• We examine travellers’ choices between autonomous vehicles (AVs) and human-driven vehicles (HVs) under different information conditions. • A repeated mode-choice game was implemented where participants made decisions based on varying levels of information about travel outcomes. • Mixed multinomial logit (MMNL) models were estimated to analyse the factors influencing travellers’ choices. • Learning patterns, heterogeneous preferences, and fairness concerns significantly influence mode choice in mixed AV-HV traffic environments. With rapid advancements in automation, autonomous vehicles (AVs) will soon coexist with human-driven vehicles (HVs) in mixed traffic. This study investigates the coordination behaviour in mode choice between AVs and HVs with positive and negative externalities, respectively. Participants play a mode choice game repeatedly under partial information (PI) or full information (FI) feedback on payoffs of the alternatives. The descriptive analysis indicates that choices under PI are close to the outcome at the Nash equilibrium; however, choices under FI are consistently below this equilibrium outcome. This discrepancy leads to a decline in social welfare, despite the provision of additional information. To test the factors influencing choice behaviour, we built a series of mixed multinomial logit (MMNL) models. The first model reveals substantial heterogeneity in individual preferences for AVs and indicates that full information feedback weakens the influence of inherent preferences while reinforcing the role of dynamic experience learning. In the second model, the memory effect diminishes further when payoff fairness is introduced. Fairness considerations significantly shape mode choices: while some participants prioritise equitable payoffs, others pursue individual payoff maximisation, producing aggregate outcomes between the distributions induced by the payoff equity and by the Nash equilibrium. By understanding the factors influencing choice behaviour and payoff preferences, policymakers and transport planners can develop targeted interventions to promote AV adoption, ultimately facilitating a smoother transition towards autonomous vehicle usage.

Passive data for active policy: Evidence-based insights from GNSS and CDR applications in transport planning

OT-ADG: Optimal-transport-driven adaptive data generation for WiFi sensing in airborne mobile networks

ABSTRACT Public transport is seen as a key player in the green transition of the transport sector in the Global North. The bus is primarily associated with positive values, reflecting a lifestyle and choice-centred sustainability discourse. The paper argues that the bus system can be thought of as an affective economy of “goodness” that stands in great contrast with bus-dependent lived experience of bus mobilities, in particular people living in marginalized urban areas. Qualitative interviews with young and adult bus riders living in disadvantaged urban neighbourhoods are contrasted with an analysis of regional transport plans to reveal discrepancies between the envisioned ideal future users and the current collective of bus users. The paper outlines how bus-dependent groups negotiate their own everyday bus experiences, and the affective economy of stigma related to their neighbourhoods, with public discourses on bus mobilities as normatively good. The article highlights the need to acknowledge how affect works in relation to public transport and the green transition, and points to the need to critically engage with questions of equality and justice in policy making and planning practices in the context of public transport.

Purpose With the continuous expansion of railway hubs, increasing functional complexity and growing capacity constraints, the coordinated and efficient utilization of transportation resources – such as stations, lines and maintenance facilities – has become a critical issue for improving hub operational efficiency. This study focuses on the division of functions within railway hubs that incorporate shared stations operating under mixed high-speed and conventional train services. Design/methodology/approach An optimization model for hub functional allocation is developed to achieve efficient resource utilization in hubs containing mixed-operation stations. A node–arc network representation combined with an improved multi-commodity flow model is employed, taking train dwell and operation time within the hub as the optimization objective. A case study is conducted to derive optimized solutions, followed by both qualitative and quantitative analyses. Findings The results indicate that optimizing train operation routes and station assignments within the hub can effectively reduce the total occupation time of train flows and significantly improve resource utilization efficiency. Originality/value The proposed model demonstrates both scientific rigor and practical effectiveness. In real-world operations, it can provide operators with preliminary and proactive functional allocation schemes, help identify key constraints limiting hub capacity utilization and offer decision support for transport plan adjustments or infrastructure and facility upgrades.

