Search Results (195)

Background: The rising costs in the metropolitan real estate market are compelling individuals to relocate outside of the city. The anticipated financial savings, however, may be undermined by long and costly commutes; raising the question of whether this trade-off is a worthwhile proposition. This paper uses a digital model of workplace commutes, income levels and house prices in England as well as Wales, to evaluate the trade-off between (i) moving to the city centre and cycling to work versus (ii) continuing to commute by car from a residence on the periphery. Methods: An indicator has been introduced that unifies the transport and housing affordability by expanding the concept of the ‘effective speed’ to include housing costs. The effective speed itself is commonly defined as the travel distance divided by the time dedicated to the transport activity (i.e., travel duration and time given to earn the money to pay for the costs incurred). Results: If only the associated fuel and mortgage costs are considered, residing on the periphery can—for those already living there—be a cost-effective option specially in cities like Cambridge and Oxford. Yet, accounting for the total ownership costs of cars or external effects, this advantage shifts in favour of relocating to the city centre. Conclusion: This study does not negate the existence of an affordable housing crisis in urban environments, though it demonstrates that strategies to cut transport emissions can produce economic gains. Full article

Cities need emergency-shelter systems that are computationally efficient, socially fair, and consistent with long-term goals for sustainable urban development. This paper proposes a GIS-integrated, two-stage location–allocation framework for urban earthquakes that jointly optimizes shelter siting and evacuee assignment under time-varying demand. The model incorporates equity constraints that cap extreme travel burdens for vulnerable groups and robust capacity safeguards against demand uncertainty, helping prevent over- or under-investment in shelter infrastructure and promoting efficient use of land and public resources. A customized Phased Nested Local Search (PNLS) heuristic enables city-scale application and is benchmarked against a mixed-integer programming baseline solved by CPLEX. In a district-level case study of Chengdu, China, the framework reduces total assignment distance by 12.3% and the 95th-percentile travel burden by 15.8% while maintaining feasibility during the peak demand window. The results show that integrating equity, robustness, and spatial efficiency in shelter planning can strengthen urban resilience and directly support SDG 11 on sustainable cities and communities. Full article

Background: Stepped-wedge cluster randomized trials (SW-CRTs) are increasingly used to evaluate population-level interventions, but trial validity depends on timely cluster transitions. Rollout feasibility is often assumed rather than modelled. In the context of a planned national trial of CPR training kiosks, we developed a Monte Carlo simulation algorithm to quantify logistical feasibility under uncertainty. Methods: A stochastic Monte Carlo algorithm was implemented to simulate deploying 100 CPR kiosks across eight Canadian cities under four team structures. Inputs included productivity (0.8–1.2 kiosks/day), disruption probabilities (weather, venue access, technical failure, staff illness, transport delays), and cost parameters (salaries, per diems, travel). Each scenario was simulated across 3000 iterations. Outputs included per-city feasibility (p ≤ 60 days), total project duration, and risk–cost trade-offs. Results: Single-team strategies required 9–10 months for full rollout, with winter-exposed cities such as Halifax and Charlottetown having up to 30% probability of exceeding 60 days. Two-team strategies halved rollout time (4–5 months) and achieved >95% on-time rollout across cities. Adding a third onsite staff member reduced risk by 5–15% with modest additional cost (~CAD 1500–2000 per city). Risk–cost analysis identified two teams with three staff as the most reliable strategy. Conclusions: Monte Carlo simulation provides a practical framework for assessing rollout feasibility in SW-CRTs. Applied to CPR kiosk deployment, it highlights the importance of staffing, seasonality, and city-level context. The approach is generalizable to other national interventions requiring phased rollout under uncertainty. Full article

Promoting sustainable mobility and socio-economic sustainability through demand management is critical for mountainous urban rail systems. This study investigates urban rail transit in mountainous cities, focusing on how passenger travel behavior responds to time-based pricing policies across different station types, with the aim of informing differentiated fare policy design. Using Chongqing—a city with pronounced mountainous terrain—as a case study, we classified stations into 12 categories based on 11 indicators, including road slope, bus transfer density, average housing price, and peak-hour train crowding within a 500 m radius. This classification was then combined with questionnaire data to quantify fare elasticity of departure time. The results show that high-value bus-transfer congested stations are concentrated in central urban clusters with dense bus networks, mitigating terrain constraints and encouraging active travel. In contrast, low-value pedestrian-transfer comfort-oriented stations are predominantly located on the urban periphery, where sparse road networks and steep terrain exert greater influence. Low-value pedestrian-transfer congested stations exhibit the highest fare elasticity across all periods, indicating greater sensitivity to fare changes, while high-value bus-transfer comfort-oriented stations demonstrate the lowest elasticity, with passengers more likely to maintain existing travel patterns. Multiple linear regression identifies six significant determinants of fare elasticity, including section-level passenger crowding, average housing price, and bus route density. Sensitivity analysis using multinomial logistic regression further reveals that increasing bus route availability enhances the stability of low-value balanced-transfer comfort-oriented stations, whereas improving walkability can shift stations toward pedestrian-transfer types. By tailoring time-of-day pricing to station heterogeneity, policymakers can achieve equitable and environmentally friendly demand management, enhance operational efficiency and support sustainable urban development in mountainous regions. Full article

