Number Of Vehicles Research Articles

A modelling framework to support the development of last-mile urban freight distribution systems based on the use of cargo bikes

Nowadays there is a growing interest in cargo bikes to mitigate the detrimental effects of urban freight transport, since the last-mile segment of freight delivery process is being challenged to reduce the number of vehicles, the distance travelled and the environmental impacts. This paper presents a modelling framework that, starting from the key reference units (freight demand, deliveries, and vehicles) and defining the relationship among stakeholders and choice dimensions, can support the implementation of freight distribution systems based on cargo bikes. In detail, the work follows a general approach and is focused on the estimation of the share of demand that can be satisfied by cargo bikes, freight flows in terms of number of deliveries and trips, obtained by assigning freight demand to the transport network. The model has been applied to the test case of the city of Ravenna by implementing several scenarios.

Implementation of a Fully Automatic Four-way Traffic Light Controller Using Verilog

The traffic control system is one of the challenging problems in metropolitan and developed cities. This is due to the large number of vehicles and the high dynamics of the traffic system. Impoverished traffic systems are the big reason for accidents and time losses. In this project, the Fully Automatic Four-way Traffic light controller is implemented by using Verilog and we have overcome some drawbacks i.e., we included the area detection parameter that improves the detection of vehicles compared to the design part in the base paper.

Computer Vision-Based Lane Detection and Detection of Vehicle, Traffic Sign, Pedestrian Using YOLOv5

There has been a global increase in the number of vehicles in use, resulting in a higher occurrence of traffic accidents. Advancements in computer vision and deep learning enable vehicles to independently perceive and navigate their environment, making decisions that enhance road safety and reduce traffic accidents. Worldwide accidents can be prevented in both driver-operated and autonomous vehicles by detecting living and inanimate objects such as vehicles, pedestrians, animals, and traffic signs in the environment, as well as identifying lanes and obstacles. In our proposed system, road images are captured using a camera positioned behind the front windshield of the vehicle. Computer vision techniques are employed to detect straight or curved lanes in the captured images. The right and left lanes within the driving area of the vehicle are identified, and the drivable area of the vehicle is highlighted with a different color. To detect traffic signs, pedestrians, cars, and bicycles around the vehicle, we utilize the YOLOv5 model, which is based on Convolutional Neural Networks. We use a combination of study-specific images and the GRAZ dataset in our research. In the object detection study, which involves 10 different objects, we evaluate the performance of five different versions of the YOLOv5 model. Our evaluation metrics include precision, recall, precision-recall curves, F1 score, and mean average precision. The experimental results clearly demonstrate the effectiveness of our proposed lane detection and object detection method.

Linear diophantine multi-fuzzy soft similarity measures: An analysis on alternative-fuel

Every day, the globe becomes more contemporary and industrialized. As a result, the number of vehicles and engines is growing. However, the energy sources utilized in these engines are scarce and dwindling over time. This circumstance prompts the search for alternate fuel. As civilization develops, transportation becomes a need for daily living. The largest issue is the diminishing supply of fossil fuels and the expanding population. As a result, everyone needs alternate energy sources for their automobiles. Therefore, in this investigation, we identify the best substitute for petrol. We offer the similarity measure(SM) for a hybrid structure of a Linear Diophantine Multi-Fuzzy Soft Set(LDMFSS) with the goal of determining this issue. Because the range of grade values has been expanded, decision-makers now have greater freedom in selecting their grade. An exemplary case study is illustrated that shows the appropriateness of our recommended approach. A comparative analysis is provided to show the outcomes of the proposed method are more achievable and beneficial than those of the existing methodologies. Additionally, its applicability and attainability are evaluated by comparing its structure to those of the already used procedures.

Experimental investigation on the behavior of thermal super insulation materials for cryogenic storage tanks in fire incidents

The number of vehicles using or transporting cryogenic fuels such as Liquefied Hydrogen (LH2) or Liquefied Natural Gas (LNG) increases fast in the land transportation sector. Does this also entail new risks? The storage of cryogenic fuels requires tanks with Thermal Super Insulations (TSI) to keep the fluid cold and limit the formation of boil-off gas. TSI has proven itself in some applications since the middle of the 20th century, but in the land transport sector they are still quite new, where accidents involving fires, collisions, and their combination are to be expected. This work focuses on investigating the behavior of different types of TSI while exposed to a heat source representing a fire. To this aim, a High-Temperature Thermal Vacuum Chamber (HTTVC) was applied, which allows the thermal loading of a thermal insulation material in a vacuum and measuring the heat flow transported through the TSI in parallel. In this study, the results of 6 samples are presented regarding 3 types of MLI, rock wool, perlites, and microspheres. The thermal exposure caused different effects on the samples. In practice, this can be connected to the rapid release of flammable gases as well as to a Boiling Liquid Expanding Vapour Explosion (BLEVE). These results are relevant for reducing the risks to people and infrastructures in the progressive establishment of tanks for cryogenic fluids in our industry and society. The data presented in the study can be used to improve the design of tanks and TSIs, the assessment of accident scenarios, and the development of measures for first responders.

