Modeling of Freeway Real-Time Traffic Crash Risk Based on Dynamic Traffic Flow Considering Temporal Effect Difference

Yang, Yang; Yin, Yuexiu; Wang, Yunpeng; Meng, Ran; Yuan, Zhenzhou

doi:10.1061/jtepbs.teeng-7717

Cited by 15 publications

(1 citation statement)

References 25 publications

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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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Section: Rnnmentioning

confidence: 99%