The research aims to develop a cloud based service framework for reducing carbon dioxide emission and
fuel consumption in intelligent transportation system .It collects traffic condition ,driving behavior ,and
video through telematics and digital tachygraphy and road-side cameras to facilitate advanced data
analytics for the reduction of fuel consumption . There are three specific features regarding this
framework . First ,a transportation cloud is built for the storage of massive data and video .This cloud-based system not only a voids the use of hard disks at client-site for energy conservation and reliability
improvement , but also allows the back - end data analytics at both server and client sites . Second , areal-time traffic condition analytic was developed by mobile machine vision techniques base don video and
data collected from road - side cameras to analyze and recognize traffic conditions,such as traffic flow,
braking events,traffic lights,and count-down timers .Then, a fuel-efficient route navigation technology
is also developed for eco-driving based on real time traffic information and a dynamic shortest path
algorithm for saving time and fuel consumption .Third, a sequential pattern mining model was proposed
to diagnose misguided driving behavior for eco - driving base don the realtime data collected from
digital tachygraphy and on-board diagnostics system . Further more ,ane Learning visualization system
was developed to provide advice and instruction for correction of misguided driving behavior . Indeed ,
the fuel consumption and power consumption can bereduced simultaneously based on the proposed
framework regarding cloud - based system and eco - driving.