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Technological advancement in automobile and infrastructure sector encourages more and longer distance travel and the way people travel to perform their daily tasks creates environmental problems. Activity-travel behavior of individual have a significant potential to be influenced to reduce environmental issues, however, the underlying factors need to be investigated. This paper investigates pro-environmental activity-travel behavior using recorded GPS-based travel-activity diaries and individual personal traits using online questionnaire and estimating a structure equation model borrowed from theory of planned behavior. The results of the study verified that individual mobility decisions were highly influenced by the attitude one has about specific travel behavior. The results are helpful in devising effective behavioral intervention. (C) 2019 The Authors. Published by Elsevier B.V. ; This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 689954. This paper reflects the authors views. The European Commission is not liable for any use that may be made of the information contained therein. The authors would also like to thank Acadia University and Natural Sciences and Engineering Research Council (NSERC) of Canada for funding the publication of this paper. ; Adnan, M (reprint author), Hasselt Univ, Transportat Res Inst IMOB, B-3590 Diepenbeek, Belgium. muhammad.adnan@uhasselt.be

Informational interventions are considered important to bring positive changes in attitudes and perception about pro-environmental life styles among individuals. In relation to mobility aspects, it is vital to identify relatively easier changes that have potential to reduce negative impacts of mobility on environment and individual health. This paper provides a comprehensive methodological framework and developed a computation algorithm that helps identify such an easy changes in the travel behavior of an individual. The development of 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. A variety of rules that are part of the computational algorithm are taken from the transport modelling literature, where constraints and factors were examined for various activity-travel decisions. Three major aspects of activity-travel behavior such as lesser 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 data collected, using citizens from Hasselt and their pro-environmental potential is determined, which has been found significant. ; This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 689954.