2021 IEEE International Conference on Big Data (Big Data), 2021
In a bike-sharing system, demand loss is primarily due to out-of-stock stations. One solution to ... more In a bike-sharing system, demand loss is primarily due to out-of-stock stations. One solution to tackle this problem is to rebalance the bike inventory of the stations through repositioning. In a large-scale bike-sharing system, bike repositioning typically works in two steps: The platform generates the reposition tasks, and then the operators execute those tasks. In this paper, we focus on the problem of determining the reposition tasks, which is a core problem for the platform operations. We model the bike-sharing system as a networked inventory system and propose a select-and-match method to generate optimal repositions, seamlessly combining inventory management and spatiotemporal value learning techniques. We compute the optimal (s, S) policy to select the stations with excess and deficit bike stocks. Subsequently, we solve a minimum cost maximum flow problem for rebalance assignment while considering the long-term effects of repositioning, characterized by a bike transition value. We design a spatiotemporal value network to learn the values. We evaluate the performance of the proposed method in a simulator based on real bike-sharing data, and the results demonstrate the superiority of our approach over the other alternatives.
We consider a class of sparse learning problems in high dimensional feature space regularized by ... more We consider a class of sparse learning problems in high dimensional feature space regularized by a structured sparsity-inducing norm that incorporates prior knowledge of the group structure of the features. Such problems often pose a considerable challenge to optimization algorithms due to the non-smoothness and non-separability of the regularization term. In this paper, we focus on two commonly adopted sparsity-inducing regularization terms, the overlapping Group Lasso penalty l 1 /l 2-norm and the l 1 /l ∞-norm. We propose a unified framework based on the augmented Lagrangian method, under which problems with both types of regularization and their variants can be efficiently solved. As one of the core building-blocks of this framework, we develop new algorithms using a partial-linearization/splitting technique and prove that the accelerated versions of these algorithms require O(1 √) iterations to obtain an-optimal solution. We compare the performance of these algorithms against that of the alternating direction augmented Lagrangian and FISTA methods on a collection of data sets and apply them to two real-world problems to compare the relative merits of the two norms.
In building intelligent transportation systems such as taxi or rideshare services, accurate predi... more In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management. Using the NYC taxi dataset, which contains taxi trips data collected from GPS-enabled taxis [1], this paper investigates the use of deep neural networks to jointly predict taxi trip time and distance. We propose a model, called ST-NN (Spatio-Temporal Neural Network), which first predicts the travel distance between an origin and a destination GPS coordinate, then combines this prediction with the time of day to predict the travel time. The beauty of ST-NN is that it uses only the raw trips data without requiring further feature engineering and provides a joint estimate of travel time and distance. We compare the performance of ST-NN to that of state-of-the-art travel time estimation methods, and we observe that the proposed approach generalizes better than state-of-the-art methods. We show that ST-NN approach significantly reduces the mean absolute error for both predicted travel time and distance, about 17% for travel time prediction. We also observe that the proposed approach is more robust to outliers present in the dataset by testing the performance of ST-NN on the datasets with and without outliers.
2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021
In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement lea... more In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to ridesharing problems. Papers on the topics of rideshare matching, vehicle repositioning, ride-pooling, and dynamic pricing are covered. Popular data sets and open simulation environments are also introduced. Subsequently, we discuss a number of challenges and opportunities for reinforcement learning research on this important domain.
2019 IEEE International Conference on Big Data (Big Data), 2019
Origin-Destination (OD) flow data is an important instrument for traffic study and management. So... more Origin-Destination (OD) flow data is an important instrument for traffic study and management. So far traditional ways like surveys or detectors are costly and only give limited availability of OD flows. Various statistical and stochastic models for OD flow estimation and prediction based on limited link volume data or automatic vehicle identification (AVI) data have been developed. However, smartphone-generated trajectory data has not been as much leveraged in this field, though the usage of smartphones in traveling is emerging in recent years. In this paper, we propose a semi-supervised deep learning based model that appropriately combines both AVI and smartphone trajectory data during training and is able to generate predictions of OD flows in an urban network solely based on the smartphone trajectory data at inference time. Our model can provide OD estimation and prediction services on larger spatial areas beyond the limited spatial coverage of AVI data. Tests of our model using real data have shown promising results, compared with an AVI input-dependent Kalman filter model. Potentially, our model can easily be embedded to a trajectory collecting platform and generate continuous real-time OD flow predictions online.
2018 IEEE International Conference on Big Data (Big Data), 2018
In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy... more 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.
