A Stacked BiLSTM based Approach for Bus Passenger Demand Forecasting using Smart Card Data
- Title
- A Stacked BiLSTM based Approach for Bus Passenger Demand Forecasting using Smart Card Data
- Creator
- Balaji V.; Anand T.; Abid T.; Ramya D.; Chauhan A.; Hazela B.
- Description
- Demand forecasting is crucial in the business sector. Despite the inherent uncertainty of the future, it is essential for any firm to be able to accurately predict the market for both short- and long-term planning in order to place itself in a profitable position. The proposed approach focus on the passenger transport sector because it is particularly vulnerable to fluctuations in consumer demand for perishable commodities. At every stage of the planning process from initial network designs to final pricing of inventory for each vehicle in a route-an accurate prediction of demand is essential. Forecasting passenger demand is crucial since passenger transportation is responsible for a substantial chunk of global commerce. The suggested method relies on three distinct techniques: data preparation, feature selection, and model training. Data modification, cleansing, and reduction are the three sub-processes that make up preprocessing. When it comes to feature selection, partition-based clustering algorithms like k-means are the norm. Let's go on to training the models with stacked BiLSTM. The proposed method is demonstrably superior to both LSTM and BiLSTM, the two most common competing approaches. The proposed method had a success rate of 98.45 percent. 2023 IEEE.
- Source
- International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings, pp. 920-925.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Demand Forecasting; Electronic Ticketing Machines (ETMs); Long Short-Term Memory (LSTM)
- Coverage
- Balaji V., Aditya Engineering College, Department of Eee, Andhra Pradesh, Surampalem, India; Anand T., Graphic Era Deemed to Be University, Department of Humanities and Social Sciences, Dehradun, India; Abid T., Nawab Shah Alam Khan College of Engineering and Technology, Department of It, Telangana, Hyderabad, India; Ramya D., Sri Eshwar College of Engineering, Department of Computer Science and Business Systems, Coimbatore, India; Chauhan A., Christ (Deemed to Be University), School of Sciences, Department of Life Sciences, Karnataka, Bengaluru, India; Hazela B., Amity University, Amity School of Engineering and Technology Lucknow, Uttar Pradesh, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835031398-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Balaji V.; Anand T.; Abid T.; Ramya D.; Chauhan A.; Hazela B., “A Stacked BiLSTM based Approach for Bus Passenger Demand Forecasting using Smart Card Data,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/19756.