Comparative study of traffic signal design using Webster and IRC method

In India, the growth of population plays an important role in increasing the congestion at intersections because every person uses a separate vehicle for movement due to this reason we know every day many people die in road accidents due to lack of traffic control system because all intersections are not well signalized. So, it is not possible to control the movement of vehicles by traffic police due to the increase in the number of vehicles at intersections. Therefore, all intersections should be well signalized and traffic signals are specially designed for the emergency vehicles like Ambulance, Fire vehicles because emergency vehicles face delay at intersections when traffic is at red light and it is very dangerous for our society. In this project, Design of traffic signals is done with the help of the Indian Road Congress (IRC) and Webster's method at Gajularamaram service road intersection. Traffic signals are a better option for effective transportation. Traffic signal systems are used to control the flow of vehicles with the help of traffic lights, where Many roads meet together and make a junction. Traffic volume studies are used to lay down mainly how many vehicles are moving on the road at a particular section during a particular time. Traffic signals are the best way to control vehicle movement at intersections without any accidents and conflicts.

Fusion of satellite and street view data for urban traffic accident hotspot identification

As the number of vehicles and the volume of traffic swell in urban centers, cities have experienced a concomitant increase in traffic accidents. Proactively identifying accident-prone hotspots in urban environments holds the promise of preventing traffic mishaps, thereby curtailing the incidence of accidents and reducing property damage. This research introduces the Two-Branch Contextual Feature-Guided Converged Network (TCFGC-Net) utilizing multimodal satellite and street view data. Designed to extract global structural features from satellite imagery and dynamic continuous features from street view imagery, the model aims to improve the accuracy of detecting urban accident hotspots. For the satellite imagery branch, we propose the Contextual Feature Coupled Convolutional Neural Network (Trans-CFCCNN) designed to extract global spatial features and discern feature correlations across adjacent regions. For the street view imagery branch, we develop the Sequential Feature Recurrent Attention Network (SFRAN) to assimilate and integrate dynamic scene features captured from successive street view images. We designed the Multi-Branch Feature Adaptive Fusion Structure (MBFAF) to aggregate different branch features for accurate identification of accident hotspots. Experimental results show that the model performs well, with an overall accuracy of 93.7 %. Ablation studies confirm that relative to standalone street view and satellite branch analyses, implementing multimodal fusion enhances the model's accuracy by 12.05 % and 17.86 %, respectively. The innovative fusion structure proposed herein garners a 4.22 % increase in model accuracy, outpacing conventional feature concatenation techniques. Furthermore, the model outperforms existing deep learning models in terms of overall efficacy. Additionally, to showcase the efficacy of the proposed model structure, we utilize Class Activation Maps (CAM) to provide visual interpretability for the model. These results suggest that the dual-branch fusion model effectively decreases false alarm occurrences and directs the model's focus toward regions more pertinent to accident hotspots. Finally, the code and model used for identifying hotspots of urban traffic accidents in this study are available for access: https://github.com/gwt-ZJU/TCFGC-Net.

Analysis of the impact of exhaust gas emissions on diesel fueled vehicles in view from the year of manufacture (Case study on the Mitsubishi L 300 vehicle)

Air pollution is a condition caused by exhaust gas from motor vehicles operating on the highway. To minimize greater air pollution, the government is making preventive efforts by carrying out regular vehicle emission tests which are currently being carried out targeting transport vehicles. This is triggered by the increasing development of the automotive industry so that every year manufacturers produce these vehicles to meet the vehicle market, especially transport vehicle. With the increase in the number of vehicles every year, this is one of the causes of air pollution, so it needs to be controlled. This research aims to determine the effect of vehicle exhaust emissions on the year of vehicle production, in diesel fueled vehicles. The method used is an experiment using two-way Anova analysis on the results of tests carried out on Mitsubhisi L 300 vehicles with a total of 30 test samples, by testing vehicles produced in 2017 - 2022. The results of the research show that older vehicles will produce gas emissions. tall one. This is in accordance with the results of the ANOVA test which accepts the statement that there is at least one vehicle age that has a significant effect on smoke concentration, so it can be concluded that vehicles with an old age, in this case 2017, have the potential to produce high emission gases, therefore preventive efforts need to be taken. to vehicle engines

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.

