Abstract. Here we sketch the rudiments of what constitutes a smart city which we define as a city in which ICT is merged with traditional infrastructures, coordinated and integrated using new digital technologies. We first sketch our vision defining seven goals which concern: developing a new understanding of urban problems; effective and feasible ways to coordinate urban technologies; models and methods for using urban data across spatial and temporal scales; developing new technologies for communication and dissemination; developing new forms of urban governance and organisation; defining critical problems relating to cities, transport, and energy; and identifying risk, uncertainty, and hazards in the smart city. To this, we add six research challenges: to relate the infrastructure of smart cities to their operational functioning and planning through management, control and optimisation; to explore the notion of the city as a laboratory for innovation; to provide portfolios of urban simulation which inform future designs; to develop technologies that ensure equity, fairness and realise a better quality of city life; to develop technologies that ensure informed participation and create shared knowledge for democratic city governance; and to ensure greater and more effective mobility and access to opportunities for a

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Since the first GPS studies in the mid-1990s, this way of surveying individual travel behaviour has gained increasing attention in transport research. Compared to classic travel survey methods researchers benefit from more accurate and reliable information. At the same time, the participants' burden is reduced substantially if the GPS data collection is not accompanied by elaborate questioning. However, without additional information, such as modes and trip purposes, extensive post-processing is required to derive data that can be used for analysis and model estimation. The corresponding procedures are still an ongoing research issue.This paper describes a post-processing procedure that needs no other input than the most basic GPS raw data: three-dimensional positions and the corresponding timestamps. First, the data is thoroughly cleaned and smoothed. Second, trips and activities are determined. Third, the trips are segmented into single-mode stages and the transport mode for each of the stages is identified. The procedure is applied to GPS records collected in the Swiss cities of Zurich, Winterthur and Geneva. 4882 participants carried an on-person GPS-receiver for 6.65 days on average. The results are compared to the Swiss Microcensus 2005 to demonstrate that derived data is ready for further applications, such as discrete choice model estimations.

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