The increasing reliance on transportation network companies (TNCs) and delivery services has transformed the use of curb space. The curb space is also an important interface for bikeways, bus lanes, street vendors, and paratransit stops for passengers with disabilities. These various demands are contributing to a lack of parking, resulting in illegal and double-parking and excessive cruising for spaces and causing traffic disturbance, congestion, andhazardous situations.

The increasing use of transportation network companies and delivery services has transformed the utilization of curb space, resulting in a lack of parking and contributing to congestion. No systematic method exists for identifying curb usage patterns, but emerging machine learning technologies and low-tech data sources, such as dashboard cameras mounted on vehicles that routinely travel the area, have the potential of monitoring curb usage. To demonstrate how video data can be used to recognize usage patterns, we conducted a case study on Bancroft Way in Berkeley, CA. The project collected video footage with GPS data from a dashboard camera installed on a shuttle bus that circles the area. We trained a machine learning model to recognize different types of delivery vehicles in the data images, and then used the model to visualize curbside usage trends. The findings include identifying hot spots, analyzing arrival patterns by delivery vehicle type, detecting bus lane blockage, and assessing the impact of parking on traffic flow. The proof-ofconcept study demonstrated that machine learning techniques, when coupled with affordable hardware like a dashboard camera, can reveal curb usage patterns. The data can be used to efficiently manage curb space, facilitate goods movement, improve traffic flow, and enhance safety.