Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility
Abstract: In urban settings, bus transit stands as a significant mode of public transportation, yet faces hurdles in delivering accurate and reliable arrival times. This discrepancy often culminates in delays and a decline in ridership, particularly in areas with a heavy reliance on bus transit. A prevalent challenge is the mismatch between actual bus arrival times and their scheduled counterparts, leading to disruptions in fixed schedules. Our study, utilizing New York City bus data, reveals an average delay of approximately eight minutes between scheduled and actual bus arrival times. This research introduces an innovative, AI-based, data-driven methodology for predicting bus arrival times at various transit points (stations), offering a collective prediction for all bus lines within large metropolitan areas. Through the deployment of a fully connected neural network, our method elevates the accuracy and efficiency of public bus transit systems. Our comprehensive evaluation encompasses over 200 bus lines and 2 million data points, showcasing an error margin of under 40 seconds for arrival time estimates. Additionally, the inference time for each data point in the validation set is recorded at below 0.006 ms, demonstrating the potential of our Neural-Net-based approach in substantially enhancing the punctuality and reliability of bus transit systems.
- (2017). New york city bus data.
- (2017). Smart public transport – key to solving the urban challenge - telia company. Accessed on Mar. 12, 2023.
- (2019). Transport, data analytics and ai: Why tfl’s latest initiative is good news. Accessed on Mar. 12, 2023.
- (2022). September 2022 apta public transportation ridership update. Accessed on Mar. 15, 2023.
- (2023). Google maps vs waze – which one is better? Accessed on Mar. 15, 2023.
- (2023). New york city transit key performance metrics. Accessed on Mar. 15, 2023.
- Dynamic bus travel time prediction models on road with multiple bus routes. Computational intelligence and neuroscience, 2015:63–63.
- A multi-agent reinforcement learning approach for bus holding control strategies. Advances in Transportation Studies.
- Bus transit service reliability and improvement strategies: Integrating the perspectives of passengers and transit agencies in north america. Transport Reviews, 35(3):292–328.
- Data-driven optimization of public transit schedule.
- Why has public transit ridership declined in the united states? Transportation research part A: policy and practice, 161:68–87.
- Freemark, Y. (2021). Us public transit has struggled to retain riders over the past half century. reversing this trend could advance equity and sustainability. urban institute. The Urban Institute.
- Bus arrival time prediction and release: system, database and android application design. In International Conference on Algorithms and Architectures for Parallel Processing, pages 404–416. Springer.
- Performance analysis of bus arrival time prediction using machine learning based ensemble technique. In Proceedings 2019: Conference on Technologies for Future Cities (CTFC).
- Federated learning for diagnosis of age-related macular degeneration. Frontiers in Medicine, 10.
- Understanding the recent transit ridership decline in major us cities: Service cuts or emerging modes. In 98th Annual Meeting of the Transportation Research Board, Washington, DC.
- Bus single-trip time prediction based on ensemble learning. Computational intelligence and neuroscience, 2022.
- Mallett, W. J. (2022). Public transportation ridership: Implications of recent trends for federal policy. Technical report.
- Towards demand-oriented flexible rerouting of public transit under uncertainty. In Proceedings of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering, pages 35–40.
- Pishgu: Universal path prediction architecture through graph isomorphism and attentive convolution. arXiv preprint arXiv:2210.08057.
- Predict arrival time by using machine learning algorithm to promote utilization of urban smart bus.
- Distributions of bus stop spacings in the united states. Findings.
- Ancilia: Scalable intelligent video surveillance for the artificial intelligence of things. IEEE Internet of Things Journal.
- Does transit service reliability influence ridership?
- E-transit-bench: simulation platform for analyzing electric public transit bus fleet operations. In Proceedings of the Thirteenth ACM International Conference on Future Energy Systems (e-Energy 2022).
- Transit-hub: A smart public transportation decision support system with multi-timescale analytical services. Cluster Computing, 22:2239–2254.
- Real-time and predictive analytics for smart public transportation decision support system. In 2016 IEEE International Conference on Smart Computing (SMARTCOMP), pages 1–8. IEEE.
- Bus arrival time prediction with real-time and historic data. Cluster Computing, 20:3099–3106.
- A prediction model of bus arrival time at stops with multi-routes. Transportation research procedia, 25:4623–4636.
- Bus travel time prediction based on ensemble learning methods. IEEE Intelligent Transportation Systems Magazine, 14(2):174–189.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
