Transportation hazard spatial analysis using crowd-sourced social network data

Ghandour, Ali; Hammoud, Huda; Telesca, Luciano

doi:10.1016/j.physa.2019.01.025

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…The primary source of data used in this study was the national road crash database procured from the Lebanese Road Accidents Platform (LRAP). The LRAP database compiles national traffic data by crowdsourcing reported road crashes in Lebanon from social media consolidated mainly from three credible sources: Traffic Management Authority, Civil Defense, and Lebanese Red Cross, intending to study crash characteristics and contributing factors [18]. The database encompasses 8482 crash records spanning over a 4-year period from February 2015 to February 2019.…”

Section: Data Descriptionmentioning

confidence: 99%

Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach

Ghandour

Hammoud

Al-Hajj

2020

IJERPH

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Road traffic injury accounts for a substantial human and economic burden globally. Understanding risk factors contributing to fatal injuries is of paramount importance. In this study, we proposed a model that adopts a hybrid ensemble machine learning classifier structured from sequential minimal optimization and decision trees to identify risk factors contributing to fatal road injuries. The model was constructed, trained, tested, and validated using the Lebanese Road Accidents Platform (LRAP) database of 8482 road crash incidents, with fatality occurrence as the outcome variable. A sensitivity analysis was conducted to examine the influence of multiple factors on fatality occurrence. Seven out of the nine selected independent variables were significantly associated with fatality occurrence, namely, crash type, injury severity, spatial cluster-ID, and crash time (hour). Evidence gained from the model data analysis will be adopted by policymakers and key stakeholders to gain insights into major contributing factors associated with fatal road crashes and to translate knowledge into safety programs and enhanced road policies.

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Section: Data Descriptionmentioning

confidence: 99%

Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach

Ghandour

Hammoud

Al-Hajj

2020

IJERPH

Self Cite

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“…For example, Huang [21] used a hazard graph model to study the spatial pattern of flood impact on urban mobility performance using space syntax theory. Ghandour et al [22] provided a visual analysis of safety hazards on the roads network in Lebanon using spatial autocorrelation and spatial clustering theories such as the Global G method, Getis-Ord, and Hot Spot Analysis. Ahmed [15] used network analysis to identify the best route from an incident to any healthcare service providers in the Greater Cairo metropolitan area.…”

Section: Network Analysis: Best Route Analysismentioning

confidence: 99%

Impact of the Mobility Restrictions in the Palestinian Territory on the Population and the Environment

Aburas

Shahrour

2021

Sustainability

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This paper analyzes the mobility restrictions in the Palestinian territory on the population and the environment. The literature review shows a scientific concern for this issue, with an emphasis on describing mobility barriers and the severe conditions experienced by the population due to these barriers as well as the impact of mobility restrictions on employment opportunities. On the other hand, the literature review also shows a deficit in quantitative analysis of the effects of mobility restrictions on the environment, particularly on energy consumption and greenhouse gas emissions. This paper aims to fill this gap through a quantitative analysis by including data collection about mobility restrictions, using network analysis to determine the impact of these restrictions on inter-urban mobility, and analysis of the resulting energy consumption and CO2 emissions. The results show that mobility restrictions induce a general increase in energy consumption and CO2 emissions. The average value of this increase is about 358% for diesel vehicles and 275% for gasoline vehicles.

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“…Iran with 6.7 of total vehicles in 2013, among 175 counties, is in the rank of 110 (Poormoalem & Ghorbani, 2015;WHO, 2009). Use of hazard index for safety analysis has been vastly performed previously (Ghandour, Hammoud, & Telesca, 2019;Liu & Liao, 2019;Matsuoka & Amai, 2019). In a research conducted in Manizales county of Colombia, hazard index of main factors for traffic accidents including non-motor users were analysed.…”

Section: Rate Of Changes Of Index Of Dead To Ten Thousand Vehiclesmentioning

confidence: 99%

Use of Hazard Indices for Inter-City Recreational Accident Occurrence Through in Order to Analyze Road Safety Status

2020

IJTTE

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Considering the high number of inter-city recreational travels during Nowruz holidays in Iran and the changes in travel patterns, these holidays are a potential high-risk period. In this research, hazard indices of accident occurrence during these holidays through three consecutive years of 2013, 2014, and 2015 in inter-city roads, for different provinces of the country were analysed and computed. Considering hazardous travels of 20-days of Nowruz holidays, accident analysis in these days and comparing them with other days of the year, in order to identify hazardous provinces (hot spots), can have an important role in executive decision makings in the country. For these objectives, initially, injury rate has been evaluated using injury reduction equation, then hazardous provinces based on different methods were identified, and lastly perceived causes of accidents during these holidays were investigated. The results suggest that traffic exposure, accident risk and total number of traffic injuries have been decreased annually, however, injury rates per accident has been increased possibly due to the worsening of road safety conditions. Furthermore, analysis of hazard indices showed that severity-rate and crash rate methods were consistent and both pointed out Kerman to be the most high-risk province in the country during Nowruz holidays. Moreover, among the perceived causes, front carelessness was the major reason of traffic accidents during Nowruz holidays.

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Transportation hazard spatial analysis using crowd-sourced social network data

Cited by 8 publications

References 8 publications

Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach

Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach

Impact of the Mobility Restrictions in the Palestinian Territory on the Population and the Environment

Use of Hazard Indices for Inter-City Recreational Accident Occurrence Through in Order to Analyze Road Safety Status

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