Increase In Number Of Vehicles Research Articles

Smart Control of Traffic Light Using Artificial Intelligence

Traffic light management is one of the intense technical hazards of the urban areas in almost every country around the world. This is due to speedy increase in number of vehicles in order to decrease the time and complexity. The project developed by us will enable the traffic light to switch from red to green based on traffic density. The easy way to control a traffic light by using timer for each section since, we proposed a system for controlling the traffic light by image processing. The system can notice vehicles through pictures and also the image sequence can then be analyzed victimization digital image process for vehicle detection, and in line with traffic conditions on the road stoplight will be controlled.

Advanced IoT-integrated parking systems with automated license plate recognition and payment management

Urban parking management is a growing challenge with increasing vehicle numbers and limited parking space. Traditional methods often fail during peak hours, leading to inefficiencies, unauthorized usage, and revenue losses. For instance, a parking lot designed for 300 vehicles often exceeds 90% occupancy during peak times, creating congestion and billing inaccuracies. This research proposes an automated system integrating sensors, image processing, and database management to address these issues. A single camera monitors multiple parking slots, with predefined coordinates linked to IR sensors for dual verification. Image processing algorithms, including Optical Character Recognition (OCR), enable accurate license plate recognition. Testing under real-world conditions showed 95% accuracy in daylight, 90% in low light, and 93% for plates at 45-degree angles. Detection accuracy reached 88% at distances of 1.5–3 m, ensuring reliable operation even at the camera’s range limits. Occupancy tracking achieved less than a 5% error margin compared to manual methods, while the fare calculation module reduced billing errors by 90%, enhancing efficiency and revenue. The system’s scalable design supports applications in parking management, toll collection, and traffic monitoring. By improving vehicle detection, occupancy tracking, and billing accuracy, this solution addresses critical challenges in urban parking and contributes to smarter city infrastructure.

Traffic Safety Management System

Traffic safety is a critical concern for civic and pastoral surroundings likewise, with the thing of minimizing accidents, enhancing road-stoner safety, and icing effective transportation systems. This paper presents a comprehensive ‘Traffic Safety Management System’ (TSMS) designed to ameliorate road safety through the integration of advanced technologies and datadriven approaches. The system combines real-time business monitoring, predictive analytics, and proactive measures to address safety challenges. Crucial features of the TSMS include the use of Internet of effects (IoT) bias for real-time data collection, machine learning algorithms for accident vaticination, and Geographic Information Systems (Civilians) for hotspot identification. By using smart detectors surveillance systems, and communication networks, the system provides practicable perceptivity for policymakers and transportation authorities to apply targeted interventions. Also, TSMS promotes public mindfulness through stoner-friendly interfaces and dynamic feedback mechanisms. Traffic Management is one of the major issues which is arising fleetly because of significant increase in number of vehicles. To address this there is the need of a smart traffic operation system which will enable the smooth traffic flow. Case studies and simulations demonstrate significant advancements in reducing business-related losses and enhancing road safety compliance. The proposed result underscores the eventuality of integrating technology and policy for erecting safer, more sustainable transportation ecosystems.

Transport development performance with hazard-inducing variables: A frontier-based shadow pricing approach

While economic growth in a country can be spurred by the expansion of the transportation sector, traffic accidents resulting from an increase in vehicle numbers negatively impact social wellbeing. To integrate both the beneftis and costs in evaluating the transportation sector, this study incorporates traffic accidents as a risk factor (undesirable output) in analyzing the performance of road transportation across Chinese provinces. We propose a shadow price model to estimate the revenue generated and the losses incurred from the addition of each vehicle. The potential improvements in desirable outputs (freight and passenger traffic) and the possible reduction in risks (traffic accident losses) are assessed using a nonparametric approach. Our findings indicate that the overall performance of the Chinese transportation sector improved between 2001 and 2018. During this period, the shadow revenue of vehicles increased, while the shadow loss from traffic accidents showed a declining trend, attributed to enhanced transportation infrastructure and effective government regulations. However, we observe regional disparities in performance and offer targeted policy recommendations.

