Data mining for enhanced driving effectiveness: an eco-driving behaviour analysis model for better driving decisions

From data to big data in production research: the past and future trends

“…adopted DEA to allocate emission permits. Hsu, Lim, and Yang (2017) presented a fuel consumption prediction model based on drivers' driving behaviour and vehicle characteristics. Their prediction model could be used to aid vehicle design.…”

“…The recent developments of production research enabled by data have tried to address the environmental issues resulting from production processes (e.g. Sun et al 2014;Hsu, Lim, and Yang 2017). Sustainability issues have become more and more of a concern to manufacturing companies.…”

Big data as a value generator in decision support systems: a literature review

“…The second group of articles in our technique analysis was linear regression that has been observed in stock market index prediction studies (Khan et al , 2018), in DSS for collaborative logistics networks (Ilie-Zudor et al , 2015) and in predicting fuel consumption based on driving behaviors (Hsu, Lim & Yang, 2017). The least absolute shrinkage operator regression was used to estimate risk-adjusted performance in hospitals (Feuerriegel, 2016), for forecasting electricity prices based on historical data and analysis of weather conditions (Ludwig, Feuerriegel & Neumann, 2015) and for predicting population health indices from social media data to improve predictive performance (Nguyen et al , 2017).…”

“…et al (2014),Ilie-Zudor et al (2015),Neaga et al (2015),Lan et al (2016),Chen et al (2017),Hsu et al (2017), R€ onnqvist et al (2017), Aversa et al (2018), Hao et al (2018), Jamshidi et al (2018), Jin & Kim (2018), Lee et al (2018), Sathiaraj et al (2018), Wang et al (2018a) Health Population health prediction, disease detection assistance and information mapping to improve care Khansa et al (2012), Ashrafi et al (2014), Burattin et al (2015), Capobianco & Li o (2015), Feuerriegel (2016), Goh et al (2016), Baechle et al (2017), Nguyen et al (2017), Ghaddar & Naoum-Sawaya (2018), Sivamani et al (2018), Tashkandi et al (2018), Wang et al (2018a) Business and market Assistance in business decision-making and social relationship improvement Demirkan & Delen (2013), Boutkhoum et al (2016), Osuszek et al (2016), Pape (2016), Boutkhoum et al (2017), Moore (2017), Ghaddar & Naoum-Sawaya (2018), Jankovi c et al (2018), Tian et al (2018), Ghasemaghaei & Calic (2019), Mello & Martins (2019), Power et al (2019) Industry and IT Process efficiency, operating costs and activity optimization Almeida et al (2015), Sasson et al (2015), Rasiulis et al (2016), Shrestha et al (2016), Jukic et al (2017), Li et al (2017), Ahmed et al (2018), Nimmagadda et al (2018), Stein et al (2018) Supply chain Support for strategic decision-making and control of key performance indicators Groves et al (2014), Vera-Baquero et al (2015), Giannakis & Louis (2016), Long (2017), Brinch et al (2018), Papagiannidis et al (2018) Weather Climate analysis to predict retail consumption flow, traffic and crisis management Ludwig et al (2015), Drosio & Stanek (2016), Schnase et al (2017), Lee et al (2018), Sathiaraj et al (2018), Tian et al (2018) Tourism and/or social media Tourist flow trend analysis according to visit log Li et al (2016), Miah et al (2017), Nguyen et al (2017), Gao et al (2018), Giglio et al (2019) Financial Decision support in risk analysis, accounting and stock index trends Hayashi (2016), Chan & Chong (2017), Al Chahadah et al (2018), Khan et al…”

Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods

“…Eco-driving is the adoption of a driving behavior (or a driving style) that aims at saving fuel and reducing harmful emissions of greenhouse gases (GHG) [11]. In general, it refers to the adjustment of the vehicle's moving speed (in relation to traffic conditions) and the choice of routes that minimize fuel consumption [12,13]. Therefore, eco-driving can be seen as a set of choices and behaviors adopted by drivers that are connected with an energy-efficient way of using a vehicle.…”