<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This work apply a deep learning artificial neural network model – the Multilayer Perceptron – as a regression model to estimate the demand of bus passengers. Transit bus ridership and weather conditions were collected over a year from a medium-size European metropolitan area and linked under the assumption: individuals choose the travel mode based on the weather conditions that are observed during (a) the departure hour, (b) the hour before or (c) two hours prior to the travel start. The transit ridership data were also labelled according to the hour of the day, day of the week, month, and whether there was a strike and/or holiday or not. The results show that the prediction error of the model decrease by ~9% when the weather conditions observed two hours before travel start is taken into account. The model sensitivity analyses reveals that the worst performance is obtained for a strike day of a weekday in spring (typically Wednesdays or Thursdays).</p></abstract>

This work apply a deep learning artificial neural network model – the Multilayer Perceptron – as a regression model to estimate the demand of bus passengers. Transit bus ridership and weather conditions were collected over a year from a medium-size European metropolitan area and linked under the assumption: individuals choose the travel mode based on the weather conditions that are observed during (a) the departure hour, (b) the hour before or (c) two hours prior to the travel start. The transit ridership data were also labelled according to the hour of the day, day of the week, month, and whether there was a strike and/or holiday or not. The results show that the prediction error of the model decrease by ~9% when the weather conditions observed two hours before travel start is taken into account. The model sensitivity analyses reveals that the worst performance is obtained for a strike day of a weekday in spring (typically Wednesdays or Thursdays).

A Deep Learning Approach for Predicting Bus Passenger Demand Based on Weather Conditions

INESC TEC - Institute for Systems and Computer Engineering, Technology and Science

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This work apply a deep learning artificial neural network model – the Multilayer Perceptron – as a regression model to estimate the demand of bus passengers....

A Deep Learning Approach for Predicting Bus Passenger Demand Based on Weather Conditions

Data publikacji: 26 lis 2020

Zakres stron: 255 - 264

DOI: https://doi.org/10.2478/ttj-2020-0020

Słowa kluczowe
Predition, Urban public transport, Bus passenger demand, Weather conditions, Artificial neural networks

© 2020 Tânia Fontes et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Deep Learning Approach for Predicting Bus Passenger Demand Based on Weather Conditions

Data publikacji: 26 lis 2020

Zakres stron: 255 - 264

DOI: https://doi.org/10.2478/ttj-2020-0020

Słowa kluczowePredition, Urban public transport, Bus passenger demand, Weather conditions, Artificial neural networks

© 2020 Tânia Fontes et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Predition, Urban public transport, Bus passenger demand, Weather conditions, Artificial neural networks