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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <title>Residential Price Modeling Using Spatially Validated Machine Learning Methods: A Comparison Across Geographical Contexts</title>
      <link>https://trid.trb.org/View/2724620</link>
      <description><![CDATA[Land use (i.e., buildings) and transportation infrastructure are tightly coupled systems, such that residential property prices play a critical role in transportation planning. In a similar manner, transportation infrastructure influences property prices, such that accurate forecasts of both systems are related research problems. The objective of this study is to examine these interactions and evaluate the effectiveness of machine learning methods in modeling residential real estate prices across different urban contexts. Specifically, we first examine the impact of land and transportation infrastructure on residential real estate prices using Extreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning methods, and compare results between two cities representing diverse geographic and socioeconomic contexts. Second, we investigate the application of machine learning methods on spatial data and provide a comparison of non-spatial and spatial cross-validation on the performance of machine learning methods. We use SHapley Additive exPlanations (SHAP) values to study the impact of land use and transportation infrastructure on real estate prices. The models are applied, and results are compared between the Rawalpindi and Islamabad Metropolitan Area in Pakistan and the City of Toronto in Canada. We find that, despite differences in demographics and economic development, the two cities exhibit similarities in the effect of transportation infrastructure and local amenities on dwelling prices. Proximity to the major central city cores (i.e., downtowns) increases sale price. The effect of transportation infrastructure is differentiated, with high quality transit (e.g., subway and BRT) increasing and conventional bus stop proximity decreasing sale price, respectively. We confirm the previous finding in other fields that non-spatial cross-validation over-estimates the prediction accuracy of machine learning algorithms on spatially referenced datasets. We find that the XGBoost model has slightly higher performance than the RF model. We recommend careful use of machine learning methods in the case of spatial data specifically in modeling of land prices.]]></description>
      <pubDate>Thu, 09 Jul 2026 14:05:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724620</guid>
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    <item>
      <title>Toward safer streets: Investigating risk factors for pedestrian injury severity in Pakistan</title>
      <link>https://trid.trb.org/View/2688661</link>
      <description><![CDATA[Pedestrian crashes are a leading cause of road traffic fatalities and serious injuries in low- and middle-income countries, including Pakistan. However, pedestrian safety remains underexplored compared to other vulnerable road users, such as motorcyclists and motorized rickshaw occupants. In addition, most of the existing studies related to pedestrian safety in the country have primarily examined overall crash frequencies or broad injury patterns, providing limited insights into the factors influencing injury severity in pedestrian-involved crashes. This study investigates key factors associated with pedestrian injury severity in Pakistan by analyzing 4,024 crashes collected between January 1 2020 and July 31 2023. A random parameters binary probit model with heterogeneity in means and variances was applied to account for unobserved heterogeneity in injury severity outcomes. The model reveals significant heterogeneity in the effects of different variables, i.e., nighttime, outer shoulder width, and access point presence. Findings showed that the likelihood of severe pedestrian injuries increases due to demographic attributes (e.g., men pedestrians, individuals aged 41 and above), environmental and temporal characteristics (e.g., crashes occurring during or after the COVID-19 period, nighttime), and crash features (e.g., collisions involving passenger cars or heavy vehicles, excessive speeding). In addition, roadway geometric attributes (e.g., presence of intersections, narrow shoulders, access points) were found to significantly influence injury severity. The study highlights the need for targeted, health-focused interventions, including promoting gender-inclusive pedestrian safety initiatives, implementing age-friendly urban designs, and adopting speed management strategies from Vision Zero programs, to enhance pedestrian safety. These measures can help reduce pedestrian-related injury severity and support safer urban environments in Pakistan and other similar settings.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688661</guid>
    </item>
    <item>
      <title>Optimization of Public Transport Route Accessibility in High Congestion Areas Using Arc GIS: A Case Study of Rawalpindi City</title>
      <link>https://trid.trb.org/View/2686168</link>
