Abstract
Travel time information is used as input or auxiliary data for tasks such as dynamic navigation, infrastructure planning, congestion control, and accident detection. Various data-driven Travel Time Prediction (TTP) methods have been proposed in recent years. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm. The existing studies that empirically compare different TTP models only use a few models with specific features. Moreover, there is a lack of research on explaining TTPs made by black-box models. Such explanations can help to tune and apply TTP methods successfully. To fill these gaps in the current TTP literature, using three data sets, we compare three types of TTP methods (ensemble tree-based learning, deep neural networks, and hybrid models) and ten different prediction algorithms overall. Furthermore, we apply XAI (Explainable Artificial Intelligence) methods (SHAP and LIME) to understand and interpret models’ predictions. The prediction accuracy and reliability for all models are evaluated and compared. We observed that the ensemble learning methods, i.e., XGBoost and LightGBM, are the best performing models over the three data sets, and XAI methods can adequately explain how various spatial and temporal features influence travel time.
Highlights
Travel time refers to the time for a vehicle to reach a destination
The results demonstrate that the XGBoost travel time prediction model considerably enhances the performance and efficiency
For NextUp-1 data set, by looking at the RMSE, MAE, and R2 score of models, we can observe that XGBoost, LightGBM, and Hybrid models have similar performance, with XGBoost and LightGBM being the best-performing models
Summary
Travel time refers to the time for a vehicle to reach a destination. Precise prediction of travel time leads to strong route planning and emergency services, preventing the delay of public transport, decreasing fuel consumption, traffic congestion, and environmental pollution [1,2,3]. There is a lack of studies on the impact of spatial and temporal travel features on the accuracy of different types of TTP models. Understanding such impact is of great significance for improving the performance level of travel planning services [10,11]. To address the above-mentioned limitations of the TTP literature, we set the following two research questions: RQ1: To what extent can data-driven methods be applied for predicting travel time using spatiotemporal features?. To answer RQ1, we developed ten TTP models using the learning algorithms from three different categories of data-driven methods, namely classical machine learning, neural networks, and hybrid models.
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