Major Highways Research Articles

Electrical Resistivity Tomography Survey of Some Failed Segments along Selected Major Highways in Cross River State, Southeastern Nigeria

Electrical Resistivity Tomography Survey of Some Failed Segments along Selected Major Highways in Cross River State, Southeastern Nigeria

Numerical modelling of cut-slope stability in the Lesser Himalayan terrain of west-central Nepal

Area development without due consideration of geological factors has led to recurrent slope failures within the Lesser Himalayan Zone thereby rendering socio-economic impacts. This issue is well exemplified by cut-slopes in major highways of Nepal traversing through a diverse range of rocks and soils. The study has focused on addressing the stability challenges posed by highly vulnerable excavated cut-slopes with particular attention given to a debris cut-slope considered as a model for this study is located in the Lesser Himalayan terrain of west-central Nepal. The cut-slope model is analyzed by numerical modellings through both the limit equilibrium method (LEM) and finite element method (FEM). The study further evaluated the underlying causes of these failures to recommend the options for remedial measures. Data acquired from two-dimensional electrical resistivity tomography (2D-ERT), multi-channel analysis of surface waves (MASW), and laboratory testing were integrated into numerical modelling and simulation. The results showed that the lower slope below the natural benching is stable whereas the upper failed slope is unstable due to reduced gravitational loading. In LEM, factors of safety (FoS) range from 0.77 to 0.89 for cut-slope angles of 70° and 60° whereas the FoS in modified slopes (50° and 40°) were slightly greater than 1 which ranged from 1.01 to 1.18 and indicated marginal stability. Application of 25 mm diameter soil nails, each measuring 6 m in length and spaced at 2 m intervals with tensile and plate capacity of 125.6 kN, bond strength of 75.4 kN/m, and 80% of nail length have produced FoS of 1.30 or greater in LEM and FEM which is acceptable values specified by EM 1110-2-1902 and IS 456 2000 for end-of-construction and multistage loadings.

Mixed-Effects Zero-Inflated Negative Binomial Crash Predictive Models for Unsignalized Intersections along Two-Lane Highways with Minor Roads Operating with Very Low Traffic Volumes

Current and past investigations into road accidents have mainly focused on high-volume roads which have experienced a significant number of collisions. However, low-volume roads also record crash data which merit analysis to understand the causes of accidents and find potential countermeasures. This study focuses on unsignalized intersections linking major highways and minor stop-controlled local roads that operate with very low traffic volumes. In this scenario, an over-abundance of zero observations has to be accounted for. To address this issue, zero-inflated Poisson and negative binomial regression models have been calibrated and analyzed to find the geometric and operational factors which contribute most to the crash rate. Random effects have also been considered to account for unobserved heterogeneity inherent in the grouping of intersections within the database. The results indicate that the mixed-effect negative binomial model had the best prediction performance. Developing models for subgroups of crashes, for example, failure-to-yield and other types of crash, resulted in better prediction performance. The major road’s average annual daily traffic and the surface type (i.e., paved or unpaved) of minor road approaches had a significant effect on the frequency of all crash types. The type of intersection (three- or four-legs), the skew angle of intersection, deviation angle of near curves, and operating speed differences also had statistically significant effects on the frequency of crashes that do not include the failure-to-yield.

Health and economic benefits of heavy-duty diesel truck emission control policies in Beijing

PM2.5 emissions from heavy-duty diesel trucks (HDDTs) have a significant impact on air quality, human health, and climate change, and seriously threaten the UN Sustainable Development Goals. Globally, a series of emission control measures have been implemented to reduce pollution emissions from HDDTs. Current studies assessing the impact of these measures on air quality and human health have mainly used coarse-grained emission data as input to dispersion model, resulting in the inability to capture the spatiotemporal variability of pollutant concentrations and tending to increase the uncertainty of health impact assessment results. In this study, we quantified the impact of pollution control policies for HDDTs in Beijing on PM2.5 concentrations, human health, and economic losses by integrating policy scenario analysis, pollution dispersion simulation, public health impact and economic benefit assessment models, supported by high spatiotemporal resolution emission data from HDDTs. The results show that PM2.5 concentrations from HDDTs exhibit significant spatial aggregation characteristics, with the intensity of aggregation at night being about twice as high as that during the day. The emission hotspots are mainly concentrated in the sixth, fifth and fourth rings and major highways. Compared to the “business as usual” scenario in 2018, the current policy of updating the fuel standard to China VI and the emission standard to China 6 can reduce PM2.5 concentrations by 96.72%, thereby avoiding 612 premature deaths, which is equivalent to obtaining economic benefits of 1.65 billion CNY. This study further emphasizes the importance of high spatiotemporal resolution emission data during traffic dispersion modeling. The results can help improve the understanding of the effectiveness of emission reduction measures for HDDTs from a health benefit perspective.

