Breaking Through the Traffic Congestion: Asynchronous Time Series Data Integration and Xgboost for Accurate Traffic Density Prediction

The proliferation of data collection from smart cities has resulted in an exponential growth in the volume of measurements available for analysis. However, collecting all parameters concurrently at the same location is not feasible due to the complex nature of the real world. We present an innovative methodology that enriches asynchronous time series data from a variety of sources to facilitate data enrichment and city-wide behaviour simulation. A case study on OpenDataBCN attests to the efficacy of this approach via an XGBoost model, predicated on geographical coordinates and timestamp disparities. The consolidation of data from different sources improves the richness and granularity of information at disposal for analysis, thereby revealing previously hidden patterns and relationships, exhibiting new insights and underscoring the potential of this methodology for sustainable and efficient data enrichment processes as well as new possibilities for simulation based on smart city datasets.