Modeling the cost and coverage of an ad-hoc asset management system based on existing fleet vehicles

Monitoring road assets such as road signs, utility poles, features of the road itself or other structures close to where vehicles are driving is important. Such assets need to be monitored in order to maintain them and minimize accident fatalities caused by non-compliance [1]. However, traditional surveying methods that utilize dedicated vehicles equipped with high-end expensive sensors turn out to be very costly and hence, surveys can only be carried out every few years. This paper explores the feasibility of equipping existing fleet vehicles, such as taxis, with low-end, low-quality sensors that traverse the road network through their normal daily activities. The cost and coverage of such a new approach is modeled with the help of a dataset T-Drive from Microsoft that provides taxi trajectories for more than 10,000 taxis in Beijing. The paper further estimates the optimal, from a cost perspective, number of taxis needed to survey the region by considering the cost of explicitly surveying areas that have not been covered by the random trajectories of the taxis.