Car following (CF) behaviour modelling has made significant progress in both traffic engi-neering and traffic psychology during recent decades. Autonomous vehicles (AVs) have been demonstrated to optimise traffic flow and increase traffic stability. Consequently, sever-al car-following models have been proposed based on various car following criteria, leading to a range of model parameter sets. In traffic engineering, Intelligent Driving Model (IDM) are commonly used as microscopic traffic flow models to simulate a single vehicle's behav-iour on a road. Observational data can be employed to parameter calibrate IDM models, which enhances their practicality for real-world applications. As a result, the calibration of model parameters is crucial in traffic simulation research and typically involves solving an optimization problem. Within the given context, the Nelder-Mead（NM）algorithm, particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are utilized in this study for parameterizing the IDM model, using abundant trajectory data from five different road conditions. The study further examines the effects of various algorithms on the IDM model in different road sections, providing useful insights for traffic simulation and optimization.:Table of Contents
CHAPTER 1 INTRODUCTION 1
1.1 BACKGROUND AND MOTIVATION 1
1.2 STRUCTURE OF THE WORK 3
CHAPTER 2 BACKGROUND AND RELATED WORK 4
2.1 CAR-FOLLOWING MODELS 4
2.1.1 General Motors model and Gazis-Herman-Rothery model 5
2.1.2 Optimal velocity model and extended models 6
2.1.3 Safety distance or collision avoidance models 7
2.1.4 Physiology-psychology models 8
2.1.5 Intelligent Driver model 10
2.2 CALIBRATION OF CAR-FOLLOWING MODEL 12
2.2.1 Statistical Methods 13
2.2.2 Optimization Algorithms 14
2.3 TRAJECTORY DATA 21
2.3.1 Requirements of Experimental Data 22
2.3.2 Data Collection Techniques 22
2.3.3 Collected Experimental Data 24
CHAPTER 3 EXPERIMENTS AND RESULTS 28
3.1 CALIBRATION PROCESS 28
3.1.1 Objective Function 29
3.1.2 Errors Analysis 30
3.2 SOFTWARE AND METHODOLOGY 30
3.3 NM RESULTS 30
3.4 PSO RESULTS 37
3.4.1 PSO Calibrator 37
3.4.2 PSO Results 44
3.5 GA RESULTS 51
3.6 OPTIMIZATION PERFORMANCE ANALYSIS 58
CHAPTER 4 CONCLUSION 60
REFERENCES 62
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:88957 |
| Date | 11 January 2024 |
| Creators | Yang, Zhimin |
| Contributors | Okhrin, Ostap, Hirte, Georg, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:masterThesis, info:eu-repo/semantics/masterThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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