Increased urbanisation has led to significant challenges for public transport operators. Inconsistent demand leads to peaks in passenger activity on the network. Moreover, the COVID-19 pandemic has introduced a need for social distancing as well, limiting the desired capacity of vehicles. To combat this, intelligent real-time and data-driven decision making is required. In many cases, the data required is lacking or not available in real-time. Our research addresses these challenges by providing means to gain insight into the passenger load of public transport vehicles. The focus of this research is to investigate how using in-vehicle sensor data can help in constructing an estimate of the passenger load and evaluate its contribution.

By combining in-vehicle sensor signals with historical passenger flow patterns, a novel fusion model
based on gradient boosting machines is constructed that can make real-time predictions of the passenger load using this data as input features. The evaluation shows that its estimates have a mean absolute error (MAE) score of 7.83, outperforming a random forest model baseline by 37%. Moreover, a crowding indicator analysis demonstrated that when predicting crowding indicators, the model achieves a weighted F1 score of 0.828. An ablation study found that excluding the in-vehicle features from the model reduces the model’s performance significantly, it could reduce the performance by up to 42%. In fact, the same experiment showed that having only the in-vehicle features is preferable to historical passenger flow features. Therefore, we conclude that using in-vehicle sensor data can be a feasible alternative to historical AFC data for predicting the passenger load.

The methodology has been extended by constructing a short-term forecasting model based on Seasonal ARIMA and GARCH that uses real-time signals of the passenger load to update its forecasts. The results show that while the forecasts lack accuracy initially, once the model is updated the forecasts improve up to almost a negligible error. When model predictions are used as update signals, the forecasting model is still able to improve and the results are competitive, despite the error contained in the signals.

Overall, we conclude that the proposed real-time model offers a suitable method for passenger load prediction and clearly demonstrates the effectiveness of using in-vehicle sensor data as input features. Moreover, we have presented a feasible method for using these features in a forecasting setting with a real-time model as an intermediary

Scenwise is a business working on innovative and sophisticated solutions in the domain of traffic management. Leveraging data science and IT systems, Scenwise delivers products to institutions to facilitate efficient traffic management. In order to manage the highly complex network of infrastructure on the road network, traffic managers need to use and analyze data that is collected all across the network in order to support decision makers in management of this network. However, there is often a mismatch in expertise between traffic management experts and decision makers. Traffic management experts use highly technical visualization techniques that require significant background knowledge in the traffic management domain. In addition, the visualization techniques are spread out over a multitude of systems that do not work together. In order to bridge the knowledge gap, a product needs to be created that allows experts to extract and visualize relevant data using their traffic domain knowledge while providing intuitive and clear visualizations which are clear to both experts and non-experts. The ultimate goal of this product would be to facilitate efficient traffic management in order to improve the lives of commuters by contributing to a better organized infrastructure. Our project group has designed and implemented a product for Scenwise that offers this solution. A web-based application has been created that retrieves and stores traffic data. The product is able to traverse the road network and provide helpful insights into the traffic network’s state at either the present moment, or moments in history. The application is able to provide dynamic traffic contour plots, draw fundamental diagrams, show live traffic intensity over the entire Dutch road network and provide information related to traffic events like accidents and matrix sign states. The product is able to do all of this while providing a seamless and intuitive user interface. The system has been designed and implemented over a span of ten weeks by a group of five students. A SCRUM methodology was adopted and through careful discussion with the client and a continuous feedback loop a product was delivered that fits both the clients needs and the wider product vision that has been defined.