72858 - Transport Planning M

Learning outcomes

The course of Transport Planning M has the objective of providing the principal scientific fundamentals for the simulation of transportation systems: in particular, the course provides the fundamental tools for the quantitative evaluation of impacts perceived by the users in relation to alternative projects of transportation system.

Course contents

Requirements/Prior knowledge

A prior knowledge and understanding of basic topics of the engineering of transportation system is required to attend with profit this course. In addition, students should master the Tecnica ed Economia dei Trasporti T.

Fluent spoken and written Italian is a necessary pre-requisite: all lectures and tutorials, and all study material will be in Italian.

Introduction

Transportation systems engineering and planning process. levels, objectives and analysis tools in transport planning. Structure of the models for the simulation of transportation systems.

Modelling transportation system

Study area. zoning; location of zone centroids. Zoning rules. Internal and external centroids: zoning practical examples. Assumption of intra-period and inter-period stationarity.

Transportation supply

Transportation supply models. Review of graph and network models. Graph construction and functional classification of urban streets. Graphs for a road intersection. Road network representation: link flow and link cost function. Path flow vector and path cost vector. Separable and non-separable cost functions. Capacity constraints in road transport networks: practical examples.

Environmental capacity constraints in urban transport networks: air pollution modelling

Passenger travel demand

Review of random utility models (multinomial Logit models). The single-level hierarchical Logit model. System of demand models (destination and mode choice). Path choice models.

Traffic assignment to road transportation networks

Classification factors of assignment models. Path choice behaviour. Wardrop principles. Rigid demand user equilibrium assignment models. System optimal assignment models. Braess' paradox. Traffic assignment to road transportation networks: stochastic user equilibrium models; relationship between stochastic and deterministic equilibrium flows.

Algorithms for traffic assignment to road transportation networks

Calculation of rigid demand deterministic user equilibrium link flows with separable cost functions: Frank-Wolfe algorithm. Practical examples. Diagonalization algorithm

Estimation of O-D demand flows using traffic counts

The assignment matrix. Update of the origin-destination matrix using traffic counts. Calculation of the assignment matrix.

 

Elements of cycling mobility.

Manual and automatic methods for bicycle counting both on- and off-street. Bike route choice modeling.

 

Methods for the comparison of alternative projects.

Methods for choosing among alternative transportation projects: Multi-criteria analysis. Compensatory and non- compensatory methods: the Electre I method; the analytic hierarchy process. Practical examples.

Readings/Bibliography

P. Ferrari, Fondamenti di pianificazione dei Trasporti, Pitagora Editrice, 2001.

E. Cascetta, Modelli per i sistemi di trasporto Teoria ed applicazioni, ed. UTET, 2006.

A. Pratelli, Ingegneria dei sistemi di trasporto Esercizi ed esempi, Pitagora Editrice, 2007.

M. Lupi, Dispense del corso “Tecnica ed Economia dei Trasporti”, 2002

P. Volta, F. Rupi, "Trasporto merci: da Babele a sistema. Il valore della programmazione nella movimentazione delle merci", Gruppo24ore, 2011

S. Maffii, R. Parolin, R. Scatamacchia, "Guida alla valutazione economica di progetti di investimento nel settore dei trasporti", FrancoAngeli, 2011

E. Cascetta, Transportation System Analysis: Models and Applications, Springer, New York, (2009) 

Juan de Dios Ortuzar, Luis G. Willumsen, Modelling Transport, 4th Edition, Wiley, 2011

Teaching methods

The course will include lectures and exercises.

Assessment methods

Achievements will be assessed by the means of a final exam. This is based on an analytical assessment of the "expected learning outcomes" described above. In order to properly assess such achievement the examination is oral with discussion of the project.

Higher grades will be awarded to students who demonstrate an organic understanding of the subject, a high ability for critical application, and a clear and concise presentation of the contents. To obtain a passing grade, students are required to at least demonstrate a knowledge of the key concepts of the subject, some ability for critical application, and a comprehensible use of technical language. A failing grade will be awarded if the student shows knowledge gaps in key-concepts of the subject, inappropriate use of language, and/or logic failures in the analysis of the subject.

Teaching tools

The slides that are shown in class are made available to students through the link AMS Campus. Software for the project and lecture notes are made available to students too.

Office hours

See the website of Federico Rupi