Informational interventions are important to bring positive changes in attitudes and perception among individuals. In relation to the individual’s mobility behavior, habits, attitudes, and perceptions are difficult to change. Therefore, it is vital to identify relatively soft aspects of travel behavior with a potential to reduce the negative impacts of mobility on the environment and individual health. This paper provides a methodological framework and describes the development of a computational algorithm that helps to identify soft changes in the travel behavior. The algorithm is based on a variety of different data sources such as activity-travel diaries and related constraint information, meteorological conditions, bicycle and public transport supply data, and emissions and air pollutant concentrations data. A variety of rules that are part of the algorithm are derived from the transport modeling literature, where constraints and factors were examined for activity-travel decisions. Three major aspects of activity-travel behavior, such as reduced car use, cold start of car engines, and participation in non-mandatory outdoor activities are considered in assessing pro-environmental potential. The algorithm is applied to collected small datasets from citizens of Hasselt (Belgium), Bologna (Italy), and Guildford (UK). A significant replaceable potential for car trips within 3 km to cycling and car trips to public transport has been found. The replaceable potential of excessive cold starts and participation in non-mandatory outdoor activities were also found, to some extent, to bring positive changes in the environment. In future research, these identified potentials are reported back to individuals with their consequence as part of a mobility-based informational intervention.

Ahmed, S., Adnan, M., Janssens, D., Brattich, E., Yasar, A., Kumar, P., et al. (2019). Estimating pro-environmental potential for the development of mobility-based informational intervention: a data-driven algorithm. PERSONAL AND UBIQUITOUS COMPUTING, 23(5-6), 653-668 [10.1007/s00779-018-1187-5].

Estimating pro-environmental potential for the development of mobility-based informational intervention: a data-driven algorithm

Brattich, Erika;di Sabatino, Silvana;
2019

Abstract

Informational interventions are important to bring positive changes in attitudes and perception among individuals. In relation to the individual’s mobility behavior, habits, attitudes, and perceptions are difficult to change. Therefore, it is vital to identify relatively soft aspects of travel behavior with a potential to reduce the negative impacts of mobility on the environment and individual health. This paper provides a methodological framework and describes the development of a computational algorithm that helps to identify soft changes in the travel behavior. The algorithm is based on a variety of different data sources such as activity-travel diaries and related constraint information, meteorological conditions, bicycle and public transport supply data, and emissions and air pollutant concentrations data. A variety of rules that are part of the algorithm are derived from the transport modeling literature, where constraints and factors were examined for activity-travel decisions. Three major aspects of activity-travel behavior, such as reduced car use, cold start of car engines, and participation in non-mandatory outdoor activities are considered in assessing pro-environmental potential. The algorithm is applied to collected small datasets from citizens of Hasselt (Belgium), Bologna (Italy), and Guildford (UK). A significant replaceable potential for car trips within 3 km to cycling and car trips to public transport has been found. The replaceable potential of excessive cold starts and participation in non-mandatory outdoor activities were also found, to some extent, to bring positive changes in the environment. In future research, these identified potentials are reported back to individuals with their consequence as part of a mobility-based informational intervention.
2019
Ahmed, S., Adnan, M., Janssens, D., Brattich, E., Yasar, A., Kumar, P., et al. (2019). Estimating pro-environmental potential for the development of mobility-based informational intervention: a data-driven algorithm. PERSONAL AND UBIQUITOUS COMPUTING, 23(5-6), 653-668 [10.1007/s00779-018-1187-5].
Ahmed, Shiraz; Adnan, Muhammad; Janssens, Davy; Brattich, Erika; Yasar, Ansar-ul-Haque; Kumar, Prashant; di Sabatino, Silvana; Shakshuki, Elhadi M....espandi
File in questo prodotto:
File Dimensione Formato  
11585_670613.pdf

Open Access dal 25/11/2019

Tipo: Postprint
Licenza: Licenza per accesso libero gratuito
Dimensione 1.65 MB
Formato Adobe PDF
1.65 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/670613
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 4
social impact