A risk-based decision policy to aid the prioritization of unsafe sidewalk locations for maintenance and rehabilitation

Air pollution and a general concern for lack of physical activity in North America have motivated governments to encourage non-motorized modes of transportation. A key infrastructure component for these forms of transportation is sidewalks. The City of Saskatoon has identified the need to formalize sidewalk management policies to demonstrate diligence for community protection regarding sidewalk safety. Prioritization of sidewalk maintenance and rehabilitation actions must be objective and minimize risk to the community. Most research on prioritization of pedestrian facilities involved new construction projects. This research proposes a decision model that prioritizes a given list of existing unsafe sidewalk locations needing maintenance or rehabilitation using a direct measure of pedestrian safety, namely, quality-adjusted life years lost per year. A decision model was developed for prioritizing a given list of unsafe sidewalk locations, aiding maintenance and rehabilitation decisions by providing the associated risk to pedestrian safety. The model used data mostly from high quality sources that had already been collected and validated. Probabilities and estimations were used to produce value-added decision policy. The decision analysis framework applied probability and multi-attribute utility theories. This study differed from other research due to the inclusion of age and gender groups. Total average daily population of the city was estimated. This population was distributed to sidewalk locations using probabilities for trip purposes and a location’s ability to attract people relative to the city total. Then trip injury events were predicted. Age and gender distribution and trip injury type estimations were used to determine the impact of those injuries on quality of life.There exist much observable high quality data that can be used as indicators of unknown or unobserved events. A decision policy was developed that prioritizes unsafe sidewalk locations based on the direct safety impact on pedestrians. Results showed that quality-adjusted life years lost per year sufficiently prioritized a given list of unsafe sidewalk locations. It was demonstrated that the use of conditional probabilities (n=594) allowed for the ability to abstract data representing a different source population to another. Average daily population confined and distributed within the city boundary minimized problems of accuracy. Gender-age distribution was important for differentiating the risk at unsafe sidewalk locations. Concepts from this research provide for possible extension to the development of sidewalk service levels and sidewalk priority maps and for risk assessment of other public services