Assessment of Regulatory Ecosystem Services of Amasya University Hakimiyet Campus

Background: Population growth, increasing vehicle numbers, unplanned urbanization and rural-urban migration are reducing green spaces and exacerbating environmental problems such as air, water and noise pollution. In this context, university campuses serve as important small-scale urban models that play a crucial role in maintaining environmental and social well-being within urban ecosystems. Objectives: To evaluate the regulating ecosystem services provided by the tree canopy at Amasya University Hâkimiyet Campus (AUHC), such as air quality, energy savings, and carbon storage. Method: In this study, the land cover and ecosystem services of the AUHC were assessed using the i-Tree Canopy model. Six land cover classes defined for the study area (tree/shrub, grass/herbaceous vegetation, soil/bare ground, impervious buildings, impervious roads, other impervious surfaces) were assessed using 4000 random points and ecosystem services such as air quality were calculated. Results: The tree and shrub canopy covering 31.30% of the AUHC removes 261.53 kg of gaseous and particulate pollutants from the air annually, sequesters 36.57 tons of carbon, and stores a total of 918.42 tons of carbon. The economic value of these ecosystem services was calculated as $758 for air pollution removal and $44420 for carbon storage. The land cover distribution of the campus shows that 57.35% consists of impervious surfaces (buildings, roads) and 42.05% is green space. Conclusion: The tree canopy at the AUHC makes a significant contribution to ecosystem services such as improved air quality, carbon sequestration and storage, and these contributions and economic benefits could be further enhanced by increasing tree cover.

An Analysis of User Perceptions Towards Public Parking Management in Kampala City

The parking problems in Kampala City have become more pronounced due to the increasing vehicle numbers resulting from a growth in population and income levels. Understanding public perceptions and the various stakeholders’ parking needs and preferences is fundamental for identification of priority areas for improvement. This paper assessed the user perceptions of public parking management and the parking needs and preferences for various stakeholders in Kampala City. The study was evaluative and employed a cross sectional research design with a range of data diverse triangulation quantitative and qualitative techniques. From 328 questionnaires, participants were asked to assess fifteen (15) public parking management attributes from a point of view of their satisfaction. Also, a public parking stakeholder analysis was done and the level of influence by each stakeholder category in regards to parking management in the city and their parking needs and preferences were established. The study results revealed- that the customer satisfaction Index (CSI) of parkers was 52.4% indicating a relatively lower satisfaction level of the parking management in Kampala. From the fifteen public parking management attributes, four (4) of them to include affordability of parking, availability of parking spaces, accessibility to parking spaces and as well Safety and security were accorded the greatest importance. While thirteen (13) public parking stakeholders in Kampala City that included Ministry of Works and Transport (MoWT), Kampala Capital City Authority (KCCA), Multiplex Limited, Private Parking suppliers, public transport operators, Business Community, Commuters, Private Car Drivers, Logistics Vehicles, Traffic Police, Residents, shoppers and tourists and the academia were identified. The study recommends prioritization of those factors that parkers deem very important by the relevant authorities, stakeholder involvement in parking planning and management, establishment of an efficient enforcement and control mechanism, having a public parking vision with clear goals and objectives and as well the use of technology to enhance efficiency for both parkers and parking suppliers.

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

A macroscopic safety indicator for road segment: application of entropy theory

Road traffic safety is confronted with unique challenges worldwide due to the varied road network, high population density, and the rapid increase in vehicle numbers. This necessitates an effective evaluation of traffic safety at the road segment level to improve traffic safety management. Existing safety indicators, such as crash frequency (which lacks real-time availability) and Surrogate Safety Measures (focused on micro-level interactions), fall short in quantifying the overall safety status of road segments. To overcome these limitations, we introduce the concept of entropy, typically used to measure disorder in complex systems, into road traffic safety assessment. Our research presents a novel macroscopic safety indicator named ‘velocity entropy,’ along with its calculation method, which involves discretization and the Parzen window technique. The efficacy of velocity entropy is validated using the UTE dataset, a high-fidelity trajectory dataset from China, showing a more significant correlation with traffic conflicts than other macroscopic traffic properties. Notably, velocity entropy can track the temporal and spatial evolution of traffic safety, providing insights into the dynamic development of traffic risks. Overall, velocity entropy offers an innovative, practical, and theoretically meaningful tool for assessing and predicting road safety.

