Run-off-road crashes are one of the most common crash types, especially in rural roadway environments contributing significantly to fatalities and severe injuries. These crashes are complex and multi-dimensional events, and factors like road geometry, driver behaviour, traffic characteristics and roadside features contribute to their occurrence, separately or interactively. Sudden changes in road geometry, in particular, can influence driver behaviour, and therefore, in developing a micro-level crash risk model for run-off-road crashes, one of the challenges is incorporating the effects of driver behaviour (disaggregated information) that may arise from the variations in road geometry (aggregated information). This study aims to examine the interaction between road geometry and driver behaviour through a set of measures for design consistency on two-lane rural roads. Multiple data sources, including crash data for 2014–18, traffic data, probe speed data and roadway geometric data, for twenty-three highways in Queensland, Australia, have been fused for this study. Seventeen types of design consistency measures with regard to alignment consistency, operating speed consistency and driving dynamics are tested. A run-off-road crash risk model is estimated by employing the Random Parameters Negative Binomial Lindley regression framework, which accounts for excess zeros in the crash counts and captures the effects of unobserved heterogeneity in the parameter estimates. Results indicate that the geometric design consistency capturing the interaction between driver behaviour and operational factors better predicts run-off-road crashes along rural highways. In addition, roadside attributes like clear zone width, infrastructures, terrain, and roadway remoteness also contribute to run-off-road crashes. The findings of the study provide a comprehensive understanding of the influence of variations in roadway geometry on driver behaviour and run-off-road crashes along rural highways.@en

Increasing the use of non-motorized modes of transport, such as walking, is a worldwide objective aimed at improving the sustainability of cities. However, pedestrians may not choose to walk if the infrastructure fails to meet their needs or if they hold unfavourable perceptions regarding the built environment (BE). The current study aims to identify the significance of route attributes and perceptions of attractiveness, safety, and security at the route level, which influence pedestrians' preferences for last-mile route choices. A cross-sectional design was employed, utilizing a questionnaire comprising a stated preference (SP) experiment and a perception survey. The study considered theory-informed attributes influencing pedestrian route choice preferences, including: (1) individual-level determinants, (2) physical-level determinants, and (3) time of day. Two separate models were developed, considering the time of day, to examine the differences in trade-offs within pedestrians' route choice preferences between day and night. The results revealed that both the BE and perceptions of the BE play a crucial role in determining pedestrian route choice behaviour. Pedestrians showed a preference for routes fully encompassed by mixed or residential land uses during the daytime. The presence of vacant land along the walking route significantly decreased the likelihood of choosing a route at night. Generally, pedestrians favoured shorter walking times, lower posted speed limits, and comfortable walkway grades in their routes. Female pedestrians tended to avoid routes that were not well-lit and pleasant at night. Lowering roadway speed limits emerged as a strategy to encourage walking in suburban areas. The findings of this study hold the potential to play an essential role in the development of effective policy initiatives targeted at pedestrians in cities.@en

Pedestrian route choice (PRC) is influenced by a wide range of factors, yet an understanding of the trends, patterns, and findings of PRC research is a gap in the literature. The present study applied the PRISMA framework to systematically identify published studies on PRC. The identified studies were synthesized by answering the systematic classification scheme (SCS) questions. Results show that prior studies have used a total of 105 factors associated with PRC. The identified factors were categorized under three groups (i.e. pedestrian socio-demographic factors, built environment factors, trip characteristics). The findings of this systematic review identified several critical research gaps for studying PRC behaviour. Future PRC research should aim to determine the influence of social, physical, and individual factors associated with PRC and the nature of the relationships of those factors with PRC.@en

