Conference Papers
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Item Public Transit Travel Time Analysis Using GPS Data: A Case Study of Mysore ITS(Institute of Electrical and Electronics Engineers Inc., 2018) Harsha, M.M.; Mulangi, R.H.A prominent research emphasis on Intelligent Transportation Systems in the present world of technologies is Big Data. The use of big data in many of the projects consisting of Intelligent Transportation Systems especially for public transit vehicles such as buses is currently a subject of interest for many researches. Public transit is of greater importance when it comes to the reduction of traffic congestion and delays in present scenario of rapid increase in private vehicles on the roads. The present study explains the intelligent transport technology applied to the public transit system of Mysore city. The system assists in the collection, storage and analysis of a large data using the latest tools of information technology. This study also tries to make use of the data obtained from Intelligent Transportation System, Mysore to analyze the performance of public transport system. The work involves retrieving of the Global Positioning System data from the database server and analyzing the travel time characteristics of public transit. The travel time data can be utilized for the improvement of service provided by the transit agencies. © 2018 IEEE.Item Analysis of Bus Travel Time Variability using Automatic Vehicle Location Data(Elsevier B.V., 2020) Harsha, M.M.; Mulangi, R.H.; Kumar, H.D.D.Transit service reliability is a measure of the quality of service offered by the public transit systems. The nature and pattern of the travel time variability can be described by the travel time distribution and it is the prerequisite in the reliability analysis. Many research works have been carried out to understand the travel time distribution, but there are very few studies on the heterogeneous traffic in developing countries like India. In this study, an attempt has been made to analyze the travel time distribution at different spatial and temporal aggregation scales using Automatic Vehicle Location (AVL) data of public bus route in Mysore city. The results suggest that, Generalised extreme value (GEV) distribution is the better fit for travel times at both route and segment levels and the performance of the Generalised extreme value (GEV) distribution is comparatively better in both temporal and spatial aggregation compared to other distributions. This distribution can be utilized by the transit operators in the operation and performance evaluation of transit systems. © 2020 The Authors. Published by Elsevier B.V.Item Study on Travel Time Characteristics of Hubli-Dharwad Bus Rapid Transit System in Comparison with Heterogeneous Traffic Lane(Springer Science and Business Media Deutschland GmbH, 2022) Halyal, S.; Mulangi, R.H.; Harsha, M.M.; Laddha, H.Bus Rapid Transit System, which is also known as BRTS, is a very effective transit system in terms of travel time reduction. It has advantages compared to conventional road-based public transport like dedicated lane and pre-board fare collections. In the current study, performance-based travel time characteristics like travel time and travel speeds are evaluated by conducting the speed and delay survey for the BRT buses and private vehicles separately. The survey was performed by moving the car observation method to find out travel time and respective delays that are currently hampering the operations. Speed and delay survey for BRT buses was performed through the manual observation by travelling in the bus. Based upon the study, it has been found that there is a positive side effect of a dedicated corridor on the operation of BRTS buses, as obtained trial running speed values are almost very near to measured free-flow speed values on the selected study corridors. Meanwhile, it has been observed that the BRTS bus operation has faced many delays due to bus bunching, dwell time at the stations and delays at the intersections but delays occurring at the stations have contributed major proportion in the total delay. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Delay Variability Analysis at Intersections Using Public Transit GPS Data(Springer Science and Business Media Deutschland GmbH, 2023) Lal, A.; Mulangi, R.H.; Harsha, M.M.India is competing with the fastest-growing countries in the world in terms of urbanization and development. Rapid urbanization and motorization have led to congestion in urban roads in India. Delay forms a significant part of congestion. It is necessary to analyze variability in delay for mitigation of traffic congestion. In recent years, GPS data has emerged as a novel data source for traffic state monitoring and analysis due to its better accuracy, coverage, and accessibility. However, little work has been done for control delay estimation especially in Indian traffic condition. In this paper, an attempt has been made to estimate control delay at selected intersections in Mysore city using GPS data from transit buses. A vehicle trajectory-based formulation is adopted for the estimation of delay. The results are fitted to statistical distributions to analyze variability in delay. Kolmogorov- Smirnov (K-S) test for goodness of fit is used to estimate best fitting distribution. Generalized extreme value (GEV) distribution is found to best-fit delay in terms of fitting performance, robustness, and accuracy. The performance analysis indicates greater variability in delay during morning and evening peak hours. Successful estimation of delay variability allows for the analysis of traffic state at various intersections, thus paving the way for effective congestion mitigation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Analysis of Bus Stop Delay Variability Using Public Transit GPS Data(Springer Science and Business Media Deutschland GmbH, 2023) Ayana, H.; Mulangi, R.H.; Harsha, M.M.Travel time is considered as a direct measure of the efficiency and service reliability of a public transit system and increased travel time is contributed by delays at bus stops. Delay at bus stops affects the efficiency of bus operations and the level of service of public transportation. Day-to-day delay variability decreases passenger confidence in perceived reliability, causing uncertainty in making travel decisions. Many research works have been carried out to find delays at bus stops, but there are very few studies about the delay at bus stops in India using automatic vehicle location (AVL) data. In the present study, an attempt has been made to analyze delays at various bus stops in Mysore city using vehicle trajectory-based formulation based on AVL data collected from the Mysore Intelligent Transportation System (ITS). Delay variability has been analyzed using the coefficient of variation (COV) and also by fitting various probability distributions to the data, since distribution fitting helps in estimating the pattern of delay variability. The goodness of fit is tested by the Kolmogorov–Smirnov (KS) test. The results suggest that bus stops in the commercial area face more variability than the bus stops in residential areas. By fitting various distributions to delay data, it was observed that the performance of the generalized extreme value (GEV) distribution in fitting the data is better than other distributions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Visualisation of Transit Passenger's Mobility from Automatic Fare Collection Data (AFC): Case Study of Hubli–Dharwad BRTS(Springer Science and Business Media Deutschland GmbH, 2023) Halyal, S.; Mulangi, R.H.; Harsha, M.M.The automatic fare collection system is an essential part of many public transportation systems and adopting more and more by public transit agencies. Even though the primary purpose is to collect revenue, they also produce large quantities of transaction data. The produced data is much needed for many transit planners for the long-term planning of transit network requirements. This paper aims to evaluate the application of automatic fare collection data, focusing on the visualisation of the aggregate pattern of passenger characteristics in terms of boarding and alighting values. Hubli–Dharwad Bus Rapid Transit System (HDBRTS) was used as a case study. The passenger flow characteristics are recognized by the spatial distribution of six months boarding and alighting data of all the network stations. The interactive plots were developed and analysed to understand passenger mobility variations between network stations concerning different days of the week. Thus, ultimately, the paper gives a complete and inclusive view of the passenger's flow characteristics. It helps transits agencies take decisions on the utility-based priority for improving strategies on various available routes of the network. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.