The behavior of cyclists when choosing the path to follow along a road network is not uniform. Some of them are mostly interested in minimizing the travelled distance, but some others may also take into account other features such as safety of the roads or level of pollution, including carbon dioxide emission by the cars or even the noise pollution. Identifying the different groups of users, estimating the numerical consistency of each of these groups, and reporting the weights assigned by each group to different characteristics of the road network, is quite relevant. Indeed, when decision makers need to assign some budget for infrastructural interventions, they need to know the impact of their decisions, and this is strictly related to the way users perceive different features of the road network. In this paper, we propose an optimization approach to detect the weights assigned to different road features by various user groups, leveraging knowledge of the true paths followed by them, accessible, for example, through data collected by bike-sharing services.
Identification of Cyclists' Route Choice Criteria / Ardizzoni, S.; Laurini, M.; Praxedes, R.; Consolini, L.; Locatelli, M.. - (2024), pp. 6975-6980. ( 63rd IEEE Conference on Decision and Control, CDC 2024 Allianz MiCo Milano Convention Centre, ita 2024) [10.1109/CDC56724.2024.10886090].
Identification of Cyclists' Route Choice Criteria
Ardizzoni S.;Laurini M.;Consolini L.;Locatelli M.
2024-01-01
Abstract
The behavior of cyclists when choosing the path to follow along a road network is not uniform. Some of them are mostly interested in minimizing the travelled distance, but some others may also take into account other features such as safety of the roads or level of pollution, including carbon dioxide emission by the cars or even the noise pollution. Identifying the different groups of users, estimating the numerical consistency of each of these groups, and reporting the weights assigned by each group to different characteristics of the road network, is quite relevant. Indeed, when decision makers need to assign some budget for infrastructural interventions, they need to know the impact of their decisions, and this is strictly related to the way users perceive different features of the road network. In this paper, we propose an optimization approach to detect the weights assigned to different road features by various user groups, leveraging knowledge of the true paths followed by them, accessible, for example, through data collected by bike-sharing services.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


