENTS

DECLARATION.....................................................................................................................i

COPYRIGHT...........................................................................................................................ii

DEDICATION........................................................................................................................iii

ACKNOWLEDGEMENT.......................................................................................................iv

ABSTRACT............................................................................................................................v

TABLE OF CONTENTS.........................................................................................................vi

LIST OF FIGURES.................................................................................................................xi

LIST OF ABBREVIATIONS.................................................................................................xii

Chapter

Introduction

1.1. Overview

1.2. Background of the Study

1.3. Statement of the Problem

1.4. Purpose of the Study

1.5. Research Objectives

1.6. Research Questions

1.7. Expected Outcomes

1.8. Assumptions of Simulation

1.9. Scope of Simulation

1.10. Limitations of the Simulator

1.11. Significance of the Study

1.12. Theoretical Framework

1.13. Conceptual Framework

Chapter

Literature Review

2.1. Overview

2.2. Different Types of State-of-the-Art Traffic Simulators

2.3. Tools for Assessing Alternative Configurations and Model Impacts in Modelling

2.3.1. Microscopic Models

2.3.2. Driver Diversity

2.3.3. Driving Multitasking Engagement

2.3.4. Attitude and Matureness

2.3.5. Psychoactive Agents

2.3.6. How Signage Affects

2.3.7. Signage Immoderate

2.3.8. Each Sign's Uniqueness

2.3.9. Traffic Signage Working Area

2.3.10. Errors while Driving

2.3.11. Experience and Habits

2.3.12. The Spiral Pattern and Car Following Behavioral Patterns

2.3.13. Vehicle Model Based on Vectors

2.3.14. Traffic Research Institute

2.3.15. Kurt Lewin

2.4. Domain Specific Languages Used to Create a Unified Modelling Approach

2.4.1. O2 DSL Model

2.4.2. ATHOS

2.4.3. DBMs

2.4.4. How Single Model Frameworks Are Being Implemented

2.5. Gaps Findings Level

Chapter

Methodology

3.1. Overview

3.2. Research Design

3.3. Area of Study

3.4. Sample and Sampling Procedure

3.5. Pilot Study

3.6. Validity and Reliability of Instruments

3.7. Method of Data Analysis

3.8. Ethical Considerations

3.9. Model Development

Chapter

RESULTS

4.1. Overview

4.2. TFFF Architecture

4.3. TFFF Framework Core

4.3.1. Built-in Factors

4.3.2. Internal DSL

4.3.3. Distractions

4.3.4. Attributes

4.3.5. Calculation Model

4.4. MATSim Integration

4.4.1. Input Data

4.4.2. Startup Handler

4.4.3. Runtime

4.4.4. Adapter

4.4.5. MATSim Configuration

4.5. TFFF Infrastructures and Factors Simulation

4.6. The Simulator Infrastructures

4.6.1. TFFF Core

4.6.2. TFFF MATSim Integration

4.6.3. TFFF Visualizer

4.7. The Factors Simulation

4.7.1. Terms

4.7.2. Calculation Model

4.7.3. Driver and Environment Attributes

4.7.4. Built-in Factors

4.7.5. Aggression Adjusts

4.7.6. Reaction Time Adjusts

4.7.7. Speed Limiting

4.7.8. Weather Factor

4.7.9. Traffic Camera Factor

4.7.10. Traffic Police Factor

4.8. Domain Specific Language (DSL) of TFFF

4.8.1. How it Works

4.8.2. Calculated Factors

4.8.3. Applicable Factors

4.8.4. Use Case MATSim Integration

Chapter

Discussion

5.1. Overview

5.2. Experiment Settings Scenario: Tverskaya

5.2.1 Trip Times and Distances

5.2.2 Simulation Results Regarding Speed Statistics

5.2.3 . Simulation Results Regarding Traffic Flow Factors

Chapter

Conclusion and Future Recommendation

6.1. Conclusion

6.2. Future Recommendation

References

Appendices

LIST OF FIGURES

Figure 1: Simulator Architecture

Figure 2: TFFF Architecture

Figure 3: TFFF Launcher GUI

Figure 4: OTF Visualizer

Figure 5: TFFF Network Editor

Figure 6: TFFF Visualizer

Figure 7: Histogram Bin

Figure 8: Calculation Model

Figure 9: Unified Factors Deterministic Model Calculation Algorithm Flowchart

F igure 10: Interfaces

Figure 11: Built-in Factor Classes

Figure 12: Driving Aggressiveness versus Age and Gender

Figure 13: Reaction Time

Figure 14: Speed versus Aggressiveness

Figure 15: Speed versus Cognitive Load

Figure 16: Plot for Different Base Reaction Times and Road Lengths when Moving Between

