基于细粒度交通流的旅行时间估算预测的多尺度学习

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Computational Intelligence Pub Date : 2024-09-17 DOI:10.1111/coin.12693
Zain Ul Abideen, Xiaodong Sun, Chao Sun
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引用次数: 0

摘要

在智能交通系统(ITS)中,实现精确的旅行时间估算(TTE)至关重要,就像路线规划一样。精确预测不同城市区域的旅行时间至关重要,而这些特权的一个基本要求是掌握城市的细粒度知识。与之前局限于粗粒度数据的研究不同,我们将交通流量预测的范围扩大到了细粒度,这就带来了明确的挑战:(1)细粒度数据中普遍存在的网格间转换,为捕捉全球范围内网格单元间的空间依赖关系带来了复杂性。(2) 源自动态时间依赖性。为了应对这些挑战,我们提出了多尺度混合模型(MSHM)作为一种新方法。首先,使用多向卷积层获取每个单元的高层描述,从局部和全局两方面检索路网的语义属性。接下来,我们结合路网特征和粗粒度流量特征,利用增强型深度超分辨率(EDSR)技术对道路相关交通流的局部和全局空间分布建模进行正则化处理。得益于 EDSR 方法,我们的方法可以生成高质量的细粒度交通流地图。此外,为了利用精心设计的多尺度特征建模,持续提供准确的交通流量预测,我们对每个路段进行了多尺度特征表达,捕捉不同尺度上错综复杂的细节和重要特征,以优化交通流量预测。我们在两个真实世界数据集(BJTaxi 和 NYCTaxi)上进行了全面试验,旨在取得优于基线方法的结果。
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Multi-scale learning for fine-grained traffic flow-based travel time estimation prediction

In intelligent transportation systems (ITS), achieving accurate travel time estimation (TTE) is paramount, much like route planning. Precisely predicting travel time across different urban areas is vital, and an essential requirement for these privileges is having fine-grained knowledge of the city. In contrast to prior studies that are restricted to coarse-grained data, we broaden the scope of traffic flow forecasting to fine granularity, which provokes explicit challenges: (1) the prevalence of inter-grid transitions within fine-grained data introduces complexity in capturing spatial dependencies among grid cells on a global scale. (2) stemming from dynamic temporal dependencies. To address these challenges, we propose the multi-scaling hybrid model (MSHM) as a novel approach. Initially, a multi-directional convolutional layer is first used to acquire high-level depictions for each cell to retrieve the semantic attributes of the road network from local and global aspects. Next, we incorporate the characteristics of the road network and coarse-grained flow features to regularize the local and global spatial distribution modeling of road-relative traffic flow using an enhanced deep super-resolution (EDSR) technique. Benefiting from the EDSR method, our approach can generate high-quality fine-grained traffic flow maps. Furthermore, to continuously provide accurate TTE over time by leveraging well-designed multi-scale feature modeling, we incorporate a multi-scale feature expression of each road segment, capturing intricate details and important features at different scales to optimize the TTE. We conducted comprehensive trials on two real-world datasets, BJTaxi and NYCTaxi, aiming to achieve superior results compared to baseline methods.

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来源期刊
Computational Intelligence
Computational Intelligence 工程技术-计算机:人工智能
CiteScore
6.90
自引率
3.60%
发文量
65
审稿时长
>12 weeks
期刊介绍: This leading international journal promotes and stimulates research in the field of artificial intelligence (AI). Covering a wide range of issues - from the tools and languages of AI to its philosophical implications - Computational Intelligence provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The journal is designed to meet the needs of a wide range of AI workers in academic and industrial research.
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