P2P Energy Trading of EVs Using Blockchain Technology in Centralized and Decentralized Networks: A Review

Peer-to-peer (P2P) energy trading has attracted a lot of attention and the number of electric vehicles (EVs) has increased in the past couple of years. Toward sustainable mobility, EVs meet the standard development goals (SDGs) for attaining a sustainable future in the transport sector. This development and increasing number of EVs creates an opportunity for prosumers to trade electricity. Considering this opportunity, this review article aims to provide an in-depth analysis of P2P energy trading of EVs using blockchain in centralized and decentralized networks, which enables prosumers to exchange energy directly with one another. The paper is aimed to provide the reader with a state-of-the-art review on the P2P energy trading for EVs, considering different blockchain algorithms that are practically implemented or still in the research phase. Moreover, the paper presents blockchain applications, current trends, and future challenges of EVs’ energy trading. P2P energy trading for EVs using blockchain algorithms can be successfully implemented considering real-time scenarios and economically benefits smart sustainable societies.

Full-Scale Fire Extinguishing Experiments to Analyze the Adaptability of Firefighting Facilities for Electric Vehicle Fires in Underground Parking Lots

Recently, the number of electric vehicles (EVs) has rapidly increased owing to an increase in international environmental problems and domestic eco-friendly policies. With the increase in the number of EVs, the number of EV fires is also increasing. However, full-scale experiments for fire risk analysis or verification studies on the adaptability of present firefighting facilities are lacking , except for EV fire suppression experiments. In this study, to analyze the adaptability of firefighting facilities to EV fires in underground parking lots, fire extinguishing experiments on EV fires were conducted by considering the conditions of firefighting facilities applied to existing buildings. The first experiment simulated a current underground parking sprinkler system (pre-action system). In the second experiment, a lower sprinkler system was added to the upper sprinkler system. Experiment #1 confirmed that preventing the spread of thermal runaway in EV battery packs was difficult but blocking fire spread to adjacent vehicles was possible. Experiment #2 confirmed that thermal runaway occurred in approximately 50% of EV battery packs. These experiments concluded that fire spread to adjacent vehicles could be blocked by the spray from the upper sprinkler system.

Traffic Signal Timing Scheme Based on the Improved Harris Hawks Optimisation

With the continuous increase of urban vehicles, traffic congestion becomes severe in the metropolitan areas and higher car utilisation areas. The traffic signal timing scheme can effectively alleviate traffic congestion at intersections. We need to make a profound study in the traffic signal timing. An optimisation model is established, which not only takes the average delay time of vehicles, the number of vehicle stops and the traffic capacity, but also takes the exhaust emissions as the evaluation indexes. The model is too complex and involves too many variables to be solved by using multi-objective programming. Thus, the Harris Hawks Optimisation (HHO) with few parameters and high search accuracy was used to solve the model. To avoid the disadvantages of poor search performance and easy to fall into local optimisation of the Harris Hawks Algorithm, multi-strategy improvements were introduced. The experimental effects show that during the peak hours of traffic flow, the improved algorithm can reduce the average vehicle delay by 36.7%, the exhaust emission by 31.2% and increase the vehicle capacity by 41.6%. The above indicators have also been upgraded during the low peak stage.

Traffic Signal Controller System and Vehicle Count-Based Road Congestion Detection

Abstract: Traffic congestion is one of the major modern-day crisis in every big city in the world. Recent study of World Bank has shown that average vehicle speed has been reduced from 21 km to 7 km per hour in the last 10 years in Dhaka. Inter metropolitan area studies suggest that traffic congestion reduces regional competitiveness and redistributes economic activity by slowing growth in county gross output or slowing metropolitan area employment growth. As more and more vehicles are commissioning in an already congested traffic system, there is an urgent need for a whole new traffic control system using advanced technologies to utilize the already existent infrastructures to its full extent. Since building new roads, flyovers, elevated expressway etc. needs extensive planning, huge capital and lots of time; focus should be directed upon availing existing infrastructures more efficiently and diligently. Previously different techniques had been proposed, such as infra-red light sensor, induction loop etc. to acquire traffic date which had their fair share of demerits. In recent years, image processing has shown promising outcomes in acquiring real time traffic information using CCTV footage installed along the traffic light. Different approaches have been proposed to glean traffic data. Some of them count total number of pixels , some of the work calculate number of vehicles .These methods have shown promising results in collecting traffic data.

