Key Techniques Of Intelligent Analysis Over Traffic GPS Big Data And Their Applications

With booming process of the world urbanization and rapid acceleration of modernization,mobile sensors are widely used in all aspects of our lives,especially the application of Global Positioning System(GPS).Taxies equipped with GPS sensor play a key role in Intelligent Transportation Systems.The generated taxi GPS data can be used to reflect,analyze and predict the commercial activities,social behaviors,living habits and travel law of people in urban area.Different from other public transport and private cars,taxies serve the needs of a huge number of people driven by diverse demands with flexible time.As a consequence,taxi GPS big data which record spatial and temporal law of people provide invaluable information for the analysis of urban transportation system.Besides,a win-win situation could be formed among taxi drivers,passengers,transportation bureau and city planners if all of them take good advantage of the analysis result.For a long time,taxi service faces many typical challenges,e.g.,passengers may be detours overcharged by some unethical taxi drivers,especially when traveling in unfamiliar cities.What's worse,a passenger may not be aware of a fraud when it is ongoing.As a result,it's important to detect taxi drivers' misbehavior through taxi's GPS big data analysis in a real-time manner for enhancing the quality of taxi services.In addition,with the acceleration of population urbanization and urban land expansion,new urban areas and economic zones spring up rapidly.However,the current urban public transport planning is deadly dependent on human research and the research outcomes suffer from sample size,information asymmetry,subjective factors of people,etc.A rational and scientific urban transportation planning reference standard is deadly needed among these new urban areas.In view of these challenges,based on real taxi GPS raw data,an online anomalous trajectory detection method is firstly investigated in this paper for improving taxi services.This method mainly consists of two steps:route recommendation and online detection.Secondly,an assistant decision-supporting method for urban bus route planning among new city communities is designed.Specifically,a traffic hotline discovery principle is devised to provide a reference standard for new urban traffic hotspots.