The rapid increase in the number of vehicles in urban areas has led to significant challenges in parking management, including traffic congestion, fuel wastage, and driver inconvenience. This paper presents a Smart Parking System with web application support, implemented as a prototype using Internet of Things (IoT) technologies, to improve parking efficiency and user experience in urban environments.

The proposed system enables users to view real-time parking availability and reserve parking slots through a web-based interface, thereby reducing the need for manual searching. Each parking slot is equipped with ultrasonic sensors that detect vehicle presence. These sensors communicate with an ESP32 microcontroller, which processes the data and updates a cloud-based database to ensure accurate and real-time parking information.

To evaluate parking demand, the system considers real-time occupancy data, frequency of slot usage, and peak-hour variations, which provide insights into urban parking patterns. This data-driven approach can be extended using Transport System Models (TSM) to predict demand and optimize parking allocation. Furthermore, the integration of Information and Communication Technology (ICT) with TSM enables intelligent decision-making and scalable urban mobility solutions.

Additionally, the system incorporates a wrong-parking detection mechanism that triggers a buzzer alert and sends notifications through the web application when improper parking is detected. The proposed system demonstrates a practical, scalable, and cost-effective solution for smart urban parking management.

Public transit infrastructure is essential to ensure the mobility of the elderly, enabling them to travel efficiently for various purposes such as recreational and medical. However, although the phenomenon of rapidly ageing communities has become a global problem, the public transport infrastructure is yet to be fully adapted in several countries to cater for the needs of these growing users, thus leading to accessibility issues. It is therefore essential to devise appropriate tools to assess the conditions of the existing infrastructure and ultimately devise appropriate measures.

This study was conducted using the newly built Light Rail Transit (LRT) network in the urban areas of Rose Hill and Quatre Bornes in Mauritius as case studies. Following a comprehensive literature review, it was deduced that the accessibility at transit stations is dependent on three main factors, namely, quality of pathway, security, and furniture present. A toolkit consisting of these three major parameters and sub-components was developed to rate the accessibility of transit stations in the selected study area using a walk over survey. The results of the toolkit were used to calculate the scores of three LRT stations in the study area and demonstrated using spider charts.

Our findings indicate that the infrastructure at LRT stations satisfied several accessibility criteria for the elderly in terms of good surface quality and continuity of footpath, adequate street lighting, provision of furniture such as benches and shelters, and cleanliness. However, they fared poorly in terms of kerb height, presence of various obstructions along the pathway and the lack of priority crossings. The findings highlight that although public transport infrastructure is being built to high standards, additional consideration can be given during the planning and design phase to specific aspects to better cater for the mobility needs of elderly pedestrians.

The accelerating impacts of climate change in tropical megacities necessitate robust evaluation mechanisms to ensure that urban climate policies deliver measurable, inclusive, and sustainable outcomes. This study proposes a comprehensive implementation analytics framework for the Kuala Lumpur Climate Action Plan (KLCAP) in Kuala Lumpur, focusing on programme effectiveness, socio-environmental co-benefits, and distributive equity. The framework integrates greenhouse gas (GHG) inventories, building energy performance data, public transport usage statistics, urban heat island mapping, flood vulnerability indices, and socio-demographic datasets within a multi-criteria evaluation architecture. Key performance indicators (KPIs) are structured across three domains: (i) mitigation effectiveness (carbon intensity reduction, renewable penetration, energy efficiency gains), (ii) adaptation performance (flood risk reduction, thermal comfort improvement, infrastructure resilience), and (iii) socio-economic co-benefits (public health enhancement, green employment generation, mobility accessibility, and air quality improvements). A spatially weighted Urban Climate Equity Index (UCEI) is introduced to assess whether climate investments equitably benefit low-income and climate-vulnerable communities. Scenario-based modelling compares baseline implementation pathways with equity-optimised resource allocation strategies. Results indicate that embedding equity metrics into programme prioritisation enhances cumulative social welfare benefits while maintaining carbon reduction trajectories. The proposed analytics model provides city planners and policymakers with a transparent, data-driven decision-support system to strengthen accountability, optimise resource allocation, and advance a just and resilient urban transition.

The compact city concept has traditionally been associated with transit-oriented urban structures; however, many regional cities continue to rely heavily on automobile-based mobility due to limited public transport networks. This study raises critical questions regarding how future urban development strategies should be designed in car-oriented contexts. Using the concept of the “15-minute city” as an analytical framework rather than a normative goal, this study examines multi-modal accessibility to urban functional facilities in several regional Japanese cities.

A GIS-based network analysis was conducted to compare 15-minute accessibility across four transport modes—walking, bicycle, public transport, and private automobiles. Accessibility was evaluated not only in terms of spatial coverage but also in terms of population coverage, using high-resolution census mesh data. The analysis covers five regional cities and six types of frequently used urban facilities, including healthcare, retail, and administrative services.

