Overview:
Mobility Behaviour and Sustainable Mobility
Cascetta, E. (2009). Transportation systems analysis: models and applications
Ben-Elia, E., & Ettema, D. (2011). Rewarding rush-hour avoidance: A study of commuters’ travel
behavior.
Gärling, T., & Fujii, S. (2009). Travel behavior modification: Theories, methods, and programs.
Transportation modes dynamics and new mobilities
Soza-Parra, J., & Cats, O. (2023). The Role of Personal Motives in Determining Car Ownership and Use:
Kalter, M. J. O., Puello, L. L. P., & Geurs, K. T. (2020). Do changes in travellers’ attitudes towards car
use and ownership over time affect travel mode choice? A latent transition approach in the Netherlands.
Zijlstra, T., Durand, A., Hoogendoorn-Lanser, S., & Harms, L. (2020). Early adopters of
Mobility-as-a-Service in the Netherlands.
Ceccato, R., & Diana, M. (2018). Substitution and complementarity patterns between traditional transport
means and car sharing: a person and trip level analysis.
Shaping today’s mobility landscape
Khavarian-Garmsir, A. R., Sharifi, A., & Sadeghi, A. (2023). The 15-minute city
Ibraeva, A., De Almeida Correia, G. H., Silva, C., & Antunes, A. P. (2020). Transit-oriented development
Hidalgo, D., Urbano, C., Olivares, C. L. H., Tinjacá, N., Pérez, J. M. S., Pardo, C. F., Rodríguez, M. M.,
Granada, I., Navas, C., Glen, C., Ramos, C. C., Gutiérrez, M. C., & Pedraza, L. (2020). Mapping
universal access experiences for public transport in Latin America
Transport Poverty and Transport Justice
Lucas, K., & Jones, P. (2012). Social impacts and equity issues in transport
Pereira, R. H., Schwanen, T., & Banister, D. (2017). Distributive justice and equity in transportation
Israel, F., D. Ettema & Van Lierop, D. (2023), Equity Implications for the (Non-) Adoption of Electric
Mobility in the Early Stage of the Energy Transition
,Lecture 1: Mobility Behaviour and Sustainable Mobility (Dick Ettema, 11-11)
Cascetta, E. (2009). Transportation systems analysis: models and applications (Vol. 29). Springer Science
& Business Media. (pp. 89-100, Chapter 3 up to 3.3.1)
Chapter 3: Random Utility Theory
3.1 Introduction
● Transport flows result from the aggregation of individual trips. Each trip is the result of a number
of choices made by transport system users.
○ Infrequently choices: where to reside and work and whether to own a vehicle
○ Choices each trip: whether to make the trip, at what time, to what destination or
destinations, by what mode, and using what path
○ Each choice context, defined by the available alternatives, evaluation factors, and
decision procedures, is known as a “choice dimension.”
→ finite number of discrete alternatives
● Behavorial models: Travel demand models attempt to reproduce user’s choice behaviour
● Random utility theory: the richest and by far the most widely used theoretical paradigm
for modeling transport-related choices and more generally, choices among discrete
alternatives (! not the only behavioral model)
3.2 Basic assumptions
Random utility theory is based on the hypothesis that every individual is a rational decision-maker,
maximizing utility relative to his or her choices. Specifically, the theory is based on the following
assumptions.
1) The generic decision-maker i, in making a choice, considers mi mutually exclusive alternatives
that constitute her choice set Ii . The choice set may differ according to the decision-maker (e.g.,
in the choice of transport mode, the choice set of an individual without a driver’s license or car
obviously should not include the alternative “car as a driver”);
2) Decision-maker i assigns to each alternative j in his choice set a perceived utility or
“attractiveness” Ui j and selects the alternative that maximizes this utility;
3) The utility assigned to each choice alternative depends on a number of measurable characteristics,
or attributes, of the alternative itself and of the decisionmaker: Ui j = Ui (Xi j ), where Xi j is the
vector of attributes relative to alternative j and to decision-maker i;
4) Because of various factors described later, the utility assigned by decisionmaker i to alternative j
is not known with certainty by an external observer (analyst) wishing to model the
decision-maker’s choice behavior, thus Ui j must be represented in general by a random variable.
