Data Mining - Concepts and Techniques.

Matrix Factorization Techniques for Recommender Systems

We present a novel order dispatch algorithm in large-scale on-demand ride-hailing platforms. While traditional order dispatch approaches usually focus on immediate customer satisfaction, the proposed algorithm is designed to provide a more efficient way to optimize resource utilization and user experience in a global and more farsighted view. In particular, we model order dispatch as a large-scale sequential decision-making problem, where the decision of assigning an order to a driver is determined by a centralized algorithm in a coordinated way. The problem is solved in a learning and planning manner: 1) based on historical data, we first summarize demand and supply patterns into a spatiotemporal quantization, each of which indicates the expected value of a driver being in a particular state; 2) a planning step is conducted in real-time, where each driver-order-pair is valued in consideration of both immediate rewards and future gains, and then dispatch is solved using a combinatorial optimizing algorithm. Through extensive offline experiments and online AB tests, the proposed approach delivers remarkable improvement on the platform's efficiency and has been successfully deployed in the production system of Didi Chuxing.

Large-Scale Order Dispatch in On-Demand Ride-Hailing Platforms: A Learning and Planning Approach

Linear Diophantine fuzzy set and its applications towards multi-attribute decision-making problems

The power Bonferroni mean (PBM) operator can relieve the influence of unreasonable aggregation values and also capture the interrelationship among the input arguments, which is an important generalization of power average operator and Bonferroni mean operator, and Pythagorean fuzzy set is an effective mathematical method to handle imprecise and uncertain information. In this paper, we extend PBM operator to integrate Pythagorean fuzzy numbers (PFNs) based on the interaction operational laws of PFNs, and propose Pythagorean fuzzy interaction PBM operator and weighted Pythagorean fuzzy interaction PBM operator. These new Pythagorean fuzzy interaction PBM operators can capture the interactions between the membership and nonmembership function of PFNs and retain the main merits of the PBM operator. Then, we analyze some desirable properties and particular cases of the presented operators. Further, a new multiple attribute decision making method based on the proposed method has been presented. Finally, a numerical example concerning the evaluation of online payment service providers is provided to illustrate the validity and merits of the new method by comparing it with the existing methods.

https://onlinelibrary.wiley.com/doi/epdf/10.1002/int.22204

Pythagorean fuzzy interaction power Bonferroni mean aggregation operators in multiple attribute decision making

Bike sharing systems, aiming at providing the missing links in public transportation systems, are becoming popular in urban cities A key to success for a bike sharing systems is the effectiveness of rebalancing operations, that is, the efforts of restoring the number of bikes in each station to its target value by routing vehicles through pick-up and drop-off operations There are two major issues for this bike rebalancing problem: the determination of station inventory target level and the large scale multiple capacitated vehicle routing optimization with outlier stations The key challenges include demand prediction accuracy for inventory target level determination, and an effective optimizer for vehicle routing with hundreds of stations To this end, in this paper, we develop a Meteorology Similarity Weighted K-Nearest-Neighbor (MSWK) regressor to predict the station pick-up demand based on large-scale historic trip records Based on further analysis on the station network constructed by station-station connections and the trip duration, we propose an inter station bike transition (ISBT) model to predict the station drop-off demand Then, we provide a mixed integer nonlinear programming (MINLP) formulation of multiple capacitated bike routing problem with the objective of minimizing total travel distance To solve it, we propose an Adaptive Capacity Constrained K-centers Clustering (AdaCCKC) algorithm to separate outlier stations (the demands of these stations are very large and make the optimization infeasible) and group the rest stations into clusters within which one vehicle is scheduled to redistribute bikes between stations In this way, the large scale multiple vehicle routing problem is reduced to inner cluster one vehicle routing problem with guaranteed feasible solutions Finally, the extensive experimental results on the NYC Citi Bike system show the advantages of our approach for bike demand prediction and large-scale bike rebalancing optimization