2021 IEEE International Conference on Big Data (Big Data), 2021
In a bike-sharing system, demand loss is primarily due to out-of-stock stations. One solution to ... more In a bike-sharing system, demand loss is primarily due to out-of-stock stations. One solution to tackle this problem is to rebalance the bike inventory of the stations through repositioning. In a large-scale bike-sharing system, bike repositioning typically works in two steps: The platform generates the reposition tasks, and then the operators execute those tasks. In this paper, we focus on the problem of determining the reposition tasks, which is a core problem for the platform operations. We model the bike-sharing system as a networked inventory system and propose a select-and-match method to generate optimal repositions, seamlessly combining inventory management and spatiotemporal value learning techniques. We compute the optimal (s, S) policy to select the stations with excess and deficit bike stocks. Subsequently, we solve a minimum cost maximum flow problem for rebalance assignment while considering the long-term effects of repositioning, characterized by a bike transition value. We design a spatiotemporal value network to learn the values. We evaluate the performance of the proposed method in a simulator based on real bike-sharing data, and the results demonstrate the superiority of our approach over the other alternatives.
We consider a class of sparse learning problems in high dimensional feature space regularized by ... more We consider a class of sparse learning problems in high dimensional feature space regularized by a structured sparsity-inducing norm that incorporates prior knowledge of the group structure of the features. Such problems often pose a considerable challenge to optimization algorithms due to the non-smoothness and non-separability of the regularization term. In this paper, we focus on two commonly adopted sparsity-inducing regularization terms, the overlapping Group Lasso penalty l 1 /l 2-norm and the l 1 /l ∞-norm. We propose a unified framework based on the augmented Lagrangian method, under which problems with both types of regularization and their variants can be efficiently solved. As one of the core building-blocks of this framework, we develop new algorithms using a partial-linearization/splitting technique and prove that the accelerated versions of these algorithms require O(1 √) iterations to obtain an-optimal solution. We compare the performance of these algorithms against that of the alternating direction augmented Lagrangian and FISTA methods on a collection of data sets and apply them to two real-world problems to compare the relative merits of the two norms.
In building intelligent transportation systems such as taxi or rideshare services, accurate predi... more In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management. Using the NYC taxi dataset, which contains taxi trips data collected from GPS-enabled taxis [1], this paper investigates the use of deep neural networks to jointly predict taxi trip time and distance. We propose a model, called ST-NN (Spatio-Temporal Neural Network), which first predicts the travel distance between an origin and a destination GPS coordinate, then combines this prediction with the time of day to predict the travel time. The beauty of ST-NN is that it uses only the raw trips data without requiring further feature engineering and provides a joint estimate of travel time and distance. We compare the performance of ST-NN to that of state-of-the-art travel time estimation methods, and we observe that the proposed approach generalizes better than state-of-the-art methods. We show that ST-NN approach significantly reduces the mean absolute error for both predicted travel time and distance, about 17% for travel time prediction. We also observe that the proposed approach is more robust to outliers present in the dataset by testing the performance of ST-NN on the datasets with and without outliers.
2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021
In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement lea... more In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to ridesharing problems. Papers on the topics of rideshare matching, vehicle repositioning, ride-pooling, and dynamic pricing are covered. Popular data sets and open simulation environments are also introduced. Subsequently, we discuss a number of challenges and opportunities for reinforcement learning research on this important domain.
2019 IEEE International Conference on Big Data (Big Data), 2019
Origin-Destination (OD) flow data is an important instrument for traffic study and management. So... more Origin-Destination (OD) flow data is an important instrument for traffic study and management. So far traditional ways like surveys or detectors are costly and only give limited availability of OD flows. Various statistical and stochastic models for OD flow estimation and prediction based on limited link volume data or automatic vehicle identification (AVI) data have been developed. However, smartphone-generated trajectory data has not been as much leveraged in this field, though the usage of smartphones in traveling is emerging in recent years. In this paper, we propose a semi-supervised deep learning based model that appropriately combines both AVI and smartphone trajectory data during training and is able to generate predictions of OD flows in an urban network solely based on the smartphone trajectory data at inference time. Our model can provide OD estimation and prediction services on larger spatial areas beyond the limited spatial coverage of AVI data. Tests of our model using real data have shown promising results, compared with an AVI input-dependent Kalman filter model. Potentially, our model can easily be embedded to a trajectory collecting platform and generate continuous real-time OD flow predictions online.
2018 IEEE International Conference on Big Data (Big Data), 2018
In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy... more 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.
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Papers by Tony Qin