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Author

Ivana Semanjski

Other affiliations: University of Zagreb
Bio: Ivana Semanjski is an academic researcher from Ghent University. The author has contributed to research in topics: Smart city & Big data. The author has an hindex of 10, co-authored 38 publications receiving 406 citations. Previous affiliations of Ivana Semanjski include University of Zagreb.

Papers
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Journal ArticleDOI
TL;DR: A comprehensive review of the recent development in air pollution monitoring, including both the pollution data acquisition and the pollution assessment methods, and presents the efforts of applying these models on the mobile sensing data and discusses the future research of fusing the stationary and mobile sensingData.
Abstract: The impact of urban air pollution on the environments and human health has drawn increasing concerns from researchers, policymakers and citizens. To reduce the negative health impact, it is of great importance to measure the air pollution at high spatial resolution in a timely manner. Traditionally, air pollution is measured using dedicated instruments at fixed monitoring stations, which are placed sparsely in urban areas. With the development of low-cost micro-scale sensing technology in the last decade, portable sensing devices installed on mobile campaigns have been increasingly used for air pollution monitoring, especially for traffic-related pollution monitoring. In the past, some reviews have been done about air pollution exposure models using monitoring data obtained from fixed stations, but no review about mobile sensing for air pollution has been undertaken. This article is a comprehensive review of the recent development in air pollution monitoring, including both the pollution data acquisition and the pollution assessment methods. Unlike the existing reviews on air pollution assessment, this paper not only introduces the models that researchers applied on the data collected from stationary stations, but also presents the efforts of applying these models on the mobile sensing data and discusses the future research of fusing the stationary and mobile sensing data.

114 citations

Journal ArticleDOI
TL;DR: In this paper, a systematic overview of relevant and scientifically sound indicators that cover different aspects of sustainable mobility that are applicable in different social and economic contexts around the world is presented, together with an overview of the applied measures described across the literature review.
Abstract: The role of sustainable mobility and its impact on society and the environment is evident and recognized worldwide. Nevertheless, although there is a growing number of measures and projects that deal with sustainable mobility issues, it is not so easy to compare their results and, so far, there is no globally applicable set of tools and indicators that ensure holistic evaluation and facilitate replicability of the best practices. In this paper, based on the extensive literature review, we give a systematic overview of relevant and scientifically sound indicators that cover different aspects of sustainable mobility that are applicable in different social and economic contexts around the world. Overall, 22 sustainable mobility indicators have been selected and an overview of the applied measures described across the literature review has been presented.

65 citations

Journal ArticleDOI
TL;DR: The role of spatial context of human movements in inferring transport mode from mobile sensed data is investigated and a support vectors machines-based model is developed to infer five transport modes and achieve success rate of 94%.

52 citations

Journal ArticleDOI
03 Jul 2015-Sensors
TL;DR: A gradient boosting trees algorithm is applied to model individuals’ mobility decision making processes (particularly concerning what transportation mode they are likely to use) and the applicability of crowdsourced data for this purpose is explored.
Abstract: Mobility management represents one of the most important parts of the smart city concept. The way we travel, at what time of the day, for what purposes and with what transportation modes, have a pertinent impact on the overall quality of life in cities. To manage this process, detailed and comprehensive information on individuals’ behaviour is needed as well as effective feedback/communication channels. In this article, we explore the applicability of crowdsourced data for this purpose. We apply a gradient boosting trees algorithm to model individuals’ mobility decision making processes (particularly concerning what transportation mode they are likely to use). To accomplish this we rely on data collected from three sources: a dedicated smartphone application, a geographic information systems-based web interface and weather forecast data collected over a period of six months. The applicability of the developed model is seen as a potential platform for personalized mobility management in smart cities and a communication tool between the city (to steer the users towards more sustainable behaviour by additionally weighting preferred suggestions) and users (who can give feedback on the acceptability of the provided suggestions, by accepting or rejecting them, providing an additional input to the learning process).

