The initial segment of a journey, specifically the first mile connectivity between a traveler’s starting point (origin) and a primary public transportation hub such as a bus stop, railway station, or metro station, is a crucial element influencing the overall efficiency and attractiveness of multimodal transportation systems. Improving first mile connectivity is necessary to promote sustainable mobility and reduce dependency on customized automobiles in Indian cities where the rapid pace of urbanization and population growth has increased traffic congestion and travel time. By using the multinomial logit model, a discrete choice model, this study aims to develop an access time index and an average accessibility index to enhance first mile connectivity in Indian cities. The authors will assess the first-mile connectivity conditions in the selected Indian cities, ascertain the significance of the factors influencing commuters’ first-mile choices, and offer suggestions for particular actions meant to improve the intermodal integration of feeder, private, and walking modes with the primary public transportation system. It was discovered that the city of Bhopal has an Average Accessibility Index (AAI) of 0.48 and an Access Time Index (ATI) of 0.52. Because an optimum ATI would be closer to 0 (meaning access times would be shorter) and an optimal AAI would be closer to 1 (meaning accessibility would be better), these numbers suggest that Bhopal city’s first mile connectivity can be improved. This research will be useful in developing evidence-based policies and initiatives to enhance first-mile connectivity in Indian cities, which would ultimately result in sustainable urban mobility and improved living circumstances for city dwellers.

Travel time and ridership growth are two indicators of how interconnectivity affects the effectiveness of public transportation. Using a comparison of Extreme Gradient Boosting, polynomial regression (Degree 2), and the semi-linear regression approach, this research report attempts to determine the impact of interconnectivity and identify the most effective technique for estimating ridership demand for multi-modal transport systems. The influence of several factors on ridership demand is evaluated in three cities where the metro and bus rapid transit system () are operational. The results show that ridership is impacted by interconnectedness. The metro-BRTS transfer sites and the number of boardings in Ahmedabad show a strong correlation, indicating well-established multimodal integration (R-2 = 0.974). According to Jaipur, route length was the most important feature, indicating that transfers are more efficient over longer routes with fewer interchanges. Pune showed moderate predictability, indicating changes in intermodal connections and increased variability in passenger behaviour. The (Shapley Additive exPlanations) and (Local Interpretable Model-agnostic Explanations) analyses found that the number of interchange locations and metro alighting volumes had the greatest positive impact on ridership. However, long travel times and frequent stops discourage ridership and deter commuters.

Rapidly growing urban populations necessitate the development of integrated multimodal transport systems (MMTS) for efficient and sustainable mobility. This bibliometric study reviews research on MMTS travel time modeling and evaluation over a 30-year period, encompassing 1402 documents, including articles, reviews, and proceedings papers. The annual publication trajectory highlights the increasing research interest, with peaks in 2021–2022. Core journals driving MMTS discourse include ‘Transportation Research Record’ and ‘Sustainability.’ Keyword analysis reveals clusters focusing on travel time, reliability, behavior, and public transport integration. The quantitative synthesis presents a comprehensive perspective of influential studies, authors, events, and thematic connections surrounding MMTS travel time research. Results show a growth in studies utilizing big data analytics and AI to evaluate reliability and congestion impacts across interconnected multimodal networks. However, the quantification and modeling specifically focused on travel time performance remain limited. This review establishes an informative foundation and identifies critical areas requiring further research to advance multimodal transport planning and operations. It signals promising avenues at the intersection of sustainable, technology-driven MMTS and travel time optimization for urban commuters. The identified research gap suggests that this study will be valuable for researchers, and the bibliometric analysis provides empirical insights to guide future scholarly activity in this domain.

