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The present study was carried out to observe the variation of volatile organic compounds (VOCs) namely benzene, toluene, ethylbenzene, and xylene isomers (BTEX) at three different sites of Delhi, during 2016–2017. Four hourly sampling was carried out day and night separately. Results showed that BTEX concentration was highest in post-monsoon and lowest in monsoon season. Again, daily variation shows that benzene (47%) and toluene (35%) were more during night than day when it was 44% and 33% respectively. Mean concentration of BTEX was observed in following order: ethylbenzene ~ o-xylene < m,p-xylene < toluene < benzene, while overall seasonal variation was observed as follows: post-monsoon > summer > winter > monsoon. Possible emission sources of BTEX were also established through corresponding ratios of individual compounds. Xylene isomers together accounted highest ozone formation potential. The risk assessments of BTEX were carried out in terms of non-cancer (the hazard quotient, HQ) and cancer (the incremental lifetime cancer risk, ILCR) regarding the inhalation exposure only. It was observed that benzene and xylene isomers possessed higher HQs than ethylbenzene and toluene at all sites throughout the study. Again, benzene was found with higher mean ILCR (3.58 × 10 −5 ) than ethylbenzene (1.47 × 10 −5 ).

Traditional automated monitoring systems adopted for Intersection Traffic Control still face challenges, including high costs, maintenance difficulties, insufficient coverage, poor multimodal data integration, and limited traffic information analysis. To address these issues, the study proposes a sovereign AI-driven Smart Transportation governance approach, developing a mobile AI solution equipped with multimodal perception, task decomposition, memory, reasoning, and multi-agent collaboration capabilities. The proposed system integrates computer vision, multi-object tracking, natural language processing, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to construct a Pipeline-based Traffic Analysis System (PTAS). The PTAS can produce real-time statistics on pedestrian and vehicle flows at intersections, incorporating potential risk factors such as traffic accidents, construction activities, and weather conditions for multimodal data fusion analysis, thereby providing forward-looking traffic insights. Experimental results demonstrate that the enhanced DuCRG-YOLOv11n pre-trained model, equipped with our proposed new activation function βsilu, can accurately identify various vehicle types in object detection, achieving a frame rate of 68.25 FPS and a precision of 91.4%. Combined with ByteTrack, it can track over 90% of vehicles in medium- to low-density traffic scenarios, obtaining a 0.719 in MOTA and a 0.08735 in MOTP. In traffic flow analysis, the RAG of Vertex AI, combined with Claude Sonnet 4 LLMs, provides a more comprehensive view, precisely interpreting the causes of peak-hour congestion and effectively compensating for missing data through contextual explanations. The proposed method can enhance the efficiency of urban traffic regulation and optimizes decision support in intelligent transportation systems.

As vehicular networking has recently been developed and commercialized, vehicular cloud computing has received much attention in various research areas, such as intelligent transportation systems and vehicular ad hoc networks. An efficient intersection traffic control using vehicular cloud computing is one of the key research topics in intelligent transportation systems. To efficiently deal with intersection traffic control via vehicle-to-vehicle communications, we design a distributed mutual exclusion algorithm that does not rely on broadcast, which introduces communication overheads; instead, our algorithm use point-to-point messages sent between the vehicles to keep network traffic load lower. In our algorithmic design, to pass an intersection, the lead vehicle on a lane must get permissions from a subset of other vehicles and its following vehicles on the same lane can follow the lead vehicle without permissions unlike the previous research. To evaluate the performance of our distributed mutual exclusion algorithm, we conduct extensive experiments. The results show that our algorithmic design is both effective and efficient.

We present a three-dimensional (3D) intersection traffic management platform for small autonomous Unmanned Aerial Vehicles (UAVs), particularly quadcopters, in urban airspace. Assuming many autonomous UAVs are approaching a shared airspace, where UAVs have varying sources and destinations, we propose a system model for a 3D intersection that aims to provide safe and systematic management of UAVs. We also devised a scheduling scheme to ensure that the intersection is efficiently utilized and that there are no collisions among the UAVs in the intersection. The scheduling scheme applies the reservation-based approach, which is sensitive to the sequence of the UAVs in scheduling, thus genetic algorithm is used to determine the best sequence of the UAVs. Simulations were performed to evaluate the efficiency of the system. We also show through the simulations that our scheduling scheme reduces the UAVs’ average time in the system by 27 percent compared with when the UAVs are scheduled in a first-come, first-served manner for the highly crowded intersection.

Priority and sign-controlled intersections under heterogeneous and mixed traffic conditions behave in an uncontrolled manner with drivers and other road users neglecting right-of-way and priority rules. Microscopic traffic flow simulation is an effective way to explain such complex vehicular interactions without much effort and cost in shortage of specific video-image data. This research aims at enhancing potential capacity of priority-controlled (unsignalized in nature) intersections using grid-based cellular automata (GBCA) models in context of agent-based computer simulations. The grid-based cellular automata (GBCA) model proposed considers three-dimensional visualizations of road objects like links, nodes, connectors with vehicles in OpenGL environment inside the NetLogo and Java platform. Twenty-three unsignalized (both four-legged and three-legged) intersection traffic and geometric data collected from different regions of India have been utilized to create the GBCA models. Various intersection dimensions with several vehicle types and maximum velocities are considered for validating the proposed GBCA model. Four uncontrolled intersection vehicle entering and leaving rules based on virtual priority abstract of Indian driving behaviour are also included as traffic operating scenarios within the proposed GBCA model simulation framework in NetLogo. Findings from the study suggests sign and priority-controlled intersections in India need re-modification in terms of driver turning rules. According to the GBCA simulations in NetLogo and Java, the fourth traffic rule re-modification proposed in this study helps reduce service delays by 17% but increases potential intersection capacity by 20% at priority-controlled intersections. The average potential capacities for right turning traffic as obtained from the proposed model are 2478 vehicles/h/lane and 1289 vehicles/h/lane for major and minor roads, respectively, which showed perfect correlations with field estimated gap acceptance capacities.