47 citations

Journal ArticleDOI
05 Jul 2016-Sensors
TL;DR: The role of smartphones as mobility behavior sensors is investigated and the responsivity of different attitudinal profiles towards personalized route suggestion incentives delivered via mobile phones are evaluated to illustrate smart city platform potential as a tool for development of personalized mobility management campaigns and policies.
Abstract: Sustainable mobility and smart mobility management play important roles in achieving smart cities’ goals. In this context we investigate the role of smartphones as mobility behavior sensors and evaluate the responsivity of different attitudinal profiles towards personalized route suggestion incentives delivered via mobile phones. The empirical results are based on mobile sensed data collected from more than 3400 people’s real life over a period of six months. The findings show which user profiles are most likely to accept such incentives and how likely they are to result in more sustainable mode choices. In addition we provide insights into tendencies towards accepting more sustainable route options for different trip purposes and illustrate smart city platform potential (for collection of mobility behavior data and delivery of incentives) as a tool for development of personalized mobility management campaigns and policies.

39 citations


Cited by
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01 Jan 2002

9,314 citations

01 Jan 2012
TL;DR: A systematic review of the current state of research in travel time reliability, and more explicitly in the value oftravel time reliability is presented.
Abstract: Travel time reliability is a fundamental factor in travel behavior. It represents the temporal uncertainty experienced by users in their movement between any two nodes in a network. The importance of the time reliability depends on the penalties incurred by the users. In road networks, travelers consider the existence of a trip travel time uncertainty in different choice situations (departure time, route, mode, and others). In this paper, a systematic review of the current state of research in travel time reliability, and more explicitly in the value of travel time reliability is presented. Moreover, a meta-analysis is performed in order to determine the reasons behind the discrepancy among the reliability estimates.

352 citations

Journal ArticleDOI
TL;DR: A robust random forest method is proposed to analyze travel mode choices for examining the prediction capability and model interpretability of people’s travel behavior and results show that the random Forest method performs significantly better in travel mode choice prediction for higher accuracy and less computation cost.
Abstract: The analysis of travel mode choice is important in transportation planning and policy-making in order to understand and forecast travel demands Research in the field of machine learning has been exploring the use of random forest as a framework within which many traffic and transport problems can be investigated The random forest (RF) is a powerful method for constructing an ensemble of random decision trees It de-correlates the decision trees in the ensemble via randomization that leads to an improvement of forecasting and reduces the variance when averaged over the trees However, the usefulness of RF for travel mode choice behavior remains largely unexplored This paper proposes a robust random forest method to analyze travel mode choices for examining the prediction capability and model interpretability Using the travel diary data from Nanjing, China in 2013, enriched with variables on the built environment, the effects of different model parameters on the prediction performance are investigated The comparison results show that the random forest method performs significantly better in travel mode choice prediction for higher accuracy and less computation cost In addition, the proposed method estimates the relative importance of explanatory variables and how they relate to mode choices This is fundamental for a better understanding and effective modeling of people’s travel behavior

200 citations

20 Mar 2010
TL;DR: The authors explores the huge disparity of pan-regional cultures, economies, and regulatory environments and examines the plethora of obstacles facing alternative trading venues in the Asia-pacific region and concludes that although there are certainly hurdles to be overcome in the Asian region, change is taking hold and alternative trading platforms are starting to pose a real challenge to established exchanges.
Abstract: This article explores the huge disparity of pan-regional cultures, economies, and regulatory environments and examines the plethora of obstacles facing alternative trading venues in the region. It concludes that although there are certainly hurdles to be overcome in the Asian region, change is taking hold and alternative trading venues are starting to pose a real challenge to established exchanges. Indeed, the article suggests that there is now a genuine appetite for technology that can help firms take advantage of the structural changes in the markets as well as provide the flexibility to accommodate a rapidly evolving Asian landscape.

183 citations

Journal ArticleDOI
TL;DR: A preliminary evaluation of the health burden attributable to air pollution generated by bushfires during this period of unprecedented bushfires in Australia found that smoke affected large numbers of people in New South Wales, Queensland, the Australian Capital Territory and Victoria.
Abstract: Weather conditions conducive to extreme bushfires are becoming more frequent as a consequence of climate change.1 Such fires have substantial social, ecological, and economic effects, including the effects on public health associated with smoke, such as premature mortality and exacerbation of cardiorespiratory conditions.2,3 During the final quarter of 2019 and the first of 2020, bushfires burned in many forested regions of Australia, and smoke affected large numbers of people in New South Wales, Queensland, the Australian Capital Territory and Victoria. The scale and duration of these bushfires was unprecedented in Australia. We undertook a preliminary evaluation of the health burden attributable to air pollution generated by bushfires during this period.

175 citations