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This paper presents a data-driven approach for predicting the propagation of traffic congestion at road segments as a function of the congestion in their neighboring segments. In the past, this problem has mostly been addressed by modelling the traffic congestion over some standard physical phenomenon through which it is difficult to capture all the modalities of such a dynamic and complex system. While other recent works have focused on applying a generalized data-driven technique on the whole network at once, they often ignore intersection characteristics. On the contrary, we propose a city-wide ensemble of intersection level connected LSTM models and propose mechanisms for identifying congestion events using the predictions from the networks. To reduce the search space of likely congestion sinks we use the likelihood of congestion propagation in neighboring road segments of a congestion source that we learn from the past historical data. We validated our congestion forecasting framework on the real world traffic data of Nashville, USA and identified the onset of congestion in each of the neighboring segments of any congestion source with an average precision of 0.9269 and an average recall of 0.9118 tested over ten congestion events. 

Internet of Things (IoT), edge/fog computing, and the cloud are fueling rapid development in smart connected cities. Given the increasing rate of urbanization, the advancement of these technologies is a critical component of mitigating demand on already constrained transportation resources. Smart transportation systems are most effectively implemented as a decentralized network, in which traffic sensors send data to small low-powered devices called Roadside Units (RSUs). These RSUs host various computation and networking services. Data driven applications such as optimal routing require precise real-time data, however, data-driven approaches are susceptible to data integrity attacks. Therefore we propose a multi-tiered anomaly detection framework which utilizes spare processing capabilities of the distributed RSU network in combination with the cloud for fast, real-time detection. In this paper we present a novel real time anomaly detection framework. Additionally, we focus on implementation of our framework in smart-city transportation systems by providing a constrained clustering algorithm for RSU placement throughout the network. Extensive experimental validation using traffic data from Nashville, TN demonstrates that the proposed methods significantly reduce computation requirements while maintaining similar performance to current state of the art anomaly detection methods.