We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO 2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.

This paper proposes a three-dimensional pedestrian position estimation algorithm using a waist-mounted Inertial Measurement Unit (IMU) sensor. A Pedestrian Dead Reckoning (PDR) system has been implemented using a single IMU sensor to locate a pedestrian who is carrying the PDR system on his waist in a building in which GPS signals are too weak to be utilized for localization. The position estimation system using IMU data suffers from degraded accuracy caused by the bias drift of gyroscope values. This approach adopts the quaternion to reduce the bias drift and obtain the precise Euler angles to localize the pedestrian in two-dimensional space with the step length estimation. To extend the localization into three-dimensional space to incorporate situations in which the pedestrian is ascending or descending the stairs, Zero-Velocity UpdaTe (ZUPT) scheme has been applied for processing gyroscope data from the IMU sensor. Real experiments have been performed to show the proposed algorithm's effectiveness in both two-and three-dimensional spaces.

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