      <description><![CDATA[Due to increasing urbanization and environmental concerns, there is a growing need to enhance and optimize public transportation systems to make them more accessible, efficient, and sustainable. The aim of this study is to improve the public transportation network by optimization of the routes in high congested areas of Rawalpindi city. Geographic Information Systems (GIS) have emerged as a powerful tool for achieving such goal. The integration process involves collecting and analyzing geospatial data, including route information, passenger demographics, and real-time traffic conditions, to optimize transit routes. By incorporating real-time traffic data into the GIS model, the analysis provided insights into congestion configurations and prospective solutions. GIS-based maps presented performance metrics such as reduced travel times, enhanced accessibility scores, and better public transport usage. These metrics were critical for validating the proposed solutions and ensuring their feasibility for implementation. GIS-based route optimization study also proposed BRT systems in our study area (Rawalpindi) to create efficient, timesaving, and passenger-centric public transport options. To maximize its impact, cities need to encourage people to switch from cars to BRT. The success of a GIS-based optimization transportation system requires close collaboration between the public and private sectors.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:29:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686168</guid>
    </item>
    <item>
      <title>15-minute city and life satisfaction: Evidence from university students in Lahore, Pakistan</title>
      <link>https://trid.trb.org/View/2681556</link>
      <description><![CDATA[The 15-minute city concept seeks to promote health, well-being, and quality of life by ensuring that essential services are located within a 15-minute walking or cycling distance from housing, accessible through sustainable modes of transportation. This study aims to evaluate the compliance of this concept in a developing country context and provide empirical evidence by examining the effects of 15-minute access to basic services on perceived health, perceived accessibility, and life satisfaction.To assess pedestrian accessibility in Lahore, Pakistan, we adapted the NEXT proximity index—originally developed as part of the Landscape Metropolis Project in Italy—which scores 15-minute access using open data sources. A network analysis was conducted to determine the shortest travel times to various points of interest, including education, transportation, healthcare, shops, restaurants, leisure spaces, places of worship, and financial services. Each hexagonal unit in the study area was assigned an access score proportional to its proximity to these facilities. These access scores were then analyzed through linear regression models, using survey data collected from 520 university students on their perceived health, perceived accessibility, and life satisfaction. Calculations made using WorldPop estimates on Lahorés population reveal that only up to 30% of the citýs population resides in areas that qualify as a 15-minute city for each facility type. Moreover, access to bus stops significantly enhances both perceived accessibility and life satisfaction, while proximity to healthcare services shows the strongest positive association with life satisfaction.]]></description>
      <pubDate>Mon, 06 Jul 2026 15:58:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681556</guid>
    </item>
    <item>
      <title>Assessing transit-oriented development readiness: a GIS multi-criteria evaluation of Lahore’s orange line corridor</title>
      <link>https://trid.trb.org/View/2681532</link>
      <description><![CDATA[This study examines the Transit-Oriented Development (TOD) potential of stations along the Orange Line metro rail corridor in Lahore, Pakistan, focusing on various metrics such as population density, commercial activity, pedestrian infrastructure, and safety. Utilizing a comprehensive TOD readiness index, we rank the stations’ ability to facilitate TOD development. Applying the Geographic Information System Multi-Criteria Decision-making Analysis approach to calculate the index score for each station, the analysis reveals that top-performing stations, such as Ali Town and Thokar Niaz Baig, exhibit high population and commercial densities, robust pedestrian infrastructure, and diverse land uses, reflecting successful TOD characteristics. In contrast, low-performing stations, including Hanjarwal, Pakistan Mint, and Islam Park, suffer from inadequate pedestrian amenities, low commercial activity, and limited transfers to other routes, contributing to their underutilization. The findings highlight the need for targeted interventions in underperforming areas to enhance walkability, promote mixed-use development, and improve overall transit access. By identifying key indicators influencing TOD readiness, this study offers valuable insights for urban planners and policymakers in developing countries aiming to foster sustainable transport systems and create vibrant, livable urban spaces around new transit systems. Understanding policy levers that enhance the TOD potential of mass transit stations can boost ridership and contribute to the broader goals of urban sustainability and livability.]]></description>
