Crash prediction model for two-lane rural highways in the Ashanti region of Ghana

Crash Prediction Models (CPMs) have been used elsewhere as a useful tool by road Engineers and Planners. There is however no study on the prediction of road traffic crashes on rural highways in Ghana. The main objective of the study was to develop a prediction model for road traffic crashes occurring on the rural sections of the highways in the Ashanti Region of Ghana. The model was developed for all injury crashes occurring on selected rural highways in the Region over the three (3) year period 2005–2007. Data was collected from 76 rural highway sections and each section varied between 0.8 km and 6.7 km. Data collected for each section comprised injury crash data, traffic flow and speed data, and roadway characteristics and road geometry data. The Generalised Linear Model (GLM) with Negative Binomial (NB) error structure was used to estimate the model parameters. Two types of models, the ‘core’ model which included key exposure variables only and the ‘full’ model which included a wider range of variables were developed. The results show that traffic flow, highway segment length, junction density, terrain type and presence of a village settlement within road segments were found to be statistically significant explanatory variables (p < 0.05) for crash involvement. Adding one junction to a 1 km section of road segment was found to increase injury crashes by 32.0% and sections which had a village settlement within them were found to increase injury crashes by 60.3% compared with segments with no settlements. The model explained 61.2% of the systematic variation in the data. Road and Traffic Engineers and Planners can apply the crash prediction model as a tool in safety improvement works and in the design of safer roads. It is recommended that to improve safety, highways should be designed to by-pass village settlements and that the number of junctions on a highway should be limited to carefully designed ones.     

道路因素对典型较严重道路交通事故严重性的影响分析

基于道路交通事故数据探究事故影响因素对于认识事故的影响因素、提高交通安全水平具有重要意义。利用近年来国内典型较严重道路交通事故数据,应用泊松模型和负二项模型,以区分事故形态的方式建立追尾事故、侧碰事故及撞行人事故的事故死亡率的道路影响因素分析模型。这些模型以三类事故中涉及人员的死亡数为因变量,以一系列道路因素为自变量,将事故涉及人数作为偏移变量。模型的具体形式以过离散系数及赤池信息量准则(AIC)为依据进行选择。结果显示,追尾事故的死亡率与道路等级、路侧防护设施显著相关;侧碰事故则与天气、路表情况、路口路段位置、坡度以及道路结构有关;撞行人事故与路表情况、道路等级、车道数、平曲线半径有关。本文拓展了事故严重性研究的深度,其研究成果对于更好地利用重特大事故的深入调查数据有现实意义,也可为事故分析及道路设计等提供借鉴。      

基于动态风险饱和度的高速公路交通安全分析

为了反映高速公路运营安全状况，提出了动态风险饱和度理论，构建了动态风险饱和度模型和计算方法。依据路段不同交通饱和度下车辆的驾驶行为，以路段交通安全为约束，研究了跟车行驶和换道行驶2种驾驶状态下，考虑车速变化及雾天等特殊天气条件影响的路段平均最小安全车头时距计算方法，利用建立的安全车头时距与安全流量之间的转换关系，得到不同驾驶状态下的路段安全流量。在不同车辆驾驶状态切换阈值下，计算路段实际交通流量与路段安全流量的比值得到高速公路路段动态风险饱和度值。以G3高速公路某改扩建路段所在路网为例进行验证，计算得到了路网中各路段不同切换阈值下的动态风险饱和度值。动态风险饱和度随着交通饱和度的增大，呈现稳定的先增大后减小的规律，且在换道行驶状态时达到最大，在跟车行驶状态时开始下降，与现有交通安全状态分析相吻合。相较于交通饱和度，动态风险饱和度更能够反应出高速公路路段交通安全动态变化的规律。      

Inclusion of speed and weather measures in safety performance functions for rural roadways

The research on relationships among vehicle operating speed, roadway design elements, weather, and traffic volume on crash outcomes will greatly benefit the road safety profession in general. If these relationships are well understood and characterized, existing techniques and countermeasures for reducing crash frequencies and crash severities could potentially improve, and the opportunity for new methodologies addressing and anticipating crash occurrence would naturally ensue. This study examines the prevailing operating speeds on a large scale and determines how traffic speeds and different speed measures interact with roadway characteristics and weather condition to influence the likelihood of crashes. This study used three datasets from Washington and Ohio: 1) Highway Safety Information System (HSIS), 2) the National Performance Management Research Dataset (NPMRDS), and 3) National Oceanic and Atmospheric Administration (NOAA) weather data. State-based conflated databases were developed using the linear conflation of HSIS and NPMRDS. The results show that certain speed measures were found to be beneficial in quantifying safety risk. Annual-level crash prediction models show that increased variability in hourly operating speed within a day and an increase in monthly operating speeds within a year are both associated with a higher number of crashes. Safety practitioners can benefit from the current study in addressing the issue of speed and weather in crash outcomes.     