This study examines the relationship between transportation expenditure and economic performance across 41 countries over the period 1990–2023, highlighting the global role of transportation investment in economic development. Using descriptive statistics, correlation analysis, and hierarchical linear regression, the research investigates the association between gross domestic product (GDP) and multiple dimensions of transportation expenditure. The results indicate a strong positive relationship between GDP and total transportation expenditure, as well as passenger transportation expenditure, underscoring the close linkage between transportation investment and economic activity. In contrast, a significant negative relationship is observed between GDP and transportation expenditure as a share of GDP, suggesting that higher-income countries invest more in transportation in absolute terms while allocating a smaller proportion of national output to this sector, likely reflecting efficiency gains and economies of scale in mature transportation systems. Hierarchical regression results indicate that transportation expenditure is a statistically significant predictor of GDP, with the baseline model accounting for a substantial share of cross-country variation. The inclusion of relative expenditure measures yields modest additional explanatory power, with the second model providing the most stable specification by effectively mitigating multicollinearity. Overall, the findings emphasize the continued importance of sustained transportation investment, while highlighting differences in investment intensity across stages of economic development. For developing economies, strategic and efficient transportation investment remains critical for supporting long-term economic growth. The study contributes to the literature by offering a comprehensive, cross-national perspective on the scaling of transportation expenditure with economic performance and its implications for transportation policy and planning.

This paper presents a matrix-based approach for modelling and analysing transportation networks using concepts from graph theory and linear algebra. The incidence matrix and its transformation into the adjacency matrix through the product MMT are employed to represent structural relationships within the network. Matrix operations and their powers are used to study both direct and indirect connectivity, while the reachability matrix provides an effective algebraic criterion for determining accessibility among nodes. The theoretical results establish a connection between matrix formulations and graph connectivity, offering a systematic framework for network analysis. The applicability of the proposed method is demonstrated through several transportation models, including regional and large-scale networks, where key hubs, connectivity patterns, and efficiency are identified. The study shows that matrix-based techniques provide a scalable and practical tool for transportation planning, route optimization, and analysis of complex network systems.

Understanding active transportation is critical for transportation planning, infrastructure development, and safety improvements. Unlike motor vehicles, which have widespread automated counting stations, cycling and walking automated counting has limited coverage. Given the limited data and unique characteristics of active transportation, it is crucial to evaluate the accuracy of counting technologies and account for temporal variations, weather effects, and transferability when estimating volumes. Data from four sites in Wisconsin were analyzed with 5 years of hourly sensor, weather, and Strava data, along with 268 h of manually processed ground truth video data. Ground truth hourly count trends showed that pedal cycles were the main users in the shared paths (78%–87%). There were peak and directional hourly trends by week or weekend days, higher volumes and a shift in the type of user were observed on weekends. Automatic sensor count data accuracy from inductive loop and infrared sensors was evaluated and compared with ground truth data. Inductive loop counting technology showed high levels of pedal cycle count accuracy (91%–92%). Infrared sensors counted passersby with a reduced degree of accuracy (54%–67%). Negative binomial regression modeling was implemented to account for overdispersion in the count data. Key predictors included time of day, day of the week, month, temperature, precipitation, and Strava counts. Site-specific models were developed, transferability across sites was assessed, and models were generalized with data from sites that shared similar characteristics applicable to high-volume, urban commuting and recreational paths. Models were not transferable to isolated sites with low volume and unreliable sensor count data.

