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Simulating Urban Traffic Scenarios

Overview of attention for book
Cover of 'Simulating Urban Traffic Scenarios'

Table of Contents

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    Book Overview
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    Chapter 1 Application of the SCRUM Software Methodology for Extending Simulation of Urban MObility (SUMO) Tools
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    Chapter 2 Multi-resolution Traffic Simulation for Large-Scale High-Fidelity Evaluation of VANET Applications
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    Chapter 3 How Does the Traffic Behavior Change by Using In-Vehicle Signage for Speed Limits in Urban Areas?
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    Chapter 4 Connecting Macroscopic and Microscopic Traffic Assignment
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    Chapter 5 A SUMO Extension for Norm-Based Traffic Control Systems
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    Chapter 6 Public Transport, Logistics and Rail Traffic Extensions in SUMO
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    Chapter 7 Accurate Vehicle Simulation in Logistic and Manufacturing Planning
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    Chapter 8 Flood Impacts on Road Transportation Using Microscopic Traffic Modelling Techniques
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    Chapter 9 Improving Traffic Lights System Management by Translating Decisions of Traffic Officer
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    Chapter 10 eNetEditor: A Platform-Independent Tool for Rapid Creation of Urban Traffic Scenarios and for the Optimization of Their Energy Supply Infrastructure
Attention for Chapter 6: Public Transport, Logistics and Rail Traffic Extensions in SUMO
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Citations

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Chapter title
Public Transport, Logistics and Rail Traffic Extensions in SUMO
Chapter number 6
Book title
Simulating Urban Traffic Scenarios
Published by
Springer, Cham, January 2019
DOI 10.1007/978-3-319-33616-9_6
Book ISBNs
978-3-31-933614-5, 978-3-31-933616-9
Authors

Andreas Kendziorra, Melanie Weber, Kendziorra, Andreas, Weber, Melanie

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 33%
Student > Master 1 17%
Other 1 17%
Unknown 2 33%
Readers by discipline Count As %
Engineering 4 67%
Unknown 2 33%

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