Perceptions of the walking environment can encourage or discourage walking for transport. However, the influence of the built environment (BE) on pedestrians’ perceptions of the walking environment has not been fully understood. To address this gap, the present research investigates how BE characteristics of a suburban walking environment are associated with the perceptions of attractiveness (i.e., pleasantness, friendliness), safety, and security. Using a cross-sectional design, 995 participants reported their perceptions about the attractiveness, safety, and security of different suburban BE scenarios in Brisbane (Australia). Univariate and bivariate random effect ordered probit models were estimated to identify the associations between BE characteristics and perceived attractiveness, safety, and security whilst controlling for psychosocial factors. Results indicated that perceived attractiveness (i.e., pleasantness and friendliness) was higher for recreational areas than residential land use. Women perceived the walking environment as more pleasant with trees. The perception of safety was higher for recreational and vacant land compared to residential areas. Young pedestrians perceived that the likelihood of being assaulted/robbed/harassed at night was lower if they walked through an area with commercial and mixed land use. Women pedestrians perceived that the corresponding risk at night was higher in recreational areas. Overall, the findings suggest that urban design strategies such as increasing land use diversity and providing adequate trees enhance perceived environmental attractiveness, safety, and security, ultimately resulting in more walking for transport.@en

Introduction: Studies have shown that perceived security discourages pedestrians from walking, which in turn reduces physical activities and associated health benefits. However, there is a dearth of research about what elements of the built environment contribute to perceived security among pedestrians and, in particular, how the perception varies amongst women and amongst men in suburban environments. The present study aims to address this gap in the literature. Methods: A range of scenarios (i.e. more/less diversity of land use, presence/absence of trees) were presented in an online experiment to 995 participants in Brisbane (Australia). Participants reported their perceptions of being assaulted/robbed/harassed in each scenario. Random effects ordered probit models were estimated for men and women to identify the association between perceived security and built environment factors, controlling for other confounders such as age and attitudes. Results: Women have a higher perceived risk of being assaulted/robbed/harassed compared with men in all scenarios tested in the present study. Importantly, the results indicated that while residential, commercial, and mixed land use provide a sense of security for pedestrians compared to vacant land, the effect was larger for women compared to men. There were no significant differences between vacant and recreational land use in terms of perceived security. At night, pedestrians perceived suburban environments as insecure, and the change in the level of security was higher for women than men. Also, night time security varied over different land-use types between men and women. Conclusions: The findings suggest that targeted urban design practices (e.g., functional use of vacant land, increasing diversity of land use, adequate street lights) could be used to improve perceived security and also reduce gender inequality in security perceptions. In turn, improved perceptions of security can help to increase walking as a mode of transport.@en

The availability of advisory warnings via Vehicle-to-Vehicle and Vehicle-to-Infrastructure communication in the connected environments is expected to gradually increase over the next few years. Much of the research on advisory warning systems have examined driving behaviour in response to unexpected driving hazards; however, very little research has been conducted on common driving interactions such as interacting with pedestrians at pedestrian crossings. Therefore, the aim of this study is to investigate the effects of the connected environment on driving behaviour at pedestrian crossings. The connected environment was designed within the CARRS-Q advanced driving simulator. A combination of auditory (beep sound) and imagery message was simultaneously displayed on the windscreen to advise the driver on the presence of a pedestrian entering from a sidewalk. Seventy-eight licensed drivers drove the simulator in two driving conditions, namely, baseline and connected environment. The participants were 18–65 years old, and a third of them were females. Drivers' response to the driving aids and the braking behaviour were analysed in the latent response phase and the observable response phase, and the corresponding response times were modelled using the hazard-based duration modelling approach. In particular, this study applied the Weibull accelerated failure time model with shared frailty accounting for multiple observations from the same driver. Results showed that the time taken to respond to the pedestrian in the latent response phase was longer when the advisory warning was provided to the drivers, but the corresponding time in the observable response phase was shorter, indicating that drivers take an informed decision in the connected environment. Moreover, the safety margin—measured in terms of time-to-collision—was higher in the connected environment than the traditional driving environment, indicating a safer behavioural adaptation towards the connected environment.@en