Carbon, Particulate Matter, and Heavy Metal Human Toxicity Footprint of IoT-based Micro Electric Vehicle (EV) Sharing for Urban Mobility

Purpose : The purpose of this study is to calculate and compare carbon footprint, particulate matter footprint, and heavy metal human toxicity footprint with operation data of IoT-based Micro Electric Vehicle (EV) Sharing for Urban Mobility, which was conducted in Chungbuk Innovation City (Jinchen Ducksaneup, Eumsung Mangdongmyun), Ochangeup (Cheongju), Osongeup(Cheongju) as a MOLIT Smart City Challenge Project, with petrol and diesel vehicles.Methods : In the assessment area, ten new Micro Electric Vehicles were used within the ‘TAYOU’ platform, which is an IoT-based micro electric vehicle sharing platform for smart mobility. These vehicles were operated for two months (1,021 times and 1,996 km). Data including vehicle numbers, charging start and end times, operating durations, return times, distances traveled (km), user information (name, driver's license, telephone number), and more were collected. Using this data, the carbon, particulate matter, and heavy metal human toxicity footprint were calculated and compared with those of petrol and diesel vehicles.Results and Discussion : The carbon footprints were 188.74 kg CO<sub>2</sub> eqv., 409.67 kg CO<sub>2</sub> eqv., 389.55 kg CO<sub>2</sub> eqv., in IoT-based Micro Electric Vehicle (EV), and petrol and diesel vehicle, respectively. The particulate matter footprint was 263.36 gPM<sub>2.5</sub> eqv., 21.37 gPM<sub>2.5</sub> eqv., and 439.47 gPM<sub>2.5</sub> eqv., respectively. The heavy metal human toxicity footprint was calculated at 0.029 g1.4 DCB eqv., 8.26 g1.4 DCB eqv., and 7.42 g1.4 DCB eqv., respectively; even though the pilot project was completed in just two months, the results showed a meaningful reduction in carbon, particulate matter, and heavy metals. If the service system can be extended, we can expect a more significant decrease in all environmental footprints. In the transition period from petrol and diesel vehicles to electric vehicles for carbon neutrality in road transportation systems, these results showed that electric vehicles can contribute significantly to reducing carbon, particulate matter, and heavy metals.Result : Carbon, particulate matter, and heavy metals reduction efforts are continuously needed in transportation. This study calculated and compared carbon, particulate matter, and heavy metal human toxicity footprints with electric, petrol, and diesel vehicles. As a result, in the future, this new small electric mobility (“TAYOU” Smart mobility vehicle) sharing service system can support and contribute to reducing carbon, particulate matter, and heavy metals emissions and footprints.

Enhanced Surveillance: Developing a Computer Vision-based CCTV Footage Analysis Tool for Improved Vehicle Number Plate Recognition

Abstract: Smart parking systems serve as integral components in augmenting the efficiency and sustainability of modern urban environments. Nonetheless, prevailing systems predominantly rely on sensors to monitor parking space occupancy, entailing substantial installation and maintenance costs while offering limited functionality in tracking vehicle movement within parking lots. To confront these challenges head-on, our solution introduces a multistage learning-based approach, capitalizing on the existing surveillance infrastructure within parking facilities and a meticulously curated dataset of Saudi license plates. Our approach amalgamates YOLOv5 for license plate detection, YOLOv8 for character detection, and a novel convolutional neural network architecture tailored for enhanced character recognition. We illustrate the superiority of our approach over single-stage methods, achieving an impressive overall accuracy of 96.1%, a notable advancement from the 83.9% accuracy of single-stage approaches. Furthermore, our solution seamlessly integrates into a web-based dashboard, facilitating real-time visualization and statistical analysis of parking space occupancy and vehicle movement, all accomplished with commendable time efficiency. This endeavor underscores the potential of leveraging existing technological resources to bolster the operational efficiency and environmental sustainability of smart cities. By harnessing the power of machine learning and computer vision, our work exemplifies a paradigm shift towards smarter and more adaptive urban infrastructure

دراسة تحليلية لتطور تأمين المركبات في المملكة العربية السعودية

The analytical study of the development of vehicle insurance in the Kingdom of Saudi Arabia addresses the importance of insurance in Saudi society and its impact on the economy and social development. The study begins by highlighting the Industrial Revolution and the development of the global economy, and how this led to an increase in the number of vehicles and an increasing need for insurance. The study shows that insurance has become an indispensable social necessity due to the huge number of factories and economic companies in the Kingdom of Saudi Arabia. Insurance works to compensate for damages and disasters to which individuals are exposed, and represents solidarity and support in society. The study indicates that insurance companies provide different types of insurance that meet the needs of individuals and companies. It also points to the close relationship between economic growth and the growth of the insurance sector, as the development of the economy contributes to supporting the sector and increasing its investments. The study concludes with the importance of developing the insurance sector in the Kingdom of Saudi Arabia and improving the services provided, in addition to enhancing awareness of the importance of insurance and motivating citizens to benefit from insurance services to protect their property and enhance financial and social stability in the country.

