Identifying and Ranking Dangerous Road Segments a Case of Hawassa-Shashemene-Bulbula Two-Lane Two-Way Rural Highway, Ethiopia

Abebe, Mandefro Terefe; Belayneh, Moltot Zewdie

doi:10.4236/jtts.2018.83009

Cited by 5 publications

(8 citation statements)

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“…Contrary to this problem, however, only few studies were carried out in Ethiopia, in this regard, the Berhanu's dissertation on the effect of road and traffic factors on road safety in the selected main road sites and junctions in the city of Addis Ababa and along Addis Ababa-Nazareth (or Adama) two-lane rural road in 2000 could be worth mentioning. In addition to this, even though dangerous road segments have been identified and ranked on Hawassa-Shashemene-Bulbula two-lane rural highway with total length of 64.5 km, in our previous publication [4], the influence of road section parameters on road safety have not been correlated along this study area even by any other study. Therefore, due attention should be given to identify the broader underlying inter-related factors contributing to the rising magnitude and burden of road traffic accidents in Ethiopia.…”

Section: Introductionmentioning

confidence: 64%

“…The general formula for the accident prediction model, E(k), is composed of three components: 1) base model, 2) accident modification factors, and 3) calibration factor as given by [2]. The HSM Safety Performance Function (SPF) for base condition on rural two-lane two-way highway was calibrated for the local condition and also the fitness of SPF with the local data was confirmed in [4] along the same study area of this study. Accordingly, the predicted number of total roadway accidents per year after the application of calibration factor that has been fitted for the local condition and can be estimated by using Equation…”

Section: Methodsmentioning

confidence: 99%

“…Secondary data: expected number of accidents and other related data obtained from published article [4], road as built drawing and average Annual Daily Traffic (AADT) collected from Ethiopian Road Authority (ERA).…”

Section: Sources Of Datamentioning

confidence: 99%

“…Since the expected number of accidents with the empirical Bayes (EB) adjustments have been estimated by using road, three years (2012/13-2014/15) traffic accidents and traffic volumes (in terms of AADT) data in our previous publication [4], it has been used for this research as a secondary data.…”

Section: Traffic Accident Datamentioning

confidence: 99%

“…The expected number of accidents (i.e. EB safety estimate), which were estimated to be the weighted average of observed and predicted number of accidents, for the ranked dangerous road segments are presented in Table 1 [4].…”

Section: Observed Predicted and Expected Number Of Accidents For Thementioning

confidence: 99%

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Quantifying the Influence of Road Geometric Parameters on Road Safety (Case Study: Hawassa-Shashemene-Bulbula Rural Two-Lane Highway, Ethiopia)

Abebe¹

2019

JTTs

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Road safety depends on humans, vehicles, and highway conditions. These factors influence road safety separately or in combination. Despite these facts, the police report indicated that only a single cause of accident has been reported for a given accident in Ethiopia and the number of accidents that had been caused by road defect accounted around 1% of total accidents per year, which underestimates its contribution to road accidents. Beside these facts, only a few studies were carried out in Ethiopia on this issue. Even though dangerous road segments were identified and ranked along the same study area of this paper in our previous publication, Hawassa-Shashemene-Bulbula two-lane rural highway, the influences of road geometric parameters on road safety were not correlated along this study area. Therefore, it is imperative to clarify the relationship between the expected number of traffic accidents and the geometric conditions of the road under study to reduce accidents and provide safe driving environment. The main objective of this study was to quantify the influence of road geometric parameters on road safety. Using historical accident data, traffic data, and road data, the expected numbers of accidents were estimated by using the empirical Bayes (EB) method. Then, the expected numbers of accidents have been correlated with one or more road geometric parameters at a time. Accordingly, the identified dangerous road segments in the previous publication

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Section: Introductionmentioning

confidence: 64%

Section: Methodsmentioning

confidence: 99%

Section: Sources Of Datamentioning

confidence: 99%

Section: Traffic Accident Datamentioning

confidence: 99%

Section: Observed Predicted and Expected Number Of Accidents For Thementioning

confidence: 99%

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Quantifying the Influence of Road Geometric Parameters on Road Safety (Case Study: Hawassa-Shashemene-Bulbula Rural Two-Lane Highway, Ethiopia)

Abebe¹

2019

JTTs

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Effectiveness of road safety interventions: An evidence and gap map

