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It is these existing challenges that we review in this paper and offer suggestions for future work

Short-term traffic forecasting: Where we are and where we’re going

Transportation Research Part C: Emerging Technologies, (2014): 3-19

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摘要

•Recent short-term traffic forecasting literature is categorized and analyzed.•Relevant work in 10 challenging, yet relatively under researched, directions is thoroughly discussed.•Suggestions for future work are provided and discussed.

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简介
  • Short term traffic forecasting has been a very important consideration in many areas of transportation research for more than 3 decades.
  • A novel research area, based on data driven empirical algorithms, has been systematically growing in parallel to the well-founded mathematical models that are based on macroscopic and microscopic theories of traffic flow (Wang and Papageorgiou, 2005; Yuan et al, 2012; Treiber and Kesting, 2012; Fowe and Chan, 2013; Kerner et al, 2013)
  • This significant leap from analytical to data driven modeling has been marked by an overwhelming increase of Computational Intelligence (CI) – Data Mining (DM) approaches to analyzing the data.
  • Researchers have moved from what can be considered as a classical statistical perspective, to Neural and evolutionary computational approaches (Karlaftis and Vlahogianni, 2011)
重点内容
  • Short term traffic forecasting has been a very important consideration in many areas of transportation research for more than 3 decades
  • A novel research area, based on data driven empirical algorithms, has been systematically growing in parallel to the well-founded mathematical models that are based on macroscopic and microscopic theories of traffic flow (Wang and Papageorgiou, 2005; Yuan et al, 2012; Treiber and Kesting, 2012; Fowe and Chan, 2013; Kerner et al, 2013). This significant leap from analytical to data driven modeling has been marked by an overwhelming increase of Computational Intelligence (CI) – Data Mining (DM) approaches to analyzing the data
  • To avoid overlaps with already published work, in Tables 1–4 we summarize the available literature for the periods 2004–2006, 2007–2009, 2010–2011, 2012–2013 respectively, and categorize papers based on certain criteria that can give a good sense of where most research effort has concentrated over the past decade
  • Findings support the shift of research interest towards: i. responsive forecasting schemes for non-recurrent conditions, ii. developing prediction systems with increased algorithmic complexity, iii. attempting to understand data coming from novel technologies and fuse multi-source traffic data to improve predictions, and iv. the applicability of Artificial Intelligence methodologies to the short-term traffic prediction problem
  • The analysis of the literature with relation to the 10 challenging issues has shown that, much work has been conducted in short-term traffic forecasting, there are still important research directions that will attract the interest of researcher in the following years (Table 6)
  • Researchers seem to be unprepared to answer two important questions: are we confident that our models are better, in terms of accuracy, than models developed 30 years ago? And, what have we learnt about prediction that has significantly changed our perception for traffic operations and management? The above imply that both research and practice in short-term traffic forecasting are entering a maturity phase, where models and methods must be critically assessed to produce solid knowledge on the concepts and processes involved with short-term traffic forecasting
方法
结论
  • In this paper the authors revisited much of the literature on short-term traffic forecasting and its advancements over the last decade.
  • The analysis of the literature with relation to the 10 challenging issues has shown that, much work has been conducted in short-term traffic forecasting, there are still important research directions that will attract the interest of researcher in the following years (Table 6).
  • The literature on short-term traffic forecasting has covered and used an impressive amount of models and data specifications.
  • Researchers seem to be unprepared to answer two important questions: are the authors confident that the models are better, in terms of accuracy, than models developed 30 years ago?
  • Researchers seem to be unprepared to answer two important questions: are the authors confident that the models are better, in terms of accuracy, than models developed 30 years ago? And, what have the authors learnt about prediction that has significantly changed the perception for traffic operations and management? The above imply that both research and practice in short-term traffic forecasting are entering a maturity phase, where models and methods must be critically assessed to produce solid knowledge on the concepts and processes involved with short-term traffic forecasting
表格
  • Table1: Literature for the period between 2004 and 2006
  • Table2: Literature for the period between 2007 and 2009
  • Table3: Literature for the period between 2010 and 2011
  • Table4: Literature for the period between 2012 and 2013
  • Table5: Existing challenges in short-term traffic forecasting and relevant literature
  • Table6: Directions for further research in relation to the 10 challenges
Download tables as Excel
基金
  • Reviews existing research with an explicit focus on identifying, briefly discussing, and offering information on 10 areas wbelieves that the technological and analytical challenges lie for the generation of short term forecasting research
  • Summarizes the available literature for the periods 2004–2006, 2007–2009, 2010–2011, 2012–2013 respectively, and categorize papers based on certain criteria that can give a good sense of where most research effort has concentrated over the past decade
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