http://localhost:8081/jspui/handle/123456789/19175 Sun, 05 Apr 2026 21:46:37 GMT 2026-04-05T21:46:37Z http://localhost:8081/jspui/handle/123456789/20228 Title: ELECTRIC VEHICLE ADOPTION CHALLENGES AND CHARGING INFRASTRUCTURE PLANNING IN THE INDIAN URBAN ENVIRONMENT Authors: Mall, Shaurya Abstract: Electric vehicles are being widely promoted currently. Globally, their adoption rate is increasing. A similar trend is also evident in India as well. The Government of India has been running various policies like Faster adoption and Manufacturing of Hybrid & Electric Vehicles (FAME) and National Electric Mobility Mission Plan (NEMMP) to significantly increase the adoption and manufacturing rate by promoting awareness and providing incentives. This has resulted in growth in the Electric Vehicles (EV) sector, demonstrating that India has a high hope for its EV market. However, according to actual scenario, the growth could be more substantial. Barriers can hinder adoption at various levels, such as social, economic, or technical. With minimal evidence on the decision-making process of potential users and factors affecting EV adoption in Indian cities in the recent studies, there is also a glaring deficiency of a comprehensive evaluation framework for charging station planning, especially in developing countries like India. The present study aims to develop a comprehensive charging station evaluation framework for promoting EVs in India using potential users’ perception data from two cities of India, i.e., Delhi and Dehradun. To achieve this, the research objectives of the present study are (i) to identify barriers affecting the adoption of EVs in Indian cities; (ii) to understand the decision-making process and adoption intention of potential users; (iii) to propose a district-level plan for the locating potential alternate locations for charging stations; (iv) to propose a plan for charging infrastructure site selection; and, (v) to develop a framework for the evaluation and selection of EV charging technology. This study has utilised potential users’ perception survey data from 515 respondents from Delhi and 315 respondents from Dehradun and expert data from a total of 40 experts for accomplishing the research objectives. This study investigates how potential users' perception of the newly introduced technology has a major role in adopting two-wheelers and four-wheelers. For this purpose, logistic regression is used to identify the significant factors affecting the adoption of two-wheelers and four-wheelers in Delhi. For two-wheelers, a total sample size of 230 people is collected through semi-structured interviews. Of these, 209 are used for further analysis after cleaning the data. The data set is first trained and then tested. Wed, 01 Nov 2023 00:00:00 GMT http://localhost:8081/jspui/handle/123456789/20228 2023-11-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/20095 Title: DEVELOPMENT OF ROAD ASSET MANAGEMENT SYSTEM (RAMS) FOR SMART CITIES OF INDIA Authors: Jain, Bhavesh Abstract: Urbanisation has paved the way for higher comfort levels and standards of living. In pursuit of these, the rural population continues to move rapidly to urban regions. More than a third of India’s population is now residing in urban areas, which is expected to be over 50% by 2050. It has provided people with better jobs, living standards, healthcare, and education while fostering social and economic development. Despite this, growing urbanisation has created several problems, such as lack of space, increase in energy (fuel) demand, environmental pollution, and associated health problems. Urban area development has been significantly aided by the transportation sector and vice versa. The development of smart transport infrastructure has ensured better connectivity across the country and supported other infrastructural developments for the nation’s overall economic and social welfare. The road infrastructure is one of the most complex and highly diversified of all other transport modes in urban regions. In India, urban roads constitute about 8.5% or 5.42 lakh km of the total road length. Road assets deteriorate with ageing, and reconstruction may not always be the sustainable solution while considering the impact of construction activities on the environment and the vast sum of public funds involved, which would otherwise be utilised for social welfare. Further, the decaying pavement performance and condition lead to problems like a hike in vehicular operating costs (VOC) for road users, increased vehicular emissions, and risk of road accidents. However, if coped with proper operations and timely maintenance, they are long-lived, and the adverse effects of pavement’s untimely deterioration are minimised. Therefore, there is a pressing need to overhaul the conventional practice, i.e., worst first, and opt for asset management practices that believe in the proper maintenance and management of pavements including all their assets immediately after construction. Asset management in the transportation sector is an extended and more holistic phenomenon of information and management systems being developed by transportation organisations, which usually focus on individual classes of assets (for example, bridges, tunnels, highway pavements, airport runways and ancillary road assets). The comprehensive framework, like an asset management system, integrates information and recommendations obtained from different tools at a common platform within the administration's scope. It promotes information-based decision-making