A new curb lane monitoring and illegal parking impact estimation approach based on queueing theory and computer vision for cameras with low resolution and low frame rate

The rapid development of the internet of things (IoT), sensing technologies, and machine learning and deep learning techniques, along with the growing variety and volume of data, have yielded new perspectives on how novel technologies can be applied to obtain new sources of curb data to achieve cost-effective curb management. This study presents a new computer vision-based data acquisition and analytics approach for curb lane monitoring and illegal parking impact assessment. The proposed "rank, detect, and quantify impacts" system consists of three main modules: 1) hotspot identification based on rankings generated by local spatial autocorrelation analysis, 2) curb lane occupancy estimation leveraging traffic cameras and computer vision techniques, and 3) illegal parking traffic impact quantification using an M/M/infinity queueing model. To demonstrate the feasibility and validity of the proposed approach, it was tested and empirically validated using field data collected from three case study sites in Midtown Manhattan, New York City (NYC)-one of the most complex urban transportation networks in the world. Different types of curb lane occupancy, including parking and bus lanes, and different frequencies of illegal parking (high, moderate, low) were investigated. The proposed method was proven to be effective even for low resolution and discontinuous video feeds obtained from publicly available traffic cameras. All three case study sites achieved good detection accuracy (86% to 96%) for parking and bus lane occupancy, and acceptable precision and recall in detecting illegal parking events. The queueing model was also proven to effectively quantify link travel time with the appearance of illegal parking events of different frequencies. The proposed "rank, detect, and quantify impacts" system is friendly for large-scale real-time implementation and is highly scalable to help evaluate the impact of other modes such as bike or mobility-on-demand (MOD) services. It can also be easily adopted by other cities to provide transportation agencies with effective data collection and innovative curb space management strategies.