Study On Advanced Vehicle Number Plate Detection Systems-A Review

In response to the rising volume of vehicles on roadways, there's an urgent demand for advanced technologies to bolster security and streamline traffic-related operations. This project introduces an innovative solution: an Advanced Automatic Number Plate Detection System. Leveraging Python, Easy OCR, and Open CV, this system aims to develop a sophisticated Automatic Number Plate Recognition (ANPR) mechanism capable of accurately detecting license plates, extracting alphanumeric characters, and providing actionable insights for various applications. Python is chosen for its versatility and ease of use, serving as the primary programming language. OpenCV is employed for its robust image processing tools and license plate detection capabilities, while Easy OCR simplifies character extraction from license plates. Key features include license plate detection through OpenCV algorithms, character recognition via Easy OCR, and real-time processing suitable for tasks like traffic monitoring and surveillance. The project follows a structured approach encompassing data collection, image pre-processing, machine learning model training, integration, and thorough testing. Anticipated outcomes include a fully operational ANPR system with high accuracy in license plate detection and character recognition, surpassing conventional methods. Its potential integration into real-world applications such as traffic management underscores its significance in addressing challenges posed by increasing vehicle numbers, with YOLO v8 utilized for precise outcomes. Keywords- Python, Open CV, YOLO, tensorflow, Machine learning, ANN, CNN, and Easy OCR

Vehicle detection and classification using an ensemble of EfficientDet and YOLOv8.

With the rapid increase in vehicle numbers, efficient traffic management has become a critical challenge for society. Traditional methods of vehicle detection and classification often struggle with the diverse characteristics of vehicles, such as varying shapes, colors, edges, shadows, and textures. To address this, we proposed an innovative ensemble method that combines two state-of-the-art deep learning models i.e., EfficientDet and YOLOv8. The proposed work leverages data from the Forward-Looking Infrared (FLIR) dataset, which provides both thermal and RGB images. To enhance the model performance and to address the class imbalances, we applied several data augmentation techniques. Experimental results demonstrate that the proposed ensemble model achieves a mean average precision (mAP) of 95.5% on thermal images, outperforming the individual performances of EfficientDet and YOLOv8, which achieved mAPs of 92.6% and 89.4% respectively. Additionally, the ensemble model attained an average recall (AR) of 0.93 and an optimal localization recall precision (oLRP) of 0.08 on thermal images. For RGB images, the ensemble model achieved mAP of 93.1%, AR of 0.91, and oLRP of 0.10, consistently surpassing the performance of its constituent models. These findings highlight the effectiveness of proposed ensemble approach in improving vehicle detection and classification. The integration of thermal imaging further enhances detection capabilities under various lighting conditions, making the system robust for real-world applications in intelligent traffic management.

Nomadic ungulate movements under threat: Declining mobility of Mongolian gazelles in the Eastern Steppe of Mongolia

Increasing habitat fragmentation and disturbance threaten long-distance movements of ungulates. While the effects of impermeable barriers on ungulate migrations have been well researched, quantitative evidence for gradual, long-term changes of mobility remains rare.We investigated changes in movement behavior of Mongolian gazelle Procapra gutturosa using GPS tracking data collected from 62 gazelle individuals between 2007 and 2021. We quantified 16-day displacement distances as a metric for long-distance movements before using linear mixed models, generalized additive models and quantile regressions to assess how anthropogenic and environmental factors affected gazelle movement behavior.Long-distance 16-day movements decreased by 36 %, from 142 km in 2007 to 92 km in 2021. Changes in mobility were affected by increasing vehicle numbers in Mongolia, but could not be explained by concurrent changes in other environmental factors like temperature, precipitation or vegetation greenness. Gazelle movement decreased close to roads, and gazelles stayed further away from roads during the snow-free season, when traffic likely is most intense.Conserving landscape permeability is essential for maintaining populations of highly mobile species. Our study provides evidence for a gradual decline in gazelle mobility over fifteen years as a response to increasing anthropogenic impact. The transportation infrastructure permeating the Eastern Steppe does not pose physical barriers, yet our findings suggest that increasing traffic volume may create semipermeable barriers to gazelle movement. As human activity is increasing, interactions between ungulates and vehicle traffic need to be closely monitored to identify and mitigate semipermeable barrier effects before landscape permeability is severely altered.

A Driver Safety System using Android (Mind your Route)

Abstract: The initiative of the project is to develop an Android application which increases road safety. We aim to utilize the android studio for developing a mobile application. As there is a drastic Increase in number of vehicles so are the accidents caused in day-to-day life. So, Road Safety is the major concern. Logistics plays a vital role as it is the supply chain for delivering or transportation of goods. The heavy vehicles which are involved in this process are observed in accidents which cause huge damage to people's lives. To Improvise the safety, we are here to implement an Android Application which provides or pushes notifications/alarms.

