Traffic systems have been built as a result of recent technological advancements. In application, dependable communication technology is essential to link any system needs. VANET technology is used to communicate data about intelligent traffic lights, which are focused on infrastructure during traffic accidents and mechanisms to reduce traffic congestion. To ensure reliable data transfer in VANET, appropriate routing protocols must be used. This research aims to improve data transmission in VANETs implemented in intelligent traffic lights. This study investigates the capability of combining the DSDV routing protocol with the routing protocol AODV to boost AODV on an OMNET++ simulator utilizing the 802.11p wireless standard. According to the simulation results obtained by analyzing the delay parameters, network QoS, and throughput on each protocol, the DSDV-AODV routing protocol performs better in three scenarios compared to QoS, delay, and throughput parameters in every scenario that uses network topology adapted to the conditions on the road intersections. The topology with 50 fixed + 50 mobile nodes yields the best results, with 0.00062 s delay parameters, a network QoS of 640 bits/s, and a throughput of 629.437 bits/s. Aside from the poor results on the network QoS parameters, the addition of mobile nodes to the topology influences both the results of delay and throughput metrics.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383356 | PMC |
| http://dx.doi.org/10.3390/s23146426 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Spatiotemporal multi-feature fusion vehicle trajectory anomaly detection for intelligent transportation: An improved method combining autoencoders and dynamic Bayesian networks.
Accid Anal Prev
January 2025
School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK.
With the continuous development of intelligent transportation systems, traffic safety has become a major societal concern, and vehicle trajectory anomaly detection technology has emerged as a crucial method to ensure safety. However, current technologies face significant challenges in handling spatiotemporal data and multi-feature fusion, including difficulties in big data processing, and have room for improvement in these areas. To address these issues, this paper proposes a novel method that combines autoencoders, Mahalanobis distance, and dynamic Bayesian networks for anomaly detection.
View Article and Find Full Text PDFDriver's journey from historical traffic violations to future accidents: A China case based on multilayer complex network approach.
Accid Anal Prev
December 2024
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 611756, China; Institute of System Science and Engineering, Southwest Jiaotong University, Chengdu, Sichuan 611756, China; National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu, Sichuan 611756, China. Electronic address:
Traffic violation records serve as key indicators for predicting drivers' future accidents. However, beyond statistical correlations, the underlying mechanisms linking historical traffic violations to future accidents remain inadequately understood. This study introduces a research framework to address this gap: Using Propensity Score Matching and an adapted mutual information-based feature selection algorithm to precisely identify correlations and optimal time windows between drivers' historical traffic violations and future accidents.
View Article and Find Full Text PDFIntrinsic motivation and challenges of road safety volunteers: the interplay of educational role, coping strategies, resilience, and social impact.
BMC Psychol
December 2024
Department of Behavioral Sciences, Zefat Academic College, Safed, Israel.
Road safety is a critical concern worldwide, impacting individuals, communities, and societies. As mobility increases, so does the risk of accidents and injuries on roads, emphasizing the need for preventive measures. Road safety volunteers contribute significantly to promoting and maintaining road safety, making it important to understand their motivations and resilience sources.
View Article and Find Full Text PDFFactors contributing to road traffic accidents in the Gaza Strip a comprehensive analysis.
Sci Rep
December 2024
Department of Electrical Engineering, Imam Khomeini Naval Science University of Nowshahr, Nowshahr, Iran.
Road traffic crashes (RTCs) are considered one of the major public health issues in many countries worldwide. Investigating factors of traffic crashes, accidents, and disasters can facilitate and aid in identifying measures to mitigate their frequency and severity as well as occurrence and impact, thereby enhancing road safety. This study aims to investigate the factors that contribute to road traffic accidents in the Gaza Strip, Palestine.
View Article and Find Full Text PDFDeep Learning-Based Method for Detecting Traffic Flow Parameters Under Snowfall.
J Imaging
November 2024
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China.
In recent years, advancements in computer vision have yielded new prospects for intelligent transportation applications, specifically in the realm of automated traffic flow data collection. Within this emerging trend, the ability to swiftly and accurately detect vehicles and extract traffic flow parameters from videos captured during snowfall conditions has become imperative for numerous future applications. This paper proposes a new analytical framework designed to extract traffic flow parameters from traffic flow videos recorded under snowfall conditions.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!