Driver's response to a pedestrian crossing requires braking, whereby both excess and inadequate braking is directly associated with crash risk. The highly anticipated connected environment aims to increase drivers’ situational awareness by providing advanced information and assisting them during critical driving tasks such as braking. Focussing on this crucial behaviour and combined with the promise of a connected environment, the objective of this study is to examine the braking behaviour of drivers in response to a pedestrian at a zebra crossing in a connected environment. Seventy-eight participants from diverse backgrounds performed this driving task in the CARRS-Q Advanced Driving Simulator in two randomised driving scenarios: a baseline scenario (without driving aids) and a connected environment (with driving aids) scenario. A Weibull accelerated failure time duration modelling approach is adopted to model the braking behaviour of drivers. In particular, this duration model is specified to capture the panel nature of the data and unobserved heterogeneity through correlated grouped random parameters with heterogeneity-in-the-means in the Bayesian framework. Results indicate that, for most drivers in the connected environment, it takes longer to reduce their speed with less speed variation and a larger safety margin. In addition, a decision tree analysis for the braking time suggests that for older drivers, when the distance to the zebra crossing is larger in the connected environment than that in the baseline scenario, braking time is likely to increase. The model also reveals that the braking time of female drivers is longer in the connected environment compared to that of male drivers. Overall, the connected environment is associated with increased braking time by providing advanced information, giving drivers additional time to smoothly reduce their speed in response to a pedestrian at a zebra crossing, and ultimately making the vehicle–pedestrian interaction safer.

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Mobile phone distracted drivers have been reported to initiate risk-compensating behaviour depending on a multitude of factors such as roadway environment and traffic characteristics, personal demographics and psychological attributes, and mobile phone task characteristics. However, the complexities of drivers’ decisions in engaging in such behaviour are not well known. This study aims to fill this gap by developing a comprehensive multivariate ordered model in Bayesian framework for risk-compensating behaviour of distracted drivers. The multivariate setting captures the common unobserved factors between multiple types of risk-compensating behaviour. In addition, an instrumental variable is employed to account for the endogeneity between crash risk and driving behaviour. To capture the varying effects of exogenous factors as well as varying propensity of initiating risk-compensating behaviour, the model is specified with grouped random parameters and random thresholds. This model is then empirically tested by data from a survey, which was specifically designed to understand the risk-compensating behaviour of mobile phone distracted drivers in Queensland, Australia. Results indicate that the grouped random parameters random thresholds ordered model has a substantially improved fit compared to its fixed parameters/fixed thresholds counterparts, indicating that the unobserved heterogeneity is significant, both in the effects of exogenous factors and in the propensity of initiating risk-compensating behaviour. It is found that drivers’ decisions to engage in different types of risk-compensating behaviour are correlated, indicating that they generally initiate different types of risk-compensating strategies simultaneously. Overall, the perceived crash risk has been found to increase the likelihood of risk-compensating behaviour among distracted drivers. Demanding secondary tasks and complex road traffic environment are also found to initiate risk-compensating behaviours such as increasing headway, reducing driving speed and visual scanning of the surrounding environment.@en

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.

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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.

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The connected environment provides real-time information about surrounding traffic; such information can be helpful in complex driving manoeuvres, such as lane-changing, that require information about surrounding vehicles. Lane-changing modelling in the connected environment has so far received little attention. This is due to the novelty of connected environment, and the consequent scarcity of data. A behaviourally sound lane-changing model is not even available for the traditional environment; that is, an environment without driving aids. To address this need, this study develops a game theory-based mandatory lane-changing model (AZHW model) for the traditional environment and extends it for the connected environment. The CARRS-Q advanced driving simulator is used to collect high-quality vehicle trajectory data for the connected environment. The developed models (for traditional environment and connected environment) are calibrated using NGSIM and simulator data in a bi-level calibration framework. The performance of the models has been rigorously evaluated using various performance indicators. These include the true positive, false positive, detection rate, false alarm rate, time prediction error, and location prediction error. Results consistently show that the developed game theory-based models can effectively capture mandatory lane-changing decisions with a high degree of accuracy. Furthermore, the performance of the developed AZHW models is compared with representative game theory-based lane-changing models in the literature. The comparative analysis reveals that the AZHW models developed in this study outperform existing models.@en

Missing data can lead to biased and inefficient parameter estimates in statistical models, depending on the missing data mechanism. Count regression models are no exception, with missing data leading to incorrect inferences about the effects of explanatory variables. A convenient approach for dealing with missing data is to remove observations with incomplete records prior to the analysis – often referred to as case-wise deletion. Removing incomplete records, however, reduces the sample size, increases standard errors and, if data are not missing completely at random, produces biased parameter estimates. A more complex approach is multiple imputation, which provides an estimate of the modelling uncertainty created by the data ‘missing-ness’, as distinct from the natural variation in the data. However, multiple imputation produces biased parameter estimates if the probability of missing data is related to the observed data – or is endogenous. Latent variable modelling has recently been introduced as an alternative approach for dealing with missing data, but it comes at a high computational cost and complexity. Despite fairly extensive methodological advancements in statistical literature, case-wise deletion is commonly employed to deal with missing data in statistical models of transport, while the multiple imputation and latent variable approaches remain relatively unexplored. More importantly, the performance of these approaches has not been tested across different types of data missing-ness. To address these gaps, this study aims to contrast case-wise deletion with multiple imputation and latent variable approaches in dealing with missing data in count regression models. We compare the performance of these three approaches using crash count models estimated against empirical data obtained from state controlled roads in Queensland, Australia. A quasi-experimental evaluation of data missing-ness is then conducted by extracting three data subsets from the original dataset, each with a unique missing data mechanism (with terminology adopted from the statistical literature): missing completely at random, missing at random, and missing not at random. The three approaches are then applied to each data subset and the results are compared in terms of bias, precision of parameter estimates, and goodness-of-fit. The findings indicate that multiple imputation is the most effective approach when data are missing either completely at random or at random, whereas the latent variable approach is more effective when data are missing not at random. However, the effectiveness of the latent variable approach is dependent on the availability of suitable variables as instruments in the data.@en

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.@en

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.@en