Model free Reinforcement Learning to determine pricing policy for car parking lots

Finding a parking space has not only become painful but also costs a lot in most of the metropolitan cities. With the increase in number of vehicles and limited resources such as manpower and space, the need for effective management of parking lots has increased. Improper management of parking lots can have negative consequences such as traffic congestion, wastage of time in search of parking spaces, air pollution and even loss of revenue for the parking lot managers. Dynamic pricing is a powerful tool to control the behavior of drivers by diverting them towards the unoccupied and cheaper parking lots. Though there are several existing dynamic pricing strategies, determining the right prices is quite challenging due to lack of knowledge of drivers’ behavior and several uncertainties like harsh weather and special days. In this paper Reinforcement Learning(RL) technique called Q-learning is used to calculate the dynamic prices for parking lots on hourly basis without the need of prior information about the system. Crucial factors like distance of the parking lots from the city centers, weather and holidays are considered in the proposed algorithm to achieve better accuracy. Price Elasticity of Demand (PED) is used in the proposed work to calculate the new state(occupancy) when an action(dynamic price charged by the parking lot owner) is taken place. Hourly prices are estimated using the proposed algorithm and simulation results show that the calculated prices can efficiently manage parking occupancy during peak and off peak hours. The simulation output also shows that the proposed algorithm can successfully increase the revenue of the parking lot owners.

Optimal performance assessment of intelligent controllers used in solar-powered electric vehicle

Introduction. Increasing vehicle numbers, coupled with their increased consumption of fossil fuels, have drawn great concern about their detrimental environmental impacts. Alternative energy sources have been the subject of extensive research and development. Due to its high energy density, zero emissions, and use of sustainable fuels, the battery is widely considered one of the most promising solutions for automobile applications. A major obstacle to its commercialization is the battery's high cost and low power density. Purpose. Implementing a control system is the primary objective of this work, which is employed to change the energy sources in hybrid energy storage system about the load applied to the drive. Novelty. To meet the control objective, a speed condition-based controller is designed by considering four separate math functions and is programmed based on different speed ranges. On the other hand, the conventional/intelligent controller is also considered to develop the switching signals related to the DC-DC converter’s output and applied the actual value. Methods. According to the proposed control strategy, the adopted speed condition based controller is a combined conventional/intelligent controller to meet the control object. Practical value. In this work, three different hybrid controllers adopted speed condition based controller with artificial neural network controller, adopted speed condition based controller with fuzzy logic controller, and adopted speed condition based controller with proportional-integral derivative controller are designed and applied separately and obtain the results at different load conditions in MATLAB/Simulink environment. Three hybrid controller’s execution is assessed based on time-domain specifications.

Effect of Driver Response on Efficiency of Vehicular Communication using Penalty Cost Function (EVCPCF)

Background and Objective: This study examines and takes into account three key timing factors that have an impact on the effectiveness of human-machine interfaces (HVI). A threshold-based mechanism is created to account for both cooperative driving and advanced vehicle control system (AVCS) scenarios. For AVCS and cooperative driving, the developed model takes into account on-board machine interface time, human interface time, and transmission time. Methods: A threshold function that represents the penalty cost of a slow driver reaction is presented in order to enable adaptive intelligence, enhance HVI design, and increase vehicle safety. The Penalty Cost Function (PCF) is used to make vehicle control systems intervene and take control in situations where the driver responds slowly to safety and warning messages. Additionally, this study demonstrates that AVCS-based vehicular systems are more responsive overall and are less impacted by the PCF function than cooperative systems. Results: The mathematical models created through this work allowed for a limiting efficiency value and capping for each driving scenario, according to comparative plots. This will improve the creation of more reliable control systems as part of a vehicle's mechatronics, impacting how vehicles communicate with one another in a cooperative setting. MATLAB simulation was used to verify the mathematical model. The simulation covered two limiting cases of 0.33 and 0.5 and used incrementing numbers of vehicles (10, 20, 30, 40, 50) to check the impact of increasing vehicle numbers on communication efficiency and examine whether both AVCS and AVCS with cooperative will have close levels and converge at limiting values. Conclusion: The successfully completed simulation demonstrated that throughput decreased as the number of vehicles increased, although in the limiting case, both scenarios and the driving system changed virtually by the same percentage.

