Survey and empirical evaluation of nonhomogeneous arrival process models with taxi data

Hamid R. Sayarshad, Joseph Y.J. Chow

Research output: Contribution to journalArticlepeer-review


Arrival processes are important inputs to many transportation system functions, such as vehicle prepositioning, taxi dispatch, bus holding strategies, and dynamic pricing. We conduct a comprehensive survey of the literature which shows that many transport systems employ basic homogeneous arrival process models or static nonhomogeneous processes. We conduct an empirical experiment to compare five state of the art arrival process short term prediction models using a common transportation system data set: New York taxi passenger pickups in 2013. Pickup data is split between 672 observations for model estimation and 96 observations for validation. From our experiment, we obtain evidence to support a recent model called FM-IntGARCH, which is able to combine the benefits of both time series models and discrete count processes. Using a set of seven performance metrics from the literature, FM-IntGARCH is shown to outperform the offline models—seasonal factor method, piecewise linear model—as well as the online models—ARIMA, Gaussian Cox process. Implications for operating data-driven “smart” transit systems and urban informatics are discussed.

Original languageEnglish (US)
Pages (from-to)1275-1294
Number of pages20
JournalJournal of Advanced Transportation
Issue number7
StatePublished - Nov 1 2016


  • Gaussian Cox process
  • New York taxi data
  • arrival process
  • non-homogeneous Poisson process
  • time series

ASJC Scopus subject areas

  • Automotive Engineering
  • Economics and Econometrics
  • Mechanical Engineering
  • Computer Science Applications
  • Strategy and Management


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