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Connected and automated vehicles (CAVs) are being developed and designed to operate on existing roads. Their safe and efficient operation during roadworks, where traffic management measures are often introduced, is crucial. Two alternative measures are commonly applied during roadworks on motorways: (i) closing one or multiple lanes (ii) narrowing one or all lanes. The former can cause delays and increased emissions, while the latter can pose safety risks. This study uses a VISSIM‐based traffic microsimulation to compare the effectiveness of these two strategies on traffic efficiency and safety, considering various market penetration rates (MPR) of CAVs. The model was calibrated and validated with the data collected from M1 motorway in the United Kingdom. Results show that average delays per vehicle‐kilometre‐travelled decreased from 102.7 to 2.5 s (with lane closure) and 23.6 to 0.6 s (with narrow lanes) with 0% and 100% CAV MPR, respectively. Moreover, safety in narrow lanes improved by 4.8 times compared to 1.5 times improvement in lane closure with a 100% CAV MPR; indicating that narrow lanes would result in better safety performance. These findings could assist transport authorities in designing temporary traffic management measure that results in better CAV performance when navigating through roadworks. [ABSTRACT FROM AUTHOR]

Lane width is crucial for traffic safety management. Previous studies have shown that inappropriate width will lead to an increase in accident probability and unsafe driving behaviors. However, there have been limited studies on determination method of special lane width for different models, especially for passenger cars. To tackle this problem, the lateral driving behavior characteristics of the expressway need to be clarified. This work aims to obtain the appropriate lane width according to the vehicles' width and lateral position characteristics. In this study, the lane lateral residual width (the distance between the vehicle body's contour and the lane marking on the same side) and the lateral safety margin are extracted as characterization indices of vehicle lateral characteristics from natural driving trajectory data of expressways. The effect of vehicle type, driving speed, and lane position on the expressway's trajectory behavior is investigated. The results show that the utilization rate of lane width is higher in the outer lane compared to the inner lane. As driving speed increases, vehicles in the inner and middle lanes exhibit shy away effect, moving away from obstacles. Substantial variations exist in the lateral width of lanes among different vehicle types. When driving in the same lane, passenger cars' lane lateral residual width that is 0.4–0.5 m wider than that of heavy vehicles. The recommended lane width for the safe operation of trucks on expressways is 3.75 m. After a comprehensive analysis of vehicle width, trajectory oscillation, and lateral safety margin, this study proposes a minimum lane width of 3.25 m and a recommended general width of 3.5 m for a passenger car‐only expressway. This study holds great significance in enhancing road safety and provides a valuable theoretical foundation for the design of lane width in a passenger car‐only expressway. [ABSTRACT FROM AUTHOR]

Precise real-time traffic flow prediction is crucial for route guidance and traffic fine control. With the development of autonomous driving, the mixed traffic flow state composed of Connected Automated Vehicles (CAVs) and Human-driven Vehicles (HVs) provides new insight into traffic flow prediction. In this paper, we innovatively consider the interaction between heterogeneous traffic flow as well as the mutual effect of traffic flow on different lanes and develop a hybrid model based on deep learning for urban expressway lane-level mixed traffic flow prediction, including three modules. First, the feature selection module is applied to screen the features with a high spatio-temporal correlation to the prediction object and construct the input matrix. Then, it is input to the feature attention module to quantify the importance of the input features on the prediction object, thereby assigning attention weights. Finally, the spatio-temporal information fusion module is adopted to capture the global spatio-temporal dynamics of traffic flow at horizontal and vertical spatial scales, as well as learn the complex coupling characteristics of heterogeneous traffic flow, thus obtaining predictions. An urban expressway mixed traffic flow simulation environment is built to collect experimental datasets for prediction accuracy evaluation. The results indicate that the proposed model outperforms the benchmarks in single-step and multi-step mixed traffic flow predictions on each lane. Furthermore, the proposed model shows the best performance and strong robustness under different penetration rates of connected automated vehicles. [ABSTRACT FROM AUTHOR]