      <pubDate>Mon, 06 Jul 2026 15:58:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681532</guid>
    </item>
    <item>
      <title>A data-driven decision-support framework for evaluating hybrid-electric BRT systems: a case study of Lahore, Pakistan</title>
      <link>https://trid.trb.org/View/2679216</link>
      <description><![CDATA[Traffic congestion, environmental degradation, and energy inefficiency are major challenges for developing countries like Pakistan. The Bus Rapid Transit (BRT) system offers a practical solution to address these issues through sustainable mobility. This study evaluates the operational and environmental performance of conventional diesel and hybrid-electric BRT systems along the Lahore corridor using a segment-wise Data Envelopment Analysis (DEA) framework. Key variables include travel time, fuel consumption, fuel cost, and carbon dioxide (CO2) emissions. DEA was implemented separately for each technology, with each segment benchmarked against its technology-specific frontier, ensuring that efficiency scores reflect relative performance among peers within the same system. Results indicate that hybrid-electric buses generate substantially lower total CO2 emissions (approximately 72 kg) compared to diesel buses (approximately 120 kg) over the analyzed corridor. Within their respective DEA frontiers, hybrid-electric buses achieve efficiency scores of 1.0 at more than half of the analyzed stations, indicating operation close to best-practice performance among peer units. These results highlight the operational characteristics and environmental advantages observed for hybrid-electric buses in developing urban contexts. The proposed DEA-based framework provides a replicable tool for evaluating transit alternatives in data-limited settings. Future extensions could integrate life-cycle assessment (LCA) or sensitivity analysis within the DEA structure to better capture uncertainty in energy use and emission parameters, offering policy-relevant guidance for cities transitioning toward low-carbon and sustainable public transport systems.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:54:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679216</guid>
    </item>
    <item>
      <title>Work-related neck pain and its association with postural and ergonomic factors among Pakistani professional drivers</title>
      <link>https://trid.trb.org/View/2708421</link>
      <description><![CDATA[BACKGROUND: Professional drivers are most likely to experience work-related neck pain due to awkward sitting positions for extended periods of time. Globally, millions of people depend on drivers who dedicate their lives to the craft. However, there is a paucity of data available on professional drivers suffering from neck pain or other musculoskeletal pain in Pakistan. OBJECTIVE: To investigate the prevalence of neck pain and its association with postural and ergonomic factors among Pakistani professional drivers. METHODS: This was a cross-sectional study conducted from January to June 2022 among 369 professional drivers located in Faisalabad, Pakistan. The data were collected by using a questionnaire comprising different sections, including personal, postural and ergonomic factors among drivers. The Statistical Package for the Social Sciences (SPSS) 25 was used for data entry and analysis. RESULTS: The mean age of the participants was 40.83±9.27 years. Among the 369 participants, 129 reported neck pain. The period and point prevalence of neck pain were 35% (n = 129) and 31% (n = 115), respectively. Professional drivers reported a significant association between habitual forward posture and head–neck posture (p = 0.000) and between habitual forward posture and trunk posture (p = 0.000) with neck pain. In addition, ergonomics training (p = 0.002), ergonomics awareness (p = 0.002), and mobile use while driving (p = 0.000) were significantly associated with neck pain. CONCLUSION: This study revealed that drivers have greater period prevalence of neck pain than point prevalence. Moreover, this study revealed that age, BMI, lifestyle, health status, medication use, and smoking habits were associated with neck pain in drivers. Drivers who had ergonomic training and awareness were significantly less likely to suffer from lower neck pain. Drivers with a habitual forward posture are more likely to suffer from neck pain than are drivers with a prone posture.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708421</guid>
    </item>
    <item>
      <title>Improving public transport for social mobility and economic progress in three Pakistani cities</title>
      <link>https://trid.trb.org/View/2712020</link>
