Traffic Sign Classification

The traffic signs engraved on the streets nowadays improve traffic security by advising the driver regarding speed limits or any further potential perils like profound thrilling streets, inescapable fix street works or any common intersections. With the quick improvement of economy and innovation in the cutting edge society, vehicles have become an imperative method for transportation in the day by day travel of individuals. Albeit the fame of autos has acquainted impressive comfort with individuals, it has additionally caused a various traffic security issues that can't be overlooked, for example, gridlock and successive street mishaps. Traffic security issues are to a great extent brought about by abstract reasons identified with the driver, like obliviousness, inappropriate driving activity and resistance with traffic rules, and keen vehicles have become a compelling way to wipe out these human components. Self-driving innovation can help, or even autonomously complete the driving activity, which is vital to free the human body and extensively lessen the rate of mishaps. Traffic sign identification and acknowledgment are significant in the advancement of astute vehicles, which straightforwardly influences the execution of driving practices. Traffic sign identification and grouping is of vital significance for the fate of independent vehicle innovation. We benchmark the commented on dataset with AI baselines Convolutional Neural Organizations (CNN). Computational strategies for AI (ML) have shown their importance for the projection of possible outcomes for educated choices. AI calculations have been applied for quite a while in numerous applications. An information driven methodology with higher precision as here can be extremely valuable for a proactive reaction from the public authority and residents. At long last, we propose a bunch of exploration openings and arrangement justification for additional useful applications. Keywords: Convolutional Neural Networks, Traffic sign detection, Traffic safety, Computational Methods, machine Learning Algorithms

Research on the General Algorithm and Benefit of Real-Time Positioning Advertising System—Based on the Use of 5G Base Station Data

Due to the limitations of mobile phone positioning technology in the past, it is difficult for an advertising system to obtain users’ locations. Although there are many advertising delivery ideas in the market, time and location-based advertising delivery system have not been able to meet the delivery needs of advertisers. This study investigates various time and location-based advertising needs in the market and finds a universal data processing model that can quickly organize user positioning data from telecom base stations into screening information. It helps us to find specific audiences and to deliver advertisements to target audiences quickly. This study evaluated various advertising scenarios’ effectiveness and explained why there is a performance gap usually between five to ten times worse compared to the past. This study discusses the problems encountered when implementing the advertising system and how to improve them. This study also examines advertising interaction methods based on 5G features, business cooperation and profit splitting with telecom operators, and more diversified data application orientations and possibilities such as market surveys, urban traffic, security issues, and epidemic prevention. The future challenge is to develop footprint tracking technologies based on 5G and provide smart cities with lives-protecting or cost-saving solutions.

The impact of subway operation on urban traffic: A GRA-BNs based study

With the development of urbanization, urban traffic has exposed many problems. To study the subway’s influence on urban traffic, this paper collects data on traffic indicators in Nanchang from 2008 to 2018. The research is carried out from three aspects: traffic accessibility, green traffic, and traffic security. First, Grey Relational Analysis is used to select 18 traffic indicators correlated with the subway from 22 traffic indicators. Second, the data is discretized and learned based on Bayesian Networks to construct the structural network of the subway’s influence. Third, to verify the reliability of using GRA and the effectiveness of Bayesian Networks (GRA-BNs), Bayesian Networks with full indicators analysis and other four algorithms (Naive Bayes, Random Decision Forest, Logistic and regression) are employed for comparison. Moreover, the receiver operating characteristic (ROC) area, true positive (TP) rate, false positive (FP) rate, precision, recall, F-measure, and accuracy are utilized for comparing each situation. The result shows that GRA-BNs is the most effective model to study the impact of the subway’s operation on urban traffic. Then, the dependence relations between the subway and each index are analyzed by the conditional probability tables (CPTs). Finally, according to the analysis, some suggestions are put forward.

A Review over AI Methods Developed for Maritime Awareness Systems

Due to their operations against illegal activities, maritime threats or collision prevention analysis, maritime surveillance plays a vital role in maritime traffic security and safety management. Today's maritime surveillance and awareness systems can integrate multiple data sources like: coastal, HFSWR and SAR radars, AIS or satellite imagery; and this process produces massive amounts of data. That available data can be processed, with the use of Artificial Intelligence (AI) methods and algorithms, to automatically monitor the maritime traffic and its implications in safety, security, economy and environment. This paper's purpose is to briefly reveal current AI techniques that have been researched and deployed in the industry, and to seize the opportunity of implementing them.

INTERNATIONAL STANDARDS IMPACT ON THE REGULATION OF NATIONAL TAXI AGGREGATOR MARKETS

The development of the sharing economy has affected the change in the passenger transportation market, as online platforms for taxi services have appeared. For example, Uber Technologies plays a significant role in the market, which in 2017 won first place in the top 10 startups of the sharing economy. Currently, there are many problems that arise with the regulation of online taxi platforms. The Organization for Economic Co-operation and Development, together with the International Transport Forum, is conducting a study of the law enforcement practices of countries regarding the regulation of online taxi platforms (or taxi aggregators), in particular, the problems of deploying the online platform for the transport of passengers and baggage by passenger taxi as a regular online platform or as a transport provider, and licensing problems for such online platforms, problems of control over obtaining permission directly by the driver, problems of vehicle safety of control over the drivers, ensure quality of service, traffic security problems and etc.

A Cognitive Contemplation of Road Accident Predicton Through Deep Learning

The research based on the vehicle accidents step to collect and structure a progressive secure transportation unfortunately vehicle crashes were unavoidable. The accident prediction related with the risky environment data collection and arrangements based on the high priority of reality of accidents. The social activity and roadway structures are useful in the progression of traffic security control approach. We believe that to secure the best possible setback decline impacts with limited budgetary resources, it is basic that measures be established on coherent and objective studies of the explanations behind mishaps and seriousness of wounds. A survey based on the different algorithms able to predict the road accidents prevention methods. This paper demonstrates a couple of models to predict the reality of harm that occurred in the midst of car accidents using three artificial intelligent approaches (AI). The proposed scheme contributes a neural systems prepared utilizing choice trees and fluffy c implies bunching strategy for division.