Neural Architecture and Feature Search for Predicting the Ridership of Public Transportation Routes

Afiya Ayman, Juan Martinez, Philip Pugliese, Abhishek Dubey, Aron Laszka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Accurately predicting the ridership of public-transit routes provides substantial benefits to both transit agencies, who can dispatch additional vehicles proactively before the vehicles that serve a route become crowded, and to passengers, who can avoid crowded vehicles based on publicly available predictions. The spread of the coronavirus disease has further elevated the importance of ridership prediction as crowded vehicles now present not only an inconvenience but also a public-health risk. At the same time, accurately predicting ridership has become more challenging due to evolving ridership patterns, which may make all data except for the most recent records stale. One promising approach for improving prediction accuracy is to fine-tune the hyper-parameters of machine-learning models for each transit route based on the characteristics of the particular route, such as the number of records. However, manually designing a machine-learning model for each route is a labor-intensive process, which may require experts to spend a significant amount of their valuable time. To help experts with designing machine-learning models, we propose a neural-architecture and feature search approach, which optimizes the architecture and features of a deep neural network for predicting the ridership of a public-transit route. Our approach is based on a randomized local hyper-parameter search, which minimizes both prediction error as well as the complexity of the model. We evaluate our approach on real-world ridership data provided by the public transit agency of Chattanooga, TN, and we demonstrate that training neural networks whose architectures and features are optimized for each route provides significantly better performance than training neural networks whose architectures and features are generic.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages56-61
Number of pages6
ISBN (Electronic)9781665481526
DOIs
StatePublished - 2022
Event8th IEEE International Conference on Smart Computing, SMARTCOMP 2022 - Espoo, Finland
Duration: Jun 20 2022Jun 24 2022

Publication series

NameProceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022

Conference

Conference8th IEEE International Conference on Smart Computing, SMARTCOMP 2022
Country/TerritoryFinland
CityEspoo
Period6/20/226/24/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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