Title page for ETD etd-01282011-162408

Type of Document Dissertation
Author Zhou, Guoqing
Author's Email Address gzhou@odu.edu
URN etd-01282011-162408
Title Co-Location Decision Tree for Enhancing Decision-Making of Pavement Maintenance and Rehabilitation
Degree PhD
Department Civil Engineering
Advisory Committee
Advisor Name Title
Wang, Linbing Committee Chair
Abbas, Montasir M. Committee Member
Flintsch, Gerardo W. Committee Member
Hobeika, Antoine G. Committee Member
Trani, Antonio A. Committee Member
  • Maintenance and Rehabilitation
  • Decision Tree
  • Spatial Data Mining
  • Co-Location
  • Pavement Management
  • GIS
Date of Defense 2011-01-17
Availability unrestricted
A pavement management system (PMS) is a valuable tool and one of the critical elements of the

highway transportation infrastructure. Since a vast amount of pavement data is frequently and

continuously being collected, updated, and exchanged due to rapidly deteriorating road

conditions, increased traffic loads, and shrinking funds, resulting in the rapid accumulation of a

large pavement database, knowledge-based expert systems (KBESs) have therefore been

developed to solve various transportation problems. This dissertation presents the development

of theory and algorithm for a new decision tree induction method, called co-location-based

decision tree (CL-DT.) This method will enhance the decision-making abilities of pavement

maintenance personnel and their rehabilitation strategies. This idea stems from shortcomings in

traditional decision tree induction algorithms, when applied in the pavement treatment strategies.

The proposed algorithm utilizes the co-location (co-occurrence) characteristics of spatial

attribute data in the pavement database. With the proposed algorithm, one distinct event

occurrence can associate with two or multiple attribute values that occur simultaneously in

spatial and temporal domains.

This research dissertation describes the details of the proposed CL-DT algorithms and steps of

realizing the proposed algorithm. First, the dissertation research describes the detailed colocation

mining algorithm, including spatial attribute data selection in pavement databases, the

determination of candidate co-locations, the determination of table instances of candidate colocations, pruning the non-prevalent co-locations, and induction of co-location rules. In this step, a hybrid constraint, i.e., spatial geometric distance constraint condition and a distinct event-type constraint condition, is developed. The spatial geometric distance constraint condition is a

neighborhood relationship-based spatial joins of table instances for many prevalent co-locations

with one prevalent co-location; and the distance event-type constraint condition is a Euclidean

distance between a set of attributes and its corresponding clusters center of attributes. The

dissertation research also developed the spatial feature pruning method using the multi-resolution

pruning criterion. The cross-correlation criterion of spatial features is used to remove the nonprevalent co-locations from the candidate prevalent co-location set under a given threshold. The dissertation research focused on the development of the co-location decision tree (CL-DT)

algorithm, which includes the non-spatial attribute data selection in the pavement management

database, co-location algorithm modeling, node merging criteria, and co-location decision tree

induction. In this step, co-location mining rules are used to guide the decision tree generation and

induce decision rules. For each step, this dissertation gives detailed flowcharts, such as flowchart of co-location decision tree induction, co-location/co-occurrence decision tree algorithm, algorithm of colocation/co-occurrence decision tree (CL-DT), and outline of steps of SFS (Sequential Feature Selection) algorithm. Finally, this research used a pavement database covering four counties, which are provided by NCDOT (North Carolina Department of Transportation), to verify and test the proposed method. The comparison analyses of different rehabilitation treatments proposed by NCDOT, by the traditional DT induction algorithm and by the proposed new method are conducted. Findings and conclusions include: (1) traditional DT technology can make a consistent decision for road maintenance and rehabilitation strategy under the same road conditions, i.e., less interference from human factors; (2) the traditional DT technology can increase the speed of decision-making because the technology automatically generates a decision-tree and rules if the expert knowledge is given, which saves time and expenses for PMS; (3) integration of the DT and GIS can provide the PMS with the capabilities of graphically displaying treatment decisions, visualizing the attribute and non-attribute data, and linking data and information to the geographical coordinates. However, the traditional DT induction methods are not as quite intelligent as one’s expectations. Thus, post-processing and refinement is necessary. Moreover, traditional DT induction methods for pavement M&R strategies only used the non-spatial attribute data. It has been demonstrated from this dissertation research that the spatial data is very useful for the improvement of decision-making processes for pavement treatment strategies. In addition, the decision trees are based on the knowledge acquired from pavement management engineers for strategy selection. Thus, different decision-trees can be built if the requirement changes.

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