Suchergebnisse

Intro -- Acknowledgement -- Abstract -- Zusammenfassung -- Contents -- List of Figures -- List of Tables -- List of Algorithms -- Acronyms -- Glossary -- 1 Introduction -- 1.1 Controlling traffic lights -- 1.2 Data acquisition methods -- 1.3 Airborne sensors -- 1.4 Self-Organizing Traffic Lights -- 1.5 Traffic optimization goals -- 1.6 Greenhouse gas -- 1.7 Vehicles' emissions -- 1.8 Hypothesis and the research questions -- 1.9 Organization of the thesis -- 2 Comparison of travel time es-timations using intelligent in-frastructure and floating car data -- 2.1 Introduction -- 2.2 State of the art -- 2.3 Simulation environment -- 2.4 Methods -- 2.5 Measurements -- 2.6 Results -- 2.7 Summary -- 3 Integration of cellular automata traffic simulation with carbon dioxide emission model -- 3.1 Traffic simulators -- 3.2 Greenhouse gases and carbon dioxide emis-sion -- 3.3 CO2 emission model -- 3.4 Microscopic traffic simulator -- 3.5 Cellular automata traffic simulator -- 3.6 Integration of cellular automata traffic sim-ulation with emission model -- 3.6.1 Data acquisition -- 3.6.2 Data preparation -- 3.6.3 Vehicles' acceleration rate -- 3.6.4 Data analysis -- 3.6.5 Distribution of the acceleration -- 3.6.6 Deceleration -- 3.6.7 Adaptation of Rule 184 -- 3.7 Rule 184-based CO2 traffic emission model -- 3.7.1 Implementation note -- 3.8 NaSch-based CO2 traffic emission model -- 3.9 Critics, discussion and future work -- 4 The impact of dynamic route guidance system driven by travel time measurements on carbon dioxide emission -- 4.1 Introduction -- 4.2 Dynamic route guidance system architec-ture -- 4.2.1 Shortest Paths Algorithm -- 4.2.2 Cost function -- 4.2.3 Path with minimal cost -- 4.3 Methodology and measurements -- 4.4 Simulation -- 4.4.1 Virtual cities -- 4.5 Test scenarios and results -- 4.5.1 First scenario -- 4.5.2 Second scenario.