This study investigates the strength characteristics of full depth reclamation (FDR) mixes by varying cement contents (4, 5, and 6

India's rapidly expanding urban road infrastructure has made flexible (bituminous) pavement maintenance a critical challenge for municipal and state road agencies. This study develops quadratic regression prediction models for seven flexible pavement distress types: longitudinal cracking, transverse cracking, ravelling, potholes, rutting, edge cracking, and bleeding/flushing as its primary contribution, based on a two-year visual inspection survey of ten road sections in Bhopal, India, at 500-m intervals and three-month observation cycles. Ensemble machine learning models (Random Forest and Gradient Boosting) are deployed exclusively as nonparametric sensitivity validators-not as competing prediction methods-to verify the quadratic functional form adequacy and establish an accuracy ceiling against which regression performance is benchmarked. Quadratic regression achieved R2 values of 0.931–0.974 across all seven distress types, providing closed-form predictive equations directly usable by municipal engineers via standard statistical software. Gradient Boosting attained R2 of 0.951–0.987; the marginal gain of ΔR2 ≤0.02 over regression confirms that the quadratic functional form is correctly specified rather than indicating a genuine machine learning advantage. Feature importance analysis independently confirms time elapsed as the dominant predictor (61–68

Developing multimodal public transportation systems is essential to meet the growing mobility requirements in cities all across the world and realize more sustainable communities. However, the efficiency of these systems depends on the quality of service the consumers received. The aim of this study is to find the factors determining passenger satisfaction and quality of service in the efficient public transportation network of Bhopal. In order to analyze the data obtained from the study conducted among 1177 passengers via a self-administered questionnaire, EFA, CFA, HLM, and LCA were carried out. EFA and CFA identified six critical service quality dimensions: convenience, customer satisfaction, implementation, security, ease, comfort, and dependability. HLM revealed that reliability, integration, and comfort played a major role in affecting passenger satisfaction and that the impact of factors varied with different modes of transport. LCA identified three distinct passenger segments: Some are as follows: “Highly Satisfied”, “Moderate Satisfied” and “Dissatisfied” presented. The findings can assist policymakers to improve significantly the understanding of the components needed to improve service quality and passenger satisfaction by focusing on reliability, convenience, and integration, and emerging needs of heterogeneous passenger groups and different modes of transportation of burgeoning users. The above introduced plan provides a systematic approach towards enhancing the sustainability and appeal of the integrated and multimodal public transport system in Bhopal as well as supplementing the available literature on the quality of service of the public transport system.

Multimodal public transport systems are crucial for sustainable urban mobility in growing cities like India. This study investigates the factors determining service quality in Bhopal's multimodal public transport system using factor analysis. A survey of 650 regular users was conducted to assess service quality perceptions. Exploratory factor analysis revealed six significant factors: integration (22.12 % of variance explained), reliability (14.30 %), comfort (9.38 %), safety (8.07 %), accessibility (6.85 %), and customer service (5.74 %). The factors demonstrated high internal consistency reliability (Cronbach's alpha > 0.8). Satisfaction levels varied across factors, with accessibility and comfort receiving higher ratings than reliability. Demographic comparisons revealed significant differences based on gender and age. The findings provide insights into the critical factors shaping service quality perceptions and offer a framework for assessment. The results highlight the need for targeted interventions, particularly in areas with lower satisfaction levels. This research contributes to the literature on public transport service quality in developing cities and underscores the importance of considering passenger perspectives in multimodal transport planning and management. This research contributes to the growing body of literature on public transport service quality in developing cities and underscores the importance of considering passenger perspectives in the planning and management of multimodal transport systems.

This study explores the opportunities and challenges of advancing multimodal integration for sustainable urban mobility in India. With rapid urbanization and increasing motorization, Indian cities face issues of congestion, air pollution, and social inequity. Multimodal integration, the seamless integration of different transportation modes, is a promising approach to address these challenges. The study assesses the current state of urban mobility in India, examines the concepts and benefits of multimodal integration, and identifies key opportunities, including supportive policies, technological advancements, and public-private partnerships. It also discusses challenges such as institutional barriers, financial constraints, and the need for behavioral change. Case studies of successful initiatives in Delhi and Ahmedabad demonstrate the potential benefits of integrated transport systems. The study proposes recommendations for advancing multimodal integration, focusing on policy reforms, infrastructure development, capacity building, and stakeholder engagement. It concludes by summarizing key findings and identifying future research directions, emphasizing the need for further investigation into long-term impacts, innovative funding mechanisms, emerging technologies, comparative policy analysis, and social and behavioral aspects of sustainable urban mobility. This research contributes to the growing knowledge on multimodal integration and sustainable urban mobility in India, providing valuable insights for policymakers, urban planners, and transportation professionals working towards creating more sustainable, efficient, and inclusive cities.