The set of available alternatives I i , or choice set, significantly influences the choice probabilities
, Those related to the service offered by the transport system are known as level-of-service or performance
attributes (times, costs, service frequency, comfort, etc.). Those related to the land-use characteristics of
the study area (e.g., the number of shops or schools in each zone) are known as activity system attributes.
Those related to the decisionmaker or to his household (income, holding a driver’s license, number of cars
in the household, etc.) are referred to as socioeconomic attributes.
ChatGPT:
The excerpt from Transportation Systems Analysis by Ennio Cascetta (pp. 89-100) focuses on Random
Utility Theory, a widely used framework for modeling transportation choices.
Overview of Random Utility Theory
● Random Utility Theory (RUT) models the decision-making process by which individuals select
among discrete alternatives, such as transport modes or travel routes.
● It assumes that individuals aim to maximize their utility, which is composed of a systematic
(deterministic) component and a random (stochastic) component due to unobserved factors.
Basic Assumptions of RUT
1. Discrete Choices: Individuals make choices from a set of mutually exclusive alternatives (e.g.,
car, bus, walking).
2. Utility Maximization: The utility for each alternative is based on measurable attributes like cost
and time, combined with random residuals that capture unknown influences.
3. Choice Probability: The probability of choosing a specific option is determined by the
comparative utility of all available alternatives.
Ben-Elia, E., & Ettema, D. (2011). Rewarding rush-hour avoidance: A study of commuters’ travel
behavior. Transportation Research Part A: Policy and Practice, 45(7), 567-582.
https://doi.org/10.1016/j.tra.2011.03.003
- The potential of rewards as a policy mean for changing commuter behavior
- A 13 week field study was organized in The Netherlands with the purpose of longitudinally
investigating the impacts of rewards on commuter behavior.
- The results suggest that rewards can be effective tools in changing commuting behavior.
Specifically rewards reduce the shares of rush-hour driving, shift driving to off-peak times and
increase the shares of public transport, cycling and working from home.
- By rewarding those commuters who are willing to shift their commuting times or switch to
alternative travel modes, overall penalization of drivers through tolling is avoided and overall
welfare could well be improved by reducing peak demand.
- These studies reveal that commuters’ habitual behavior of rush-hour driving and constraints
related to household and work schedules are important factors which limit the positive impact of
rewarding on car travel.
Mobility Behaviour and Sustainable Mobility
Cascetta, E. (2009). Transportation systems analysis: models and applications
Ben-Elia, E., & Ettema, D. (2011). Rewarding rush-hour avoidance: A study of commuters’ travel
behavior.
Gärling, T., & Fujii, S. (2009). Travel behavior modification: Theories, methods, and programs.
Transportation modes dynamics and new mobilities
Soza-Parra, J., & Cats, O. (2023). The Role of Personal Motives in Determining Car Ownership and Use:
Kalter, M. J. O., Puello, L. L. P., & Geurs, K. T. (2020). Do changes in travellers’ attitudes towards car
use and ownership over time affect travel mode choice? A latent transition approach in the Netherlands.
Zijlstra, T., Durand, A., Hoogendoorn-Lanser, S., & Harms, L. (2020). Early adopters of
Mobility-as-a-Service in the Netherlands.
Ceccato, R., & Diana, M. (2018). Substitution and complementarity patterns between traditional transport
means and car sharing: a person and trip level analysis.
Shaping today’s mobility landscape
Khavarian-Garmsir, A. R., Sharifi, A., & Sadeghi, A. (2023). The 15-minute city
Ibraeva, A., De Almeida Correia, G. H., Silva, C., & Antunes, A. P. (2020). Transit-oriented development
Hidalgo, D., Urbano, C., Olivares, C. L. H., Tinjacá, N., Pérez, J. M. S., Pardo, C. F., Rodríguez, M. M.,
Granada, I., Navas, C., Glen, C., Ramos, C. C., Gutiérrez, M. C., & Pedraza, L. (2020). Mapping
universal access experiences for public transport in Latin America
Transport Poverty and Transport Justice
Lucas, K., & Jones, P. (2012). Social impacts and equity issues in transport
Pereira, R. H., Schwanen, T., & Banister, D. (2017). Distributive justice and equity in transportation
Israel, F., D. Ettema & Van Lierop, D. (2023), Equity Implications for the (Non-) Adoption of Electric
Mobility in the Early Stage of the Energy Transition
,Lecture 1: Mobility Behaviour and Sustainable Mobility (Dick Ettema, 11-11)
Cascetta, E. (2009). Transportation systems analysis: models and applications (Vol. 29). Springer Science
& Business Media. (pp. 89-100, Chapter 3 up to 3.3.1)
Chapter 3: Random Utility Theory
3.1 Introduction
● Transport flows result from the aggregation of individual trips. Each trip is the result of a number
of choices made by transport system users.
