http://localhost:8081/jspui/handle/123456789/18898 2026-10-16T13:40:48Z http://localhost:8081/jspui/handle/123456789/21352 Title: PLANNING FOR NON-MOTORIZED TRANSPORT (NMT) IN MIXED-LANDUSE AND MIXED-TRAFFIC URBAN AREAS Authors: Bhatnagar, Apoorva Abstract: A non-motorized mode of transportation (NMT) involves mainly walking, cycling, and rickshaws. Their carbon footprints are small, their energy consumption is low, and their local emissions are zero. Furthermore, they are not dependent on fossil fuels, making them more affordable than motorised transport`. NMT plays a significant role in the lives of low income households in India. Additionally, active transportation such as walking, and cycling has numerous health benefits. Despite this, NMT is far too often overlooked as a means of mobility in favour of capital-intensive modes of transportation. The design of most Indian cities is compact, with high population densities and mixed land use - ideal factors for the influence of NMT as a form of mobility for short trips and as a feeder to other modes of transportation. In addition to offering environmental benefits, NMT offers a holistic range of benefits to both individuals and cities. Health, equity, a cleaner environment, poverty reduction, safer roads, liveable cities, and equal opportunities for all regardless of socio-economic background are among these. A large majority of trips in Indian cities, especially those less than 4 km in length, are made by walking and cycling. Conventional planning approaches have focused on the movement of vehicles rather than people. In addition to promoting healthy lifestyles and economic independence, NMT modes are environmentally friendly. In addition to improving social equity, they also make it easier for people from all economic backgrounds to get around. Although this has been true, pedestrians and non-motorized vehicles have largely been devalued in Indian cities. Transport planners should pay special attention to NMT in realigning their planning priorities. Due to the high prevalence share of modes, planning for NMT is both economically reasonable and contributes to social equity by improving access to jobs and housing. Aside from this, NMT modes are entirely non-polluting, thus helping to reduce the environmental impact of city life. The purpose of the report is to find the critical factors that are involved in the planning of non-motorized transport and perform a comparison of Germany and India based on the factors identified in the literature review. Both the cases are mixed land use and mixed land use conditions. 2022-05-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/21326 Title: IMAGE PROCESSING AND MACHINE LEARNING-BASED TOLL COLLECTION MODEL FOR INDIAN HIGHWAYS Authors: Kumar, Amrish Abstract: The road transport system plays a crucial role in fostering economic and societal growth. However, it faces numerous obstacles, such as traffic congestion, inefficient toll collection methods, and insufficient enforcement of traffic regulations. This study proposes a Distance-Based Toll Collection Model using Automatic Number Plate Recognition (ANPR) and machine learning techniques to address these problems. The model aims to enhance the efficacy, equity, and safety of toll collection while promoting optimal road network utilisation. The proposed system employs ANPR technology to automatically identify vehicles through licence plate recognition, thereby eliminating the need for manual toll collection and decreasing congestion at toll booths. The collection of real-time data enables efficient traffic monitoring and management. The distance-based toll collection model ensures a fair and equitable system by charging tolls based on the actual distance travelled by vehicles. This promotes cost effectiveness and encourages optimal road network utilisation. The application of machine learning techniques, particularly the Indian Number Plate Character Recognition (INPCR) model, improves the accuracy and dependability of number plate recognition, even for Indian number plates with varying designs. In terms of accuracy, precision, recall, F1 score, and Receiver Operating characteristic - Area Under Curve, the evaluation results are promising. The implementation of the proposed system contributes economically to fuel and time savings. By minimising toll booth wait times and optimising traffic flow, congestion and delays are reduced, resulting in substantial fuel savings and decreased emissions. While the proposed system offers several advantages, there are obstacles and research gaps that need to be addressed. Variability in licence plate designs, deviations from ideal image conditions, the inability to generalise to non-standard plates, and the need for real-time performance optimisation are examples of these challenges. Distance-Based Toll Collection Model utilising ANPR and machine learning techniques provides a comprehensive and effective solution to road transportation system challenges. The proposed system contributes to a sustainable and user-friendly road network by reducing congestion, enhancing safety, promoting cost-effectiveness, and enhancing services. Future research and development should concentrate on overcoming obstacles to improve the system's efficacy and dependability. 