Accessibility is crucial for the integration and efficiency of multimodal transportation systems in Indian cities. This study develops an accessibility index tailored for Indian cities to measure the accessibility of multimodal transport systems and identify improvement strategies. The study combines the accessibility index with the multinomial logit model to investigate commuter decision factors and determine the relative proportions of private and feeder modes compared to walking. The accessibility index is constructed using carefully selected variables that capture the key dimensions of accessibility in Indian cities and is applied to assess the accessibility of the multimodal public transport system. The multinomial logit model is illustrated through a case study of Bhopal city, demonstrating its practical utility in improving accessibility. The study highlights the effectiveness of the developed accessibility index in measuring accessibility and informing improvement strategies. The findings contribute to the growing body of knowledge on accessibility measurement and provide a foundation for future studies in the Indian context.

Urban Indian roads are under increasing stress due to rapid urbanization, escalating traffic volumes, and deteriorating infrastructure. This Study explores how integrating predictive technologies such as Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), drones, and blockchain within a Performance-Based Asset Management (PBAM) framework enhances road maintenance efficiency. AI-powered drone inspections improved Pavement Condition Index (PCI) assessment accuracy by 85

Rapid growth of technology-based transportation services has drastically altered the nature of transportation networks as well as individuals’ travel behaviour and lifestyle. People generally rely on two transportation networks such as public and private for travel. However, the existing transportation network faces a lot of crises such as traffic congestion, environmental impact and cost consumption. So, an effective study must be conducted to analyse the access and egress mode choice of travellers to solve these above mentioned issues and to improve the transit accessibility and comfort of the travellers. Most of the existing mode choice analysis studies are conducted in the main cities of India. However, the accessibility of transit in other cities of India is not known to the people. Thus, this current research target is to analyse the access and egress mode choice of travellers using the MNP-INL regression model in Bhopal city. Initially, questionnaires are framed to conduct surveys from respondents. Based on the responses collected from the respondents the dataset needed for analysis is prepared. After that, the correlation prevailing between traveller mode choice and variable factor is found by calculating the correlation coefficient. The variable factors include the user and trip characteristics. Then, based on the correlation the choice of access and egress mode used by the traveller is estimated using regression models such as the Multinomial Probit Model (MNP) and Improved Nested Logit (INL) model. The nested logit model is improved through optimizing inclusive parameters using Honey Badger Optimization (HBO). The behaviour of travellers is significantly influenced by various factors. From that, the quality Parameter related to the main mode is essential and has a great influence in analysing the traveller’s mode. Simulation analysis showed that 8.8

This study develops strategies to enhance feeder services and improve accessibility in multimodal transport systems in Indian cities. An accessibility index tailored for Indian cities is developed, incorporating parameters such as travel time, service frequency, network coverage, multimodal integration, and accessibility to key destinations. The study integrates this index with a logit model to investigate commuter decision factors and determine the relative proportions of private and feeder modes compared with walking. A case study of Bhopal city demonstrates the model’s practical utility, revealing that enhancing feeder service coverage from 22% to 92% significantly improves Bhopal’s accessibility metrics, with the access time index decreasing from 0.4 to near 0 and the accessibility attractiveness index increasing from 0.6 to almost 1.0. The findings highlight the effectiveness of the developed approach in measuring accessibility and informing targeted strategies for enhancing feeder services in Indian cities, contributing to the growing body of knowledge on accessibility measurement and providing a foundation for future research on improving multimodal transport systems in India.

Urban mobility in India faces significant challenges, including inefficient public transit systems, extended travel times, and difficulties in seamlessly connecting different modes of transportation. To address these complex urban transportation issues, there is a pressing need to develop and evaluate multimodal public transportation systems. This study proposes a comprehensive methodology for assessing the travel time performance of multimodal transport systems in urban areas. The proposed methodology consists of three key steps: (1) Identification of critical travel time performance parameters, (2) Development of individual indices for these parameters, and (3) Evaluation of an overall Travel-Time Performance Index. This approach allows for a systematic assessment of access, in-vehicle, and egress travel times within a multimodal context. To demonstrate the application of this methodology, a case study was conducted in Bhopal, India. The study utilized a combination of survey data and analytical techniques, including Fuzzy Analytic Hierarchy Process (AHP), to evaluate the city’s multimodal transport system performance. The results provide insights into the relative performance of different components of Bhopal’s multimodal transport system, highlighting areas of strength and opportunities for improvement. This methodology offers a valuable tool for transportation planners and policymakers to assess and enhance the efficiency of multimodal transport systems in Indian cities. By facilitating a comprehensive evaluation of travel time performance, this study contributes to the development of more efficient public transit systems, potentially leading to reduced congestion, improved air quality, and enhanced urban mobility. The proposed framework can be adapted and applied to other urban areas, supporting evidence-based decision-making in transportation planning and management.

