Prediction of bus passenger trip flow based on artificial neural network

Yu, Shaoqiang; Shang, Caiyun; Yu, Yating; Zhang, Shuyuan; Yu, Wenbo

doi:10.1177/1687814016675999

Cited by 18 publications

(14 citation statements)

References 23 publications

(28 reference statements)

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“…In another study, in which taxi passengers' demands were estimated using the ARIMA model, error rates of up to 25% were obtained [23]. Other studies on the estimation of bus passengers' demands found estimation values of up to 78% [24,25,26]. But in some studies there are 89-90% [27,28].…”

Section: Discussion and Outcomementioning

confidence: 94%

Arranging Bus Behaviour by Finding the Best Prediction Model with Artificial Neural Networks

Aydemir

Gülseçen

2019

Teh. vjesn.

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Artificial Neural Networks (ANNs) were used in this study to estimate the hourly passenger populations at certain stations in İstanbul. To do this, the details were collected from various sources regarding the passengers in a station. This study aims to show what can be implemented for the passenger numbers in the decision support system and makes some recommendations for the regulation of the bus lines. Trials were conducted using an ANN with a backpropagation model and various inner layers for the estimations. The MAE score was 10.301 for the stations studied. Qualitative interviews were conducted with 32 passengers and 12 drivers, and solutions were searched for the density of the lines. A proposal system was developed with the c# software resulting from the combination of the prediction model with these proposals.

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Section: Discussion and Outcomementioning

confidence: 94%

Arranging Bus Behaviour by Finding the Best Prediction Model with Artificial Neural Networks

Aydemir

Gülseçen

2019

Teh. vjesn.

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“…The applied trip demand model was based on a Poisson point process, which does not consider local travel conditions, e.g., residences of passengers or existing road-or PT networks (Hyytiä et al, 2010). Yu et al (2016) and Ke et al (2017) apply neural networks for predicting passenger demand. A drawback of this approach is the required size of the training dataset.…”

Section: Simulation Of Demand For Drtmentioning

confidence: 99%

Modelling Commuting Activities for the Simulation of Demand Responsive Transport in Rural Areas

Dytckov

Lorig

Davidsson

et al. 2020

Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems

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For the provision of efficient and high-quality public transport services in rural areas with a low population density, the introduction of Demand Responsive Transport (DRT) services is reasonable. The optimal design of such services depends on various socio-demographical and environmental factors, which is why the use of simulation is feasible to support planning and decision-making processes. A key challenge for sound simulation results is the generation of realistic demand, i.e., requests for DRT journeys. In this paper, a method for modelling and simulating commuting activities is presented, which is based on statistical real-world data. It is applied to Sjöbo and Tomelilla, two rural municipalities in southern Sweden.

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“…In the earlier days, some researchers usually predicted the long-term passenger flow trend for days or years through ANN with manual survey data. For example, Yu [56] uses ANN to forecast the bus passenger trip flow between different city zones. Jiang [57] uses RBF-ANN and BP-ANN to predict the long-term passenger flow in one-year interval respectively, the results show the accuracy of RBF-ANN is better than BP-ANN.…”

Section: Applications Of Ann In Short-term Bus Passenger Flow Predictionmentioning

confidence: 99%

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

et al. 2018

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In order to meet the real-time public travel demands, the bus operators need to adjust the timetables in time. Therefore, it is necessary to predict the variations of the short-term passenger flow. Under the help of the advanced public transportation systems, a large amount of real-time data about passenger flow is collected from the automatic passenger counters, automatic fare collection systems, etc. Using these data, different kinds of methods are proposed to predict future variations of the short-term bus passenger flow. Based on the properties and background knowledge, these methods are classified into three categories: linear, nonlinear and combined methods. Their performances are evaluated in detail in the major aspects of the prediction accuracy, the complexity of training data structure and modeling process. For comparison, some long-term prediction methods are also analyzed simply. At last, it points that, with the help of automatic technology, a large amount of data about passenger flow will be collected, and using the big data technology to speed up the data preprocessing and modeling process may be one of the directions worthy of study in the future.

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Prediction of bus passenger trip flow based on artificial neural network

Cited by 18 publications

References 23 publications

Arranging Bus Behaviour by Finding the Best Prediction Model with Artificial Neural Networks

Arranging Bus Behaviour by Finding the Best Prediction Model with Artificial Neural Networks

Modelling Commuting Activities for the Simulation of Demand Responsive Transport in Rural Areas

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

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