Analysis of Bus Trip Characteristic Analysis and Demand Forecasting Based on GA-NARX Neural Network Model

Sun, Fang Hong; Wang, Xiaolin; Zhang, Yang; Liu, Weixuan; Zhang, Rongji

doi:10.1109/access.2020.2964689

Cited by 16 publications

(5 citation statements)

References 10 publications

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“…Section passenger flow survey is a regular sampling survey of passenger flow, and one or two sections can be selected for the survey. Generally, the maximum passenger flow section is investigated, and the investigators use the direct observation method to investigate the number of passengers in the vehicle [8,9].…”

Section: Sample Survey Of Passengersmentioning

confidence: 99%

Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Zhang

2022

Mathematical Problems in Engineering

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Based on spatial data dynamic analysis technology, the characteristics of urban rail transit passenger flow are analyzed. This study analyzes the passenger flow characteristics of urban rail transit from the perspective of operation and planning by using the dynamic analysis technology of spatial data, combined with social and economic development. Based on the analysis of research data from the study of passenger traffic and passenger traffic data measurement, the characteristics of passenger traffic from urban railways are derived from the time distribution, spatial distribution, and transfer of passengers waiting on the platform. The final length of line 3 is 39.7 m, the passenger volume is 102, with 11 people/day, the passenger flow density is 1.8/2.3, and the high section in peak hours is 2.4/3.2. By considering the various influencing factors, efforts should be made to develop urban rail transit projects to promote the growth of social and economic benefits.

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Section: Sample Survey Of Passengersmentioning

confidence: 99%

Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Zhang

2022

Mathematical Problems in Engineering

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“…Data mining has emerged as an alternative tool for modeling and forecasting due to its ability to capture the non-linearity in the data. While the shortcoming of data mining is a large amount of training data [14], with the advent of big data era, data mining has recently been widely used for demand forecasting in the various fields, where data can be collected easily, such as energy [10,15], tourism [16,17], transportation [18][19][20], water management [21,22], remanufacturing [23], bike sharing [24,25], retail pharmacies [26], hospitals [27,28], logistics [14], and spare parts management [14,[29][30][31], showing its usefulness.…”

Section: Reviews On Related Workmentioning

confidence: 99%

Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea

Choi¹,

Suh²

2020

Sustainability

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In a weapon system, the accurate forecasting of the spare parts demand can help avoid the excess inventory, leading to the efficient use of budget. It can also help develop the combat readiness of the weapon system by improving weapon system utilization. Moreover, as performance-based logistics (PBL) projects have recently emerged, the accurate demand forecasting of spare parts has become an important issue for the PBL contractors as well. However, for the demand forecasting of spare parts, the time series methods, typically used in the military sector, have low prediction accuracies and the PBL contractors are mostly based on the judgment of practitioners. Meanwhile, most of the previous studies in the military sector have not considered the managerial characteristics of spare parts (e.g., reparability and the irregularity of maintenance). No previous work has considered any such features, which can indicate the reliability of spare parts (e.g., mean time between failures (MTBF)), although they can affect the spare parts demand. Therefore, to develop a more accurate forecasting of the spare parts demand of military aircraft, we designed and examined a systematic approach that uses data mining techniques. To fill up the research gaps of related works, our approach also considered the managerial characteristics of spare parts and included the new features that represent the reliability of spare parts. Consequently, given the case of South Korea and the full feature set, we found random forest gave better results than the other data mining techniques and the conventional time series methods. Using the best technique Random Forest, we identified the contribution of each managerial feature set to improving the prediction accuracy, and we found the reliability and operation environment are valuable feature sets in a significant way, so they should be collected, managed more carefully, and included for better prediction of spare parts demand of military aircraft.

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“…Several works have studied human mobility [7] and mobility of vehicular networks [8], [9]. Mobility is useful in several applications, such as the prediction of public transportation passengers' flow [10], the increase of vehicular safety protection [11], and to estimate the spread of contagious diseases such as COVID-19 [12], [13]. Most of geolocated datasets available in the community are based on GPS traces sampled over time [14].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Evaluation of Probabilistic and Deep Learning Approaches for Vehicular Trajectory Prediction

Irio

Oliveira

2021

IEEE Open J. Veh. Technol.

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This work compares two innovative methodologies to predict the future locations of moving vehicles when their current and previous locations are known. The two methodologies are based on: (a) a Bayesian network model used to infer the statistics of prior vehicles' trajectory data that is further adopted in the estimation process; (b) a deep learning approach based on recurrent neural networks (RNNs), more specifically Long Short-term Memory (LSTM) networks. Both methodologies are evaluated with GPS traces of multiple taxis servicing Porto city in Portugal. After a characterization of the dataset, we present experimental results obtained with both prediction methodologies. The results indicate that the prediction accuracy is improved in both methods as more information about prior vehicle mobility is available. The Bayesian network-based method is advantageous because the statistical inference can be updated in real-time as more trajectory data is known. On the contrary, the RNN-based method requires a time-consuming learning task every time new data is added to the inference dataset. However, the RNN achieves a higher prediction accuracy performance (3% to 5% higher) than the Bayesian method. Additionally, we show that the computational cost to predict the next position a vehicle will move to can be substantially reduced when the Bayesian network is adopted, a scenario where the RNN method requires more computational time. But when the quantity of prior data used in the prediction increases, the computational time required by the RNN-based method can be two orders of magnitude lower than the Bayesian network-based approach, showing that the RNN method is advantageous in both accuracy and computational time. Finally, we highlight that both methods achieve a next position successful prediction rate higher than 90%, confirming the applicability and validity of the proposed methods.

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Analysis of Bus Trip Characteristic Analysis and Demand Forecasting Based on GA-NARX Neural Network Model

Cited by 16 publications

References 10 publications

Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea

A Comparative Evaluation of Probabilistic and Deep Learning Approaches for Vehicular Trajectory Prediction

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