Accurate passenger demand forecasting is crucial for operational planning and service reliability in public transportation systems. Despite the effectiveness of traditional models, existing approaches often struggle with nonlinear fluctuations in demand, which limits their ability to adapt to real-world variability. This study proposes a hybrid forecasting framework that combines the Autoregressive Integrated Moving Average (ARIMA) model with a Multi-Layer Perceptron (MLP) neural network for short-term passenger demand prediction. By using ARIMA to capture linear components like trend, seasonality, and autocorrelation, and MLP to model the residuals that contain nonlinear patterns, the proposed approach integrates the strengths of both models. This hybrid method addresses gaps in current forecasting techniques by improving adaptability and precision. Empirical analysis was conducted using daily passenger count data from Bus Trans Jatim during 2023–2024. Data preprocessing included exploratory time series analysis, variance stabilization, and outlier assessment to ensure compatibility with the modeling assumptions. Forecast performance was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that the hybrid ARIMA–MLP model achieved a MAPE of 4.95%, outperforming the standalone ARIMA model in providing more adaptive and accurate short-term forecasts. These findings have practical implications for public transportation planning, enabling more responsive and efficient operations, particularly for forecasting demand fluctuations.

Forest harvesting transportation planning must balance operational efficiency with environmental sustainability, because timber transportation can cause both soil disturbance and carbon emissions. However, most vehicle routing studies primarily focus on economic objectives such as distance or cost minimization, whereas environmental impacts are often considered separately. The integrated optimization of ecological disturbance and carbon emissions remains limited in forest transportation planning. To address this gap, this study formulates a multi-vehicle routing optimization model for timber transportation that simultaneously minimizes transportation distance, makespan, soil disturbance, and CO2 emissions within a hierarchical forest road network. An enhanced evolutionary algorithm, Eco-Constrained Lévy-flight Local Search NSGA-II (ECLS-NSGA-II), is proposed to improve convergence and maintain environmentally favorable routing solutions. Simulation experiments comparing ECLS-NSGA-II with NSGA-II, MOPSO, MOEA/D, and WS-GA demonstrate that the proposed method achieves superior performance across all objectives, producing shorter routes, lower completion times, and reduced CO2 emissions while maintaining minimal ecological disturbance. Additional experiments on randomly generated networks further confirm the robustness of the proposed approach. These results indicate that the proposed framework provides an effective methodological tool for environmentally sustainable timber transportation planning in forest operations.

Urban transportation demand is strongly influenced by household socioeconomic characteristics and accessibility to transportation infrastructure. Understanding the determinants of work trip generation is essential for urban transport planning and policy formulation. This study aims to model household work trip generation based on socioeconomic characteristics and transportation accessibility using a logistic regression approach. Data were collected through a household questionnaire survey conducted in urban residential areas with 320 respondents. The variables analyzed include household income, number of family members, number of working household members, vehicle ownership, and accessibility to public transportation. Logistic regression analysis was used to estimate the probability of households generating higher work trips. The results indicate that household income, number of working members, vehicle ownership, and accessibility to public transportation significantly influence work trip generation. Households with higher income and better transport accessibility tend to generate more work trips. The model achieved a Nagelkerke R² value of 0.41, indicating a moderate explanatory capability. The developed model can support travel demand forecasting and provide useful insights for transportation planning policies in urban areas. Keywords: Household Travel Demand, Logistic Regression, Socioeconomic Factors, Transport Accessibility, Trip Generation.

Vehicle miles traveled (VMT) is increasingly used as a metric for assessing the environmental effects of development projects, given its associations with greenhouse gas emissions, traffic collisions, and health outcomes. In 2013, California Senate Bill 743 mandated the replacement of the level of service (LOS) with VMT for transportation effect analysis under the California Environmental Quality Act. This study examines how transportation professionals are adapting to the implementation of VMT-based analysis, and particularly the development of off-site VMT mitigation strategies. Using a two-phased qualitative interview process, first in 2021 ( n = 19) and again in 2024 ( n = 24), this study explores evolving perceptions, challenges, and strategies related to VMT tools, legal defensibility, and equitable implementation. The findings reveal persistent uncertainty around modeling and evaluation, legal risk, and the administrative complexity of equitably distributing off-site mitigation. While grounded in California’s regulatory context, the insights offer broader relevance for jurisdictions pursuing climate-aligned transportation planning. This study calls for the development of an integrated approach to VMT modeling and evaluation, and a nuanced, multifactor understanding of the context in which new developments are being proposed when implementing off-site mitigation measures.