An in-depth analysis of head-on crash severity and fatalities in Ghana

Head-on collisions are often linked to more serious injuries compared to other types of crashes, due to the intense impact they cause. In low- and middle-income countries, these collisions frequently involve high occupancy public transportation vehicles, leading to higher fatality rates per crash. Given the high risk of injury and potential for multiple casualties, this study delves into the factors influencing the outcomes of head-on crashes and the number of fatalities in Ghana. The study analyzed six years of historical head-on collision data from Ghana and developed two models to address the issue. The injury-severity analysis was performed using a random parameter multinomial logit with heterogeneity in means and variances approach and aimed to identify the factors that have a significant impact on the severity of injuries sustained in head-on collisions, while the random parameters negative binomial fatality count model was designed to examine the factors that contribute to the number of fatalities in these crashes in the country. Results showed that head-on collisions with drivers over 65, buses, motorcycles, and those between 25 and 65 years of age were more likely to result in fatalities. Speeding and vehicle malfunctions were also found to be significant contributing factors to fatal head-on collisions. Head-on crashes involving minibuses and incidents where the driver was attempting to overtake another vehicle were found to be more likely to result in a higher number of fatalities. The results of this study uncover an intriguing interaction between human-related elements and socioeconomic factors, which pose obstacles to the Government's endeavor to upgrade the major highways in the country. Additionally, the increasing need for transportation has led to the presence of vehicles on the roads that may not meet safety standards. Consequently, it is no surprise that several of the study's findings align with expectations. Nevertheless, within the specific context of Ghana, these findings furnish compelling data-driven evidence supporting the adoption and implementation of the safe systems approach as a means to tackle fatal head-on collisions in the country.

Transit station area walkability: Identifying impediments to walking using scalable, recomputable land-use measures

Transit station area land-use characteristics can increase or decrease the perceived costs of riding rail relative to driving or taking other modes. This paper focuses on those characteristics that create discomfort to riders who are walking between stations and destinations, with the aim of providing researchers and planners with a tool that can be used to identify pain points in any existing or potential station areas. We propose and demonstrate a scalable, recomputable method of measuring pedestrian quality for trips that relies solely on datasets readily available for almost any location in the United States, and we compare results using data from a global source, OpenStreetMap. We illustrate our tool in neighborhoods surrounding the Blue Line light rail in Minneapolis, Minnesota, calculating the population-weighted distribution of land uses within pathway buffers of walks from stations to nearby destinations. We focus on land uses that pose a disutility to pedestrians such as major highways or industrial tracts, and we compare disamenity levels across station areas. Despite their simplicity, our measures capture important differences in land-use-related pedestrian experiences and reveal the inadequacy of using circular buffers to designate and characterize station catchment areas.

Microsimulation Modelling and Scenario Analysis of a Congested Abu Dhabi Highway

Today’s roadways are subject to traffic congestion, the deterioration of surface-assets (often due to the overreliance on private vehicle traffic), increasing vehicle-operation and fuel costs, and pollutant emissions. In Abu Dhabi, private car traffic forms the major share on urban highways, as the infrastructure was built to a high quality and the public transport network needs expansion, resulting in traffic congestion on major highways. These issues are arguably addressable by appropriate decisions at the planning stage. Microsimulation modeling of driving behavior in Abu Dhabi is presented for empirical assessment of traffic management scenarios. This paper presents a technique for developing, calibrating, validating, and the scenario analysis of a detailed VISSIM-based microsimulation model of a 3.5 km section of a 5-lane divided highway in Abu Dhabi. Traffic-count data collected from two sources, i.e., the local transport department (year 2007) and municipality (2007 and 2015–2016) were used. Gaps in traffic-counts between ramps and the highway mainline were noted, which is a common occurrence in real-world data situations. A composite dataset for a representative week in 2015 was constructed, and the model was calibrated and validated with a 15% (<100 vehicles per hour) margin of error. Scenario analysis of a potential public bus transport service operating at 15 min headway and 40% capacity was assessed against the base case, for a 2015–2020 projected period. The results showed a significant capacity enhancement and improvement in the traffic flow. A reduction in the variation between vehicle travel times was observed for the bus-based scenario, as less bottlenecking and congestion were noted for automobiles in the mainline segments. The developed model could be used for further scenario analyses, to find optimized traffic management strategies over the highway’s lifecycle, whereas it could also be used for similar evaluations of other major roads in Abu Dhabi post-calibration.

Geodynamic ‘Hotspots’ in a Periglacial Landscape: Natural Hazards and Impacts on Productive Activities in Chilean Fjordlands, Northern Patagonia

In this paper, we study natural hazards and their potential impacts on productive activities in the Comau Fjord in Chilean Northern Patagonia. We carried out hazard mapping to identify areas with evidence of geomorphological activity on slopes in terms of landslides, river/tsunami flooding, and glacial retreat. The assessment of different geomorphic processes was carried out by both detailed fieldwork and analysis of satellite remote sensing and aerial photography information. We identified terrain units that are subject to multi-hazards overlapping different spatially distributed hazard maps. This information was overlaid with spatial data of economic activities in the area in order to establish the impacts of the natural hazards on the local salmon and mussel farming infrastructure (risk). The results suggest significant exposure levels for these productive activities and potential damage due to the occurrence of natural hazards. The extension of a major highway (CH-7 Austral Highway) on the east coast of the Comau Fjord will be a new incentive for economic development in the area. However, the highway construction sites show a high level of exposure to natural hazards, especially floodings and landslides. Our study highlights that the geohazard potential might have a high negative impact on future productive activity in the fjord as well as on the new highway infrastructure.

Exploring the effect of COVID-19 on driver injury severities in freeway single-vehicle crashes accounting for unobserved heterogeneity

ABSTRACT The COVID-19 pandemic, characterized by travel restrictions and reduced traffic volumes, heightened the risk of severe single-vehicle crashes on Florida's freeways. This study utilized random parameter multinomial logit models, accounting for heterogeneity in means and variances, to analyze driver injury severities in 2020 and compare variations in the magnitude of factors contributed to these injuries across different freeway systems. The estimated models identified 31 statistically significant variables across Florida's major freeways (I-4, I-10, I-75, and I-95). Among these variables, only two—normal driving and restraint usage—were statistically significant across all four freeway systems. Moreover, the model results indicated that factors contributing to severe driver injuries were most prominent on I-95 compared to the other freeways in 2020. These findings improve our understanding of freeway safety measures during a pandemic and provide valuable insights to enhance traffic management strategies for state highway agencies, prioritizing operational safety in potential future pandemics.

Distribution Center Location Selection in Humanitarian Logistics Using Hybrid BWM–ARAS: A Case Study in Türkiye

Abstract This study investigates the criteria affecting the location of humanitarian logistics distribution centers in the Sakarya province of Turkey, an area prone to natural disasters. The study identifies potential distribution center locations and uses the Best-Worst Method (BWM) to determine criteria such as population, distance to major highways and airports, public transportation availability, natural disaster risk, and suitable infrastructure. BWM is used to assign weights to each criterion and rank them based on their importance. The Additive Ratio Assessment (ARAS) method is then used to evaluate potential distribution center locations based on the established criteria. Disaster management experts and academicians provide their opinions through an online and face-to-face survey. Based on the results, Adapazarı is identified as the most suitable district for a humanitarian logistics distribution center. The study highlights the importance of considering multiple criteria when selecting distribution center locations and provides a framework for using multi-criteria decision-making methods in logistics planning. Disaster managers and policymakers can use the results to make informed decisions about the location of humanitarian logistics distribution centers.

Association of multi-criteria derived air toxics hazard score with lung cancer incidence in a major metropolitan area.

Lung cancer remains a major health problem world-wide. Environmental exposure to lung cancer carcinogens can affect lung cancer incidence. We investigated the association between lung cancer incidence and an air toxics hazard score of environmental carcinogen exposures derived previously under the exposome concept. Lung cancer cases diagnosed in Philadelphia and the surrounding counties between 2008 and 2017 were identified from the Pennsylvania Cancer Registry. Age-adjusted incidence rates at the ZIP code level were calculated based on the residential address at diagnosis. The air toxics hazard score, an aggregate measure for lung cancer carcinogen exposures, was derived using the criteria of toxicity, persistence, and occurrence. Areas with high incidence or hazard score were identified. Spatial autoregressive models were fitted to evaluate the association, with and without adjusting for confounders. Stratified analysis by smoking prevalence was performed to examine potential interactions. We observed significantly higher age-adjusted incidence rates in ZIP codes that had higher air toxics hazard score values after controlling for demographic variables, smoking prevalence, and proximity to major highways. Analyzes stratified by smoking prevalence suggested that exposure to environmental lung carcinogens had a larger effect on cancer incidence in locations with higher smoking prevalence. The positive association between the multi-criteria derived air toxics hazard score and lung cancer incidence provides the initial evidence to validate the hazard score as an aggregate measure of carcinogenic exposures in the environment. The hazard score can be used to supplement the existing risk factors in identifying high risk individuals. Communities with higher incidence/hazard score may benefit from greater awareness of lung cancer risk factors and targeted screening programs.

Metal contents and size distributions of brake and tire wear particles dispersed in the near-road environment

Traffic related non-tailpipe particulate matter emissions can rival the continuously decreasing tailpipe emissions in modern fleets. Non-tailpipe emissions have become the dominating source of traffic emissions in California already. This study measured ambient PM2.5 and PM10 concentrations at near road environments for two major highways in California, I-5 in Anaheim and I-710 in Long Beach. A total of 51 elements were measured from filter samples collected over four-hour intervals for a two-week period in the winter of 2020 before the statewide lockdown by the COVID-19 pandemic. Iron was the most abundant element in ΔPM10 (differences between downwind and upwind sites), contributing to 30 % and 24 % of total measured elements in ΔPM10 at the I-5 and I-710 locations, respectively. Iron correlated highly with other brake wear markers (e.g., titanium, copper, barium, manganese, and zirconium) with coefficient of determination (r2) ranging from 0.67 to 0.90 in both PM2.5 and PM10. Silicon was the second most abundant element, contributing to 21 % of total measured elements in ΔPM2.5 and ΔPM10. Silicon showed strong correlations with crustal elements such as calcium (r2 = 0.90), aluminum (r2 = 0.96), and potassium (r2 = 0.72) in ΔPM2.5, and the correlations were even higher in ΔPM10. Barium had a weak correlation with zinc, a commonly used maker for tire wear, with r2 = 0.63 and r2 = 0.11 for ΔPM10 at the I-5 and I-710 locations respectively. Barium showed a positive correlation with crosswind speed and could serve as a good brake wear PM marker. Hourly PM2.5 concentrations of iron and zinc showed cyclic peaks from 0800 to 1000 h at I-5 during weekdays. Particle mass distributions showed peaks near ~7 μm, while particle number distributions showed peaks near 2.1 μm and 6.5 μm, respectively. This is consistent with brake wear and road dust size ranges previously reported.

Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula

Abstract. In orbit since late 2017, the Tropospheric Monitoring Instrument (TROPOMI) is offering new outstanding opportunities for better understanding the emission and fate of nitrogen dioxide (NO2) pollution in the troposphere. In this study, we provide a comprehensive analysis of the spatio-temporal variability of TROPOMI NO2 tropospheric columns (TrC-NO2) over the Iberian Peninsula during 2018–2021, considering the recently developed Product Algorithm Laboratory (PAL) product. We complement our analysis with estimates of NOx anthropogenic and natural soil emissions. Closely related to cloud cover, the data availability of TROPOMI observations ranges from 30 %–45 % during April and November to 70 %–80 % during summertime, with strong variations between northern and southern Spain. Strongest TrC-NO2 hotspots are located over Madrid and Barcelona, while TrC-NO2 enhancements are also observed along international maritime routes close the strait of Gibraltar, and to a lesser extent along specific major highways. TROPOMI TrC-NO2 appear reasonably well correlated with collocated surface NO2 mixing ratios, with correlations around 0.7–0.8 depending on the averaging time. We investigate the changes of weekly and monthly variability of TROPOMI TrC-NO2 depending on the urban cover fraction. Weekly profiles show a reduction of TrC-NO2 during the weekend ranging from −10 % to −40 % from least to most urbanized areas, in reasonable agreement with surface NO2. In the largest agglomerations like Madrid or Barcelona, this weekend effect peaks not in the city center but in specific suburban areas/cities, suggesting a larger relative contribution of commuting to total NOx anthropogenic emissions. The TROPOMI TrC-NO2 monthly variability also strongly varies with the level of urbanization, with monthly differences relative to annual mean ranging from −40 % in summer to +60 % in winter in the most urbanized areas, and from −10 % to +20 % in the least urbanized areas. When focusing on agricultural areas, TROPOMI observations depict an enhancement in June–July that could come from natural soil NO emissions. Some specific analysis of surface NO2 observations in Madrid show that the relatively sharp NO2 minimum used to occur in August (drop of road transport during holidays) has now evolved into a much broader minimum partly de-coupled from the observed local road traffic counting; this change started in 2018, thus before the COVID-19 outbreak. Over 2019–2021, a reasonable consistency of the inter-annual variability of NO2 is also found between both datasets. Our study illustrates the strong potential of TROPOMI TrC-NO2 observations for complementing the existing surface NO2 monitoring stations, especially in the poorly covered rural and maritime areas where NOx can play a key role, notably for the production of tropospheric O3.

An Illusion of Barriers to Gene Flow in Suburban Coyotes (Canis latrans): Spatial and Temporal Population Structure across a Fragmented Landscape in Southern California

Carnivores with large home ranges are especially vulnerable to habitat fragmentation. As coyotes (Canis latrans) are often found living in highly modified landscapes, it is unclear how urban and suburban development impact gene flow between their populations. This study evaluated gene flow among coyotes inhabiting California sage scrub fragments within the highly developed Pomona Valley, California. We genotyped microsatellites from scat samples collected from four study sites to examine population structure between coyotes separated by a major freeway, coyotes separated by suburban development, and finally, coyotes in contiguous, natural habitat sites over 15 months. Though coyotes from all four sites were genetically distinct, near-complete turnover of individuals in sites and examination of temporal genetic structure and relatedness within one site indicated the movement of family groups through natural fragments over time. Thus, we argue that solely examining spatial genetic structure may create the illusion of genetic barriers among coyote populations where they may not exist, and that incorporating temporal components of genetic variation is critical to understanding gene flow across space and time in highly mobile animals. Understanding how to better study and manage coyotes, an apex predator, is key to the conservation of the endangered California sage scrub ecosystem.

On quality and complexity: non-conformance failures, management perspectives and learning outcomes on a highways megaproject

PurposeThe construction industry continues to struggle to deliver a right first time culture, seeking a panacea for improvement whilst maintaining project milestones. Complex construction projects demand stringent programmes, however, (un)foreseen changes, political influences and human behaviours all have significant impacts on delivering schemes without error. Previous studies have questioned the ability of the construction industry to successfully learn from errors. A major barrier has been the sharing of sensitive data from failed outcomes. Hence, this paper investigates non-conformance on an existing scheme and suggests avenues for improvement.Design/methodology/approachA mixed-method approach was adopted whereby 1,260 non-conformance reports (NCRs) from a highways megaproject were interrogated using root cause analysis (RCA) techniques to uncover the most frequent and costly areas. This was followed by a survey to industry professionals within a tier 1 principal contractor to gain insight into their perceptions of non-conformance and rework on construction projects.FindingsUsing Pareto analysis, the authors find that materials management, workmanship (poor quality execution) and supervision issues are the most frequently occurring and costly root causes of non-conformance on a major highways scheme. Furthermore, the authors link corresponding viewpoints of two project professional groups to the posed, achieving a high degree of consensus for the areas requiring development. Lastly, the authors suggest avenues for improvement via lessons learnt. These include greater emphasis on quality culture via a strong leadership mandate, enhanced vetting of workforce competence and improving the way materials are managed by embracing technology to drive efficiency.Originality/valueThis paper interrogates a current highways scheme using a uniquely rich, sensitive dataset to determine how the construction sector may improve efforts to achieving right first time outcomes.

Spatio-Temporal Evolution and Driving Factors of Landscape Pattern in a Typical Hilly Area in Southern China: A Case Study of Yujiang District, Jiangxi Province

As the most intuitive manifestation of land use/land cover change, the spatio-temporal evolution of landscape patterns has significant implications for optimizing regional landscape pattern and land use management. Based on multi-period remote sensing data, we selected an optimal scale (570 m) and used the geographic detector model to analyze the spatio-temporal changes in the landscape pattern of a typical hilly area (Yujiang District, Yingtan City, Jiangxi Province) in southern China. The results showed that from 2009 to 2018, the area of urban land, other construction land, rural residential land, and cultivated land expanded by 33.27%, 21.23%, 19.42%, and 1.07%, respectively. In contrast, the area of grassland, forest land, and water area shrank by 18.18%, 5.41%, and 2.19%, respectively, over the past 10 years. At the landscape level, the patch shape became more complex over time, with increased landscape fragmentation and diversity. At the class level, cultivated land, forest land, and grassland tended to be fragmented, whereas rural residential land exhibited an aggregation tendency. Slope gradient, gross regional product, and distance from major highways had a strong ability to explain the spatial differences in landscape pattern change. The results of this study enable a dynamic understanding of landscape pattern evolution in typical hilly areas in southern China and provide a reference for landscape pattern optimization in similar geomorphic settings.

Structure and species composition of tree stands on verges and slopes along a major highway in Hong Kong

Structure and species composition of tree stands on verges and slopes along a major highway in Hong Kong

Contributions of Engineering Geological Properties of Subgrade Soils to Premature Failure of Major Highways in Southwestern Nigeria: A Case Study of Akure-Ikere Ekiti Highway

Contributions of Engineering Geological Properties of Subgrade Soils to Premature Failure of Major Highways in Southwestern Nigeria: A Case Study of Akure-Ikere Ekiti Highway

Even minor logging road development can decrease the functional diversity of forest bird communities: Evidence from a biodiversity hotspot

Although the effects of major roads and highways on biodiversity have received considerable attention, there is a lack of research considering how smaller and less-used roads might also impact wildlife, especially during the initial re-used development phase of abandoned roads, despite these minor roads being much more extensive and pervasive. Here, we examined how even minor redevelopment of former-abandoned logging roads can impact bird community alpha and beta diversity indices at the taxonomic, functional, and phylogenetic levels in a biodiversity conservation hotspot in central China. The minor redevelopment (i.e., re-paved) of former-abandoned roads generally reduced bird alpha and beta diversity compared to undeveloped abandoned roads, especially at the functional level. Compared with continuous-use roads, minor road redevelopment shows only a slight impact on avian diversity. Notably, compared to old-growth forest reference stands and undeveloped abandoned roads, minor road redevelopments have negative effects on the recovery process of avian diversity. Furthermore, the structure and composition of the high tree layer significantly affected avian functional and phylogenetic diversity, and low shrub layer vegetation mainly influenced taxonomic and phylogenetic diversity. To mitigate the effects of minor forest logging roads on avian biodiversity in priority biodiversity conservation areas, we propose the following: (1) roads that have fallen into disuse should be allowed and encouraged to re-naturalise; (2) roads should only be paved, and cleared back as strictly necessary, allowing grassy or gravel roads to suffice where possible; and (3) if road development is necessary, key umbrella tree species should be planted deliberately to support maximal community diversity and to maintain ecosystem functions and processes.

Framework for rainfall-triggered landslide-prone critical infrastructure zonation

Rainfall-induced landslides cause frequent disruptions to critical infrastructure in mountainous countries. Climate change is altering rainfall patterns and localizing extreme rainfall events, increasing the occurrence of landslides. For planning climate-resilient critical infrastructure in landslide-prone regions, it is urgent to understand the changing landslide susceptibility in relation to changing rainfall extremes and spatially overlay them with critical infrastructure to determine risk zones. As such, areas requiring financial reinforcements can be prioritized. In this paper, we develop a framework linking changing rainfall extremes to landslide susceptibility and intensity of critical infrastructure — exemplified on a national scale using Nepal as a case study. First, we define a set of 21 different unique rainfall indices that describe extreme and localized rainfall. Second, we prepare a new annual (2016–2020) inventory of 107,900 landslides in Nepal mapped on PlanetScope satellite imagery. Next, we prepare a landslide susceptibility map by training a random forest model using the collected extreme rainfall indices and landslide locations in combination with spatial data on topography. Fourth, we construct a gridded critical infrastructure spatial density map that quantifies the intensity of infrastructure (i.e., transportation, energy, telecommunication, waste, water, health, and education) at each grid location using OpenStreetMap. The landslide susceptibility map classified Nepal's topography into low (36 %), medium (33 %), and (32 %) high rainfall-triggered landslide susceptibility zones. The landslide susceptibility map had an average area under the receiver characteristic curve value of 0.94. Finally, we overlay the landslide susceptibility map with the critical infrastructure intensity to identify areas needing financial reinforcement. Our framework reasonably mapped critical infrastructure hotspots in Nepal prone to landslides on a 1 km grid. The hotspots are mainly concentrated along major national highways and in provinces 4, 3, and 1, highlighting the need for improved land management practices. These hotspots need spatial prioritization regarding climate-resilient critical infrastructure financing and slope conservation policies. The research data, output maps, and code are publicly released via an ArcGIS WebApp and GitHub repository. The framework is scalable and can be used for developing infrastructure financing strategies for landslide mountain regions and countries.