Sharing roads with automated vehicles: A questionnaire investigation from drivers', cyclists' and pedestrians' perspectives.

Despite the promised benefits, the introduction of Automated Vehicles (AVs) on roads will be confronted by many challenges, including public readiness to use those vehicles and share the roads with them. The risk profile of road users is a key determinant of their safety on roads. However, the relation of such risk profiles to road users' perception of AVs is less known. This study aims to address the above research gap by conducting a cross-sectional survey to investigate the acceptance of Fully Automated Vehicles (FAVs) among different non-AV-user groups (i.e., pedestrians, cyclists, and conventional vehicle drivers). A total of 1205 road users in Queensland (Australia) took part in the study, comprising 456 pedestrians, 339 cyclists, and 410 drivers. The Theory of Planned Behaviour (TPB) is used as the theoretical model to examine road users' intention towards sharing roads with FAVs. The risk profile of the participants derives from established behavioural scales and individual characteristics are also included in the acceptance model. The study results show that pedestrians reported lowest intention in terms of sharing roads with FAVs among the three groups. Drivers and cyclists in a lower risk profile group were more likely to report higher intention to share roads with FAVs than those in a higher risk profile group. As age increased, pedestrians were less likely to accept sharing roads with FAVs. Drivers who had more exposure time on roads were more likely to accept sharing roads with FAVs. Male drivers reported higher intention towards sharing roads than female drivers. Overall, the study provides new insights into public perceptions of FAVs, specifically from the non-AV-user perspective. It sheds light on the obstacles that future AVs may encounter and the types of road users that AV manufacturers and policymakers should consider closely. Specifically, groups such as older pedestrians and road users who engage in more risky behaviours might resist or delay the integration of AVs.

Effects of design consistency on run-off-road crashes: An application of a Random Parameters Negative Binomial Lindley model.

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.

"I did not see that coming": A latent variable structural equation model for understanding the effect of road predictability on crashes along horizontal curves.

Driver anticipation plays a crucial role in crashes along horizontal curves. Anticipation is related to road predictability and can be influenced by roadway geometric design. Therefore, it is essential to understand which geometric design elements can influence anticipation and cause the road to be (un)predictable. This exercise, however, is not straightforward because anticipation is individual-specific whereas road geometric design is location-specific; anticipation is latent and measuring it may not be trivial; anticipation may have several stages from the preceding tangent until the midst of the curve; and not all drivers anticipate in the same way and thus there may well be unobserved heterogeneity in the effect of anticipation on crash risk. Despite methodological advancements in crash risk modelling, there is no econometric model that can adequately explain the above complexities. This study aims to fill this gap by developing an econometric model with a new latent variable, named 'predictability' that is measured by individual-specific driving behaviour indicators and predicted by location-specific road geometric factors. The model is specified with random parameters to account for unobserved heterogeneity and is empirically tested by a unique dataset including detailed geometric design and driver behaviour data obtained for 156 curves in the Netherlands. Results indicate that higher exposure and uphill vertical grade are associated with increased likelihood of vehicle crashes along horizontal curves, whereas adequate superelevation and higher predictability are associated with decreased likelihood of those crashes. Pavement friction influences this likelihood too but it has varied effects. Road predictability is influenced by the differences in angle of horizontal curves, vertical grades, and width of consecutive road segments.

A home-based approach to understanding seatbelt use in single-occupant vehicles in Tennessee: Application of a latent class binary logit model.

Although the enforcement of seatbelt use is considered to be an effective strategy in reducing road injuries and fatalities, lack of seatbelt use still accounts for a substantial proportion of fatal crashes in Tennessee, United States. This problem has raised the need to better understand factors influencing seatbelt use. These factors may arise from spatial/temporal characteristics of a driving location, type of vehicle, demographic and socioeconomic attributes of the vehicle occupants, driver behaviours, attitudes, and social norms. However, the above factors may not have the same effects on seatbelt use across different individuals. In addition, the behavioural factors are usually difficult to measure and may not always be readily available. Meanwhile, residential locations of vehicle occupants have been shown to be associated with their behavioural patterns and thus may serve as a proxy for behavioural factors. However, the suitability of geographic and residential locations of vehicle occupants to understand the seatbelt use behaviour is not known to date. This study aims to fill the above gaps by incorporating the residential location characteristics of vehicle occupants in addition to their demographics and crash characteristics into their seatbelt use while accounting for the varying effects of these factors on individual seatbelt use choices. To achieve this goal, empirical data are collected for vehicular crashes in Tennessee, United States, and the home addresses of vehicle occupants at the time of the crash are geocoded and linked with the census tract information. The resulting data is then used as explanatory variables in a latent class binary logit model to investigate the determinants of vehicle occupants' seatbelt use at the time of the crash. The latent class specification is employed to capture the unobserved heterogeneity in data. Results show that Tennessean drivers belong to two general categories-conformist and eccentric-with gender, vehicle type, and income per capita determining the likelihood of these categories. Overall, male drivers, younger drivers, and drivers who have consumed drugs are less likely to wear a seatbelt, whereas drivers who come from areas with higher population density, travel time, and income per capita are more likely to wear a seatbelt. In addition, driving during the day and in rainy weather are associated with an increased likelihood of seatbelt use. The findings of this study will help developing effective policies to increase seatbelt use rate and improve safety.

Applying a joint model of crash count and crash severity to identify road segments with high risk of fatal and serious injury crashes.