Ensuring equitable access to basic education is a core issue for promoting balanced regional development and sustainable educational outcomes. As a vast and sparsely populated region with relatively slow development, the Qinghai–Tibet Plateau faces particular challenges in ensuring educational accessibility and equity. Using a 100 m × 100 m travel time cost raster constructed from OSM road networks and the cost-distance method, together with local spatial autocorrelation, Lorenz curve, and Gini coefficients, as well as the Geodetector method, this study examines the spatial equity and factors influencing the accessibility of primary and secondary schools across 2798 townships at three time points (2016, 2020, and 2024) under three scenarios: Scenario 1 (nearby schooling), Scenario 2 (schooling within the prefecture-level city), and Scenario 3 (schooling within the county). The results show that: (1) Overall accessibility improved from 2016 to 2024, with primary schools being more accessible than secondary schools. Western townships, although initially disadvantaged, experienced the most notable gains. However, accessibility declined markedly when administrative-boundary constraints were imposed, with the greatest losses observed in ultra-high-altitude remote areas such as Ngari and Nagqu. (2) Spatial equity also improved, but when administrative boundaries were imposed, population-weighted inequities became even more pronounced than disparities in accessibility itself. Equity declined most sharply under county-level constraints, with pronounced impacts on both primary and secondary schooling. (3) Spatial variations in accessibility were jointly driven by multiple factors. In Scenario 1, road network density and population density had the strongest explanatory power. Under administrative boundary constraints, however, county type and ethnic autonomy became increasingly influential. In conclusion, in ultra-high-altitude areas where natural conditions remain difficult to overcome, improving educational equity depends less on transport expansion or facility provision and more on relaxing county-level boundary restrictions that constrain access to services. These findings may provide useful evidence to inform targeted policy interventions and resource allocation strategies aimed at promoting equitable access to basic education in underdeveloped and high-altitude regions. Full article

Policies rarely act on simple circles around project sites. We develop a policy-semantics-to-geometry workflow that converts clause-level rules in ordinances into auditable Policy Spatial Footprints (PSFs) with explicit boundaries, timing markers, and intensity tiers, and we measure exposure in network time on road–rail graphs. Using 1.10 million arm’s-length parcel transactions from five Yangtze River Delta cities (2012–2024) and a catalog of 64 policies across regulatory, transport, and industrial/functional families, we estimate dynamic capitalization under staggered roll-outs while separating direct footprint effects from adjacency diffusion. Direct exposures are associated with policy-relevant uplifts that build over several years and then stabilize; spillovers attenuate within a few minutes of network travel time. Effects are systematically larger in thicker markets and where pre-policy regulatory headroom is greater. The PSF framework yields estimator-consistent maps with provenance and uncertainty tiers, providing a transparent basis for land-value-capture scheduling and equity-aware carve-outs. Full article

This paper assesses the correlates of perceived well-being among young women aged 18 to 30 in five Balkan cities: Athens, Greece; Plovdiv, Bulgaria; Bucharest, Romania; Nis, Serbia; and Shtip, North Macedonia, by integrating urban, travel behavioural, and socio-economic features. A cross-sectional survey was employed using standard questionnaires including the Warwick–Edinburgh Mental Well-being Scale (WEMWBS), the short version of the International Physical Activity Questionnaire (IPAQ), and the adapted ALPHA environmental questionnaire. To answer research questions, linear regression models were developed to analyse predictors of well-being at both regional and national levels. Results show that neighbourhood and mobility features play a significant role in shaping mental well-being. Access to walkable sidewalks, green spaces, mixed land-use structure, and attractive local facilities (e.g., shops, recreational centres in the neighbourhood) were consistently associated with higher levels of well-being. Conversely, perceived insecurity, especially at night or regarding bicycle theft, significantly reduced well-being. Physical activity levels, particularly days of walking and vigorous activity, showed strong positive associations, underscoring the role of active lifestyles in promoting mental health. Socio-economic variables, including financial status, relationship status, and work status, were also found to be linked to perceived well-being. Cycling-related variables may affect Greek well-being up to 16.5 times. Perception of crime during the night may negatively affect both Bulgarian and Serbian well-being (up to 10 times), while Romanian well-being is mostly affected by the existence of shopping facilities. Finally, the most impactful factors for well-being in North Macedonia refer to cycling safety and scooter accessibility. Full article