Using a surrogate safety approach to prioritize hazardous segments in a rural highway in a developing country

Road safety is a global concern particularly in developing countries where some road sections are disproportionately more vulnerable in terms of the frequency and severity of crashes. Other than using historical crash data based reactive approaches, those sections need to be identified proactively, so that mitigation measures can be applied. Moreover, those approaches are sometimes questioned mainly due to data reliability issues in developing countries. The study reported here is aimed at highlighting the applicability of traffic conflict techniques as surrogate safety measures to identify those sections of a rural highway in a developing country, which are most likely at risk. An adapted framework is demonstrated to identify traffic conflicts using combined surrogate indicators acknowledging the limited resources and facilities in developing countries. A new model is put forwarded using a count data modelling approach. Both fixed and random parameters model derivatives have been explored as an alternative methodological approach to relate the factors affecting the number and probability of conflicts. The partial effects of individual independent variables were estimated to gain a better insight of their impact. The results show that the model can predict high risk segments in terms of probability of conflicts as well as safety risk, as well as prioritize road sections according to the likelihood of their safety level. The model provides a less expensive alternative to the collection of historical crash data in order to identify hazardous road locations or black spots on two-lane highways in developing countries.¹     

基于负二项分布的高速公路交通事故影响因素分析

为分析高速公路交通事故的影响因素,构建基于负二项分布的事故分析模型,探究事故数与交通特性、公路线形及路面性能间关系.鉴于传统固定参数模型难以刻画各因素对事故风险影响的异质性,引入了随机参数建模方法.结果表明:相比于固定参数负二项模型,构建的随机参数负二项模型有更好的拟合优度,且能更合理地反映各因素对事故的作用效果;将随...     

Modeling two-way stop-controlled intersection crashes with zero-inflated models on Louisiana rural two-lane highways

Intersection safety continues to be a crucial issue throughout the United States. In 2016, 27% of the 37,461 traffic fatalities on U.S. roadways occurred at or near intersections. Nearly 70% of intersection-related fatalities occurred at unsignalized intersections. At such intersections, vehicles stopping or slowing to turn create speed differentials between vehicles traveling in the same direction. This is particularly problematic on two-lane highways. Research was performed to analyze safety performance for intersections on rural, two-lane roadways, with stop control on the minor roadway. Roadway, traffic, and crash data were collected from 4148 stop-controlled intersections of all 64 Parishes (counties) statewide in Louisiana, for the period of 2013 to 2017. Four count approaches, Poisson, Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) were used to model the number of intersection crashes for different severity levels. The results indicate that ZIP models provide a better fit than all other models. In addition to traffic volume, larger curve radii of major and minor roads and wider lane widths of major roads led to significantly smaller crash occurrences. However, higher speed limits of major roads led to significantly greater crash occurrences. Four-leg stop-controlled intersections have 35% greater total crashes, 49% greater fatal and injury crashes, and 25% greater property damage only (PDO) crashes, relative to three-leg intersections.     

What affects pedestrian crossing difficulty at urban intersections in a developing country?

In developing countries, road traffic crashes involving pedestrians have become a foremost concern. At present, road safety assessment plans and selection of interventions are primarily restricted to traditional approaches that depend on the investigations of historical crash data. However, in developing countries such as India, the availability, consistency, and accuracy of crash data are major concerns. In contrast, proactive approaches such as studying road users' risk perception have emerged as a substitute method of examining potential risk factors. An individual's risk perception offers vital information on probable crash risk, which may be beneficial in detecting high-risk locations and major causes of crashes. Since the pedestrian fatality risk is not uniform across the urban road network level, it may be expected that pedestrians' perceived risk measured in terms of “crossing difficulty” would also vary across the sites. In this perspective, the present paper establishes a mathematical association between the pedestrians' perceived “crossing difficulty” and actual crashes. The model outcome confirms that pedestrians' perceived crossing difficulty is a good surrogate of fatal pedestrian crashes at the intersection level in Kolkata City, India. Subsequently, to examine the impact of traffic exposures, road infrastructure, land use, spatial factors, and pedestrian-level attributes on pedestrians' “crossing difficulty”; a set of Ordered Logit models are developed. The model outcomes show that high vehicle and pedestrian volume, vehicular speed, absence of designated bus stop, the presence of inaccessible pedestrian crosswalk, on-street parking, lack of signalized control (for both vehicle and pedestrian), inadequate sight distance, land use pattern, slum population, pedestrian-vehicular post encroachment time, waiting time before crossing, road width, and absence of police enforcement at an intersection significantly and positively increase pedestrian's crossing difficulty at urban intersections. To end, the model findings are advantageously utilized to develop a set of countermeasures across 3E's of road safety.     

