Abstract
This study contributes to knowledge by advancing the transportation management plan (TMP) development efforts for expressway work zones. Using text mining techniques to a large-scale transportation data set that contains descriptively narrated texts, this research analyzes the association between words related to the type of work being performed and the type of lane closure in expressway work zone areas. It found that recurrent everyday tasks and bridge repair works tend to cause shoulder lane closure, while works—such as tunnel repair, night work, pavement, median barrier, road surface repair, and line marking—are more associated with main lane closure. Moreover, the findings further clarify the characteristic patterns shared between the number of closed lanes, and the respective lane position in two- and three-lane expressways. These offer significant insights into the decision-making process for the development of work zone TMPs, which can further be integrated into the various components of TMP to make the plan more effective and, at the same time, ensure an efficient throughput flow throughout the work zone, reduced congestion, and improved safety.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Federal Highway Administration (2005) Developing and implementing transportation management plans for work zones. US Department of Transportation, Washington, DC
California Department of Transportation (2015) Transportation management plan guidelines. Division of Traffic Operations, Office of Traffic Management, California Department of Transportation, Sacramento, California
Colorado Department of Transportation (2014) Work zone safety and mobility rule. Procedures Document, Colorado Department of Transportation, Denver, Colorado
Illinois Department of Transportation (2010) Chapter 13: Work zone transportation management plans. Bureau of Design and Environment Manual, Illinois Department of Transportation, Springfield, Illinois
Oregon Department of Transportation (2013) Transportation management plan (TMP). Project Level Guidance Manual, Oregon Department of Transportation, Salem, Oregon
Ministry of Land, Infrastructure and Transport (2013) Korea highway capacity manual. Infrastructure and Transport, Republic of Korea, Ministry of Land
Han J, Pei J, Kamber M (2012) Data Mining: Concept and Techniques, 3rd edn. Morgan Kaufmann Publishers, Burlington
Blei D, Lafertty J (2009) Topic models. In: Srivastava A, Sahami M (eds) Text Mining: Theory and Applications. Taylor and Francis, Oxfordshire
Pang B, Lee L (2008) Opinion mining and sentiment analysis. Foundations and trends in information retrieval 2:1–135, Hanover, Germany
Mei Q, Zhai ChengXiang (2006) A mixture model for contextual text mining. In: Proceedings of the 2006 ACM SIGKDD International Conference on Knowledge Discovery in Databases (KDD ‘06), ACM, New York, New York, pp 649–655
Mafrur R, Nugraha GD, Choi D (2015) Modeling and discovering human behavior from smartphone sensing life-log data for identification purpose. Hum Cent Comput Inf Sci 5:31. doi:10.1186/s13673-015-0049-7
Lee M, Park Y-S, Kim M-H (2016) A convergence data model for medical information related to acute myocardial infarction. Hum Cent Comput Inf Sci 6:15. doi:10.1186/s13673-016-0071-4
James AP (2015) Heart rate monitoring using human speech spectral features. Hum Cent Comput Inf Sci 5:33. doi:10.1186/s13673-015-0052-z
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the \(20^{{\rm th}}\) International Conference on Very Large Databases, Vol. 1215, Morgan Kaufmann Publishers, San Francisco, California, pp 487–499
Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, pp 207–216
Young SC, Song CM (2015) Recommender system using periodicity analysis via mining sequential patterns with time-series and FRAT analysis. J Converg 6:9–17
Jeeva SC, Rajsingh EB (2016) Intelligent phishing URL detection using association rule mining. Hum Cent Comput Inf Sci 6:10. doi:10.1186/s13673-016-0064-3
Jurafsky D, Martin JH (2009) Speech and language processing, 2nd edn. Prentice Hall, Upper Saddle River
Hahsler M, Buchta C, Gruen B, Hornik K, Johnson I, Borgelt C (2016) Package ‘arules’: mining association rules and frequent itemsets. R Package Version 1.5-0
Jeon H, Kim T (2016) KoNLP: Korean NLP package. R Package Version (80)
Feinerer I, Hornik K (2015) tm: Text mining package. R Package Version 0.6-2
Hahsler M, Chelluboina S (2016) arulesViz: Visualizing association rules and frequent itemsets. R Package Version 1.2-0
Acknowledgements
This research was supported by the BISA Research Grant of Keimyung University in 2014.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Park, S.H., Synn, J., Kwon, O.H. et al. Apriori-based text mining method for the advancement of the transportation management plan in expressway work zones. J Supercomput 74, 1283–1298 (2018). https://doi.org/10.1007/s11227-017-2142-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-017-2142-3