The adaptive behaviour of mobile phone distracted drivers has been a topic of much discussion in the recent literature, but the mechanisms of behavioural adaptation are still unclear. This study investigated the influence of driving demands, secondary task characteristics, and personal characteristics on behavioural adaptation of mobile phone distracted drivers. In particular, distracted drivers’ self-regulation at strategic, tactical, and operational levels was investigated through a driving simulator experiment. In a high-fidelity driving simulator, participants driving through various driving conditions (e.g. interactions with pedestrian crossings, signalized intersections, merging ramps, roundabouts, etc.) needed to decide where and how to perform the following four mobile phone tasks: (a) ring a doctor and cancel an appointment, (b) text a friend and tell him/her that the participant will be arriving 10 min late, (c) share the doctor's phone number with a friend, and (d) take a 'selfie’. At a strategic level, the decision to pull over was modelled as a function of self-reported personal/attitudinal characteristics with a logistic regression model. Similarly, tactical self-regulation (decision to engage in a task while driving in a specific situation) and operational self-regulation (decision to temporarily stop the mobile phone task) were modelled as a function of driving demands and personal/attitudinal characteristics using a random-effects logistic regression model, which accounts for correlations resulting from multiple observations of a driver. Results suggest that tactical self-regulation is more common among distracted drivers followed by operational and strategic self-regulation. Personal beliefs regarding how safe it is to use the mobile phone for texting/browsing while driving were predictors of self-regulation for all levels. Drivers were observed to use the mobile phone more when the driving demands are low, e.g. while stopped at an intersection. This research suggests that distracted drivers engage in various levels of self-regulation, and future research could be focused on further theoretical refinement and development of technology-based interventions.@en

Purpose – The purpose of this chapter is to review the methodological and empirical underpinnings of transport network screening, or management, as it relates to improving road safety. As jurisdictions around the world are charged with transport network management in order to reduce externalities associated with road crashes, identifying potential blackspots or hotspots is an important if not critical function and responsibility of transport agencies. Methodology – Key references from within the literature are summarised and discussed, along with a discussion of the evolution of thinking around hotspot identification and management. The theoretical developments that correspond with the evolution in thinking are provided, sprinkled with examples along the way. Findings – Hotspot identification methodologies have evolved considerably over the past 30 or so years, correcting for methodological deficiencies along the way. Despite vast and significant advancements, identifying hotspots remains a reactive approach to managing road safety – relying on crashes to accrue in order to mitigate their occurrence. The most fruitful directions for future research will be in the establishment of reliable relationships between surrogate measures of road safety – such as ‘near misses’ – and actual crashes – so that safety can be proactively managed without the need for crashes to accrue. Research implications – Research in hotspot identification will continue; however, it is likely to shift over time to both closer to ‘real-time’ crash risk detection and considering safety improvements using surrogate measures of road safety – described in Chapter 17. Practical implications – There are two types of errors made in hotspot detection – identifying a ‘risky’ site as ‘safe’ and identifying a ‘safe’ site as ‘risky’. In the former case no investments will be made to improve safety, while in the latter case ineffective or inefficient safety improvements could be made. To minimise these errors, transport network safety managers should be applying the current state of the practice methods for hotspot detection. Moreover, transport network safety managers should be eager to transition to proactive methods of network safety management to avoid the need for crashes to occur. While in its infancy, the use of surrogate measures of safety holds significant promise for the future.@en

Objective: The speed selection behavior of drivers has been reported to vary across driver demographics, psychological attributes, and vehicle-specific factors. In contrast, the effects of roadway geometric, traffic characteristics, and site-specific factors on speed selection are less well known. In addition, the relative degree of speeding has received little attention and thus remains relatively unexplored. This study aims to investigate the effects of roadway geometrics, traffic characteristics, and site-specific factors on speeding behavior of drivers. Methods: A panel mixed logit fractional split model is estimated to analyze the proportion of speed limit violations across highway segments. To account for possible unobserved heterogeneity, the suitability of latent class model specification is also tested. Speeding data were collected from speed cameras along major arterials and highways in Queensland, Australia, and were merged with several other data sources including roadway geometric characteristics, spatial features of the surrounding environment, and driver behavioral factors. Results: The results of the panel mixed logit fractional split model suggest a tendency among drivers to commit minor speed limit violations irrespective of causal factors. Among potential road geometric and traffic factors, radius of horizontal curves, percentage of heavy vehicle traffic on segments with divided median, posted speed limit, and road functional classification are factors that influence speeding behavior. Additionally, the deployment of covert speed cameras is found to decrease the likelihood of major speed limit violations along arterials or highways. Conclusions: An understanding of the influence of roadway geometrics and traffic characteristics on speeding behavior of drivers will inform the design of targeted countermeasures in order to reduce speed limit violations along highways.@en