山区高速公路隧道路段与开放路段的事故影响因素分析

高速公路隧道构造特殊且通行环境复杂,因而通常事故多发。为探究高速公路隧道路段与开放路段事故影响因素和严重程度致因机理的差异,采集沪昆高速邵怀段2011—2016年期间1 537起事故为研究样本;以事故发生路段为响应变量构建逻辑回归模型,解释各种风险因素对事故发生路段倾向性的影响差异;分别针对隧道路段与开放路段建立模型研究事故伤害严重程度的影响因素。建立二元Logit回归模型分析事故的发生倾向性和2类路段的事故严重程度的影响因素;采用随机参数Logit模型以反映异质性条件对参数的影响。统计表明:与疲劳驾驶、未保持安全距离相关的事故发生在隧道路段的概率更高,其事故发生概率分别是开放路段的2.373和2.482倍;与隧道路段事故严重程度正相关的因素包括下坡（坡度2%以上）、夏季和超速行驶,其中下坡（坡度2%以上）段的严重事故发生的概率为上坡（坡度2%以上）的3.397倍,夏季的严重事故发生概率为秋季的3.951倍,超速行驶相关的严重事故发生概率为其他不当驾驶行为的4.242倍;与开放路段事故严重程度正相关的因素包括超速行驶和疲劳驾驶,其中超速行驶相关的严重事故概率是其他不当驾驶行为的2.713倍,疲劳驾驶相关的严重事故概率是其他不当驾驶行为的4.802倍。研究表明,山区高速公路隧道路段与开放路段的事故发生概率及其严重程度的影响因素存在一定的差异性,研究结论可为山区高速公路差异管理方案制定提供依据。      

基于GIS分析的深圳市道路交通事故空间分布特征研究

掌握城市道路交通事故空间分布特征是城市道路交通安全管理的重要基础。基于深圳市2014~2016年的道路交通事故数据，首先应用地理编码方法对原始事故记录进行空间定位，形成事故的空间分布。其次针对考虑/不考虑路网密度的2种情况，应用密度分析方法对道路交通事故多发的区域和事故严重程度较高的区域进行鉴别，比较2种情况下区域分布的差异并分析造成这种差异的可能原因。最后利用异常点分析和热点分析2种空间聚类分析模型对事故严重程度较高的区域进行进一步鉴别，并对密度分析和聚类分析2种方法得到的结果进行了比较。密度分析结果表明：就事故频度而言，深圳市中心城区单位面积上的交通事故频度较高，而郊区单位长度道路上的交通事故分布更为密集；就事故严重程度而言，郊区的交通事故平均严重程度高于市中心区域。造成上述差异的原因可能与郊区道路限速较高等因素有关。聚类分析结果与密度分析结果相近，在郊区形成了高严重程度的事故聚类，而在中心城区形成了低严重程度的事故聚类，说明郊区的交通事故严重程度总体高于市中心区域。从2种方法的比较来看，密度分析简单易行，有助于交通管理部门对城市交通事故空间分布特征直观快速的了解；聚类分析可精确到事故点，为精细化的交通安全管理工作提供支撑。研究结果表明基于密度分析和聚类分析的研究方法对于确定道路交通事故空间分布特征有良好的作用。     

