ORCID Profile
0000-0003-1016-110X
Current Organisations
University of Colorado at Boulder
,
Queensland University of Technology (QUT)
,
Queensland University of Technology
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Transport Engineering | Road transportation and freight services | Transport engineering | Transportation logistics and supply chains | Civil Engineering | Road Transportation and Freight Services | Transportation and Freight Services
Road Safety | Road Passenger Movements (excl. Public Transport) |
Publisher: Elsevier BV
Date: 09-2023
Publisher: Elsevier BV
Date: 09-2023
Publisher: Elsevier BV
Date: 06-2023
Publisher: Elsevier BV
Date: 05-2023
Publisher: Elsevier BV
Date: 05-2014
DOI: 10.1016/J.AAP.2014.01.007
Abstract: Hot spot identification (HSID) aims to identify potential sites-roadway segments, intersections, crosswalks, interchanges, r s, etc.-with disproportionately high crash risk relative to similar sites. An inefficient HSID methodology might result in either identifying a safe site as high risk (false positive) or a high risk site as safe (false negative), and consequently lead to the misuse the available public funds, to poor investment decisions, and to inefficient risk management practice. Current HSID methods suffer from issues like underreporting of minor injury and property damage only (PDO) crashes, challenges of accounting for crash severity into the methodology, and selection of a proper safety performance function to model crash data that is often heavily skewed by a preponderance of zeros. Addressing these challenges, this paper proposes a combination of a PDO equivalency calculation and quantile regression technique to identify hot spots in a transportation network. In particular, issues related to underreporting and crash severity are tackled by incorporating equivalent PDO crashes, whilst the concerns related to the non-count nature of equivalent PDO crashes and the skewness of crash data are addressed by the non-parametric quantile regression technique. The proposed method identifies covariate effects on various quantiles of a population, rather than the population mean like most methods in practice, which more closely corresponds with how black spots are identified in practice. The proposed methodology is illustrated using rural road segment data from Korea and compared against the traditional EB method with negative binomial regression. Application of a quantile regression model on equivalent PDO crashes enables identification of a set of high-risk sites that reflect the true safety costs to the society, simultaneously reduces the influence of under-reported PDO and minor injury crashes, and overcomes the limitation of traditional NB model in dealing with preponderance of zeros problem or right skewed dataset.
Publisher: Elsevier BV
Date: 11-2016
Publisher: Wiley
Date: 19-07-2017
DOI: 10.1111/JACE.15071
Publisher: Elsevier BV
Date: 05-2023
Publisher: Public Library of Science (PLoS)
Date: 21-06-2018
Publisher: SAGE Publications
Date: 2009
DOI: 10.3141/2103-05
Abstract: This study proposes a framework of a model-based hot spot identification method by applying full Bayes (FB) technique. In comparison with the state-of-the-art approach [i.e., empirical Bayes method (EB)], the advantage of the FB method is the capability to seamlessly integrate prior information and all available data into posterior distributions on which various ranking criteria could be based. With intersection crash data collected in Singapore, an empirical analysis was conducted to evaluate the following six approaches for hot spot identification: ( a) naive ranking using raw crash data, ( b) standard EB ranking, ( c) FB ranking using a Poisson-gamma model, ( d) FB ranking using a Poisson-lognormal model, ( e) FB ranking using a hierarchical Poisson model, and ( f) FB ranking using a hierarchical Poisson (AR-1) model. The results show that ( a) when using the expected crash rate–related decision parameters, all model-based approaches perform significantly better in safety ranking than does the naive ranking method, and ( b) the FB approach using hierarchical models significantly outperforms the standard EB approach in correctly identifying hazardous sites.
Publisher: SAGE Publications
Date: 2016
DOI: 10.3141/2601-11
Abstract: 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.
