Lighting is an integral element of every pedestrian environment, making it a promising tool for crowd management. However, limited knowledge exists on how different lighting conditions shape pedestrian choice behavior. This study systematically examines how both light intensity and light color influence pedestrian exit choice using data from a large field experiment in which varying light settings were applied to two building exits. Two multinomial logit (MNL) models, a light-intensity model and a light-color model, were estimated to quantify these effects. Findings indicate that only a limited subset of light-intensity and light-color conditions meaningfully influence pedestrian exit choice, with Off-Neutral, Bright-Neutral, White-Green, and Red-Green showing moderate, time-dependent effects. At the same time, contextual factors such as origin, local density, and time of day remain far stronger predictors of behavior. Moreover, learning effects emerge selectively and often counterintuitively, with pedestrians increasingly favoring the darker or red-lit exits in conditions where opposite directional responses are expected. The MNL models suggest that lighting can modestly influence pedestrian routing, provided it is applied with careful attention to contextual conditions and time of day.

The deployment of automated vehicles (AVs) on public roads remains limited due to concerns about their interaction with human-driven vehicles (HDVs) in mixed traffic. While previous studies suggest that AVs influence HDV behaviour, the nature of this influence is still not well understood. This study examines how AVs affect HDV car-following behaviour in mixed traffic conditions. Empirical data were collected through a driving simulator experiment in which participants followed a lead vehicle in four scenarios varying in vehicle appearance (AV or HDV) and driving style (AV-like or HDV-like). Car-following behaviour was analysed using the Intelligent Driver Model (IDM) and an extended version (IDM+). The results show that HDVs adapt their behaviour when following AVs, exhibiting smaller jam spacing distances and shorter safe time headways compared to following HDVs. These findings support more accurate assessments of traffic safety and efficiency and contribute to the safe integration of AVs into mixed traffic.

Bicycle traffic, as a sustainable mode of transport, helps reduce urban emissions and promote livable cities. Accurate short-term prediction of bicycle traffic flow can support real-time and near-real-time traffic management decisions in urban cycling systems. However, this task is challenging due to limited high-quality bicycle data, sparse sensor coverage, and high sensitivity to external factors. In this work, we propose a Bicycle Spatial-Temporal Large Language Model (BiSTLLM) for bicycle traffic prediction, inspired by the strong time series modeling capabilities of large language models (LLMs). In BiSTLLM, we define the historical traffic sequence at each location as tokens, allowing the model to learn complex spatial-temporal patterns. Specifically, we first introduce a weather lagging processor using causal temporal convolution to capture the delayed effects of weather on bicycle traffic. We also incorporate semantic embeddings of Point-of-Interest (POIs) and bicycle lane accessibility to represent land use and infrastructure, enhancing the model’s ability to learn both localized activity patterns and broader spatial-temporal dynamics. We then further introduce a fine-tuning strategy to better tailor the general knowledge of a pre-trained LLM to the specific characteristics of bicycle traffic. Experiments on real-world bicycle traffic flow datasets demonstrate that BiSTLLM outperforms existing state-of-the-art models. Notably, BiSTLLM exhibits strong performance even in few-shot settings, highlighting its potential for accurate bicycle traffic prediction in data-sparse environments.

At the drone densities anticipated for urban and contested airspace over the coming decades, drone traffic must be managed by decentralised conflict-resolution schemes to ensure feasibility. This paper presents such a decentralised scheme using differential game theory. Each drone optimises its own trajectory over a receding prediction horizon while anticipating the behaviour of its neighbours. By choosing a parameterisation of the cost function, we represent cooperative, Nash and explicit adversarial assumptions about the opponent. The optimal conditions for the ego drone are solved by Pontryagin’s minimum principle using an iterative forward–backward sweep with Anderson-accelerated relaxation.We benchmark the framework against a calibrated reactive social-forces model, tuned so that any remaining differences can be attributed to anticipation. Pairwise experiments cover head-on, orthogonal crossing, overtaking and a one-on-one adversarial encounter; multi-drone experiments cover bi-directional head-on flow, orthogonal crossing flow, and a bottleneck with two cylindrical obstacles. We show that the anticipatory model resolves every cooperative encounter cleanly where the reactive benchmark fails in a distinct way — a “kissing stall” in the symmetric head-on, an off-axis force balance in the crossing, a trailing trap in the overtake — and these failures carry over at scale into reduced safety margins. In the adversarial case the framework absorbs pursuit-evasion under the same solver with a single sign change on one cost parameter, recovering the three qualitative regimes (escape, stalemate, capture) without modifying the iteration. Moreover, the framework reproduces similar efficient self-organising flow patterns (lanes, diagonal stripes) known from pedestrian flow theory in 3D space.

