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Ambient data is generated from a collective of smart devices, connected environments and pervasive platforms, so well-integrated and finely tuned that we no longer think about connecting, syncing, or controlling. Pers Ubiquit Comput. https://doi.org/10.1007/s00779-020-01371-w 7 Ahmed A, Outay F, Zaidi SOR, Adnan M, Ngoduy D (2020) Examining queue-jumping phenomenon in heterogeneous traffic stream at signalized intersection using UAV-based data. Pers Ubiquit Comput. https://doi.org/10.1007/s00779-019-01333-x 3 Beg A, Qureshi AR, Sheltami T, Yasar A (2020) UAV-enabled intelligent traffic policing and emergency response handling system for the smart city. [Extracted from the article]

This special issue is based on the best papers from 8th International Conference on Ambient Systems, Networks and Technologies (ANT-2017, http://cs-conferences.acadiau.ca/ANT-17/) and the 7th International Conference on Sustainable Energy Information Technology (SEIT 2017, http://cs-conferences.acadiau.ca/seit-17/) held in Madeira, Portugal, from May 16-19, 2017. 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. Another paper is by Al-Ruithe et al. [[14]] entitled " I A systematic literature review of data governance and cloud data governance i ". 14 Al-Ruithe M, Benkhelifa E, Hameed K (2018) A systematic literature review of data governance and cloud data governance. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]