Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining

In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand. For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS trip data. Secondly, we develop a carpooling simulation environment for RL training, with the output of ST-NN and using the NYC taxi trip dataset. In order to maximize transportation efficiency and minimize traffic congestion, we choose the effective distance covered by the driver on a carpool trip as the reward. Therefore, the more effective distance a driver achieves over a trip (i.e. to satisfy more trip demand) the higher the efficiency and the less will be the traffic congestion. We compared the performance of RL learned policy to a fixed policy (which always accepts carpool) as a baseline and obtained promising results that are interpretable and demonstrate the advantage of our RL approach. We also compare the performance of ST-NN to that of state-of-the-art travel time estimation methods and observe that ST-NN significantly improves the prediction performance and is more robust to outliers.Comment: Accepted at IEEE International Conference on Big Data 2018. arXiv admin note: text overlap with arXiv:1710.0435

Refractive-index-modified-dot Fabry-Perot fiber probe fabricated by femtosecond laser for high-temperature sensing

An optical fiber Fabry-Perot probe sensor for high-temperature measurement is proposed and demonstrated, which is fabricated by inducing a refractive-index-modified-dot (RIMD) in the fiber core near the end of a standard single mode fiber (SMF) using a femtosecond laser. The RIMD and the SMF end faces form a Fabry-Perot interferometer (FPI) with a high-quality interference fringe visibility (<20dB). As a high-temperature sensor, such an FPI exhibits a sensitivity of 13.9 pm/°C and 18.6 pm/°C in the range of 100-500 °C and 500-1000 °C, respectively. The fabrication process of this device is quite straightforward, simple, time saving, and the sensor features small size, ease of fabrication, low cost, assembly-free, good mechanical strength, and high linear sensitivity