International audience; In today's modern cities, mobility is of crucial importance, and public transportation is particularly concerned. The main objective is to propose solutions to a given, practical problem, which specifically concerns the bus arrival time at various bus stop stations, by taking to account local traffic conditions. We show that a global prediction approach, under some global macro-parameters (e.g., total number of vehicles or pedestrians) is not feasible. This observation leads us to the introduction of a finer granularity approach, where the traffic conditions are represented in terms of a traffic density matrix. Under this new paradigm, the experimental results obtained with both linear and neural networks (NN) approaches show promising prediction performances. Thus, the NN approach yields 24% more accurate prediction performances than a basic, linear regression

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Published on 01/01/2018

Volume 2018, 2018
DOI: 10.1109/euvip.2018.8611683
Licence: CC BY-NC-SA license

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