Intersections

Figure 17: Plot of Factor Values for Multiple Weather Interference Values by Temperature

Figure 18: Portion of Tverskaya Region

Введение диссертации (часть автореферата) на тему «Средства моделирования дорожного движения / Traffic Flow Factor Framework»

ABSTRACT

The objective of this study was to implement an appropriate and user-friendly tool for modeling traffic flow factors. Traffic flow can be influenced by numerous factors, including the driver's mental state or logical condition, weather conditions, road conditions, vehicle performance characteristics, traffic laws factor, traffic signage factor, and so many others. Therefore, the importance of introducing a convenient tool that could facilitate throughput, homogeneously describing the effects of these factors cannot be overemphasized. The outlined factors were loaded into the overarching principle of motion modeling and the framework modeling during the simulation analysis. This work proposed the development and implementation of a software simulation system or simulator named the Traffic Flow Factor Framework (TFFF or TF3) and its internal Domain Specific Language (DSL). In addition to designing a Unified Factors Deterministic Model (UFDM) and a Unified Factors Deterministic Model Calculation Algorithm (UFDMCA), the above approach will straightforwardly encompass deterministic models in and out of designed UFDM algorithms thereby, simulating each factor.

The new model UFDM is geared to simulate various traffic flow factors through TFFF, a software simulation model developed as well as the internal DSL of the TFFF created. Using deterministic model analysis, the framework will assess the impact of these factors. Furthermore, the model created acknowledges the influence of other stimulants from the surroundings. The more realistic interpretation and prediction of a driver's behavior and its consequences was accomplished by creating a system based along each driver's awareness or reaction to significant factors. The UFDM seeks to measure and model factors on a roadway that could influence a driver's decision, such as traffic signs, pedestrians and vehicles in the surroundings, lane closure, and several others.

Заключение диссертации по теме «Другие cпециальности», Хабиншути Франсуа Ксавье

Chapter 6

Conclusion and Future Recommendation 6.1.Conclusion

In this project, we addressed the problem of integrating real-world factors (weather, driver attributes, etc.) into agent-based transport simulations. One of the fundamental ideals of the system was to provide a unified framework for expressing and embedding mathematical models of real-world factors into a simulation engine to enrich the simulation and make it more realistic.

A unified calculation model named Unified Factors Deterministic Model (UFDM), which aims to unite input attributes like weather interference, temperature, driver age, speed limits, etc. by using a synthesis of scientific methods (factors) to obtain intermediary or final parameter adjustments, which can further be supplied to the simulation engine.

The presented technique adopts its foundation in deterministically calculated mathematical functions (models), which are expressed as functions of multiple parameters (attributes), returning a single value of a corresponding type which is further combined by using combinators with related factors to obtain final results.

The deterministic nature of models ensures their reproducibility and testability, and they can easily be fitted or proven by using existing real-world data, and adjusted to fit a specific simulation scenario. Another contribution is that the system was developed to decipher driver distractions, a major factor that affects driving performance. Distraction is an activity that takes attention away from driving. Examples include the use a smartphone, talking, eating and drinking, etc. Distractions slow down drivers and increases the risks of road accidents.

In addition, an approach for describing and implementing custom factors was provided by implementing a DSL, which is based on Apache Groovy, which is utilized to add additional factors to the model for a particular scenario without altering the source code. One-sample scenario based

on road networks was provided and analyzed, thereby confirming the correct and realistic behavior in the network while also, highlighting the possible issues and inefficiencies for improvement.

6.2.Future Recommendation

Many additional factors and experiments were left for the future due to the limited time and the paucity of real-time data to base them on. Our model currently addresses only resulting speeds due to state-of-the-art simulation engine, it does not have a way to express other attributes.

Some suggestions that could be recommended for future inclusion are;

i) To determine if the implemented factors also have an effect on the possibilities of crashing (like weather, aggressive driving, etc.), since the simulation engine we have examined currently does not have the ability or systems to add traffic accidents to the simulation. It will be interesting to add extra complexity to the implemented factors by allowing them to also influence accident rates, and implement accidents into the simulation engine and observe the resulting behavior.

ii) It should be possible to add an ability to define custom calculation models either through DSL or by any other means. The proportion of tasks recommended for the core API to support these operations should be minimal, but there should be related entry points on the simulation side for these values plugged in, which returns us to the question of implementing additional systems for the simulation engine.

iii) The framework intended should be adaptable, and given enough time and data, those ideas could be implemented, enriching the simulation and adding more information for further analysis thereby, solving real-world traffic problems.

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