Publisher: Elsevier
Date: 2021
Publisher: Elsevier BV
Date: 05-2017
DOI: 10.1016/J.AAP.2017.03.002
Abstract: Mountainous highways generally associate with complex driving environment because of constrained road geometries, limited cross-section elements, inappropriate roadside features, and adverse weather conditions. As a result, single-vehicle (SV) crashes are overrepresented along mountainous roads, particularly in developing countries, but little attention is known about the roadway geometric, traffic and weather factors contributing to these SV crashes. As such, the main objective of the present study is to investigate SV crashes using detailed data obtained from a rigorous site survey and existing databases. The final dataset included a total of 56 variables representing road geometries including horizontal and vertical alignment, traffic characteristics, real-time weather condition, cross-sectional elements, roadside features, and spatial characteristics. To account for structured heterogeneities resulting from multiple observations within a site and other unobserved heterogeneities, the study applied a random parameters negative binomial model. Results suggest that rainfall during the crash is positively associated with SV crashes, but real-time visibility is negatively associated. The presence of a road shoulder, particularly a bitumen shoulder or wider shoulders, along mountainous highways is associated with less SV crashes. While speeding along downgrade slopes increases the likelihood of SV crashes, proper delineation decreases the likelihood. Findings of this study have significant implications for designing safer highways in mountainous areas, particularly in the context of a developing country.
Publisher: American Physiological Society
Date: 06-2001
DOI: 10.1152/JAPPL.2001.90.6.2420
Abstract: Heat exposure early in ovine pregnancy results in placental insufficiency and intrauterine growth restriction (PI-IUGR). We hypothesized that heat exposure in this model disrupts placental structure and reduces placental endothelial nitric oxide synthase (eNOS) protein expression. We measured eNOS protein content and performed immunohistochemistry for eNOS in placentas from thermoneutral (TN) and hyperthermic (HT) animals killed at midgestation (90 days). Placental histomorphometry was compared between groups. Compared with the TN controls, the HT group showed reduced delivery weights (457 ± 49 vs. 631 ± 21 g P 0.05) and a trend for reduced placentome weights (288 ± 61 vs. 554 ± 122 g P = 0.09). Cotyledon eNOS protein content was reduced by 50% in the HT group ( P 0.03). eNOS localized similarly to the vascular endothelium and binucleated cells (BNCs) within the trophoblast of both experimental groups. HT cotyledons showed a reduction in the ratio of fetal to maternal stromal tissue (1.36 ± 0.36 vs. 3.59 ± 1.2 P≤ 0.03). We conclude that eNOS protein expression is reduced in this model of PI-IUGR and that eNOS localizes to both vascular endothelium and the BNC. We speculate that disruption of normal vascular development and BNC eNOS production and function leads to abnormal placental vascular tone and blood flow in this model of PI-IUGR.
Publisher: Informa UK Limited
Date: 04-10-2022
Publisher: Springer US
Date: 2009
Publisher: Elsevier BV
Date: 04-2017
DOI: 10.1016/J.AAP.2017.01.018
Abstract: 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.
Publisher: Elsevier BV
Date: 02-2022
Publisher: Public Library of Science (PLoS)
Date: 06-09-2017
Publisher: Elsevier BV
Date: 02-2022
DOI: 10.1016/J.AAP.2021.106527
Abstract: The Empirical Bayes approach for before-after evaluation methodology utilizing the negative binomial model does not account well for unobserved heterogeneity. Building on the Empirical Bayes approach, the objective of this study was to propose a framework to accommodate unobserved heterogeneity in before-after countermeasure evaluation. In particular, this study has proposed a simulation-based Empirical Bayes approach by applying the panel random parameters negative binomial model with parameterized overdispersion (PRNB-PO) to evaluate the effectiveness of engineering treatments. The proposed framework has been tested for the wide centerline treatment (WCLT) on rural two-lane two-way highways in Australia. The empirical analysis included 511 km of WCLT treated highways in a before-after evaluation within a time period of 2010 - 2018 and 430 km of reference sites in Queensland, Australia. The PRNB-PO models outperformed the traditional negative binomial models in terms of goodness-of-fit and prediction performance for total injury crashes, and fatal and serious injury (FSI) crashes. The simulation-based Empirical Bayes approach using the PRNB-PO model resulted in more precise estimates of crash modification factors than the standard Empirical Bayes approach. The WCLT is found to result in significant reductions in total injury crashes by 28.21% (95% confidence interval (CI) = 22.92 - 33.50%), FSI crashes by 13.90% (95% CI = 6.99 - 20.81%), and head-on crashes by 25.45% (95% CI = 14.87 - 36.03%). Overall, WCLT is an effective engineering treatment and should be considered a low-cost countermeasure on rural two-lane two-way highways.
