Vision-based map-matching with HD map for high precision vehicle localization has gained great attention for its low-cost and ease of deployment. However, its localization performance is still unsatisfactory in accuracy and robustness in numerous real applications due to the sparsity and noise of the perceived HD map landmarks. This article proposes the tightly-coupled monocular map-matching localization algorithm (TM³Loc) for monocular-based vehicle localization. TM³Loc introduces semantic chamfer matching (SCM) to model monocular map-matching problem and combines visual features with SCM in a tightly-coupled manner. By applying the sliding window-based optimization technique, the historical visual features and HD map constraints are also introduced, such that the vehicle poses are estimated with an abundance of visual features and multi-frame HD map landmark features, rather than with single-frame HD map observations in previous works. Experiments are conducted on large scale dataset of 15 km long in total. The results show that TM³Loc is able to achieve high precision localization performance using a low-cost monocular camera, largely exceeding the performance of the previous state-of-the-art methods, thereby promoting the development of autonomous driving.

This study proposes an AI-powered digital twin (DT) platform designed to support real-time traffic risk prediction, decision-making, and sustainable mobility in smart cities. The system integrates multi-source data—including static infrastructure maps, historical traffic records, telematics data, and camera feeds—into a unified cyber–physical platform. AI models are employed for data fusion, anomaly detection, and predictive analytics. In particular, the platform incorporates telematics–video fusion for enhanced trajectory accuracy and LiDAR–camera fusion for high-definition work-zone mapping. These capabilities support dynamic safety heatmaps, congestion forecasts, and scenario-based decision support. A pilot deployment on Madison’s Flex Lane corridor demonstrates real-time data processing, traffic incident reconstruction, crash-risk forecasting, and eco-driving control using a validated Vehicle-in-the-Loop setup. The modular API design enables integration with existing Advanced Traffic Management Systems (ATMSs) and supports scalable implementation. By combining predictive analytics with real-world deployment, this research offers a practical approach to improving urban traffic safety, resilience, and sustainability.

As cities expand and long-distance travel demand increases, expressways are usually constructed to enhance regional connectivity. In the mixed road networks of urban roads and expressways, coordinating the distinct traffic dynamics of the two networks is a challenge. To address this challenge, we propose a cooperative flow control method for large-scale mixed networks. First, we develop an integrated traffic model that models urban regions using the macroscopic fundamental diagram (MFD) and expressways using the multi-class cell transmission model (CTM), achieving route tracking of vehicles throughout the entire mixed network. Next, a route choice model is developed to allocate new traffic demands within the mixed network. To coordinate traffic flow, a perimeter control (PC) is conducted to manage transfer flows between region boundaries, ramp metering (RM) to regulate flows entering expressways from urban regions, and variable speed limit (VSL) to control mainline speeds on expressways. We establish this cooperative flow control method within a model predictive control (MPC) framework. Case studies show that, based on the implementation of PC, the combined application of RM and VSL to the expressway system is more effective in reducing congestion and improving traffic efficiency in the mixed network than using RM and VSL independently.