Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach

Lin, Lei; He, Zhengbing; Peeta, Srinivas

doi:10.1016/j.trc.2018.10.011

Cited by 376 publications

(225 citation statements)

References 34 publications

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“…Researchers have recently explored the use of deep learning techniques in the field of ITS [25] and have obtained very promising results. However, data in the context of the problem addressed within this paper are highly irregular and sparse and deep learning techniques are not always the best and obvious choice.…”

Section: Related Workmentioning

confidence: 99%

Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

Passow

Paluszczyszyn

et al. 2021

IEEE Intell. Transport. Syst. Mag.

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Travel time is a basic measure based on which intelligent transportation systems such as traveller information systems, traffic management systems, public transportation systems are developed. Although many methodologies have been proposed, they have not yet adequately solved many challenges associated with travel time, in particular, travel time estimation for all links in a large and dynamic urban traffic network is still an open problem that needs addressing. Typically focus is placed on major roads such as motorways and main city arteries but there is an increasing need to know accurate travel times for minor urban roads. Such information is crucial for tackling air quality problems, accommodate the growing number of cars and provide accurate information for routing. This study aims to address the aforementioned challenges by introducing a methodology, namely Similar Model Searching (SMS), to estimate travel times by using historical sparse travel time data. The SMS learns the temporal and spatial relationship between the travel time of adjacent links and utilise labelled data of similar models in order to improve its overall performance. The effectiveness of the proposed method is evaluated on a section of Leicestershire traffic network in the UK. The obtained results show that SMS efficiently estimates travel time of target links using models of adjacent traffic links.

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Section: Related Workmentioning

confidence: 99%

Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

Passow

Paluszczyszyn

et al. 2021

IEEE Intell. Transport. Syst. Mag.

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show abstract

“…Intuitively, the passenger flow volumes between nearby stations may affect each other. This can be effectively handled by one GCNN layer which has shown a powerful ability to hierarchically capture spatial structural information [33,34]. Additionally, since metro systems connect two locations separated by a large distance, this leads to spatiotemporal dependencies between distant stations.…”

Section: Using Deep Gcnns To Capture Distant Spatiotemporal Dependencmentioning

confidence: 99%

Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks

Han

Wang

Ren

et al. 2019

IJGI

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Predicting the passenger flow of metro networks is of great importance for traffic management and public safety. However, such predictions are very challenging, as passenger flow is affected by complex spatial dependencies (nearby and distant) and temporal dependencies (recent and periodic). In this paper, we propose a novel deep-learning-based approach, named STGCNNmetro (spatiotemporal graph convolutional neural networks for metro), to collectively predict two types of passenger flow volumes-inflow and outflow-in each metro station of a city. Specifically, instead of representing metro stations by grids and employing conventional convolutional neural networks (CNNs) to capture spatiotemporal dependencies, STGCNNmetro transforms the city metro network to a graph and makes predictions using graph convolutional neural networks (GCNNs). First, we apply stereogram graph convolution operations to seamlessly capture the irregular spatiotemporal dependencies along the metro network. Second, a deep structure composed of GCNNs is constructed to capture the distant spatiotemporal dependencies at the citywide level. Finally, we integrate three temporal patterns (recent, daily, and weekly) and fuse the spatiotemporal dependencies captured from these patterns to form the final prediction values. The STGCNNmetro model is an end-to-end framework which can accept raw passenger flow-volume data, automatically capture the effective features of the citywide metro network, and output predictions. We test this model by predicting the short-term passenger flow volume in the citywide metro network of Shanghai, China. Experiments show that the STGCNNmetro model outperforms seven well-known baseline models (LSVR, PCA-kNN, NMF-kNN, Bayesian, MLR, M-CNN, and LSTM). We additionally explore the sensitivity of the model to its parameters and discuss the distribution of prediction errors.

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“…In the first case, people can rent a bike from a certain dock -or station -and deliver it to a different dock belonging to the same operator. In the second case, users can leave their bike wherever they want, removing the need for a specific station/infrastructure [2], [4]. [4].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Finally, Station-level approaches are the most precise, as they aim at predicting the demand for every single station in the system [10]. The main advantage is that correlations between different docks can be estimated and used to have more reliable and interpretable results [2]. On the other hand, given a certain number of observations, the potential estimation error increases with the number of variables [11].…”

Section: Literature Reviewmentioning

confidence: 99%

A low dimensional model for bike sharing demand forecasting

Cantelmo

Kucharski

Antoniou

2019

2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

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Big, transport-related datasets are nowadays publicly available, which makes data-driven mobility analysis possible. Trips with their origins, destinations and travel times are collected in publicly available big databases, which allows for a deeper and richer understanding of mobility patterns.This paper proposes a low dimensional approach to combine these data sources with weather data in order to forecast the daily demand for Bike Sharing Systems (BSS). The core of this approach lies in the proposed clustering technique, which reduces the dimension of the problem and, differently from other machine learning techniques, requires limited assumptions on the model or its parameters.The proposed clustering technique synthesizes mobility data quantitatively (number of trips) and spatially (mean trip origin and destination). This allows identifying recursive mobility patterns that -when combined with weather data -provide accurate predictions of the demand.The method is tested with real-world data from New York City. We synthesize more than four million trips into vectors of movement, which are then combined with weather data to forecast the daily demand at a city-level. Results show that, already with a one-parameters model, the proposed approach provides accurate predictions. 1

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Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach

Cited by 376 publications

References 34 publications

Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks

A low dimensional model for bike sharing demand forecasting

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