      <description><![CDATA[An affordable and efficient public transport system is fundamental for the well-being of the inhabitants of a city. It improves their access to economic opportunities, enabling them to rise out of poverty and overcome social inequities. A good public transport system benefits the low-income and marginalised segments of society the most, resulting in greater social inclusion. Pakistan lacks an effective public transport system even in large cities, undermining its economic potential. Thus, developing efficient public transport systems is vital for urban mobility in Pakistan. However, the government lacks resources, and the private sector is reluctant to invest due to limited policy support and incentives. Against this background, this study proposes low-cost interventions to improve public transport in three main Pakistani cities. These interventions are backed by data from a stated choice survey, analysed using state-of-the-art choice modelling techniques. Findings reveal that travellers tend to value level-of-service improvements more than reduced fares. At the same time, they show a strong dislike for vehicle transfer, probably due to high uncertainty in waiting times during transfers. Small improvements such as providing free WI-FI and reserved seats for women in buses may increase its market share up to 8%.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712020</guid>
    </item>
    <item>
      <title>A comparative study of predicting travel mode choice of school children using explainable machine learning techniques</title>
      <link>https://trid.trb.org/View/2701780</link>
      <description><![CDATA[Prediction of mode choice of school children is an important research topic for transportation planning. Traditionally, mode choice studies of school children are conducted using statistical or simple machine learning techniques. Though statistical techniques provide a good basis for theoretical learning and interpretability, they are mostly based on unrealistic assumptions which might lead to biased predictions. Alternatively, machine learning approaches do not provide any theoretical basis, with poor interpretability and do not provide any insights about factors affecting behavioral aspects. To fill this gap, this research proposes explainable machine learning approaches to comprehend the mode choice prediction of school children in Sahiwal, Pakistan. Data was collected from different schools in Sahiwal district through questionnaire survey and 1,498 completed responses were collected for further analysis. Different explainable machine learning techniques (such as Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, and Light Gradient Boosting) were developed to model the mode choice of school children. Results showed that the Random Forest outperformed as compared to other models. In order to avoid Blackbox criticism of machine learning models and improve their interpretability, variable importance and SHAP dependency analysis were also performed. The results showed that predictors such as travel cost, monthly household income, distance to school, class grade and number of family members were significantly influencing mode choice of school children. These findings can be better used for effective modeling and planning of mode choice preferences of school children.]]></description>
      <pubDate>Thu, 28 May 2026 09:03:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701780</guid>
    </item>
    <item>
      <title>Inclusive mobility at risk: Perceived safety and women's intentions to use paratransit in Lahore</title>
      <link>https://trid.trb.org/View/2698541</link>
      <description><![CDATA[In many developing countries, women face limited mobility options and frequently rely on paratransit due to its affordability, flexible routing, and broad spatial coverage. While paratransit plays a vital role in supporting inclusive urban mobility, especially in underserved areas, its informal nature raises significant safety and accessibility concerns for women. This study employs an extended Theory of Planned Behavior (TPB) framework to examine women's behavioral intentions to use paratransit in Lahore, Pakistan. The model includes TPB core variables, such as attitude, subjective norms, and perceived behavioral control, along with additional variables, including perceived safety risks, perceived likelihood of harassment, and perceived freedom to use paratransit. Through a questionnaire survey, data were collected from 319 women and analyzed using Structural Equation Modeling. The findings reveal that attitude (β = 0.241, p < 0.001) and perceived behavioral control (β = 0.366, p < 0.001) are significant positive predictors of intention to use paratransit. Conversely, subjective norms and perceived freedom to use paratransit were not statistically significant. Perceived safety risks were found to have a direct negative influence on behavioral intention (β = −0.175, p < 0.01), while the perceived likelihood of harassment indirectly reduced intentions to use paratransit by negatively impacting attitude and perceived behavioral control. These findings suggest that women's intentions to use paratransit are shaped not only by service attributes but also by perceived safety risks. Strengthening confidence through safer infrastructure, effective complaint systems, and gender-sensitive operator training can improve the safety and support inclusive urban mobility.]]></description>
      <pubDate>Wed, 27 May 2026 13:05:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698541</guid>
    </item>
    <item>
