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<p>Traffic congestion in Shimla, a hill city in the Himalayas, represents a complex urban challenge exacerbated by its steep terrain, limited road infrastructure, and a seasonal influx of tourists. This study adopts a mixed-methods approach integrating advanced geospatial and statistical analyses, predictive analytics, and comprehensive stakeholder surveys to critically evaluate the socio-economic, environmental, and governance impacts of traffic congestion while assessing the efficacy of smart city technologies implemented under India’s Smart Cities Mission. Geospatial methods, such as kernel density estimation (KDE) and Moran’s I, identify Victory Tunnel and Mall Road as critical congestion hotspots, with traffic volumes exceeding capacity by up to 395%. Statistical tests, including one-way ANOVA and Kruskal–Wallis, reveal significant delays, with 78% of residents experiencing over 45-min delays, correlating with respiratory health issues (r = 0.74, <i>p</i> &lt; 0.01). Predictive analytics provide crucial insights into peak tourist seasons, forecasting delays of up to 85&#xa0;min during winter months. Stakeholder surveys highlight challenges such as tourist dissatisfaction, leading to an 18% decline in spending (Z = − 3.45, <i>p</i> &lt; 0.01), and business revenue losses averaging 15% in the hospitality and retail sectors (R<sup>2</sup> = 0.68, <i>p</i> &lt; 0.01). Smart city technologies, such as Intelligent Traffic Management Systems (ITMS) and smart parking solutions, have achieved a 28% reduction in travel times (t = 9.12, <i>p</i> &lt; 0.001) and a 50% decrease in parking violations (χ<sup>2</sup> = 15.48, <i>p</i> &lt; 0.01). However, scalability is hindered by fragmented governance, limited geographical coverage, and seasonal factors, including snowfall, which exacerbate delays and disrupt urban mobility. Shimla’s interventions align with global frameworks like the United Nations’ Sustainable Development Goals (SDGs), advancing SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action) by fostering inclusive transportation systems and mitigating emissions. This study establishes Shimla as a replicable model for urban mobility in constrained geographies, integrating localized smart city solutions with global sustainability objectives. Actionable recommendations include expanding ITMS coverage, implementing elevation-sensitive infrastructure, and strengthening inter-agency coordination, offering scalable insights for similarly constrained mountainous cities, particularly across the Global South.</p>

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Geostatistical analysis of traffic congestion and adaptive smart urban mobility solutions in Shimla a Himalayan mountain city

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  • 发表日期：

    2025年12月14日

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  • Ajitesh Singh Chandel

摘要

Traffic congestion in Shimla, a hill city in the Himalayas, represents a complex urban challenge exacerbated by its steep terrain, limited road infrastructure, and a seasonal influx of tourists. This study adopts a mixed-methods approach integrating advanced geospatial and statistical analyses, predictive analytics, and comprehensive stakeholder surveys to critically evaluate the socio-economic, environmental, and governance impacts of traffic congestion while assessing the efficacy of smart city technologies implemented under India’s Smart Cities Mission. Geospatial methods, such as kernel density estimation (KDE) and Moran’s I, identify Victory Tunnel and Mall Road as critical congestion hotspots, with traffic volumes exceeding capacity by up to 395%. Statistical tests, including one-way ANOVA and Kruskal–Wallis, reveal significant delays, with 78% of residents experiencing over 45-min delays, correlating with respiratory health issues (r = 0.74, p < 0.01). Predictive analytics provide crucial insights into peak tourist seasons, forecasting delays of up to 85 min during winter months. Stakeholder surveys highlight challenges such as tourist dissatisfaction, leading to an 18% decline in spending (Z = − 3.45, p < 0.01), and business revenue losses averaging 15% in the hospitality and retail sectors (R2 = 0.68, p < 0.01). Smart city technologies, such as Intelligent Traffic Management Systems (ITMS) and smart parking solutions, have achieved a 28% reduction in travel times (t = 9.12, p < 0.001) and a 50% decrease in parking violations (χ2 = 15.48, p < 0.01). However, scalability is hindered by fragmented governance, limited geographical coverage, and seasonal factors, including snowfall, which exacerbate delays and disrupt urban mobility. Shimla’s interventions align with global frameworks like the United Nations’ Sustainable Development Goals (SDGs), advancing SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action) by fostering inclusive transportation systems and mitigating emissions. This study establishes Shimla as a replicable model for urban mobility in constrained geographies, integrating localized smart city solutions with global sustainability objectives. Actionable recommendations include expanding ITMS coverage, implementing elevation-sensitive infrastructure, and strengthening inter-agency coordination, offering scalable insights for similarly constrained mountainous cities, particularly across the Global South.