ABSTRACT Integrating forest biomass into bioenergy systems poses logistical challenges due to seasonal variations in quality and the dispersed nature of supply. We develop a mixed-integer linear programming model that jointly optimizes procurement timing, multimodal transport (truck−rail−barge), chipping and drying locations, and inventory levels at supply nodes, terminals, and the biorefinery. The model embeds process-state transitions, seasonal moisture profiles, and infrastructure limits. In a large-scale Quebec case study (500 − 3000 dry metric tonne (DMT)/day), integrating rail reduces total system costs by 2.8 − 4.8% and yields mill-gate costs around CAD 119 − 121 per DMT. Terminals near the biorefinery decouple procurement from conversion and support buffer-based strategies through high-moisture periods. The optimization model is computationally tractable and provides a reusable template for planning forest biomass logistics that accounts for seasonal quality, preprocessing, and mode-choice interactions.

Understanding trip generation at large-scale commercial land uses is essential for effective urban transport planning, particularly in rapidly growing cities in developing countries. This study investigates trip-generation patterns at extra-regional commercial centers in Mashhad, Iran, using an exploratory structural equation modeling (SEM) framework that jointly examines physical attributes, built-environment characteristics, and socio-economic factors. Using observed trip counts from 33 extra-regional commercial sites, two dependent measures were analyzed: the total number of trips and trip rates normalized by floor area (trips per 100m2). The findings suggest that, within the analyzed sample and under the current exploratory model specification, physical scale and surrounding built-environment characteristics are associated with higher total trip volumes, whereas the examined socio-economic indicators did not show statistically significant associations. In contrast, no statistically significant relationships were identified between the examined explanatory factors and trip rates normalized by floor area.Given the limited sample size and model-fit constraints, the results should be interpreted as exploratory rather than confirmatory. This study provides preliminary empirical evidence on commercial trip generation in a data-constrained context and underscores the importance of distinguishing between total trip volumes and density-based trip measures. Future research using larger samples and alternative modeling approaches is needed to validate and extend these findings.

Birth preparedness and complication readiness (BPCR) remains a critical safe motherhood strategy aimed at reducing maternal and neonatal morbidity and mortality by promoting timely access to skilled obstetric care. Despite ongoing efforts to improve maternal health outcomes in Nigeria, preventable maternal deaths persist due to delays in decision-making, transportation challenges, financial constraints, and limited access to skilled providers. This study assessed the factors associated with BPCR among pregnant women attending antenatal clinic at Mgbundukwu Model Primary Health Center, Port Harcourt, Rivers State. A descriptive cross-sectional design was adopted. The target population comprised pregnant women attending antenatal clinic at the facility, with an estimated monthly attendance of 150. Using the Taro Yamane formula and a 10% non-response adjustment, a sample size of 120 was determined. A total of 109 correctly completed questionnaires were analyzed, yielding a response rate of 90.8%. Data were collected using a structured, self-administered questionnaire and analyzed with SPSS version 25 using descriptive and inferential statistics. Findings revealed that most respondents were aged 26–40 years (86.2%), married (89.0%), and had attained at least a Diploma-level education (56.9%). Logistic factors emerged as important determinants of BPCR: 45.9% reported difficulty obtaining transportation at night, and 51.4% experienced traffic congestion en route to the hospital. Financial constraints were also notable, with 27.5% lacking adequate funds for BPCR requirements and 18.3% reporting unstable income. However, cultural and religious barriers were minimal, as only 15.6% believed emergency planning invites misfortune and 8.3% reported cultural resistance to hospital delivery or caesarean section. Family support was strong, with 92.7% reporting spousal involvement and 85.3% having access to potential blood donors. Nonetheless, 29.4% of respondents were unable to confidently identify skilled birth attendants. The study concludes that while socio-cultural resistance is low and family support is high, logistical challenges, financial instability, and knowledge gaps regarding skilled birth attendants significantly influence BPCR in this urban primary healthcare setting. Strengthening antenatal education, improving emergency transport planning, and enhancing financial preparedness strategies are recommended to improve safe delivery outcomes.