Publisher: Elsevier BV
Date: 12-2023
Publisher: Elsevier BV
Date: 09-2018
DOI: 10.1016/J.AAP.2018.03.020
Abstract: 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.
Publisher: Elsevier BV
Date: 07-2021
Publisher: Elsevier BV
Date: 05-2021
Publisher: Elsevier BV
Date: 09-2019
Publisher: Elsevier BV
Date: 09-2019
Publisher: Elsevier BV
Date: 05-2013
Publisher: Elsevier BV
Date: 2015
Publisher: Inderscience Publishers
Date: 2014
Publisher: Elsevier BV
Date: 2024
Publisher: Wiley
Date: 24-09-2020
DOI: 10.1111/JACE.17470
Publisher: Elsevier BV
Date: 2023
DOI: 10.1016/J.AAP.2022.106897
Abstract: Injury severity studies typically rely on police-reported crash data to examine risk factors associated with traffic injuries. The police crash database includes essential information on roadways, crashes and driver-vehicle characteristics but may not contain accurate and sufficient information on traffic injuries. Despite sizable efforts on injury severity modelling, very few studies have employed hospital records to classify injury severities accurately. As such, the inferences drawn from the police-recorded injury severity classifications may be questionable. This study investigates factors affecting road traffic injuries of motor vehicle crashes in two approaches (1) police-reported injury severity data and (2) a data fusion approach linking police and hospital records. Data from 2015 to 2019 were collected from the Abu Dhabi Traffic Police Department and linked with hospital records by the Department of Health, Abu Dhabi. A total of 6,333 casualty crashes were categorised into non-severe, severe, and fatal crashes following police-reported data and non-hospitalised, hospitalised and fatal crashes based on the police-hospital linked data. The state-of-the-art random thresholds random parameters hierarchical ordered Probit models were then employed to examine the differences in factors affecting crash-injury severities between police-reported and police-hospital linked data. While there are similarities between these two approaches, there are numerous notable differences in injury severity factors. For instance, head-on collisions are associated with high crash-injury severities in the model with police-hospital linked data, but they tend to show low injury severities in the model with police-reported data. In addition, the police-reported approach identifies that crashes occurred in remote areas and angle collisions are associated with low injury severities, which is not intuitive. These findings highlight that modelling the misclassified injury severity in police crash data may lead to wrong estimations and misleading inferences. Instead, the data fusion approach of police-hospital linked data provides critical and accurate insights into road traffic injuries and is a valuable approach for understanding traffic injuries.