Nowadays, the development of autonomous vehicles has emerged as an approach to considerably improve the traffic management in urban zones. Thanks to automation in vehicles as well as in other sectors, the probability of errors, typically due to repetitive tasks, has been drastically reduced. Therefore, technological aids in current driving systems are aimed to avoid or reduce human errors like imprudences or distractions. According to this, it is possible to tackle complex scenarios such as the automation of the vehicles traffic at intersections, as this is one of the points with the highest probability of accidents. In this sense, the coordination of autonomous vehicles at intersections is a trending topic. In the last few years, several approaches have been proposed using centralized solutions. However, centralized systems for traffic coordination have a limited fault-tolerance. This paper proposes a distributed coordination management system for intersections of autonomous vehicles through the employment of some well-defined rules to be followed by vehicles. To validate our proposal, we have developed different experiments in order to compare our proposal with other centralized approaches. Furthermore, we have incorporated the management of communication faults during the execution in our proposal. This improvement has also been tested in front of centralized or semi-centralized solutions. The introduction of failures in the communication process demonstrates the sensitivity of the system to possible disturbances, providing a satisfactory coordination of vehicles during the intersection. As final result, our proposal is kept with a suitable flow of autonomous vehicles still with a high communication fails rate.

This paper investigates the possibilities for improving the traffic organization at a traffic light-controlled intersection in urban conditions through simulation modelling and evaluation of a potential alternative - conversion to a roundabout. By comparing scenarios with the existing state, optimized traffic light control and a design proposal for a roundabout, the effects on throughput, average delay and safety are established.

Traffic light optimization is known to be a cost-effective method for reducing congestion and energy consumption in urban areas without changing physical road infrastructure. However, due to the high installation and maintenance costs of vehicle detectors, most intersections are controlled by fixed-time traffic signals that are not regularly optimized. To alleviate traffic congestion at intersections, we present a large-scale traffic signal re-timing system that uses a small percentage of vehicle trajectories as the only input without reliance on any detectors. We develop the probabilistic time-space diagram, which establishes the connection between a stochastic point-queue model and vehicle trajectories under the proposed Newellian coordinates. This model enables us to reconstruct the recurrent spatial-temporal traffic state by aggregating sufficient historical data. Optimization algorithms are then developed to update traffic signal parameters for intersections with optimality gaps. A real-world citywide test of the system was conducted in Birmingham, Michigan, and demonstrated that it decreased the delay and number of stops at signalized intersections by up to 20% and 30%, respectively. This system provides a scalable, sustainable, and efficient solution to traffic light optimization and can potentially be applied to every fixed-time signalized intersection in the world. Without relying on any infrastructure-based vehicle detectors, the authors present a scalable traffic signal re-timing system that uses a small percentage of connected vehicle trajectories as the only input. Real-world tests demonstrate that the system decreases both delays and number of stops.

Long queues of vehicles are often found at various traffic light intersection in cities. Such situation is caused by many factors, including the duration of traffic lights that do not match with the arrival of the vehicles. In this article, we propose a model of queue at a road intersection with traffic lights where it can be determine the appropriate traffic light duration based on the arrival of the vehicle, so that the queue waiting time can be obtained that is as far as possible under the driver's time stress threshold. The waiting time in the queue is very dependent on the accuracy of the traffic light time duration setting, both red and green lights on all intersection lines. The research method that will be carried out is firstly to determine the vehicle waiting time model in the queue at the red light phase, followed by the waiting time model in the green light phase with the arrivals by Poisson process with M/M/1 queuing model, then, the waiting time of all vehicles will be determined in one traffic light cycle. Through the implementation of the queue waiting time model, it is expected to obtain an average waiting time for each of vehicle, based on the arrival process and the duration time of traffic lights. The application of SimEvents MATLAB-Simulink is used to demonstrate the calculation from the model that being built for the queue waiting time.

The present era is marked by rapid improvement and advances in technology. One of the most essential areas that demand improvement is the traffic signal, as it constitutes the core of the traffic system. This demand becomes stringent with the development of Smart Cities. Unfortunately, road traffic is currently controlled by very old traffic signals (tri-color signals) regardless of the relentless effort devoted to developing and improving the traffic flow. These traditional traffic signals have many problems including inefficient time management in road intersections; they are not immune to some environmental conditions, like rain; and they have no means of giving priority to emergency vehicles. New technologies like Vehicular Ad-hoc Networks (VANET) and Internet of Vehicles (IoV) enable vehicles to communicate with those nearby and with a dedicated infrastructure wirelessly. In this paper, we propose a new traffic management system based on the existing VANET and IoV that is suitable for future traffic systems and Smart Cities. In this paper, we present the architecture of our proposed Intelligent Traffic Management System (ITMS) and Smart Traffic Signal (STS) controller. We present local traffic management of an intersection based on the demands of future Smart Cities for fairness, reducing commute time, providing reasonable traffic flow, reducing traffic congestion, and giving priority to emergency vehicles. Simulation results showed that the proposed system outperforms the traditional management system and could be a candidate for the traffic management system in future Smart Cities. Our proposed adaptive algorithm not only significantly reduces the average waiting time (delay) but also increases the number of serviced vehicles. Besides, we present the implemented hardware prototype for STS.