## 3 Materials and Methods
### Proposed ATM System Design and Implementation
#### 3.3.5 Machine Learning in ITM
The vehicle density is lower with the rise in range for the proposed ATM system as the current scheme tries to resolve road traffic without too many difficult situations. The automobiles are also redirected to the alternative route to prevent congestion. The proposed ATM system significantly outperformed the other existing structure. Apart from previous designs, the proposed ATM accepts different countries' traffic situations and traffic movement patterns and communicates in actual environments. It supports day-to-day operations with downstream monitoring and a consumer environment. In terms of achieving highway interchanges green-green coordination throughout the distribution zone, traffic information from crossings is aggregated in a centralized transport network. Advanced traffic control algorithms generate optimum red-green cycles of traffic lights. In live time, the ATM constantly reacts to changing traffic situations. ATM analyzes real-time traffic information from automobile detection using a machine learning approach to calculate the signal durations that are best for current traffic situations. ## 5 Conclusions

This research provides an ITM system for tracking LAVs and Non-LAVs vehicles related to potential highway-vulnerability factors. The proposed ATM model enables in-location services of automobiles, parking management, and implementing traffic-management techniques for development of an intelligent transport system. The scheme helps to monitor automobile movement, thereby examining the traffic in a specific region. Automatic accident detection has become a popular topic in vehicular traffic-management systems. Surveillance of an accident can help us to avoid possible similar incidents in the future, and it will facilitate security agencies in reopening the road segment to a number of vehicles. We successfully demonstrated that vehicular activity could be evaluated, utilizing vehicular locations and average speeds. Additionally, abnormal events on the highway can be considered a future challenge for drivers who have already been nearest to the accident region. It was found that the proposed ATM system had a superior performance to the existing conventional systems. Future work will integrate energy-efficient systems and security into the proposed ATM system. The proposed system will be implemented in a real-time environment in the place of the simulator with real-time traffic flow. U.K.L., S.S., S.K.P. and N.G. were responsible for the conceptualization of the topic; U.K.L. carried out article gathering and organization; manuscript writing, original drafting, and formal analysis were carried out by U.K.L., A.L.I., C.-T.L., S.S., S.K.P., N.G., A.K. and C.-C.L., writing of reviews and editing was carried out by U.K.L., A.L.I., C.-T.L., S.S., S.K.P., N.G., A.K. and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simulation Duration** & **Vehicle Count(in Each Road** & **Cluster Type** & **Cluster Type** \\
**in Seconds** & **Segment)** & **(Normal)** & **(Anomaly)** \\ \hline
60 & 75 & 70 & 1 \\
70 & 77 & 72 & 1 \\
80 & 80 & 75 & 1 \\
90 & 82 & 76 & 2 \\
100 & 85 & 78 & 2 \\
110 & 87 & 79 & 3 \\
120 & 88 & 81 & 3 \\
130 & 90 & 82 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Clustering Outcomes of DBSCAN and ML methods for accident detection. C.-C.L. ; and S.K.P., led the overall research activity. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data that supports the findings of this paper is available from the corresponding author upon reasonable request. The authors would like to thank the anonymous reviewers of the journal _Sensors_ for their careful and helpful comments. The work of [PERSON] is supported by the Nigerian Petroleum Technology Development Fund (PTDF) and the German Academic Exchange Service (DAAD) through the Nigerian-German Postgraduate Program under grant 57473408.