利用主动对抗逆向强化学习和变形器估算个性化的出发地-目的地旅行时间

IF 8.3 1区 工程技术 Q1 ECONOMICS
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引用次数: 0

摘要

旅行时间估算对于即时配送、车辆路由选择和打车服务非常重要。大多数研究估算的是指定路线的旅行时间,只有少数研究关注无指定路线的起点-终点旅行时间估算(OD-TTE)。此外,大多数关于 OD-TTE 的研究都忽略了个性化路线偏好和数据标注成本。为了填补这一研究空白,我们分析了个人路线偏好,并提出了一种基于主动对抗逆强化学习(AA-IRL)和 Transformer 的个性化起点-终点旅行时间估算方法。为了分析个性化路线偏好,我们将对抗逆强化学习与主动学习相结合,从而有效降低了样本标注的成本。在推断出可能的路线后,我们提出了用于旅行时间估算的 AdaBoost 多融合图卷积变换器网络(AMGC-Transformer)。在中国的打车和在线送餐轨迹上进行的数值实验验证了我们方法的优势。与相关研究相比,我们的方法可将路线推断的 F1 分数提高 2.50-3.35%,将 OD-TTE 的平均绝对误差降低 7.44-11.66%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Personalized origin–destination travel time estimation with active adversarial inverse reinforcement learning and Transformer
Travel time estimation is important for instant delivery, vehicle routing, and ride-hailing. Most studies estimate the travel time of specified routes, and only a few studies pay attention to origin–destination travel time estimation (OD-TTE) without a specified route. Moreover, most of these studies on OD-TTE ignore the personalized route preference and the cost of data annotation. To fill this research gap, we analyze the individual route preference and propose a personalized origin–destination travel time estimation method based on active adversarial inverse reinforcement learning (AA-IRL) and Transformer. To analyze the personalized route preference, we integrate adversarial inverse reinforcement learning with active learning, which effectively reduces the cost of sample annotation. After inferring the possible routes, we propose AdaBoost multi-fusion graph convolutional Transformer network (AMGC-Transformer) for travel time estimation. Numerical experiments conducted on ride-hailing and online food delivery trajectories in China validate the advantage of our method. Compared to relevant studies, our approach can improve F1-score of route inference by 2.50–3.35% and reduce the mean absolute error of OD-TTE by 7.44–11.66%.
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来源期刊
CiteScore
16.20
自引率
16.00%
发文量
285
审稿时长
62 days
期刊介绍: Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management. Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.
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