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Mendes, O.B.B.; Larocca, A.P.C.; Rodrigues Silva, K.; Pirdavani, A. Highway Safety Manual for Brazilian Multilane Highways. Encyclopedia. Available online: (accessed on 01 December 2023).
Mendes OBB, Larocca APC, Rodrigues Silva K, Pirdavani A. Highway Safety Manual for Brazilian Multilane Highways. Encyclopedia. Available at: Accessed December 01, 2023.
Mendes, Olga Beatriz Barbosa, Ana Paula Camargo Larocca, Karla Rodrigues Silva, Ali Pirdavani. "Highway Safety Manual for Brazilian Multilane Highways" Encyclopedia, (accessed December 01, 2023).
Mendes, O.B.B., Larocca, A.P.C., Rodrigues Silva, K., & Pirdavani, A.(2023, July 19). Highway Safety Manual for Brazilian Multilane Highways. In Encyclopedia.
Mendes, Olga Beatriz Barbosa, et al. "Highway Safety Manual for Brazilian Multilane Highways." Encyclopedia. Web. 19 July, 2023.
Highway Safety Manual for Brazilian Multilane Highways

This research assesses the performance of Highway Safety Manual (HSM) predictive models when applied to Brazilian highways. The research evaluates five rural multilane highways and calculates calibration factors (Cx) of 2.62 for all types of crashes and 2.35 for Fatal or Injury (FI) crashes. The Goodness of Fit measures show that models for all types of crashes perform better than FI crashes.

road safety highway safety manual transferability

1. Introduction

Road safety is a global concern that has prompted nations to implement measures to reduce the fatalities and injuries resulting from road crashes. Despite some success in reducing the number of deaths in road crashes [1], the problem persists, with the proportion of fatal crashes increasing in recent years, causing more than 15 deaths per 100 thousand inhabitants yearly [2]. This number is about three times higher for emerging countries than developed countries [3], which might be related to the rise in motorization across Latin American countries that has led to a significant increase in exposure to traffic risks [4].
Therefore, countries must devise strategies to decrease this figure, including implementing stricter regulations to manage key risk factors and allocating greater resources to initiatives and studies that enhance road safety. By comprehending the factors that significantly influence the likelihood of accidents, it becomes feasible to forecast the probability of their incidence [5][6][7]. Establishing standardized definitions and methodologies for collecting comprehensive data on accidents, risk factors, and exposure occurrence is imperative to facilitate global and regional comparisons [4]. As a result, there is a lack of uniformity in the organization and collection of crash data across different regions and municipalities within the country. Each state and municipality may have its system for collecting crash data, leading to inconsistencies and challenges in data management and analysis [4][8].
Despite a decrease in the total fatalities on federal highways in Brazil over the past ten years, there has been an alarming increase in the proportion of fatal crashes [2][9]. This discrepancy may be attributed to changes in the crash reporting system since 2015, particularly the introduction of self-reporting for non-injury crashes. This could have led to an underreported number of property damage only (PDO) crashes [8][10]. Additionally, Brazil’s technological backwardness resulting from the economic and political crisis that began in 2014 may have contributed to this trend [11]. Thereby, further investigation is needed to address road safety on Brazilian highways, including investments in infrastructure and technology for accident prevention [12].
Developing effective strategies to address road safety requires a comprehensive understanding of contributing factors, which can be achieved through data-driven approaches like safety performance functions (SPF). The Highway Safety Manual (HSM) offers predictive models that integrate SPF with crash modification factors to estimate the crash frequency and identify high-risk areas and scenarios. However, it is crucial to assess the transferability of HSM predictive models when applied to an international context, particularly on Brazilian highways where data availability is limited and local SPFs are lacking. The aim is to bridge this gap by evaluating the performance of HSM predictive models on Brazilian rural multilane highways, thereby contributing to developing effective road safety strategies and advancing the United Nations Sustainable Development Goals.