      <title>School travel mode choice in two medium-sized south Asian cities: Cross-city transferability and explainable machine learning approaches</title>
      <link>https://trid.trb.org/View/2663847</link>
      <description><![CDATA[Trips for educational purposes represent a significant portion of morning and evening peak hour trips. These trips, if carried out by private transport, can lead to several negative consequences including increased traffic congestion, air and noise pollution, and driver discomfort. This study aimed at predicting the mode choices of school-going students in two medium-sized South Asian cities, Kandy, Sri Lanka, and Sahiwal, Pakistan. City-specific classification models were developed for each city, followed by cross-city evaluations using a subset of common features. SHapley Additive exPlanations (SHAP) were employed to interpret model behavior and assess the stability of learned decision logic across contexts. Ensemble models, particularly CatBoost and Gradient Boosting, consistently outperformed linear and single-tree classifiers in both cities, with substantially stronger predictive performance observed in Sahiwal due to richer household and contextual information. SHAP analyses reveal a shared behavioral foundation across cities in which cost-related variables dominate mode choice decisions. Higher costs for both private and sustainable modes are associated with continued reliance on the corresponding mode, indicating necessity-driven, mode-aligned behavior rather than cost-induced switching. Distance, income, and school type exert secondary but context-dependent effects within cities. Cross-city transferability analysis demonstrates limited and asymmetric generalizability. Models trained in one city experience pronounced performance degradation and systematic classification biases when applied to the other. SHAP-based diagnostics show that transferred models undergo marked reconfiguration of decision logic, including reduced spatial sensitivity and disproportionate reliance on cost signals, with evidence of decision-structure collapse under certain transfer directions. These results highlight the strong context dependence of school travel behavior and the need for locally calibrated, explainable modeling approaches.]]></description>
      <pubDate>Thu, 14 May 2026 17:04:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663847</guid>
    </item>
    <item>
      <title>Understanding cycling uptake on university campuses in developing countries</title>
      <link>https://trid.trb.org/View/2692460</link>
      <description><![CDATA[University campuses in developing countries face growing mobility pressures, yet cycling remains underused despite its affordability and sustainability benefits. This study investigates the social, infrastructural, and demographic factors influencing cycling adoption within a major university campus in Peshawar, Pakistan. A questionnaire captured sociodemographic characteristics, perceptions of stigma, safety, social norms, infrastructure, and bike-sharing accessibility. A total of 441 valid responses from students, faculty, and staff were analyzed. Exploratory Factor Analysis identified four key dimensions shaping cycling perceptions: Status Stigma and Safety Concerns, Social Norms and Influences, Supportive Cycling Infrastructure, and Bike-Sharing Services. Structural Equation Modeling was applied to assess both direct and mediated effects of demographic variables on the intention to cycle. Results show that actual bicycle use on campus is very low, while car and public transport dominate commuting choices. Students demonstrated a higher willingness to adopt cycling than faculty, while staff showed the highest overall acceptance. Gender, income, and travel distances significantly influenced the four perceptual factors, which in turn strongly predicted cycling intentions. Supportive infrastructure and bike-sharing services showed significant positive effects, while women reported substantially lower willingness. Status stigma remained prevalent but did not affect willingness to cycle. The findings underscore the need for integrated interventions that combine cycling infrastructure, traffic calming measures, secure parking, end-of-trip facilities, and strategically located bike-sharing stations. Addressing gender-specific barriers and shifting social norms are also essential. The study provides context-specific evidence for promoting cycling as a viable, equitable, and sustainable travel mode on university campuses in developing-country settings.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:18:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692460</guid>
    </item>
    <item>
      <title>Transport poverty and gendered inequalities: Evidence from expert interviews in Karachi</title>
      <link>https://trid.trb.org/View/2652302</link>