○ Infrequently choices: where to reside and work and whether to own a vehicle
○ Choices each trip: whether to make the trip, at what time, to what destination or
destinations, by what mode, and using what path
○ Each choice context, defined by the available alternatives, evaluation factors, and
decision procedures, is known as a “choice dimension.”
→ finite number of discrete alternatives
● Behavorial models: Travel demand models attempt to reproduce user’s choice behaviour
● Random utility theory: the richest and by far the most widely used theoretical paradigm
for modeling transport-related choices and more generally, choices among discrete
alternatives (! not the only behavioral model)
3.2 Basic assumptions
Random utility theory is based on the hypothesis that every individual is a rational decision-maker,
maximizing utility relative to his or her choices. Specifically, the theory is based on the following
assumptions.
1) The generic decision-maker i, in making a choice, considers mi mutually exclusive alternatives
that constitute her choice set Ii . The choice set may differ according to the decision-maker (e.g.,
in the choice of transport mode, the choice set of an individual without a driver’s license or car
obviously should not include the alternative “car as a driver”);
2) Decision-maker i assigns to each alternative j in his choice set a perceived utility or
“attractiveness” Ui j and selects the alternative that maximizes this utility;
3) The utility assigned to each choice alternative depends on a number of measurable characteristics,
or attributes, of the alternative itself and of the decisionmaker: Ui j = Ui (Xi j ), where Xi j is the
vector of attributes relative to alternative j and to decision-maker i;
4) Because of various factors described later, the utility assigned by decisionmaker i to alternative j
is not known with certainty by an external observer (analyst) wishing to model the
decision-maker’s choice behavior, thus Ui j must be represented in general by a random variable.
The set of available alternatives I i , or choice set, significantly influences the choice probabilities
, Those related to the service offered by the transport system are known as level-of-service or performance
attributes (times, costs, service frequency, comfort, etc.). Those related to the land-use characteristics of
the study area (e.g., the number of shops or schools in each zone) are known as activity system attributes.
Those related to the decisionmaker or to his household (income, holding a driver’s license, number of cars
in the household, etc.) are referred to as socioeconomic attributes.
ChatGPT:
The excerpt from Transportation Systems Analysis by Ennio Cascetta (pp. 89-100) focuses on Random
Utility Theory, a widely used framework for modeling transportation choices.
Overview of Random Utility Theory
● Random Utility Theory (RUT) models the decision-making process by which individuals select
among discrete alternatives, such as transport modes or travel routes.
● It assumes that individuals aim to maximize their utility, which is composed of a systematic
(deterministic) component and a random (stochastic) component due to unobserved factors.
Basic Assumptions of RUT
1. Discrete Choices: Individuals make choices from a set of mutually exclusive alternatives (e.g.,
car, bus, walking).
2. Utility Maximization: The utility for each alternative is based on measurable attributes like cost
and time, combined with random residuals that capture unknown influences.
3. Choice Probability: The probability of choosing a specific option is determined by the
comparative utility of all available alternatives.
Ben-Elia, E., & Ettema, D. (2011). Rewarding rush-hour avoidance: A study of commuters’ travel
behavior. Transportation Research Part A: Policy and Practice, 45(7), 567-582.
https://doi.org/10.1016/j.tra.2011.03.003
- The potential of rewards as a policy mean for changing commuter behavior
- A 13 week field study was organized in The Netherlands with the purpose of longitudinally
investigating the impacts of rewards on commuter behavior.
- The results suggest that rewards can be effective tools in changing commuting behavior.
Specifically rewards reduce the shares of rush-hour driving, shift driving to off-peak times and
increase the shares of public transport, cycling and working from home.
- By rewarding those commuters who are willing to shift their commuting times or switch to
alternative travel modes, overall penalization of drivers through tolling is avoided and overall
welfare could well be improved by reducing peak demand.
- These studies reveal that commuters’ habitual behavior of rush-hour driving and constraints
related to household and work schedules are important factors which limit the positive impact of
rewarding on car travel.
