A BASIC STUDY ON THE REQUIREMENTS FOR LARGE-SCALE ROAD MAPS SUITABLE FOR LANE-BASED ROAD TRAFFIC ANALYSIS

This paper introduces a new approach of hybrid meta-heuristics based optimization technique for decreasing the computation time of the shortest paths algorithm. The problem of finding the shortest paths is a combinatorial optimization problem which has been well studied from various fields. The number of vehicles on the road has increased incredibly. Therefore, traffic management has become a major problem. We study the traffic network in large scale routing problems as a field of application. The meta-heuristic we propose introduces new hybrid genetic algorithm named IOGA. The problem consists of finding the k optimal paths that minimizes a metric such as distance, time, etc. Testing was performed using an exact algorithm and meta-heuristic algorithm on random generated network instances. Experimental analyses demonstrate the efficiency of our proposed approach in terms of runtime and quality of the result. Empirical results obtained show that the proposed algorithm outperforms some of the existing technique in term of the optimal solution in every generation.

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Performance Evaluation of Hadoop-based Large-scale Network Traffic Analysis Cluster

MATEC Web of Conferences ◽

10.1051/matecconf/20165605015 ◽

2016 ◽

Vol 56 ◽

pp. 05015

Author(s):

Ran Tao ◽

Yuanyuan Qiao ◽

Wenli Zhou

Keyword(s):

Performance Evaluation ◽

Network Traffic ◽

Large Scale ◽

Traffic Analysis ◽

Network Traffic Analysis ◽

Large Scale Network ◽

Scale Network

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Personal Exposure to BC, PM and NO2 in the Paris Region Measured by Portable Sensors Worn by Volunteers

Toxics ◽

10.3390/toxics10010033 ◽

2022 ◽

Vol 10 (1) ◽

pp. 33

Author(s):

Baptiste Languille ◽

Valérie Gros ◽

Bonnaire Nicolas ◽

Cécile Honoré ◽

Anne Kaufmann ◽

...

Keyword(s):

Large Scale ◽

Road Traffic ◽

Personal Exposure ◽

Portable Sensors ◽

Promising Solution ◽

Field Campaigns ◽

Paris Region ◽

The Impact ◽

First Time ◽

Indoor And Outdoor

Portable sensors have emerged as a promising solution for personal exposure (PE) measurement. For the first time in Île-de-France, PE to black carbon (BC), particulate matter (PM), and nitrogen dioxide (NO2) was quantified based on three field campaigns involving 37 volunteers from the general public wearing the sensors all day long for a week. This successful deployment demonstrated its ability to quantify PE on a large scale, in various environments (from dense urban to suburban, indoor and outdoor) and in all seasons. The impact of the visited environments was investigated. The proximity to road traffic (for BC and NO2), as well as cooking activities and tobacco smoke (for PM), made significant contributions to total exposure (up to 34%, 26%, and 44%, respectively), even though the time spent in these environments was short. Finally, even if ambient outdoor levels played a role in PE, the prominent impact of the different environments suggests that traditional ambient monitoring stations is not a proper surrogate for PE quantification.

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Road Traffic Analysis Using Computer Vision

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37630 ◽

2021 ◽

Vol 9 (8) ◽

pp. 2002-2006

Author(s):

Needhi U. Gaonkar

Keyword(s):

Computer Vision ◽

Traffic Flow ◽

Traffic Management ◽

Road Traffic ◽

Vehicle Detection ◽

Traffic Analysis ◽

Transportation Systems ◽

Visual Object ◽

Highway Traffic ◽

Analysis System

Abstract: Traffic analysis plays an important role in a transportation system for traffic management. Traffic analysis system using computer vision project paper proposes the video based data for vehicle detection and counting systems based on the computer vision. In most Transportation Systems cameras are installed in fixed locations. Vehicle detection is the most important requirement in traffic analysis part. Vehicle detection, tracking, classification and counting is very useful for people and government for traffic flow, highway monitoring, traffic planning. Vehicle analysis will supply with information about traffic flow, traffic summit times on road. The motivation of visual object detection is to track the vehicle position and then tracking in successive frames is to detect and connect target vehicles for frames. Recognising vehicles in an ongoing video is useful for traffic analysis. Recognizing what kind of vehicle in an ongoing video is helpful for traffic analysing. this system can classify the vehicle into bicycle, bus, truck, car and motorcycle. In this system I have used a video-based vehicle counting method in a highway traffic video capture using cctv camera. Project presents the analysis of tracking-by-detection approach which includes detection by YOLO(You Only Look Once) and tracking by SORT(simple online and realtime tracking) algorithm. Keywords: Vehicle detection, Vehicle tracking, Vehicle counting, YOLO, SORT, Analysis, Kalman filter, Hungarian algorithm.

