Urban air pollution from traffic poses serious public health risks. Pollution exposure can be minimised through traffic routing systems; these currently rely on detailed local environmental information, which is often difficult to collect or generalise within and across cities. Here, we introduce a new data-driven approach for ready application to different urban road networks by directly relating NO2 to traffic density in a time-dependent and weather-corrected manner. We demonstrate this application by comparing pollution-optimal routings, using our novel direct NO2/density approach, to the conventional traffic assignment minimising user travel time, in a case study of Sheffield, UK. There, we find user-optimal traffic flows result in 21% higher total NO2 concentrations than pollution-optimal routings, while saving only 9% in total travel time: an average of 0.3 min per road. Our generalisable framework offers a practical alternative to current emissions-based models for air-quality-aware traffic control and environmental zone planning.

Large Language Models zijn AI-systemen die menselijke taal begrijpen en zich er ook in kunnen uiten. Ze zijn de basis onder populaire applicaties als ChatGPT, Gemini en Copilot. Maar inmiddels is de technologie zó breed inzetbaar dat ze ook doordringt in de mobiliteitssector. Hoe werken de Large Language Models? Hoe kunnen ze van nut zijn in ons vakgebied? En wat zijn de mitsen en maren

Rerouting drivers from selfish route choices to system-optimal traffic patterns has the potential to improve the performance of existing infrastructure. Previous research has looked into assessing the potential of rerouting through the empirical price of anarchy, a measure of network efficiency. However, studies using real-world measurements have been limited by methodological accuracy and network size. Also, they have lacked understanding of the spatial distribution of benefits from rerouting and the relationship with marginal external cost road charges that can be used for implementation. In this article, we create an accurate data-driven traffic assignment model of England's Strategic Road Network. We use it to calculate the national price of anarchy, which is found to be almost 1 implying gains from rerouting at the national scale are minimal and smaller than in other studies. The results show the distribution of rerouting benefits varies strongly with different network zones and demand profiles. This did not match the distribution of marginal external cost charges. Some zones have noticeable benefits from rerouting although the overall network benefit is small, however, these zones do not coincide with where the largest road charges have to be applied for system-optimal rerouting. These results have implications for rerouting implementation.

Urban traffic congestion remains a critical challenge in modern cities, with traffic signal control systems often struggling to manage congestion during peak travel times. Perimeter control of a Protected Network (PN) has emerged as a potential solution to reducing gridlock in urban networks. This paper proposes a novel auction-based mechanism for green time allocation at signalized intersections, for effective perimeter control application. Utilising a Sealed Bid, Second Price auction framework, our approach combines real-time traffic monitoring with market-inspired mechanisms to regulate vehicle inflows into PN areas. Unlike existing methods that focus primarily on gated links, our system allocates budgets to individual traffic movements, providing greater flexibility in managing multi-directional flows. We evaluate the proposed mechanism using a test case intersection with a single controlled inflow, comparing it against a volume-based fixed-time approach. The results demonstrate that our auction-based method controls flows into the PN with improved accuracy, outperforming the volume-based approach in terms of inflow regulation, queue management and delays. The framework can be applied in real time to any generic intersection, offering a scalable solution for urban traffic management. This work bridges the gap between perimeter control and market-based intersection auctions, providing a pathway for further research on adaptive traffic management systems.

This paper presents a novel auction-based traffic signal control mechanism aimed at optimizing multimodal traffic flow at signalized intersections through \textcolor{red}{connected vehicles}. The proposed framework, which utilizes a second price sealed bid auction mechanism, allocates green time dynamically based on user bids, incorporating policy-oriented modal priority. This approach addresses the limitations of current signal control systems by providing a computationally fast and distributable method that considers the priority hierarchy of traffic modes, thereby enhancing the efficiency and equity of intersection management.

Key innovations include a dynamic bid distance determination method and a modified bidding scheme that prioritize certain traffic modes according to predefined policies. The effectiveness of these methods is demonstrated through a case study focusing on bicycle prioritization at a real-world intersection in Bordeaux, France.

Simulation results indicate significant improvements in service levels for prioritized modes without substantially increasing delays for other users. The methods' flexibility for adaptation to different intersection configurations and computational feasibility ensure their applicability to a wide range of intersection types and traffic conditions. Our findings suggest that the sealed bid second price auction mechanism can be a useful tool for policymakers aiming to implement multimodal traffic priorities, contributing to reduced travel delays and more effective control at intersections.

Are you using tools like ChatGPT in your daily life to help write an email or even draft a construction plan? Just ten years ago, these kinds of capabilities would have seemed unimaginable. Today, they’re becoming part of everyday life for ordinary people. Behind these powerful tools are technologies known as Large Language Models (LLMs)—AI systems that can understand and generate human-like text and now even create images and videos. But what exactly are LLMs? Could they help transform fields like transportation and traffic management? Can they really do everything, or are there still limitations? In this article, we’ll walk you through a general introduction to LLMs: what they are, how they work, and what opportunities—and challenges—they bring to the transportation sector.

Accurate static traffic assignment models are important tools for the assessment of strategic transportation policies. In this article we present a novel approach to partition road networks through network modularity to produce data-driven static traffic assignment models from loop detector data on large road systems. The use of partitioning allows the estimation of the key model input of Origin–Destination demand matrices from flow counts alone. Previous network tomography-based demand estimation techniques have been limited by the network size. The amount of partitioning changes the Origin–Destination estimation optimisation problems to different levels of computational difficulty. Different approaches to utilising the partitioning were tested, one which degenerated the road network to the scale of the partitions and others which left the network intact. Applied to a subnetwork of England’s Strategic Road Network and other test networks, our results for the degenerate case showed flow and travel time errors are reasonable with a small amount of degeneration. The results for the non-degenerate cases showed that similar errors in model prediction with lower computation requirements can be obtained when using large partitions compared with the non-partitioned case. This work could be used to improve the effectiveness of national road systems planning and infrastructure models.

The ability to build accurate traffic assignment models on large-scale major road networks is essential for effective infrastructure planning. Static traffic assignment models often utilize standard formulations of congestion functions which suffer from various inaccuracies. Conversely, newer approaches in the literature rely on inverse optimisation to provide enhanced accuracy but incur significantly heavy computational costs. The work in this article develops density-based congestion function fitting in order to compute traffic assignment patterns. Computational efficiency makes the method amenable to be used on real-world networks at national scale. The methodology is applied on the motorway network connecting the primary metropolitan areas in England using Motorway Incident Detection and Automatic Signalling system data. The results demonstrate that the use of density-based congestion functions provides significant improvement in terms of computational runtime in the order of 11,000 times (22 secs vs 68 hours). Correspondingly, prediction error from this method (3.9 to 6.9% for time prediction and 10.4 to 10.7% for flow prediction) slightly outperforms the state-of-the-art Inv-Opt method (5.3 to 8.8% for time prediction and 10.5 to 11% for flow prediction). The increased accuracy provides greater confidence in modelling results for applications such as cost-benefit analysis and price of anarchy calculations.