Browsing by Author "Halyal, S."

Now showing 1 - 6 of 6

Forecasting public transit passenger demand: With neural networks using APC data
(Elsevier Ltd, 2022) Halyal, S.; Mulangi, R.H.; Harsha, H.
The implementation of Intelligent Transportation Systems (ITS) as a part of smart mobility is crucial for solving the current problems of the transportation industry. The setting up and maintenance of ITS requires not only the current passenger demand but also the future passenger demand. The future passenger demand can be obtained with time-series forecasting carried out with different techniques. With the advancements in the technological field, modern and more advanced methods of time-series forecasting using deep learning are being preferred over traditional forecasting techniques. However, the research carried out in this regard is quite limited, particularly considering the Indian scenario. Hence this research work focuses on exploring the performance of deep learning forecasting techniques considering the aspects mentioned previously. Here, the forecasting of passenger demand was done with Long Short-Term Memory (LSTM) using the three months Automatic Passenger Counter (APC) data of the Hubballi-Dharwad Bus Rapid Transit System (HDBRTS) as part of a case study. Then the forecasting of passenger demand was also done with Seasonal Autoregressive Integrated Moving Average (SARIMA), and the comparison of the forecasting accuracy of both methods was made using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Furthermore, to validate the results, novel approach has been adopted for the process, by following some more time-series resampled with different time intervals. Study shows that LSTMs will be used satisfactorily in the traffic conditions of developing counties, for forecasting passenger demand using APC data. Study also provides detailed guiding methodologies of advanced methods of passenger forecasting along with conventional ones. © 2022 World Conference on Transport Research Society
Short-term passenger demand modelling using automatic fare collection data: A case study of hubli-dharwad brts
(Aracne Editrice, 2023) Halyal, S.; Mulangi, R.H.; Harsha, H.M.
A well-planned Intelligent Transport System (ITS), is the need of the hour for solving the problem of excessive traffic and unacceptable travel durations in public transit systems. However, for the successful operation of an ITS, the forecasting of passenger demand on a regular basis is essential. Obtaining reliable and accurate passenger data required for forecasting passenger demand is a genuinely tedious task for most of the researchers. The current study focused on short-term forecasting of passenger demand using reliable ITS-based data from Hubli-Dharwad Bus Rapid Transit System (HDBRTS) as a case study. The aggregate pattern of passenger demand data was visualized using one month of Automated Fare Collection System (AFCS) data. Subsequently, Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA models were used on the data of selected stations and tested for their forecasting accuracy. Outcomes of the current case study show that Seasonal ARIMA models are the most suitable for forecasting the passenger data over the conventional ARIMA models. The most suitable and reliant models were evidenced through lower values of Mean Absolute Percentage Error (MAPE), Akaike’s Information Criterion (AIC), and Bayesian Information Criterion (BIC). The current case study helps in having an inclusive view of the passenger flow characteristics for the further advancement of the system. Research work in the Indian context, especially on the usage of AFCS data, is quite limited, this study makes to explore the possibility of using AFCS data efficiently in forecasting passenger demand. © 2023, Aracne Editrice. All rights reserved.
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
Spatiotemporal Speed Characterization of Bus Rapid Transit System
(Springer Science and Business Media Deutschland GmbH, 2024) Halyal, S.; Angadi, V.S.; Mulangi, R.H.
Performance analysis of an urban transportation system, particularly a bus rapid transit system (BRTS), must report on its operations and dependability. The BRTS performance analysis includes many factors, including speed, directly impacting how the system functions and serves commuters. This study employs an experimental technique on a standardized basis to remark on the performance of the Hubballiâ€“Dharwad BRTS by estimating the corridor's average speed at various spatial and temporal fluctuations. The BRTS route chosen included segregated (exclusive traffic environment) and unsegregated (mixed traffic environment) stretches. The current study primarily employed video graphics-based and manual surveys to collect data on identified spatial and temporal trends at different BRTS bus stations. The primary data required consists of dwell time (DT)-based data at each station, summarizing the total time a bus services a station for a unit of time, signal cycle data, and section speed, which corresponds to the speed of the buses between two BRT bus stations while observing spatial and temporal trends. The acceleration and deceleration rates were calculated using the section speed, followed by the acceleration and deceleration time. The addition of delays, errors, and bus congestion resulted in the average speed of various stretches. The average section speeds then facilitated the average facility speed. The variations in average section speed between stations were graphically presented, both spatially and temporally, which gave the conclusion that there was a higher section speed in the segregated stretch and a lower rate in the unsegregated stretch, followed by a higher speed in the off-peak hours and lower speed in the peak hours. The Level of Service (LOS) was developed for complete study sections considering the average facility speeds with and without the impedance, and the results were compared. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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. Â© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Visualisation of Transit Passenger's Mobility from Automatic Fare Collection Data (AFC): Case Study of Hubliâ€“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. Hubliâ€“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. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.