Multi-stage deep learning approaches to predict boarding behaviour of bus passengers

Tang, Tianli; Fonzone, Achille; Liu, Ronghui; Choudhury, Charisma F.

doi:10.1016/j.scs.2021.103111

Cited by 24 publications

(8 citation statements)

References 66 publications

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“…In general, the data imbalance issue is more acute in predicting individual behaviour or a particular type of event. In our previous study of predicting public transport board demand [30], we showed that the prediction is good at the aggregated level, but is poor when we tried to predict the hourly boarding behaviour of individual bus users, due to the data imbalance issue. In Section III-A, we introduce this particular data imbalance issue relating to public transport demand prediction.…”

Section: Research Gaps and Scopesmentioning

confidence: 98%

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Predicting Hourly Boarding Demand of Bus Passengers Using Imbalanced Records From Smart-Cards: A Deep Learning Approach

Tang

Liu

Choudhury

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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The tap-on smart-card data provides a valuable source to learn passengers' boarding behaviour and predict future travel demand. However, when examining the smart-card records (or instances) by the time of day and by boarding stops, the positive instances (i.e. boarding at a specific bus stop at a specific time) are rare compared to negative instances (not boarding at that bus stop at that time). Imbalanced data has been demonstrated to significantly reduce the accuracy of machinelearning models deployed for predicting hourly boarding numbers from a particular location. This paper addresses this data imbalance issue in the smart-card data before applying it to predict bus boarding demand. We propose the deep generative adversarial nets (Deep-GAN) to generate dummy travelling instances to add to a synthetic training dataset with more balanced travelling and non-travelling instances. The synthetic dataset is then used to train a deep neural network (DNN) for predicting the travelling and non-travelling instances from a particular stop in a given time window. The results show that addressing the data imbalance issue can significantly improve the predictive model's performance and better fit ridership's actual profile. Comparing the performance of the Deep-GAN with other traditional resampling methods shows that the proposed method can produce a synthetic training dataset with a higher similarity and diversity and, thus, a stronger prediction power. The paper highlights the significance and provides practical guidance in improving the data quality and model performance on travel behaviour prediction and individual travel behaviour analysis.

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Section: Research Gaps and Scopesmentioning

confidence: 98%

“…In our own recent research [30], we demonstrate that smartcard data combined with machine learning techniques can be a powerful approach for predicting the spatial and temporal patterns of bus boarding. The predictions were found to be highly accurate at an aggregated level, averaged over all travellers.…”

mentioning

confidence: 97%

Predicting Hourly Boarding Demand of Bus Passengers Using Imbalanced Records From Smart-Cards: A Deep Learning Approach

Tang

Liu

Choudhury

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…When predicting multiple targets for the whole traffic network, this kind of method maintains various models for different objects. As for the machine learning methods, they convert the time series into a supervised learning problem, solved by machine learning algorithms [9].…”

Section: Introductionmentioning

confidence: 99%

A Bus Passenger Flow Prediction Model Fused with Point-of-Interest Data Based on Extreme Gradient Boosting

Ouyang³

et al. 2022

Applied Sciences

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Bus operation scheduling is closely related to passenger flow. Accurate bus passenger flow prediction can help improve urban bus planning and service quality and reduce the cost of bus operation. Using machine learning algorithms to find the rules of urban bus passenger flow has become one of the research hotspots in the field of public transportation, especially with the rise of big data technology. Bus IC card data are an important data resource and are more valuable to passenger flow prediction in comparison with manual survey data. Aiming at the balance between efficiency and accuracy of passenger flow prediction for multiple lines, we propose a novel passenger flow prediction model based on the point-of-interest (POI) data and extreme gradient boosting (XGBoost), called PFP-XPOI. Firstly, we collected POI data around bus stops based on the Amap Web service application interface. Secondly, three dimensions were considered for building the model. Finally, the XGBoost algorithm was chosen to train the model for each bus line. Results show that the model has higher prediction accuracy through comparison with other models, and thus this method can be used for short-term passenger flow forecasting using bus IC cards. It plays a very important role in providing decision basis for more refined bus operation management.

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“…It is necessary to expand infrastructure construction in order to overcome these di culties. It is needed to install intelligent equipment in scenic areas, convert user behavior and information into data resources, increase the scienti c management of the whole industry, and provide visitors with a higher-quality, intelligent all-round tourism experience [5].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tourism Demand in Liuzhou Region Based on Machine Learning

2022

Mobile Information Systems

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Liuzhou is a rich tourism city in China. It is well known for its ethnic and prehistoric culture, folk songs, rare stones, and urban landscape. The demand of tourism in Liuzhou city is increasing day by day. Therefore, a system is needed to accurately predict the increasing demand of tourism in Liuzhou city. For this reason, based on the examination of historical visitor data in natural sites especially in Liuzhou city, this research employs an important machine learning and deep learning approach. The purpose of this study is to identify the consumption patterns and improve prediction, prejudgment, and preparation abilities by incorporating them into an intelligent tourism service platform, preventing visitors from travelling at inconvenient times and providing appropriate suggestions to scenic site managers. In this paper, the Sparse Principal Component Analysis-Long-Short-Term Memory (SPCA-LSTM) model has been updated to the Sparse Principal Component Analysis Convolutional Neural Network Long-Short-Term Memory (SPCA-CNNLSTM) model to accurately predict the tourist traffic during the holidays. Convolutional and pooling layers are added to the network topology to extract the local characteristics of the input data. The data of passenger traffic and influencing factors of Liuzhou scenic area from the months of Sept. 2015 to Nov. 2019 were used as the data set for the experiments. A hybrid-forecasting model is also proposed in the paper, which first removes noise from international crude oil data using compression-aware denoising preprocessing and then combines compression-aware denoising with machine learning using artificial neural network (ANN) and support vector regression (SVR). The experimental result shows that the SPCA-CNNLSTM model predicts better values than the SPCA-LSTM model.

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Multi-stage deep learning approaches to predict boarding behaviour of bus passengers

Cited by 24 publications

References 66 publications

Predicting Hourly Boarding Demand of Bus Passengers Using Imbalanced Records From Smart-Cards: A Deep Learning Approach

Predicting Hourly Boarding Demand of Bus Passengers Using Imbalanced Records From Smart-Cards: A Deep Learning Approach

A Bus Passenger Flow Prediction Model Fused with Point-of-Interest Data Based on Extreme Gradient Boosting

Prediction of Tourism Demand in Liuzhou Region Based on Machine Learning

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