700.372 (17W) Simulation Lab for Transportation and Logistics

Wintersemester 2017/18

Anmeldefrist abgelaufen.

Erster Termin der LV
04.10.2017 12:00 - 14:00 L4.1.02 ICT-Lab Off Campus
... keine weiteren Termine bekannt

Überblick

Lehrende/r
LV-Titel englisch Simulation Lab for Transportation and Logistics
LV-Art Kurs (prüfungsimmanente LV )
Semesterstunde/n 2.0
ECTS-Anrechnungspunkte 3.0
Anmeldungen 5 (30 max.)
Organisationseinheit
Unterrichtssprache Englisch
LV-Beginn 04.10.2017
eLearning zum Moodle-Kurs

Zeit und Ort

Liste der Termine wird geladen...

LV-Beschreibung

Intendierte Lernergebnisse

This lecture familiarises students with the development of simulatlion algorithms (using MATLAB and SIMULINK) for the analysis of systems, scenarios and phenomena in transportation. Various MATLAB codes are developed to solve linear and nonlinear optimization problems. MATLAB codes are also developed for solving the shortest path problem (SPP) and the  traveling salesman problem (TSP) in graph networks. SIMULINK is further used to implement the „Car-following“ model and the analysis of the dynamics of „HEADWAY“ on  arterial roads. Students are also familiarized (through this lecture) with the simulation toolboxes for traffic signal control at junctions. An overview of the traffic simulation tools is provided. The signal optimization Tool- SYNCHRO 7/9 is used as application example for traffic control at isolated junction. Using SYNCHRO 7/9 different signal control strategies are implemented: The pretimed-, Actuated-, Semi actuated, and Roundabout- control strategies.

Overall, the main objectives of this lecture are expressed by the following keywords: Simulation algorithms for the analysis of traffic flow; Simulation algorithms for solving linear and nonlinear optimization problems; Simulation algorithms for solving the shortest path problem and the traveling salesman problem; Design of the SIMULINK scheme for the analysis of „Car following“ model. Use of the traffic optimization tool SYNCHRO 7/9 for the implementation of different signal control strategies.

The general expectation regarding the knowledge to be provided/acquired is as follows:

  • Understanding of the basic functioning principles of traffic simulation tools.
  • Mastering of the development of simulation algorithms for solving linear and nonlinear optimization problems.
  • Mastering of the development of simulation algorithms for solving the shortest path problems in graph networks.
  • Mastering of the development of simulation algorithms for solving the traveling salesman problem in graph networks.
  • Mastering of the design and implementation of the SIMULINK scheme for solving the „Car-following“ model in transportation and the analysis of the dynamics of „HEADWAY“ on arterial roads.
  • Mastering of the development of simulation algorithms for solving models of macroscopic traffic flow (PDEs) and models of microscopic traffic flow (ODEs).
  • Mastering of the use of „SYNCHRO 7/9“ to implement different signal control startegies (Pretimed-, Actuated- and Roundabout- strategies) at isolated traffic junctions.

Lehrmethodik inkl. Einsatz von eLearning-Tools


1. The Lecturer provides full explanation of how to write numerical codes to solve the exercises proposed in each chapter of the theoretical part of the lecture entitled: 700.303 (17W) Methods of Transportation Informatics and Logistics.

The following codes will be explained by the Lecturer:

* Numerical codes for linear optimization: Thelinear programming (LP) toolbox in MATLAB.

* Numericalcodes for nonlinear optimization: The quadratic programming (QP) toolbox inMATLAB.

*  MATLAB codes/algorithms for solving the shortestpath problem (SPP) using the  BasicDifferential Multiplier Method (BDMM)- Neurocomputing.

*  MATLAB codes/algorithmsfor solving the traveling salesman problem (TSP) using the  Basic Differential Multiplier Method (BDMM)-Neurocomputing.

* SIMULINK scheme for „Car-following“ model.

* SYNCHRO 7/9 toolbox for traffic signal optimization atisolated junctions

2. Students ingroups of two must develop/write numerical codes to solve the exercices proposedby the Lecturer as additional application examples for the good understanding ofthe lecture.

3. Numerical codes are developed by students (in groups of two)as projects. These codes are developed in accordance to each of the chaptersconsidered in the theoretical part of the lecture entitled: 700.303 (17W)Methods of Transportation Informatics and Logistics.

Inhalt/e

Chapter 1. Introduction to traffic simulation, management and control.

Chapter 2. Overview on traffic simulation models: Macroscopic and microscopic models

Chapter 3. Overview on traffic Management, Control and Optimization Systems/tools: Synchro7, Cube5, Vissim, Visum, Spot, Utopia, Transyt, Sidra, SCOOT, SCAT, etc.

