Comparison of two non-parametric models for daily traffic forecasting in Hong Kong

Hing Keung William Lam, Y. F. Tang, Mei Lam Tam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

38 Citations (Scopus)

Abstract

The most up-to-date annual average daily traffic (AADT) is always required for transport model development and calibration. However, the current-year AADT data are not always available. The short-term traffic flow forecasting models can be used to predict the traffic flows for the current year. In this paper, two non-parametric models, non-parametric regression (NPR) and Gaussian maximum likelihood (GML), are chosen for short-term traffic forecasting based on historical data collected for the annual traffic census (ATC) in Hong Kong. These models are adapted as they are more flexible and efficient in forecasting the daily vehicular flows in the Hong Kong ATC core stations (in total of 87 stations). The daily vehicular flows predicted by these models are then used to calculate the AADT of the current year, 1999. The overall prediction and comparison results show that the NPR model produces better forecasts than the GML model using the ATC data in Hong Kong.
Original languageEnglish
Pages (from-to)173-192
Number of pages20
JournalJournal of Forecasting
Volume25
Issue number3
DOIs
Publication statusPublished - 1 Apr 2006

Keywords

  • Annual average daily traffic (AADT)
  • Gaussian maximum likelihood (GML)
  • Non-parametric regression (NPR)
  • Short-term daily traffic forecasting

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Strategy and Management
  • Management Science and Operations Research

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