Smart Cities Center

Transportation systems are becoming increasingly complex with the evolution of emerging technologies, including deeper connectivity and automation, which will require more advanced control mechanisms for efficient operation (in terms of energy, mobility, and productivity). Stakeholders, including government agencies, industry, and local populations, all have an interest in efficient outcomes, yet there are few tools for developing a holistic understanding of urban dynamics. Simulating large-scale, high-fidelity transportation systems can help, but remains a challenging task, due to the...

In this paper, we address the problem of recovering traveled geospatial paths on a transportation network from time sampled location traces. Determining the proper sampling rate for path reconstruction has not traditionally been addressed ahead of the collection process. Instead various uncertainty models have been created and tuned to estimate possible geospatial paths from an existing set of location measurements. This paper suggests that the geospatial road density sets a fundamental constraint on the sampling frequency. The result shows that a sufficient sampling rate is determined by...

An approach is provided for semantic-free traffic prediction. The approach involves dividing a travel-speed data stream into a plurality of travel-speed patterns. The travel-speed data stream represents vehicle travel speeds occurring in a road network. The approach also involves representing each of the plurality of travel-speed patterns by a respective token. The respective token is selected from a dictionary of tokens representing a plurality of travel-speed templates determined from historical travel-speed data. The approach further involves matching a sequence of the respective...

Traffic forecasting approaches are critical to developing adaptive strategies for mobility. Traffic patterns have complex spatial and temporal dependencies that make accurate forecasting on large highway networks a challenging task. Recently, diffusion convolutional recurrent neural networks (DCRNNs) have achieved state-of-the-art results in traffic forecasting by capturing the spatiotemporal dynamics of the traffic. Despite the promising results, however, applying DCRNNs for large highway networks still remains elusive because of computational and memory bottlenecks. This paper presents...