A Convolutional Long Short-Term Memory Neural Network Based Prediction Model

In recent years, the market demand for online car-hailing service has expanded dramatically. To satisfy the daily travel needs, it is important to predict the supply and demand of online car-hailing in an accurate manner, and make active scheduling based on the predicted gap between supply and demand. This paper puts forward a novel supply and demand prediction model for online carhailing, which combines the merits of convolutional neural network (CNN) and long short-term memory (LSTM). The proposed model was named convolutional LSTM (C-LSTM). Next, the original data on online car-hailing were processed, and the key features that affect the supply and demand prediction were extracted. After that, the C-LSTM was optimized by the AdaBound algorithm during the training process. Finally, the superiority of the C-LSTM in predicting online car-hailing supply and demand was proved through contrastive experiments

Decision-making for Vehicle Path Planning

This dissertation presents novel algorithms for vehicle path planning in scenarios where the environment changes. In these dynamic scenarios the path of the vehicle needs to adapt to changes in the real world. In these scenarios, higher performance paths can be achieved if we are able to predict the future state of the world, by learning the way it evolves from historical data. We are relying on recent advances in the field of deep learning and reinforcement learning to learn appropriate world models and path planning behaviors. There are many different practical applications that map to this model. In this dissertation we propose algorithms for two applications that are very different in domain but share important formal similarities: the scheduling of taxi services in a large city and tracking wild animals with an unmanned aerial vehicle. The first application models a centralized taxi dispatch center in a big city. It is a multivariate optimization problem for taxi time scheduling and path planning. The first goal here is to balance the taxi service demand and supply ratio in the city. The second goal is to minimize passenger waiting time and taxi idle driving distance. We design different learning models that capture taxi demand and destination distribution patterns from historical taxi data. The predictions are evaluated with real-world taxi trip records. The predicted taxi demand and destination is used to build a taxi dispatch model. The taxi assignment and re-balance is optimized by solving a Mixed Integer Programming (MIP) problem. The second application concerns animal monitoring using an unmanned aerial vehicle (UAV) to search and track wild animals in a large geographic area. We propose two different path planing approaches for the UAV. The first one is based on the UAV controller solving Markov decision process (MDP). The second algorithms relies on the past recorded animal appearances. We designed a learning model that captures animal appearance patterns and predicts the distribution of future animal appearances. We compare the proposed path planning approaches with traditional methods and evaluated them in terms of collected value of information (VoI), message delay and percentage of events collected

A Sequence Learning Model With Recurrent Neural Networks For Taxi Demand Prediction

In this paper, we focus on an application of recurrent neural networks for learning a model that predicts taxi demand based on the requests in the past. A model that can learn time series data is necessary here since taxi requests in the future relate to the requests in the past. For instance, someone who requests a taxi to a movie theater, may also request a taxi to return home after few hours. We use Long Short Term Memory (LSTM), one of the best models for learning time series data. For training the network, we encode the historical taxi requests from the official New York City taxi trip dataset and add date, day of the week and time as impacting factors. Experimental results show that our approach outperforms the prediction heuristics based on feed-forward neural networks and naive statistic average