Nonlinear and threshold effects of the built environment, road vehicles and air pollution on urban vitality

Online real-time traffic flow prediction typically offers better real-time performance than offline prediction. However, existing studies rarely discussed online real-time traffic flow prediction and the balance between prediction accuracy and computational costs. Training and predicting traffic flow data with complex patterns by artificial intelligence models is usually time-consuming. Some high-accuracy statistical learning methods also reach a performance bottleneck in terms of computational speed. Therefore, a Fast Autoregressive Tensor Decomposition (FATD) algorithm is proposed for online real-time traffic flow prediction. First, the historical tensor data is decomposed by Tucker Decomposition into factor matrices and easy-to-compute core tensors, and these core tensors are modeled by Tensor Seasonal Autoregressive Integrated Moving Average (Tensor SARIMA). Second, a future core tensor is predicted by Tensor SARIMA. By the Inverse Tucker Decomposition, the traffic flow data to be predicted is recovered. The experimental results show that the FATD algorithm can reduce the computational cost while maintaining high accuracy. Compared with baselines, the FATD algorithm reduces MAE, RMSE, and the computational cost by approximately 35.04%, 26.86%, and 99.28% on average, respectively. 

Fast autoregressive tensor decomposition for online real-time traffic flow prediction

Air pollution is a primary concern, causing around 7 million premature deaths annually, with traffic-related sources contributing 23 %-45 % of emissions. While several studies have surveyed vehicle emission models, they are either outdated or focus on specific data-driven models. This paper systematically reviews vehicle emission prediction models, comparing traditional approaches with data-driven emission models. The traditional emission models can be divided into average-speed, modal, and other models, noting their reliance on empirical assumptions and parameters that may not be universally applicable. In contrast, we delve into data-driven models utilizing dynamometer and on-road test data for time-series and spatial-temporal predictions. The application of these models is discussed across various scenarios, highlighting the progress and gap. We observed that traditional models, primarily estimating total traffic emissions in study regions, lack micro-level detail crucial for tailored decisions. The direct link between road emission model accuracy and input data quality poses challenges in disaggregating on-road vehicle emission inventories. Due to unique transportation instruments, traffic fleet components, and patterns, exploring the effects of emission-reduction policies in specific cities or regions is urgent. Vehicle characteristics, environmental conditions, traffic scenarios, and prediction scales are common effect factors, while instantaneous driving profiles prove effective in model calibration. In data-driven models, ANN outperforms in estimating emissions and performance of low-power diesel engines with errors not exceeding 5 %. However, no single data-driven method performed excellently in predicting all pollutants. Besides, integrated methods utilizing LSTM, GRU, and RNN outperform individual models. To enhance prediction accuracy considering the inherent connectivity of road networks and spatiotemporal variation patterns of vehicle emissions, GCN is an emerging approach for capturing spatial-temporal relationships based on remote sensing data. Moreover, limited data-driven studies have been performed to forecast particle matter emissions, the main contributors to urban pollution, calling for more attention for future research. 

Models for predicting vehicle emissions: A comprehensive review.

The unprecedented COVID-19 pandemic has caused significant short-term travel disruptions and profound changes in daily travel routines. Yet, the long-term impact on travel behavior and mobility remains unclear. The present study examines the enduring effects of COVID-19 on travel patterns in the United Arab Emirates (UAE) through a comprehensive survey assessing long-term impacts and participants' travel behavior changes during different stages of the pandemic. The survey covered travel modes, frequency, duration, and distance, unveiling a pandemic-driven shift towards increased personal vehicle usage due to public transportation concerns. Nevertheless, as the pandemic gradually recedes, individuals are slowly reverting to their pre-pandemic travel habits. Moreover, the results indicate that travel patterns vary depending on the distance traveled. Short-distance travel has largely returned to pre-pandemic levels, while medium-distance travel continues to lag. Intriguingly, post-pandemic levels of long-distance travel have exceeded the pre-pandemic figures, indicating a potential alteration in travel behaviors. These observed changes were substantiated by a paired t-test, demonstrating a significant difference across nearly all periods examined. In summary, this research sheds light on the long-term consequences of COVID-19 on travel behavior and mobility in the UAE. The survey data uncovers a noteworthy transformation in travel patterns during the pandemic, with subsequent gradual reversion to pre-pandemic norms. Notably, the findings suggest potential shifts in travel preferences across different distances, with long-distance travel surpassing pre-pandemic levels. These insights contribute to a deeper understanding of the lasting impacts of COVID-19 on transportation and travel choices. 

Assessing the long-term impact of COVID-19 on travel behavior: The United Arab Emirates perspective

Changes in visitor behaviour across COVID-19 pandemic: Unveiling urban visitation dynamics and non-linear relationships with the built environment using mobile big data

https://doi.org/10.1016/j.cities.2023.104246

Investigating impacts of COVID-19 on urban mobility and emissions

Variable Speed Limit (VSL) is a commonly applied active traffic management measure for urban motorways. In recent years, model-based and model-free approaches have been extensively adopted to solve VSL optimization problems. However, the success of model-based VSL relies heavily on the nature of the environmental model adopted (e.g., traffic flow model). Implicit environment models may result in inappropriate control actions. Although model-free approaches are able to directly map raw measurements to control actions without a need for an environment model, they usually require large amounts of training data. In order to address these issues, we propose an Imagination-Augmented Agent (I2A) for VSL control. The I2A consists an imagination path and a model-free path, which work together to generate appropriate control actions. The simulation results show that the proposed I2A agent outperforms other tested Reinforcement Learning (RL) agents in terms of Total Time Spent and bottleneck volume. 

Imagination-Augmented Reinforcement Learning Framework for Variable Speed Limit Control

Real-time traffic monitoring represents a key component for transportation management. The increasing penetration rate of connected vehicles with positioning devices encourages the utilization of trajectory data for real-time traffic monitoring. The use of commercial fleet trajectory data could be seen as the first step towards mobile sensing networks. The main objective of this research is to estimate space occupancy of a single road segment with partially observed trajectories (commercial fleet trajectories in our case). We first formulate the trajectory-based traffic estimation as a video computing problem. Then, we reconstruct trajectory series into video-like data by performing spatial discretization. Following this, video input is embedded using a tubelet embedding strategy. Finally, a Revised Video Vision Transformer (RViViT) is proposed to estimate traffic state from video embeddings. The proposed RViViT is tested on a public dataset of naturalistic vehicle trajectories collected from German highways around Cologne during 2017 and 2018. The results witness the effectiveness of the proposed method in traffic estimation with partially observed trajectories.

A Revised Video Vision Transformer for Traffic Estimation With Fleet Trajectories

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Papers

Investigating impacts of COVID-19 on urban mobility and emissions

Imagination-Augmented Reinforcement Learning Framework for Variable Speed Limit Control

A Revised Video Vision Transformer for Traffic Estimation With Fleet Trajectories