| Traffic management is an important part of urban planning and development.From a global perspective,traffic safety and congestion,air pollution,energy consumption and other issues are increasingly prominent.Effective measures are urgently needed to solve them in order to promote the sustainable development of the city.With the rapid integration and development of communication technology and automobile industry,many vehicle service platforms emerge as the times require,such as vehicle remote diagnosis system,vehicle mobile positioning data positioning terminal,vehicle traveling recorder,vehicle non parking charge management,parking lot automation management,etc.,which initially meet the service needs of vehicle users and management departments.However,these vehicle service platforms still fail to solve the problems of traffic safety,traffic congestion,energy consumption and so on.Therefore,with the growing maturity of the Internet of vehicles technology,the application platform with the Internet of vehicles technology as the core is becoming a hot research and development object.It is expected that through the application platform based on the Internet of vehicles,the interconnection between vehicles,vehicles and roads,vehicles and people can be realized,the data between the platforms can be shared,play the role of data mining,and then coordinate the management of traffic conditions.Through literature research,research interviews,and personal experience of vehicle terminal service system,this paper understands the construction needs of different types of customers’ vehicle networking multi-functional service platform,and combs out the application scenarios of four types of users,including government,service industry,manufacturing enterprises and individual consumers.Based on the four types of users,a multi-functional service platform scheme of "four interconnected,one platform,five intelligent" Internet of vehicles with integrated and predictive functions is constructed.Based on cluster analysis technology,this paper analyzes driving behavior data.For a group of daily trip driving data,using clustering analysis algorithm,the user trip rule display is obtained,and the prediction effect of vehicle operation data is preliminarily explored.Based on GPS technology,under the condition of ensuring the driving route and mileage of vehicles,a practical study on the fuel saving effect of hybrid bus is carried out.Taking different vehicle load,operation period,line congestion and other situations as the research object,an analysis experiment on the fuel saving effect of hybrid bus and traditional fuel consuming bus is carried out.The experimental conclusion is that the fuel-saving effect of hybrid bus in various kinds of When the load,line type and operation period change,the fuel consumption is relatively stable and has a wide range of applicability.This paper studies the application of the multi-functional vehicle service platform of theInternet of vehicles from two aspects of data application analysis and experimental comparison,initially obtains the platform service function experience,and provides some practical research data and examples for the application scenarios and data mining direction of the Internet of vehicles platform. |
