A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data

Wang, Hongjian; Li, Zhenhui; Kuo, Yu-Hsuan; Kifer, Daniel

doi:10.48550/arxiv.1512.08580

Cited by 2 publications

(1 citation statement)

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“…On the other hand, data-driven approaches treat travel time as a regression task and estimate the travel time for an entire path or route based on historical data, implicitly capturing the complexities of traffic patterns. Data-driven approaches can be further classified into trajectory-based methods [15,16] and origin-destination (OD)-based methods [17]. Trajectory-based methods utilize road network and trajectory data to predict travel time, while OD-based methods solely consider pickup and drop-off location data for travel time estimation.…”

Section: Related Workmentioning

confidence: 99%

Predicting Bus Travel Time in Cheonan City through Deep Learning Utilizing Digital Tachograph Data

Mustafa,

Hwang,

Cho

2024

Electronics

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Urban transportation systems are increasingly burdened by traffic congestion, a consequence of population growth and heightened reliance on private vehicles. This congestion not only disrupts travel efficiency but also undermines productivity and urban resident’s overall well-being. A critical step in addressing this challenge is the accurate prediction of bus travel times, which is essential for mitigating congestion and improving the experience of public transport users. To tackle this issue, this study introduces the Hybrid Temporal Forecasting Network (HTF-NET) model, a framework that integrates machine learning techniques. The model combines an attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, enhancing its predictive capabilities. Further refinement is achieved through a Support Vector Regressor (SVR), enabling the generation of precise bus travel time predictions. To evaluate the performance of the HTF-NET model, comparative analyses are conducted with six deep learning models using real-world digital tachograph (DTG) data obtained from intracity buses in Cheonan City, South Korea. These models includes various architectures, including different configurations of LSTM and GRU, such as bidirectional and stacked architectures. The primary focus of the study is on predicting travel times from the Namchang Village bus stop to the Dongnam-gu Public Health Center, a crucial route in the urban transport network. Various experimental scenarios are explored, incorporating overall test data, and weekday and weekend data, with and without weather information, and considering different route lengths. Comparative evaluations against a baseline ARIMA model underscore the performance of the HTF-NET model. Particularly noteworthy is the significant improvement in prediction accuracy achieved through the incorporation of weather data. Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), consistently highlight the superiority of the HTF-NET model, outperforming the baseline ARIMA model by a margin of 63.27% in terms of the RMSE. These findings provide valuable insights for transit agencies and policymakers, facilitating informed decisions regarding the management and optimization of public transportation systems.

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Section: Related Workmentioning

confidence: 99%