A new approach for resource allocation for black spot treatment (case study: The road network of Iran)

Abdolmanafi, Seyed Ebrahim; Karamad, Sina

doi:10.1016/j.jsr.2019.03.001

Cited by 9 publications

(5 citation statements)

References 10 publications

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“…For a more critical study, a more thorough investigation of the causes and a thorough examination of efficient countermeasures are required. In order to successfully address accident black spots and improve road safety, future research and assessment should concentrate on collecting empirical data, conducting indepth analyses, and offering specific and evidence-based measures (McGuigan, 1981;Elvik, 2007;Fekadu, & Wuezon, 2013;Ghadi & Török, 2019;Abdolmanafi, & Karamad, 2019;Elena, Sergei, & Jarosław, 2020).…”

Section: Causes Of Black Spotsmentioning

confidence: 99%

Assessing Road Accident Black Spots and Countermeasures in the State of Perak, Malaysia

Hussein,

Abd Rahman,

Mohd Saim

et al. 2023

IJSCET

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To develop a successful strategy for improving road safety, the initial step involves is to identify the black spots that are more susceptible to high-risk traffic accidents. However, due to the high cost of implementing safety measures in these locations, only a limited number of spots/sites can be thoroughly examined with the available funding. Therefore, it is crucial to prioritize the high-risk sites and their corresponding safety measures in order to make the most efficient use of the available finances. Hence, this paper presents black spots ranking and low-cost countermeasures based on the Perak Federal trunk road traffic crash (RTC) data for three years from 2018 to 2020 recorded by the Royal Malaysian Police (RMP). The weightage point method was used for each black spot ranking exercise where as, the selection for countermeasures were based on RTC situations which are norms practiced by the Public Works Department (PWD). In total 22 black spots were ranked and it was found that the highest rank of the 2020 black spots in Perak was along route FT005 in Kinta district which had 18 RTCs and with a total weightage points (TWP) of 118.2. In balancing between limited financial allocation and improving road safety, the countermeasures were priorotized for implementation in 2021 based on the black spots ranking results. However, only six black spots were treated in 2021 with the entire cost was RM 1,834,027.19. Where for route FT005 in Kinta district, the countermeasure was done at Section 684 with a total cost of RM350,000 under the 2022 black spots countermeasure program which is an annual program funded by the Ministry of Works Malaysia. It is recommended that Perak State PWD form a Road Safety Unit to reduce the black spots along the state roads which is the second highest road fatalities recorded in Perak.

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Section: Causes Of Black Spotsmentioning

confidence: 99%

Assessing Road Accident Black Spots and Countermeasures in the State of Perak, Malaysia

Hussein,

Abd Rahman,

Mohd Saim

et al. 2023

IJSCET

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“…The concept of using the weight connection to determine the relevance of predicting variables in an ANN model was initially proposed by Garson [45]. The overall absolute weight contribution fraction of the variables with regard to each neuron was calculated using the Garson algorithm to determine the relative importance of the predicting variable, as indicated in Equation (7). Olden and colleagues later claimed that the net sum of the weight contribution for each predicting variable across the neurons yielded more accurate rankings [44], as given in Equation ( 8).…”

Section: Relative Importance Analysis and Rankingmentioning

confidence: 99%

“…Up to 1.3 million people lose their lives, and another 50 million sustain serious injuries due to road accidents each year, and the numbers keep increasing [6]. Annual road accidents (ARAs), which cause serious and irreparable economic and social harm to nations, are thus one of the main concerns of transportation managers around the world [7]. Successful implementation of road infrastructural safety management requires sufficient funding in addition to regulations, institutional capacity, and the availability of safety data [8].…”

Section: Introductionmentioning

confidence: 99%

“…Road management authorities are faced with the difficult task of identifying and prioritizing new projects and maintenance actions. Several optimization tools for resource allocation in road asset management have been adopted for efficient and well-informed decision-making processes [7,[9][10][11][12][13]. Due to budget constraints, most of these optimization tools focus on the best alternative solutions within available budget constraints [14].…”

Section: Introductionmentioning

confidence: 99%

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Road Infrastructure Investment Limits Based on Minimal Accidents Using Artificial Neural Network