零截尾负二项模型在交叉口事故预测中的应用

事故预测模型是广泛采用的交通安全定量分析方法，但往往要求具有完备的道路、交通和事故数据。然而，基础数据相对不健全是包括中国在内的发展中国家交通安全管理面临的主要问题之一，例如仅有发生事故路段或者交叉口的相关属性特征（即零截尾数据）。为此，为确保基础数据不全的情况下交叉口事故预测的准确性，提出了基于零截尾的广义负二项回归模型；采集了246个非信号控制交叉口的交通与事故数据，采用传统负二项模型和新提出的零截尾负二项模型对全数据和零截尾数据分别进行对比分析。结果表明：在针对截尾数据的分析中，零截尾负二项模型明显优于传统负二项模型，并且零截尾负二项模型的参数估计值与基于全数据的负二项基准模型的估计值非常接近；在所有模型中，交叉口的主路交通量和支路交通量与交叉口的安全性之间存在较大的正关联。此外，同等条件下，十字形交叉口的事故数量高于T形交叉口的事故数量；利用传统负二项分布模型分析截尾数据得到的事故预测模型与使用全数据的基准模型有显著差异，其结果不可靠；采用零截尾负二项分布模型的参数结果与基准模型基本一致，截尾模型的置信区间包含基准模型相应的参数估计值。当受条件所限无法获取全部数据时，可以考虑使用零截尾负二项模型进行安全分析。     

Factors affecting severity of motorcycle accidents on Thailand's arterial roads: Multiple correspondence analysis and ordered logistics regression approaches

Thailand was classified as a middle-income country and ranked second highest in terms of road traffic fatality rate in the world in 2015. By 2018, this ranking went up to ninth in world which may be because of various earnest safety policies implementation, supporting road safety research and establishing a road safety directing center. However, crash fatality rate has considerably remained high until recent year, indicating a clear need for further related research. Considering severity of the crashes, the majority of fatal crashes involved the motorcycle road user. Therefore, motorcycle crashes are important issues and should be considered to mitigate fatality due to immoderate proportion of motorcycle road user and motorcyclist fatality. This study aims to identify factors that influence the severity of motorcycle accidents on Thailand's arterial roads by employing ordered logistic regression and multiple correspondence analysis. The results demonstrated that although both analyses were relatively different, they provided similar results. Age, road lanes, and helmet wearing were significant factors that influenced the severity of motorcycle accidents. The results could serve as reference for planning strategies or organizing campaigns to reduce and prevent death owing to road traffic accidents, which may enhance the overall image of road traffic safety in Thailand.     

Effectiveness of enforcement resources in the highway patrol in reducing fatality rates

Motor vehicle crashes are a leading cause of death in the United States. Wyoming initiated a safety study to investigate the underlying causes of high crash rates since it has one of the highest fatality rates in the nation. Research has shown relationships between increased enforcement activity and road crash/fatality reduction. However, little research has attempted to quantitatively measure the impact of various forms of police enforcement, such as the percentage of enforcement time and the quantity of resources, on fatality rate. Therefore, this study was set forward to fill this gap. Data from the highway patrol in Wyoming and the surrounding states were used in this study. Although Wyoming and these nearby states have very similar features in terms of geography and weather, they are different in terms of road mileage and traffic. Therefore, the data was normalized based on highway mileage and miles traveled. Enforcement efforts were compared in terms of allocated enforcement budget, number of sworn officers, and time spent patrolling. The results indicated that there are negative relationships between fatality rate and budget, number of officers, and active hours on the field. This paper also investigated which variable is the best predictor of fatality rate. The results indicated that time spent on the field by highway patrol officers is the best indicator of fatality rate. It was found that although some states like Wyoming have a higher number of sworn officers, they spend less time actively enforcing highway safety. This study provides information needed for authorities to allocate more funding to the highway patrol, and for the highway patrol to spend more time on the road.     

Using machine leaning techniques for evaluation of motorcycle injury severity

There is a growing interest in the application of the machine learning techniques in predicting the motorcycle crash severity. This is partly due to a progress in autonomous vehicles technology, and machine learning technique, which as a main component of autonomous vehicle could be implemented for traffic safety enhancement. Wyoming's motorcycle crash fatalities constitute a concern since the count of riders being killed in motorcycle crashes in 2014 was 11% of the total road fatalities in the state. The first step of crash reduction could be achieved through identification of contributory factors to crashes. This could be accomplished by using a right model with high accuracy in predicting crashes. Thus, this study adopted random forest, support vector machine, multivariate adaptive regression splines and binary logistic regression techniques to predict the injury severity outcomes of motorcycle crashes. Even though researchers applied all the aforementioned techniques to model motorcycle injury severities, a comparative analysis to assess the predictive power of such modeling frameworks is limited. Hence, this study contributes to the road safety literature by comparing the performance of the discussed techniques. In this study, Wyoming's motorcycle crash injury severities are modeled as functions of the characteristics that give rise to crashes. Before conducting any analyses, feature reduction was used to identify a best number of predictors to be included in the model. Also to have an unbiased estimation of the performance of different machine learning techniques, 5-fold cross-validation was used for model performance evaluation. Two measure, Area under the curve (AUC), and confusion matrix were used to compare different models' performance. The machine learning results indicate that random forest model outperformed the other models with the least misclassification and higher AUC. It was also revealed that a dichotomous response variable, with fatality and incapacitation injury in one category, along with all other categories in another group would result in a lower misclassification rate than a polychotomous response variable. This might result from the nature of motorcycle crashes, lacking a protection compared with passenger cars, preventing machine learning technique to get trained properly. Moreover, the most important variables identified by the random forest model are those related to the operating speed, resentful other party, traffic volume, truck traffic volume, riding under the influence, horizontal curvature, wide roadway with more than two lanes and rider's age.     

