  The sensing and monitoring of the urban road network contribute to the
efficient operation of the urban transportation system and the functionality of
urban systems. However, traditional sensing methods, such as inductive loop
sensors, roadside cameras, and crowdsourcing data from massive urban travelers
(e.g., Google Maps), are often hindered by high costs, limited coverage, and
low reliability. This study explores the potential of drive-by sensing, an
innovative approach that employs large-scale ridesourcing vehicles (RVs) for
urban road network monitoring. We first evaluate RV sensing performance by
coverage and reliability through historical road segment visits. Next, we
propose an optimal trip-based RV rerouting model to maximize the sensing
coverage and reliability while preserving the same level of service for the
RVs' mobility service. Furthermore, a scalable column generation-based
heuristic is designed to guide the cruising trajectory of RVs, assuming trip
independence. The effectiveness of the proposed model is validated through
experiments and sensitivity analyses using real-world RV trajectory data of
over 20,000 vehicles in New York City. The optimized rerouting strategy has
yielded significantly improved results, elevating explicit sensing coverage of
the road network by 15.0\% to 17.3\% (varies by time of day) and achieving an
impressive enhancement in sensing reliability by at least 24.6\% compared to
historical records. Expanding the path-searching space further improved sensing
coverage of up to 4.5\% and reliability of over 4.2\%. Moreover, considering
incentives for RV drivers, the enhanced sensing performance comes at a
remarkably low cost of \$0.10 per RV driver, highlighting its
cost-effectiveness.