2022

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Road traffic accidents are still among the top major global causes of death, injury, and disability. Despite this cause for alarm and several preventive initiatives, global road accident statistics are not improving. This study modeled annual road accidents (ARAs) as a function of demographic, economic, passenger movement, freight movement, and road capital investment indicators. The research is based on 22 years of data from more than 36 Organization for Economic Co-operation and Development (OECD) member and partner countries. Artificial neural network (ANN), multiple linear regression (MLR), and Poisson regression (PR) analysis were employed for this purpose. The ANN model outperformed the regression models by far, thus making it possible for reliable new insights and accurate results to be obtained. The ANN’s superior performance was shown to be a result of the non-linear relationship between ARA and some of the predicting variables. The average relative contribution of each variable in describing the ARA models was estimated using connection weight analysis (from the ANN model) and relative weight analysis for the regression model. The profile method was used to perform sensitivity analysis and to establish the partial variation trend of the ARA with each of the variables. The Existing Road Maintenance Investment (ERMI) and New Road Infrastructural Investment (NRII) showed a nonlinear concave-up relationship with ARA for given demography, economy, freight, and passenger movements. A combination of per capita NRII and ERMI corresponding to the minimum ARA exists. These sets of NRII and ERMI were considered safe road investment limits. The ANN-ARA model was utilized to estimate these limits with their relative proportion for diverse combinations of demography, economy, freight level, and passenger movement.

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“…According to the report published by the World Health Organization in 2018, the death rate due to traffic accidents is 18,2 per 100,000 people. Traffic accidents, which rank eighth as the cause of death worldwide, show that it is one of the important issues that should be taken into consideration (Briz-Redón, Martínez-Ruiz and Montes, 2019;Casado-Sanz and Guirao, 2018;Abdolmanafi and Karamad, 2019). According to the report of the World Health Organization, it is predicted that by 2030, traffic accidents, which are the eighth causes of death, will rise to the fifth place (Bhavan, 2019).…”

Section: Introductionmentioning

confidence: 99%

Trafi̇k Kazalarinin Zamansal Ve Mekânsal İncelenmesi̇: Eski̇şehi̇r Şehi̇r Örneği̇

ZERENOĞLU¹,

HAYBAT²

2021

Lnternational Journal of Geography and Geography Education

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Türkiye'de şehir nüfusu her geçen yıl artmaktadır. Nüfusu her yıl artan şehirlerden biri de Eskişehir'dir. Şehirdeki nüfusun artması aynı zamanda araç kullanımını arttırmaktadır. Araç ve sürücü sayısındaki artış trafikte problemlerin ortaya çıkmasına neden olmaktadır. Ortaya çıkan bu problemlerden bir tanesi de trafik kazalarıdır. Gerçekleştirilen bu çalışma ile birlikte Eskişehir'in iki merkez ilçesinde meydana gelen trafik kazaları zamansal ve mekânsal olarak incelenmiştir. Bu merkez ilçelerin seçilmesindeki sebepler ise nüfusun ve trafik akışının bu alan içerisinde yoğunluk göstermesidir. Araştırmada kullanılan veriler; 2016-2017 yılına ait trafik kaza ve 2012 yılına ait arazi kullanım verileridir. Analizlerin ve bulguların tespit edilmesi için ArcGIS 10.6 versiyonunda iki araç kullanılmıştır. Bu araçlar; nokta yoğunluk ve toplam vaka araçlarıdır. Bu araçların kullanılmasındaki amaç trafik kazalarının zamansal ve mekânsal bağlantılarını ortaya çıkarmaktır. Zamansal bağlantılar içerisinde trafik kazaları; iki yıl, mevsim, ay, haftanın günleri ve saat olarak incelenmiştir. Mekânsal bağlantılar içerisinde ise ilçe, mahalle ve karayolları üzerinde gerçekleşen trafik kazaları incelenmiştir. Analizler sonucunda trafik kazalarının; yıl bakımından hangi alanlarda yoğunlaştığı, hangi mevsimlerde artış gösterdiği, hangi aylarda arttığı, haftanın hangi günlerinde artış gösterdiği, saatlere göre dağılımı, ilçelere göre dağılımı, mahallelere göre dağılımı, karayollarına göre dağılımı incelenmiş ve kara noktalar tespit edilmiştir. Zaman bakımından trafik kazaları; ay bazında en fazla temmuz, gün bazında pazartesi ve cuma günleri, saat bazında 12.00 -18.00 saatleri arasında gerçekleştiği çalışma sonucunda ortaya çıkarılmıştır. Mekân bakımından trafik kazaları; Odunpazarı ilçesindeki kazalar Tepebaşı ilçesine göre daha fazla gerçekleşmiştir. Mahalle bazındaki incelemelerde ise trafik kazaları en fazla Merkez Mahallesi'nde meydana geldiği tespit edilmiştir.

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A new approach for resource allocation for black spot treatment (case study: The road network of Iran)

Cited by 9 publications

References 10 publications

Assessing Road Accident Black Spots and Countermeasures in the State of Perak, Malaysia

Assessing Road Accident Black Spots and Countermeasures in the State of Perak, Malaysia

Road Infrastructure Investment Limits Based on Minimal Accidents Using Artificial Neural Network

Trafi̇k Kazalarinin Zamansal Ve Mekânsal İncelenmesi̇: Eski̇şehi̇r Şehi̇r Örneği̇

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