Both crash count and severity are thought to quantify crash risk at defined transport network locations (e.g. intersections, a particulate section of highway, etc.). Crash count is a measure of the likelihood of occurring a potential harmful event, whereas crash severity is a measure of the societal impact and harm to the society. As the majority of safety improvement programs are focused on preventing fatal and serious injury crashes, identification of high-risk sites-or blackspots-should ideally account for both severity and frequency of crashes. Past research efforts to incorporate crash severity into the identification of high-risk sites include multivariate crash count models, equivalent property damage only models and two-stage mixed models. These models, however, often require suitable distributional assumptions for computational efficiency, neglect the ordinal nature of crash severity, and are inadequate for capturing unobserved heterogeneity arising from possible correlations between crash counts of different severity levels. These limitations can ultimately lead to inefficient allocation of resources and misidentification of sites with high risk of fatal and serious injury crashes. Moreover, the implication of these models in blackspot identification is an important, unanswered question. While a joint econometric model of crash count and crash severity has the flexibility to account for the limitations mentioned previously, its ability to identify high-risk sites also needs to be examined. This study aims to fill this research gap by employing the joint model for blackspot identification. Using data from state-controlled roads in Queensland, Australia, a new risk score is developed based on predicted crash counts by severity, weighted by the cost ratio of severity levels. This weighted risk score is then used for identifying road segments with high risk of fatal and injury crashes. Results show that the joint model of crash count and crash severity has substantially improved prediction accuracy compared to the traditional count models. The correlation between crash counts of different severity levels captures the unobserved heterogeneity caused by the extra-variation in total crash counts and moderates the parameters in the joint model. In comparison with the traditional approaches, the proposed weighted risk score approach with the joint model of crash count and crash severity leads to the identification of a higher number of fatal and serious injury crashes in the top ranked sites flagged for safety improvements.

Incorporating behavioral variables into crash count prediction by severity: A multivariate multiple risk source approach.

The frequency and severity of traffic crashes have commonly been used as indicators of crash risk on transport networks. Comprehensive modeling of crash risk should account for both frequency and injury severity-capturing both the extent and intensity of transport risk for designing effective safety improvement programs. Previous research has revealed that crashes are correlated across severity categories because of the combined influence of risk factors, observed or unobserved. Moreover, crashes are the outcomes of a multitude of factors related to roadway design, traffic operations, pavement conditions, driver behavior, human factors, and environmental characteristics, or in more general terms: factors reflect both engineering and non-engineering risk sources. Perhaps not surprisingly, engineering risk sources have dominated the list of variables in the mainstream modeling of crashes whereas non-engineering sources, in particular, behavioral factors, are crucially omitted. It is plausible to assume that crash contributing factors from the same risk source affect crashes in a similar manner, but their influences vary across different risk sources. Conventional crash frequency modeling hypothesizes that the total crash count at any roadway site is well-approximated by a single risk source to which several explanatory variables contribute collaboratively. The conventional formulation is not capable of accounting for variations between risk sources; therefore, is unable to discriminate distinct impacts between engineering variables and non-engineering variables. To address this shortcoming, this study contributes to the development of multivariate multiple risk source regression, a robust modeling technique to model crash frequency and severity simultaneously. The multivariate multiple risk source regression method applied in this study can effectively capture the correlation between severity levels of crash counts while identifyinging the varying effects of crash contributing factors originated from distinct sources. Using crashes on Wisconsin rural two-lane highways, two risk sources - engineering and behavioral - were employed to develop proposed models. The modeling results were compared with a single equation negative binomial (NB) model, and a univariate multiple risk source model. The results show that the multivariate multiple risk source model significantly outperforms the other models in terms of statistical fit across several measures. The study demonstrates a unique approach to explicitly incorporating behavioral factors into crash prediction models while taking crash severity into consideration. More importantly, the parameter estimates provide more insight into the distinct sources of crash risk, which can be used to further inform safety practitioners and guide roadway improvement programs.

Effects of globally obtained informative priors on bayesian safety performance functions developed for Australian crash data.

The precision and bias of Safety Performance Functions (SPFs) heavily rely on the data upon which they are estimated. When local (spatially and temporally representative) data are not sufficiently available, the estimated parameters in SPFs are likely to be biased and inefficient. Estimating SPFs using Bayesian inference may moderate the effects of local data insufficiency in that local data can be combined with prior information obtained from other parts of the world to incorporate additional evidence into the SPFs. In past applications of Bayesian models, non-informative priors have routinely been used because incorporating prior information in SPFs is not straightforward. The previous few attempts to employ informative priors in estimating SPFs are mostly based on local prior knowledge and assuming normally distributed priors. Moreover, the unobserved heterogeneity in local data has not been taken into account. As such, the effects of globally derived informative priors on the precision and bias of locally developed SPFs are essentially unknown. This study aims to examine the effects of globally informative priors and their distribution types on the precision and bias of SPFs developed for Australian crash data. To formulate and develop global informative priors, the means and variances of parameter estimates from previous research were critically reviewed. Informative priors were generated using three methods: 1) distribution fitting, 2) endogenous specification of dispersion parameters, and 3) hypothetically increasing the strength of priors obtained from distribution fitting. In so doing, the mean effects of crash contributing factors across the world are significantly different than those same effects in Australia. A total of 25 Bayesian Random Parameters Negative Binomial SPFs were estimated for different types of informative priors across five sample sizes. The means and standard deviations of posterior parameter estimates as well as SPFs goodness of fit were compared between the models across different sample sizes. Globally informative prior for the dispersion parameter substantially increases the precision of a local estimate, even when the variance of local data likelihood is small. In comparison with the conventional use of Normal distribution, Logistic, Weibull and Lognormal distributions yield more accurate parameter estimates for average annual daily traffic, segment length and number of lanes, particularly when sample size is relatively small.

Applying a random parameters Negative Binomial Lindley model to examine multi-vehicle crashes along rural mountainous highways in Malaysia.

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.

Applying fractional split model to examine the effects of roadway geometric and traffic characteristics on speeding behavior.

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.