Abstract
Accurate predictions of bus arrival times help passengers arrange their trips easily and flexibly and improve travel efficiency. Thus, it is important to manage and schedule the arrival times of buses for the efficient deployment of buses and to ease traffic congestion, which improves the service quality of the public transport system. However, due to many variables disturbing the scheduled transportation, accurate prediction is challenging. For accurate prediction of the arrival time of a bus, this research adopted a recurrent neural network (RNN). For the prediction, the variables affecting the bus arrival time were investigated from the data set containing the route, a driver, weather, and the schedule. Then, a stacked multilayer RNN model was created with the variables that were categorized into four groups. The RNN model with a separate multi-input and spatiotemporal sequence model was applied to the data of the arrival and leaving times of a bus from all of a Shandong Linyi bus route. The result of the model simulation revealed that the convolutional long short-term memory (ConvLSTM) model showed the highest accuracy among the tested models. The propagation of error and the number of prediction steps influenced the prediction accuracy.
Highlights
Accurate predictions of bus arrival times help passengers arrange their trips and flexibly and improve travel efficiency. us, it is important to manage and schedule the arrival times of buses for the efficient deployment of buses and to ease traffic congestion, which improves the service quality of the public transport system
The variables affecting the bus arrival time were investigated from the data set containing the route, a driver, weather, and the schedule. en, a stacked multilayer recurrent neural network (RNN) model was created with the variables that were categorized into four groups. e RNN model with a separate multi-input and spatiotemporal sequence model was applied to the data of the arrival and leaving times of a bus from all of a Shandong Linyi bus route. e result of the model simulation revealed that the convolutional long short-term memory (ConvLSTM) model showed the highest accuracy among the tested models. e propagation of error and the number of prediction steps influenced the prediction accuracy
Models with LSTM processed the historical data of the global position system (GPS) and bus stop locations with the influence of different routes, drivers, weather conditions, time distribution [9], heterogeneous traffic flow, and real-time data [10,11,12]. e temporal and spatial RNN network with ConvLSTM or a spatiotemporal property model (STPM) was originally used to predict the precipitation [13]
Summary
A recurrent neural network (RNN) [19] has a feedback structure that processes sequential data for time-series prediction or classification. Hidden layers of LSTM use memory blocks that store the previous sequence information, while increasing the performance of three gates: input, output, and forget gates. E linear transformation for rt , ht−1, and the input tensor is combined with the activation function of equation (4) to calculate the updated value of the hidden state. E input layer has the sequence of the arrival time series input, and the other two layers use a fully connected prediction network. A two-part network used the time series-related input data such as route number, driver, departure time, and route length for LSTM processing.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
