Modelling and comparing two modes of sharing parking spots at residential area: Real‐time and fixed‐time allocation

Yang, Yang; Yang, Bo; Yuan, Zhenzhou; Meng, Ran; Wang, Yunpeng

doi:10.1049/itr2.12343

Cited by 19 publications

(19 citation statements)

References 24 publications

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“…Yang et al. [4] proposed real‐time and fixed‐time shared parking spot allocation models based on time window constraints. The real‐time model adopts dynamic response service mechanism and introduces multi‐objective decision weighting method to construct weighted evaluation function.…”

Section: Papers In the Special Issuementioning

confidence: 99%

Guest Editorial: Electro‐mobility for urban traffic and transportation

Zhang,

Mohammed,

Calvi

2024

IET Intelligent Trans Sys

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Section: Papers In the Special Issuementioning

confidence: 99%

Guest Editorial: Electro‐mobility for urban traffic and transportation

Zhang,

Mohammed,

Calvi

2024

IET Intelligent Trans Sys

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“…Nath, Kumbhar and Khoa (2022) applied the RNN algorithm to machine translation successfully. Yang et al (2023a;2023b) applied real-time and fixed-time allocation to the parking space sharing model in residential areas to find a favorable RNN algorithm. The improvement of this algorithm was applied in real-time traffic accident risk.…”

Section: Rnnmentioning

confidence: 99%

A Mining Algorithm to Improve LSTM for Predicting Customer Churn in Railway Freight Traffic

Zhao¹,

Dong²,

Peng³

et al. 2023

SIC

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Railway freight is at risk of losing customers due to intense competition in the market. Effectively managing potential customer loss is a long-term problem for railway freight. Big data technology has been widely researched and applied in recent years, and using data mining techniques to fully extract information from railway freight ticket data and discover potential customer loss is a research topic. In this study, a mining algorithm is proposed to improve the accuracy of predicting customer churn in railway freight using Long Short-Term Memory (LSTM) network. The algorithm involves three steps: constructing the time series of railway freight volume, predicting customer churn trends using a modified LSTM model, and analyzing the characteristics of customers with different degrees of loss and the characteristics of transportation demand of lost and potential lost customers. Experimental verification was conducted using customer freight ticket data from the Shanghai Railway Bureau in China and compared the proposed modified LSTM model was compared with other commonly used machine learning algorithms for churn prediction. The results showed that the present algorithm demonstrated good accuracy and adaptability. The study proposed in this paper enriches the theoretical basis of railway freight customer management, provides an effective method for predicting customer loss in railway freight, and offers technical support for railway freight management practices.

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“…It is concluded that LSTM network is more effective than Arima and BP neural network. Yang et al [16] have carried on the modelling and the comparison to the parking lot vehicle assignment forecast. Zhang [17] applies Bayesian network model to the analysis and forecast of e-commerce logistics.…”

Section: Related Work 21 Time Series Prediction Algorithmmentioning

confidence: 99%

Analysis and Forecast of Railway Freight Volume based on Prophet-Deep AR Model

2023

Teh. vjesn.

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The research on railway freight volume forecast is of great significance to the allocation of railway freight transport resources, the formulation of transport plans and the organization of railway freight transport. This study, by fully mining the railway freight ticket data information, put forward the precise forecast model of railway freight volume under different types of freight. Firstly, the railway freight ticket data are cleaned, regulated and integrated, and the time series of the daily number of railway freight trains are constructed, get the trend, periodicity and holiday data of railway traffic data, and set the parameters of Chinese holidays and rest days according to the demand characteristics of different categories. Secondly, the forecasting result of the Prophet is taken as a cooperative parameter. DeepAR is used to forecast, and a combined model of the Prophet-DeepAR is constructed. Finally, the combined model was validated with Shanghai Railway Bureau data from January 1, 2015 to December 31, 2018 for the food and tobacco category, and with Prophet-DeepAR, LSTM, Wavelet, BILSTM, and Prophet-LSTM, prophet-wavelet and Prophet-Bilstm are used to compare the prediction results. The results show that the Prophet-DeepAR model can extract the multi-dimensional periodicity of freight traffic and mine the trend information of freight traffic, and get the prediction result with high precision. It has better accuracy than other models.

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Modelling and comparing two modes of sharing parking spots at residential area: Real‐time and fixed‐time allocation

Cited by 19 publications

References 24 publications

Guest Editorial: Electro‐mobility for urban traffic and transportation

Guest Editorial: Electro‐mobility for urban traffic and transportation

A Mining Algorithm to Improve LSTM for Predicting Customer Churn in Railway Freight Traffic

Analysis and Forecast of Railway Freight Volume based on Prophet-Deep AR Model

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