Mobile phone distracted driving is a recurrent issue in road safety worldwide. Recent research on driving behaviour of distracted drivers suggests that in certain circumstances drivers seem to assume safer behaviours while using a mobile phone. Despite a high volume of research on this topic, self-regulation by mobile phone distracted drivers is not well understood as many driving simulator experiments are designed to impose an equal level of distraction to participants being tested for their driving performance. The aim of this research was to investigate the relationship between self-regulatory secondary task performance and driving. By a driving simulator experiment in which participants were allowed to perform their secondary tasks whenever they feel appropriate, the driving performance of 35 drivers aged 18–29 years was observed under three phone conditions including non-distraction (no phone use), hands-free interactions and visual-manual interactions in the CARRS-Q advanced driving simulator. Drivers’ longitudinal and lateral vehicle control observed across various road traffic conditions were then modelled by Generalized Estimation Equations (GEE) with exchangeable correlation structure accounting for heterogeneity resulting from multiple observations from the same driver. Results show that the extent of engagement in the secondary task influence both longitudinal and lateral control of vehicles. Drivers who engaged in a large number of hands-free interactions are found to select lower driving speed. In contrast, longer visual-manual interactions are found to result in higher driving speed among drivers self-regulating their secondary task. Among the road traffic conditions, drivers distracted by their self-regulated secondary tasks are found to select lower speeds along the s-curve compared to straight and motorway segments. In summary, the applied human-machine system approach suggests that road traffic demands play a vital role in both secondary task management and driving performance.@en

This study investigated how situational characteristics typically encountered in the transport system influence drivers’ perceived likelihood of engaging in mobile phone multitasking. The impacts of mobile phone tasks, perceived environmental complexity/risk, and drivers' individual differences were evaluated as relevant individual predictors within the behavioral adaptation framework. An innovative questionnaire, which includes randomized textual and visual scenarios, was administered to collect data from a sample of 447 drivers in South East Queensland-Australia (66% females; n = 296). The likelihood of engaging in a mobile phone task across various scenarios was modeled by a random parameters ordered probit model. Results indicated that drivers who are female, are frequent users of phones for texting/answering calls, have less favorable attitudes towards safety, and are highly disinhibited were more likely to report stronger intentions of engaging in mobile phone multitasking. However, more years with a valid driving license, self-efficacy toward self-regulation in demanding traffic conditions and police enforcement, texting tasks, and demanding traffic conditions were negatively related to self-reported likelihood of mobile phone multitasking. The unobserved heterogeneity warned of riskier groups among female drivers and participants who need a lot of convincing to believe that multitasking while driving is dangerous. This research concludes that behavioral adaptation theory is a robust framework explaining self-regulation of distracted drivers.@en

Road safety in rural mountainous areas is a major concern as mountainous highways represent a complex road traffic environment due to complex topology and extreme weather conditions and are associated with more severe crashes compared to crashes along roads in flatter areas. The use of crash modelling to identify crash contributing factors along rural mountainous highways suffers from limitations in data availability, particularly in developing countries like Malaysia, and related challenges due to the presence of excess zero observations. To address these challenges, the objective of this study was to develop a safety performance function for multi-vehicle crashes along rural mountainous highways in Malaysia. To overcome the data limitations, an in-depth field survey, in addition to utilization of secondary data sources, was carried out to collect relevant information including roadway geometric factors, traffic characteristics, real-time weather conditions, cross-sectional elements, roadside features, and spatial characteristics. To address heterogeneity resulting from excess zeros, three specialized modelling techniques for excess zeros including Random Parameters Negative Binomial (RPNB), Random Parameters Negative Binomial – Lindley (RPNB-L) and Random Parameters Negative Binomial – Generalized Exponential (RPNB-GE) were employed. Results showed that the RPNB-L model outperformed the other two models in terms of prediction ability and model fit. It was found that heavy rainfall at the time of crash and the presence of minor junctions along mountainous highways increase the likelihood of multi-vehicle crashes, while the presence of horizontal curves along a steep gradient, the presence of a passing lane and presence of road delineation decrease the likelihood of multi-vehicle crashes. Findings of this study have significant implications for road safety along rural mountainous highways, particularly in the context of developing countries.@en

