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The travel time data collected from widespread traffic monitoring sensorsnecessitate big data analytic tools for querying, visualization, andidentifying meaningful traffic patterns. This paper utilizes a large-scaletravel time dataset from Caltrans Performance Measurement System (PeMS) systemthat is an overflow for traditional data processing and modeling tools. Toovercome the challenges of the massive amount of data, the big data analyticengines Apache Spark and Apache MXNet are applied for data wrangling andmodeling. Seasonality and autocorrelation were performed to explore andvisualize the trend of time-varying data. Inspired by the success of thehierarchical architecture for many Artificial Intelligent (AI) tasks, weconsolidate the cell and hidden states passed from low-level to the high-levelLSTM with an attention pooling similar to how the human perception systemoperates. The designed hierarchical LSTM model can consider the dependencies atdifferent time scales to capture the spatial-temporal correlations ofnetwork-level travel time. Another self-attention module is then devised toconnect LSTM extracted features to the fully connected layers, predictingtravel time for all corridors instead of a single link/route. The comparisonresults show that the Hierarchical LSTM with Attention (HierLSTMat) model givesthe best prediction results at 30-minute and 45-min horizons and cansuccessfully forecast unusual congestion. The efficiency gained from big dataanalytic tools was evaluated by comparing them with popular data science anddeep learning frameworks.

Big Data Analytics for Network Level Short-Term Travel Time Prediction with Hierarchical LSTM