# Controlling Traffic Congestion in a Residential Area via GLOSA Development
### Related Work  
In a 2020 study exploring the shortcut roads phenomenon and its impact on traffic trends, disruptions to mobility, and congestion patterns over one year, researchers discovered that an increasing number of drivers may be taking shortcuts, resulting in a 4X increase in flow in some locations and a 25% fall in speed on some main roadways. This underscores the necessity for traffic management applications to collaborate with the current urban infrastructure to optimize the movement of traffic effectively [12]. Furthermore, the use of navigation apps like Google Maps and Apple Maps has led to an increase in traffic congestion in residential neighborhoods as drivers are being routed through these areas as shortcuts to their destinations [13]. This has resulted in negative impacts on residential areas, including increased noise pollution, safety concerns for pedestrians and cyclists, and decreased quality of life for residents. While the underlying routing algorithms are advantageous to consumers in the near term, they can have significant negative impacts on the places used as shortcuts [14]. In recent years, researchers have proposed techniques to estimate emissions using Global Positioning System (GPS) data from vehicles. These methods evaluate fuel use and emissions using models that presumptively depend primarily on a vehicle's path, distance traveled, and journey time [13]. Utilizing GPS data for traffic volume, these methods enable the evaluation of traffic-related emissions through the analysis of sampled vehicle tracks [15]. Trajectory data from various vehicle types have become a standard data source for monitoring traffic, fuel consumption, and emissions. Given their integral role in urban transportation networks and their significant contribution to urban traffic flows, data on trajectories from all types of cars are commonly used for such calculations. [PERSON] et al. [16] used GPS data from a fleet of vehicles to estimate traffic NOx emissions using an average-speed-dependent estimating technique. Researchers used taxi GPS trajectory data to examine trends in fuel consumption and emissions in Beijing. For every liter of gasoline, a vehicle uses, it generates about 2.3 g of CO\({}_{2}\)[17]. In a previous study in 2023, surveillance cameras and radar sensors communicated with the control processing unit using the Internet Protocol Version 6 (IPv6) communication protocol. While the control processing unit collects data and photographs, analyzes them using algorithms, and decides the speed at which vehicles must travel to avoid stopping at traffic signals, it broadcasts this information to nearby vehicles through Wi-Fi [7]. This has the potential to ultimately decrease travel duration, diminish fuel consumption, and lower CO\({}_{2}\) emissions. Additionally, the public stands to gain from this solution by receiving timely information on incidents and traffic congestion, enabling them to make informed decisions about their travel plans [5]. Implementing straightforward measures, such as enhancing roadwork planning through intelligent transportation systems (ITS) and optimizing the full capacity of the road, can contribute to maintaining a reasonable traffic flow. Intelligent transportation systems (ITS) can be realized by integrating cars with technology, surveillance cameras, and sensors, thereby using their sensing, monitoring, and communication capabilities [18]. FDM's study highlights that traffic delays caused by roadwork, uncoordinated traffic lights, and improper traffic rerouting are common causes of congestion [19]. To address these issues, traffic management apps need to consider the capacity and characteristics of the road network when suggesting routes to users [20; 21]. Additionally, local governments can implement traffic-calming measures such as speed bumps, traffic circles, and road narrowing to discourage cut-through traffic in residential areas.