## 2 Related Works
An innovative technique to showing such information is illustrated using a generalized framework visualization system. In a quick, interactive browser-based ecosystem, this system remembers almost a year's benefit of public transportation reliability information to a great detailed degree. There may still be issues with the data itself when dealing with a large amount of data. As a result, real-time data may not always be accurate due to network and GPS problems. As a result of these concerns, operators might use \"general transit feed specification (GTFS-RT) validation tools.\" If GTFS-RT is widely used to monitor systems on a continuous rather than real-time basis, it will be easier to exchange knowledge and improve the tools for creating and validating data feeds. [PERSON], et al. [18] discussed traffic management system categorization, evaluation, difficulties, and prospects. A qualitative investigation was also conducted using the traffic management systems (TMS) mentioned. Lastly, they explained how they plan to enhance TMS efficacy and resilience to accomplish the desired level of precision and traffic monitoring, with significance on concentrating open complexities. Moreover, they have detected and discovered numerous feasible solutions. In addition to cloud computing's inherent security issues, TMS also adds to the difficulty of securing a system. [PERSON], et al. [19] suggested a crowdsourcing-dependent traffic surveillance strategy, which allows transportation management to obtain road traffic information at road crossings in a time-saving, reliable, and confidential way. This approach's main drawback is its inability to present huge datasets. They will use blockchain technology to assess the legitimacy of a crowdsourcing-based traffic surveillance situation in the future. [PERSON] et al. [20] suggested a newer control scheme depending upon the combination of \"software defined networks (SDN)\" and \"Internet of Things (IoT)\" in the nation's surroundings. This system is activated when an emergency occurs and changes the regular and urgent traffic routes in urban areas to minimize the time it takes for emergency resources to arrive at the scene of an emergency. Studies reveal that the average time it takes emergency responders to arrive at an emergency site is decreased from 17 to 12 milliseconds. The proposed algorithm controls the resource requests and the route modification for helping the emergency service vehicle's movement. This system's inability to capture multidimensional perspectives of the data being shown is a major flaw. [PERSON], et al. [21] presented a new trafficking network in real-time based on an end-to-end deep learning (E2 EDL) technology. The suggested framework includes the network's spatial and temporal l congestion profile correlations and links them with effective traffic management techniques. The E2 EDL model is trained with a lab-generated data set that includes pairs of current traffic characteristics and effective traffic control strategies planned to deal with them. As a result of the system's recommendation of routes to the same spot, however, congestion is created in different parts of town. [PERSON], et al. [22] described a travel route suggestion procedure that can be used to suggest the best congestion-aware path in a network. Congestion indexes are calculated using both equipped and non-equipped automobiles, as well as driving distraction variables. This procedure is employed for designing smarter transportation systems to encounter traffic congestion issues. This system's main drawback is that, in order to alleviate traffic congestion, it selects lengthy routes. [PERSON], et al. [23] suggested vehicle social networks based on the VIoT in this research are made up of a larger number of sensors that wirelessly transfer data. The efficacy of traditional layered protocol solutions and existing cross-layer solutions for wireless systems is limited by the great heterogeneity in equipment capacities of things and quality of service (QoS) necessities for diverse uses.