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1. On the impacts of roadway hierarchy on the network Macroscopic Fundamental Diagram

   Xu, Guanhao; Gayah, Vikash V. (January 2021, 100th Annual Meeting of the Transportation Research Board)

   null (Ed.)

   Relationships between average network productivity and accumulation or density aggregated 2 across spatially compact regions of urban networks—so called network Macroscopic Fundamental 3 Diagrams (MFDs)—have recently been shown to exist. Various analytical methods have been put 4 forward to estimate a network’s MFD as a function of network properties, such as average block 5 lengths, signal timings, and traffic flow characteristics on links. However, real street networks are 6 not homogeneous—they generally have a hierarchical structure where some streets (e.g., arterials) 7 promote higher mobility than others (e.g., local roads). This paper provides an analytical method 8 to estimate the MFDs of hierarchical street networks by considering features that are specific to 9 hierarchical network structures. Since the performance of hierarchical networks is driven by how 10 vehicles are routed across the different street types, two routing conditions— user equilibrium and 11 system optimal routing—are considered in the analytical model. The proposed method is first 12 implemented to describe the MFD of a hierarchical one-way limited access linear corridor and 13 then extended to a more realistic hierarchical two-dimensional grid network. For both cases, it is 14 shown that the MFD of a hierarchical network may no longer be unimodal or concave as 15 traditionally assumed in most MFD-based modeling frameworks. These findings are verified using 16 simulations of hierarchical corridors. Finally, the proposed methodology is applied to demonstrate 17 how it can be used to make decisions related to the design of hierarchical street network structures. 

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2. Classification of Roadway Infrastructure and Collaborative Automated Driving System

   https://doi.org/10.4271/12-06-04-0026  

   Ran, Bin; Cheng, Yang; Li, Shen; Li, Hanchu; Parker, Steven (December 2023, SAE International Journal of Connected and Automated Vehicles)

   The latest developments in vehicle-to-infrastructure (V2I) and vehicle-to-anything (V2X) technologies enable all the entities in the transportation system to communicate and collaborate to optimize transportation safety, mobility, and equity at the system level. On the other hand, the community of researchers and developers is becoming aware of the critical role of roadway infrastructure in realizing automated driving. In particular, intelligent infrastructure systems, which leverage modern sensors, artificial intelligence, and communication capabilities, can provide critical information and control support to connected and/or automated vehicles to fulfill functions that are infeasible for automated vehicles alone due to technical or cost considerations. However, there is limited research on formulating and standardizing the intelligence levels of road infrastructure to facilitate the development, as the SAE automated driving levels have done for automated vehicles. This article proposes a five-level intelligence definition for intelligent roadway infrastructure, namely, connected and automated highway (CAH). The CAH is a subsystem of the more extensive collaborative automated driving system (CADS), along with the connected automated vehicle (CAV) subsystem. Leveraging the intelligence definition of CAH, the intelligence definition for the CADS is also defined. Examples of how the CAH at different levels operates with the CAV in the CADS are also introduced to demonstrate the dynamic allocation of various automated driving tasks between different entities in the CADS. 

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3. Drivers’ Attentional Instability on a Winding Roadway

   https://doi.org/10.1109/THMS.2019.2906612  

   Jagacinski, Richard J.; Rizzi, Emanuele; Bloom, Benjamin J.; Turkkan, O. Anil; Morrison, Tyler N.; Su, Haijun; Wang, Junmin (December 2019, IEEE Transactions on Human-Machine Systems)
4. Automated Detection of Roadway Obstructions Using UAVs and Reference Images

   https://doi.org/10.1061/9780784485262.105  

   Opanasopit, Chonnapat; Louis, Joseph (March 2024, American Society of Civil Engineers)

   Natural disasters such as wildfires, landslides, and earthquakes result in obstructions on roads due to fallen trees, landslides, and rocks. Such obstructions can cause significant mobility problems for both evacuees and first responders, especially in the immediate aftermath of disasters. Unmanned Aerial Vehicles (UAVs) provide an opportunity to perform rapid and remote reconnaissance of planned routes and thus provide decision-makers with information relating to a route’s feasibility. However, detecting obstacles on roads manually is a laborious and error-prone task, especially when attention is diverted to needs that are more urgent during disaster scenarios. This paper thus proposes a computer vision and machine-learning framework to detect obstacles on a road automatically to ensure its possibility in the aftermath of disasters. The framework implements the YOLO algorithm to detect and segment roads on images from UAVs and reference images from publicly available datasets. The images retrieved from UAVs and reference images are segmented and counted pixels of the roadway for comparison of the difference in pixels to identify the obstruction on the road. In addition, the method is proposed to automatically detect obstructions found in the region of interest (ROI) only on a roadway with images and videos from UAVs. Preliminary results from test runs are presented along with the future steps for implementing a real-time UAV-based road obstruction system. 

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5. Investment and financing of roadway digital infrastructure for automated driving

   https://doi.org/10.1016/j.trb.2024.103146  

   Ahmadian, Amir; Bahrami, Sina; Nourinejad, Mehdi; Yin, Yafeng (February 2025, Transportation Research Part B: Methodological)