## 7 Conclusions  
In this study, unidirectional and bidirectional LSTM models were developed using data from a busy arterial road in Melbourne, Australia, to predict speed and traffic volumes. The models were trained/tested using historical field data collected by Intelematics INSIGHT Studio for a forecasting horizon up to 60 min into the future. A total of 70,072 data observations were extracted for a 12-month period from Hoddle Street's southbound and northbound directions. The data represented different patterns and variable traffic conditions, including peak, non-peak, weekday, weekend, and incident data. A comprehensive and rigorous procedure was adopted to evaluate the suitability of different architectures and modelling parameters. The results showed superior performance for the BiLSTM compared to unidirectional Uni-LSTM, RNN, Elman and DLBP for both speed and traffic volume forecasts. For speed, the model was able to provide a prediction accuracy above 99% for up to 60 min into the future in both directions (southbound and northbound). For volume, the BiLSTM model was able to achieve an accuracy above 96% for up to 60 min into the future in both directions (northbound and southbound). It was also noticed that the accuracy did not deteriorate substantially as the prediction horizon increased, which shows the ability of the system to capture the complexity of longer speed/volume prediction horizons. The study also investigated the robustness of the AI models when developed under variable weather conditions such as rainfall observation in mm, air temperature in degrees Celsius, relative humidity and wind (1 min) speed in km/h. The experimental results showed that BiLSTM and LSTM models can effectively predict traffic speed/volume under various weather conditions. In summary, the results showed that the model has excellent stability and prediction accuracy when tested on data collected from an arterial road. The analysis also highlighted the robustness of deep learning models when incorporating weather data, with a slight improvement in prediction accuracy achieved in some instances. Accurate traffic prediction models have significant implications for emergency response and disaster management. Models such as BiLSTM, which can accurately forecast traffic conditions even under dynamic or incomplete data conditions, are essential for planning evacuation routes, prioritising emergency vehicle access, and optimising traffic flow during crises. By anticipating congestion points and adjusting response routes based on predicted traffic volumes and speeds, authorities can improve the safety and efficiency of emergency operations, ensuring timely assistance and enhancing community resilience in disaster scenarios. While this study demonstrates the effectiveness of BiLSTM models in traffic prediction, future research should focus on a more detailed statistical analysis of weather variables to further enhance model robustness. Methods such as correlation analysis, regression models, and sensitivity analysis can help identify weather factors--such as rain intensity, wind speed, humidity, and temperature--that significantly impact traffic flow and speed predictions. Future studies could also benefit from case studies examining model performance under varied weather conditions (e.g., heavy rain, fog, snow) to assess prediction accuracy in challenging contexts. Additionally, investigating the temporal effects of weather on traffic, as well as interactions with other factors (e.g., time of day, special events), could provide a holistic view of weather impacts. These insights would improve the applicability of BiLSTM models for Intelligent Transport Systems (ITS) under diverse environmental conditions. H.D. and R.A.: Research planning. R.A. Methodology and generation of results. R.A. and S.L. : Drafting of paper content, editing and updating. H.D. : Reviewing, editing, and structuring. H.D. : Supervising and mentoring PhD students. All authors have read and agreed to the published version of the manuscript.