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A review of AI-enabled and model-based methodologies for travel demand estimation in urban transport networks

In: Handbook on Artificial Intelligence and Transport

Author

Listed:
  • Sajjad Shafiei
  • Hussein Dia

Abstract

Information on urban travel movements is a priority input for traffic models and intelligent transportation system (ITS) applications because of their usefulness in predicting and mitigating traffic congestion. The growing availability of new surveillance travel data, such as mobile phone data, geolocated trajectories, automatic number plate recognition, and public transport smartcard data, provides a neoteric opportunity to capture urban travel movement patterns. Even though these new technologies collect data at lower costs and time compared with classical travel demand surveys, such travel demand information still suffers from a high level of inaccuracy and is unsuitable to be directly applied to transport applications. Therefore, the need for artificial intelligence (AI)-enabled and model-based methods has increased significantly over recent years since they are able to analyze big data and estimate travel demands more accurately. This chapter provides a comprehensive review of the literature on travel pattern prediction approaches and identifies trends, challenges, and opportunities.

Suggested Citation

  • Sajjad Shafiei & Hussein Dia, 2023. "A review of AI-enabled and model-based methodologies for travel demand estimation in urban transport networks," Chapters, in: Hussein Dia (ed.), Handbook on Artificial Intelligence and Transport, chapter 14, pages 411-433, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:21868_14
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    File URL: https://www.elgaronline.com/doi/10.4337/9781803929545.00025
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