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Estimating traffic volumes on intercity road locations using roadway attributes, socioeconomic features and other work-related activity characteristics

Author

Listed:
  • Noelia Caceres

    (AICIA)

  • Luis M. Romero

    (University of Seville)

  • Francisco J. Morales

    (University of Seville)

  • Antonio Reyes

    (University of Seville)

  • Francisco G. Benitez

    (University of Seville)

Abstract

Traffic volume data are key inputs to many applications in highway design and planning. But these data are collected in only a limited number of road locations due to the cost involved. This paper presents an approach for estimating daily and hourly traffic volumes on intercity road locations combining clustering and regression modelling techniques. With the aim of applying the procedure to any road location, it proposes the use of roadway attributes and socioeconomic characteristics of nearby cities as explanatory variables, together with a set of previously discovered patterns with the hourly traffic percent distribution. Test results show that the proposed approach significantly produces accurate estimates of daily volumes for most locations. The accuracy at hourly level is a bit more reduced but, for periods when traffic is significant, more than half of the estimates are within 20% of absolute percentage error. Moreover, the main peak period is approximately identified for most cases. These findings together with its great applicability make this approach attractive for planners when no traffic data are available and an estimate is helpful.

Suggested Citation

  • Noelia Caceres & Luis M. Romero & Francisco J. Morales & Antonio Reyes & Francisco G. Benitez, 2018. "Estimating traffic volumes on intercity road locations using roadway attributes, socioeconomic features and other work-related activity characteristics," Transportation, Springer, vol. 45(5), pages 1449-1473, September.
    • Handle: RePEc:kap:transp:v:45:y:2018:i:5:d:10.1007_s11116-017-9771-5
    DOI: 10.1007/s11116-017-9771-5
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    References listed on IDEAS

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    Cited by:

    1. Sfyridis, Alexandros & Agnolucci, Paolo, 2020. "Annual average daily traffic estimation in England and Wales: An application of clustering and regression modelling," Journal of Transport Geography, Elsevier, vol. 83(C).

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