Extremely serious crashes on urban roadway networks: Patterns and trends

Extremely serious traffic crashes, defined as having a death toll of two and greater than two, have become a serious safety concern on urban roadways in Louisiana. This study examined the different contributing factors of these crashes to determine significant trends and patterns. We collected traffic crash data from Louisiana during the period of 2013 to 2017 and found that a total of 72 extremely serious crashes (around 2% of all traffic fatalities) occurred on Louisiana urban roadway networks. As crash data contain an enormous list of contributing factors, there was an issue of ‘more features than data points’ in solving the research problem. Most of these variables are categorial in nature. We selected a dimension reduction tool called Taxicab Correspondence Analysis (TCA) to investigate the complex interaction between multiple factors under a two-dimensional map. Findings of the study reveal several key clusters of attributes that show patterns of association between different crash attributes. The conclusions of this study are exploratory, and the results can help in better visualizing the association between key attributes of crashes. The findings have potentials in designing suitable countermeasures to reduce extremely serious crashes.     

Severity analysis of tree and utility pole crashes: Applying fast and frugal heuristics

A roadway departure (RwD) crash is defined as a crash that occurs after a vehicle crosses an edge line or a center line, or otherwise leaves the designated travel path. RwD crashes account for approximately 50% of all traffic fatalities in the U.S. Additionally, crashes related to roadside fixed objects such as trees, utility poles, or other poles (TUOP) make up 12–15% of all fatal RwD crashes in the U.S. Data spanning over seven years (2010–2016) shows that TUOP crashes account for approximately 22% of all fatal crashes in Louisiana, which is significantly higher than the national statistic. This study aims to determine the effect of crash, geometric, environmental, and vehicle characteristics on TUOP crashes by applying the fast and frugal tree (FFT) heuristics algorithm to Louisiana crash data. FFT identifies five major cues or variable threshold attributes that contribute significantly to predicting TUOP crashes. These cues include posted speed limit, primary contributing factor, highway type, weather, and locality type. The balanced accuracy is around 56% for both training and test data. The current model shows higher accuracies compared to machine learning models (e.g., support vector machine, CART). The present findings emphasize the importance of a comprehensive understanding of factors that influence TUOP crashes. The insights from this study can help data-driven decision making at both planning and operation levels.     

Impact of driving style,behaviour and anger on crash involvement among Iranian intercity bus drivers

In developing countries such as Iran, due to the inadequate infrastructure for rail and air transportation facilities, intercity buses are the most common type of transportation for long distances. Because of the long hours of driving, bus driving is considered a challenging job. Moreover, given the high capacity of these vehicles, a small error from the driver could endanger many passengers' health. So, studying drivers' behaviours can be a key factor in decreasing the risk factors of crash involvement in these drivers. However, few studies have focused on intercity bus drivers' behaviours. This research uses a sample of 254 professional drivers that answered a self-report questionnaire on driving style (MDSI), driving behaviour (DBQ), and driving anger (DAS). A structural equation modelling (SEM) is used to investigate the psychometric properties of these questionnaires. The results show a positive correlation between maladaptive driving styles and driving behaviour, and a negative correlation between adaptive styles and driving behaviour. Significant differences are observed among drivers with and without crash history on their maladaptive driving styles and their driving anger scale. A binary logistic regression model is also developed to predict traffic crashes as a function of driving misbehaviour. The results suggest that factors related to driving anger are the main factors that increase the probability of misbehaviour and traffic crashes. The results also suggest that driving style and driving behaviour significantly predict crash risk among bus drivers. Aggressive driving is associated with an increased probability of crash involvement among intercity bus drivers. The findings can be used to inform the health promotion policies and provide regular interventions designed to improve driving safety among intercity bus drivers.     

