Cited by 13Traffic Prediction and Management TechniquesTransportation Planning and OptimizationUrban Transport Systems Analysis

Bus Arrival Time Prediction: A Comprehensive Review

Kumar, B. Anil, Singh, Ramanand, Shaji, Hima Elsa, Vanajakshi, Lelitha·2025·IEEE Transactions on Intelligent Transportation Systems

Abstract

Bus arrival prediction systems play a crucial role in enhancing the efficiency and reliability of public transportation networks. Existing reviews in this area mainly focus on travel time prediction, without paying enough attention to the data, the patterns in the data, which can help in identifying the regressors for the models, various implementation strategies adopted and on performance evaluation techniques. To address these short-comings, this paper aims to provide a comprehensive overview of 1) methods available to collect and analyse the travel time data; 2) modelling of bus travel time using naive approaches to physics based and data driven approaches; 3) pattern identification techniques that can identify inputs for the modelling; 4) various implementation strategies including route discretization, multi-step prediction and utilization of data obtained from single route and multiple routes; 5) evaluation metrics and performance evaluation strategies adopted by bus transit agencies followed by challenges faced and way forward. Thus, this paper aims to guide researchers toward developing more accurate, reliable, and user-centric bus arrival time prediction systems, which are crucial for enhancing public transportation efficiency and user experience.

Integrity signals

Registry

Crossref · 10.1109/tits.2025.3545695

Cites

166 of 166 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Cites

Flagged first, then by citations

Research works printed but not linked: 9

  1. 2007, Public Transit Planning and Operation: Theory, Modeling and Practice
  2. Altinkaya, 2013, Urban bus arrival time prediction: A review of computational models, Int. J. Eng. Technol., 2, 164
  3. Mathew, 2017, Implementation of probe data performance measures
  4. 10.5555/3001460.3001507 · DOI
  5. 10.2307/99769 · DOI
  6. Maybeck, 1984, Stochastic Models, Estimation and Control
  7. 2015, San Francisco Transit Prediction Accuracy and Howswyft Helps You Commute Smarter
  8. 2019, Metro’s New Bus Prediction System is Pretty Accurate, but Leave Yourself Some Extra Time
  9. Machlab, A customerfocusedmethodology for determining prediction accuracy usingautomatically collected data

Cited by

Sorted by citations

Authors
Kumar, B. A., Singh, R., Shaji, H. E., & Vanajakshi, L.
Affiliations
  • Indian Institute of Technology Patna (IN)
  • Indian Institute of Technology Madras (IN)
Venue
IEEE Transactions on Intelligent Transportation Systems
Published
2025-06
Volume
26
Issue
6
Pages
7362-7379
ISSN
  • 1524-9050 (ISSN-L)
  • 1558-0016
OpenAlex
W4408047520
Cited by
13
References
165
Citations by year
2026: 10 · 2025: 3
FWCI
4.74
Type
journal article
Language
en
Open access
closed
Locations
IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
Topics
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
  • Social Sciences › Transportation › Transportation Planning and Optimization
  • Physical Sciences › Engineering › Civil and Structural Engineering › Urban Transport Systems Analysis
Keywords
bus arrival time prediction, public transport, pattern identification, multi-step forecasting, performance indicators, physics-based modeling, data-driven modeling, transit agencies

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review