Smooth Traffic Flow Research Articles

Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control

The integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicle driving behaviors—cautious, normal, aggressive, and platooning—affect key traffic metrics, including queue lengths, travel times, vehicle delays, emissions, and fuel consumption. A four-leg signalized intersection in Balgat, Ankara, was modeled and validated using field data, with twenty-one scenarios simulated to assess the effects of various autonomous vehicle behaviors at penetration rates from 25% to 100%, alongside human-driven vehicles. The results show that while cautious autonomous vehicles promote smoother traffic flow, they also result in longer delays and higher emissions due to conservative driving patterns, especially at higher penetration levels. In contrast, aggressive and platooning autonomous vehicles significantly improve traffic flow and reduce delays and emissions. Mixed-behavior scenarios reveal that different driving styles can coexist effectively, balancing safety and efficiency. These findings emphasize the need for optimized autonomous vehicle algorithms and signal control strategies to harness the potential benefits of autonomous vehicle integration in urban traffic systems fully, particularly in terms of improving traffic performance and sustainability.

Mobile laboratory measurements of air pollutants in Baltimore, MD elucidate issues of environmental justice

ABSTRACT The City of Baltimore, MD has a history of problems with environmental justice (EJ), air pollution, and the urban heat island (UHI) effect. Current chemical transport models lack the resolution to simulate concentrations on the scale needed, about 100 m, to identify the neighborhoods with anomalously high air pollution levels. In this paper we introduce the capabilities of a mobile laboratory and an initial survey of several pollutants in Baltimore to identify which communities are exposed to disproportionate concentrations of air pollution and to which species. High concentrations of black carbon (BC) stood out at some locations – near major highways, downtown, and in the Curtis Bay neighborhood of Baltimore. Results from the mobile lab are confirmed with longer-term, low-cost monitoring. In Curtis Bay, higher concentrations of BC were measured along Pennington Ave. (mean [5th to 95th percentiles] = 2.08 [2.0–10.9] μg m−3) than along Curtis Ave. just ~ 150 m away (0.67[0.1 – 1.8] μg m−3). Other species, including criteria pollutants ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine particulate matter (PM2.5), showed little gradient. Observations with high spatial and temporal resolution help isolate the mechanisms leading to locally high pollutant concentrations. The difference in BC appears to result not from heavier truck traffic or slower dispersion but from the interruptions in traffic flow. Pennington Ave. has three stoplights while Curtis Ave. has none. As heavy-duty diesel-powered vehicles accelerate, they experience turbo-lag and the resulting rich air-fuel mixture exacerbates BC emissions. Immediate mediation might be achieved through smoother traffic flow, and the long-term solution through replacing heavy-duty trucks with electric vehicles. Implications: We present results documenting the locations within Baltimore of high concentrations of Black Carbon pollution and identify the likely source – diesel exhaust emissions exacerbated by stop-and-go traffic and associated turbo-lag. This suggests solutions (smoother traffic, retrofit particulate filters, replacement of diesel with electric vehicles) that would enhance Environmental Justice (EJ) and could be applied to other cities with EJ problems. Synopsis: This paper presents observations of atmospheric black carbon aerosol showing impacts on environmental justice, then identifies causes and suggests solutions.

Vehicle Detection and Counting for Traffic Congestion Estimation Using YOLOv5 and DeepSORT in Smart Traffic Light Application

