网络空间搜索意图作为主动交通热点检测的先行指标
Cyberspace Search Intentions as Leading Indicators for Proactive Traffic Hotspot Detection
AI总结：
本研究提出以网络空间搜索记录为先行指标的网络-物理数据驱动框架，通过ODT张量模型处理搜索数据，可主动检测高速公路交通热点，验证了搜索强度与交通流量的强相关性，为交通拥堵早期预测提供了可靠方法。
AI中文摘要：
本研究提出一种基于网络-物理数据驱动的框架，用于主动检测高速公路交通热点与热区。该框架将网络空间中用户的在线搜索记录作为早期指标。为处理大规模且不规则的搜索记录，提出一种起讫时间（Origin Destination Time，ODT）张量模型，用于表示路线搜索数据的时空结构并加速计算。利用从这些记录中提取的按目的地划分的流入序列，开发了一种系统方法，用于自动识别表明新兴交通热点及对应区域的异常激增。为验证该框架，使用涵盖高速公路网络内2728个互通式立交（IC）节点的一年真实数据集开展实验，此外整合搜索数据与实际交通流量进行评估。结果显示搜索强度与交通流量存在强相关性，表明在线搜索行为可作为预测交通动态的可靠代理。这些发现表明，网络空间中的路线搜索记录可有效用于主动交通监测，并凸显了拥堵模式早期预测的潜力。
英文摘要：
This study proposes a cyber-physical data-driven framework for proactive detection of highway traffic hotspots and hot regions. The proposed framework bridges users' online search records in cyberspace as early indicator. To handle large-scale and irregular search records, we propose an Origin Destination Time (ODT) tensor model to represent the spatio-temporal structure of route search data and accelerate computation. Using destination-wise inflow sequences derived from these records, we develop a systematic method to automatically identify anomalous surges that indicate emerging traffic hotspots and further the regions. To validate the framework, we conduct experiments using a one-year real-world dataset covering 2,728 interchange (IC) nodes within a highway network. Furthermore, we integrate and compare search data with actual traffic volumes for evaluation. The results reveal a strong correlation between search intensity and traffic flow, demonstrating that online search behavior serves as a reliable proxy for anticipating traffic dynamics. These findings suggest that route search records in cyberspace can be effectively utilized for proactive traffic monitoring and highlight the potential for early prediction of congestion patterns.
