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

Sajjad Shafiei and Hussein Dia

Chapter 14 in Handbook on Artificial Intelligence and Transport, 2023, pp 411-433 from Edward Elgar Publishing

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.

Keywords: Economics and Finance; Environment; Geography; Innovations and Technology; Law - Academic; Politics and Public Policy Urban and Regional Studies (search for similar items in EconPapers)
Date: 2023
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