# Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service +
## 7 Conclusions  
In this work, a novel edge computing enabled Smart Urban Traffic Monitoring system named SurMon is investigated. Compared with cloud computing, edge computing facilitates data processing, anomaly detection, pattern recognition, and intelligent decision-making at the network edge with low latency. However, due to the limited computing capabilities of edge nodes, lightweight algorithms are required. A novel mSSA anomaly detection approach is proposed to catch vehicles with abnormal behaviors. Different from traditional ways that identify change points in the time domain, the SurMon system detects anomalies in the space domain. By identifying anomaly vehicles through detecting changes/differences in behaviors, pre-defined normal patterns are not required. Consequently, it relieves the burden of collecting and labelling a huge amount of traffic data, which is relatively labor intensive and impractical. In addition, a cascaded Capsules Network is adopted to interpret these behaviors to decide whether they are legal moves or violations. A fundamental assumption of our mSSA based anomaly detection algorithm is that most vehicles on roads follow traffic rules and behave legitimately, and outliers are very likely violators. Consequently, it is reasonable to identify vehicles that present different characteristics from others as objects of interest, and more analysis should be conducted on them. In situations where a significant amount or even the majority of the vehicles on the road do not follow traffic rules, our mSSA algorithm may not be an ideal candidate. Meanwhile, a rule-based target-tracking or classification approach using ML is more appropriate. The results reported here are still at an early stage toward a complete and mature smart urban traffic surveillance application. Several typical driving behaviors are selected to validate the feasibility of the SurMon system. While the experimental results are very  
Figure 11: Training and testing accuracy. Figure 10: Training and testing time. encouraging, a lot of open problems are yet to be tackled. In this paper, we focus on the system design, and the experimental study is mainly used to validate the feasibility of the system. Therefore, we tested the effectiveness of mSSA and the multi-target tracking algorithms at the edge for the purpose of suspicious/anomaly vehicles detection, and then switched to the CapNet for behavior Interpretation. Currently, we are extending the implement the CapNet to test its performance on the suspicious behavior detection part, the success of which will provide an alternative solution for devices that are not able to handle the mSSA algorithm. Our other on-going efforts include mainly two directions. The first is to collect more real-world data and get a comprehensive understanding of the driving behavior on the road. Secondly, since the vehicle information like trajectory is very critical, the security and privacy of driver should not be tampered with. However, edge nodes which facilitate the traffic surveillance tasks will not always be on the white list, and the mobility is also preventing the creation of such a white list. Blockchain is a nice candidate to solve this problem. It allows multiple nodes to reach a consensus within a trustless environment. Conceptualization, N.C. and Y.C. ; methodology, N.C.; software, N.C.; validation, N.C. and Y.C. ; formal analysis, N.C.; investigation, N.C.; resources, Y.C. ; data curation, N.C.; writing--original draft preparation, N.C.; writing--review and editing, Y.C. ; visualization, N.C.; supervision, Y.C. ; project administration, Y.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable, the study does not report any data. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: