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Accurate and robust forecasting of Emergency Medical Services (EMS) demand is crucial for ensuring timely ambulance dispatch and efficient resource allocation, particularly in low-resource public health systems, such as those in India. While most prior EMS forecasting studies have focused on urban settings in developed countries with rich, granular data, limited research has explored district-level forecasting using real-world ambulance dispatch data from India. Moreover, existing models often trade off robustness for accuracy or rely on complex black-box architectures, limiting their interpretability and real-world deployment. This study examines whether a heterogeneous ensemble of interpretable and complementary learners can outperform traditional and state-of-the-art regressors for district-level EMS forecasting, utilizing limited real-world features. To address this challenge, we propose EM-LR (Ensembled Meta-Learner with Linear Regression), a meta-learning framework that integrates four diverse base models-Lasso Regression, Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGB)-via a linear regression meta-learner. Unlike prior meta-learners that stack similar tree-based or linear models, EM-LR combines low-variance, diverse learners to enhance robustness while maintaining model interpretability through SHAP-based feature analysis and transparent ensemble weights. Using only temporal and meteorological inputs, EM-LR forecasts daily EMS call volumes across five diverse districts in the state of Uttar Pradesh. We benchmark EM-LR against traditional models and recent advanced variants, including Twin Bounded Least Squares Support Vector Regression (TBLSSVR), Asymmetric-Huber based Extreme Learning Machine (AHELM), and Mexican-Hat Kernelized Large Margin Distribution Machine-based Regression (MHKLDMR), demonstrating superior accuracy and reduced prediction variance. Experimental results show up to 9.5% reduction in RMSE and over 40% variance reduction. EM-LR thus offers a scalable and interpretable forecasting solution tailored to the operational constraints of developing public health systems, supporting data-driven emergency planning and equitable healthcare delivery.

This study investigates the relationship between weather conditions and two-wheeler road crashes in Uttar Pradesh, India, which experiences diverse climatic conditions. A novel framework, the Weather-Influenced Clustering and Random Sampling (WICRS) model, is proposed for Relative Accident (crash) Risk (RAR) analysis. Initially, a preliminary analysis of crash data based on location, human, and environmental factors provides insights into contributing factors. Building on these findings, the WICRS model categorizes weather patterns using highly randomized sampling-based clustering, a departure from traditional matched pair analysis (MPA). The study also conducts a stratified RAR analysis, considering variables such as gender, road type, and time of day. The effectiveness of the WICRS model is validated by comparing its impact with MPA, specifically examining risk analysis for wet and non-wet days. The dataset includes over 954,000 two-wheeler crash incidents, combined with historical weather data over six years. The findings highlight the significance of weather conditions in two-wheeler crashes and support the use of the WICRS model for detailed RAR analysis and road safety policy formulation.

Encouraging people to walk and use public transport can be a beneficial approach to tackle social and environmental issues associated with traffic and transportation. To motivate walking as a mode of choice of people, policymakers need to accord importance to pedestrians’ needs and expectations. Developing countries like India are lacking proper design guidelines for safer pedestrian infrastructure. With this background, it is essential to understand the concept of pedestrian needs for a safe and comfortable walking environment in Indian cities and provide a framework for planners to develop proper design guidelines for pedestrian infrastructures. The present study enhances the comprehension of decision-making process of pedestrians using Analytical Hierarchy Process to acquire priorities for various criteria that affects pedestrians’ choice of walking. A questionnaire survey was conducted in ten zones of Thiruvananthapuram city (Kerala, India) to recognize pedestrian priorities for walking characteristics within four main criteria: ‘Safety,’ ‘Security,’ ‘Comfort and Convenience’ and ‘Mobility and Infrastructure’ identified based on literature review. The study found that pedestrians perceived ‘Safety’ as the most important factor than conventionally used pedestrian infrastructure design factor ‘Mobility and Infrastructure.’ This paper also found a possible approach to quantify the importance of qualitative attributes that are applicable to pedestrian decision process. The findings of this study highlighted the importance of pedestrian-oriented assessment in better understanding of their decision-making process. These results will help urban planners and experts to rank the attributes defining the hierarchy of pedestrian needs and allocating investments into pedestrian facilities based on the needs and expectations of pedestrians.

