Sustainable urban mobility represents one of the greatest challenges for medium-sized Latin American cities, where public transport plays a fundamental role in reducing air pollution and traffic congestion and improving access to basic services. This paper examines the impact of public transportation on urban mobility in Celaya, Guanajuato, using Geographic Information Systems (GIS) to study the public transportation network in terms of coverage, accessibility, and efficiency. By compiling georeferenced data on public transport routes, such as departure frequencies and passenger flows, together with details on road infrastructure and sociodemographic data from open sources. Geographic information systems were used to construct thematic maps and spatial accessibility models, which made it possible to identify areas with poor coverage, long travel times, and disparities in urban connectivity. The findings show that, although public transport covers most of the urban areas, there are peripheral areas with poor accessibility and high dependence on private transport, which negatively affects sustainable mobility in developing industrial cities. Likewise, strategic corridors were identified where improving frequencies and modal integration would significantly increase the efficiency of the system. Finally, it is essential to include spatial analysis through GIS in the design of public transport, as this enables fairer and more sustainable mobility policies to be implemented, helping to reduce congestion and improve the quality of life in cities.

• Joint probabilistic forecasting of link travel times for all running buses on a route. • A time-varying multivariate Gaussian mixture distribution captures intra-bus and inter-bus correlations. • Kronecker-structured covariance enables scalable high-dimensional parameter estimation. • Real-time conditional forecasting achieves competitive accuracy on three real-world bus routes. Recent advancements in statistical and machine learning models have substantially enhanced bus travel time forecasting accuracy. However, these studies primarily rely on deterministic models and fail to quantify forecasting uncertainty, which is crucial for travelers and transit operators. Moreover, existing probabilistic methods are either computationally prohibitive or restricted to local dependencies, preventing them from modeling the joint travel time correlation among multiple running buses and links along the route. To address these issues, this paper introduces a probabilistic deep structured Gaussian model that performs joint forecasting of link travel times for all running buses on a route by explicitly capturing both intra-bus and inter-bus correlations. We model the travel time of each bus on every link as a random variable, whose joint correlations are captured using a time-varying multivariate Gaussian mixture distribution. Efficient high-dimensional parameter estimation is achieved through a novel deep neural network that incorporates a Kronecker product-based structure for the covariance matrix, with the multivariate Gaussian mixture likelihood as the loss function. This specialized architecture enables the network to effectively learn dynamic intra-bus and inter-bus correlations by fusing spatiotemporal features encoded from the travel times of the preceding buses. Probabilistic forecasting is then conducted by computing the conditional distribution of downstream bus link travel times based on partially observed upstream travel times, facilitating real-time predictions for all remaining links of all running buses. We evaluate the proposed model with three routes from two bus systems. Compared with other baseline models, results show that our approach achieves an average improvement of 0.44% in MAPE, 1.12 in RMSE, 0.36 in CRPS, 0.20 in 0.5-risk, and 0.24 in 0.9-risk. Furthermore, the model provides interpretable operational insights, capturing time-varying inter-bus correlations with complex short-range and long-range intra-bus dependencies.

Urban traffic network congestion propagation prediction model: A case of non-recurrent congestion

As urban transportation evolves, shared e-mobility is increasingly recognized as a socio-technical system shaping urban equity, social inclusion, and mobility behaviour. However, existing platforms often lack multimodal integration and user-centric adaptability, limiting their ability to address diverse and behaviourally heterogeneous travel preferences. This study proposes a cloud-based shared e-mobility platform integrating docking electric cars, e-bikes, and e-scooters with large language model assistance for natural-language preference interpretation. The system enables human-centred decision-making through lexicographic multi-objective route optimization. The platform is evaluated using 500 expert-annotated queries, assessing both preference alignment and optimization performance. Results show that gpt-4.1 achieves the highest semantic alignment score (0.928) and best route quality measured by a Mean Optimality Gap of 18.62%. To jointly capture alignment and optimization performance, we introduce the Combined Alignment–Performance Metric, under which gpt-4o achieves the highest score (1.0594). Optimization experiments demonstrate high system robustness under varying traffic conditions, as key metrics such as travel time ( p = 0 . 771 ), risk ( p = 0 . 341 ), and walking distance ( p = 0 . 153 ) show no statistically significant differences. Furthermore, the platform exhibits significant scalability, where increasing e-hub density from 20 to 100 stations reduced the mean travel time from 1771 ± 497 s to 955 ± 343 s ( p < 0 . 001 ). These findings contribute to interdisciplinary transportation research by linking optimization with human-centred mobility analysis, offering actionable insights for equity-aware urban planning, inclusive mobility system design, and policy development supporting adaptive and sustainable transport systems. • Cloud platform enables multimodal routing across e-bikes, e-scooters and e-cars. • LLMs translate user travel preferences into lexicographic optimization priorities. • Eight LLMs benchmarked for preference alignment and routing performance. • Compact models achieve comparable route quality with faster response time. • Denser e-hub networks significantly reduce average multimodal travel time.