The escalating number of vehicles on the roads has exacerbated trafficcongestion, presenting a significant and inevitable challenge for roadusers.Trafficcongestionnotonlyincreasesthetraveltimeofroadusers;it also causes air pollution since more vehicles are stuck on theroad. To address this issue, deep learning algorithms, including YOLOv5and DeepSORT, have been leveraged to enable vehicle detection and estimate the number of vehicles through image processing. This study focuses on training a custom YOLOv5 dataset of vehicles and compares its performance with a pretrained YOLOv5 dataset in terms of feasibility for detecting and estimating the number of vehicles. In the proposed approach, YOLOv5 is employed to detect vehicles in video streams, DeepSORT is used for vehicle tracking and counting as they pass through the detection zone, and the number of vehicles in each lane is estimated with the aid of Supervision. The custom dataset's validation demonstrates promising results, with the precision curve indicating convergence for all classes at 97.4% precision and an 87% accuracy in predicting vehicle classes. Additionally, the precision-recall curve assesses the ability to detect individual vehicle categories, such as bus, car, motorcycle, and truck, with accuracies of 68.2%, 95.2%, 79.9%, and 70.1%, respectively. Furthermore, the overall accuracy for detecting all vehicle classes combined is 78.3%. The F1 curve indicates that the system achieves a confidence level of 30.9% F1 score, ensuring 78% confidence in accurately predicting all classes. Both the pretrained and custom datasets exhibit similar accuracy in counting the total number of vehicles passing through the detection zone as well as estimating the number of vehicles in each lane. However, it is evident that the custom dataset's performance can be further improved by incorporating more extensive and diverse datasets during the training process to enhance the accuracy of vehicle detection and estimation. In conclusion, the integration of deep learning algorithms, specificallyYOLOv5 and DeepSORT, offers a promising solution for addressing traffic congestion byefficiently detecting and estimating vehicle numbers, which allow traffic systems to identify which lane is congested and prioritise ensuring the congested lane has a smooth traffic flow. Continued research with larger datasets can lead to further advancements and refined results in the field of traffic management and optimization.

Sustainable Transportation System for Rajshahi City, Bangladesh

The ultimate objective of this project is to make a meaningful contribution to the ongoing research on sustainable development and to aid in developing and implementing an efficient transportation system that effectively tackles the challenges of mobility and environmental security challenges in the bustling city of Rajshahi. As part of this effort, our paper will present a comprehensive overview of recent advancements and key findings in the field of sustainable transportation. In the process, the paper will offer an overview of some of the most recent and useful developments in the subject of study. One of the key challenges in achieving sustainable transportation in Rajshahi is the inadequate road infrastructure, such as ill-defined road markings, lack or insufficiency of traffic signs, and rampant illegal parking, particularly in peripheral areas. These factors not only hinder smooth traffic flow but also threaten the safety of drivers, increasing their vulnerability to accidents. Additionally, they contribute to traffic congestion and other inconveniences for commuters. Hence, it is imperative to adopt a holistic approach to tackling these issues to promote sustainable transportation within Rajshahi City. This requires collaboration between various stakeholders and implementing comprehensive solutions that address multiple facets of sustainable transportation. Only through such concerted efforts can we effectively overcome the challenges hindering efficient mobility in Rajshahi.

Hybrid deep learning-based traffic congestion control in IoT environment using enhanced arithmetic optimization technique

The Internet of Things (IoT) is essential in several Internet application areas and remains a key technology for communication technologies. Shorter delays in transmission between Roadside Units (RSUs) and vehicles, road safety, and smooth traffic flow are the major difficulties of Intelligent Transportation System (ITS). Machine Learning (ML) was an advanced technique to find hidden insights into ITSs. This article introduces an Improved Arithmetic Optimization with Deep Learning Driven Traffic Congestion Control (IAOADL-TCC) for ITS in Smart Cities. The presented IAOADL-TCC model enables traffic data collection and route traffic on existing routes for avoiding traffic congestion in smart cities. The IAOADL-TCC algorithm exploits a hybrid convolution neural network attention long short-term memory (HCNN-ALSTM) method for traffic congestion control. In addition, an IAOA-based hyperparameter tuning strategy is derived to optimally modify the parameters of the HCNN-ALSTM model. The presented IAOADL-TCC model effectively enhances the flow of traffic and reduces congestion. The experimental validation was performed using the road traffic dataset from the Kaggle repository. The proposed model obtained an average accuracy of 98.03 % with an error rate of 1.97 %. The experimental analysis stated the superior performance of the IAOADL-TCC approach over other DL methods.

