A connected autonomous vehicle (CAV) network can be defined as a set of connected vehicles including CAVs that operate on a specific spatial scope that may be a road network, corridor, or segment. The spatial scope constitutes an environment where traffic information is shared and instructions are issued for controlling the CAVs movements. Within such a spatial scope, high-level cooperation among CAVs fostered by joint planning and control of their movements can greatly enhance the safety and mobility performance of their operations. Unfortunately, the highly combinatory and volatile nature of CAV networks due to the dynamic number of agents (vehicles) and the fast-growing joint action space associated with multi-agent driving tasks pose difficultly in achieving cooperative control. The problem is NP-hard and cannot be efficiently resolved using rule-based control techniques. Also, there is a great deal of information in the literature regarding sensing technologies and control logic in CAV operations but relatively little information on the integration of information from collaborative sensing and connectivity sources. Therefore, we present a novel deep reinforcement learning-based algorithm that combines graphic convolution neural network with deep Q-network to form an innovative graphic convolution Q network that serves as the information fusion module and decision processor. In this study, the spatial scope we consider for the CAV network is a multi-lane road corridor. We demonstrate the proposed control algorithm using the application context of freeway lane-changing at the approaches to an exit ramp. For purposes of comparison, the proposed model is evaluated vis-à-vis traditional rule-based and long short-term memory-based fusion models. The results suggest that the proposed model is capable of aggregating information received from sensing and connectivity sources and prescribing efficient operative lane-change decisions for multiple CAVs, in a manner that enhances safety and mobility. That way, the operational intentions of individual CAVs can be fulfilled even in partially observed and highly dynamic mixed traffic streams. The paper presents experimental evidence to demonstrate that the proposed algorithm can significantly enhance CAV operations. The proposed algorithm can be deployed at roadside units or cloud platforms or other centralized control facilities.

Highway agencies continue to show interest in measuring pavement condition effects on safety. This paper estimates univariate negative binomial (UNB) and random-parameters seemingly-unrelated negative binomial (RPSUNB) regression models. The latter account for unobserved heterogeneity and correlation in crash frequencies across the crash severity levels. The analysis was carried out for two-lane and multi-lane highways, and the results suggest that at the latter, the pavement condition generally has a far more significant safety impact compared to the former. This could be due to risk compensation effects where drivers offset the safety hazard associated with inherently less safe situations by driving more carefully. It was determined that compared to UNB, RPSUNB models have superior efficacy in addressing seemingly unrelated correlations among the crash severity levels.