The Relative Index of Metropolisation of the Territory: Quantifying spatial reorganisation driven by land-based transport

Delayed initiation of antenatal care and its determinants in Ethiopia: A systematic review and meta-analysis.

Normothermic machine perfusion (NMP) and normothermic regional perfusion (NRP) have expanded donation after circulatory death (DCD) liver transplantation (LT). However, cold ischemia time (CIT) potentially restricts access to DCD LT for recipients living far from transplant centers. The impact of perfusion technology on DCD LT access for geographically distant patients remains underexplored. All adult DCD LT at a transplant center from January 2017 to December 2024 were reviewed. Recipients were categorized by perfusion method:static cold storage (SCS) only, NRP, or NMP. Real-world travel distances and times to transplant center were calculated using Google Maps application programming interface. Social determinants of health, including Distressed Community Index (DCI) scores, were assessed. Among 208 DCD LT recipients (n = 80 SCS; n = 106 NRP; n = 22 NMP), median travel distances were 63 (17-122), 61 (23-92), and 156 (78-386) miles for SCS, NRP, and NMP, respectively (NMP versus SCS and NRP, p = 0.043 and 0.010). Median travel times were 1.23 (0.38-2.40), 1.20 (0.50-1.80), and 3.15 (1.58-7.73) h for SCS, NRP, and NMP, respectively (NMP versus .SCS and NRP, P = 0.025 and 0.007). Although DCI distribution did not differ by procurement type, New Mexico (NM) recipients had significantly higher DCI scores (67.7, at risk) than Colorado (CO) recipients (26.7, comfortable) ( P < 0.001). Notably, 21% of NM recipients received NMP LT compared with 7% of CO residents. An early center experience with DCD LT demonstrates that perfusion technologies facilitated transplantation for geographically vulnerable recipients. NMP recipients on average lived over 2 times farther away and traveled twice as long compared to SCS and NRP recipients. NMP allowed us to mitigate historical barriers of prolonged CIT, especially for recipients from NM, who live further and in highly distressed communities.

An IFC-based framework for semantic integration of BIM and mobile crowd sensing in real-time evacuation routing

Real-time route optimization in smart cities via Bidirectional A* algorithm

Pedestrians, especially wheelchair users, face significant navigation challenges due to inadequate infrastructure. Addressing these challenges requires pathfinding services to offer optimal routes tailored to user needs. Such routes depend on detailed infrastructure data, often absent from crowdsourced maps. This study enhances OpenStreetMap (OSM) data and presents a multi-criteria, context-aware routing approach. It integrates Dijkstra’s algorithm, a Decision Tree (DT), and Ordered Weighted Averaging (OWA) for personalized navigation. The proposed framework makes two key contributions: (1) a Large Language Model (LLM)-driven OSM enrichment pipeline and (2) a custom OWA-weighted DT algorithm that adapts routes according to user preferences. Experiments on 190 routes in Calgary show the enhanced network reduces total walking distance by 25.89 km (12.45%) and travel time by 47.78 min (9.83%) compared to the original OSM-based network. The customized pathfinding method enables preference-aware routing based on criteria such as surface quality, slope, crossing safety, and lighting. Results show improvements in safety-focused profiles, with a 2.2% increase in safety-related attributes. Accessibility-focused profiles see a 5.3% increase in surface quality. Personalized routes remain efficient, averaging only 1.8% longer than the shortest available path. In 97.4% of cases, the distance stays within a 10% margin. This supports real-time routing with low computational overhead. Limitations include dependence on attribute completeness. Future research will incorporate real-time environmental conditions, expand preference-learning mechanisms, and evaluate generalizability across a wider range of urban contexts, supporting real-time routing with minimal computational overhead. • Enhances OpenStreetMap data using a multi-source enrichment pipeline. • Combines human expertise with Large Language Models to improve OSM geospatial data quality. • Cold-start routing framework combining rule-based paths, OWA, and a DT model. • Uses explicit and implicit preferences to create context-aware pedestrian routes. • Walking speed model that adapts to infrastructure and user demographic factors. • Tailored models and enriched data yield routes that better reflect user preferences.

