Passenger Demand Prediction with Cellular Footprints

Chu, Jian; Qian, Kun; Wang, Xu; Yao, Lina; Xiao, Fu; Li, Jianbo; Miao, Xin; Yang, Zheng

doi:10.1109/sahcn.2018.8397114

Cited by 15 publications

(15 citation statements)

References 28 publications

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“…External factors are further leveraged in [2] to improve the prediction accuracy. Chu et al [4] try to incorporate the spatialtemporal dependencies and external factors by using fixed parameter matrixes learned during model training. However, all the above existing works are limited in incorporating different spatial-temporal features and external factors together, since fixed notice is paid to them without considering the impacts of contextual information.…”

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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“…We first compare our method with three representative traditional baselines: 1) Historical Average (HA); 2) Ordinary Linear Regression (OLR); 3) XGBoost [Chen and Guestrin, 2016] [Chu et al, 2018]; 10)DCRNN ; 11) STGCN .…”

Section: Next-step Prediction Comparisonmentioning

confidence: 99%

“…Based on the GGCM, two encoder modules named long-term encoder and short-term encoder are designed to encode historical passenger demand and integrate new predictions, separately. Compared to the chain structured RNN, the hierarchical GCN structure shortens the path to capture the long-range temporal dependency [Gehring et al, 2017]. Besides, having two distinct encoders allows our model to utilize last step's prediction to generate the next step's prediction without requiring a RNN to act as a decoder, which reduces the associated issue of error accumulation.…”

Section: Introductionmentioning

confidence: 99%

STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

Bai

Yao

Kanhere

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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194

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Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multistep citywide passenger demand prediction based on a graph and use a hierarchical graph convolutional structure to capture both spatial and temporal correlations simultaneously. Our model consists of three parts: 1) a long-term encoder to encode historical passenger demands; 2) a shortterm encoder to derive the next-step prediction for generating multi-step prediction; 3) an attentionbased output module to model the dynamic temporal and channel-wise information. Experiments on three real-world datasets show that our model consistently outperforms many baseline methods and state-of-the-art models.

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“…Definition 2: Crowd Outflow [11] We use P T (r i ) to denote the set of people in region r i at time T. The crowd outflow of region r i during time interval t can be defined as C t (r i ) = P T (r i ) \ P T +∆T (r i ). Passenger Demand Prediction Let S t (r i ) denotes all the historically observed data (passenger demand, crowd outflow) for region r i in time period t, E t+1 (r i ) denotes all external features in time interval t + 1 (since the weather in time interval t + 1 is unknown, we can use the predicted weather or the weather in time t), passenger demand prediction aims to to predict:…”

Section: Notation 1: Regionmentioning

confidence: 99%

Passenger Demand Forecasting with Multi-Task Convolutional Recurrent Neural Networks

Bai

Yao

Kanhere

et al. 2019

Lecture Notes in Computer Science

Self Cite

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Accurate prediction of passenger demands for taxis is vital for reducing the waiting time of passengers and drivers in large cities as we move towards smart transportation systems. However, existing works are limited in fully utilizing multi-modal features. First, these models either include excessive data from weakly correlated regions or neglect the correlations with similar but spatially distant regions. Second, they incorporate the influence of external factors (e.g., weather, holidays) in a simplistic manner by directly mapping external features to demands through fully-connected layers and thus result in substantial bias as the influence of external factors is not unified. To tackle these problems, we propose an end-to-end multi-task deep learning model for passenger demand prediction. First, we select similar regions for each target region based on their Point-of-Interest (PoI) information or historical demand and utilize Convolutional Neural Networks (CNN) to extract their spatial correlations. Second, we map external factors to future demand levels as part of the multi-task learning framework to further boost prediction accuracy. We conduct experiments on a large-scale real-world dataset collected from a city in China with a population of 1.5 million. The results demonstrate that our model significantly outperforms the stateof-the-art and a set of baseline methods.

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Passenger Demand Prediction with Cellular Footprints

Cited by 15 publications

References 28 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

STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

Passenger Demand Forecasting with Multi-Task Convolutional Recurrent Neural Networks

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