Geometric features and traffic dynamic analysis on 4-leg intersections

AbstractWith the escalating density of vehicles converging at road intersections, the surge in road accidents, traffic conflicts, and traffic congestion has emerged as a pressing concern. This research paper addresses these challenges by employing MC (manual control) techniques to mitigate encroachment issues at three selected intersections. These intersections were identified through a comprehensive analysis of the Ranking-based Instance Selection (RIS), enabling the design of suitable measures to facilitate smooth traffic flow and minimize the occurrence of crashes. In order to gather pertinent data, the study incorporates various parameters such as traffic volume, peak flow rate (PFR), traffic conflicts, accidents, and intersection inventory. Through the implementation of our proposed approaches, which involve both MC techniques and signalized operation, a supreme level of service (LOS) is attainable. Notably, our findings demonstrate a remarkable reduction in the volume-to-capacity ratio (v/c ratio) of up to 0.62. This paper thus serves as a significant contribution to the field of traffic management, offering practical insights for optimizing intersection design and effectively addressing the challenges posed by increased traffic density.

Advanced Traffic Management System

Abstract: Traffic Management is one of the major issues which is arising rapidly because of significant increase in number of vehicles. To address this there is the need of a smart traffic management system which will enable the smooth traffic flow. Today there are traffic signals which work on the timeframe setting and switch signals after the certain fixed time frame. This system works but a problem arises that is if within the timeframe if the lane becomes empty before the time ends then the signal will not immediately switch as it will complete the timeframe and then switch according to its default setting

Traffic Sign Detection Under Foggy Environment Using Machine Learning

Road signs are important to ensure smooth traffic flow without bottle necks or mishaps. Road symbols are the pictorial representations having different necessary information required to be understood by driver. Road signs in front of the vehicle are ignored by the drivers and this can lead to catastrophic accidents. This paper presents an overview of the traffic sign board detection and recognition and implements a procedure to extract the road sign from a natural complex image, processes it and alerts the driver using voice command It is implemented in such a way that it acts as a boon to drivers to make easy decisions. Traffic sign recognition is a critical component of modern intelligent transportation systems, contributing to road safety and efficient traffic management. This paper presents a novel approach to traffic sign recognition based on Convolutional Neural Networks (CNNs). Key Words: CNN, Pre-processing, Feature Extraction.

Modeling of traffic at a road crossing and optimization of waiting time of the vehicles

Traffic management is a critical activity, the population is increasing day by day and so the traffic on the road is also increasing. Traffic jams and long waiting queues of vehicles at the road crossing are now part of everyone's life. The traffic lights used at the crossing to regulate the traffic play a vital role in the smooth functioning of traffic movement. At a crossing of four roads, it has been observed that giving an equal amount of green light to all roads is meaningless since the arrival of traffic on different paths is different. Importantly, the arrival rate is responsible for all traffic jams, long queues, and increased waiting time. Therefore, this paper suggests a green light allocation scheme for all paths i depending on the arrival rate of the vehicles. Thus, the allocation of green light will be dynamic. Further, weight is also computed, where more arrival rate means more weight, thereby assigning more time to the green signal. This will help in reducing the long queue length, residual traffic, and long waiting times. On simulating the traffic with the traffic data, the proposed optimized green light allocation scheme to path i reduces the residue traffic to negligible, allowing smooth traffic flow even during peak hours. The work also provides a proficient optimization of the waiting time of vehicles accumulated during the red light. According to the simulation, the maximum time assigned for the green signal during the peak hour of 9:30 AM to 10:00 AM for paths i, where 1≤i≤4 is 39.96, 33.36, 26.64, and 20.04 seconds respectively. Similarly, during the second rush hour of 5:00 PM to 6:00 PM, the simulation assigns a green signal time of 41.4, 37.2, 24.84, and 16.56 seconds for corresponding paths 1–4. Thus, the proposed work suggests an effective traffic management scheme at the four-road crossing.