for the management of road assets and a result-oriented (performance-based) approach. Wed, 01 Feb 2023 00:00:00 GMT http://localhost:8081/jspui/handle/123456789/20095 2023-02-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/19900 Title: INCLUSIVE MOBILITY FOR ELDERLY PILGRIMS IN INDIA: A STUDY IN HARIDWAR, UTTARAKHAND Authors: Jaiswal, Swapneel S Abstract: The research aims to develop guidelines for inclusive mobility for elderly people in the Indian pilgrimage cities. Accordingly, four objectives are set to meet the purpose including identifying factors of mobility for the elderlies in a pilgrimage environment, to develop a framework for mobility studies, to identify the mobility problems faced by elderly and suggest strategies and interventions for policies and design in the Indian context. The study adopts an exploratory approach with a field based study of Haridwar city, a prominent pilgrim city of the country. The study undertakes a systematic strategic exploration of the subject by first taking critical literature review followed by the development of the framework for research. Further, it takes detail review of the case study Haridwar through secondary sources and field investigation. Thereafter, the study captured elderly pilgrims’ perception, elderly profile, mobility behaviour, infrastructure assessment, stakeholder input and policy review using the developed framework. Based on the findings by implementing the developed framework in Haridwar city, conclusions are drawn and recommendations are made in the thesis. The developed framework includes Space-Time Model, Stages of Travel, Mobility Perception, Pilgrims Profiling, Mobility Behaviour analysis, Infrastructure Assessment, Stakeholder’s Engagement, Policy Review and Comprehensive Analysis and Validation. The study showed that there are strong motivation and intention to come to Haridwar city despite the travel constraints and limitation of the city itself with respect to infrastructure. Causes of this gap identified by the study include inconsistency in the policy at different levels in translating the international and national agenda of elderly inclusiveness and barrier-free environment. Pilgrim travel to these destinations because of their strong motivation and intentions and strongly perceive the low level of mobility. The infrastructure assessment also shows the gap in various places and levels. Stakeholders’ input also shows wide gap in preparedness of host community. Elderly pilgrims’ profile indicates the need for intervention as most of the pilgrims face travel constraints including physical and economic limitations. This further lowers their participation in mobility and especially women’s participation. Mobility behavior does conform the need to improve the public transportation design intervention and introduce economically innovative mechanism to make the city inclusive. Wed, 01 Jul 2020 00:00:00 GMT http://localhost:8081/jspui/handle/123456789/19900 2020-07-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/19657 Title: ARTIFICIAL INTELLIGENCE BASED SOLUTIONS FOR URBAN TRANSPORTATION SYSTEMS Authors: Chaturvedi, Narayan Abstract: In urban transportation systems, traffic monitoring and management can suggest better routes to the travelers, reduce travel time and fuel consumption, increase road capacity, and passenger safety. Such transportation applications require huge amount of dataset to analyze and derive urban traffic situation. Recent innovations in communication and information technology have opened various sources of data generation. For example, different social media platforms and Traffic sensors made it possible to collect a large dataset and process it to improve the urban transportation systems. These large collected datasets give rise to the need for techniques to discover and understand hidden patterns. These discovered trends and patterns can further be used in various decision making. With the advent of such massive data collection ways, urban transportation systems have been changed significantly into more powerful Artificial Intelligence (AI) algorithm driven systems with optimized performance. Data-driven AI-based learning algorithms employ large datasets from different sources and can be processed into patterns necessary for transport contributors. However, larger datasets come with new challenges in data processing steps. Further, With the expansion of Intelligent Transportation Systems (ITS), it is becoming more challenging to accurately predict urban transport systems and analyze city traffic. The decision ability of machines powered by Artificial Intelligence and Machine learning algorithms has made a paradigm shift in modern society. This thesis proposes different machine learning (ML) algorithms considering urban transportation characteristics, useful for solving urban transportation problems utilizing unstructured text-based social media datasets and numerical traffic sensor datasets. Primarily, this work addresses the issues like accurately detecting traffic information and analyzing the stakeholder’s sentiments from the Twitter-based social media datasets and precise Abstract traffic information utilizing traffic sensor data. A few of the urban transportation challenges proposed in this work are discussed further. With the rapid urbanization and exponential increase of motorized vehicles, frequently occurring traffic events like accidents, potholes, traffic-jam, road maintenance etc., affect daily life traveling. Confined use of road sensors limits the effectiveness