DeepSD: Supply-Demand Prediction for Online Car-Hailing Services Using Deep Neural Networks

Wang, Dong; Cao, Wei; Li, Jian; Ye, Jieping

doi:10.1109/icde.2017.83

Cited by 174 publications

(92 citation statements)

References 20 publications

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“…Recently, deep neural networks, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), are widely used in taxi demand prediction, to capture spatial and temporal features. Fullyconnected layers and residual networks are employed in [10] to automatically learn features to assist taxi demand prediction. Xu et al [11] propose a LSTMbased sequential learning framework to model temporal dependencies of taxi demand in recent moments.…”

Section: Dnn-based Methodsmentioning

confidence: 99%

CACRNN: A Context-Aware Attention-Based Convolutional Recurrent Neural Network for Fine-Grained Taxi Demand Prediction

Liu

Yang

2020

Lecture Notes in Computer Science

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As taxis are primary public transport in metropolises, accurately predicting fine-grained taxi demands of passengers in real time is important for guiding drivers to plan their routes and reducing the waiting time of passengers. Many efforts have been paid to provide accurate taxi demand prediction, and deep neural networks are leveraged recently. However, existing works are limited in properly incorporating multi-view taxi demand predictions together, by simply assigning fixed weights learned by training to the predictions of each region. To solve this problem, we apply the attention mechanism for leveraging contextual information to assist prediction, and a context-aware attentionbased convolutional recurrent neural network (CACRNN) is proposed. Specially, we forecast fine-grained taxi demands with considering multiview features, including spatial correlations among adjacent regions, short-term periodicity, long-term periodicity, and impacts of external factors. Local convolutional (LC) layers and gated recurrent units (GRUs) are utilized to extract the features from historical records. Moreover, a context-aware attention module is employed to incorporate the predictions of each region with considering different features, which is our novel attempt. This module assigns different weights to the predictions of a region according to its contextual information such as weather, index of time slots, and region function. We conduct comprehensive experiments based on a large-scale real-world dataset from New York City, and the results show that our method outperforms state-of-the-art baselines.

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Section: Dnn-based Methodsmentioning

confidence: 99%

CACRNN: A Context-Aware Attention-Based Convolutional Recurrent Neural Network for Fine-Grained Taxi Demand Prediction

Liu

Yang

2020

Lecture Notes in Computer Science

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“…Recently, various deep learning methods have been used to capture complex non-linear spatial-temporal correlations and predict spatial-temporal series, such as stacked fully connected network [24,35], convolutional neural network (CNN) [23,41] and recurrent neural network (RNN) [39]. Several hybrid models have been proposed to model both spatial and temporal information [7,33,34].…”

Section: Spatial-temporal Predictionmentioning

confidence: 99%

Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction

Yao

Liu

Wei

et al. 2019

The World Wide Web Conference

183

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Spatial-temporal prediction is a fundamental problem for constructing smart city, which is useful for tasks such as traffic control, taxi dispatching, and environment policy making. Due to data collection mechanism, it is common to see data collection with unbalanced spatial distributions. For example, some cities may release taxi data for multiple years while others only release a few days of data; some regions may have constant water quality data monitored by sensors whereas some regions only have a small collection of water samples. In this paper, we tackle the problem of spatialtemporal prediction for the cities with only a short period of data collection. We aim to utilize the long-period data from other cities via transfer learning. Different from previous studies that transfer knowledge from one single source city to a target city, we are the first to leverage information from multiple cities to increase the stability of transfer. Specifically, our proposed model is designed as a spatial-temporal network with a meta-learning paradigm. The meta-learning paradigm learns a well-generalized initialization of the spatial-temporal network, which can be effectively adapted to target cities. In addition, a pattern-based spatial-temporal memory is designed to distill long-term temporal information (i.e., periodicity). We conduct extensive experiments on two tasks: traffic (taxi and bike) prediction and water quality prediction. The experiments demonstrate the effectiveness of our proposed model over several competitive baseline models.

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“…For example, Yao et al [8] proposed a periodically shifted attention mechanism by taking long-term periodic information and temporal shifting simultaneously. Wang et al [11] used a Softmax Layer to get the weight vector of representation. Only a few works attempt to deal with Semantic similarity to enhance the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Attention-Based Supply-Demand Prediction for Autonomous Vehicles

Zhang

Dong

et al. 2019

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

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As one of the important functions of the intelligent transportation system (ITS), supply-demand prediction for autonomous vehicles provides a decision basis for its control. In this paper, we present two prediction models (i.e. ARLP model and Advanced ARLP model) based on two system environments that only the current day's historical data is available or several days' historical data are available. These two models jointly consider the spatial, temporal, and semantic relations. Spatial dependency is captured with residual network and dimension reduction. Short term temporal dependency is captured with LSTM. Long term temporal dependency and temporal shifting are captured with LSTM and attention mechanism. Semantic dependency is captured with multi-attention mechanism and autocorrelation coefficient method. Extensive experiments show that our frameworks provide more accurate and stable prediction results than the existing methods.

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DeepSD: Supply-Demand Prediction for Online Car-Hailing Services Using Deep Neural Networks

Cited by 174 publications

References 20 publications

CACRNN: A Context-Aware Attention-Based Convolutional Recurrent Neural Network for Fine-Grained Taxi Demand Prediction

CACRNN: A Context-Aware Attention-Based Convolutional Recurrent Neural Network for Fine-Grained Taxi Demand Prediction

Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction

Attention-Based Supply-Demand Prediction for Autonomous Vehicles

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