Multilevel and holonic model for dynamic holarchy management: Application to large-scale road traffic

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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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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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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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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A hybrid gravity and route choice model to assess vector traffic in large-scale road networks

Royal Society Open Science ◽

10.1098/rsos.191858 ◽

2020 ◽

Vol 7 (5) ◽

pp. 191858

Author(s):

S. M. Fischer ◽

M. Beck ◽

L.-M. Herborg ◽

M. A. Lewis

Keyword(s):

Large Scale ◽

Road Traffic ◽

Choice Model ◽

Route Choice ◽

Gravity Models ◽

Sampling Effort ◽

Long Distance ◽

General Terms ◽

Rigorous Model ◽

Stochastic Gravity

Human traffic along roads can be a major vector for infectious diseases and invasive species. Though most road traffic is local, a small number of long-distance trips can suffice to move an invasion or disease front forward. Therefore, understanding how many agents travel over long distances and which routes they choose is key to successful management of diseases and invasions. Stochastic gravity models have been used to estimate the distribution of trips between origins and destinations of agents. However, in large-scale systems, it is hard to collect the data required to fit these models, as the number of long-distance travellers is small, and origins and destinations can have multiple access points. Therefore, gravity models often provide only relative measures of the agent flow. Furthermore, gravity models yield no insights into which roads agents use. We resolve these issues by combining a stochastic gravity model with a stochastic route choice model. Our hybrid model can be fitted to survey data collected at roads that are used by many long-distance travellers. This decreases the sampling effort, allows us to obtain absolute predictions of both vector pressure and pathways, and permits rigorous model validation. After introducing our approach in general terms, we demonstrate its benefits by applying it to the potential invasion of zebra and quagga mussels ( Dreissena spp.) to the Canadian province British Columbia. The model yields an R 2 -value of 0.73 for variance-corrected agent counts at survey locations.

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Moving Towards Electrification of Workers’ Transportation: Identifying Key Motives for the Adoption of Electric Vans

Sustainability ◽

10.3390/su11143878 ◽

2019 ◽

Vol 11 (14) ◽

pp. 3878

Author(s):

Jon Martin Denstadli ◽

Tom Erik Julsrud

Keyword(s):

Decision Process ◽

Large Scale ◽

Commercial Vehicle ◽

Road Traffic ◽

Web Survey ◽

Policy Makers ◽

Car Market ◽

Low Emission ◽

Commercial Market ◽

Logistic Regressions

The large-scale diffusion of low-emission vehicles is required to increase the sustainability of the transport system. Statistics show strong and continued growth in the sales of electric and other low-emission vehicles in the passenger car market. The commercial market, however, has thus far been a different story, despite the fact that vans and other utility vehicles constitute an increasing share of total road traffic and emissions. The present study investigates the potential for increasing the adoption of electric vans (e-vans) among small- and medium-sized enterprises (SMEs). Data gathered in a web survey of 264 SME managers show that 25% of the managers expressed intentions to adopt e-vans within the next two years and another 27% within the next five years. Results from logistic regressions show that a combination of attributes related to the vehicle, the firm and the firm-environment relationships drives adoption intentions. Costs and vehicle reliability are typically important drivers of commercial vehicle purchases. E-vans, however, bring symbolic features into the decision process since they are seen as a measure to improve the green legitimacy of the enterprise. Various measures relevant to manufacturers/dealers and policy makers to stimulate the adoption of e-vans are discussed.

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An epidemiological study of road traffic accidents among patients admitted in a tertiary care hospital in Puducherry

International Journal of Community Medicine and Public Health ◽

10.18203/2394-6040.ijcmph20183062 ◽

2018 ◽

Vol 5 (8) ◽

pp. 3362

Author(s):

Muthukumar T. ◽

Zile Singh ◽

Vishnu Prasad R. ◽

Abel K. Samuel ◽

Raja T. K.

Keyword(s):

Large Scale ◽

Traffic Accidents ◽

Road Traffic ◽

Tertiary Care Hospital ◽

Seat Belt ◽

Tertiary Care ◽

Public Health Problem ◽

Care Hospital ◽

Road Traffic Accidents ◽

Modifiable Factors

Background: Road traffic accidents (RTA) are the only public health problem for which society and decision makers still accept death and disability among young people on a large scale. This study was undertaken to determine the various epidemiological factors influencing RTA with a view to suggest appropriate remedial measures for prevention of accidents.Methods: A hospital based prospective observational study was conducted at a tertiary care hospital in Puducherry. All the patients admitted in the hospital due to RTA from Jan to Dec 2014 were interviewed by using a pre-tested and pre-designed questionnaire. Data was entered in MS Excel and analysed using SPSS version 21.0. Institute ethics committee clearance was obtained.Results: Among 541 participants, 50% of the victims were in the productive age group of 20-40 yrs. The approximate speed of majority of the vehicles (55.6%) at the time of accident was 40-80 km/hr. Among the accident victims, 85.8% of the two wheeler riders did not use helmet and 70.9% of the car drivers did not wear seat belt at the time of accident. Around 39% of the drivers were under the influence of alcohol at the time of accident. Mobile phone is the major cause of distraction (51.9%) at the time of accident.Conclusions: Based on this study it is evident that prevention and control of most of the modifiable factors can bring about a change in the statistics that would ensure our future to be safer.

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Comparison of Logistics Time of R3A and R3B for Transportation of Lychee Product

International Journal of Geoinformatics ◽

10.52939/ijg.v17i6.2059 ◽

2021 ◽

pp. 15-25

Keyword(s):

Traffic Congestion ◽

Road Traffic ◽

Prediction Method ◽

Transportation Safety ◽

Traffic Density ◽

Traffic Data ◽

Management Application ◽

Heavy Burden ◽

Chinese Markets ◽

Local Farmers

This research presents the logistics management information system (LMIS) for the supply chain of lychee products of Phayao Province, Thailand. The main aim of this research is to develop a management application for Phayao’s agricultures to improve their competitive abilities on Chinese markets by utilizing a prediction method for traffic congestion based on both real-time and anticipated road traffic. The loss of productivity caused by traffic congestion has become a huge and increasingly heavy burden on Phayao farmers. Therefore, the prediction of urban road network traffic flow and the rapid and accurate evaluation of traffic congestion is of great significance to solve this problem. By using traffic data obtained by distance, road conditions, transportation safety, traffic density, and customs clearance, the local farmers in Phayao can deliver lychee products on time and reduce the loss of high emissions and environmental pollution caused by traffic congestion effectively.

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