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

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 activitytravel 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.

A route to school informational intervention for air pollution exposure reduction

“…Mobility based informational interventions have a pull effect and ability to effect socio-cognitive aspects of individual travel behavior by applying strategies based on information dissemination and persuasion [43]. Therefore the efficacy of such an intervention primarily depends on the ways how the information is designed.…”

Semantic Traffic Sensor Data: The TRAFAIR Experience

“…However, for this dream to come true, the development of suitable data management strategies that can provide citizens and administrations with the information they need is a key issue. Thus, for example, according to [16], informational interventions are vital to encourage changes in attitudes and perceptions of people. Along the same lines, the work presented in [17] emphasizes that "open data can impact positively on citizens in particular and society in general".…”

Special issue on ambient systems, networks and technologies

“…Another paper is by Ahmed et al [2] entitled BEstimating pro-environmental potential for the development of mobilitybased informational intervention: a data-driven algorithm^. This paper provides a methodological framework and describes the development of a computational algorithm that helps to identify soft changes in the travel behavior.…”