2. The Highway Safety Manual Predictive Model

The existing literature on crash prediction models primarily attributes crashes to inadequate driving performance with the demands of the road environment. Factors such as traffic flow, geometric attributes, road signs, and vehicle characteristics have been identified as contributing to this mismatch [13][14][15][16][17][18]. Moreover, SPFs have been developed to estimate crash rates within a specific timeframe or exposure [5][19][20][21][22][23][24]. These SPFs utilize statistical models that analyze risk indicators, including absolute numbers, frequency, and crash rates, as defined by Equation (1).
λ = N × p,
where λ is the expected crash number, N is the exposure, and p is the crash rate. The introduction of the HSM has provided a systematic approach to assessing crashes by employing analytical techniques and tools that quantify the impacts of road network planning, design, operation, and maintenance decisions. In research-based studies, the HSM has played a significant role in evaluating crashes.
The SPFs included in the HSM were developed using negative binomial (NB) regression models. These models were constructed using a generalized linear modeling (GLM) procedure, as outlined by Srinivasan et al. [25]. The SPFs consider both the infrastructure and operational characteristics.
Equation (2) illustrates how the predicted number of crashes (Npredicted) is determined using the SPF [26]. The SPF equation is specific to each facility, considering its base conditions, and adjusted by a calibration factor (Cx) and multiple crash modification factors (CMFs). Each CMF accounts for the operational and geometric characteristics (y) of the facility (x).
Npredicted = NSPFx × Cx × (CMF1x × CMF2x × … × CMFyx)
To determine Cx, Equation (3) provides the necessary calculation. The observed crashes are summed up across all sites and divided by the predicted crashes across all sites. The resulting Cx value is rounded to two decimal places and applied to the predictive model.
Cx = ∑ observed crashes/∑ predicted crashes
Calculating the corresponding Cx value for each facility type and year is advisable to customize the model. By substituting default values with locally derived values, the reliability of the predictive model can be improved. To apply this methodology, the HSM recommends a minimum desirable sample of 30 to 50 sites, representing at least 100 crashes annually [26]. Following the initial calibration, the HSM suggests utilizing the Empirical Bayes (EB) method to enhance the reliability of results and account for the regression-to-the-mean effect.
However, the model has limitations, particularly regarding its failure to consider speed limits. A study by Shirazinejad et al. [27] demonstrated that increasing the speed limit from 70 mph to 75 mph led to a significant 27% increase in total crashes and a notable 35% increase in fatal and injury crashes. Additionally, the HSM methodology fails to account for factors such as road infrastructure damage and unreasonable road design, all of which have been identified as impacting traffic safety [28].

3. Previous Studies on the Transferability of the HSM Model

Numerous studies have investigated the transferability and calibration of the HSM predictive model in different countries and regions. Over the past decade, researchers have explored the performance and parameters of the HSM model to assess its applicability and effectiveness in various contexts. The following studies shed light on the transferability and calibration challenges and the practical solutions and results in different countries.
Sun et al. conducted a statewide calibration of the HSM model for rural divided multilane highways in the US [29]. Their findings indicated that the HSM model reasonably predicted crashes in Missouri, with a calibration factor (Cx) of 0.98. In a study on rural two-lane roads in Arizona, Srinivasan et al. identified limitations in applying the HSM predictive models [30]. They emphasized the importance of gathering a larger sample and exploring the estimation of calibration functions to fit local data better. The overall calibration factor in this study was 1.079, indicating the success of the HSM model for US cases. D’Agostino examined the calibration factor for Italian motorways and found that the HSM model underestimated observed crash counts, with a Cx of 1.26 [31]. La Torre et al. concluded that a jurisdiction-specific base model derived from the HSM’s SPF provided a solid and reliable tool for crash prediction on the Italian freeway network [32].
In Brazilian studies, Rodrigues-Silva applied the HSM predictive model to two-lane highways in São Paulo State and found a calibration factor of 3.73 [33]. Barbosa et al. developed SPFs for intersections in Belo Horizonte, Brazil, with a calculated Cx of 2.06 [34]. Another study in Fortaleza city found a calibration factor of 0.65, highlighting the challenges in developing a nationwide SPF. Waihrich & Andrade investigated the calibration of the HSM model for multilane highways in the states of Minas Gerais and Goiás, Brazil. The resulting Cx values were 2.37 and 1.58 for each region, respectively, indicating a lack of transferability of the original HSM model in these scenarios [35]. Rodrigues-Silva compared the transferability between the HSM method and a local SPF for two-lane highways in different regions of Brazil. The calculated calibration factors were 3.67, 3.77, and 2.60 for São Paulo, Minas Gerais, and Paraná, respectively. This study highlighted the need for more parameters and knowledge in models for different facility types [36].
Studies conducted in Egypt by Elagamy et al. and in California, Maine, and Washington by Matarage & Dissanayake found that the HSM model overpredicted crash occurrences on multilane rural roads [37][38]. These studies emphasized the importance of considering local conditions and conducting calibration to improve the accuracy of predictions. Dadvar et al. proposed a method to adjust the HSM crash prediction model to provide a better fit for local data, as misallocating resources due to incorrect calibration factors can be problematic [39]. Al-Ahmadi et al. studied multilane rural highway segments in Saudi Arabia [40]. They found Cx values ranging from 0.63 to 0.78, emphasizing the need for in-depth local calibration and assessment of SPF quality. Researchers agree that the transferability of a model is dependent on the similarity of site characteristics to base conditions, and models must be built by associating regions with similar characteristics. The effectiveness of the local calibration factor as a method for transferring SPFs is widely discussed, considering socio-economic characteristics, traffic safety data distributions, and traffic flow influences on the transferability process. Kronprasert et al. compared different regression models for prediction accuracy, and the calibrated HSM SPF was the most effective model in predicting crashes on horizontal curve segments, underscoring its usefulness [41]. In a comprehensive overview, Heydari S. et al. [42] addressed road safety in low-income countries (LICs). They stressed the importance of accurate and complete road crash data for effective road safety interventions. They acknowledged that traditional sources such as police records suffer from varying levels of under-reporting, especially in LICs. They also emphasized the need to improve the quality and accuracy of road crash data through techniques like combining police and hospital records.
Countries like Brazil, with comprehensive databases integrating crash counts, traffic volume, and infrastructure data, must evaluate the performance of crash prediction models to shape investment planning strategies effectively. Conducting local calibration exercises considering regional peculiarities is crucial to enhance the transferability and precision of the HSM model across diverse countries and regions. These efforts aim to optimize the reliability of crash predictions, facilitating sustainable and informed interventions in transportation systems.

