Integration of GIS and Data Mining Technology to Enhance the Pavement Management Decision Making

This paper presents a research effort undertaken to explore the applicability of data mining and knowledge discovery (DMKD) in combination with Geographic Information System (GIS) technology to pavement management to better decide maintenance strategies, set rehabilitation priorities, and make investment decisions. The main objective of the research is to utilize data mining techniques to find pertinent information hidden within the pavement database. Mining algorithm C5.0, including decision trees and association rules, has been used in this analysis. The selected rules have been used to predict the maintenance and rehabilitation strategy of road segments. A pavement database covering four counties within the state of North Carolina, which was provided by North Carolina DOT (NCDOT), has been used to test this method. A comparison was conducted in this paper for the decisions related to a rehabilitation strategy proposed by the NCDOT to the proposed methodology presented in this paper. From the experimental results, it was found that the rehabilitation strategy derived by this paper is different from that proposed by the NCDOT. After combining with the AIRA Data Mining method, seven final rules are defined. Using these final rules, the maps of several pavement rehabilitation strategies are created. When their numbers and locations are compared with ones made by engineers at the Institute for Transportation Research and Education (ITRE) at North Carolina State University, it has been found that error for the number and the location are various for the different rehabilitation strategies. With the pilot experiment in the project, it can be concluded: (1) use of the DMKD method for the decision of road maintenance and rehabilitation can greatly increase the speed of decision making, thus largely saving time and money, and shortening the project period; (2) the DMKD technology can make consistent decisions about road maintenance and rehabilitation if the road conditions are similar, i.e., interference from human factors is less significant; (3) integration of the DMKD and GIS technologies provides a pavement management system with the capabilities to graphically display treatment decisions against distresses; and (4) the decisions related to pavement rehabilitation made by the DMKD technology is not completely consistent with that made by ITRE, thereby, the postprocessing for verification and refinement is necessary.

[1]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[2]  Nii O. Attoh-Okine.  Combining use of rough set and artificial neural networks in doweled-pavement-performance modeling: A hybrid approach , 2002 .

[3]  Sou-Sen Leu,et al.  Data mining for tunnel support stability: neural network approach , 2001 .

[4]  António Pais Antunes,et al.  PROBABILISTIC SEGMENT-LINKED PAVEMENT MANAGEMENT OPTIMIZATION MODEL , 2002 .

[5]  Weng Tat Chan,et al.  Road-maintenance planning using genetic algorithms. I: Formulation , 1994 .

[6]  Fabian C. Hadipriono,et al.  USING A KNOWLEDGE-BASED EXPERT SYSTEM AND FUZZY LOGIC FOR MINOR REHABILITATION PROJECTS IN OHIO , 1995 .

[7]  Feng Wang,et al.  Decision-Making Problem for Managing Pavement Maintenance and Rehabilitation Projects , 2003 .

[8]  Dong Ha Lee,et al.  Data mining approach to policy analysis in a health insurance domain , 2001, Int. J. Medical Informatics.

[9]  Ryszard S. Michalski,et al.  AQ15: Incremental Learning of Attribute-Based Descriptions from Examples: The Method and User's Guide , 1986 .

[10]  Khaled Nassar.  Application of data-mining to state transportation agencies' projects databases , 2007, J. Inf. Technol. Constr..

[11]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[12]  Hosin Lee,et al.  Development of Geographic Information System-Based Pavement Management System for Salt Lake City , 1996 .

[13]  Philip J. Stone,et al.  Experiments in induction , 1966 .

[14]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[15]  Weng Tat Chan,et al.  ROAD-MAINTENANCE PLANNING USING GENETIC ALGORITHMS.. , 1994 .

[16]  Kenneth A. Kaufman,et al.  Learning in an Inconsistent World: Rule Selection in AQ18 , 1999 .

[17]  Nii O. Attoh-Okine.  Rough set application to data mining principles in pavement management database , 1997 .

[18]  Nada Lavrac,et al.  The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains , 1986, AAAI.

[19]  Lucio Soibelman,et al.  Generating Construction Knowledge with Knowledge Discovery in Databases , 2000 .

[20]  Gary S. Spring,et al.  IDENTIFICATION OF HAZARDOUS HIGHWAY LOCATIONS USING KNOWLEDGE-BASED GIS: A CASE STUDY , 1995 .

[21]  Mark D. Abkowitz,et al.  Adaptation of Geographic Information Systems to Highway Management , 1990 .

[22]  D. Goulias.  Management Systems And Spatial Data Analysis In Transportation And Highway Engineering , 2002 .

[23]  Xudong Jia,et al.  A FRAMEWORK FOR INTEGRATING GIS-T WITH KBES: A PAVEMENT MANAGEMENT SYSTEM EXAMPLE , 1995 .

[24]  Bo Gao,et al.  Multiyear Pavement-Rehabilitation Planning Enabled by Geographic Information System: Network Analyses Linked to Projects , 2004 .

[25]  Ryszard S. Michalski,et al.  AQVAL/1 (AQ7) User's Guide and Program Description , 1975 .

[26]  Peter Clark,et al.  The CN2 Induction Algorithm , 1989, Machine Learning.

[27]  Ram B. Kulkarni,et al.  Pavement Management Systems: Past, Present, and Future , 2003 .