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With rapid development of the urbanization, how to improve the traffic lights efficiency has become an urgent issue. The traditional traffic light control is a method that calculates a series of corresponding timing parameters by optimizing the cycle length. However, fixing sequence and duration of traffic lights is inefficient for dynamic traffic flow regulation. In order to solve the above problem, this study proposes a traffic light timing optimization scheme based on deep reinforcement learning (DRL). In this scheme, the traffic lights can output an appropriate phase according to the traffic flow state of each direction at the intersection and dynamically adjust the phase length. Specifically, we first adopt Proximal Policy Optimization (PPO) to improve the convergence speed of the model. Then, we elaborate the design of state, action, and reward, with the vehicle state defined by Discrete Traffic State Encoding (DTSE) method. Finally, we conduct experiments on real traffic data via the traffic simulation platform SUMO. The results show that, compared to the traditional timing control, the proposed scheme can effectively reduce the waiting time of vehicles and queue length in various traffic flow modes. [ABSTRACT FROM AUTHOR]

The vigorous progress in artificial intelligence and the widespread availability of computing power and big data have resulted in remarkable achievements in applying deep learning methods to traffic engineering. However, the complexity of deep learning models poses a significant challenge to their interpretability, despite their scalability to big data. As a result, academia has shown a keen interest in interpreting these intricate models. To address these limitations, this study introduces the Gated Recurrent Convolution Network (GRCN) for predicting spatiotemporal crash risks. Moreover, to transform the opaque nature of the GRCN into a transparent framework, SHapley Additive Explanations (SHAP) was employed, a game theory-based approach. The application of SHAP analysis not only enhances the explainability of the GRCN model but also reveals complex causal relationships regarding crash risks, using extensive datasets encompassing taxi trips data, law enforcement, and weather conditions. The study incorporates both local and global interpretability analyses at the grid level. Additionally, it indicates that intersection red light violations tend to elevate crash risks. The findings further demonstrate that adverse weather conditions, such as low visibility and strong crosswinds, negatively affect driving behavior and correlate with higher crash risks. However, the presence of cloudy skies is associated with a decrease in crash risks. By shedding light on the intricate dynamics of crash risk factors, this study contributes to the field of traffic engineering and emphasizes the importance of interpretability in deep learning models. The insights gained from this research can inform the development of effective interventions and policies aimed at reducing crash risks and improving overall transportation safety. [Display omitted] • Introducing a novel model that advances the field of spatiotemporal crash risk prediction. • Proposing a method rooted in game theory to enhance the interpretability of the model. • Delving into the causality inference of traffic law enforcement violations on crash risk. • Investigating the causality inference related to the impact of weather conditions on crash risks. [ABSTRACT FROM AUTHOR]

Smart traffic engineering and intelligent transportation services are in increasing demand from governmental authorities to optimize traffic performance and thus reduce energy costs, increase the drivers' safety and comfort, ensure traffic laws enforcement, and detect traffic violations. In this paper, we address this challenge, and we leverage the use of Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs) to develop an AI-integrated video analytics framework, called TAU (Traffic Analysis from UAVs), for automated traffic analytics and understanding. Unlike previous works on traffic video analytics, we propose an automated object detection and tracking pipeline from video processing to advanced traffic understanding using high-resolution UAV images. TAU combines six main contributions. First, it proposes a pre-processing algorithm to adapt the high-resolution UAV image as input to the object detector without lowering the resolution. This ensures an excellent detection accuracy from high-quality features, particularly the small size of detected objects from UAV images. Second, it introduces an algorithm for recalibrating the vehicle coordinates to ensure that vehicles are uniquely identified and tracked across the multiple crops of the same frame. Third, it presents a speed calculation algorithm based on accumulating information from successive frames. Fourth, TAU counts the number of vehicles per traffic zone based on the Ray Tracing algorithm. Fifth, TAU has a fully independent algorithm for crossroad arbitration based on the data gathered from the different zones surrounding it. Sixth, TAU introduces a set of algorithms for extracting twenty-four types of insights from the raw data collected. These insights facilitate the traffic understanding using curves, histograms, heatmaps, and animations. The present work presents a valuable added value for academic researchers and transportation engineers to automate the traffic video analytics process and extract useful insights to optimize traffic performance. TAU is a ready-to-use framework for any Transportation Engineer to better understand and manage daily road traffic (video demonstrations are provided here: https://youtu.be/wXJV0H7LviU and here: https://youtu.be/kGv0gmtVEbI). The source code is available at: https://github.com/bilel-bj/TAU. [ABSTRACT FROM AUTHOR]