No abstract

This research investigated the effect of ultraviolet absorber (UV‐531) on the properties of epoxy asphalt (EA) binders. EA binders were modified by adding UV‐531 absorber at contents of 0, 0.3, 0.6, 0.9, and 1.2 wt%, and composites were then subjected to ultraviolet (UV)‐based aging, including UV, oxygen, and heat treatment. Laboratory tests were conducted to assess UV absorption, viscosity, mechanical parameters, contact angle, microstructure, and chemical functional groups of absorber‐modified EA composites before and after aging. The results showed that the addition of UV‐531 resulted in enhanced UV absorption capacity, lower initial viscosity, and longer allowable application time for EA composites. The UV‐531 absorber has good compatibility with EA binders and does not weaken the performance of EA binders. From anti‐aging perspective, UV‐531 absorber effectively delayed the degradation of the physical, mechanical, and chemical structures of EA composites. The breakage of alkyl and ester groups of EA composites containing absorber was significantly reduced under aging conditions. Overall, the presence of UV‐531 retarded severe performance deterioration in EA composite. Considering aging improvement and economic cost, it is recommended to use a content of 0.9 wt% UV‐531 absorber as an anti‐aging agent for EA composites.

A comparison study is conducted between two simulation methods to estimate conflicts between road users. An improved Cellular Automata (CA) model is proposed to estimate occurrences and severity of traffic conflicts (both vehicle-vehicle and vehicle-pedestrian) at signalized intersections. The proposed CA model is compared with a calibrated method of Surrogate Safety Assessment Model (SSAM) based on VISSIM. Simulated conflicts from both methods are compared with observed vehicle conflicts from automated vehicle tracking for both occurrences and severity. Simulation results show CA approach is able to replicate realistic conflicts. However, SSAM tends to over-estimate occurrences and under-estimate severity of rear-end and lane-changing conflicts. SSAM is also found to over-estimate the severity of crossing conflicts. Furthermore, the proposed CA model is able to estimate conflicts between vehicles and pedestrians.

As a cutting-edge strategy to reduce travel delay and fuel consumption, platooning of connected and autonomous vehicles (CAVs) at signal-free intersections has become increasingly popular in academia. However, when determining optimal platoon size, few studies have attempted to comprehensively consider the relations between the size of a CAV platoon and traffic conditions around an intersection. To this end, this study develops an adaptive platoonbased autonomous intersection control model, named INTEL-PLT, which adopts deep reinforcement learning technique to realize the optimization of multiple dynamic objectives (e.g., efficiency, fairness, and energy saving). The framework of INTEL-PLT has a two-level structure: The first level employs a reservationbased policy integrated with a nonconflicting lane selection mechanism to determine the lanes' releasing priorities; and the second level uses a deep Qnetwork algorithm to identify the optimal platoon size based on real-time traffic conditions (e.g., traffic density, vehicle movement, etc.) of an intersection. The model is validated and examined on the simulator Simulation of Urban Mobility. It is found that the proposed model exhibits superior performances on both travel efficiency and fuel conservation as compared with state-of-the-art methods in three typical traffic conditions. Moreover, several in-depth insights learned from the simulations are provided in this paper, which could better explain the relation between platoon size and traffic condition.

No abstract