The bicycle has become a key mode of transport for promoting sustainable urban mobility in Lima. However, various studies show that its use is not gender-neutral. Men and women experience urban mobility differently. This study analyzes university cycling from a gender perspective.

The research examines students who use a bicycle as a means of transportation for their trips to and from the Pontificia Universidad Católica del Perú (PUCP), located in the district of San Miguel, Lima. The study adopts a mixed-methods approach consisting of two stages. In the first stage, structured surveys were administered to student cyclists to investigate gender differences in cycling behavior. The instrument collected data on frequency, schedules, routes, and perceptions of safety. In the second stage, photographic records were made of the bike lanes most frequently used by students, and maps were produced to analyze the information obtained.

The results show clear differences in mobility patterns by gender. Women report lower nighttime use and a higher perception of risk. Men report greater circulation on shared roads. Female students prioritize routes with segregated infrastructure. The analysis confirms that cycling does not operate as a neutral practice but is shaped by gender inequalities. This study provides evidence for more inclusive urban mobility planning.

Introduction: In Zambian cities, over 70% of daily trips are on foot or in informal minibuses, yet pedestrians and cyclists account for over 60% of road fatalities, rooted in infrastructure designed for motorised vehicles. Kitwe exemplifies this: residents walk and cycle out of necessity while the built environment provides no dedicated non-motorised transport infrastructure. Meanwhile, corridor investments in Ndola–Kitwe and Kitwe–Chingola dual carriageways reshape connectivity without transit-oriented development, potentially entrenching car-centric spatial configurations in a city where fewer than 10% of households own private vehicles.
Methods: A two-stage methodology is applied. First, a GIS-based walkability and cyclability audit maps existing NMT infrastructure, including footpaths, pedestrian crossings, cycle lanes, and street lighting within two-kilometre catchments of Kitwe’s primary arterials and dual carriageway interchanges, scored against the ITDP Pedestrians First framework. Second, a transit-oriented development readiness assessment evaluates land use density, diversity, and design parameters at six interchange nodes using the TOD Standard, benchmarked against modal split data from household travel surveys.
Results: Fewer than 5% of arterial road segments incorporate dedicated pedestrian facilities meeting ITDP minimum standards, and purpose-built cycle infrastructure is absent across the entire surveyed network. Interchange grade separations extend pedestrian crossing distances by 300–800 metres, creating significant severance barriers for non-motorised users. TOD readiness scores remain uniformly low, constrained by single-use zoning, the absence of mixed-income housing near transit nodes, and the lack of scheduled public transport services to anchor development. Copperbelt Province accounts for 15% of national traffic crashes, and total annual economic losses from road injuries reach 4.7% of GDP nationwide.
Conclusions: Kitwe’s dual carriageway investments represent a rapidly closing window for NMT and TOD integration. Retroactive pedestrian infrastructure, mixed-use zoning overlays at interchanges, and formalised feeder transit services are essential to prevent reproduction of auto-dependent patterns that undermine mobility equity in resource-constrained cities.

Transportation sector pollution is one of the most critical environmental challenges affecting urban sustainability, public health, and occupant comfort in rapidly growing cities. This paper analyses the factors of the urban mobility, using an in-depth evaluation of the urban traffic flow, modal share, vehicle composition, and the emission loads as a basis of proposing the innovative and data-driving approaches in accordance with the principles of sustainable transportation. The Traffic Volume Count survey for Gwalior, India shows that it has a large daily vehicular capacity of well more than 155,000 vehicles with two wheelers and intermediate public transport (IPT) as the major modes of transportation. The proportion of short-range (≤5 km) trips ( > 50 percent of daily trips) indicates a huge potential of the modal shift on active mobility and the integration of the public transit. Average journey speeds ranging between 22–27 km/h highlight systemic congestion and operational inefficiencies. Nevertheless, infrastructure fragmentation, poor pedestrian infrastructure, and the lack of transit-centered planning are the current limitations to the adoption of sustainable mobility.

The paper focuses on TOD and the corridor-based planning to reorganize the patterns of growth around high capacity public transport corridors. The introduction of electric intra-city buses and the electrification of IPT fleets facilitates the low-emission mobility transitions. It is suggested that smart infrastructure (e.g. ITS, digital fleet management systems, GPS-based tracking) can improve operational efficiency and minimize the emissions associated with the idling issue.

Promotion of active mobility is a key component of the strategy. The continuous footpath, cycle tracks, safe last-mile connectivity are geared towards promoting local walking and cycling to make short journeys. These solutions decrease vehicle kilometers traveled as well as enhancing accessibility, inclusivity, and population health.

Planned industrial townships were historically designed with compact neighborhoods, functional land-use distribution, and strong internal accessibility; however, rising private vehicle dependence and spatial dispersion are weakening their inherent pedestrian orientation. Prior studies on sustainable mobility in such settlements have primarily examined walkability, urban form, or low-carbon transport transitions in isolation. The interactive or synergic relationship between pedestrian-supportive environments and carbon transition readiness—defined as the spatial and infrastructural capacity to enable low-emission mobility—remains insufficiently understood, particularly in industrial township contexts.