The escalating demand for urban mobility has significantly contributed to increased CO₂ emissions, necessitating a shift towards sustainable, low-carbon transportation solutions. Emerging modes and concepts such as micro-mobility, shared mobility, electric mobility and mobility hubs offer promising pathways to reduce vehicle CO₂ emissions. This review explores the role of these modes in emission reduction, with particular attention to the integrative function of mobility hubs. This review synthesized current knowledge on the role of emerging transport modes in reducing urban CO₂ emissions. Our analysis through the Life-Cycle Assessment framework and Dynamic Mitigation Model demonstrates that while these modes can lower emissions by facilitating a shift away from private cars, their success is not a guaranteed outcome. Instead, their environmental benefit depends on managing the balance between modal substitution, operational logistics, and vehicle life-cycles. Mobility hubs are a pivotal strategy for mitigating the life cycle emissions associated with shared transport modes by enhancing integration and minimizing indirect emissions. Therefore, the review argues that advancing shared mobility from a niche option to a mainstream solution, supported by strategically implemented mobility hubs, is essential for achieving significant climate benefits. Prioritizing the coordinated deployment of emerging modes and hubs can capture their synergistic advantages, minimizing life-cycle CO₂ emissions and advancing the transition toward sustainable urban transport.

Estimating queue lengths at signalized intersections is a long-standing challenge in traffic management. Partial observability of vehicle flows complicates this task despite the availability of two privacy-preserving data sources: (i) aggregated vehicle counts from loop detectors near stop lines, and (ii) aggregated floating car data (aFCD) that provide segment-wise average speed measurements. However, how to integrate these sources with differing spatial and temporal resolutions for queue length estimation is rather unclear. Addressing this question, we present Q-Net: a queue estimation framework built upon a state-space formulation. This design addresses key challenges in queue modeling, such as violations of traffic conservation assumptions. Q-Net follows the Kalman predict-update structure and maintains physical interpretability in both the state evolution and measurement models. Q-Net uses an AI-augmented Kalman filter to learn time-varying gain dynamics from data. The framework supports real-time implementation and improves spatial transferability by grouping aFCD measurements into fixed-size local groups, making the number of learnable parameters independent of section length. Evaluations on urban main roads in Rotterdam, the Netherlands, show that Q-Net outperforms baseline methods, tracks queue formation and dissipation accurately, and mitigates aFCD-induced delays. By combining data efficiency, interpretability, real-time applicability, and spatial transferability, Q-Net makes accurate queue length estimation possible without costly sensing infrastructure like cameras or radar.

At the end of 2019, SARS-CoV-2 rapidly spread across the globe within a few months. Since then, tackling the virus has been high on national agendas for over three years. As with other respiratory viruses, physical distancing (i.e., requiring sufficient space between individuals) became a key measure to prevent airborne virus transmission between individuals. However, this measure significantly reduces the capacity of pedestrian infrastructure, as more space is needed between people. This paper develops a capacity framework designed to calculate the capacity of pedestrian infrastructure, evaluate its state, and propose tailored interventions when physical distancing regulations are enforced. The framework is founded on the current state-of-the-art in pedestrian operational movement dynamics and determines capacity using three independent key performance indicators: flow rate, density, and interactions. Through two case studies from the COVID-19 pandemic, this paper demonstrates how the framework identifies when and why pedestrian infrastructures become unsafe and enables targeted interventions. The framework's relevance extends beyond the COVID-19 pandemic, offering insights into crowd management and resilient pedestrian infrastructure design for future airborne disease outbreaks.