The results indicate that automobile-based accessibility remains dominant in dispersed urban structures. In most cases, over 80–90% of the urban population can reach major facilities within 15 minutes by car, whereas the corresponding walking coverage remains substantially lower. Bicycles substantially expand accessibility compared with walking and provide moderate coverage in compact urban areas. Public transport improves accessibility in certain corridors; for example, the population reachable within 15 minutes increases by over 13,000 people in some cities, while in others the increase remains limited to only a few hundred residents, indicating significant spatial disparities.

Rather than advocating a universal transition toward transit-oriented compact cities, the findings suggest that future urban strategies in car-dependent regional contexts should adopt hybrid approaches that enhance local accessibility through walking and bicycle networks while strategically integrating public transport where feasible.

Achieving carbon neutrality in the building sector is central to Malaysia’s urban decarbonisation strategy. This study examines the implementation pathways of the Kuala Lumpur Net Zero Carbon Buildings (NZCB) Roadmap in Kuala Lumpur, focusing on regulatory compliance mechanisms, lifecycle cost implications, and data-driven retrofit prioritisation. The research develops an integrated assessment framework combining building energy benchmarking, operational carbon intensity analysis, embodied carbon accounting, and financial feasibility modelling. Compliance pathways are evaluated across new developments and existing building stock, considering alignment with national energy efficiency regulations, municipal planning guidelines, and green building certification standards. A multi-criteria retrofit prioritisation model is proposed, incorporating energy savings potential, marginal abatement cost (MAC), payback period, occupant comfort improvement, and carbon reduction per square metre. Scenario simulations compare incremental efficiency upgrades, deep energy retrofits, and renewable energy integration strategies under different carbon pricing assumptions. Results indicate that targeted retrofits in high-intensity commercial and mixed-use buildings yield the greatest emissions reductions per unit investment, while bundled measures combining efficiency and on-site solar generation optimise long-term cost performance. The study provides a structured decision-support framework for municipal authorities, property owners, and investors to accelerate NZCB adoption. By aligning compliance requirements with economic feasibility and carbon impact metrics, the proposed approach strengthens policy implementation, enhances transparency, and supports Kuala Lumpur’s transition toward a resilient, low-carbon urban built environment.

Urban transport systems in India are undergoing rapid transformation, with significant investments in metro rail, Bus Rapid Transit (BRT), and related infrastructure. Despite these investments, the performance of several systems has consistently fallen short of expectations, resulting in considerable economic, social, and financial implications. A key contributing factor is the persistent overestimation of ridership forecasts in Detailed Project Reports (DPRs), which undermines the long-term viability of these projects.

This study critically evaluates the performance of operational metro, BRT, and Intermediate Public Transport (IPT) corridors using a Projected vs. Achieved Performance framework. The magnitude of forecasting error is first quantified using the Mean Absolute Percentage Error (MAPE), which reveals an average forecast bias exceeding 1500% in newly operational metro systems, highlighting the scale of the planning discrepancy.

To investigate the underlying causes of this bias, a Multiple Linear Regression (MLR) model is employed to examine the relationship between achieved ridership and key planning variables commonly used in DPR forecasts, including income levels, network expansion, and modal competition. The results indicate that the forecasting bias is significantly associated with an overestimation of the effects of network expansion and a systematic underestimation of competition from Intermediate Public Transport (IPT).

A comparative analysis of high-error and low-error projects further provides insights into the structural limitations of existing forecasting practices. Based on these findings, this study proposes data-driven recommendations for adopting more robust, econometrically grounded, and locally adaptive forecasting methodologies, which are essential for improving the sustainability and planning effectiveness of future urban transit investments in India.

Urban river degradation is frequently framed as a technical failure of waste management or infrastructure. This study advances an alternative hypothesis: that governance exclusion itself functions as a structural driver of socio-ecological collapse. Using the Nairobi River Basin as a case, we examine how pollution gradients, ecosystem decline, and disease burden intersect with institutional marginalisation in riverside communities, with implications for SDGs 3, 6, 11, and 13.

A convergent mixed-methods design integrated four datasets covering the period 2018–2024: (1) secondary water quality records across thirteen parameters (BOD, DO, COD, heavy metals, microbial indicators) benchmarked against WHO standards; (2) outpatient morbidity records from health facilities serving Kibera, Mukuru, and Mathare; (3) purposive household interviews (n = 12 most-impacted households identified through community leadership networks); and (4) a multi-stakeholder validation forum involving residents, community leaders, waste operators, and municipal actors. Field observations were conducted from upstream catchment zones, including Ondiri Swamp, to downstream industrial and informal settlement corridors. Pearson correlation was used to test contamination–disease associations. Ethical approval and informed consent were obtained prior to primary data collection.

A pronounced downstream pollution gradient was observed. Peak E. coli concentrations reached 100,000 CFU/100 mL (≈800× WHO guideline), while lead exceeded permissible limits by up to 50×. E. coli levels were strongly associated with cholera incidence (r = 0.79, p < 0.01). Waterborne diseases accounted for approximately 35% of outpatient visits at Riverside facilities. Ecological assessment documented near-total loss of sensitive aquatic taxa downstream. Qualitative findings revealed high levels of localised ecological knowledge and active stewardship practices; however, residents reported zero formal representation within river governance structures.