of such traffic disturbing event detection. In this context, social media platforms and microblogging websites like Facebook, Twitter, etc. are becoming popular among the people to share the events and things which affect their daily life. In our first objective, an integrated methodology is suggested that uses machine learning (ML) models to detect the traffic events from usergenerated social media data. In order to collect the traffic related tweets, a dictionary of traffic-related keywords has been formed. The novel combinatorial feature generation approach (CFGA) is the main contribution of this work. The proposed CFGA uncovers appropriate associations among the keywords of tweet and extracts the correlated keywords from the collected data. Such keywords are denoted as set phrase. The set phrases may comprise of single or multiple words of a tweet. These set phrases may be used as keywords for event-related data collection for further analysis. The frequently occurring set phrases are identified using the notion of support, which signifies the percentage of tweets containing relevant keywords. Since the nature of different events may also be different, therefore, a hard-coded value for support threshold will not be beneficial. A hyper-parameter named as support (!) is tuned for finding threshold value that is used to obtain the set phrase. This process sets up a database of frequently occurring set phrases that can signalize traffic-related events. This database of set phrases are then used as input for ML-based classifier to identify the text that contains traffic related events. The results of the proposed approach depict that if suitable support is chosen, then proposed CFGA increases the accuracy of supervised classification models for extracting traffic information from twitter data. Transportation is an essential part of human life, therefore, it is important to understand the opinion of the general public in different aspects of transportation. Furthermore, the citizens’ opinion is the actual customer-based performance metric which can be used to assign priorities to the urban traffic issues. Further, Social media is the cost effective source of large amount of dataset, which reflects sentiments and provides the possibility of opinion vi Abstract mining in this context. This work of the thesis proposes an opinion mining approach based on traffic-related tweets to find the citizens’ sentiment for urban transportation issues. In order to show the prevalence of transportation on social media, the location-based traffic related tweets, written by individuals expressing their sentiments about different transport services have been mined, preprocessed, and then a dictionary-based approach is used for the calculation of sentiment and classification of sentiment polarity to evaluate the satisfaction of transportation users. ML covers the biggest part of artificial intelligence and is used extensively in transportation systems from travel prediction to route choice modeling. The third work of the thesis utilizes ML-based techniques to obtain more understanding about urban traffic patterns by analyzing hourly and daily variation in urban traffic flow dataset. A model has been developed for the analysis of spatial and temporal patterns in urban traffic data. Model development involves the formulation of algorithms to be applied to the data and choice of various metrics to evaluate the ML algorithms. Final results of the work are analyzed to determine the various factors that affect the traffic flow patterns in an urban area. The sensor-based average speed estimation of vehicles on road segments is desirable while determining the route of choice. Such estimation activity requires large traffic data to develop an effective traffic prediction model. The sensors deployed on road networks collect traffic data continuously. This results into the generation of large time series of traffic data. Accurately capturing the temporal dynamics of such time-series traffic data with improved prediction performance is an open challenge. The fourth work of the thesis proposes a solution to the problem of route choice behaviors and traffic congestion. An innovative travel speed prediction model using decomposition based smooth time series has been proposed for non-stationary and non-linear temporal dynamics of traffic time series data. The decomposition of time series data is aimed to capture the temporal characteristics of traffic data by improved data representation. The purpose of smoothing is to filter out the irrelevant and excessive fluctuations in time-series data, which may lead to improved model complexity and performance. This way, the thesis proposes solutions to the urban transportation related problems of accurately predicting traffic information using a text-based social media dataset and numerivii Abstract cal traffic sensor dataset. The work promotes the applications of ML algorithms in intelligent urban transportation systems. The proposed solutions of the thesis are tested with their existing counterparts. In the first and second problems, Twitter based social media data is used to develop the model, and precision, recall, and F-measure are used to evaluate the model’s performance. Whereas numerical traffic data is used in third and fourth problems and Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) error is considered for evaluating the model’s performance in fourth work. Sat, 01 Jan 2022 00:00:00 GMT http://localhost:8081/jspui/handle/123456789/19657 2022-01-01T00:00:00Z