Goel,

Tiwari,

Varghese

et al. 2024

Campbell Systematic Reviews

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BackgroundRoad Traffic injuries (RTI) are among the top ten leading causes of death in the world resulting in 1.35 million deaths every year, about 93% of which occur in low‐ and middle‐income countries (LMICs). Despite several global resolutions to reduce traffic injuries, they have continued to grow in many countries. Many high‐income countries have successfully reduced RTI by using a public health approach and implementing evidence‐based interventions. As many LMICs develop their highway infrastructure, adopting a similar scientific approach towards road safety is crucial. The evidence also needs to be evaluated to assess external validity because measures that have worked in high‐income countries may not translate equally well to other contexts. An evidence gap map for RTI is the first step towards understanding what evidence is available, from where, and the key gaps in knowledge.ObjectivesThe objective of this evidence gap map (EGM) is to identify existing evidence from all effectiveness studies and systematic reviews related to road safety interventions. In addition, the EGM identifies gaps in evidence where new primary studies and systematic reviews could add value. This will help direct future research and discussions based on systematic evidence towards the approaches and interventions which are most effective in the road safety sector. This could enable the generation of evidence for informing policy at global, regional or national levels.Search MethodsThe EGM includes systematic reviews and impact evaluations assessing the effect of interventions for RTI reported in academic databases, organization websites, and grey literature sources. The studies were searched up to December 2019.Selection CriteriaThe interventions were divided into five broad categories: (a) human factors (e.g., enforcement or road user education), (b) road design, infrastructure and traffic control, (c) legal and institutional framework, (d) post‐crash pre‐hospital care, and (e) vehicle factors (except car design for occupant protection) and protective devices. Included studies reported two primary outcomes: fatal crashes and non‐fatal injury crashes; and four intermediate outcomes: change in use of seat belts, change in use of helmets, change in speed, and change in alcohol/drug use. Studies were excluded if they did not report injury or fatality as one of the outcomes.Data Collection and AnalysisThe EGM is presented in the form of a matrix with two primary dimensions: interventions (rows) and outcomes (columns). Additional dimensions are country income groups, region, quality level for systematic reviews, type of study design used (e.g., case‐control), type of road user studied (e.g., pedestrian, cyclists), age groups, and road type. The EGM is available online where the matrix of interventions and outcomes can be filtered by one or more dimensions. The webpage includes a bibliography of the selected studies and titles and abstracts available for preview. Quality appraisal for systematic reviews was conducted using a critical appraisal tool for systematic reviews, AMSTAR 2.Main ResultsThe EGM identified 1859 studies of which 322 were systematic reviews, 7 were protocol studies and 1530 were impact evaluations. Some studies included more than one intervention, outcome, study method, or study region. The studies were distributed among intervention categories as: human factors (n = 771), road design, infrastructure and traffic control (n = 661), legal and institutional framework (n = 424), post‐crash pre‐hospital care (n = 118) and vehicle factors and protective devices (n = 111). Fatal crashes as outcomes were reported in 1414 records and non‐fatal injury crashes in 1252 records. Among the four intermediate outcomes, speed was most commonly reported (n = 298) followed by alcohol (n = 206), use of seatbelts (n = 167), and use of helmets (n = 66). Ninety‐six percent of the studies were reported from high‐income countries (HIC), 4.5% from upper‐middle‐income countries, and only 1.4% from lower‐middle and low‐income countries. There were 25 systematic reviews of high quality, 4 of moderate quality, and 293 of low quality.Authors' ConclusionsThe EGM shows that the distribution of available road safety evidence is skewed across the world. A vast majority of the literature is from HICs. In contrast, only a small fraction of the literature reports on the many LMICs that are fast expanding their road infrastructure, experiencing rapid changes in traffic patterns, and witnessing growth in road injuries. This bias in literature explains why many interventions that are of high importance in the context of LMICs remain poorly studied. Besides, many interventions that have been tested only in HICs may not work equally effectively in LMICs. Another important finding was that a large majority of systematic reviews are of low quality. The scarcity of evidence on many important interventions and lack of good quality evidence‐synthesis have significant implications for future road safety research and practice in LMICs. The EGM presented here will help identify priority areas for researchers, while directing practitioners and policy makers towards proven interventions.

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Network Screening on Low-Volume Roads using Risk Factors

Huda,

Al-Kaisy

2023

Preprint

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This paper proposes a new method for network screening on rural low-volume roads. These roads are important as they provide critical access to agricultural land and tourist attractions. Most low-volume roads belong to the lowest functional class (local rural roads) and thus are built to lower design standards. The conventional hot spot network screening techniques may not be appropriate for low-volume roads due to the sporadic nature of crashes occurring on these roads. Contrarily sophisticated network screening approaches require extensive roadway and traffic data that are often unavailable to local agencies for lack of resources, and/or lack of technical expertise. This research attempts to address these obstacles in low-volume roads network screening which aims to identify candidate sites for safety improvements. The research used an extensive low-volume road sample from the state of Oregon and the Empirical Bayes expected number of crashes in developing the proposed models for network screening. The proposed models do not require exact measurement of roadway geometric features as all geometric variables were classified into categories that are easy to compile by local agencies. Further, the method could be used with and without traffic data, without compromising the effectiveness of the network screening process.

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Identifying and Ranking Dangerous Road Segments a Case of Hawassa-Shashemene-Bulbula Two-Lane Two-Way Rural Highway, Ethiopia

Cited by 5 publications

References 14 publications

Quantifying the Influence of Road Geometric Parameters on Road Safety (Case Study: Hawassa-Shashemene-Bulbula Rural Two-Lane Highway, Ethiopia)

Quantifying the Influence of Road Geometric Parameters on Road Safety (Case Study: Hawassa-Shashemene-Bulbula Rural Two-Lane Highway, Ethiopia)

Effectiveness of road safety interventions: An evidence and gap map

Network Screening on Low-Volume Roads using Risk Factors

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