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A Survey of National Physicians Working in an Active Conflict Zone: The Challenges of Emergency Medical Care in Iraq

Prehospital and Disaster Medicine ◽

10.1017/s1049023x12000519 ◽

2012 ◽

Vol 27 (2) ◽

pp. 153-161 ◽

Cited By ~ 16

Author(s):

Ross I. Donaldson ◽

Patrick Shanovich ◽

Pranav Shetty ◽

Emma Clark ◽

Sharaf Aziz ◽

...

Keyword(s):

Emergency Medicine ◽

Medical Care ◽

Emergency Departments ◽

Large Scale ◽

Road Traffic ◽

Health Workers ◽

Emergency Medical Care ◽

Convenience Sample ◽

Emergency Medical ◽

Conflict Zone

AbstractIntroductionThere has been limited research on the perspectives and needs of national caregivers when confronted with large-scale societal violence. In Iraq, although the security situation has improved from its nadir in 2006-2007, intermittent bombings, and other hostilities continue. National workers remain the primary health resource for the affected populace.ProblemTo assess the status and challenges of national physicians working in the Emergency Departments of an active conflict area.MethodsThis study was a survey of civilian Iraqi doctors working in Emergency Departments (EDs) across Iraq, via a convenience sample of physicians taking the International Medical Corps (IMC) Doctor Course in Emergency Medicine, given in Baghdad from December 2008 through August 2009.ResultsThe 148 physician respondents came from 11 provinces and over 50 hospitals in Iraq. They described cardiovascular disease, road traffic injuries, and blast and bullet injuries as the main causes of death and reasons for ED utilization. Eighty percent reported having been assaulted by a patient or their family member at least once within the last year; 38% reported they were threatened with a gun. Doctors reported seeing a median of 7.5 patients per hour, with only 19% indicating that their EDs had adequate physician staffing. Only 19% of respondents were aware of an established triage system for their hospital, and only a minority had taken courses covering ACLS- (16%) or ATLS-related (24%) material. Respondents reported a wide diversity of prior training, with only 3% having some type of specialized emergency medicine degree.ConclusionsThe results of this study describe some of the challenges faced by national health workers providing emergency care to a violence-stricken populace. Study findings demonstrate high levels of violent behavior directed toward doctors in Iraqi Emergency Departments, as well as staffing shortages and a lack of formal training in emergency medical care.Donaldson RI, Shanovich P, Shetty P, Clark E, Aziz S, Morton M, Hasoon T, Evans G. A survey of national physicians working in an active conflict zone: the challenges of emergency medical care in Iraq. Prehosp Disaster Med. 2012;27(2):1-9.

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Detection of Botnet Based Attacks on Network

Handbook of Research on Network Forensics and Analysis Techniques - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-4100-4.ch007 ◽

2018 ◽

pp. 101-116

Author(s):

Prachi

Keyword(s):

Large Scale ◽

Flow Analysis ◽

Traffic Analysis ◽

High Accuracy ◽

Machine Learning Techniques ◽

Botnet Detection ◽

Learning Techniques ◽

Proposed Model ◽

Benchmark Datasets ◽

Traffic Flow Analysis

This chapter describes how with Botnets becoming more and more the leading cyber threat on the web nowadays, they also serve as the key platform for carrying out large-scale distributed attacks. Although a substantial amount of research in the fields of botnet detection and analysis, bot-masters inculcate new techniques to make them more sophisticated, destructive and hard to detect with the help of code encryption and obfuscation. This chapter proposes a new model to detect botnet behavior on the basis of traffic analysis and machine learning techniques. Traffic analysis behavior does not depend upon payload analysis so the proposed technique is immune to code encryption and other evasion techniques generally used by bot-masters. This chapter analyzes the benchmark datasets as well as real-time generated traffic to determine the feasibility of botnet detection using traffic flow analysis. Experimental results clearly indicate that a proposed model is able to classify the network traffic as a botnet or as normal traffic with a high accuracy and low false-positive rates.