Road traffic is the major source of noise pollution leading to human health impacts in urban areas. This study presents the relation between changes in human brain waves due to road traffic noise exposure in heterogeneous conditions. The results are based on Electroencephalogram (EEG) data collected from 12 participants through a listening experience of traffic scenarios at 14 locations in New Delhi, India. Energetic, spectral and temporal characteristics of the noise signals are presented. The impact of noise events on spectral perturbations and changes in the relative power (RP) of EEG signals are evaluated. Traffic noise variations modulate the rate of change in α and θ EEG bands of temporal, parietal and frontal lobe of the brain. The magnitude of event-related spectral perturbation (ERSP) increases with each instantaneous increase in traffic noise, such as honking. Individual noise events impact the temporal lobe more significantly in quieter locations compared with noisy locations. Increase in loudness changes the RP of α band in frontal lobe. Increase in temporal variation due to intermittent honking increases the RP of θ bands, especially in right parietal and frontal lobe. Change in sharpness leads to variation in the RP of right parietal lobe in theta band. Whereas, inverse relation is observed between roughness and the RP of right temporal lobe in gamma band. A statistical relationship between noise indicators and EEG response is established.

The transportation sector is a major source of environmental air pollution, contributing 25% of total greenhouse gas (GHG) emissions globally. Among India’s transport sector, the road sector alone is responsible for 80%–90% of total GHG emissions. Besides emissions from construction and maintenance and rehabilitation (M&R) activities of road infrastructure and vehicular tailpipe emissions (TPEs) are responsible for about 80%–90% of total emissions in case of an urban environment. TPEs are directly correlated to vehicular fuel consumption (FC), and hence, influenced by the pavement performance and application of different maintenance strategies. This paper presents the development of a road asset management system (RAMS) to manage urban roads in India sustainably. The developed framework implements Highway Development and Management (HDM‐4) software to study the urban road network under condition‐based maintenance scenario in three aspects: technical (pavement performance), economic and environmental (emissions estimation). Three additional scenarios were defined to analyse the effectiveness of maintenance strategies: do nothing (DN), routine and scheduled‐based. This study was conducted on urban roads of Pune Smart City over a network of 116.16 km in length, consisting of both bituminous (BT) and cement concrete (CC) pavements. The case study results showed that condition‐based is the most economical scenario and estimates the maximum reduction in emissions. Over Indian Rupees (INR) 285.76 million of funds and 6028.73 tonnes of carbon dioxide (CO 2 ) can be saved in 10 years by opting for a condition‐based over scheduled‐based scenario. The decline in GHG emissions was even more significant compared to other scenarios.

Investigating travel time variability is critical for pre-trip planning, reliable route selection, traffic management, and the development of control strategies to mitigate traffic congestion problems cost-effectively. Hence, a large number of studies are available in the literature which determine the most suitable distribution to fit the travel time data, but these studies recommend different distributions for the travel time data, and there is a disagreement on the best distribution option for fitting to the travel time data. The present study proposes a novel framework to determine the best distribution to represent the travel time data obtained from probe vehicles by using the modern machine learning technique. This study employs vast travel time data collected by fitting GPS tracking units on the probe vehicles and offers a comprehensive investigation of travel time distribution in different scenarios generated due to spatiotemporal variation of the travel time. The study also considers the effect of weather and uses the three most commonly used non-parametric goodness-of-fit tests (namely, Kolmogorov–Smirnov test, Anderson–Darling test, and chi-squared test) to fit and rank a comprehensive set of around 60 unimodal statistical distributions. The framework proposed in the study can determine the travel time distribution with 91% accuracy. Additionally, the distribution determined by the framework has an acceptance rate of 98.4%, which is better than the acceptance rates of the distributions recommended in existing studies. Because of its robustness and applicability in many different traffic situations, the proposed framework can also be used in developing countries with heterogeneous disordered traffic conditions to evaluate the road network’s performance in terms of travel time reliability.