In the safety literature, motor vehicle crashes are modelled predominately using single equation regression models, albeit with a variety of distributional assumptions and econometric enhancements. These models rely on a single linear additive predictive equation—which becomes multiplicative with a log transform—to specify the expected mean crash count conditioned on predictors. The models also specify the distribution of observations around the conditional mean, with common examples including the Poisson, Negative Binomial, and Conway-Maxwell distribution among others. This mainstream probabilistic conceptualization (i.e. model) of motor vehicle crash causation assumes that crashes are well-approximated by a single source of risk, wherein several contributing factors exert their collective, non-independent influences on the occurrence of crashes via a linear predictor. This study first postulates, and then demonstrates empirically, that crash occurrence may be more complex than can be adequately captured by a single equation regression model. The total crash count recorded at a transport network location (e.g. road segment) may arise from multiple simultaneous and inter-dependent sources of risk, rather than one. Each of these sources may uniquely contribute to the total observed crash count. For instance, a site's crash occurrence may be dominated by contributions from driver behaviour issues (e.g. speeding, impaired driving), while another site's crashes might arise predominately from design and operational deficiencies such as deteriorating pavements and worn lane markings. Stated succinctly, this research hypothesises that the unobserved heterogeneity in the accumulation of motor vehicle crashes at transport network locations arises because multiple sources of risk, not one, better captures complexity in the crash occurrence process. A stochastic multiple risk source methodological approach is developed to correspond with and empirically test this hypothesis. A joint econometric model with random parameters and instrumental variables demonstrates the applicability of the proposed theory and the corresponding methodological approach. The proposed model assumes that complexity of crash occurrence is well approximated using three sources of risk comprised of engineering, unobserved spatial, and driver behavioural factors. It is empirically tested using crash data from state controlled roads in Queensland, Australia. Finally, the multiple risk source model is compared to the traditional single risk source model to assess the viability of the proposed approach based on the sample data. The multiple risk source model significantly outperformed the single risk source model in terms of prediction ability and goodness of fit measures. In addition, while the single risk source model predicts total crash counts for individual sites, the multiple risk source model predicts crash count proportions contributed by each source of risk, and predicts crashes by risk source. The improvement in fit combined with the theoretical appeal of a multiple risk source model to explain unobserved heterogeneity in crashes suggests—at least for the sample used in the study—that the complexity in crash occurrence is better explained using multiple equation linear predictors. Further research should examine other datasets for repeatability and should further explore and test risk sources.

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Distracted driving is one of the most significant human factor issues in transport safety. Mobile phone interactions while driving may involve a multitude of cognitive and physical resources that result in inferior driving performance and reduced safety margins. The current study investigates characteristics of usage, risk factors, compensatory strategies in use and characteristics of high-frequency offenders of mobile phone use while driving. A series of questions were administered to drivers in Queensland (Australia) using an on-line questionnaire. A total of 484 drivers (34.9% males and 49.8% aged 17–25) participated anonymously. At least one of every two motorists surveyed reported engaging in distracted driving. Drivers were unable to acknowledge the increased crash risk associated with answering and locating a ringing phone in contrast to other tasks such as texting/browsing. Attitudes towards mobile phone usage were more favourable for talking than texting or browsing. Lowering the driving speed and increasing the distance from the vehicle in front were the most popular task-management strategies for talking and texting/browsing while driving. On the other hand, keeping the mobile phone low (e.g. in the driver’s lap or on the passenger seat) was the favourite strategy used by drivers to avoid police fines for both talking and texting/browsing. Logistic regression models were fitted to understand differences in risk factors for engaging in mobile phone conversations and browsing/texting while driving. For both tasks, exposure to driving, driving experience, driving history (offences and crashes), and attitudes were significant predictors. Future mobile phone prevention efforts would benefit from development of safe attitudes and increasing risk literacy. Enforcement of mobile phone distraction should be re-engineered, as the use of task-management strategies to evade police enforcement seems to dilute its effect on the prevention of this behaviour. Some countermeasures and suggestions were proposed in the design of public education campaigns and driver-mobile phone interaction.@en