This research proposes an integrated predictive framework to evaluate how walkability and carbon readiness jointly influence sustainable mobility potential. Composite indices are constructed using primary travel behavior surveys, pedestrian environment assessment, and spatial metrics including connectivity, land-use proximity, density, and modal accessibility. Multi-criteria aggregation and spatial overlay analysis are employed to identify convergence zones where pedestrian-friendly morphology coincides with high readiness for low-carbon mode adoption. Scenario-based testing further estimates the potential shift of short motorized trips toward non-motorized and public transport modes under targeted improvement strategies.

The results demonstrate that areas exhibiting both high walkability and carbon readiness present disproportionate opportunities for reducing short-distance motorized travel and associated emissions. By linking urban form characteristics with transition readiness, the study advances an integrated methodological approach for planning sustainable mobility in industrial townships. The framework offers decision-support for prioritizing pedestrian-oriented and low-carbon mobility interventions in similar planned industrial settlements undergoing motorization transition.

Growing vehicle density and insufficient parking facilities have made urban parking management increasingly challenging. Drivers often waste valuable time circling in a futile search for parking, which results in wasted gas, traffic congestion, and pollution. To mitigate these concerns, the proposed research presents an Internet of Things (IoT)- supported smart parking system that will automate and manage parking spaces in real time. The proposed system includes a microcontroller-based control unit that serves as the system's main processing unit. The control unit continuously monitors the occupancy status of each parking space using sensors to classify whether each space is empty or occupied. With the help of a specialized website, users can see parking space availability in real time and reserve parking in advance. The user opens the website and books a slot of their choice. Immediately after booking, an Entry OTP is generated and stored in Firebase. The user receives this OTP, which must be entered at the parking keypad within the 10- minute grace period. When the user decides to leave, the system calculates the total time spent based on the entry and current timestamps. The website displays the total payable amount (for example, ₹50/hour). Once the user completes the online payment, the system automatically generates a unique Exit OTP and sends it to the user. The user enters the Exit OTP on the keypad module. Upon validation, the servo motor opens the exit gate. The slot status is updated to Available, and the exit timestamp and payment confirmation are stored in Firebase. This control unit connects directly to the internet; therefore, there is no reliance on intermediate hardware to connect to the internet. The hardware saves user/consumer time and costs during implementation, improving the user experience.

The rapid expansion of the city's population, together with the increase in vehicles, has made vehicle parking a major problem today. These days, finding a parking slot is time-consuming, and there is no safety for the vehicle. To address these limitations, we have created a smart parking system that includes a webpage to see the available slots. From this web interface, the user can book a slot and make a payment. ANPR technology for number plate recognition and piezoelectric technology to generate electrical energy for backup. This system uses IR sensors that detect vehicles in the slots and update the data on the webpage using the cloud-based ESP32 Devkit microcontroller. The microcontroller sends data to the Flask-based backend, where it is stored and managed in the PostgreSQL database. This paper uses Automatic Number Plate Recognition (ANPR) with cloud-based Convolutional Neural Network (CNN) technology for vehicle identification and protection. Additionally, this project includes a piezoelectric system to generate electrical energy. Electrical energy is generated by the mechanical pressure of a vehicle passing through the piezoelectric sensor. This proposed solution provides budget-friendly, expandable parking operations and efficient parking management.

Abstract
Urbanization and the rapid increase in vehicle ownership have intensified parking
challenges, leading to congestion, wasted time, fuel consumption, and
unauthorized access in public and private facilities. Traditional parking systems
relying on manual supervision and paper tickets are inefficient and prone to
errors. To address these issues, this project proposes an IoT-based Smart
Parking System integrated with Face Recognition for vehicle access control.
The system employs an ESP32-CAM to capture and authenticate the driver’s
face against a registered database. Authorized users gain seamless entry through
a servomotor-controlled gate, ensuring enhanced security. Parking slot detection
is achieved using an IR sensors, which continuously monitor occupancy and
updates availability in real time. This data is transmitted via WiFi to a cloud
platform such as Firebase, enabling remote monitoring and management through
a website dashboard. The methodology follows a structured workflow: vehicle
detection at the entrance, face capture and recognition, automated gate control,
slot occupancy detection, cloud synchronization, and real-time display of parking
status. A field survey conducted among drivers, students,s and parents confirmed
the pressing need for such automation, highlighting delays and inefficiencies in
conventional parking systems. This integrated solution leverages IoT and sensor
technologies to deliver secure, efficient, and user-friendly parking management.
It reduces waiting time, prevents unauthorized access, ss and optimizes space
utilization. The proposed system is highly adaptable for deployment in offices and apartments,nts contributing to sustainable urban mobility. By combining intelligent
sensing, cloud connectivity, and biometric authentication, the project
demonstrates a scalable model for next-generation parking systems that enhance
convenience, security, and operational efficiency in modern urban environments.

