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The provision of accurate travel time information holds paramount significance for optimizing traffic operations and management. However, existing approaches typically address multiple travel time learning tasks independently, neglecting the correlation of tasks and necessitating the construction of separate models for each task. As the scale of the road network expands, this practice becomes increasingly infeasible. To bridge this gap, this paper presents an adaptive deep multi-task learning model to solve multiple travel time estimation tasks collaboratively from a citywide perspective. The novel model seamlessly integrates geographical, environmental, and spatial-correlated features, capturing the complex non-linear characteristics of travel time in the urban road network through a deep network learning feature representations layer by layer from the input data. Through the paradigm of deep multi-task collaborative learning, the knowledge learned from one task serves as an inductive bias for other tasks, thereby facilitating the learning of related tasks. By modeling the uncertainty of travel time with a probabilistic model, the weights of jointly learned tasks are adaptively fine-tuned in a principled way. Additionally, a novel algorithm is devised to fully leverage the information embedded within the data with partial information. The proposed methodology is evaluated through a case study on the citywide road network in Beijing, China. Empirical results of extensive experiments demonstrate the adeptness of the proposed model in effectively harnessing information transferred from parallel tasks, adaptively fine-tuning the weights of joint learning tasks, and proficiently exploiting data characterized by incomplete labels, therefore leading to superior performance compared to competing methods. The average estimation error of the four jointly learned travel time estimation tasks is about 28.48%, yielding an improvement of over 4% compared to single-task learning models. The results verify the effectiveness and robustness of the proposed model for travel time estimation in the urban road network. [ABSTRACT FROM AUTHOR]

Accurate traffic congestion estimation and prediction are critical building blocks for smart trip planning and rerouting decisions in transportation systems. Over the decades, there have been many studies focusing on traffic congestion estimation and prediction with different statistical approaches (e.g., Markov chain) and machine learning models (e.g., clustering, Bayesian networks, and artificial neural networks). However, there is a lack of a unified framework to address the mechanisms of different models and integrate the advantages of different methods through combinations. This paper introduces the FD-Markov-LSTM model, a hybrid interpretable approach that combines the fundamental diagram (FD), Markov chain, and long short-term memory (LSTM). The aim is to estimate and predict traffic states by integrating statistical data in both congested and uncongested scenarios. The FD-Markov-LSTM model leverages the FD to identify hierarchical traffic states and utilizes the Markov process to capture the probabilistic transitions between these states. We employ the LSTM model to further capture the residual time series produced by the Markov chain model (assuming a memoryless property) to enhance the estimation and prediction performance. The proposed model's accuracy in estimating and predicting traffic flow is evaluated using empirical data from three case studies conducted in Beijing and Los Angeles. The results highlight a significant improvement in accuracy compared to classical benchmark models such as the Markov model, ARIMA model, k-Nearest Neighbor model, Random Forest model, and LSTM. Specifically, the FD-Markov-LSTM model achieves reductions of over 39% in mean absolute error, 35% in root mean squared error, and 7.4% in mean absolute percentage error. These results clearly demonstrate that the FD-Markov-LSTM model outperforms the benchmark models, enabling more precise predictions of traffic flow. [ABSTRACT FROM AUTHOR]

• A multitask learning model for short-term inflow prediction of multi-traffic modes is proposed. • The adaptive matrix and attention mechanism are used to extract the spatiotemporal features of multi-traffic modes. • We analyze the interaction mechanism in the multi-traffic modes system. • The research highlights the significance of jointly predicting the inflow of multi-traffic modes. Network-wide short-term inflow prediction is important in efficiently managing the urban transportation system. Nowadays, all kinds of traffic modes gradually become interconnected and form a complex multi-traffic modes system, while extensive studies focus on the single-traffic mode and ignore the correlations among different traffic modes. There exist some challenges for short-term inflow prediction of multi-traffic modes: (1) the interaction mechanism among multi-traffic modes is difficult to learn and few studies explore the mechanism, (2) the data of multi-traffic modes are usually heterogenous due to the different spatial units of different traffic modes, and (3) it is challenging to extract the complex and dynamic features of the multi-traffic modes and most existing methods apply static spatiotemporal correlations among multi-traffic modes, while the genuine correlations among different traffic modes might be missing. To tackle these challenges, this study proposed a multitask-learning-based model called M ulti M ode-former (M2-former) with the encoder-decoder structure for network-wide short-term inflow prediction of the multi-traffic modes system. Specifically, the encoder is designed to learn and capture the complex and dynamic spatiotemporal correlations of multi-traffic modes, and the decoder is designed to extract the features of the target traffic mode and share knowledge among multi-traffic modes. Extensive experiments are conducted based on the real-world multi-traffic modes system data of Beijing, China. Results prove the superiority of the M2-former. In addition, the spatial and temporal information interaction mechanisms among multi-traffic modes are also explored. This paper can provide a reliable method and critical insights for the management and understanding of a multi-traffic modes system. [ABSTRACT FROM AUTHOR]