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1. 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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2. 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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3. Re-Routing Strategy of Connected and Automated Vehicles Considering Coordination at Intersections

   Bang, H. ; Malikopoulos, A.A. ( May 2023 , 2023 American Control Conference)

   In this paper, we propose a re-routing strategy for connected and automated vehicles (CAVs), considering coordination and control of all the CAVs in the network. The objective for each CAV is to find the route that minimizes the total travel time of all CAVs. We coordinate CAVs at signal-free intersections to accurately predict the travel time for the routing problem. While it is possible to find a system-optimal solution by comparing all the possible combinations of the routes, this may impose a computational burden. Thus, we instead find a person-by-person optimal solution to reduce computational time while still deriving a better solution than selfish routing. We validate our framework through simulations in a grid network. 

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4. 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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5. Legal Framework for Rear-End Crashes in Mixed-Traffic Platooning: A Matrix Game Approach

   https://doi.org/10.3390/futuretransp3020025  

   Chen, Xu ; Di, Xuan ( June 2023 , Future Transportation)

   Autonomous vehicles (AV) hold great potential to increase road safety, reduce traffic congestion, and improve mobility systems. However, the deployment of AVs introduces new liability challenges when they are involved in car accidents. A new legal framework should be developed to tackle such a challenge. This paper proposes a legal framework, incorporating liability rules to rear-end crashes in mixed-traffic platoons with AVs and human-propelled vehicles (HV). We leverage a matrix game approach to understand interactions among players whose utility captures crash loss for drivers according to liability rules. We investigate how liability rules may impact the game equilibrium between vehicles and whether human drivers’ moral hazards arise if liability is not designed properly. We find that compared to the no-fault liability rule, contributory and comparative rules make road users have incentives to execute a smaller reaction time to improve road safety. There exists moral hazards for human drivers when risk-averse AV players are in the car platoon.

    

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