Traffic Management System Using IOT

Abstract: Traffic congestion is a major threat to transportation sector in every urban city around the world. This causes many adverse effects like, heavy fuel consumption, increased waiting time, pollution, etc. and causes major challenge to the movement of emergency vehicles. The reaction time required by the emergency responders plays a vital role to handle the situation. The greatest challenge they face is congestion of traffic flow. This paper presents an approach to schedule emergency vehicles in traffic. The approach combines the controlling of traffic congestion by IR sensors and detection of emergency vehicle using by RFID scanner method. The IR sensors is responsible for vehicle counting and RFID for the emergency vehicle. An IR sensors continuously count the passing vehicles & records the delay time between two vehicles. Flow of vehicles is normal means there is smooth traffic flow, or if in case, the passing time between two vehicles is decreased that means traffic congestion is happen ahead. In this situation traffic light turns to green, so that the emergency vehicle will reach its destination as fast as possible. The aim of this article is to identify and monitor the congestion system in order to provide efficient facilities. This journal also sets out a method that uses 433 MHZ RF Transmitter and receiver Wireless communication device technique and Internet of Things (IoT) to transmit the treatment request from the ambulance to the nearby hospitals and at the same time the LCD is installed on the road junction which displays the nearby hospital name and contact number for the ambulance, this smart traffic system which in turn changes the traffic signal cycle. This system can be implemented throughout the city thereby to reducing the delay.

Artificial intelligence of things for smart cities: advanced solutions for enhancing transportation safety

In the context of smart cities, ensuring road safety is crucial due to increasing urbanization and the interconnected nature of contemporary urban environments. Leveraging innovative technologies is essential to mitigate risks and create safer communities. Thus, there is a compelling imperative to develop advanced solutions to enhance road safety within smart city frameworks. In this article, we introduce a comprehensive vehicle safety framework tailored specifically for smart cities in the realm of Artificial Intelligence of Things (AIoT). This framework seamlessly integrates a variety of sensors, including eye blink, ultrasonic, and alcohol sensors, to bolster road safety. The utilization of eye blink sensor serves to promptly detect potential hazards, alerting drivers through audible cues and thereby enhancing safety on smart city roads. Moreover, ultrasonic sensors provide real time information about surrounding vehicle speeds, thereby facilitating smoother traffic flow. To address concerns related to alcohol consumption and its potential impact on road safety, our framework incorporates a specialized sensor that effectively monitors the driver’s alcohol levels. In instances of high alcohol content, the system utilizes GPS and GSM technology to automatically adjust the vehicle’s speed while simultaneously notifying pertinent authorities for prompt intervention. Additionally, our proposed system optimizes inter-vehicle communication in smart cities by leveraging Li-Fi technology, enabling faster and more efficient data transmission via visible light communication (VLC). The integration of Li-Fi enhances connectivity among connected vehicles, contributing to a more cohesive and intelligent urban transportation network. Through the structured integration of AIoT technologies, our framework lays a robust foundation for a safer, smarter, and more sustainable future in smart city transportation. It offers significant advancements in road safety and establishes the groundwork for further enhancement in intelligent urban transportation networks.

IOT BASED MOVABLE ROAD DIVIDING SYSTEM

Road dividers conventionally serve the purpose of segregating traffic on roads into lanes for ongoing and incoming vehicles, facilitating smooth traffic flow. Typically, an equal number of lanes are designated for each direction of traffic. However, static road dividers present a limitation as they do not adapt to varying traffic demands. Our objective is to devise an automated road divider system capable of dynamically adjusting lane allocation, thereby accommodating the prevailing traffic patterns. The potential benefits of such a system include significant savings in time and fuel consumption, particularly by reallocating lanes to the direction experiencing higher traffic volume. This innovation promises to alleviate traffic congestion effectively and enhance traffic management strategies.

Effects of mobile phone-related distraction on driving performance at roundabouts: Eye movements tracking perspective

Modern road infrastructures are complex networks featuring various elements such as roads, bridges, intersections, and roundabouts, with advanced control systems. Roundabouts have gained prominence as a safer alternative to traditional intersections promoting smoother traffic flow and fewer collisions by guiding traffic in one direction, encouraging reduced speed, and minimizing conflict points.This study investigated driver behavior within roundabouts, focusing on gaze behavior, particularly the left-side mirror and window, under mobile phone distraction conditions. In addition, the effects of roundabout specifications (i.e., number of lanes and size of the central island) and the drivers’ characteristics (i.e., driving experience) were examined.In total, 43 participants, aged 19–56 years including 30 males and 13 females, held a valid driving license, drove through a virtual simulated urban road containing four roundabouts, implemented in a static driving simulator, under baseline condition (no distraction) as well as mobile-induced distraction. Driving simulator data were collected and drivers’ gaze direction and fixation on nine areas of interest were captured with an eye tracker. Resultsshowed that experienced drivers exhibit a more fixation on the left-side mirror and window and were less distracted. Moreover, the road environment, i.e., the number of cars and the roundabout size, significantly influenced the drivers’ attention. As regards the driving performance, the number of infractions increased when the drivers diverted focus from the left side of the car. The outcomes of the present study might help to improve traffic safety at roundabouts.