Regression based neural network model for prediction of road traffic congestion : A case study of Bhubaneswar

The prediction of road traffic congestion is the most important and essential aspect to reduce the suffering of population of urban cities which is primarily carried by roads. The lack of a traffic congestion data unavailability and evaluation standard makes the effect of traffic congestion prediction more difficult and worsen. Traffic congestion occurs due to increase in number of vehicles on roads which reduces speed of vehicles, increases delay time, and increasing vehicular queuing in traffic. Due to traffic congestion, not only delaying time increases but also its threat to slower down the economic growth rate of our country and also have high impact on our personal growth, living condition with high level of pollution and undesirable feature of overloaded streets. Traffic congestion predicting modeling plays very important role so we need a innovative approach to predicting the congestion on roads. In this paper, we aim to provide a model which studies the real time environment characteristics of the road, and analyzed the data, congestion location identification, directional movement of all those locations surveyed and forecasting traffic location where traffic congestion may occur in near future.

Environmental conditions during hot summer day in Belgrade - interaction of thermal risk and noise pollution

In urban environments, people's exposure to harmful ambient conditions and noise represents a particular challenge in the modern world. Global climate change and increasing population density, along with the increasing number of vehicles in urban areas, are constantly exacerbating this problem. The idea of this study is to assess the effects of temperature risks and noise pollution in two microlocations. An analysis of the ambient conditions and noise in the urban environment was conducted at two locations in Belgrade, which differed in configuration. Simultaneous measurements of temperature (Ta and Tg), humidity, wind speed, and equivalent noise level were taken during a hot summer day (maximum temperature over 30?C). It was shown that the noise level exceeds the prescribed noise level values for the analyzed acoustic zone. The noise level values differ for the two locations due to the different traffic densities in the environment and different urban characteristics. This study showed that the concept of green space, but also of built-up type in urban planning could has importance impact in regulating thermal and noise conditions and obtained improved urban environments.

Effect of Vehicular Stream Characteristics on Traffic Noise

Human health can be negatively impacted by exposure to loud noise, which can harm the auditory system. Traffic noise is the leading cause of noise pollution. This paper studies the problem of noise pollution on the roads in Baghdad, Iraq. Due to the increase in vehicle numbers and road network modifications in Baghdad, noise levels became a serious topic to be studied. The aim of the paper was thus to study traffic noise levels and the effect of the traffic stream on noise levels and to formulate a prediction model that identified the guidelines used for designing or developing future roads in the city. Then, the noise levels were measured based on five variables: the functional classification of roads, traffic flow, vehicle speed, distance from the carriageway, and skid number. An analysis of traffic noise prediction was conducted using a simple linear regression model to accurately predict the equivalent sound levels. Finally, the findings have shown that the formulated prediction model gives acceptable prediction noise levels since the R2 is 88.83%. The results showed that the noise levels measured were 23.1% and 48.8% higher than the allowable noise levels limited by Iraqi standards during the daytime and nighttime, respectively. Further, studying the alternatives used to improve the performance of the environment on the existing roads in Baghdad can be considered as a future research direction. Doi: 10.28991/CEJ-2022-08-12-016 Full Text: PDF

Tunnel Traffic Evolution during Capacity Drop Based on High-Resolution Vehicle Trajectory Data

Capacity drop is the critical phenomenon that triggers traffic congestion, while traffic evolution is very complex during a capacity drop. This study applied the empirical vehicle trajectory data to explore the traffic characteristics during the capacity drop at the tunnel bottleneck section. We first construct a capacity drop analysis model using image processing technology to extract high-precision vehicle trajectories. We then analyze the characteristics of the evolution process of the capacity drop at the bottleneck area. The results show that the capacity drop is a dynamic evolution process from free flow to congested flow where traffic operation is distinct. The capacity drop shows the difference between congested flow and non-congested flow. The driving characteristics of drivers in the two states are also different. The influence of lane-changing behavior on the capacity drop is estimated. In the free flow state, the disturbance caused by lane-changing can be quickly eliminated. With the increase in vehicle numbers in the area, the frequent lane-changing behavior accumulates disturbance. When the disturbance reaches a certain degree, congestion will occur, and the vehicle’s speed will drop sharply, resulting in a capacity drop.

Vehicle Parking Management System

The aim of the paper is to develop application for vehicle parking management. As you can see lot of increase in number of vehicles which is the major problem for traffic control and below standard parking management. Another issue to vehicle owner is to get availability of space to park vehicle. So we intent to create an application that user can book or pre-book parking slot for their vehicle at parking areas. The user can use the application by signing up and then selecting the space or slot for vehicle according to vehicle type at parking area. This application can be applicable in big multi-national companies, shopping centres, airports and event at large public parking areas etc.