Although there have been assorted car-following (CF) models for connected vehicles (CVs), studying their impacts in mixed traffic flow of human-driven vehicles (HVs) and CVs remains a challenge. Considering the multiple front vehicles’ optimal speed changes with memory, this study proposed a new CF model (MVCM model) implemented through vehicle-to-everything (V2X) technology in CVs environment in terms of OVCM (Optimal Velocity Changes with Driving Memory) model. The stability condition of MVCM model was derived through linear stability analysis. Then, the disturbance propagation of MVCM model was compared with that of both classical FVD (Full Velocity Difference) model and MHOVA (Multiple Headway Optimal Velocity and Acceleration) model. Finally, a case study was conducted in VISSIM to analyze the impact of CV (with MVCM model) rates on traffic characteristics, including the average speed, delay time and travel time. Results show that 1) considering more front vehicles’ optimal speed strengthens the stability of traffic flow and the optimal considered vehicle number is 4; 2) MVCM model shows better resistance to disturbance than FVD model; 3) and obtain the same stability with MHOVA model, but with fewer front vehicles considered (only 3); 4) larger rate of CVs leads to higher average speed, smaller average travel time and the delay time of CVs and HVs; and 5) CVs’ positive effects reaches stable when CV rate approaches 0.6.

Road geometric information and a digital model based on light detection and ranging (LiDAR) can perform accurate geometric inventories and three-dimensional (3D) descriptions for as-built roads and infrastructures. However, unorganized point clouds and complex road scenarios would reduce the accuracy of geometric information extraction and digital modeling. There is a standardization need for information extraction and 3D model construction that integrates point cloud processing and digital modeling. This paper develops a framework from semantic segmentation to geometric information extraction and digital modeling based on LiDAR data. A semantic segmentation network is improved for the purpose of dividing the road surface and infrastructure. The road boundary and centerline are extracted by the alpha-shape and Voronoi diagram methods based on the semantic segmentation results. The road geometric information is obtained by a coordinate transformation matrix and the least square method. Subsequently, adaptive road components are constructed using Revit software. Thereafter, the road route, road entity model, and various infrastructure components are generated by the extracted geometric information through Dynamo and Revit software. Finally, a detailed digital model of the road scenario is developed. The Toronto-3D and Semantic3D datasets are utilized for analysis through training and testing. The overall accuracy (OA) of the proposed net for the two datasets is 95.3 and 95.0%, whereas the IoU of segmented road surfaces is 95.7 and 97.9%. This indicates that the proposed net could accomplish superior performance for semantic segmentation of point clouds. The mean absolute errors between the extracted and manually measured geometric information are marginal. This demonstrates the effectiveness and accuracy of the proposed extraction methods. Thus, the proposed framework could provide a reference for accurate extraction and modeling from LiDAR data.

Roadside monitoring Lidars (RMLs) will be a crucial part of the future intelligent transportation system. Current approaches for optimizing RMLs’ placement at intersections work in hypothetical environments which do not well reflect real-world situations. This article proposes a new virtual method (VM) for optimizing the deployment of RMLs at as-built intersections. The proposed VM operates in a virtual environment where both static background and dynamic agents are modeled by dense point clouds. The agents are driven by real-world motion data. Using RMLs’ parameters as inputs, a coarse-to-fine subsampling approach is developed to generate laser scans in the virtual world. An objective function is then defined by comparing the agents’ points in the generated laser scan sequences against their original models. Bayesian optimization is applied to maximize the objective function by setting the RMLs’ positions and poses as decision variables. Besides, batch processing strategy and parallel computing are used to accelerate the optimization process. The effectiveness of the proposed VM is demonstrated in a case study. The VM shall help road administrators make decisions on RMLs’ deployment at as-built intersections.

Icy pavement is one of the primary causes affecting driving safety in winter, and deicing asphalt mixture could effectively resist pavement icing. This study evaluated the effect of a slow-release deicing agent on pavement performance and ice-melting characteristics of the asphalt mixture. The asphalt mixture containing four different contents (0%, 30%, 50%, and 70% using the internal mixing method) by replacing mineral filler was designed. Pavement performance tests were used to investigate the effect of the deicing agent on the high-temperature stability, cracking resistance, and water stability of the asphalt mixture. Qualitative and quantitative tests were designed to compare the ice-melting characteristics and predict the ice-melting durability with different replacement amounts. The experimental results show that the high-temperature stability, low-temperature cracking resistance, and water stability of the asphalt mixture decrease with the increase inthe deicing agent. The mineral filler with a content of 50% deicing agent will enhance the stability of the mixture in the short term. Deicing asphalt mixture can significantly improve the ice and snow melting ability of the pavement, and the asphalt mixture with a content of 50% deicing agent will reduce the interface adhesion between mixture and ice by more than 55%. The slow-release deicing asphalt mixture can reach the maximum release concentration within two hours under rain and snow. The recommended replacement amount of slow-release deicing agent is 50%, and the predicted durability of deicing asphalt mixture is 5–8 years.