The Crack Diffusion Model: An Innovative Diffusion-Based Method for Pavement Crack Detection

Pavement crack detection is of significant importance in ensuring road safety and smooth traffic flow. However, pavement cracks come in various shapes and forms which exhibit spatial continuity, and algorithms need to adapt to different types of cracks while preserving their continuity. To address these challenges, an innovative crack detection framework, CrackDiff, based on the generative diffusion model, is proposed. It leverages the learning capabilities of the generative diffusion model for the data distribution and latent spatial relationships of cracks across different sample timesteps and generates more accurate and continuous crack segmentation results. CrackDiff uses crack images as guidance for the diffusion model and employs a multi-task UNet architecture to predict mask and noise simultaneously at each sampling step, enhancing the robustness of generations. Compared to other models, CrackDiff generates more accurate and stable results. Through experiments on the Crack500 and DeepCrack pavement datasets, CrackDiff achieves the best performance (F1 = 0.818 and mIoU = 0.841 on Crack500, and F1 = 0.841 and mIoU = 0.862 on DeepCrack).

Assessment of Traffic Characteristic at Major Urban Road Intersection of Kathmandu Valley: A Case Study of Khanivivag, Kesarmahal, and Narayanhiti Intersections

This study addresses the prevalent issue of traffic congestion at critical intersections in urban areas, leading to slowed traffic and increased queue lengths. The centralization of the Kathmandu Valley has contributed to a gradual rise in population thus impacting the efficiency of the traffic management system. This study focuses on three of the busiest intersections of the valley i.e. Kesharmahal, Narayanhiti, and Khanibhivag intersections and aims to provide insights into current traffic conditions and propose measures for enhancing traffic flow. Data collected is analyzed to determine current traffic flow, saturation flow, existing capacity, Level of Service (LOS), and intersection delay using the HCM Manual. The analysis involves assessing present conditions and making comparisons with standardized data. Three days of traffic volume data during peak hours and geometric features of the intersection were gathered. Results indicate that the intersections are experiencing over-saturation, exceeding their capacity based on existing geometry and signalization. Additionally, the vehicle replacement strategy showed limited effectiveness in reducing saturation flow, with the flow persisting at an over-saturated level. These findings highlight the need for targeted measures to address the challenges posed by congestion and enhance the efficiency of traffic management at these intersections for smooth traffic flow.

Optimization of lane based signalized intersections through vissim at outer ring road Bengaluru

Signalized intersections are indispensable parts of urban traffic networks. In developing countries, there are many types of vehicles, which causes heterogenous traffic conditions. Lack of lane discipline, varying composition, and a variety of vehicle situations are all possible issues that get worse with time. For such a dynamic traffic situation, microsimulation modeling is appropriate. The traffic flow characteristics of a particular road are frequently used in traffic simulation research on signalized intersections. The methodology outlines a systematic process for optimizing traffic signals using VISSIM software, including data collection, calibration, and signal timing adjustments at the Iblur and Silkboard Junction, Bengaluru, Karnataka, India. Results show significant improvements in key performance metrics after optimizing signal timings, leading to reduced congestion and smoother traffic flow. The study underscores the significance of accurate data collection for developing and validating simulation models. The successful development and validation of a simulation model in VISSIM, along with efforts to enhance intersection performance through signal timing adjustments, have been achieved. Future research directions include expanding calibration efforts, incorporating real-time field data, and exploring adaptive signal control systems.