Publisher: Elsevier BV
Date: 08-0990
Publisher: SAGE Publications
Date: 2008
DOI: 10.3141/2048-08
Abstract: Crash statistics in Singapore from 2001 to 2005 have shown that motorcycles were involved in about 54% of intersection crashes. The overall involvement of motorcycles in crashes as the not-at-fault party was about 43%, but at intersections the corresponding percentage is increased to 57%. Quasi-induced exposure estimates have shown that the motorcycle exposure rate at signalized intersections was 41.7% even though motorcycles accounted for only 19% of the vehicle population. This study seeks to examine, in greater detail, the problem of motorcycle exposure at signalized intersections—in particular, the exposure caused by potential crashes with red-light-running vehicles from the conflicting stream. For that purpose, four signalized intersections are investigated. Results show that motorcycles are more exposed because they tend to accumulate near the stop line during the red phase to facilitate an earlier discharge during the initial period of the green, which is the more vulnerable period. At sites in which there are more weaving opportunities because the lanes are wider or there are exclusive right-turn lanes, the accumulation is higher and hence exposure is increased. The analysis also shows that the presence of heavy vehicles tends to decrease motorcycle exposure because motorcyclists’ weaving opportunities become restricted and they are more reluctant to weave past or queue alongside the heavy vehicles effects intensify for narrower lane widths.
Publisher: Elsevier BV
Date: 09-2020
Publisher: Elsevier BV
Date: 12-2022
Publisher: Elsevier BV
Date: 03-2009
DOI: 10.1016/J.AAP.2008.12.010
Abstract: Singapore crash statistics from 2001 to 2006 show that the motorcyclist fatality and injury rates per registered vehicle are higher than those of other motor vehicles by 13 and 7 times, respectively. The crash involvement rate of motorcyclists as victims of other road users is also about 43%. The objective of this study is to identify the factors that contribute to the fault of motorcyclists involved in crashes. This is done by using the binary logit model to differentiate between at-fault and not-at-fault cases and the analysis is further categorized by the location of the crashes, i.e., at intersections, on expressways and at non-intersections. A number of explanatory variables representing roadway characteristics, environmental factors, motorcycle descriptions, and rider demographics have been evaluated. Time trend effect shows that not-at-fault crash involvement of motorcyclists has increased with time. The likelihood of night time crashes has also increased for not-at-fault crashes at intersections and expressways. The presence of surveillance cameras is effective in reducing not-at-fault crashes at intersections. Wet-road surfaces increase at-fault crash involvement at non-intersections. At intersections, not-at-fault crash involvement is more likely on single-lane roads or on median lane of multi-lane roads, while on expressways at-fault crash involvement is more likely on the median lane. Roads with higher speed limit have higher at-fault crash involvement and this is also true on expressways. Motorcycles with pillion passengers or with higher engine capacity have higher likelihood of being at-fault in crashes on expressways. Motorcyclists are more likely to be at-fault in collisions involving pedestrians and this effect is higher at night. In multi-vehicle crashes, motorcyclists are more likely to be victims than at-fault. Young and older riders are more likely to be at-fault in crashes than middle-aged group of riders. The findings of this study will help to develop more targeted countermeasures to improve motorcycle safety and more cost-effective safety awareness program in motorcyclist training.
Publisher: Elsevier BV
Date: 08-2023
Publisher: Elsevier BV
Date: 2019
DOI: 10.1016/J.AAP.2018.09.020
Abstract: 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 r s, 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.
Publisher: Elsevier BV
Date: 02-2020
Publisher: Springer Nature Singapore
Date: 2020
Publisher: Elsevier BV
Date: 11-2022
Publisher: SAGE Publications
Date: 22-03-2019
Abstract: Response time (RT) is a critical human factor that influences traffic flow characteristics and traffic safety, and is governed by drivers’ decision-making behavior. Unlike the traditional environment (TE), the connected environment (CE) provides information assistance to drivers. This in-vehicle informed environment can influence drivers’ decision-making and thereby their RTs. Therefore, to ascertain the impact of CE on RT, this study develops RT estimation methodologies for TE (RTEM-TE) and CE (RTEM-CE), using vehicle trajectory data. Because of the intra-lingual inconsistency among traffic engineers, modelers, and psychologists in the usage of the term RT, this study also provides a ubiquitous definition of RT that can be used in a wide range of applications. Both RTEM-TE and RTEM-CE are built on the fundamental stimulus–response relationship, and they utilize the wavelet-based energy distribution of time series of speeds to detect the stimulus–response points. These methodologies are rigorously examined for their efficiency and accuracy using noise-free and noisy synthetic data, and driving simulator data. Analysis results demonstrate the excellent performance of both the methodologies. Moreover, the analysis shows that the mean RT in CE is longer than the mean RT in TE.