2023-06-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/21325 Title: APPRAISAL OF PEDESTRIANIZATION EXTERNALITIES Authors: Bhadola, Debashish Abstract: Motorized private vehicles have plagued urban areas with their incessant use and consequent negative externalities. Thus, the concept of active transport modes as a substitute is gaining prominence. The aim of this study is to investigate pedestrianization impact on value creation. Firstly, fundamental concepts and approaches are evaluated by reviewing related literature of pedestrianization and open spaces, and economics. In the second stage, the impact of pedestrianization impacts is realized with respect to abutting property values in terms of rental income and land value increment, by employing econometric tools like Hedonic Price Method (HPM). Since there is no market for value improvement arising out of pedestrian infrastructure, there occurs a lacuna on the appraisal of incidental bystander gains for pedestrians in terms of air environmental, economic, and other gains. These values can be translated to the likeability of that place attracting flocks of people and booming commercial growth especially in pedestrianized markets. Pertinent to note that a few abutting landowners apportion these gains in terms of land value increment which need to be captured by the government for an equitable and fair distribution. Thus, appraisal of the overall value creation (if any) becomes important. The Hedonic Price Method (HPM) can be used to estimate the effect of relevant variables on the price of the asset in question. The study seeks to put forth pedestrianization and pedestrianized streets as a public open space bestowing a relative measurable economic value like other public realms, its impacts on the sale and rental price of properties in form of positive externalities. Hedonic Regression analysis will allow us to show the effect of the pedestrian way, when the level of perceived quality of pedestrian way are well appreciated by pedestrians, can significantly be an underlying factor for property prices in form of shopkeepers’ willingness to pay for the premium rent. Consequently, the approach seeks to justify the expenditure incurred on pedestrianization projects. Previous research and case studies have reported open spaces like green belts, lakes, riverfront etc. providing a premium for property prices but research on pedestrian way as an open space in this regard has been limited. Thus, Hedonic Price Method can be a promising tool in measuring the impact of environmental attributes of property on its rental prices. 2023-06-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/21324 Title: STUDY OF FACTORS INFLUENCING TRIP GENERATION IN INDIAN CITIES Authors: Rathod, Jatoth Jai Kumar Abstract: This dissertation investigates the variables that influence the trips generation of people take in urban areas. With the rapid growth of urban population, understanding the factors that influence travel behaviour has become crucial for effective urban planning and transportation management. The study employs robust methodology to examine different variables and their effects on trip-generating patterns by considering statistical and neural network techniques. Primary data collected through household surveys by Central Road Research Institute and the data has been consider for the analysis. The primary data focusing on residents’ travel patterns and socio demographic and travel data of the households within specific urban areas. The study is complemented with secondary data from transportation databases, urban planning documents, and related literature. The study highlights numerous variables that influence trip generation in metropolitan environments including travel habits, factors affecting socioeconomic status, land use patterns, transportation infrastructure, and demographic characteristics. The analysis emphasis intricate interactions between these variables and their diverse influences on trip generation across different urban settings. This study considered Multiple Liner Regression and Neural networks to develop the relationship between the trip frequency and various explanatory variables considered in this study. The influence of various variable on trip frequency for private vehicle modes (Car and 2W) were investigated in this study. By understanding the factors that shape travel behaviour, urban planners and policymakers can develop effective strategies to manage transportation demand, enhance accessibility, and promote environmentally friendly transportation options. In conclusion, this study contributes to the understanding of the factors influencing trip generation in urban areas. (Number of Family Members, Trip Time, Monthly Expenditure on Transport for Cars and House type, Total Vehicles owned, Trip Time, Trip Length for 2 Wheelers in the cities Lucknow and Visakhapatnam). 2023-06-01T00:00:00Z