Climate change is acknowledged to be the single biggest threat to human health this century and healthcare is responsible for 4-5% of global carbon emissions. This is the first study of the carbon footprint of brachytherapy delivery as practiced in the UK. Nine UK centres collected data for four brachytherapy pathways: high dose rate (HDR) prostate, low dose rate (LDR) prostate, HDR cervix and HDR vaginal vault. Data was collected for power use from imaging and treatment machines, power use from time on wards and in theatre, anaesthetics, consumables waste and patient travel. Emissions were quantified by itemising each component of the process and using a conversion coefficient in units of kg CO2e per unit of activity. Expressed in kg CO2e per patient, HDR vault had the lowest associated emissions (median 35, mean 59) whereas LDR and HDR prostate had similar carbon emissions per patient (LDR: median 117, mean 134; HDR: median 132, mean 128), and HDR cervix had the largest (median 182, mean 184). Patient travel was a significant contributor in all pathways, particularly LDR prostate (51% of total CO2e for the pathway) and HDR vault (77%). Theatre and ward time contributed between 31% and 61% for pathways other than vault, and consumables contributed a similar amount (10-14%) in each pathway. This initial estimate of the carbon footprint of the UK brachytherapy process suggests median figures from 35kg CO2e per patient (for a vault treatment) to 182kg CO2e per patient (for a cervix treatment). Significant contributors were patient travel, theatre and ward time, and consumables use.

Exploring the robustness of metro-bus composite networks considering the heterogeneity of travel time and passenger flow

The vulnerability of transportation infrastructure is critical to the optimal performance of public transit systems, particularly rail networks, which have consistently been targeted by terrorist attacks. This article aims to identify critical arcs vulnerable to attacks and to evaluate attacker and defender behavior under varying budget levels. We present a continuous nonlinear bi-objective optimization model in which the attacker seeks to maximize the average travel time and the weighted variance, whereas the defender aims to minimize them. We focus on defense measures that are rapidly reallocated and can be kept partially or fully unobservable (e.g., allocation of guards to protect the railway), motivating a simultaneous model. Durable, observable hardening investments are beyond our current scope and are more appropriately addressed with sequential models. To solve the model at scale, we employ a combination of the Lagrange relaxation method and the Frank-Wolfe algorithm to transform the Minimax model into a convex nonlinear minimization model. Results show that the proposed model successfully identifies critical arcs and demonstrates that increased defensive investment can significantly reduce attacker impact. A case study on Iran's railway reveals a linear, nonlinear, or peak-like attacker-defender behavior by considering budget thresholds beyond which attacks become ineffective.

Location-based accessibility is widely assessed using zonal frameworks that aggregate high-resolution results into summary statistics. This aggregation-induced error is often assumed to be negligible, yet this assumption is rarely verified and may obscure critical disparities. This study introduces “Accessibility Misrepresentation” as the deviation between high-resolution accessibility and zonal proxies. Using building-level public transport accessibility in Metropolitan Melbourne, the research evaluates the magnitude, spatial patterns, and equity implications of misrepresentation. Results show systematic variation across travel time thresholds, built environment characteristics, aggregation methods, and service levels. Missed detection of populations without accessibility peaks at 15–20 min thresholds commonly used in neighbourhood accessibility policies, predominantly affecting densely populated middle suburbs where public transport catchments misalign with administrative boundaries. Continuous errors in accessibility values show no systematic directional bias; however, minorities can experience substantial misstatements sufficient to alter normative assessments. Equity analysis reveals disproportionate impacts on middle-status populations often overlooked by conventional frameworks. Population-weighted medians substantially outperform means in reducing misrepresentation. However, no single statistic adequately captures internal variation, particularly with multimodal distributions. A dual-scale approach is recommended: high-resolution computation to characterise within-zone distributions, combined with zonal summaries accompanied by distributional diagnostics, including share of residents without accessibility and upper-tail error indicators. This approach enhances transparency, supports equity-focused and defensible planning decisions without costing additional resources. • Zonal summaries cause “Accessibility Misrepresentation”. • Well-served zones may mask large accessibility gaps. • Middle socioeconomic group has large underserved subpopulations. • Medians outperform means as summaries of zonal accessibility. • Dual-scale analyses expose disparities and preserve interpretability.

This study aims to identify the potential of Pura Mengening in Saraseda Village, Tampaksiring District, Gianyar Regency as a spiritual tourism destination based on the 4A components. This research is qualitative descriptive research using data collection methods through observation and interviews related to the 4A components. The data were analyzed descriptively, with the research subjects being the Head of Saraseda Village, the Traditional Village Chief of Saraseda, and the managers of the Pura Mengening tourism destination. The research object is the perception or experience of tourists visiting Pura Mengening. The results of the study indicate that Pura Mengening has strong potential as a spiritual tourism destination based on the four main components (4A): attraction, access, amenities, and ancillary services. The attraction includes the natural beauty of the area with cool air, the sanctity of the temple that is still well-preserved, and the spiritual experience felt by tourists after undergoing the melukat ritual. Access is considered good with roads that are easily reachable, although some points still need improvement, and the travel time from Denpasar city is only 1 hour and 30 minutes. Available amenities include supporting facilities such as toilets, changing rooms, parking areas, and signboards. Ancillary services include the active role of the community and tour guides in maintaining the sanctity of the temple while assisting tourists in the melukat procession. In conclusion, Pura Mengening has great potential to be developed as a sustainable spiritual tourism destination that reflects the harmony of Tri Hita Karana, which is the balance of the relationship between humans, nature, and God.

