The uneven utilisation of modes of transport has a big impact on traffic in transport pathway infrastrutures. For motor vehicles for instance, this situation explains rapid road deterioration and the large amounts of money invested in maintenance and development due to overuse. There are many approaches to managing this problem; however, the impact of individual users in infrastructural maintenance is mostly ignored. In this position paper, we hypothesise that important changes torwards a more efficient use of the transport network start with its users and their behavioural changes. To this end, we introduce our vision on how to employ data driven, intelligent agent-based modelling, incorporating human factors aspects, as a toolset to understand travellers and to stimulate behavioural changes. The aim is to achieve better balanced and integrated mobility usage within the transport network. The idea is explored with a few guided questions, and a methodology is proposed. We employ 1) cognitive work analysis to investigate the reasons for travellers' mode choice; 2) computational intelligence to extract and represent knowledge from related datasets; 3) agent-based modelling to represent the real-world and to observe both individual and emergent behaviours. Future directions to adapt our methodology to alternative smart mobility projects are also discussed.
Position Paper: The Usefulness of Data-driven, Intelligent Agent-Based Modelling for Transport Infrastructure Management
2018-11-01
396938 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Network modelling simplifies infrastructure data management
Online Contents | 1998
Network modelling simplifies infrastructure data management
IuD Bahn | 1998
|Network modelling simplifies infrastructure data management
British Library Online Contents | 1998