Faculty Publications

Now showing 1 - 3 of 3

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
Probability distributions analysis of travel time variability for the public transit system
(KeAi Communications Co., 2022) Harsha, H.; Mulangi, R.H.
Travel time variability (TTV) plays a significant role in analysing the reliability of public transit system. The research works carried out on travel time variability under Indian traffic conditions are very few and these studies did not analyse the performance of travel time distribution in detail, considering different temporal and spatial aggregations. In this study, travel time variability is analysed using travel time distributions considering different temporal and spatial aggregations. The Automatic Vehicle Location (AVL) data of four transit routes of Mysore City, Karnataka, India are used to evaluate travel time distributions with respect to temporal aggregations (peak period, off-peak period, 60 minutes, 30 minutes and 15 minutes) and spatial aggregations (route level and segment level). The performance of travel time distributions is analysed using the Anderson-Darling (AD) test. The segments with signalised intersections and different land-use types are analysed to evaluate the distribution fit for various conditions. The results of both route and segment level analysis report highest accuracy and robustness values for Generalised Extreme Value (GEV) distribution. The distribution is proved to be superior in describing travel time variability of public transit. © 2021 Tongji University and Tongji University Press
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.

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