Comparing two hybrid neural network models to predict real-world bus travel time

Publikation: Beiträge in ZeitschriftenKonferenzaufsätze in FachzeitschriftenForschung

Authors

  • Thummaporn Nimpanomprasert
  • Lin Xie
  • Natalia Kliewer
In order to enhance the efficiency and reliability of bus transportation systems, it is important to predict travel time precisely in the planning phase. With the precise prediction of bus travel times, the cost can be reduced for bus companies, such as from planning fixed buffer times between trips, while a better service can be provided for passengers. In this study, historical bus travel data from a bus line of the HOCHBAHN bus company in Germany in 2019 are used for the analysis. We develop two neural network models, namely multilayer perceptrons and a long short-term memory neural network, for predicting the bus travel time between timepoints for a trip occurring at a given time of day, on a given day of the week, and in given weather conditions. Both neural networks are combined with a genetic algorithm and a Kalman filter to improve accuracy. The genetic algorithm is implemented to tune the neural network parameters, such as the number of hidden layers and neurons in a hidden layer, while the Kalman filter algorithm is used to adjust the travel time prediction for the next trip using the latest bus operation information. In the experiment, we test our models month by month and split data for each month into three parts: the data of the first two weeks for training, one week for validation and the last week for prediction. The experiment results show that the hybrid model is powerful for predicting the bus travel time. In particular, the combination of the multilayer perceptrons with the genetic algorithm and Kalman filter provides the best travel time prediction (with an improvement of 56.2% compared with the planned bus travel time from the bus company). In order to make a recommendation for bus companies to plan vehicles with more accurate bus travel times, we test our hybrid models with larger training sets to predict the travel times e.g. in August. However, due to the structure of the planning problem, the plan for buses should be estimated before a new month begins. We cannot consider the real information during the planning, therefore we apply our hybrid models without the Kalman filter and they provide on average about 26% improvement compared with the planned travel time.
OriginalspracheEnglisch
ZeitschriftTransportation Research Procedia
Jahrgang62
Seiten (von - bis)393-400
Anzahl der Seiten8
ISSN2352-1457
DOIs
PublikationsstatusErschienen - 01.01.2022

Bibliographische Notiz

Funding Information:
The authors want to greatly thank HOCHBAHN for providing us with data for the analysis. This work is a contribution to the “Robust integrated vehicle scheduling, crew scheduling and rostering in public bus transit” project, which is funded by the German Research Foundation grants KL 2152/7-1 and LX 156/2-1.

Publisher Copyright:
© 2022 Elsevier B.V.. All rights reserved.

Dokumente

DOI

Zuletzt angesehen

Publikationen

  1. Foundation of digital badges and micro-credentials
  2. Solar Rotation Velocity Determined by Coronal Bright Points – New Data and Analysis
  3. The impact of digital innovation on path-dependent decision-making
  4. NECESSARY HRM PRACTICES FOR EXTENDED WORKING LIVES IN TIGHT AND LOOSE SOCIETIES: A COMPARATIVE PERSPECTIVE
  5. Disabling barriers—Coping with accessibility of nature in Biosphere Reserves
  6. Daten, Wahn, Sinn
  7. Mathematics-specific motivations for choosing a mathematics teaching degree study programme
  8. Comparative effectiveness of three versions of a stepped care model for insomnia differing in the amount of therapist support in internet-delivered treatment
  9. Centralized and decentral approaches to succeed the 100% energiewende in Germany in the European context – A model-based analysis of generation, network, and storage investments
  10. Microstructure and degradation performance of biodegradable Mg-Si-Sr implant alloys
  11. What Do We Know About Modernization Today?
  12. Einführung
  13. Managing Interorganizational Relations
  14. From incremental to fundamental substitution in chemical alternatives assessment
  15. An integrative analysis of energy transitions in energy regions
  16. The plastic yield and flow behavior in metallic glasses
  17. Foraging wireworms are attracted to root-produced volatile aldehydes
  18. Personality in personnel selection and assessment
  19. The Effects of Social Interaction and Social Norm Compliance in Pay-What-You-Want Situations
  20. Scaffolding inquiry-based science and chemistry education in inclusive classrooms
  21. Mathematik 1
  22. Eco-Controlling for Environmental Management
  23. Sight or scent: lemur sensory reliance in detecting food quality varies with feeding ecology.
  24. A look down the drain
  25. Kinetic Spectra of the Planar Multipole Resonance Probe
  26. Promoting electric vehicles in Germany via subsidies – An efficient strategy?
  27. Innovative Popular Science Communication? Materiality, Aesthetics and Gender in Science Slams
  28. Compensation-related institutional investor activism.
  29. The sources of international investment law
  30. Kinderbetreuung im Unternehmen
  31. Radiokunst analysieren und vermitteln
  32. Unraveling the relationship between presidential approval and the economy
  33. § 348 Erfüllung Zug-um-Zug
  34. I’m in a Hurry, I Don't Want to Know! Strategic Ignorance Under Time Pressure
  35. Product-service systems as enabler for sustainability-oriented innovation
  36. Falling “fortresses”
  37. Medienklangräume
  38. Über Mathematik reden
  39. Prosodie und Zeichensetzung
  40. Privatisierung, Deregulierung und Freie und staatlich gebundene Freie Berufe