Assessment and mapping of noise pollution in recreation spaces using geostatistic method after COVID-19 lockdown in Turkey.

Increased use of recreational areas after the lifting of COVID-19 pandemic restrictions has led to increased noise levels. This study aims to determine the level of noise pollution experienced in recreational areas with the increasing domestic and international tourism activities after the lifting of pandemic lockdowns, to produce spatial distribution maps of noise pollution, and to develop strategic planning suggestions for reducing noise pollution in line with the results obtained. Antalya-Konyaaltı Beach Recreation Area, the most important international tourism destination of Turkey, is determined as the study area. To determine the existing noise pollution, 31 measurement points were marked at 100m intervals within the study area. Noise measurements were taken during the daytime (07:00-19:00), evening (19:00-23:00), and nighttime (23:00-07:00) on weekdays (Monday, Wednesday, Friday) and weekends (Sunday) over 2months in the summer when the lockdown was lifted. In addition, the sound level at each measurement point was recorded for 15min, while the number of vehicles passing through the area during the same period was determined. The database created as a result of measurements and observations was analyzed using statistical and geostatistical methods. After the analysis of the data, it was found that the co-kriging-stable model showed superior performance in noise mapping. Additionally, it was revealed that there is a high correlation between traffic density and noise intensity, with the highest equivalent noise level (Leq) on weekdays and weekend evenings due to traffic and user density. In conclusion, regions exposed to intense noise pollution were identified and strategic planning recommendations were developed to prevent/reduce noise sources in these identified regions.

Reactive Traffic Congestion Control by Using a Hierarchical Graph

Traffic management is one of the major factors in growth strategy formulation in urban centers across the globe. The increasing population and, therefore, the increase in the number of vehicles on roads in urban centers cause congested traffic patterns. These patterns typically emerge on intersections in busy city roads at various times during the day, especially during peak hours. A direct consequence of congestion is the increase in commute time and pollution. This paper presents a hierarchical graph-based congestion control (HGCC) method. Congestion values are set and evaluated as a two-level hierarchical graph. The least congestion path algorithm (LCP)is integrated with the HGCC to compute the optimal route between source and destination. The experimental results for a Manhattan-like grid network, together with the paired-sample t-test, show that the proposed method is efficient in achieving good congestion-avoiding routes.

Review on Accident Detection and Alerting System

This project aims to address the critical issue of individuals being left without assistance in the event of an accident while riding vehicle. With the implementation of an accident detection and alert system, this project seeks to provides solution to this problem. The system utilizes an Arduino, GPS Receiver, and GSM module to control the entire process. The GPS Receiver identifies the vehicle's direction, while the GSM module sends an SMS containing the directions and a link to Google Maps to the assigned contact. The system can detect accidents using a Vibration sensor. The microcontroller sends this information to the GSM module, which transmits the data, including the victim's precise location, to the assigned contact. The contact can then use the GPS MODEM to locate the victim and provide immediate assistance. The implementation of the Accident Detection and Alert System using Arduino is a highly effective solution, particularly in developing nations such as Nepal, India, and Bangladesh where the number of vehicles on the road is rapidly increasing. With the rise in vehicular accidents, fatalities have also been on the rise. This system is a valuable investment in ensuring the safety of drivers and passengers alike. Key Words: GSM module, GPS MODEM, Accident Detection, Arduino.

Effective Traffic Signal Control System Using Deep Learning

Traffic congestion is becoming a serious problem with a large number of vehicles on the roads. The present traffic system is a timer-based system that operates irrespective of the amount of traffic if there exists an ambulance. So, this Deep Learning project is designed in such a way that the traffic control system is based on vehicle density in a lane and also detecting the ambulance’s lane and let that particular lane pass considering as a first priority. In fact, we use computer vision to have the characteristics of the competing traffic rows at the signals. This is done by a object detection model based on a Deep Learning model called You Only Look Once (YOLO)v8. Then traffic signal phases are optimized according to collected data, mainly queue density and waiting time per vehicle, to enable as much as more vehicles to pass safely with minimum waiting time. Keywords— Object detection, ambulance, YOLOv8, Deep learning