The service quality of multimodal public transport system plays a crucial role in maintaining the overall efficiency and sustainability of urban mobility and the seamless integration of transportation. This study evaluates the determinants of service quality in the multimodal public transport system of Bhopal, India, using Structural Equation Modeling (SEM), Factor Analysis, Subgroup Analysis, and Importance-Performance Analysis (IPA). A detailed survey of passengers identified six latent variables: reliability, comfort, safety, accessibility, information, and customer service. SEM analysis revealed significant positive relationships between all six factors and passenger satisfaction, with reliability (beta = 0.358, p < 0.001) and comfort having the strongest influence. Subgroup analysis showed variations in the importance of service quality factors among different age groups, genders, and usage frequencies. The IPA grid identified comfort and accessibility as priority areas for improvement. The findings highlight the importance of prioritizing these factors to enhance passenger satisfaction and promote sustainable urban mobility in developing countries. The study provides actionable insights for policymakers and transit authorities to effectively improve service quality and meet passengers' satisfaction in multimodal public transport systems.

Insufficient multimodal transportation infrastructure in Indian cities increases travel time, waiting time, traffic congestion, traffic accidents, travel costs, fast-growing energy consumption, and walking distance. The multimodal transportation system's journey time performance must be analysed to tackle these transportation concerns. Thus, this research provides a fundamental method for assessing multimodal transportation system trip time performance. This study proposes three steps for assessing travel time performance: identifying factors, generating individual indices, and evaluating the Multimodal Transport System Travel-Time Performance Index. The travel-time performance index of Bhopal city was 0.79 after evaluating the results of the individual indices (Access 0.49, In-vehicle 0.39, and Egress 0.38). Consequently, this study will help identify areas for improvement in Bhopal's multimodal transportation system and provide insights for enhancing its efficiency and user satisfaction. The proposed methodology can be applied to other cities facing similar challenges, aiding in the development of more sustainable and user-friendly transportation systems.

Insufficient multimodal transportation infrastructure in Indian cities increases travel time, waiting time, traffic congestion, traffic accidents, travel costs, fast-growing energy consumption, and walking distance. The multimodal transportation system's journey time performance must be analysed to tackle these transportation concerns. Thus, this research provides a fundamental method for assessing multimodal transportation system trip time performance. This study proposes three steps for assessing travel time performance: identifying factors, generating individual indices, and evaluating the Multimodal Transport System Travel-Time Performance Index. The travel-time performance index of Bhopal city was 0.79 after evaluating the results of the individual indices (Access 0.49, In-vehicle 0.39, and Egress 0.38). Consequently, this study will help improve Bhopal's multimodal transportation system's poor locations and increase usage.

The study contains solutions for enhancing the public transportation system's travel time performance (PTS). The importance of a multimodal public transportation system resides in its travel time competence, service synchronization, and the heterogeneous nature of traffic, all of which needed improvements. The major decision influencer for commuter mode preference is PTS travel time competency versus personal trips. The identification of strategies based on previous research is an attempt to find a solution for improving travel time performance. Access time, in-vehicle and out-vehicle transit time, and egress time are all reduced by using travel time strategies. Access time strategies propose that getting to the public transportation network (PTN) is easier, that public transportation is more easily accessible, and that service coverage is better. The in-vehicle and out-vehicle travel time strategies show the key operational characteristics that influence travel time performance and how they can be improved. In-vehicle travel time plans emphasize the improvement and reorganization of areas responsible for enhancing travel time compatibility with personal trips. Interconnectivity and timely availability of cars at transfer sites were the focus of egress time initiatives. The long-term viability of public transportation is dependent on micro-level strategy enhancement.