Influence of segment length on the fitness of multivariate crash prediction models applied to a Brazilian multilane highway

Road safety modeling enables the development of crash prediction models and the investigation of which factors contribute to crash occurrence. Developing multivariate response models is also valuable, but such models are currently under-exploited. Machine learning techniques, especially artificial neural networks (ANN), have been presented as possible alternatives. Furthermore, selecting a proper roadway segmentation is one of the first tasks in the standard crash modeling workflow. However, this is a challenging task, especially in terms of choosing a segment length. This article presents a study of the influence of segment length on the development of multivariate response models (i.e., three response variables: property damage only crashes, injured victims crashes, and fatal crashes). The models use ANN for a road segment of a Brazilian divided multilane highway. The highway to be modeled was divided into segments with 10 different fixed lengths. The model characterization included geometric and operational data available for the years from 2011 to 2017. The models were evaluated in terms of errors and by residual plot analysis. The 5-km segment of the northbound carriageway and the 4.5-km segment of the southbound carriageway presented the smallest errors and the highest values of R². The residual analyses confirmed the trend to improve the model with the greater segment lengths. This was clear by the residues' distribution around zero, except for the output “Fatal crashes”. The better performance of the longer segments models was expected because these models aggregate more crashes into one segment. The reduction of no crash observations also facilitated the improvement of the models' goodness-of-fit. The use of ANNs also revealed its potential value. However, it is still important to seek strategies to deal with the excess of zeros in fatal crashes; a problem that also occurs in the traditional statistical modeling process.     

Car drivers' road safety performance: A benchmark across 32 countries

The road safety performance of a country and the success of policy measures can be measured and monitored in different ways. In addition to the traditional road safety indicators based on the number of fatalities or injured people in road traffic crashes, complementary road safety performance indicators can be used in relation to vehicles, infrastructure, or road users' behaviour. The last-mentioned can be based on data from roadside surveys or from questionnaire surveys. However, results of such surveys are seldom comparable across countries due to differences in aims, scope, or methodology.This paper is based on the second edition of the E-Survey of Road Users' Attitudes (ESRA), an online survey carried out in 2018, and includes data from more than 35,000 road users across 32 countries. The objective is to present the main results of the ESRA survey regarding the four most important risky driving behaviours in traffic: driving under the influence (alcohol/drugs), speeding, mobile phone use while driving, and fatigued driving. The paper explores several aspects related to these behaviours as car driver, such as the self-declared behaviours, acceptability and risk perception, support for policy measures, and opinions on traffic rules and penalties.Results show that despite the high perception of risk and low acceptability of all the risky driving behaviours analysed, there is still a high percentage of car drivers who engage in risky behaviours in traffic in all the regions analysed. Speeding and the use of a mobile phone while driving were the most frequent self-declared behaviours. On the other hand, driving under the influence of alcohol or drugs was the least declared behaviour. Most respondents support policy measures to restrict risky behaviour in traffic and believe that traffic rules are not being checked regularly enough, and should be stricter.The ESRA survey proved to be a valuable source of information to understand the causes underlying road traffic crashes. It offers a unique database and provides policy makers and researchers with valuable insights into public perception of road safety.     

Contribution of traffic behavior surveys for monitoring safety performance indicators in Germany: Case of mobile phone use while driving

Behavioral determinants in the form of safety performance indicators (SPIs) are increasingly being applied in addition to road traffic crash statistics to evaluate road safety. These SPIs help understand driving behaviors and adopt preventive measures for crashes. Behavior-explaining determinants, including attitude or subjective norms, are defined in the theory of planned behavior and are collected through surveys. In this study, data from three traffic behavior surveys, conducted in Germany, were employed, taking the example of mobile phone use while driving, which causes distraction and represents a road safety risk. The surveys differ in their methodology and results. While the Traffic Climate in Germany has been surveying the determinants of mobile phone use in a representative sample for several years, the E-Survey of Road Users' Attitudes (ESRA) is conducted in parallel in several countries and allows for comparison with Germany. The survey by the International Association of Traffic and Safety Science (IATSS) only included the group of young drivers in Germany. All three surveys demonstrate that although attitudes and subjective norms tend to be negative, mobile phones are nevertheless used while driving. Major differences exist depending on the mode of use (hands-free calling, texting) and recent developments. Thus, regularly surveying the determinants and mapping the latest developments in terms of content is critical. Together, these surveys provide comprehensive insights into the topic and enable prevention approaches, such as the concrete communication of information to young drivers and the emphasis on dangers, even during the hands-free use of mobile phones while driving.