A major change in people’s commute within the city, especially cities like NYC, is the development of “micromobility.” The term micromobility, according to the Merriam Webster Dictionary, means “transportation using lightweight vehicles to commute over short distances.” Some common micromobility methods include e-scooters and e-bikes. People using these micromobility methods can travel to further areas in less time while using less energy than traditional methods of non-vehicular transportations. While micromobility is seen in many different areas across the world, cities are the ideal place for the spread and popularization of micromobility, especially walkable cities like New York City. However, the integration of micromobility to our existing infrastructure and traffic system is not easy because the system did not account for the rise of micromobility upon the designing phase which could track all the way to the 19th century. As a result, the methodology of successfully adding micromobility to our current road system is crucial.

The New York City Department of Transportation (DOT) has started an East Bronx Shared E-Scooter Pilot Program. Focusing on the East Bronx, the program started in August 2021, aiming to test the possibility of shared e-scooters in areas not currently covered by the shared bike system (Citi-Bike). The goal of this program is to create a profitable shared e-scooter system that can provide enough services to all local residents while assuring citizens’ safety and keeping the roads clean from parking of the shared e-scooters. Even though this is a challenging experiment because this is the very first time the city incorporates the innovative micromobility to existing infrastructure system, the NYC DOT were not stumped by the lack of precedent. Over 86,000 riders spent more than 1,000,000 trips, and averaged 2,800 trips per day with a significantly higher ridership on weekends and warmer days. Additionally, not a single fatality was reported.

Last but not least, a very prominent term in transportation management and urban planning has to be “Smart Transportation” from the idea of “Smart City.” A major component within the smart transportation ideas has to be the various “invisible” innovations in the field of transportation systems: smart and data driven transportation systems are developed to ensure the efficiency and safety of the citizens’ daily commutes. Under the era of information and technology, with the development of the internet of things (IoT), sensing technologies, and AI, scientists are trying to bring new insights to the existing transportation systems. Among all the innovations, the following paragraphs cover a specific branch of artificial intelligence: computer vision. Computer vision is defined as the field of AI that trains computers to interpret and understand the visual world using digital images from cameras and videos and deep learning models.

C2SMART Research Lab is currently implementing a project known as ‘Exploring Cost-Effective Computer Vision Solutions for Smart Transportation Systems.’ With funding provided by USDOT, C2SMART hopes to develop a deep learning-based data acquisition and analytics tool using vision-based sensors. The lab also assesses the maturity of existing smart city applications using computer vision and hopes to illustrate the importance and effectiveness of computer vision implementation in real world applications. Aside from these major goals, the deliverables of the project also include bringing insights on the existing traffic camera systems in the U.S. With the 2 vision sensing cameras being added to the existing system, the project hopes to establish a prototype fitting NYC’s condition and can also, hopefully, be adopted by other cities in a similar way.

Even though this is an ongoing project, the implementations and benefits are clear. As a theory that has long been developed and lacked real world applications. The theory of computer vision used in this project will serve as an important paradigm and facilitate the use of computer vision by providing scientists with more hands-on experiences when it comes to “smart transportation” applications.