## 5 Discussion  
Due to the increasing importance of traffic prediction for urban transportation management, numerous traffic flow prediction models have been studied over recent decades. This paper utilizes visualization techniques, such as heatmaps, to illustrate the traffic density across various city sections throughout the day. Analyzing vehicle locations at different time intervals reveals mobility patterns and street traffic density. An interactive animation plugin is identified, enabling end users to customize the visual representation with options including overview, zoom, pause, play, loop, and variable playback speeds. Subsequently, traffic prediction is approached as a time series analysis problem, with ARIMA models being employed to forecast traffic flow across ten sections of the same street. Time series analysis, particularly ARIMA models, has historically been popular for traffic prediction due to ease of implementation and relatively high accuracy [35]. Nevertheless, the intricate non-linear spatio-temporal dependencies, coupled with external factors such as weekends, holidays, and weather, present challenges that surpass the capabilities of ARIMA. Our results indicate that none of the ARIMA models built satisfies the assumptions of independence, homoscedasticity, and normality. Thus, classical methods struggle to make accurate predictions and are not well suited to the spatio-temporal structure of traffic data. To tackle this challenge, an XGBoost model is employed for traffic prediction. XGBoost requires less prior knowledge of traffic patterns, handles non-linear variables effectively, and consistently achieves robust performance with the area under ROC curves exceeding 80%. Its use facilitates analysis of variable importance and enhances interpretability through Shapley Additive Explanations. In summary, while visualization techniques aid real-time analysis of traffic density and interactive animations for clearer pattern recognition, the XGBoost model provides insights into variable importance, interpretable outputs, and accurate predictions. By accurately predicting traffic patterns, decision-makers can make more informed about transportation infrastructure, such as the location and number of roads, highways, and public transportation systems. This can help to optimize the use of limited resources and improve the efficiency of the transportation network [36]. In terms of reducing congestion and emissions, accurate traffic prediction can help decision-makers to implement strategies to reduce congestion and improve air quality. For instance, they could implement demand-management strategies like variable tolls or congestion pricing. These measures aim to incentivize drivers to opt for alternative modes of transportation or travel during off-peak hours. Also, traffic prediction can help to identify areas where accidents are more likely to occur, allowing them to implement strategies to reduce the likelihood of accidents and improve road/street safety. Hence, by foreseeing and outlining the variables that contribute to the forecast, this paper's contribution can assist a decision-maker in making quick and effective decisions in order to enhance traffic management and mitigate traffic congestion [37]. In this study, the significant variables that contribute to the forecast are the  
Figure 8: Impact on model output by predicted class. geographical location, especially when the section starts in the North direction, the time, and the day of the month. However, some limitations need to be highlighted. In order to increase the usefulness of the data and analysis, data must be accessible, interconnected, and quantifiable to facilitate decision-making and drive innovation. In the case of the city council, this is not fully achieved, as the availability of information and the interconnection between datasets are severely restricted by the format and structure in which they are delivered.