Traffic congestion is a pertinent issue on highways, with severe consequences on environment, economy, and quality of life. Variable speed limit control can help avoid traffic jams before congestion forms, as vehicles upstream are required to decelerate at times to stop emerging congestion from propagating and expanding. This work proposes a fully decentralized, model-free, and infrastructure-free approach to variable speed limit control—V2VSL—that employs connected vehicles as communication infrastructure, as moving sensors, and as actuators. Dedicated short range communication, consensus algorithm and gossip algorithm protocols, and a Bellman controller are components of this approach. At the example of three highway bottleneck scenarios, performance is assessed by traffic micro-simulations, that show the approach is robust to gaps between platoons and capable of recovering from periods of disconnection. The proposed method achieves significant improvements in traffic states, with up to 15% higher speeds, 5% lower density, and 8% higher flows. These traffic improvements become significant at a compliance rate as low as 25%, making the method potentially viable in near-term mixed traffic environments with partial CAV penetration. V2VSL achieves efficiency gains comparable to centralized VSL systems, but without requiring roadside infrastructure, detailed traffic models, or centralized communication. An open-source implementation and computational results are provided as SUMO simulation with Python on GitHub: https://github.com/DerKevinRiehl/decentralized_vsl/ . 

Over the past few decades, efforts of road traffic management and practice have predominantly focused on maximising system efficiency and mitigating congestion from a system perspective. This efficiency-driven approach implies the equal treatment of all vehicles, which often overlooks individual user experiences, broader social impacts, the fact that users are heterogeneous in their urgency and that they experience different costs when being delayed. Even though they are the major bottleneck for traffic in cities, no dedicated instrument enables prioritisation of individual drivers at intersections. The Priority Pass is a reservation-based, economic controller that expedites entitled vehicles at signalised intersections, without causing arbitrary delays for non-entitled vehicles and without affecting transportation efficiency de trop. Particularly applicable to large, congested cities with rich sensor infrastructure, the prioritisation of vulnerable road users, emergency vehicles, commercial taxi and delivery drivers, or urgent individuals, this approach can enhance road safety, and achieve social, environmental, and economic goals. A case study of Manhattan demonstrates the feasibility of individual prioritisation (up to 40% delay reduction), and quantifies the potential of the Priority Pass to gain social welfare benefits for the people. A market for prioritisation could generate up to $ 1 million in daily revenue for Manhattan, and equitably allocate delay reductions to those in need. The findings provide a foundation for integrating user-centric prioritisation mechanisms into emerging smart city traffic management systems, supporting data-driven policymaking and equitable mobility planning.

Advancements in computer science, artificial intelligence, and control systems have catalyzed the emergence of cybernetic societies, where algorithms play a pivotal role in decision-making processes shaping nearly every aspect of human life. Automated decision-making for resource allocation has expanded into industry, government processes, critical infrastructures, and even determines the very fabric of social interactions and communication. While these systems promise greater efficiency and reduced corruption, misspecified cybernetic mechanisms harbor the threat for reinforcing inequities, discrimination, and even dystopian or totalitarian structures. Fairness thus becomes a crucial component in the design of cybernetic systems, to promote cooperation between selfish individuals, to achieve better outcomes at the system level, to confront public resistance, to gain trust and acceptance for rules and institutions, to perforate self-reinforcing cycles of poverty through social mobility, to incentivize motivation, contribution and satisfaction of people through inclusion, to increase social-cohesion in groups, and ultimately to improve life quality. Quantitative descriptions of fairness are crucial to reflect equity into algorithms, but only few works in the fairness literature offer such measures; the existing quantitative measures in the literature are either too application-specific, suffer from undesirable characteristics, or are not ideology-agnostic. This study proposes a quantitative, transactional, and distributive fairness framework based on an interdisciplinary foundation that supports the systematic design of socially-feasible decision-making systems. Moreover, it emphasizes the importance of fairness and transparency when designing algorithms for equitable, cybernetic societies, and establishes a connection between fairness literature and resource allocating systems.