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1. A Unified Framework for Data-Driven Optimal Control of Connected Vehicles in Mixed Traffic

   https://doi.org/10.1109/TIV.2023.3287131  

   Liu, Tong ; Cui, Leilei ; Pang, Bo ; Jiang, Zhong-Ping ( August 2023 , IEEE Transactions on Intelligent Vehicles)

   This paper presents a unified approach to the problem of learning-based optimal control of connected human-driven and autonomous vehicles in mixed-traffic environments including both the freeway and ring road settings. The stabilizability of a string of connected vehicles including multiple autonomous vehicles (AVs) and heterogeneous human-driven vehicles (HDVs) is studied by a model reduction technique and the Popov-Belevitch-Hautus (PBH) test. For this problem setup, a linear quadratic regulator (LQR) problem is formulated and a solution based on adaptive dynamic programming (ADP) techniques is proposed without a priori knowledge on model parameters. To start the learning process, an initial stabilizing control law is obtained using the small-gain theorem for the ring road case. It is shown that the obtained stabilizing control law can achieve general Lp string stability under appropriate conditions. Besides, to minimize the impact of external disturbance, a linear quadratic zero-sum game is introduced and solved by an iterative learning-based algorithm. Finally, the simulation results verify the theoretical analysis and the proposed methods achieve desirable performance for control of a mixed-vehicular network. 

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2. High-Fidelity Teleoperated Scaled Vehicles for Research and Development of Intelligent Transportation Technologies

   https://doi.org/10.1115/DSCC2020-3173  

   Wang, Cong ; Wang, Bohan ; Zhao, Leidi ; Maranon, Carlos ; Goswamy, Nishaant ; Lee, Jo Young ; Wang, Guiling ( October 2020 , ASME 2020 Dynamic Systems and Control Conference)

   null (Ed.)

   This paper presents a systematic design of high-fidelity tele-operated scaled vehicles to be used as a research and development platform for intelligent transportation technologies. Compared to computer simulation and full-scale physical tests, the use of high-fidelity scaled setups provides advantages on testing time and financial effectiveness. The physical design of the vehicles features a 1:14 scale with realistic appearance licensed by car manufacturers. Customized steering system and propulsion control provide high-fidelity maneuver characteristics. Remote control is deployed using a target-host structure over WiFi and can provide seamless switching between human driving and autonomous/assisted driving on the host side. Several possible solutions for real-time panoramic vision feedback are explored, with a tri-camera design based on parallel acquisition interfaces adopted. An adaptive color compression technique is developed to shorten the video streaming latency. A customized miniature LIDAR system is introduced to provide an ultra-small package for on-board installation. As a solution balancing between test fidelity and costs, the proposed scaled vehicles are especially suitable for validation tests during the early stage of research and development. With a long-term goal of developing a multi-vehicle traffic network test platform, ongoing and future work on the construction of scaled buildings and road systems is also discussed. 

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3. Centrality-based lane interventions in road networks for improved level of service: the case of downtown Boise, Idaho

   https://doi.org/10.1007/s41109-023-00532-z  

   Ahmed, Md Ashraf ; Kays, H. M. ; Sadri, Arif Mohaimin ( December 2023 , Applied Network Science)

   Recent advancements in network science showed that the topological credentials of the elements (i.e., links) in a network carry important implications. Likewise, roadway segments (i.e., links) in a road network should be assessed based on their network position along with traffic conditions at a given geographic scale. The goal of this study is to present a framework that can identify and select critical links in a road network based on their topological importance such as centrality, and the effects of systematic interventions conducted on such links in improving overall system performance (vehicle delay, travel time) to provide an adequate level of service (LOS). A real-world road network (Boise downtown) is investigated by applying lane interventions on roadways experiencing high congestion. Microscopic traffic simulation and analyses are conducted to estimate the traffic flow parameters hence the performance of the road segments. The findings of this study show that interventions applied to critical and congested road segments improve the serviceability from LOS F to LOS E as well as from LOS D to LOS C. Besides, reduced travel time and vehicular delay (after applying intervention on critical components) are also observed for high demand OD pairs of the road network. As such the proposed framework has the potential to incorporate the topological credentials with traffic flow parameters and improve the performance of the road network. This systematic approach will help traffic managers and practitioners to develop strategies that enhance road network performance.

    

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4. Identification of Spatiotemporal Relationships in Travel Speeds along Individual Roadways using Probe Vehicle Data

   https://doi.org/10.1177/0361198119842823  

   Kouhi Esfahani, Reihaneh ; Gayah, Vikash V. ( November 2019 , Transportation Research Record: Journal of the Transportation Research Board)

   The existence of spatiotemporal correlations in traffic behavior on links in a transportation network is potentially very useful. However, traffic metrics are often strongly correlated simply because of natural variations in travel demand patterns and these temporal trends might obstruct more meaningful relationships caused by the physics of traffic. To overcome this challenge, the present paper proposes a non-parametric, moving average detrending method that can be used to remove these background trends, even during non-stationary periods in which traffic states are changing with time. Cross-correlations performed on the detrended data are then used to identify more meaningful trends. The proposed method can also incorporate temporal lags in correlations between individual links, which accounts for the time it takes for information to travel between them. Links that exhibit strong correlations after detrending can then be grouped into communities which behave together using graph theory methods, and this community structure can be leveraged to improve prediction of link performance when information is missing. The proposed methodology is applied to a case study network using real-time link travel speeds obtained from probe vehicles. The results reveal that the 40 links in the network can be grouped into between eight and 12 communities, depending on the day of the week. This suggests that only a handful of links may need to be monitored to estimate travel speeds across the entire network. Furthermore, the significant overlap in the community structure across these days reveals that the network structure plays a large role in spatiotemporal correlations in link travel speeds in a network. 

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5. Resilience of Urban Street Network Configurations under Low Demands

   https://doi.org/10.1177/0361198120933269  

   Yu, Zhengyao ; Gayah, Vikash V. ( September 2020 , Transportation Research Record: Journal of the Transportation Research Board)

   null (Ed.)

   Urban street networks are subject to a variety of random disruptions. The impact of movement restrictions (e.g., one-way or left-turn restrictions) on the ability of a network to overcome these disruptions—that is, its resilience—has not been thoroughly studied. To address this gap, this paper investigates the resilience of one-way and two-way square grid street networks with and without left turns under light traffic conditions. Networks are studied using a simplified routing algorithm that can be examined analytically and a microsimulation that describes detailed vehicle dynamics. In the simplified method, routing choices are enumerated for all possible origin–destination (OD) combinations to identify how the removal of a link affects operations, both when knowledge of the disruption is and is not available at the vehicle’s origin. Disruptions on two-way networks that allow left turns tend to have little impact on travel distances because of the availability of multiple shortest paths between OD pairs and the flexibility in route modification. Two-way networks that restrict left turns at intersections only have a single shortest-distance path between any OD pair and thus experience larger increases in travel distance, even when the disruption is known ahead of time. One-way networks sometimes have multiple shortest-distance routes and thus travel distances increase less than two-way network without left turns when links are disrupted. These results reveal a clear tradeoff between improved efficiency and reduced resilience for networks that have movement restrictions, and can be used as a basis to study network resilience under more congested scenarios and in more realistic network structures. 

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