Perceived traffic safety influences cycling behavior, yet its value relative to travel time and other route attributes remains unquantified. Building on the stated choice experiment and street-level image dataset of Terra et al. (2025), we extract safety perception scores from cycling-perspective images using a computer vision model and estimate mixed logit models to test whether perceived safety affects route choice after controlling for visual street-level features. Cyclists are willing to accept 64 additional seconds of travel time for a one-unit increase in perceived safety (scale: very unsafe to very safe). Safety preferences vary across demographic groups: older cyclists, recreational cyclists, and those with positive cycling attitudes place more weight on safety, while commuters prioritize speed. These willingness-to-pay estimates enable planners to quantify perceived safety improvements for cost-benefit analyses and to score existing cycling networks for targeted infrastructure upgrades. (Replication code: https://github.com/koito19960406/cycling_safety_perception).

Previous research has shown that residential segregation often aligns with urban fragmentation in contexts where explicit segregation policies were historically implemented. However, it remains unclear whether this alignment also emerges in contemporary urban contexts where segregation is driven by market mechanisms and residential preferences. Here we analyze 520 cities across eight Western European countries using high-resolution demographic data and a Monte Carlo approach to test whether residential segregation of non-EU migrants aligns with urban fragmentation by railways, motorways, and waterways. We find that the relationship between residential segregation and urban fragmentation is highly heterogeneous across Europe. Rather than a uniform trend, our results reveal regional divergence: while the Netherlands and Germany exhibit a significant alignment, Spain, the United Kingdom, and Italy show less alignment than expected by chance. These findings suggest that urban barriers do not generally function as social frontiers in European contexts, with country-specific urban development potentially influencing the observed regional differences.

Noise pollution negatively affects health and well-being, making its monitoring important for effective mitigation strategies. Sensor systems such as sound level meters have long been used for this purpose. Nevertheless, dependence on grid power, restricted metrics beyond loudness, and high costs per unit limit current solutions. This paper presents an open hardware, off-grid sound sensor to measure loudness and complementary noise metrics. The sensor detects eleven common urban sound events, calculates acoustic sharpness, and the intermittency ratio of the acoustic environment. The sensor is based on an ESP32-S3 microcontroller on a customized printed circuit board, optimized to address the current limitations. The board includes a battery management circuit for solar charging, a real-time clock for accurate time keeping, and supports LoRaWAN to send aggregated metrics. The latter allows remote monitoring, while more detailed metrics are stored on a microSD card. A solar panel and up to two 18650 Li-Ion or LiFePo4 batteries allow the sensor to be deployed independently of mains power. The open hardware is accompanied by open firmware, which has been organized into multiple components to allow easy changes and extensions for other use cases. A lab validation showed a deviation below 2 dB for a 1 kHz test tone compared to a calibrated sound level meter.

This study sheds light on how utility derived from street-level conditions is spatially distributed, from a residential location choice perspective, at a city-wide scale. Unlike previous studies that analyse perceptions of urban environments from street-level imagery, this work maps preferences—that is, the utility residents derive from observable street-level conditions. To this end, we first develop a residential location discrete choice model that builds on two premises: (1) street-level images effectively capture street-level conditions, and (2) state-of-the-art segmentation models can extract salient information from these images and convert them into structured (i.e. tabular) data. We then apply the model to over 200 thousand geo-tagged street-level images of Rotterdam (the Netherlands) to map how utility derived from street-level conditions varies across the city. Results show strong local variation, with conditions changing rapidly even within neighbourhoods, and reveal that high real-estate prices in the city centre cannot primarily be attributed to attractive street-level conditions. As a secondary methodological contribution, the paper integrates foundation segmentation models into discrete choice analysis. Unlike conventional segmentation approaches limited to predefined object classes, our pipeline leverages prompt-based detection (GroundingDINO + SAM) to identify novel and more granular categories (e.g. transformer houses, shrubs vs. trees) overlooked in standard datasets. This integration enables a richer, fine-grained quantification of street-level conditions and demonstrates how visual information can be systematically embedded into residential location choice models. As such, this paper's findings and methodological contribution pave the way for further studies to explore integrating street-level conditions in urban planning.

Accurately and proactively alerting drivers or automated systems to emerging collisions is crucial for road safety, particularly in highly interactive and complex urban environments. Existing methods require labour-intensive annotation of sparse risk, struggle to consider varying contextual factors or are tailored to limited scenarios. Here we present the generalized surrogate safety measure (GSSM), a data-driven approach that learns collision risk from naturalistic driving without the need for crash or risk labels. Trained on diverse datasets and evaluated on 2,591 real-world crashes and near-crashes, a basic GSSM using only instantaneous motion kinematics achieves an area under the precision–recall curve of 0.9 and secures a median time advance of 2.6 s to prevent potential collisions. Incorporating more interaction patterns and contextual factors provides further performance gains. Across interaction scenarios, such as rear end, merging and turning, GSSM consistently outperforms existing baselines in terms of accuracy and timeliness. These results establish GSSM as a scalable, context-aware and generalizable foundation for identifying risky interactions before they become unavoidable and support proactive safety in autonomous driving systems and traffic incident management.