Accurate prediction of downstream vehicle arrivals is pivotal for intelligent signal control, yet many advanced controllers depend on high-resolution trajectories that are rarely available outside connected-vehicle settings. We present a deployable alternative that converts ubiquitous Automatic License Plate Recognition (ALPR) timestamps into the predictive inputs required by modern controllers. The method couples a Hidden Markov Model (HMM) for separating free-flow samples from signal-induced delays with a dynamic platoon-dispersion model that is re-estimated online in a rolling window to forecast downstream arrival profiles in real time. In a Simulation of Urban Mobility (SUMO) corridor testbed, the proposed framework consistently outperforms fixed-kernel dispersion and fixed-travel-time baselines, reducing RMSE by 57–75% and MAE by 53–73% across demand levels; ablation results confirm that HMM-based filtering is the dominant contributor to the gains. Robustness experiments further show stable parameter estimation under low ALPR matching rates, indicating suitability for real-world conditions where data quality fluctuates. Because it operates with existing roadside cameras and lightweight inference, the framework is readily integrable into adaptive signal strategies and broader smart-city traffic management. By turning discrete ALPR events into reliable arrival predictions, it bridges the gap between advanced signal control and today’s sensing infrastructure, enabling cost-effective real-time signal optimization in data-constrained urban networks. Full article

Wet markets serve as critical infrastructure for access to fresh food for urban residents in China, playing a vital role in daily life and public well-being. However, their accessibility is often shaped by disparities between urban cores and rapidly expanding peripheral districts, raising concerns over spatial equity in the urban food environment. This study investigates these disparities in Shanghai by comparing wet market accessibility in Putuo district (urban core) and Minhang district (periphery). Accessibility is measured using the Gaussian-enhanced two-step floating catchment area (2SFCA) method, incorporating travel time data from the Baidu Map API for multiple transportation modes. The Gini coefficient is further employed to evaluate the equity of accessibility distribution. The results reveal a notable disparity: residents in the periphery (Minhang) experience a higher average level of accessibility, but their access is distributed significantly less equitably compared to those in the traditional urban core (Putuo). These findings underscore a critical trade-off between development efficiency and spatial equity, highlighting the need for targeted planning strategies and policies to address spatial inequalities in fresh food access in rapidly transforming cities. Full article

As smart cities evolve, integrating new technologies into school transportation is becoming increasingly important to ensure student comfort and safety. Monitoring and enhancing comfort during daily commutes can significantly influence well-being and learning readiness. However, most existing research addresses isolated factors, which limits the development of comprehensive and scalable solutions. This study presents the design and implementation of a low-cost, generalized IoT-based system for monitoring comfort in school transportation. The system processes multiple environmental and operational signals, and these data are transmitted to a cloud computing platform for real-time analysis. Signal processing incorporates standardized metrics, such as root mean square (RMS) values from ISO 2631-1 for vibration assessment. In addition, machine learning techniques, including a Random Forest classifier and ensemble-based models, are applied to classify ride comfort levels using both road roughness and environmental variables. The results show that stacked multisensor fusion achieved a significant improvement in classification performance compared with vibration-only models. The platform also integrates route visualization with commuting time per student, providing valuable information to assess the impact of travel duration on school mobility. Full article

Urban green spaces (UGS) provide numerous benefits, but challenges in availability and accessibility often limit their full potential. This study assesses equity and disparities in car-based accessibility to Large Urban Green Spaces (LUGS > 8 ha) in the rapidly growing cities of Sulaimaniyah and Erbil in the Kurdistan Region of Iraq. Road network accessibility was analyzed using OpenRouteService (ORS) and calibrated with real-time Google Maps data to improve accuracy. Google Earth Engine (GEE) was used for NDVI-based vegetation mapping and LUGS quality assessment. Isochrones based on 5, 10, and 15 min from LUGS entrances were generated to measure served areas and population coverage at citywide and zonal levels. The findings reveal notable spatial inequities in both cities, with disparities especially evident at shorter travel times. Accessibility declines from central to outer zones. Azadi Park and Sami Abdulrahman Park emerged as key service hubs. The number of LUGS active entrances, spatial distribution, and population density are among the key determinants of car accessibility to LUGS. The study highlighted the spatial-temporal suggestion for long- and short-term implementation, with opportunities for enhancement. Full article