Purpose: To evaluate diagnostic accuracy of IS6110 insertion genes, hsp65, and Xpert MTB/RIF for rapid diagnosis of pulmonary tuberculosis.Methods: Sixty patients, medically reported HIV negative, clinically suspected of having pulmonary tuberculosis, were included in this study, and consented before enrolment.Sputum samples were gathered once, and tested by smear for Acid Fast Bacilli (AFB).Cultured in the Loewenstein-Jensen (LJ) medium for M. tuberculosis growth, M. tuberculosis DNA was detected by conventional PCR targeting IS6110, and hsp65 genes using specific primers, and automated nested real-time PCR targeting rpoB gene.Sensitivity, specificity and diagnostic accuracy were calculated for each method compared to culture.Results:Compared with culture as reference method, smear, IS6110, hsp65, and Xpert MTB/RIF had sensitivity 77.14%, 100%, 100%, and 100%, specificity 92%, 96%, 96%, and 96.97%, and diagnostic accuracy 83.33%, 98.33%, 98.33% and 98.21% respectively.Molecular diagnostic methods had the highest diagnostic accuracy, whereas smear had the lowest.No statistical significance, (p value > 0.05) was detected between the patients' demographic data and the presence or absence of TB infection.Conclusion: The diagnostic accuracy that we got from the molecular methods, confirmed the diagnostic value of molecular detection of M. tuberculosis in pulmonary cases, supporting the application of automated and conventional PCR in rapid analysis.Smear could be more efficient when used for treatment monitoring.Combination between one-molecular techniques with smear as a routine method could be valid for rapid diagnosis of TB.

This study presents basic concepts and applications of Artificial Intelligence System (AIS) for development of intelligent transport systems in smart cities in India. With growing urbanization the government has now realized the need for developing smart cities that can cope with the challenges of urban living and also be magnets for investment in India. Transport system in smart cities should be accessible, safe, environmentally friendly, faster, comfortable and affordable without compromising the future needs. The Indian cities largely lacks of Intelligent Transport System in India and there are various problems such as inefficient public transport system, severe congestion, increasing incidence of road accidents, inadequate parking spaces and a rapidly increasing energy cost etc. Therefore, development of Intelligent Transportation System is essential for smart cities due to concerns regarding the environmental, economic, and social equity. Artificial Intelligence is a key technology to resolve these issues. Therefore, there is an urgent need to adopt Artificial Intelligence system for development of Intelligent Transport System to better understand and control its operations in smart cities. Hence, the main objective of this study is to present some basic concepts of Artificial Intelligence and its applications for development of Intelligent Transport System in smart cities in India. This study concludes that Artificial Intelligence system needs to be adopted to develop smart public transport system, intelligent traffic management and control, smart traveller information system, smart parking management and safe mobility & emergency system in smart cities. It is expected that this study will pave the way for development of Intelligent Transport System in smart cities in India.

Tuberculosis(TB) is a disease of global significance, which accounts for a death in every 15 seconds. Recent studies shows TB is rising in certain parts of the world, and Saudi Arabia is one of them. Several factor contribute in predisposing the subjects for infection including but not limited to addiction to various compounds which have immune modulation properties, such as amphetamines and Heroin etc. Khat a plant whose leaves are chewed for its euphoric effect in east Africa and Arabian Peninsula including Saudi Arabia, is considered as mildly addictive, and its principle compound, Cathinone shares structural and functional similarity with amphetamine a known immunomodulator. Tuberculosis being a disease of immune modulation has a varied spectrum of complex interplay of proinflammatory molecules, resistin is one of them. In the present study, we try to explore the trinity of khat addiction, serum resistin level and tuberculosis by correlating the serum resistin level in non khat addicted healthy subjects, khat addicted healthy subjects, and in patients, both khat addicted and non khat addicted, with active tuberculosis. We observed significantly higher resistin level among the apparently healthy khat addicted subjects as compared to non addicted healthy controls. Thereafter, when we compare the resistin levels between khat addicted and non khat addicted TB patients we did not found significant difference between the two groups. However bacillary load was observe to be significantly higher among the khat addicted TB patient as compare to non addicted one. Validation of above results in animal model revealed dose dependant increase in bacillary growth in the Wistar rats treated with khat. Taken together these results suggest the role of khat in immune modulation albeit in the limited frame of resistin level.