Street closure prediction based on the combined conditions of spatially collocated municipal infrastructure assets at the segment level

Abstract

Generally, intervention activities that require excavations tend to create negative socio-economic impacts, such as increased traffic congestion and travel time, noise and air pollution, etc. A holistic form of intervention planning is needed to minimize the negative impacts and the frequency of maintenance, repair, and rehabilitation activities resulting in partial or complete street closures. This planning should be based on a unified classification model, which evaluates the conditions of different municipal assets (i.e., water and sewer pipes and road pavements) at the segment-level. The objectives of this paper are: (1) Developing a machine learning method for systematic condition classification of different spatially collocated underground municipal assets within a segment; and (2) Applying a heuristic approach for determining street closures based on the synchronized or unsynchronized interventions at the segment level induced from combining the interventions of individual assets within each segment. The conditions of all three assets are determined using three independent ensemble machine learning models, each made up of Random Forest, Gradient Boosted Trees, and Deep Learning as base learners. The required asset-level interventions are determined by these conditions combined with heuristics specific to each spatially collocated asset in a street segment to decide the segment-level synchronized or unsynchronized interventions and the nature of the street closures (partial, complete, or both) needed to undertake the interventions. The accuracies of the three models are 98.64%, 96.37%, and 82.38% for the pavement, water and sewer pipes, respectively. The segment-level intervention strategies resulted in both synchronized and unsynchronized interventions leading to complete, partial, or a combination of both partial and complete street closures. The predicted segment-level interventions had an accuracy of 79.92%. This research will aid municipal decision-makers in identifying and prioritizing synchronized segment-level interventions, therefore, improving the planning and estimation of the intervention durations and street closures.