Findings suggest that the basin’s degradation is not solely a technical deficit but a governance architecture that externalises responsibility while withholding infrastructure and decision-making power. Sustainable recovery requires institutionalising community stewardship within formal river management systems.

In the post-COVID-19 period, urban mobility patterns have changed significantly, making micromobility systems, particularly e-bikes, increasingly important for short-distance and last-mile travel. As the role of e-bikes in urban transportation continues to expand, energy efficiency has become a key issue affecting battery performance, operational feasibility, and the broader sustainability of micromobility systems. Among the factors influencing e-bike energy use, topography, especially road slope, is a key determinant of consumption and recovery potential.

This study presents a GIS-based framework for modelling slope-dependent energy consumption and regenerative energy recovery across an urban road network. Rather than relying solely on average energy values for the entire network, the study calculates segment-based consumption, recovery, and net energy indicators. It visualizes them as layered thematic maps in a GIS/Folium environment. This approach reveals the spatial heterogeneity of energy patterns and helps identify critical segments and corridors where short but steep transitions may generate disproportionately high energy costs.

The findings show a clear spatial relationship between slope and energy performance. The resulting net energy maps enable the identification of energy-advantaged and energy-disadvantaged parts of the network, thereby providing a more detailed understanding of urban topographic effects on micromobility systems. These outputs can support municipalities, micromobility operators, and urban mobility planners in developing more informed, place-sensitive, and energy-aware strategies for sustainable transport.

Although the framework is based on simplifying assumptions, excludes traffic-related operational conditions, and is limited to a single study area with constrained validation, it provides a practical basis for integrating topography-sensitive energy analysis into sustainable urban mobility planning.

Urban mobility infrastructure is a major energy consumer in rapidly growing cities. Piezoelectric flooring tiles offer a promising solution for harvesting energy from pedestrian movement and creating an eco-friendly path in public facilities with high-density occupancy. This study tests the hypothesis that installing piezoelectric tiles in high-traffic transit zones can generate measurable electrical energy. The Algiers metro station, Les Fusilles (LFU), was selected as the case study due to its consistently high passenger volume and strategic location within the city's transportation network. The methodology is structured in three stages: first, collecting baseline data on daily passenger numbers and electricity consumption at LFU station; second, conducting in situ observations of pedestrian patterns to identify optimal zones; and third, selecting the type of Waynergy tiles for this test based on their energy generation capacity, cost, and lifespan. Results show that installing the selected piezoelectric tiles in high-traffic zones could generate measurable electrical energy directly correlated with daily pedestrian frequency. This harvested energy can be used for station applications such as lighting and signage, or stored for later use. The findings confirm that Wayenergy piezoelectric flooring is a viable solution for enhancing urban sustainability, reducing carbon emissions, and supporting smart transportation systems in metropolitan environments.

Introduction

Quantifying urban walkability at fine spatial resolution is essential for evidence-based active travel planning. Yet, existing indices typically rely on coarse administrative units or proprietary data, which impedes cross-city comparisons. This study presents the Hexagonal Walkability Surface (HWS) framework: a configuration-driven pipeline for computing multi-resolution walkability indices from geospatial data.

Methods

HWS employs the H3 discrete global grid to tessellate city extents at four hierarchical resolutions (levels 8-11). Five pedestrian-environment features are extracted per hexagon: (1) footway and path density (pedestrian edge length per km²); (2) point-of-interest accessibility, combining in-cell counts with an inverse-distance-weighted proximity score; (3) land-use diversity as Shannon entropy of intersecting land-use classes; (4) greenspace proximity, integrating coverage proportion with distance decay; and (5) road-safety ratio of pedestrian-friendly to total edge length. Street network and land-use data are sourced from OpenStreetMap; place data is augmented with Overture Maps (2026-02-18) via DuckDB S3 queries. Features are min-max normalised and combined as a weighted composite score (0-100). Spatial autocorrelation (Getis-Ord Gi*) is used to identify clusters of walkability.

Results

In Nottingham and Bristol, United Kingdom, the framework produces 1,178-57,700 and 2,581-126,518 hexagons per city, respectively, across resolutions 9-11. Across the resolutions, agreement is high (Spearman ρ = 0.963-0.971 between adjacent levels, all p < 0.001), confirming multi-scale consistency. At resolution 9 (roughly neighbourhood scale), intra-urban variation is observed in both cities (σ = 11.4 and 10.1 score points respectively), with city-centre hexagons clustering as hotspots and peripheral areas as coldspots. Gini coefficients indicate greater inequality in walkability in Nottingham (0.36) than in Bristol (0.26).

Conclusions

HWS provides a reproducible, data-agnostic methodology extensible to any city with OSM coverage. The multi-resolution design could support both strategic (neighbourhood) and street-block planning decisions. By making fine-grained walkability assessment accessible to researchers and planners without proprietary data or bespoke infrastructure, HWS lowers the barrier to evidence-based active travel policy at the urban scale.