Boosted Trajectory Calibration for Traffic State Estimation Traffic state estimation is among the most critical issues in intelligent transport systems, and it has been widely explored for years considering its significance for diverse industrial applications such as vehicle dispatch and route planning. The availability of vehicle trajectories has provided a promising data source for traffic estimation, as it shed light on traffic status and characterizes human transition activates that are otherwise extremely hard to track. However, there are major challenges in incorporating trajectories in traffic estimation largely because of its inherent uncertainty from data sparsity and semantic ambiguity. Entangled in complicated road topology and spatiotemporal traffic dynamics, properly embedding trajectories in modeling is especially difficult. To address the aforementioned challenges, in this paper, we propose a Boosted Trajectory Calibration (BTRAC) framework to model traffic states of complex road networks that effectively integrates trajectory information. In the framework, we first use a deep neural network to predict the traffic states of each road individually from historical traffic information, along with the prediction uncertainty. Then we refine the predictions by an iterative boosting calibration procedure with embedded trajectories. We conduct extensive largescale empirical studies on two cities and demonstrate the effectiveness of the proposed approach.

Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers Shortterm traffic state prediction has become an integral component of an advanced traveler information system (ATIS) in intelligent transportation systems (ITS). Accurate modeling and shortterm traffic prediction are quite challenging due to its intricate characteristics, stochastic, and dynamic traffic processes. Existing works in this area follow different modeling approaches that are focused to fit speed, density, or the volume data. However, the accuracy of such modeling approaches has been frequently questioned, thereby traffic state prediction over the shortterm from such methods inflicts an overfitting issue. We address this issue to accurately model shortterm future traffic state prediction using stateoftheart models via hyperparameter optimization. To do so, we focused on different machine learning classifiers such as local deep support vector machine (LDSVM), decision jungles, multilayers perceptron (MLP), and CN2 rule induction. Moreover, traffic states are evaluated using traffic attributes such as level of service (LOS) horizons and simple ifxe2x80x93then rules at different time intervals. Our findings show that hyperparameter optimization via random sweep yielded superior results. The overall prediction performances obtained an average improvement by over 95%, such that the decision jungle and LDSVM achieved an accuracy of 0.982 and 0.975, respectively. The experimental results show the robustness and superior performances of decision jungles (DJ) over other methods.