Rebalancing Bike Sharing Systems: A Multi-source Data Smart Optimization

Extended TODIM for multi-criteria group decision making based on unbalanced hesitant fuzzy linguistic term sets

Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and transferring from station to station. An increasing number of deep learning algorithms are being utilized to forecast metro ridership due to the development of computational intelligence. However, limited efforts have been exerted to consider spatiotemporal features, which are important in forecasting ridership through deep learning methods, in large-scale metro networks. To fill this gap, this paper proposes a parallel architecture comprising convolutional neural network (CNN) and bi-directional long short-term memory network (BLSTM) to extract spatial and temporal features, respectively. Metro ridership data are transformed into ridership images and time series. Spatial features can be learned from ridership image data by using CNN, which demonstrates favorable performance in video detection. Time series data are input into the BLSTM which considers the historical and future impacts of ridership in temporal feature extraction. The two networks are concatenated in parallel and prevented from interfering with each other. Joint spatiotemporal features are fed into a fully connected network for metro ridership prediction. The Beijing metro network is used to demonstrate the efficiency of the proposed algorithm. The proposed model outperforms traditional statistical models, deep learning architectures, and sequential structures, and is suitable for ridership prediction in large-scale metro networks. Metro authorities can thus effectively allocate limited resources to overcrowded areas for service improvement.

Parallel Architecture of Convolutional Bi-Directional LSTM Neural Networks for Network-Wide Metro Ridership Prediction

Taxi and sharing bike bring great convenience to urban transportation. A lot of efforts have been made to improve the efficiency of taxi service or bike sharing system by predicting the next-period pick-up or drop-off demand. Different from the existing research, this paper is motivated by the following two facts: 1) From a micro view, an observed spatial demand at any time slot could be decomposed as a combination of many hidden spatial demand bases; 2) From a macro view, the multiple transportation demands are strongly correlated with each other, both spatially and temporally. Definitely, the above two views have great potential to revolutionize the existing taxi or bike demand prediction methods. Along this line, this paper provides a novel Co-prediction method based on Spatio-Temporal neural Network, namely, CoST-Net. In particular, a deep convolutional neural network is constructed to decompose a spatial demand into a combination of hidden spatial demand bases. The combination weight vector is used as a representation of the decomposed spatial demand. Then, a heterogeneous Long Short-Term Memory (LSTM) is proposed to integrate the states of multiple transportation demands, and also model the dynamics of them mixedly. Last, the environmental features such as humidity and temperature are incorporated with the achieved overall hidden states to predict the multiple demands simultaneously. Experiments have been conducted on real-world taxi and sharing bike demand data, results demonstrate the superiority of the proposed method over both classical and the state-of-the-art transportation demand prediction methods.

Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network

Affinity Propagation (AP) clustering has been successfully used in a lot of clustering problems. However, most of the applications deal with static data. This paper considers how to apply AP in incremental clustering problems. First, we point out the difficulties in Incremental Affinity Propagation (IAP) clustering, and then propose two strategies to solve them. Correspondingly, two IAP clustering algorithms are proposed. They are IAP clustering based on K-Medoids (IAPKM) and IAP clustering based on Nearest Neighbor Assignment (IAPNA). Five popular labeled data sets, real world time series and a video are used to test the performance of IAPKM and IAPNA. Traditional AP clustering is also implemented to provide benchmark performance. Experimental results show that IAPKM and IAPNA can achieve comparable clustering performance with traditional AP clustering on all the data sets. Meanwhile, the time cost is dramatically reduced in IAPKM and IAPNA. Both the effectiveness and the efficiency make IAPKM and IAPNA able to be well used in incremental clustering tasks.

Leilei Sun

Papers

Rebalancing Bike Sharing Systems: A Multi-source Data Smart Optimization

Extended TODIM for multi-criteria group decision making based on unbalanced hesitant fuzzy linguistic term sets

Parallel Architecture of Convolutional Bi-Directional LSTM Neural Networks for Network-Wide Metro Ridership Prediction

Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network

Incremental Affinity Propagation Clustering Based on Message Passing