Road Network Safety Screening of County Wide Road Network. The Case of the Province of Brescia (Northern Italy)

Bonera, Michela; Barabino, Benedetto; Maternini, Giulio

doi:10.1007/978-3-030-79801-7_38

Cited by 5 publications

(5 citation statements)

References 24 publications

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“… [ 49 ] 2018 5 Injury Prediction Models for Onshore Road Network Development [ 50 ] 2019 4 Modeling Conflict Risk with Real-Time Traffic Data for Road Safety Assessment: A Copula-Based Joint Approach [ 51 ] 2022 4 Road Network Safety Screening of County Wide Road Network. The Case of The Province of Brescia (Northern Italy) [ 52 ] 2022 4 Application of a Crash-predictive Risk Assessment Model to Prioritise Road Safety Investment in Australia [ 53 ] 2016 3 Optimizing Road Safety Inspections on Rural Roads [ 54 ] 2023 3 Road Safety Analysis of High-Risk Roads: Case Study in Baja California, México [ 55 ] 2020 3 Road Safety Analysis on Achmad Yani Frontage Road Surabaya [ 56 ] 2017 2 Application and Evaluation of a Non-Accident-Based Approach to Road Safety Analysis Based on Infrastructure-Related Human Factors [ 57 ] 2022 1 Application of an Innovative Network Wide Road Safety Assessment Procedure Based on Human Factors [ 58 ] 2022 1 A Proactive Decision Support Tool for Road Safety Audit of New Highway Projects Based on Crash Modification Factors and Analytical Analysis: Algeria as a Case Study …”

Section: Resultsmentioning

confidence: 99%

“…In the list of papers in Table 6 there are many papers that develop CPMs with a statistical approach similar to those proposed by the worldly known Highway Safety Manual (HSM) [ 63 , 64 ]. Among those papers there are those from Wang et al [ 33 ], Kustra et al [ 50 ], Bonera et al [ 52 ], Llopis-Castellò et al [ 37 ], and many works from Ambros et al [ 41 , 46 , 47 , 49 , 50 , 65 , 66 ].…”

Section: Resultsmentioning

confidence: 99%

“…Bonera et al [ 52 ] developed their own CPM based on the HSM approach but using the data from the Province of Brescia (Northern Italy). Only few significant variables were finally included in the model to obtain the crash frequency values.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Systematic review, evaluation and comparison of different approaches for the implementation of road network safety analysis

Paliotto,

Meocci,

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et al. 2024

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Systematic review, evaluation and comparison of different approaches for the implementation of road network safety analysis

Paliotto,

Meocci,

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et al. 2024

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“…First, once the T r,j values have been computed for each path, they are sorted from the lowest to the highest. Then, as proposed by [41], thresholds are set based on the lower, the middle, and the upper quartiles (Q1 = 25th percentile, Q2= 50th percentile and Q3 = 75th percentile, respectively) of the related distribution. Next, the interquartile range (IQR) of the distributions of the sorted T r,j is also introduced, to enable the identification of the most critical paths.…”

Section: -Safety Screening Indicator Computationmentioning

confidence: 99%

A Straightforward Framework for Road Network Screening to Lombardy Region (Italy)

Bonera¹,

Barabino

Maternini

2022

Sustainability

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It is not possible to deal with sustainable mobility without considering road safety as a key element: Target 3.6 of the Sustainable Development Goals aims at halving the number of road deaths by 2030. To do so, further effort and effective tools are required for road authorities, to implement improvement measures and enhance road safety for all. Road network screening (RNS) is the first step of the whole Road Infrastructure Safety Management (RISM) System process. It is applied to a wide scale to assess the safety performance of the whole road network and identify the worst performing roads (or sites). The literature is quite rich with RNS models and methods, which have greatly improved, recently. Moreover, although many national frameworks on road safety have been issued over time, some barriers remain, specifically related to data quality, such as accurate crash location, which is mainly used to integrate crash data with other databases. In addition, most of these frameworks adopted partial indexes to identify black spots and presented results using fixed maps for visualization. This paper fills these gaps by the proposal of a straightforward operational framework to perform RNS, based on a simple and flexible rationale to integrate raw crash, traffic, and road data. Specifically, the framework: (i) manages crash location data, without relying on plane or geographical coordinates, which are missing or inaccurate and still are a crucial issue in many European countries such as Italy; (ii) adopts an adjusted accident cost rate index that integrates frequency and severity of crashes as well as a measurement of exposure; (iii) introduces variable maps that show the results at different jurisdiction levels. A relevant case study demonstrates the usefulness of this framework using 30 000+ crash data of the whole non-urban road network of the Lombardy Region (Northern Italy). Road authorities could adopt this framework to perform an accurate safety screening on the overall regional road network. Moreover, this framework could be implemented in a road traffic safety managerial system to better prioritise safety interventions within a tight budget and help achieve sustainable development targets.

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“…Accident prediction models such as these are often useful in developing crash modification factors [21] and understanding and prioritizing accident risk indicators [16]. Some crash prediction models (CPMs) are incredibly helpful tools for quantitative road safety analysis and can be applied to road network screening to identify the network's most important portions and to more effectively guide in-depth studies [22]. Other road accident predictors, such as vehicle ownership, per capita gross domestic product (GDP), and the relative usage of transport modes, have also been investigated [23].…”

Section: Introductionmentioning

confidence: 99%

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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Road Network Safety Screening of County Wide Road Network. The Case of the Province of Brescia (Northern Italy)

Cited by 5 publications

References 24 publications

Systematic review, evaluation and comparison of different approaches for the implementation of road network safety analysis

Systematic review, evaluation and comparison of different approaches for the implementation of road network safety analysis

A Straightforward Framework for Road Network Screening to Lombardy Region (Italy)

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

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