Search

Expanding road networks to accommodate various activities has significantly increased urban noise pollution, adversely affecting human health and quality of life. Numerous factors influence the noise level in urban areas, including road characteristics, traffic characteristics, surrounding buildings, and weather conditions. While previous studies have considered many of these factors individually, this study aims to integrate all relevant variables to comprehensively monitor and analyze their combined effects on noise levels. The objective is to determine the most influential factors that could be incorporated into effective noise reduction strategies. This research focuses on Cairo, one of the most densely populated cities in the world, where high noise levels are a persistent issue. A detailed case study of Tahrir Street in Dokki, Cairo, provides the basis for this investigation. One of the most crowded areas is El-Tahrir Street in Al-Dokki, which was selected as a case study in this research. This area experiences high traffic volume, with up to 1700 vehicles passing through within a 15-min interval. This significant traffic volume is the primary driver of the elevated noise levels in the area. Traffic and noise level monitoring was conducted using a field survey using the sound level meter. Consequently, a statistical analysis was conducted to investigate the correlation between different factors and the noise level and determine the most influential factors. The study revealed that traffic volume and congestion are the most significant factors influencing noise levels on Tahrir Street, exhibiting strong positive correlations (R = 0.38). Additionally, the study found an inverse relationship between vehicle speed and noise level due to high traffic volumes and identified that building characteristics and wind direction also play roles, albeit to a lesser extent. [ABSTRACT FROM AUTHOR]

Connected and autonomous driving technologies offer a novel solution for intersection control optimization. Connected and autonomous vehicles (CAVs) can access signal plans and optimize trajectories to minimize delays and reduce fuel consumption. However, optimizing trajectories for individual vehicles significantly increases complexity, especially for joint optimization of traffic signals and vehicle trajectories. Given the current technical, regulatory, and policy constraints, a superior intersection management approach is necessary before fully automated driving is achieved. This paper introduces an adaptive coupling control (ACC) method based on vehicle platooning to optimize signal timings and vehicle trajectories in mixed traffic. Initially, vehicle platoon segmentation is conducted, led by CAVs. The study then proposes a single‐layer coupled optimization model based on vehicle platoons, simplifying the joint optimization model. To address logistic constraint difficulties, a linearization of the coupled model (LCM) method is developed. Numerical experiments demonstrate that the ACC method significantly reduces vehicle delay and fuel consumption. At high CAV penetration rates (0.8 < R <1) and high traffic volumes (over 900 pcu/h), vehicle platoon control delivers excellent performance, with delays and fuel consumption even lower than in a fully automated environment (R = 1). This surprising result suggests that the mixed platoon system (ACC method) positively impacts mixed traffic. [ABSTRACT FROM AUTHOR]

Efficient traffic flow management at intersections is vital for optimizing urban transportation networks. This paper presents a comprehensive approach to refining traffic flow by analyzing the capacity of roads and integrating fuzzy logic-based traffic light control systems. We examined the capacity of roads connecting intersections, considering factors such as road vehicle capacity, vehicle speed, and traffic flow volume, through detailed mathematical modeling and analysis. Control is determined by the maximum capacity of each road segment, providing valuable insights into traffic flow dynamics. Building upon this capacity and flow analysis, the research introduces a novel intelligent traffic light controller (ITLC) system based on fuzzy logic principles. By incorporating real-time traffic data and leveraging fuzzy logic algorithms, our ITLC system dynamically adjusts traffic light timings to optimize vehicle flow at two intersections. The paper discusses the design and implementation of the ITLC system, highlighting its adaptive capabilities in response to changing traffic conditions. Simulation results demonstrate the effectiveness of the ITLC system in improving traffic flow and reducing congestion at intersections. Furthermore, this research provides an analysis of the mathematical models used to calculate road capacity, offering insights into the underlying principles of traffic flow optimization. Through the simulation, we have validated the accuracy and reliability of our controller. [ABSTRACT FROM AUTHOR]

Numerous efforts have been made to address the section‐level travel speed prediction problem. However, section‐level predictions can hardly be used for fine‐grained applications, such as lane management and lane‐level navigation. The main reason for this is that significant speed heterogeneity exists among the lanes within one section. Thus, this study proposes a three‐dimensional (3D) dual attention convolution‐based deep learning model for predicting the lane‐level travel speed. 3D convolutions are designed to learn high‐dimensional spatiotemporal traffic flow features, that is, the relationships between different sections, lanes, and periods. Dual attention modules are used to focus on the traffic flow propagation patterns and to explain the model's mechanisms. To evaluate the proposed model, an indicator is introduced to assess the spatio‐temporal learning ability, based on targeting the lane‐level case. Evaluation experiments are conducted based on loop detector data in Shanghai, China. The results show that high accuracy is obtained by the proposed model, with a 2.9 km/h mean absolute error, thereby outperforming several existing methods. Finally, an in‐depth analysis is provided regarding the attention coefficients and interpretation of real‐world lane‐level traffic flow propagation patterns, so as to gain insights into the model's mechanism when capturing dynamic lane‐level traffic flow. [ABSTRACT FROM AUTHOR]

The article offers information on the Dominican government's presentation of the Santo Domingo Integrated Transportation System (SIT-SD), a project addressing traffic congestion and mobility issues in the capital city, with an estimated investment of 3.2 billion dollars. Topics include the initiative's focus on reducing private vehicle traffic and promoting collective transportation by connecting the entire metropolitan region of Greater Santo Domingo through a public-private partnership model.

At many airports, space on the apron is scarce and has to be used efficiently. To that end, we optimize the layout of aircraft parking positions adjacent to the airport terminal, pursuing two lexicographically ordered objectives. First, we minimize the number of aircraft that have to be diverted to remote parking positions, because positions adjacent to the terminal are not available. Second, we minimize the construction effort required for gate infrastructure. Aircraft collisions must be prevented at all times, and we consider various traffic situations, as traffic volume and fleet mix are not constant in time. We introduce the Airport Gate Layout Problem and formulate it as a mixed-integer model, which considers both greenfield and brownfield scenarios. To solve the problem efficiently, we introduce a decomposition framework that exploits the structure of the problem and employ various acceleration techniques. Our approach reduces computation times substantially, allowing us to solve instances that are intractable for CPLEX. Based on a case study for Munich Airport, we demonstrate how airports can gain valuable insights from solving the problem. • We present the Airport Gate Layout Problem and provide a mixed-integer formulation. • To solve the problem efficiently, we propose a decomposition-based approach. • Our approach solves large instances to optimality that are intractable for CPLEX. • We present a case study for a major European airport. [ABSTRACT FROM AUTHOR]