Publisher: Elsevier BV
Date: 05-2022
Publisher: Elsevier BV
Date: 09-2020
Publisher: Oxford University Press (OUP)
Date: 21-12-2022
DOI: 10.1093/TSE/TDAC040
Abstract: Freeway on-r s suffer high crash risks due to frequent merging behaviours. This study developed hazard-based duration models to investigate the merging time interval on freeway on-r s based on microscopic trajectory data. Fixed effect, random effect and random parameters Weibull distributed accelerated failure time models were developed to capture merging time as a function of various dynamic variables. The random parameters model was found to outperform the two counterparts since the unobserved heterogeneity of in idual drivers was captured. Modelling estimation results indicate that drivers along the merging section with an auxiliary lane perform a smooth merging process and are easily affected by speed variables. Dynamics of leading and following vehicles on the merging and target lanes are found to influence the merging time interval for merging without an auxiliary lane, whereas the influence of surrounding vehicles is marginal for those with an auxiliary lane. The findings of this study identify potential countermeasures for improving safety during the merging process.
Publisher: Elsevier BV
Date: 06-2020
Publisher: Cambridge University Press (CUP)
Date: 12-09-2011
DOI: 10.1017/S0373463311000257
Abstract: Navigational collisions are one of the major safety concerns for many seaports. Despite the extent of work recently done on collision risk analysis in port waters, little is known about the influential factors of the risk. This paper develops a technique for modelling collision risks in port waterways in order to examine the associations between the risks and the geometric, traffic, and regulatory control characteristics of waterways. A binomial logistic model, which accounts for the correlations in the risks of a particular fairway at different time periods, is derived from traffic conflicts and calibrated for the Singapore port fairways. Results show that the fairways attached to shoreline, traffic intersection and international fairway attribute higher risks, whereas those attached to confined water and local fairway possess lower risks. Higher risks are also found in the fairways featuring higher degree of bend, lower depth of water, higher numbers of cardinal and isolated danger marks, higher density of moving ships and lower operating speed. The risks are also found to be higher at night.
Publisher: SAGE Publications
Date: 2015
DOI: 10.3141/2512-01
Abstract: Pedestrian crashes represent about 40% of total fatal crashes in low-income developing countries. Although many pedestrian crashes in these countries occur at unsignalized intersections such as roundabouts, studies focusing on this issue are limited. The objective of this study was to develop safety performance functions for pedestrian crashes at modern roundabouts to identify significant roadway geometric, traffic, and land use characteristics related to pedestrian safety. Detailed data, including various forms of exposure, geometric and traffic characteristics, and spatial factors such as proximity to schools and to drinking establishments were collected from a s le of 22 modern roundabouts in Addis Ababa, Ethiopia, representing about 56% of such roundabouts in Addis Ababa. To account for spatial correlation resulting from multiple observations at a roundabout, both the random effect Poisson (REP) and random effect negative binomial (RENB) regression models were estimated. Model goodness-of-fit statistics revealed a marginally superior fit of the REP model to the data compared with the RENB model. Pedestrian crossing volume and the product of traffic volumes along major and minor roads had significant and positive associations with pedestrian crashes at roundabouts. The presence of a public transport (bus or taxi) terminal beside a roundabout was associated with increased pedestrian crashes. Although the maximum gradient of an approach road was negatively associated with pedestrian safety, the provision of a raised median along an approach appeared to increase pedestrian safety at roundabouts. Remedial measures were identified for combating pedestrian safety problems at roundabouts in the context of a developing country.
