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

Innovation in China: the promise and the challenge in a transition economy

Regarding innovation as an effective engine for sustainable growth model, Chinese government has launched a series of policies and regulations with the aim of becoming world-leader in innovation by 2050. The Chinese enterprises as potential main innovators are also undergoing a rapid transformation in the process of evolving from backroom producers to the world’s leading force of innovation. However, the unique political and cultural circumstances of China as emerging economy means that the innovation process in China looks very differently than it does in the rest of the world. What will China need in terms of institutional changes? Does geographical location advocated in the field of economic geography really work in China? Are there any other factors besides firm- and region-level ones determining the innovative performance of Chinese enterprises? This thesis consisting of three chapters sheds light on issues relating to innovation and tries to answer questions like these

The sustainable potential of efficient air-transportation industry and green innovation in realising environmental sustainability in G7 countries

Air transportation has a deep impact on environmental degradation due to the higher fossil fuel consumption. On the other hand, this industry also embraces the highest innovation that may alter its environmental consequences. However, there is a dearth of empirical evidence that explores the impact of air transportation and eco-innovation on environmental quality. Therefore, this study is a pioneering attempt to examine the role of air-transportation and eco-innovation in reducing environmental degradation in G7 countries using annual data from 1990 to 2019. In doing so, we employed various advance econometric approaches to handle issues arises from panel data such as Pesaran (2007) and Bai and Carrion-I-Silvestre (2009) used to examine the presence of unit root, cross-sectional dependency checked through Pesaran (2015) test, and for parameters heterogeneity through Pesaran and Yamagata (2008). Moreover, the Westerlund and Edgerton (2008) test and Cross Sectional Augmented ARDL were employed to analyse the long run and short run association among variables. The overall results show that air transportation and eco-innovation play an important role in abating environmental deterioration. Air transportation is negatively correlated with carbon emission and PM2.5 exposure (air quality) due to the improved technical structure of aircraft engines and the use of mixed ration or alternative aviation fuels. These results provide valuable suggestions for all stakeholders

Self-Learning Symmetric Multi-view Probabilistic Clustering

Multi-view Clustering (MVC) has achieved significant progress, with many efforts dedicated to learn knowledge from multiple views. However, most existing methods are either not applicable or require additional steps for incomplete MVC. Such a limitation results in poor-quality clustering performance and poor missing view adaptation. Besides, noise or outliers might significantly degrade the overall clustering performance, which are not handled well by most existing methods. In this paper, we propose a novel unified framework for incomplete and complete MVC named self-learning symmetric multi-view probabilistic clustering (SLS-MPC). SLS-MPC proposes a novel symmetric multi-view probability estimation and equivalently transforms multi-view pairwise posterior matching probability into composition of each view's individual distribution, which tolerates data missing and might extend to any number of views. Then, SLS-MPC proposes a novel self-learning probability function without any prior knowledge and hyper-parameters to learn each view's individual distribution. Next, graph-context-aware refinement with path propagation and co-neighbor propagation is used to refine pairwise probability, which alleviates the impact of noise and outliers. Finally, SLS-MPC proposes a probabilistic clustering algorithm to adjust clustering assignments by maximizing the joint probability iteratively without category information. Extensive experiments on multiple benchmarks show that SLS-MPC outperforms previous state-of-the-art methods

Asymmetric impacts of technology innovation and environmental quality on tourism development in emerging economies

Tourism development contributes to higher economic output and is highly integrated with environmental quality and associated technologies. Although many studies explore the impact of tourism on carbon emissions; however, little is known regarding the effects of environmental pollution and technology innovation on tourism growth. Therefore, this study examines the impact of technology innovation and environmental pollution on inbound tourism in emerging economies. In doing so, we employ a recently developed panel quantiles regression and found that technology innovation and economic growth stimulate inbound tourism while increasing emissions limit tourist arrivals. These effects are not equally observed across all quantiles. Particularly, the impact of technology innovation is highest at higher quantiles, while the impact of the emissions is highest at lower quantiles. These results suggest that inbound tourism is asymmetrically affected by technology innovation and environmental quality of host destinations. Hence, emerging economies should encourage sustainable tourism by integrating green technologies and minimizing ecological hazards

Uncertainty-aware Unsupervised Multi-Object Tracking

Without manually annotated identities, unsupervised multi-object trackers are inferior to learning reliable feature embeddings. It causes the similarity-based inter-frame association stage also be error-prone, where an uncertainty problem arises. The frame-by-frame accumulated uncertainty prevents trackers from learning the consistent feature embedding against time variation. To avoid this uncertainty problem, recent self-supervised techniques are adopted, whereas they failed to capture temporal relations. The interframe uncertainty still exists. In fact, this paper argues that though the uncertainty problem is inevitable, it is possible to leverage the uncertainty itself to improve the learned consistency in turn. Specifically, an uncertainty-based metric is developed to verify and rectify the risky associations. The resulting accurate pseudo-tracklets boost learning the feature consistency. And accurate tracklets can incorporate temporal information into spatial transformation. This paper proposes a tracklet-guided augmentation strategy to simulate tracklets' motion, which adopts a hierarchical uncertainty-based sampling mechanism for hard sample mining. The ultimate unsupervised MOT framework, namely U2MOT, is proven effective on MOT-Challenges and VisDrone-MOT benchmark. U2MOT achieves a SOTA performance among the published supervised and unsupervised trackers.Comment: Accepted by International Conference on Computer Vision (ICCV) 202