Bus Arrival Time Prediction and Release: System, Database and Android Application Design

Fu, Junhao; Wang, Lei; Pan, Mingyang; Zuo, Zhongyi; Yang, Qian

doi:10.1007/978-3-319-11194-0_33

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many cities offer dedicated mobile applications for their bus transit, offering bus schedules and providing passengers and commuters a way to plan their travel ahead of time [12]. These In 2020 and 2021, public transportation ridership was less than half its pre-pandemic level.…”

Section: Introductionmentioning

confidence: 99%

A Novel Neural Network Approach for Predicting the Arrival Time of Buses for Smart On-Demand Public Transit

Rashvand¹,

Hosseini²,

Azarbayjani³

et al. 2023

Preprint

View full text Add to dashboard Cite

Among the major public transportation systems in cities, bus transit has its problems, including more accuracy and reliability when estimating the bus arrival time for riders. This can lead to delays and decreased ridership, especially in cities where public transportation is heavily relied upon. A common issue is that the arrival times of buses do not match the schedules, resulting in latency for fixed schedules. According to the study in this paper on New York City bus data, there is an average delay of around eight minutes or 491 seconds mismatch between the bus arrivals and the actual scheduled time. This research paper presents a novel AI-based data-driven approach for estimating the arrival times of buses at each transit point (station). Our approach is based on a fully connected neural network and can predict the arrival time collectively across all bus lines in large metropolitan areas. Our neural-net data-driven approach provides a new way to estimate the arrival time of the buses, which can lead to a more efficient and smarter way to bring the bus transit to the general public. Our evaluation of the network bus system with more than 200 bus lines, and 2 million data points, demonstrates less than 40 seconds of estimated error for arrival times. The inference time per each validation set data point is less than 0.006 ms.

show abstract

Section: Introductionmentioning

confidence: 99%