## 1 Introduction  
Traffic congestion is a persistent problem worldwide, leading to economic and social challenges. To enhance competitiveness, smooth traffic conditions are of the utmost importance for any city. This is especially true when developing a smart city, which aims at making good use of information and communication technologies (ICT) to support the creation of a ubiquitous and interconnected network of citizens and organizations, sharing digital data and information via the internet of things (IoT) [1]. In recent years, smart mobility and smart transportation have been advocated to alleviate the above social and public problems. In general, frequent roadside loading and unloading activities have a negative impact on traffic situations. An increasing number of vehicles and insufficient data transparency regarding roadside activities, occupancy and vacancy, make the situation worse, and this issue is becoming critical in cities with high transportation density. For instance, during peak hours, limited roadside spaces are usually occupied by trucks for loading or unloading or by other vehicles for purposes of picking up or dropping off. Some vehicles are required to circle the road network or temporarily double-park while waiting for roadside spaces to become available. Figure 1 illustrates a real-life situation of roadside double-parking, caused by the loading and unloading activities of logistics trucks, company cars and other obstacles. These actions cause unnecessary blocking and create safety issues for other road users, and traffic flow can be affected. Hence, there are increasing numbers of applications for roadside surveillance systems, especially with respect to traffic surveillance related to increasing traffic congestion. However, current roadside surveillance systems have limitations, such as being affected by vehicle speeds [2]. To be useful, a roadside surveillance system requires machine learning and computer vision-based technology to facilitate and enhance the accuracy and effectiveness of detection and recognition of vehicles and objects [3]. With an increasing number of vehicles in roadside situations which may cause severe traffic congestion, a suitable and well-functioning system is needed, utilizing ICT and IoT technologies. This would enable surveillance of roadside loading and unloading bays, to prevent traffic congestion and facilitate traffic and fleet planning and management by implementing smart mobility, hence achieving a highly efficient road network in a smart city. Furthermore, smart transportation and smart parking can be established under the ontology of smart mobility. In an attempt to solve these problems, this paper presents an IoT-based system for the surveillance of roadside loading and unloading bays, namely, a computer vision-based roadside occupation surveillance system (CVROSS). By equipping a set of wireless, high-definition vision devices with machine learning and solar power in a vision-based network, real-time roadside traffic images--including recognition of loading or unloading activities--can be observed and captured automatically, to enhance the transparency of roadside occupancy and vacancy. Decision support models for parking-gap calculations and parking-space assignment are formulated, to eliminate the occurrence of double-parking, while fuzzy logic is applied to address the fuzziness in vehicle parking reservations and estimation of the time of stay for vehicles. As a result, the proposed system can evaluate average space utilization, loading and unloading activities and average waiting times for parking. Property management companies and drivers can derive benefits relating to time-saving and smoother traffic flow in busy built-up areas. To validate the proposed system's performance and feasibility, a case study was conducted in the Kwun Tong District of Hong Kong (one of the busiest districts for roadside loading and unloading activities in Hong Kong).