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High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets

10.5194/acp-2019-32 ◽

2019 ◽

Author(s):

Daoyuan Yang ◽

Shaojun Zhang ◽

Tianlin Niu ◽

Yunjie Wang ◽

Honglei Xu ◽

...

Keyword(s):

Urban Area ◽

Real World ◽

Vehicle Emissions ◽

Large Scale ◽

Emission Inventory ◽

Road Traffic ◽

Dispersion Model ◽

Traffic Monitoring ◽

Atmospheric Pollutants ◽

Link Model

Abstract. On-road vehicle emissions are a major contributor to elevated air pollution levels in populous metropolitan areas. We developed a link-level emissions inventory of vehicular pollutants, called EMBEV-Link, based on multiple datasets extracted from the extensive road traffic monitoring network that covers the entire municipality of Beijing, China (16 400 km2). We employed the EMBEV-Link model under various traffic scenarios to capture the significant variability in vehicle emissions, temporally and spatially, due to the real-world traffic dynamics and the traffic restrictions implemented by the local government. The results revealed high carbon monoxide (CO) and total hydrocarbon (THC) emissions in the urban area (i.e., within the Fifth Ring Road) and during rush hours, both associated with the passenger vehicle traffic. By contrast, considerable fractions of nitrogen oxides (NOX), fine particulate matter (PM2.5) and black carbon (BC) emissions were present beyond the urban area, as heavy-duty trucks (HDTs) were not allowed to drive through the urban area during daytime. The EMBEV-Link model indicates that non-local HDTs could for 29 % and 38 % of estimated total on-road emissions of NOX and PM2.5, which were ignored in previous conventional emission inventories. We further combined the EMBEV-Link emission inventory and a computationally efficient dispersion model, RapidAir®, to simulate vehicular NOX concentrations at fine resolutions (10 m × 10 m in the entire municipality and 1 m × 1 m in the hotspots). The simulated results indicated a close agreement with ground observations and captured sharp concentration gradients from line sources to ambient areas. During the nighttime when the HDT traffic restrictions are lifted, HDTs could be responsible for approximately 10 μg m−3 of NOX in the urban area. The uncertainties of conventional top-down allocation methods, which were widely used to enhance the spatial resolution of vehicle emissions, are also discussed by comparison with the EMBEV-Link emission inventory.

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Does changing to brighter road lighting improve road safety? Multilevel longitudinal analysis of road traffic collision frequency during the relighting of a UK city

Journal of Epidemiology & Community Health ◽

10.1136/jech-2019-212208 ◽

2020 ◽

Vol 74 (5) ◽

pp. 467-472 ◽

Cited By ~ 3

Author(s):

Paul Marchant ◽

James David Hale ◽

Jon Paul Sadler

Keyword(s):

Road Safety ◽

Large Scale ◽

Road Traffic ◽

Multilevel Modelling ◽

Personal Injury ◽

Light Emitting ◽

Road Lighting ◽

Road Traffic Collision ◽

White Light Emitting Diodes ◽

The City

BackgroundA step change in the night environment is taking place, with the large-scale installation of bright, broad-spectrum road lighting such as white light-emitting diodes (LEDs). One justification for this is a reduction in road traffic collisions (RTCs). This study aimed to estimate the effect of new lighting on personal injury RTCs within a large UK city.MethodsWe analysed a 9-year time series of weekly RTC personal injury counts in 132 areas of the city using multilevel modelling. The RTC rate over a full 24-hour period was the primary outcome; darkness and daylight RTC rates were secondary. The background change in RTC rate was separated from the change associated with the number of newly installed bright lamps by including a polynomial underlying time trend for the logarithm of the mean number of collisions per week for each area. The study was based on a rigorous, predesigned and archived protocol.ResultsWithin-area coefficients for the broad lighting effect were positive; as the number of bright lamps in an area increased, so did the RTC rate. The estimate for the increase in the within-area 24-hour RTC rate is 11% (95% CI 2% to 20%). The estimate of darkness-only RTCs is 16% (95% CI 2% to 32%). If the effect of lighting on darkness RTC rate is adjusted by that for daylight, one obtains 4% (95% CI −12% to +23%).ConclusionNo evidence was found for bright lamps leading to an improvement in road safety in any of the analyses. For this city, introducing brighter road lighting may have compromised safety rather than reducing harm.

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