A Comprehensive Comparative Analysis of the Basic Theory of the Short Term Bus Passenger Flow Prediction

Zhai, Huawei; Cui, Licheng; Nie, Yu; Xu, Xiaowei; Zhang, Weishi

doi:10.3390/sym10090369

Cited by 20 publications

(7 citation statements)

References 33 publications

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“…Automated fare collection (AFC) data, APC data, and AVL data have been among the most popular data sources used by researchers. The methodological attempts, however, have been even more diverse, ranging from statistical and mathematical models to simulation tools, to data-driven, and deep learning approaches ( 6 ). Existing methods can be divided into two main categories: linear and nonlinear methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Passenger Flow Prediction Using a Bus Network Graph Convolutional Long Short-Term Memory Neural Network Model

Baghbani

Bouguila

Patterson

2022

Transportation Research Record: Journal of the Transportation R

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Short-term passenger flow prediction is critical to managing real-time bus networks, responding to emergencies quickly, making crowdedness-aware route recommendations, and adjusting service schedules over time. Some recent studies have attempted to predict passenger flow using deep learning models. The complexity of transportation networks, coupled with emerging real-time data collection and information dissemination systems, has increased the popularity of these approaches. There has also been a growing interest in using a new deep learning approach, the graph neural network that captures graph dependence by passing messages between its nodes. Researchers in various transportation domains have used such tools for modeling and predicting transportation networks, as many of these networks consist of nodes and links and can be naturally categorized as graphs. This paper develops a bus network graph convolutional long short-term memory (BNG-ConvLSTM) neural network model to forecast short-term passenger flows in bus networks. Validating the proposed model is done using real-world data collected from the Laval bus network in Canada. Based on a set of comparisons between the proposed model and some other popular deep learning approaches, it clearly indicates that the BNG-ConvLSTM model is more scalable and robust than other baselines in making network-wide predictions for short-term passenger flows.

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Section: Literature Reviewmentioning

confidence: 99%

Short-Term Passenger Flow Prediction Using a Bus Network Graph Convolutional Long Short-Term Memory Neural Network Model

Baghbani

Bouguila

Patterson

2022

Transportation Research Record: Journal of the Transportation R

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“…Time Series Model. e following presents the modelling process for passenger flow analysis using time series model, which is based on the content described in the literature [5]. Figure 6, the sequence shows significant instability.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…In recent decades, short-term passenger flow prediction has drawn the widespread attention, and various methods have been proposed, which could be categorized as linear models, nonlinear models, and combination models generally [5]. Because the passenger flow statistics are naturally time dependent, the linear models, such as autoregressive integrated moving average (ARIMA), autoregressive moving average (ARMA), and autoregressive (AR) models, are widely used for simple short-term passenger flow prediction.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting

Zhai

Tian

Cui

et al. 2020

Journal of Advanced Transportation

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For the increasing travel demands and public transport problems, dynamically adjusting timetable or bus scheduling is necessary based on accurate real-time passenger flow forecasting. In order to get more accurate passenger flow in future, this paper proposes a novel hierarchical hybrid model based on time series model, deep belief networks (DBNs), and improved incremental extreme learning machine (Im-ELM) to forecast short-term passenger flow. The proposed model is named HTSDBNE with two modelling steps. First, referring the idea of parallelization, the hybrid model, constructed by time series model, DBN, and Im-ELM, is used to forecast short-term passenger flow in different time scales hierarchically and parallel. Second, Im-ELM is utilized to analyse the relationship of forecasting results from the first step, and the weighted outputs of Im-ELM are as the final forecasting results. Comparing with single forecasting models and typical hybrid forecasting models, the testing results indicate that HTSDBNE has better performances. The mean absolute percent error of prediction results is around 10% and fully meets the application requirements of bus operation enterprise.

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“…The large quantity of data collected by smart cards offers more detailed characteristics in the time and space dimension than any other types of data. To improve the bus service quality, an accurate and proactive passenger flow prediction approach is necessary [3,4]. Availability of smart card data has offered more opportunities for the prediction work [5].…”

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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A Comprehensive Comparative Analysis of the Basic Theory of the Short Term Bus Passenger Flow Prediction

Cited by 20 publications

References 33 publications

Short-Term Passenger Flow Prediction Using a Bus Network Graph Convolutional Long Short-Term Memory Neural Network Model

Short-Term Passenger Flow Prediction Using a Bus Network Graph Convolutional Long Short-Term Memory Neural Network Model

A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting

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

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