Emerging Data-Driven Calibration Research on an Improved Link Performance Function in an Urban Area

The reliability of urban transportation systems is crucial for ensuring smooth traffic flow and minimizing disruptions caused by external factors. This study focuses on improving the stability and efficiency of transportation systems through the calibration of a refined link performance function while building upon the U.S. Bureau of Public Roads (BPR) model. To achieve this, we propose three customized algorithms—Newton’s method, Bayesian optimization, and the differential evolutionary algorithm—to calibrate the key parameters. Additionally, we conducted a sensitivity analysis to assess the influences of the model parameters on link performance. Numerical experiments conducted in Yuyao City demonstrate the applicability and efficacy of the proposed model and solution algorithms. Our results reveal that the Newton approach is notably more efficient than the Bayesian optimization algorithm and the differential evolutionary algorithm.

Intelligent Traffic Management System with Adaptive Signal Control and Emergency Vehicle Prioritization

Abstract: Efficient traffic management is essential for ensuring smooth flow and minimizing congestion on roadways. In this project, we propose an Arduino-based solution that utilizes IR sensors for real-time traffic monitoring and signal timing adjustment, coupled with a Bluetooth module to prioritize emergency vehicles during critical situations. The IR sensors are deployed at intersections to monitor vehicle presence, enabling real-time traffic monitoring. The Arduino Uno microcontroller processes the sensor data and dynamically adjusts signal timings to ensure smooth traffic flow. Additionally, a Bluetooth module facilitates communication between the traffic management unit and ambulance vehicles, triggering an alert signal to clear the way for emergencies. The system's adaptability allows for easy integration into existing infrastructure. Results indicate improved traffic efficiency, reduced congestion, and enhanced emergency vehicle response times

Understanding the traffic flow in different types of freeway tunnels based on car-following behaviors analysis

Tunnels are an engineering solution that has gained prominence for constructing freeways in mountainous regions. The length of the tunnels can vary, depending on the geological conditions, engineering requirements, and budget constraints. Car-following is the predominant driving behavior observed in tunnels, and understanding how drivers follow each other in different types of tunnels is crucial for ensuring smooth traffic flow and safety. Each type of tunnel environment can uniquely impact car-following behavior, which allows for targeted studies to optimize traffic management. In this research, natural driving data in freeway tunnels were collected through a driving experiment conducted on the Baomao Freeway in Chongqing, China. Then, the correlations and differences in car-following data between various tunnels and sections were analyzed. Finally, car-following models were developed considering various tunnel scenarios, and the influence of tunnel types on traffic flow was analyzed by simulation. The study revealed notable variations in car-following behavior across different types of tunnels, as well as within consecutive sections of the same tunnel. As tunnel length increased, the driving stability of following vehicles decreased, but the level of driving safety risk was not positively correlated with tunnel length. Significant vehicle trajectory oscillation was observed within the inner sections of long and extra-long tunnels, and a significant relationship between the acceleration of following vehicles and the location within the tunnel section was found. Additionally, the longer the tunnel, the greater the fluctuations in traffic flow, and the negative impact of the tunnel environment on traffic flow stability increased periodically downstream. These findings offer valuable insights for understanding and modeling car-following behavior in freeway tunnels, which ultimately facilitate traffic safety and mobility.

Traffic-free emergency health corridor

The rise in vehicle traffic has resulted in traffic gridlock, more gasoline consumed, more time wasted, and more air pollution. As seen in the first coronavirus disease of 2019 (COVID-19) duration, there is a need for traffic-free paths for emergency vehicles to save someone’s life. The prototype model is demonstrated using the Arduino UNO, transmitter and receiver LORA module, DPDT switches, and LEDs. The proposed setup technology has been compared with the existing technologies. The proposed algorithm facilitates communication between onboard and roadside units, allowing for efficient traffic control. When an emergency occurs, the roadside units detect approaching emergency vehicles and manage traffic lights within reachable boundaries. To guarantee smooth traffic flow at traffic intersections, the proposed system will monitor the current location of emergency vehicles and adjust traffic signals accordingly. To provide a clear path to reach the target, our research suggests a novel strategy that uses LoRa technology to make the existing path traffic-free which is the main objective and advantage of this solution because it is not possible to create a separate corridor for emergency vehicles.