The use of mobile phones while driving remains a major human factors issue in the transport system. A significant safety concern is that driving while distracted by a mobile phone potentially modifies the driving speed leading to conflicts with other road users and consequently increases crash risk. However, the lack of systematic knowledge of the mechanisms involved in speed adaptation of distracted drivers constrains the explanation and modelling of the extent of this phenomenon. The objective of this study was to investigate speed adaptation of distracted drivers under varying road infrastructure and traffic complexity conditions. The CARRS-Q Advanced Driving Simulator was used to test participants on a simulated road with different traffic conditions, such as free flow traffic along straight roads, driving in urbanized areas, and driving in heavy traffic along suburban roads. Thirty-two licensed young drivers drove the simulator under three phone conditions: baseline (no phone conversation), hands-free and handheld phone conversations. To understand the relationships between distraction, road infrastructure and traffic complexity, speed adaptation calculated as the deviation of driving speed from the posted speed limit was modelled using a decision tree. The identified groups of road infrastructure and traffic characteristics from the decision tree were then modelled with a Generalized Linear Mixed Model (GLMM) with repeated measures to develop inferences about speed adaptation behaviour of distracted drivers. The GLMM also included driver characteristics and secondary task demands as predictors of speed adaptation. Results indicated that complex road environments like urbanization, car-following situations along suburban roads, and curved road alignment significantly influenced speed adaptation behaviour. Distracted drivers selected a lower speed while driving along a curved road or during car-following situations, but speed adaptation was negligible in the presence of high visual cutter, indicating the prioritization of the driving task over the secondary task. Additionally, drivers who scored high on self-reported safe attitudes towards mobile phone usage, and who reported prior involvement in a road traffic crash, selected a lower driving speed in the distracted condition than in the baseline. The results aid in understanding how driving task demands influence speed adaptation of distracted drivers under various road infrastructure and traffic complexity conditions.@en

Objective: The adaptive behavior of mobile phone–distracted drivers has been a topic of much discussion in the recent literature. Both simulator and naturalistic studies suggest that distracted drivers generally select lower driving speeds; however, speed adaptation is not observed among all drivers, and the mechanisms of speed selection are not well understood. The aim of this research was to apply a driver behavioral adaptation model to investigate the speed adaptation of mobile phone–distracted drivers. Methods: The speed selection behavior of drivers was observed in 3 phone conditions including baseline (no conversation) and hands-free and handheld phone conversations in a high-fidelity driving simulator. Speed adaptation in each phone condition was modeled as a function of secondary task demand and self-reported personal/psychological characteristics with a system of seemingly unrelated equations (SURE) accounting for potential correlations due to repeated measures experiment design. Results: Speed adaptation is similar between hands-free and handheld phone conditions, but the predictors of speed adaptation vary across the phone conditions. Though perceived workload of secondary task demand, self-efficacy, attitude toward safety, and driver demographics were significant predictors of speed adaptation in the handheld condition, drivers' familiarity with the hands-free interface, attitude toward safety, and sensation seeking were significant predictors in the hands-free condition. Drivers who reported more positive safety attitudes selected lower driving speeds while using phones. Conclusion: This research confirmed that behavioral adaptation models are suitable for explaining speed adaptation of mobile phone distracted drivers, and future research could be focused on further theoretical refinement.@en