Large Language Models (LLMs) are becoming widely used to support various workflows across different disciplines, yet their potential in discrete choice modelling remains relatively unexplored. This work examines the potential of LLMs as assistive agents in the specification and, where technically feasible, estimation of Multinomial Logit models. We implement a systematic experimental framework involving twelve versions of seven leading LLMs (ChatGPT, Claude, DeepSeek, Gemini, Gemma, Llama, and Mistral) evaluated under five experimental configurations. These configurations vary along three dimensions: (i) modelling goal (suggesting vs. suggesting and estimating MNL models); (ii) prompting strategy (Zero-Shot vs. Chain-of-Thoughts (CoT)); and (iii) information availability (full dataset vs. data dictionary summarising variable names and types). Each specification suggested by the LLMs is implemented, estimated, and evaluated based on goodness-of-fit metrics, behavioural plausibility, and model complexity. Our findings reveal that proprietary LLMs can generate valid and behaviourally sound utility specifications, particularly when guided by structured prompts (CoT). Open-weight models such as Llama and Gemma struggled to produce meaningful specifications. Notably, some LLMs performed better when provided with just data dictionary, suggesting that limiting raw data access may enhance internal reasoning capabilities. Among all LLMs, GPT o3, operating in an agentic setting, was uniquely capable of correctly estimating its own specifications by executing self-generated code. Overall, the results demonstrate both the promise and current limitations of LLMs as assistive agents in discrete choice modelling, not only for model specification but also for supporting modelling decision and estimation, and provide practical guidance for integrating these tools into choice modellers’ workflows.

Objective Discrete choice experiments (DCEs) are widely used in healthcare to estimate willingness-to-pay (WTP) but may be affected by hypothetical bias (HB), especially in morally sensitive contexts. While cheap-talk is proposed as a mitigation strategy, its effectiveness in health-related DCEs involving moral trade-offs remains unclear. This study examines how cheap-talk influences WTP in such settings. Methods A split-sample DCE on organ transplantation policies was conducted, involving trade-offs between cost and morally salient outcomes: saving lives (“being alive”) and improving quality of life (“having a life”). Respondents (N = 651) were randomly assigned to one of three survey arms: control (no manipulation), cheap-talk, or cheap-talk with follow-up question. Multinomial logit model in WTP space with a Taboo Trade-off Aversion (TTOA) specification was used to estimate treatment effects and interactions with religiosity. Results Exposure to the cheap-talk script reduced WTP for saving lives, indicating increased attention to financial considerations. WTP for quality-of-life improvements and avoiding taboo trade-offs remained unchanged. Religious respondents reported higher WTP to avoid taboo trade-offs in the control arm, but this gap disappeared under cheap-talk, showing that deliberation moderates monetary expressions among religious individuals without altering underlying convictions. Conclusions Cheap-talk promotes more reflective decision-making in morally sensitive health-related choices, particularly among individuals with strong moral or religious convictions. It reduces elevated WTP for taboo trade-offs, while its effect on other respondents is limited. Future research should combine stated and revealed preference data and explore models that account for non-compensatory moral decision rules to better capture complex moral preferences DCEs.

Visual imagery is indispensable to many multi-attribute decision situations. Examples of such decision situations in travel behaviour research include residential location choices, vehicle choices, tourist destination choices, and various safety-related choices. However, current discrete choice models cannot handle image data algorithmically and thus cannot incorporate information embedded in images into their representations of choice behaviour. This gap between discrete choice models’ capabilities and the real-world behaviour it seeks to model leads to incomplete and, possibly, misleading outcomes. To solve this gap, this study proposes “Computer Vision-enriched Discrete Choice Models” (CV-DCMs). CV-DCMs can handle choice tasks involving numeric attributes and images by integrating computer vision and traditional discrete choice models. Moreover, because CV-DCMs are grounded in random utility maximisation principles, they maintain the solid behavioural foundation of traditional discrete choice models. We demonstrate the proposed CV-DCM by applying it to data obtained through a novel stated choice experiment involving residential location choices. In this experiment, respondents faced choice tasks with trade-offs between commute time, monthly housing cost and street-level conditions, presented using images. We find that CV-DCMs can offer novel insights into preferences regarding features presented in images, such as what street-level conditions people find most and least attractive and how these preferences vary across age groups.

