Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data
Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data
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DOI：
10.1016/j.trc.2018.09.002
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Wei Ma;Z. Qian
中科院分区：
文献类型：
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作者：
Wei Ma;Z. Qian
动态OD需求是交通系统建模和分析的核心。动态OD需求估计问题（DODE）的研究已有几十年的历史，大多数研究都是在典型日或典型小时内进行的。目前还缺乏一种方法来估计高分辨率的动态OD需求的序列的许多连续的日子在几年内（在本研究中称为24/7 OD）。拥有多年24/7 OD需求将有助于更好地了解动态OD需求的特征及其在过去几年的演变/趋势，这是建模运输系统演变和可靠性的关键输入。本文提出了一个数据驱动的框架，估计日常的动态OD使用高粒度的交通计数和速度数据收集了多年。该框架使用t-分布随机邻居嵌入（t-SNE）和k-均值方法将日常交通数据统计聚类为典型的交通模式。提出了一种基于GPU的随机投影梯度下降法来有效地求解多年24/7 DODE问题。结果表明，该方法能够有效地估算出萨克拉门托地区I-5和SR-99公路2014 - 2016年每一天的5 min动态OD需求。由此产生的多年24/7动态OD需求揭示了一个地区出行需求的日、周、月、季和年变化，揭示了多年来有趣的需求特征。
Dynamic origin-destination (OD) demand is central to transportation system modeling and analysis. The dynamic OD demand estimation problem (DODE) has been studied for decades, most of which solve the DODE problem on a typical day or several typical hours. There is a lack of methods that estimate high-resolution dynamic OD demand for a sequence of many consecutive days over several years (referred to as 24/7 OD in this research). Having multi-year 24/7 OD demand would allow a better understanding of characteristics of dynamic OD demands and their evolution/trends over the past few years, a critical input for modeling transportation system evolution and reliability. This paper presents a data-driven framework that estimates day-to-day dynamic OD using high-granular traffic counts and speed data collected over many years. The proposed framework statistically clusters daily traffic data into typical traffic patterns using t-Distributed Stochastic Neighbor Embedding (t-SNE) and k-means methods. A GPU-based stochastic projected gradient descent method is proposed to efficiently solve the multi-year 24/7 DODE problem. It is demonstrated that the new method efficiently estimates the 5-min dynamic OD demand for every single day from 2014 to 2016 on I-5 and SR-99 in the Sacramento region. The resultant multi-year 24/7 dynamic OD demand reveals the daily, weekly, monthly, seasonal and yearly change in travel demand in a region, implying intriguing demand characteristics over the years.