CJ
C.A. Juárez Overdevest
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Master thesis
(2026)
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C.A. Juárez Overdevest, B. van Arem, M. Snelder, S. van Cranenburgh, Han Zhou
Cities increasingly implement car-reducing measures to improve sustainability, public health, and urban liveability. However, their impacts are often evaluated using aggregate indicators such as traffic volumes or modal shares, providing limited insight into the behavioural mechanisms through which travellers adapt. In particular, little is known about how responses to reduced car accessibility differ across socio-demographic groups.
This thesis investigates the effects of car-reducing measures on travel behaviour using the redesign of Hofplein in Rotterdam as a case study. The intervention reduces road capacity by transforming a car-oriented traffic square into a multimodal public space, thereby reducing car accessibility at an important node in the transport network. The study examines both the overall behavioural impacts of this accessibility reduction and the extent to which these impacts vary between traveller groups.
An activity-based transport model (ActivitySim) was used to simulate traveller behaviour before and after the intervention. Accessibility changes resulting from the Hofplein redesign were first estimated using the V-MRDH traffic assignment model and subsequently incorporated into ActivitySim. To improve behavioural realism, the model was calibrated against both observed modal shares and literature-based travel-time elasticities using Bayesian optimisation. Simulated travellers were subsequently linked to ten mobility personas to analyse behavioural responses across different socio-demographic groups.
The calibrated model reproduced both observed modal shares and benchmark travel-time elasticities reasonably well. Simulation results indicate that the intervention reduces car use while increasing walking, cycling, e-bike use, and public transport demand. However, modal shift explains only part of the response. Many travellers continue travelling by car by selecting alternative routes, while others adapt through destination changes or trip suppression. The analysis further shows that impacts are distributed unevenly across the population, with car-oriented personas experiencing the largest accessibility reductions and behavioural responses.
These findings demonstrate the value of activity-based transport models for evaluating transport interventions. By capturing individual behavioural adaptation and distributional effects, such models provide a more detailed understanding of the impacts of car-reducing measures and can support more equitable transport policy development. ...
This thesis investigates the effects of car-reducing measures on travel behaviour using the redesign of Hofplein in Rotterdam as a case study. The intervention reduces road capacity by transforming a car-oriented traffic square into a multimodal public space, thereby reducing car accessibility at an important node in the transport network. The study examines both the overall behavioural impacts of this accessibility reduction and the extent to which these impacts vary between traveller groups.
An activity-based transport model (ActivitySim) was used to simulate traveller behaviour before and after the intervention. Accessibility changes resulting from the Hofplein redesign were first estimated using the V-MRDH traffic assignment model and subsequently incorporated into ActivitySim. To improve behavioural realism, the model was calibrated against both observed modal shares and literature-based travel-time elasticities using Bayesian optimisation. Simulated travellers were subsequently linked to ten mobility personas to analyse behavioural responses across different socio-demographic groups.
The calibrated model reproduced both observed modal shares and benchmark travel-time elasticities reasonably well. Simulation results indicate that the intervention reduces car use while increasing walking, cycling, e-bike use, and public transport demand. However, modal shift explains only part of the response. Many travellers continue travelling by car by selecting alternative routes, while others adapt through destination changes or trip suppression. The analysis further shows that impacts are distributed unevenly across the population, with car-oriented personas experiencing the largest accessibility reductions and behavioural responses.
These findings demonstrate the value of activity-based transport models for evaluating transport interventions. By capturing individual behavioural adaptation and distributional effects, such models provide a more detailed understanding of the impacts of car-reducing measures and can support more equitable transport policy development. ...
Cities increasingly implement car-reducing measures to improve sustainability, public health, and urban liveability. However, their impacts are often evaluated using aggregate indicators such as traffic volumes or modal shares, providing limited insight into the behavioural mechanisms through which travellers adapt. In particular, little is known about how responses to reduced car accessibility differ across socio-demographic groups.
This thesis investigates the effects of car-reducing measures on travel behaviour using the redesign of Hofplein in Rotterdam as a case study. The intervention reduces road capacity by transforming a car-oriented traffic square into a multimodal public space, thereby reducing car accessibility at an important node in the transport network. The study examines both the overall behavioural impacts of this accessibility reduction and the extent to which these impacts vary between traveller groups.
An activity-based transport model (ActivitySim) was used to simulate traveller behaviour before and after the intervention. Accessibility changes resulting from the Hofplein redesign were first estimated using the V-MRDH traffic assignment model and subsequently incorporated into ActivitySim. To improve behavioural realism, the model was calibrated against both observed modal shares and literature-based travel-time elasticities using Bayesian optimisation. Simulated travellers were subsequently linked to ten mobility personas to analyse behavioural responses across different socio-demographic groups.
The calibrated model reproduced both observed modal shares and benchmark travel-time elasticities reasonably well. Simulation results indicate that the intervention reduces car use while increasing walking, cycling, e-bike use, and public transport demand. However, modal shift explains only part of the response. Many travellers continue travelling by car by selecting alternative routes, while others adapt through destination changes or trip suppression. The analysis further shows that impacts are distributed unevenly across the population, with car-oriented personas experiencing the largest accessibility reductions and behavioural responses.
These findings demonstrate the value of activity-based transport models for evaluating transport interventions. By capturing individual behavioural adaptation and distributional effects, such models provide a more detailed understanding of the impacts of car-reducing measures and can support more equitable transport policy development.
This thesis investigates the effects of car-reducing measures on travel behaviour using the redesign of Hofplein in Rotterdam as a case study. The intervention reduces road capacity by transforming a car-oriented traffic square into a multimodal public space, thereby reducing car accessibility at an important node in the transport network. The study examines both the overall behavioural impacts of this accessibility reduction and the extent to which these impacts vary between traveller groups.
An activity-based transport model (ActivitySim) was used to simulate traveller behaviour before and after the intervention. Accessibility changes resulting from the Hofplein redesign were first estimated using the V-MRDH traffic assignment model and subsequently incorporated into ActivitySim. To improve behavioural realism, the model was calibrated against both observed modal shares and literature-based travel-time elasticities using Bayesian optimisation. Simulated travellers were subsequently linked to ten mobility personas to analyse behavioural responses across different socio-demographic groups.
The calibrated model reproduced both observed modal shares and benchmark travel-time elasticities reasonably well. Simulation results indicate that the intervention reduces car use while increasing walking, cycling, e-bike use, and public transport demand. However, modal shift explains only part of the response. Many travellers continue travelling by car by selecting alternative routes, while others adapt through destination changes or trip suppression. The analysis further shows that impacts are distributed unevenly across the population, with car-oriented personas experiencing the largest accessibility reductions and behavioural responses.
These findings demonstrate the value of activity-based transport models for evaluating transport interventions. By capturing individual behavioural adaptation and distributional effects, such models provide a more detailed understanding of the impacts of car-reducing measures and can support more equitable transport policy development.