BACKGROUND: Long-term noise annoyance can be expected to have worse outcomes than short-term annoyance. This study investigates noise annoyance over time, its association with personality traits and potential reciprocal effects between health outcomes and noise annoyance. METHODS: Firstly, we conducted a Longitudinal Latent Class Analysis to identify noise annoyance profiles. We further analysed the effect of Big Five personality traits on the likelihood of belonging to these annoyance profiles. Secondly, we used Cross-lagged Panel Models to analyse whether changes in noise annoyance precede changes in health outcomes or vice versa. For both analyses, we used 8 years of data from the Dutch Longitudinal Internet Studies for the Social Sciences (LISS) panel. Between 2708 and 11,068 subjects were included (this varies between models). RESULTS: We found three profiles of noise annoyance, namely, chronically, occasionally and never annoyed. Among all participants, 12% were chronically annoyed by neighbour noise and 6% by street noise. Extraversion and emotional stability decreased the chance of belonging to the cluster of chronically annoyed, while openness had the opposite effect. Chronic noise annoyance showed a significant effect on self-reported heart complaints and sleeping problems, while the effects of noise annoyance profiles on high blood pressure and heart attacks were insignificant. Some potential indications for a reverse effect from health outcomes on noise annoyance were found. CONCLUSION: Noise annoyance was relatively stable over time possibly because of its correlation with personality traits. Noise had a small negative effect on health outcomes, and some health outcomes affected noise annoyance. Further research should be conducted to collect dedicated panel data.

Objectives: Taboo trade-offs can explain some of the (moral) difficulties in healthcare decision-making. The moral psychology literature suggests that individuals are averse to making trade-offs between attributes belonging to different values, such as (sacred) human lives versus (secular) money. We demonstrate and empirically test a discrete choice model designed to capture Taboo Trade-off Aversion (TTOA) behaviors in the healthcare domain. Methods: The linear-additive Random Utility Maximization (RUM) model is extended to capture TTOA behaviors by including penalties for taboo trade-offs. Using two Discrete Choice Experiments (DCEs) focusing on taboo trade-offs in public health policies, we empirically compare conventional linear-additive RUM models with TTOA models to explore differences in model and behavioral results. Results: We observe TTOA in both DCEs. In one DCE, the TTOA model separates TTOA effects from attribute-related parameters, showing inflated parameters in conventional RUM models when TTOA behavior is present. This discrepancy affected Willingness-To-Pay (WTP) estimates, with WTP to save an incremental patient life approximately 3.5 times higher in conventional RUM models compared to the TTOA models. The presence and magnitude of TTOA varied considerably across respondents. Latent Class (LC) models reveal that some respondent groups perceive trade-offs as taboo significantly, while others do not. Conclusions: Accounting for TTOA in RUM models may lead to more accurate behavioral information when choice behaviors are affected by taboo trade-offs. Researchers and policymakers can use TTOA models to obtain a more nuanced understanding of public acceptability in morally salient policy decisions – ultimately helping to navigate, rather than avoid, taboo trade-offs.

Several studies examined what drives citizens’ support for COVID-19 measures, but no works have addressed how the effects of these drivers are distributed at the individual level. Yet, if significant differences in support are present but not accounted for, policymakers’ interpretations could lead to misleading decisions. In this study, we use XGBoost, a supervised machine learning model, combined with SHAP (Shapley Additive eXplanations) to identify the factors associated with differences in policy support for COVID-19 measures and how such differences are distributed across different citizens and measures. We use secondary data from a Participatory Value Evaluation (PVE) experiment, in which 1,888 Dutch citizens answered which COVID-19 measures should be imposed under four risk scenarios. We identified considerable heterogeneity in citizens’ support for different COVID-19 measures regarding different age groups, the weight given to citizens’ opinions and the perceived risk of getting sick of COVID-19. Data analysis methods employed in previous studies do not reveal such heterogeneity of policy support. Policymakers can use our results to tailor measures further to increase support for specific citizens/measures.

We present a method to classify street networks using only geo-tagged street-level imagery. By combining pre-trained image embeddings with unsupervised clustering, it produces visually coherent street typologies without supervised training or labeled data and requires only minimal data curation. The approach is lightweight, scalable, and, in principle, transferable across urban contexts. In a Delft (Netherlands) case study, we classify approximately 2,000 road sections using over 70,000 images. Our method recovers distinct street types such as residential, arterial, and historic ones. These results show that pre-trained visual embeddings alone can support effective street classification from visual inputs, offering a practical tool for urban planning, transport analysis, and mobility research.

Low-cost sensor networks have increasingly been used to monitor noise pollution as an alternative to certified sound level meters. Existing sensor networks monitor loudness and, in some cases, classify sound sources. Most sensors do not capture metrics that are more representative of the human perception of sound, require a permanent power supply, or are relatively expensive. We develop an energy-efficient soundscape sensor with the goal of recording metrics complementary to loudness. We have implemented metrics from psychoacoustics and metrics inspired by biodiversity research, such as sharpness, intermittency, and acoustic entropy. Furthermore, the sensor predicts the source of sound events. For privacy-preservation, all audio is processed directly on the sensor. The sensor is based on a low-power microcontroller (ESP32-S3), available for a fraction of the cost of a Raspberry Pi, which is often used for sound source prediction. Finally, the sensor is solar-powered and therefore easy to install for research purposes at places without direct access to the power grid. A temporary deployment of several sensors in Amsterdam, the Netherlands, is planned.