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1. Rhythmic Control of Automated Traffic—Part II: Grid Network Rhythm and Online Routing

   https://doi.org/10.1287/trsc.2021.1061  

   Lin, Xi ; Li, Meng ; Shen, Zuo-Jun Max ; Yin, Yafeng ; He, Fang ( September 2021 , Transportation Science)

   Connected and automated vehicle (CAV) technology is providing urban transportation managers tremendous opportunities for better operation of urban mobility systems. However, there are significant challenges in real-time implementation as the computational time of the corresponding operations optimization model increases exponentially with increasing vehicle numbers. Following the companion paper (Chen et al. 2021), which proposes a novel automated traffic control scheme for isolated intersections, this study proposes a network-level, real-time traffic control framework for CAVs on grid networks. The proposed framework integrates a rhythmic control method with an online routing algorithm to realize collision-free control of all CAVs on a network and achieve superior performance in average vehicle delay, network traffic throughput, and computational scalability. Specifically, we construct a preset network rhythm that all CAVs can follow to move on the network and avoid collisions at all intersections. Based on the network rhythm, we then formulate online routing for the CAVs as a mixed integer linear program, which optimizes the entry times of CAVs at all entrances of the network and their time–space routings in real time. We provide a sufficient condition that the linear programming relaxation of the online routing model yields an optimal integer solution. Extensive numerical tests are conducted to show the performance of the proposed operations management framework under various scenarios. It is illustrated that the framework is capable of achieving negligible delays and increased network throughput. Furthermore, the computational time results are also promising. The CPU time for solving a collision-free control optimization problem with 2,000 vehicles is only 0.3 second on an ordinary personal computer. 

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2. Advanced Air Mobility Operation and Infrastructure for Sustainable Connected eVTOL Vehicle

   https://doi.org/10.3390/drones7050319  

   Al-Rubaye, Saba ; Tsourdos, Antonios ; Namuduri, Kamesh ( May 2023 , Drones)

   Advanced air mobility (AAM) is an emerging sector in aviation aiming to offer secure, efficient, and eco-friendly transportation utilizing electric vertical takeoff and landing (eVTOL) aircraft. These vehicles are designed for short-haul flights, transporting passengers and cargo between urban centers, suburbs, and remote areas. As the number of flights is expected to rise significantly in congested metropolitan areas, there is a need for a digital ecosystem to support the AAM platform. This ecosystem requires seamless integration of air traffic management systems, ground control systems, and communication networks, enabling effective communication between AAM vehicles and ground systems to ensure safe and efficient operations. Consequently, the aviation industry is seeking to develop a new aerospace framework that promotes shared aerospace practices, ensuring the safety, sustainability, and efficiency of air traffic operations. However, the lack of adequate wireless coverage in congested cities and disconnected rural communities poses challenges for large-scale AAM deployments. In the immediate recovery phase, incorporating AAM with new air-to-ground connectivity presents difficulties such as overwhelming the terrestrial network with data requests, maintaining link reliability, and managing handover occurrences. Furthermore, managing eVTOL traffic in urban areas with congested airspace necessitates high levels of connectivity to support air routing information for eVTOL vehicles. This paper introduces a novel concept addressing future flight challenges and proposes a framework for integrating operations, infrastructure, connectivity, and ecosystems in future air mobility. Specifically, it includes a performance analysis to illustrate the impact of extensive AAM vehicle mobility on ground base station network infrastructure in urban environments. This work aims to pave the way for future air mobility by introducing a new vision for backbone infrastructure that supports safe and sustainable aviation through advanced communication technology.

    

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3. On the Elliptic Curve Cryptography for Privacy-Aware Secure ACO-AODV Routing in Intent-Based Internet of Vehicles for Smart Cities

   https://doi.org/10.1109/TITS.2020.3008361  

   Safavat, Sunitha ; Rawat, Danda B. ( June 2020 , IEEE Transactions on Intelligent Transportation Systems)

   null (Ed.)

   Internet of Vehicles (IoV) in 5G is regarded as a backbone for intelligent transportation system in smart city, where vehicles are expected to communicate with drivers, with road-side wireless infrastructure, with other vehicles, with traffic signals and different city infrastructure using vehicle-to-vehicle (V2V) and/or vehicle-to-infrastructure (V2I) communications. In IoV, the network topology changes based on drivers' destination, intent or vehicles' movements and road structure on which the vehicles travel. In IoV, vehicles are assumed to be equipped with computing devices to process data, storage devices to store data and communication devices to communicate with other vehicles or with roadside infrastructure (RSI). It is vital to authenticate data in IoV to make sure that legitimate data is being propagated in IoV. Thus, security stands as a vital factor in IoV. The existing literature contains some limitations for robust security in IoV such as high delay introduced by security algorithms, security without privacy, unreliable security and reduced overall communication efficiency. To address these issues, this paper proposes the Elliptic Curve Cryptography (ECC) based Ant Colony Optimization Ad hoc On-demand Distance Vector (ACO-AODV) routing protocol which avoids suspicious vehicles during message dissemination in IoV. Specifically, our proposed protocol comprises three components: i) certificate authority (CA) which maps vehicle's publicly available info such as number plates with cryptographic keys using ECC; ii) malicious vehicle (MV) detection algorithm which works based on trust level calculated using status message interactions; and iii) secure optimal path selection in an adaptive manner based on the intent of communications using ACO-AODV that avoids malicious vehicles. Experimental results illustrate that the proposed approach provides better results than the existing approaches. 

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4. CatCharger: Deploying In-motion Wireless Chargers in a Metropolitan Road Network via Categorization and Clustering of Vehicle Traffic

   https://doi.org/10.1109/JIOT.2021.3121756  

   Yan, Li ; Shen, Haiying ; Zhao, Juanjuan ; Xu, Chengzhong ; Luo, Feng ; Qiu, Chenxi ; Zhang, Zhe ; Mahmud, Shohaib ( October 2021 , IEEE Internet of Things Journal)

   In metropolitan areas with heavy transit demands, electric vehicles (EVs) are expected to be continuously driving without recharging downtime. Wireless Power Transfer (WPT) provides a promising solution for in-motion EV charging. Nevertheless, previous works are not directly applicable for the deployment of in-motion wireless chargers due to their different charging characteristics. The challenge of deploying in-motion wireless chargers to support the continuous driving of EVs in a metropolitan road network with the minimum cost remains unsolved. We propose CatCharger to tackle this challenge. By analyzing a metropolitan-scale dataset, we found that traffic attributes like vehicle passing speed, daily visit frequency at intersections (i.e., landmarks) and their variances are diverse, and these attributes are critical to in-motion wireless charging performance. Driven by these observations, we first group landmarks with similar attribute values using the entropy minimization clustering method, and select candidate landmarks from the groups with suitable attribute values. Then, we use the Kernel Density Estimator (KDE) to deduce the expected vehicle residual energy at each candidate landmark and consider EV drivers’ routing choice behavior in charger deployment. Finally, we determine the deployment locations by formulating and solving a multi-objective optimization problem, which maximizes vehicle traffic flow at charger deployment positions while guaranteeing the continuous driving of EVs at each landmark. Trace-driven experiments demonstrate that CatCharger increases the ratio of driving EVs at the end of a day by 12.5% under the same deployment cost. 

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5. 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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