      <description><![CDATA[This paper studies the perceptions of transport experts towards the state of transport and mobility barriers faced by women in the context of Karachi, Pakistan, a representative megacity in the global South. It is based on the data gathered by ‘conversations with purpose’ with eleven experts in the transport sector to understand their attitudes towards transport provision and operation. These experts were opportunistically identified based on their presence at transport-related public events in Karachi. It was found that poorly coordinated planning, a lack of effective governance structure and investment have allowed the growth of an almost unregulated and ungovernable informal transport sector in Karachi. Apart from these issues, most experts displayed a patriarchal mindset that manifested itself by demeaning women’s importance and their contributions. Due to the lack of female representation, such views remained unchallenged, and the majority of the informants did not express a desire to integrate women into decision-making or consultation processes. It can thus be argued that improving women’s mobility requires changing the mindset of transport planners, who consider women as mainly responsible for household tasks and therefore beneath their consideration. The paper also suggests some preliminary recommendations to address the issue of breaking away from gender stereotypes in the transport sector. The study contributes to wider academic studies on gender and transport geography and will feed into and shape governmental and non-governmental interventions and policies on public transport in third-world countries.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652302</guid>
    </item>
    <item>
      <title>Fragility Assessment of Flexible Pavement Roads Subjected to Tsunami Debris Flows in Coastal Pakistan</title>
      <link>https://trid.trb.org/View/2663074</link>
      <description><![CDATA[Understanding the effects of tsunami-induced debris flows on road infrastructure is essential for developing effective mitigation strategies in coastal regions. This study develops fragility curves for the flexible pavement road network in Karachi, Pakistan, using a hybrid dataset comprising observed post-tsunami damage and synthetically generated samples to address class imbalance. Data augmentation improved the predictive performance of the machine learning-based model significantly, increasing severe damage classification accuracy from near-zero to nearly 97%. Logistic regression was used to model the probability of exceeding three damage levels (DL1–DL3) based on inundation depth. The resulting fragility curves captured a clear progression in damage severity, with confidence intervals indicating high reliability at mid-depth ranges and greater uncertainty at extreme values. Model accuracy evaluation (98.66%) and goodness-of-fit metrics supported model validity, with root mean square error ranging from 0.0701 to 0.1061 and McFadden’s R² values from 0.0237 (DL3) to 0.1724 (DL1), confirming smooth and consistent probability transitions across damage states. These regression-based fragility models offer a statistically robust and practical framework for assessing tsunami-induced road vulnerability, contributing valuable insights for disaster preparedness and infrastructure resilience planning in high-risk coastal environments.]]></description>
      <pubDate>Wed, 22 Apr 2026 14:04:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663074</guid>
    </item>
    <item>
      <title>The rising vehicle counts and deteriorating air quality in Islamabad, Pakistan</title>
      <link>https://trid.trb.org/View/2636306</link>
      <description><![CDATA[Rapid motorization in Pakistan has intensified air quality challenges, yet limited research has examined how vehicular growth contributes to environmental degradation and what institutional responses have emerged. This study investigates the linkages between rising vehicle numbers, weak regulatory enforcement and deteriorating air quality in Islamabad, Pakistan. Qualitative in-depth interviews were conducted with officials from the Capital Development Authority, Environmental Protection Agency and Islamabad Transport Authority to explores institutional capacities, policy implementation gaps and emerging strategies such as public transport expansion and electric vehicle promotion. Twelve participants were selected through purposive sampling, and the collected data were analysed thematically using MAXQDA software. The findings reveal that rapid growth in private vehicle ownership, combined with poorly maintained fleets and the absence of systematic inspection regimes, has accelerated atmospheric emissions in the capital. Institutional inertia, resource scarcity and socio-economic justifications for tolerating old vehicles further exacerbate the problem. While initiatives such as the Bus Rapid Transit system and planned electric vehicle policies signal potential, their scale remains insufficient. The current study contributes to debates on urban air quality governance in the Global South by highlighting how weak institutional frameworks and competing development priorities undermine environmental sustainability. It calls for integrated policy reforms, investment in sustainable public transport and robust enforcement mechanisms to mitigate transport-related emissions.]]></description>
      <pubDate>Thu, 26 Feb 2026 16:18:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636306</guid>
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