A Classification and Predication Framework for Taxi-Hailing Based on Big Data

Yin, Changqing; Lin, Yiwei; Yang, Chen

doi:10.1007/978-3-319-63315-2_65

Cited by 4 publications

(1 citation statement)

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“…In [25], a technique for forecasting and classifying for taxi-hailing was proposed, where the results of the K-means clustering were utilized in a neural network (NN) for making the prediction [18]. The results obtained after implementing this system on a realworld dataset, offered by a Shanghai-based taxi Later on, the researcher utilized hybrid prediction models which utilize additive advantages of machine learning and statistical approaches and produce better performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature-driven label generation for congestion detection in smart cities under big data

2022

IJATEE

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Advancements in technology, especially in the field of information and communications technology (ICT), have paved the way for the introduction of the idea of "smart city" [1]. In a smart city, various technologies are utilized to improve efficacy in social services, environmental protection, public safety, transport system, and other related areas. This in turn leads to higher comfort levels of its citizen. However, fast urbanization poses various problems in which the problem of road traffic congestion is a serious one. *Author for correspondenceThis congestion, especially in big cities, can lead to the complete immobility of traffic and the related consequences tend to be even more damaging. Hence, it is of immense significance to timely prevent traffic congestion in smart cities.The prediction of road traffic congestion has an important role in managing the transport system in smart cities. Informed travel decisions can be made with the help of such predictions by improving and providing better traveller information services. Moreover, precise traffic predictions can help improve road safety by preventing accidents and help to improve transportation costs and decrease air pollution [2]. With the recent availability of a large amount of transportation data from various sources,

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Section: Literature Reviewmentioning

confidence: 99%