A Study on Student Population Estimates and School Consolidation Criteria Based on Commutable Areas Determined by Travel Time

This paper examines the need for high-speed rail (HSR) in Romania by analyzing its economic, social, and environmental benefits. HSR has become an important part of modern transportation in Europe, and its introduction in Romania would not only improve domestic travel but also strengthen the country's connection to the wider European transport network. A well-developed HSR system would enhance cross-border mobility, support trade, attract investment, and contribute to a more efficient and sustainable transport system. To identify the best route alternatives, this study uses the National Transport Model, applying different scenarios based on traffic flows and demographic factors. The analysis also considers the natural terrain to ensure that proposed routes follow the most suitable geographical conditions. Beyond the clear advantage of reducing travel times between major cities, HSR could play a key role in regional development by reducing economic differences and encouraging more balanced growth across Romania. By aligning with European transport policies, the development of high-speed rail in Romania would be a major step toward modernizing infrastructure and improving the country's role in Europe's transport network. The objective of this study is to integrate Romania's National Transport Model with a land suitability analysis to identify the most suitable high-speed rail corridors and to evaluate their transport demand potential.

The tourism industry in Indonesia has experienced rapid growth, generating large volumes of data on tourist preferences, destinations, travel costs, and durations. Traditional travel recommendations, provided through agencies or static guidebooks, often rely on generalized assumptions and fail to adapt to individual user preferences. Consequently, tourists frequently receive generic suggestions that may not align with their specific needs, resulting in suboptimal travel experiences. To address these limitations, this study proposes a hybrid learning-based travel recommendation system that leverages Indonesian tourism datasets to deliver personalized suggestions. The system employs multiple Classification and Regression Trees (CART)-based models, including Linear Logistic Regression (LLR), Support Vector Machine (SVM), and Random Forest (RF), for comparative analysis. The primary model, Neuro-Tree-Net, integrates an Artificial Neural Network (ANN) with an Extra Trees (ET) model, combining tree-based learning with neural network-based representation learning to capture complex nonlinear relationships in tourism data. Experimental results demonstrate that the hybrid model outperforms traditional single-model approaches in prediction accuracy. Users can input constraints such as maximum cost, available travel time, and minimum rating preferences to receive personalized destination recommendations. The backend efficiently manages dataset processing, model training, and prediction using libraries including pandas, scikit-learn, Keras, and Matplotlib, while results are presented through a dynamic web interface built with Django. Beyond accurate recommendations, the system supports comprehensive analysis of tourism patterns, enabling travellers to make more informed decisions.

Multichannel ice-penetrating radar systems can be used to generate radar volumes: three-dimensional data structures that capture variability in backscattering intensity as a function of along-track position, two-way travel time and elevation angle. By digitizing surfaces within these data volumes, the production of wide-area, fine-resolution digital elevation models (DEMs) of the ice-bottom interface (measured through kilometres of ice) is now possible. This paper reviews this technique ('radar swath imaging'), explores its methodological principles, describes its operational requirements and highlights recent scientific advances enabled by radar swath imaging. Observations of glacier and substrate morphology and physical properties inferred from swath data have already been used to improve our understanding of ice-shelf melt, basal sliding and subglacial sediment and water transport. Lessons learned from these initial surveys should inform future data collection strategies, so that radar swath imaging can be deployed in the most productive way possible during upcoming major field campaigns, including the Fifth International Polar Year and beyond. This article is part of the Theo Murphy meeting issue 'Next generation ice-sheet bed measurements'.

ABSTRACT The rapid expansion of ride-sourcing (RS) has raised the important question of the interactions between RS and public transportation (PT). In this study, we tackle this question by taking a fine-grained spatiotemporal view using publicly available data. We develop a three-level methodology that encompasses the analysis of spatial and temporal transit coverage, travel time difference between RS and PT, and PT transfer burden, to determine whether an RS trip is a potential substitute for, a complement to, or independent of PT. The application of this methodology is demonstrated by performing a citywide empirical investigation in the city of Chicago. We find that RS is potentially complementary to PT, especially in areas and times with low transit coverage and services. During peaking commuting hours and in central areas, the role played by RS as a potential substitute can be on par with its role as a potential complement to PT. Based on these findings, we discuss potential policy implications and offer recommendations for improving RS provision.