The use of mobile phones while driving is increasing at an alarming rate despite the associated crash risks. A significant safety concern is that driving while distracted by a mobile phone is more prevalent among young drivers, a less experienced driving cohort with elevated crash risk. The objective of this study was to examine the gap acceptance behavior of distracted young drivers at roundabouts. The Center for Accident Research and Road Safety-Queensland Advanced Driving Simulator was used to test participants on a simulated gap acceptance scenario at roundabouts. Conflicting traffic approaching from the right of a four-legged roundabout was programmed to show a series of vehicles with the gaps between them proportionately increased from 2 s to 6 s. Thirty-Two licensed young drivers drove the simulator under three phone conditions: baseline (no phone conversation), a hands-free phone conversation, and a handheld phone conversation. Results show that distracted drivers started responding to the gap acceptance scenario when they were closer to the roundabout and they approached the roundabout at slower speeds. These drivers also decelerated at faster rates to reduce their speeds before gap acceptance compared with nondistracted drivers. Although accepted gap sizes were not significantly different across phone conditions, differences in the safety margin at various gap sizes-measured by postencroachment time (PET) between the driven vehicle and the conflicting vehicle-were statistically significant across phone conditions. PETs for distracted drivers were smaller across different gap sizes and suggest that a smaller safety margin was accepted by distracted drivers compared with nondistracted drivers.@en

The state of the practice in black spot identification uses safety performance functions based on total crash counts to identify high-risk crash sites. This paper postulates that total crash count is a result of multiple distinct risk-generating processes (RGPs), including geometric characteristics of the road, spatial features of the surrounding environment, and driver behavior factors. However, these multiple sources are ignored in current modeling methodologies that try to explain or predict crash frequencies across sites. Instead, current practice uses models that imply that a single RGP exists. This misspecification may lead to correlation of crashes with incorrect sources of contributing factors (e.g., concluding a crash is predominately caused by a geometric feature when the cause is a behavioral issue), which may ultimately lead to inefficient use of public funds and misidentification of true black spots. This study proposes a latent class model consistent with a multiple risk process theory and investigates the influence this model has on correctly identifying crash black spots. The paper presents the theoretical and corresponding methodological approach in which a Bayesian latent class model is estimated with the assumption that crashes arise from two distinct RGPs, including engineering and unobserved spatial factors. The methodology was applied to state-controlled roads in Queensland, Australia. The results were compared with an empirical Bayesian negative binomial (EB-NB) model. A comparison of goodness-of-fit measures illustrated superiority of the proposed model compared with the NB model. The detection of black spots was improved compared with the EB-NB model. In addition, modeling crashes as the result of two fundamentally separate RGPs reveals more detailed information about unobserved crash causes.@en

The use of mobile phones while driving—one of the most common driver distractions—has been a significant research interest during the most recent decade. While there has been a considerable amount research and excellent reviews on how mobile phone distractions influence various aspects of driving performance, the mechanisms by which the interactions with mobile phone affect driver performance is relatively unexamined. As such, the aim of this study is to examine the mechanisms involved with mobile phone distractions such as conversing, texting, and reading and the driving task, and subsequent outcomes. A novel human-machine framework is proposed to isolate the components and various interactions associated with mobile phone distracted driving. The proposed framework specifies the impacts of mobile phone distraction as an inter-related system of outcomes such as speed selection, lane deviations and crashes; human-car controls such as steering control and brake pedal use and human-environment interactions such as visual scanning and navigation. Eleven literature-review/meta-analyses papers and 62 recent research articles from 2005 to 2015 are critically reviewed and synthesised following a systematic classification scheme derived from the human-machine system framework. The analysis shows that while many studies have attempted to measure system outcomes or driving performance, research on how drivers interactively manage in-vehicle secondary tasks and adapt their driving behaviour while distracted is scant. A systematic approach may bolster efforts to examine comprehensively the performance of distracted drivers and their impact over the transportation system by considering all system components and interactions of drivers with mobile phones and vehicles. The proposed human-machine framework not only contributes to the literature on mobile phone distraction and safety, but also assists in identifying the research needs and promising strategies for mitigating mobile phone-related safety issues. Technology based countermeasures that can provide real-time feedback or alerts to drivers based on eye/head movements in conjunction with vehicle dynamics should be an important research direction.@en