The objective of the present study is to develop models to predict the deterioration of pavement distress of the urban road network. Genetic programming (GP) has been used to develop five models for the prediction of pavement distress: Model 1 for the cracking progression, Model 2 for the ravelling progression, Model 3 for the pothole progression, Model 4 for the rutting progression, and Model 5 for the roughness progression. The data have been collected from the roads of Patiala City, Punjab, India; during the years 2012–2015, the network of 16 roads have been selected for the data collection purposes. The data have been divided into two sets, that is, training dataset (data collected during the years 2012 and 2013) and validation dataset (data collected during the years 2014 and 2015). The two fitness functions have been used for the evaluation of the models, that is, coefficient of determination (R2) and root mean square error (RMSE), and it is inferred that GP models predict with high accuracy for pavement distress and help the decision makers for adequate and timely fund allocations for preservation of the urban road network.

Between 2008 and 2015, bus rapid transit system (BRTS) in India increased its implementation from two cities to eight cities with a significant increase in total ridership. This paper attempts to give a detailed review of BRTS implementation in cities of India. This is a systematic effort that could inform readers about the current system and network characteristics of Indian BRTS. Different system and corridor characteristics including off board and on board ticketing systems are adopted in India. Gross cost revenue collection model is adopted by almost all special purpose vehicle (SPV) companies developed to manage BRT systems. A variety of carriageway concept designs for BRTS are implemented in these cities considering a right of way of 22, 24, 30, 32, 40, 45, 60 meters respectively. Out of the eight cities, Ahmedabad has almost 30% of the total fleet size. In terms of regulatory context, SPV companies are formed in almost all eight cities after observing Ahmedabad BRT success. Documentation of these operating systems shall provide a sound database to planners and decision makers actively involved with BRT system implementation in developing countries.

There is a significant need to improve the highway safety during roadway planning, design and operations in developing countries like India. To receive appropriate consideration, safety needs to be dealt objectively within the transportation planning and highway design processes. Lack of available tools is a deterrent to quantify safety of a transportation facility during the planning or highway design process. The objective of this paper is to develop safety performance functions considering various elements involved in the planning, design and operation of a section on four-lane National Highway (NH)-58 located in the state of Uttarakhand, India. The mixed traffic on Indian multilane highways comes with a lot of variability within, ranging from different vehicle types to different driver characteristics. This could result in variability in the effect of explanatory variables on crashes across locations. Hence, explanatory variables for highway segment safety analysis considered were geometric characteristics like curvature change rate, slope change rate, transverse slope and traffic characteristics in the form of average daily traffic, light vehicle traffic, light commercial vehicle traffic, heavy vehicle traffic, two-wheelers, non-motorised traffic volume and operating speed were analysed against dependent variable as crash count per 200 m per year. Safety performance functions involving the explanatory variables are calibrated to predict crash frequency using Poisson Weibull technique and crash types are predicted using ordered logit model. Model results suggest that increase in traffic volume leads to higher probability of crash risk and traffic safety is significantly distorted by higher curvature change rate values.

Traffic noise has emerged as one major environmental concern, which is causing a severe impact on the health of urban dwellers. This issue becomes more critical near intersections in mid-sized cities due to poor planning and a lack of noise mitigation strategies. Therefore, the current study develops a precise intersection-specific traffic noise model for mid-sized cities to assess the traffic noise level and to investigate the effect of different noise-influencing variables. This study employs artificial neural network (ANN) approach and utilizes 342 h of field data collected at nineteen intersections of Kanpur, India, for model development. The sensitivity analysis illustrates that traffic volume, median width, carriageway width, honking, and receiver distance from the intersection stop line have a prominent effect on the traffic noise level. The study reveals that role of noise-influencing variables varies in the proximity of intersections. For instance, a wider median reduces the noise level at intersections, while the noise level increases within a 50-m distance from intersection stop line. In summary, the present study findings offer valuable insights, providing a foundation for developing an effective managerial action plan to combat traffic noise at intersections in mid-sized cities.