Traffic Performance Score: Measuring Urban Mobility and Online Predicting of Near-Term Traffic, like Weather Forecasting

• Published in: Transportation research record 2024-08, Vol.2678 (8), p.856-872
• Main Authors: Cui, Zhiyong, Tsai, Meng-Ju, Zhu, Meixin, Yang, Hao, Liu, Chenxi, Yin, Shuyi, Wang, Yinhai
• Format: Article
• Language: English
• ISSN: 0361-1981; 2169-4052
• DOI: 10.1177/03611981231222232

Measuring traffic performance is critical for public agencies which manage traffic and individuals who. This is the topic which the authors attempt to emphasize. One potential challenge for traffic prediction tasks is that short-term-incident-induced traffic pattern changes cannot be timely detected and the deployed model cannot adapt to the new traffic pattern. As for encountering long-term incidents, such as during COVID-19, traffic patterns are gradually changing, and the prediction model also needs to be periodically updated to avoid the so-called out-of-distribution problem. Therefore, the online training and predicting mechanisms can facilitate model updates, deployment of traffic prediction applications, and the planning of trips, especially when special events happen, such as the long-lasting COVID-19 pandemic. However, most existing traffic performance metrics narrowly focus on one aspect of the impacts but not comprehensive changes to the network. Further, during the pandemic, urban traffic patterns and travelers’ trip planning were dramatically affected and, thus, network-wide online traffic prediction became an urgent but more complicated task. To overcome such challenges, this study proposes a traffic performance score (TPS) incorporating multiple parameters for measuring both urban and freeway network-wide traffic performance. The TPS is compared with other metrics to show its superiority. To solve the challenging network-wide online traffic prediction task, this study also proposes an online training and updating strategy to predict network-wide traffic performance. Experimental results indicate that the proposed model with the online learning strategy outperforms existing methods in prediction accuracy and learning efficiency. In addition, the TPS measurement and its related online prediction functions are implemented on a publicly accessible platform and applied in real practice, which is another contribution of this work.