This paper investigates the associations between urban form, neighbourhood characteristics, socioeconomic factors and commuting mode choice and neighbourhood-level active travel (walking and cycling) in Windhoek, Namibia. Despite growing interest in sustainable mobility, limited research has examined these relationships in medium-sized African cities, particularly in distinguishing between commuting and neighbourhood travel behaviour. To address this gap, the study explores three interrelated research questions: (1) In what ways are urban form, accessibility, and socioeconomic factors associated with residents’ choices between motorised and non-motorised commuting modes? (2) What factors determine the propensity of cycling within neighbourhoods? (3) How are similar factors associated with walking propensity at the neighbourhood level? Using survey data from 1000 residents across nine constituencies and spatial analysis through GIS, the study applies binary logistic and multiple linear regression models to analyse commuting and local travel patterns. The findings show that commuting mode choice is significantly associated with socioeconomic status, car ownership, commuting time, and urban sprawl around homes, all of which reduce the likelihood of walking or cycling. Neighbourhood walking, in contrast, is largely driven by necessity in underserved, high-density areas and is positively associated with population density, perceived safety, and community belonging but constrained by inadequate infrastructure and car access. Cycling, though less frequent, is associated with perceived security, access to local amenities, and cycling competence, while negatively constrained by inexperience and cultural norms. The study concludes that fragmented urban form and socioeconomic disparities reinforce mobility exclusion and calls for equity-oriented transport planning that integrates infrastructure and behavioural change. Full article

One of the fundamental goals of contemporary mobility is to optimize transport processes in urban areas. The solution in this area seems to be the implementation of the idea of sustainable transport systems based on the Smart City concept. The article presents a case study—an assessment of the possibilities of changing mobility habits based on the idea of sustainable urban transport, taking into account the criterion of energy consumption of individual means of transport. The analyses are based on a comparison of selected means of transport occurring in the urban environment according to several key parameters for the optimization and efficiency of transport processes, i.e., cost, time, travel comfort, and impact on the natural environment, while simultaneously linking them to the criterion of energy consumption of individual means of transport. The analyzed parameters currently constitute the most important group of challenges in the area of shaping and planning optimal and sustainable urban transport. The presented research was used to indicate the connections between various areas of optimization of the transport process and the energy efficiency of individual modes of transport. Analyses have shown that the least time-consuming process of urban mobility is associated with the highest level of CO₂ emissions and, at the same time, the highest level of energy efficiency. However, combining public transport with other means of transport can meet most of the transport expectations of city residents, also in terms of energy optimization. The research results presented in the article can contribute to the creation of a strategy for the development of the transport network based on the postulates of increasing the optimization and efficiency of individual means of transport in urban areas. At the same time, recognizing the criterion of energy intensity of means of transport as leading in the development of sustainable urban mobility. Thus, confirming the important role of existing transport systems in the process of shaping and planning sustainable urban mobility in accordance with the idea of Smart City. Full article

This study explores the spatial and social implications of urban food systems in Quito, Ecuador, focusing on how food access inequalities reflect and reinforce broader urban disparities. The research addresses a critical problem in contemporary urbanization: the disconnection between food provisioning and spatial equity in rapidly growing cities. The objective is to assess and map disparities in food accessibility using a mixed-methods approach that includes field observation, participatory mapping, value chain analysis, and statistical modeling. Five traditional and emerging food markets were studied in diverse districts across the city. A synthetic accessibility function F(x) was constructed to model food access levels, integrating variables such as income, infrastructure, transport availability, and travel time. These variables were subjected to Principal Component Analysis (PCA) and hierarchical clustering to generate three typologies of territorial vulnerability. The results reveal that peripheral areas exhibit lower F(x) values and weaker integration with the formal food system, leading to higher consumer costs and limited fresh food options. In contrast, central districts benefit from multimodal infrastructure and greater diversity of supply. This study concludes that food systems should be treated as critical urban infrastructure. Integrating food equity into land use and mobility planning is essential to promote inclusive, sustainable, and resilient urban development. Full article

Reliable source term prediction for hazardous pollutant puffs in urban microenvironments is challenging, especially for risk management under strict time constraints. Puff movement is highly stochastic due to atmospheric turbulence, intensified by complex urban canopies. This complexity, combined with time limitations, makes advanced computational modeling impractical. A more efficient approach is leveraging past and present data using Machine Learning (ML) techniques. This study proposes an ML-based method, enriched with simplified physical modeling, for source term estimation of unforeseen hazardous air releases in monitored urban areas. The Random Forest Regression, commonly used in meteorology and air quality studies, has been selected. A novel variable selection method is introduced, including the following: (a) a model-derived Exposure Burden Index (EBI) reflecting plume–morphology interactions; (b) a plume travel time indicator; (c) the standard deviation of input variables capturing stochastic behavior; and (d) the total dosage-to-mass released ratio at sensor locations as the target variable. The case study examines JU2003 field experiments involving SF₆ puffs released at street level in Oklahoma City’s urban core, a challenging scenario due to the limited number of sensors and historical data. Results demonstrate the approach’s effectiveness, offering a promising, realistic alternative to traditional computationally intensive methods. Full article