4. Goodness of Fit Measures

Assessing the accuracy of crash prediction models is essential in enhancing road safety measures. One approach to improve model performance is incorporating a local calibration factor (Cx) that considers the specific conditions of the target region. However, it is equally important to evaluate the model’s goodness of fit (GOF) and examine how well it aligns with observed data. In this regard, two widely used measures of forecast accuracy, the mean absolute percentage error (MAPE) and the mean absolute deviance (MAD), are commonly employed for comparative analysis.
Moreover, in recent research, the root mean square error (RMSE) has emerged as another evaluation metric for prediction accuracy in studies conducted by Li et al., Yao et al., and Yehia et al. [43][44][45]. However, it should be noted that the effectiveness of CURE plots in assessing model performance may be limited in studies with smaller sample sizes, as highlighted by Dadvar et al. [39].

5. The Impact of COVID-19 on Traffic Safety

The COVID-19 pandemic has brought about significant changes in traffic patterns and increased interest in investigating its impact on traffic safety globally. During the period of lockdowns and restrictions, there was a noticeable reduction in traffic flow in many affected countries [46]. However, studies have revealed a concerning increase in the severity of crashes during this period [47].
Research suggests that implementing nonpharmaceutical interventions (NPIs) and the higher percentage of people staying at home have had mixed effects on traffic safety. On the one hand, these measures have been associated with potential improvements in pedestrian and cyclist safety but have also increased crash risk for motor vehicle drivers [48]. Surprisingly, the average number of cyclists killed or injured per crash has tripled compared to previous years [49].
It is important to note that simply reducing traffic volume during the pandemic does not necessarily lead to improved traffic safety. This can be attributed to the homeostasis effect, wherein drivers compensate for reduced traffic by engaging in risky driving behaviors such as speeding and failure to signal [44]. Furthermore, crashes resulting in severe injuries are more likely to occur on highways due to, i.a., increased speeding, reduced law enforcement, lack of seat belt usage, and alcohol and drug abuse [46]. Therefore, effective law enforcement mechanisms should focus on preventing these behaviors [50].
Another significant pandemic effect was the shortened trip lengths and decreased travel frequency as people engaged in more online activities as an alternative to physical travel [47]. These changes in transportation characteristics and reduced traffic intensity on the roads, driven by the rise of e-commerce, have had implications for traffic patterns.
The sudden disruptions in traffic behavior caused by the pandemic offer a valuable opportunity to broaden the understanding of risk factors and the application of SPFs. As such, the calibrated HSM SPF for 2020 is compared to the crash data count in 2020 to assess its capability in evaluating the impacts of COVID-19 on the studied highways. This analysis can provide valuable insights into the effects of the pandemic on road safety and inform future strategies and interventions.


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Update Date: 20 Jul 2023