Urban traffic congestion is a severe and widely studied problem over the decade because of the negative impacts. However, in recent years some approaches emerge as proper and suitable solutions. The Carpooling initiat...Urban traffic congestion is a severe and widely studied problem over the decade because of the negative impacts. However, in recent years some approaches emerge as proper and suitable solutions. The Carpooling initiative is one of the most representative efforts to propitiate a responsible use of particular vehicles. Thus, the paper introduces a carpooling model considering the users’ preference to reach an appropriate match among drivers and passengers. In particular, the paper conducts a study of 6 of the most avid classified techniques in machine learning to create a model for the selection of travel companions. The experimental results show the models’ precision and assess the best cases using Friedman’s test. Finally, the conclusions emphasize the relevance of the proposed study and suggest that it is necessary to extend the proposal with more drives and passengers’ data.展开更多
The paper introduces a novel paradigm to use ubiquitous computing in urban traffic control as a methodology to include the benefits of considering physical elements implicated in the environment. This promising idea a...The paper introduces a novel paradigm to use ubiquitous computing in urban traffic control as a methodology to include the benefits of considering physical elements implicated in the environment. This promising idea arises from previous results in the literature, where ubiquitous computing achieves significant and promising results in diverse scenarios. Some works in the state-of- the-art clearly state that traditional traffic light signals are not capable of offering higher service levels when they should control the vehicular mobility because of different constraints. In this sense, the paper proposes an adaptation of the main ideas of ubiquitous computing as a metaphor to facilitate the interaction between users and traffic infrastructures in order to improve the users’ experience on the road.展开更多
文摘Urban traffic congestion is a severe and widely studied problem over the decade because of the negative impacts. However, in recent years some approaches emerge as proper and suitable solutions. The Carpooling initiative is one of the most representative efforts to propitiate a responsible use of particular vehicles. Thus, the paper introduces a carpooling model considering the users’ preference to reach an appropriate match among drivers and passengers. In particular, the paper conducts a study of 6 of the most avid classified techniques in machine learning to create a model for the selection of travel companions. The experimental results show the models’ precision and assess the best cases using Friedman’s test. Finally, the conclusions emphasize the relevance of the proposed study and suggest that it is necessary to extend the proposal with more drives and passengers’ data.
文摘The paper introduces a novel paradigm to use ubiquitous computing in urban traffic control as a methodology to include the benefits of considering physical elements implicated in the environment. This promising idea arises from previous results in the literature, where ubiquitous computing achieves significant and promising results in diverse scenarios. Some works in the state-of- the-art clearly state that traditional traffic light signals are not capable of offering higher service levels when they should control the vehicular mobility because of different constraints. In this sense, the paper proposes an adaptation of the main ideas of ubiquitous computing as a metaphor to facilitate the interaction between users and traffic infrastructures in order to improve the users’ experience on the road.
