线性回归耦合瓦瑟斯坦生成对抗网络,用于芝加哥和奥斯汀的打车出行直接需求建模

Md Hishamur Rahman, Masnun Abrar, Shakil Mohammad Rifaat
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引用次数: 0

摘要

准确估算打车需求并了解其影响因素对于现代交通规划非常必要。尽管现代机器学习技术提高了对打车需求的估算能力,但仍存在一些问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Linear regression coupled Wasserstein generative adversarial network for direct demand modeling of ride-hailing trips in Chicago and Austin
Accurate estimation of ride-hailing demand and understanding of its influencing factors are necessary for modern-day transportation planning. Although modern machine learning techniques improve the...
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