Introduction: Metro station entrances are among the most acoustically exposed urban public spaces, where transport, commercial activities, and pedestrian flows converge. Yet large-scale 24-hour noise evidence for these sites remains scarce, and existing studies seldom compare activity, built-environment, and source-composition drivers within the same model or translate findings into station-level intervention priorities. This study investigates spatiotemporal noise patterns, their multi-domain drivers, and governance prioritisation at metro station entrances in Shenyang, China.

Methods: Taking Shenyang, China, as a case study, a one-day 24-hour monitoring programme was conducted at 111 station entrance sites (one entrance per station) during April–September 2025, yielding 109 valid sites. Hourly A-weighted equivalent sound levels (LAeq) were integrated with a population-heat activity proxy, built-environment indicators, and sound-source composition derived from audio event recognition. Five noise outcomes (LAeq_24h, Lday, Lnight, LPeak, LInter-peak) were evaluated using robust OLS with cross-period stability comparison. Governance outputs combined a Baseline–Surplus typology with bootstrap-tagged Top-10 prioritisation.

Results: A clear diurnal rhythm was identified (peak-to-trough difference: 7.61 dB), alongside significant spatial clustering (Moran's I = 0.196–0.246, all p = 0.001). Source composition was the most stable driver domain: traffic share was consistently positive and natural share consistently negative across all five outcomes. The Baseline–Surplus framework differentiated structurally high-burden stations from those with excess noise, and 9 of the Top 10 priority stations showed high bootstrap stability (topk_prob ≥ 0.80).

Conclusions: This study provides city-scale screening evidence for noise exposure at metro station entrances. Source composition, particularly traffic and natural sound shares, offers the most stable diagnostic signal for governance. The Baseline–Surplus typology and bootstrap stability tagging can help managers identify priority stations and allocate resources under uncertainty, rather than relying on uniform exposure ranking alone.

Urban public transport systems are traditionally designed for passenger mobility, while their latent operational capacity remains under-theorized as a strategic resource for urban logistics. Although recent research has explored freight–passenger integration and shared infrastructure efficiency, the field remains dominated by descriptive case studies and operational feasibility assessments. There is a lack of structured analytical frameworks capable of supporting systematic, multi-dimensional decision-making under technical, economic, and institutional uncertainty.

This study addresses this theoretical and methodological gap by proposing a Stage-Gate diagnostic framework grounded in Engineering Methods and Design Science Research. Rather than treating freight integration into Bus Rapid Transit (BRT) systems as an isolated operational experiment, the framework conceptualizes it as a sequential uncertainty-reduction architecture. The model structures feasibility assessment through five validation gates: (G1) contextual and territorial eligibility, (G2) physical and operational feasibility, (G3) service compatibility and interference control, (G4) logistical and economic performance, and (G5) governance, regulation, and scalability. Each gate incorporates explicit criteria, measurable indicators, and decision thresholds, enabling analytically grounded go/no-go decisions prior to implementation.

The framework is empirically applied to the TransCarioca BRT corridor in Rio de Janeiro, Brazil, using timetable analysis, occupancy patterns, and identification of idle operational windows. Our findings reveal stable off-peak capacity that can be strategically reallocated to middle mile logistics without compromising passenger service levels. Beyond empirical validation, the study contributes a formal decision architecture that advances the theoretical foundations of infrastructure sharing and freight–passenger integration by operationalizing uncertainty management within capacity-constrained urban systems.

By integrating decision-structuring logic, governance analysis, and infrastructure efficiency into a unified validation model, this research provides both conceptual advancement and a transferable methodological artefact for planners and policymakers.

Introduction: Philadelphia has one of the highest disability rates of all large US cities. The 1990 American with Disabilities Act decreased disparity between nondisabled and disabled Philadelphians. However, ongoing geographic segregation of resources needed by disabled communities illustrates the work remaining to make Philadelphia more equitable. This presentation outlines areas of geographic segregation in the Philadelphia ambulatory disabled community and how they overlap with community resources.
Methods: To examine the social context of disability in Philadelphia, we employed Geographic Information Systems (GIS). GIS enables spatial analysis of population-level data. Data sources included the U.S. Census Bureau (American Community Survey 5-Year Estimates) for demographic and socioeconomic variables at the census tract level. OpenStreetMap and City of Philadelphia GIS Hub provided spatial layers for public infrastructure. Philadelphia Department of Public Health provided data on neighborhood health centers. The Septa public transit web portal provided data on accessible transit stations. Proximity to grocery outlets and overlay with food deserts was obtained from the USDA food access research atlas. Chloropleth mapping was used to visualize the spatial distribution of each variable. Hot spot analysis was used to identify statistically significant clusters of disability prevalence. Spatial autocorrelation was used to assess the degree of clustering.
Results: The ambulatory disabled population of Philadelphia is geographically disproportionally segregated into census tracts with high rates of poverty and limited access to accessible transit stops, usable sidewalks, community health clinics, and grocery outlets. Northeast Philadelphia has amongst the highest rates of ambulatory disabled populations and lacks access to many critical community resources.
Conclusion: The ambulatory disabled population of Philadelphia is geographically segregated into census tracts with limited access to necessary community resources. Insight into the geospatial distribution of disabled communities and its relation to community resources will allow more informed public policy decisions on resource allocation and planning.