Chapter 4. Simulation algorithms for optimization: Linear programming (LP) and Quadratic programming (QP) for linear and nonlinear optimization with all types of constraints

Chapter 5. Simulation algorithms for optimization: The Basic Differential Multiplier Method (BDMM)/Neurocomputing) for linear and nonlinear optimization with all types of constraints

Chapter 6. Simulation algorithms for Shortest Path (SP) and Traveling Salesman Problem (TSP): The Neuro-dynamics concept (BDMM/Neurocomputing)

Chapter 7. Simulation of traffic flow on arterial roads: Design of the „Car-following“ model in SIMULINK and analysis of the „HEADWAY“ dynamics on arterial roads.

Chapter 8. Simulation algorithms for queueing systems: M/M/n, G/G/n, D/D/n, M/D/n, etc.

Chapter 9. Simulation tool for traffic signals control at isolated junction (SYNCHRO 7/9): Design and analysis of different signal control strategies – Pretimed control – Actuated control – Semi actuated – Roundabout.


Literatur

Textbooks 

[1] Martin Treiber, and Arne Kesting, „Traffic Flow Dynamics: Data, Models and Simulation,“ Springer-Verlag, Berlin Heidelberg, ISBN 978-3-642-32460-4, 2013

[2]. F. M. Ham and I. Kostanic, „Principles of Neurocomputing for Science , & Engineering,“ New York, NY, USA: McGraw-Hill, 2001.

[3] Adam B. Levy, „The Basics of Practical Optimization,“ SIAM, The society of industrial and applied mathematics, ISBN 978-0-898716-79-5, 2009

[4] Nocedal J. and Wright S.J., „Numerical Optimization,“ Springer Series in Operations Research, Springer, 636 pp, 1999.

[5] Saidur Rahman, „Basics of Graph Theory,“ Springer, ISBN: 978-3-319-49474-6, 2017


Journal Papers 

[1]  J. C. Platt and A. H. Barr, “Constrained differential optimization for neural networks,” American Institute of Physics, Tech. Rep. TR- 88-17, pp. 612-621, Apr. 1988.

[2] I. G. Tsoulos, D. Gavrilis, and E. Glavas, “Solving differential equations with constructed neural networks,” Neurocomputing, vol. 72, nos. 10–12, pp. 2385–2391, Jun. 2009.

Prüfungsinformationen

Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.

Prüfungsmethode/n

  • oral and written

Prüfungsinhalt/e

1. Development of MATLAB codes for the optimization of a *Linear function and a *Nonlinear function using Linear programming (LP) and Quadratic programming (QP) toolboxes. The case of all types of constraints will be considered

2.  Development of a MATLAB code based on the Basic Differential Multiplier Method (BDMM) / Neurocomputing for solving a Shortest Path Problem (SPP) in a directed graph network.

3.  Development of a MATLAB code based on the Basic Differential Multiplier Method (BDMM) / Neurocomputing for solving a Traveling Salesman Problem (TSP) in an undirected graph network.

4. Design and Synthesis of a SIMULINK scheme for solving a model of „Car-following“ on arterial roads and analysing the „HEADWAY“ dynamics .

5. Design and Synthesis using SYNCHRO 7 of 2 traffic signal control strategies (Pretimed- and Actuated- strategies) at 2 different isolated traffic junctions.

Beurteilungskriterien/-maßstäbe

  • 30%: Presence + participation in class 
  • 20%: Small project 
  • 50%: Final exam at the end of the semester

Beurteilungsschema

Note Benotungsschema

Position im Curriculum

  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Information and Communications Engineering: Supplements (NC, ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (Anhang 4) ( 0.0h VK, VO, KU / 14.0 ECTS)
        • 700.372 Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Technical Complements (NC, ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (Anhang 5) ( 0.0h VK, VO, KU / 12.0 ECTS)
        • 700.372 Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Information and Communications Engineering: Supplements (NC, ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (Anhang 4) ( 0.0h VK, VO, KU / 14.0 ECTS)
        • 700.372 Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Technical Complements (NC, ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (Anhang 5) ( 0.0h VK, VO, KU / 12.0 ECTS)
        • 700.372 Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technischer Schwerpunkt (Intelligent Transportation Systems) (Pflichtfach)
      • 1.4-1.5 Kurs oder Labor ( 4.0h KU / 6.0 ECTS)
        • 700.372 Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technische Ergänzung II (Pflichtfach)
      • 3.4-3.5 Kurs oder Labor ( 4.0h KU / 6.0 ECTS)
        • 700.372 Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Sommersemester 2023
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2022/23
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Sommersemester 2022
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Sommersemester 2021
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Sommersemester 2020
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2018/19
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2016/17
  • 700.372 KS Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2015/16
  • 700.372 KS Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2014/15
  • 700.372 KU Simulation Lab for Transportation and Logistics (1.0h / 1.5ECTS)
Wintersemester 2013/14
  • 700.372 KU Simulation Lab for Transportation and Logistics (1.0h / 1.5ECTS)
Wintersemester 2012/13
  • 700.372 KU Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)