Understanding how urban systems and traffic dynamics co-evolve is crucial for advancing sustainable and resilient cities. However, their bidirectional causal relationships remain underexplored due to challenges of simultaneously inferring spatial heterogeneity, temporal variation, and feedback mechanisms. Here we present a spatio-temporal causality framework that bridges correlation and causation by integrating spatio-temporal weighted regression with spatio-temporal convergent cross-mapping. Characterizing cities through urban structure, form, and function, the framework uncovers bidirectional causal patterns between urban systems and traffic dynamics across 30 cities on six continents. Our findings reveal asymmetric bidirectional causality, with urban systems exerting stronger influences on traffic dynamics than the reverse in most cities. Urban form and function shape mobility more profoundly than structure, even though structure often exhibits higher correlations. This does not preclude the reversed causal direction, whereby long-established mobility patterns can also reshape the built environment over time. Finally, we identify three causal archetypes: tightly coupled, pattern-heterogeneous, and workday-attenuated, which support city-to-city learning and inform context-sensitive strategies in sustainable urban and transport planning.

Assessing public transport equity: The case of Alexandria, Egypt

Buildings require deep cuts in both operational and embodied emissions from carbon-intensive materials. Carbon capture, utilization, and storage (CCUS) can support these pathways, but only if CO 2 transport and storage infrastructure remains reliable under uncertainty in pipeline deliverability and reservoir injectivity. This paper develops a network mixed-integer linear programming model for CCUS source-sink design that minimizes discounted net cost while selecting integer pipelines and injection wells. We extend the formulation with a Gamma-robust variant that hedges against per-pipeline capacity shrinkage and per-well injectivity reductions, and a Conditional Value-at-Risk penalty that limits exposure to severe storage shortfalls. The model is evaluated on a fully synthetic dataset enabling controlled comparisons among deterministic, robust, and risk-aware designs. Under representative conditions of moderate transport distances and typical cost structures, hedging a 40% pipeline capacity derate alone increases system cost by 18%, whereas hedging an equivalent injectivity derate increases cost by 52%, and hedging both simultaneously raises cost by 72%. The CVaR analysis further shows that halving the discounted tail shortfall from 200 MtCO 2 to 100 MtCO 2 requires a 52% increase in expected system cost. These results quantify how uncertainty triggers step changes in well deployment and pipeline sizing, and how risk-aware designs trade higher upfront investment for reduced exposure to extreme shortfalls. Though illustrative, the framework can be instantiated with public data for real regions, providing decision support for CCUS planning linked to net-zero building strategies.

The tightening of Emission Control Area (ECA) and Vessel Speed Reduction (VSR) regulations poses significant challenges for biomass exporters, particularly from emerging economies. These regulations raise transportation costs and reduce competitiveness, threatening the resilience of the biomass supply chain (BSC). This study develops an integrated optimization framework that combines machine learning (ML) (Bayesian XGBoost and Long ShortTerm Memory (LSTM) models) with discrete-event simulation (DES) to enhance supply chain (SC) efficiency under regulatory constraints. Using operational data from twenty-four Vietnamese wood pellet plants and international shipping records (2023–2024), the framework optimizes procurement, inland logistics, maritime transport, and demand planning. Simulation results show that full SC optimization reduces costs by nearly 40%, while combining SC optimization with power plant efficiency innovations achieves up to a 43.1% reduction in total costs and doubles exporter profits compared to the baseline. Beyond economic gains, the approach enables compliance with international environmental regulations without imposing prohibitive costs on exporters. This study contributes a novel methodological framework that bridges ML and simulation for sustainable SC optimization, offering practical guidance for exporters in emerging economies to remain competitive under tightening global environmental policies.

Incorporating built environment features in Air Passenger Demand forecasting: A spatial econometric approach for enhancing transport policy and planning