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Reconstruction of vehicle trajectories with dynamic macroscopic data

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Lately the computation of emissions has become more crucial. The level of emissions of traffic can be determined making use of simulation models, to check out ex-ante the effects of certain measures. The majority of the appropriate models are microscopic simulation models. Thess kind of models are vehicle based and cannot take into account large areas. Models which can take into account large areas usually are not vehicle based. The final results of these models are averaged values for speeds and low rates. Literature reveals that emission calculation with these averaged values is just not accurate enough. Beside this, visualization of vehicle movements enables you to understand the performance of a traffic system. Microscopic models can visualize the cars, while macroscopic models cannot do that. Alternatively, microscopic models are stochastic models. Macroscopic models are, on the other hand, deterministic and able to calculate the traffic flows in larger areas. As a result, in this study an approach is developed that is prepared to combine the best of the 2 different models by reconstructing vehicle trajectories according to dynamic macroscopic data. This technique makes it possible to visualize vehicle movements and to determine emissions, while it is still macroscopic and deterministic.

Video on vehicle trajectory reconstruction

Contents

1 Introduction
1.1 Vehicle Trajectories
1.2 Macroscopic Data
1.3 Relevance
1.4 Preview
2 Description of the research
2.1 Objective
2.2 Research question
2.3 Scope
2.4 Approach
2.4.1 Two models
2.4.2 Two data sets
2.4.3 Two validations
3 Literature Review
3.1 Trac models
3.1.1 Macroscopic models
3.1.2 Microscopic models
3.1.3 Mesoscopic models
3.2 Related issues
3.2.1 Hybrid models
3.2.2 Trajectory studies
3.2.3 Environmental studies
3.2.4 Macroscopic interpolation
3.2.5 Fundamental diagrams
4 Trajectory reconstruction methods
4.1 Simple model
4.2 Reconstruction based on interpolation techniques
4.2.1 Estimation of the fundamental diagram
4.2.2 Linear interpolation
4.2.3 Taylor series expansions
viiContents
4.2.4 Trajectory reconstruction
4.3 Other possible reconstruction methods
4.3.1 Characteristic lines
4.3.2 Stepwise interpolation
4.3.3 Trajectories solved with a di erential equation
5 Model validation
5.1 Validation methods
5.2 Network description
5.3 Validation results
5.4 Analysis of the results
5.4.1 Microscopic validation
5.4.2 Macroscopic validation
5.4.3 Conclusions
6 Case studies
6.1 Network description
6.2 Assumptions
6.3 Results
7 Conclusions
7.1 General conclusions
7.2 Discussion
7.3 Further research
Bibliography
A Calculations simple model
A.1 Simple model
A.1.1 Step 1: Headway distribution
A.1.2 Step 2: Arrival time estimation
A.1.3 Step 3: Trajectory reconstruction
B Calculations interpolation models
B.1 Derivation of the Taylor series expansion
C Results of the validation
C.1 Validation at microscopic data level
C.2 Validation at macroscopic data level
C.2.1 Speed calculation
C.2.2 Results

Source: University of Twente

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