Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
4 results
Search Results
Item Visualization and Assessment of the Effect of Roadworks on Traffic Congestion Using AVL Data of Public Transit(Springer Nature, 2022) Harsha, H.; Mulangi, R.H.; Kulkarni, V.Congestion-free movement of traffic during peak hours in urban areas is rarely witnessed nowadays. Several factors are responsible for traffic congestion, and a large amount of reliable data is necessary to investigate them. In this study, we investigated the effectiveness of automated vehicle location (AVL) data of public transit in evaluating the effect of route diversion due to roadworks on traffic congestion. The public transit vehicle data from Mysore intelligent transport system were used for the purpose. In the preliminary analysis, the spatiotemporal variations in the speed data of public transit were visualized using spatiotemporal speed plots. A comparison study of traffic states in an urban street and an arterial road was conducted using a visualization tool. The data from Inner Ring Road of Mysore city were used to evaluate the effect of roadworks on traffic congestion. The road links of Inner Ring Road were evaluated for two scenarios: normal scenario and route diversion scenario. The results revealed that the spatiotemporal visualization technique can be used to diagnose the changes in traffic congestion, especially near intersections and bus stops. It is concluded that the AVL data from public transit buses proves to be a potential data source for traffic state prediction and evaluation of traffic congestion. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item The Spatiotemporal Patterns of Bus Passengers: Visualisation and Evaluation using Non-negative Tensor Decomposition(Springer Nature, 2023) Shanthappa, N.K.; Mulangi, R.H.; Harsha, H.M.Spatiotemporal analysis of passenger mobility patterns provides valuable information regarding the travel behaviour of passengers at different spatial and temporal scales. However, in the spatiotemporal analysis of passenger mobility patterns, a few questions are yet to be answered: how does passenger travel behaviour change during different seasons? In developing countries like India where land use distribution is complex, do travel characteristics have a relationship with spatial regions of different land use? And what is the influence of people from nearby sub-urban and villages on the passenger mobility of urban areas if transit service is provided? Hence, this study developed a methodology to visualise and analyse spatiotemporal variations in the bus passenger travel behaviour among different spatial regions at hourly, daily, and monthly temporal resolutions using non-negative tensor decomposition (NTD). Six-month electronic ticketing machine (ETM) data of the Davangere city bus service is collected. Land use data is also collected from the urban development authority of Davangere city. NTD was found efficient in extracting spatiotemporal patterns. From the analysis, it is observed that passenger mobility patterns across different spatial regions varied during different seasons and within a season as well. Pertaining to spatial variations, passenger origins and destinations are aggregated with respect to spatial regions with uniform land use or similar travel characteristics without giving any geographical inputs. Also, the mobility pattern of sub-urban and village people varied unconventionally. Thus, developed research methodology has the potential of unveiling the spatiotemporal variations in passenger mobility, which can act as a base for designing transit facilities and framing policies. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Application of Public Transit AVL Data for Evaluation of Delay Variability(Institute for Transport Studies in the European Economic Integration, 2023) Harsha, M.M.; Mulangi, R.H.; Panditharadhya, B.J.The travel time is the significant factor in evaluating efficiency and performance of public transit system. A greater percentage of travel time is accounted by bus stop delays which depends on passenger count, bus stop characteristics, traffic condition, bus performance, etc. Many of the Indian transit agencies store the passenger details stage wise not stop wise, which makes it difficult to evaluate delay variability at bus stop level. In this connection, Automatic Vehicle Location (AVL) data from Intelligent Transport System (ITS) implemented at Mysore, India is considered for evaluating bus stop delay variability. The collected data is used for estimating delay at five stops by adopting trajectory-based formulation. The probability distributions have been utilized to model the variability in delay. The performance has been analysed using Kolmogorov-Smirnov (KS) test. The daily variability of delay at bus stops has been evaluated using Coefficient of Variation (COV). The results of the performance evaluation of delay distributions show that the Generalized Extreme Value (GEV) distribution is the best descriptor of the delay variability in terms of accuracy, robustness, and survival capacity. In the absence of passenger data collection systems, method of evaluation of delay using AVL data presented in this study is helpful. © 2023 Institute for Transport Studies in the European Economic Integration. All rights reserved.Item Spatiotemporal capacity estimation of bus rapid transit system based on dwell time analysis(King Saud University, 2024) Angadi, V.S.; Halyal, S.; Mulangi, R.H.The performance study of an urban transport system, particularly a Bus Rapid Transit System (BRTS), must report on its operations and reliability. Such study of BRTS comprises numerous facets, including capacity, which directly influences how the system practically operates and serves the commuters. Hubballi-Dharwad Bus Rapid Transit System (HDBRTS) has been operational since 2018. A performance study is necessary to evaluate the performance of HDBRTS, which aids in its upgradation and improvement. The current research uses the experimental technique through an innovative and inspired basis to comment on the HDBRTS's performance by estimating the corridor's operational capacity at different spatial and temporal fluctuations. The selected route of the HDBRTS comprises combined segregated (exclusive traffic environment) and unsegregated (mixed traffic environment) stretches. The current study mainly conducted a video graphics-based survey to acquire the necessary data on identified spatial and temporal trends at various HDBRTS bus stations. The essential data gathered consists of Dwell Time (DT)-based data at each station, summarising the total time a bus takes to serve passengers at a station. DT is inversely proportional to the capacity of the particular bus station, which is related to the Failure Rate (FR). FR values of all the bus stations of the route were analyzed using DT, and then capacity values were calculated at different spatiotemporal patterns. Study results show that the busiest stations of the identified routes with critical DT values have FR values in the range of 1–2%, contradicting previous studies. The variations in the capacity of the stations, both spatially and temporally, were graphically represented with the minimum capacity of the segregated stretch as 36 buses/hr and the unsegregated stretch as 31 buses/hr. Finally, the Level Of Service (LOS) of the chosen study corridor was developed using the K-Means clustering algorithm and validated using the Silhouette Coefficient technique. The silhouette coefficient values obtained range from 0.52 to 0.74, indicating a reasonable structure. © 2023 The Authors