Publisher: Elsevier BV
Date: 02-2012
Publisher: Elsevier BV
Date: 09-2022
Publisher: Elsevier BV
Date: 09-2021
Publisher: Informa UK Limited
Date: 18-01-2022
Publisher: Elsevier BV
Date: 04-2019
Publisher: SAGE Publications
Date: 2011
DOI: 10.3141/2243-05
Abstract: Achieving sustainability is a major goal of many urban transport systems. To attain an efficient, safe, and sustainable transport system, many innovative policies have been attempted in the past. Those policies often require smart technologies to assist in the implementation process and to enhance effectiveness. This paper discusses how sustainability can be promoted by embedding smart technologies in a modern transport system. In particular, this paper studies the transport system of Singapore to see how it is addressing sustainability through the use of smart technologies. Various technological initiatives in managing traffic flow, monitoring and enforcement, sharing real-time information, and managing revenues are discussed in light of their potential to address sustainability issues. The Singapore experience provides a useful reference for cities that intend to develop and promote a sustainable transport system.
Publisher: Elsevier BV
Date: 12-2015
Publisher: Elsevier BV
Date: 2023
DOI: 10.1016/J.AAP.2022.106882
Abstract: Right-turn crashes (or left-turn crashes for the US or similar countries) represent over 40 % of signalized intersection crashes in Queensland, Australia. Protected right-turn phasings are a widely used countermeasure for right-turn crashes, but the research findings on their effects across different crash types and intersection types are not consistent. Methodologically, the Empirical Bayes and Full Bayes techniques are generally applied for before-after evaluations, but the inclusion of heterogeneous models within these techniques has not been considered much. Addressing these research gaps, the objective of this study is to evaluate the effectiveness of protected right-turn signal phasings at signalized intersections employing heterogeneous count data models with the Empirical Bayes and Full Bayes techniques. In particular, the Empirical Bayes approach based on random parameters Poisson-Gamma models (simulation-based Empirical Bayes), and the Full Bayes approach based on random parameters Poisson-Lognormal intervention models (simulation-based Full Bayes) are applied. A total of 69 Cross intersections (with ten treated sites) and 47 T intersections (with six treated sites) from Southeast Queensland in Australia were included in the analysis to estimate the effects of protected right-turn signal phasings on various crash types. Results show that the change of signal phasing from a permissive right-turn phasing to the protected right-turn phasing at cross and T intersections reduces about 87 % and 91 % of right-turn crashes, respectively. In addition, the effect of protected right-turn phasings on rear-end crashes was not significant. The heterogenous count data models significantly address extra Poisson variation, leading to efficient safety estimates in both simulation-based Empirical Bayes and simulation-based Full Bayes approaches. This study demonstrates the importance of accounting for unobserved heterogeneity for the before-after evaluation of engineering countermeasures.
Publisher: Elsevier BV
Date: 12-2023
Publisher: Elsevier BV
Date: 06-2022
DOI: 10.1016/J.AAP.2022.106644
Abstract: Traffic conflict techniques represent the state-of-the-art for road safety assessments. However, the lack of research on transferability of conflict-based crash risk models, which refers to applying the developed crash risk estimation models to a set of external sites, can reduce their appeal for large-scale traffic safety evaluations. Therefore, this study investigates the transferability of multivariate peak-over threshold models for estimating crash frequency-by-severity. In particular, the study proposes two transferability approaches: (i) an uncalibrated approach involving a direct application of the uncalibrated base model to the target sites and (ii) a threshold calibration approach involving calibration of conflict thresholds of the conflict indicators. In the latter approach, the conflict thresholds of the Modified Time-To-Collision (MTTC) and Delta-V indicators were calibrated using local data from the target sites. Finally, the two transferability approaches were compared with a complete re-estimation approach where all the model parameters were estimated using local data. All three approaches were tested for a target set of signalized intersections in Southeast Queensland, Australia. Traffic movements at the target intersections were observed using video cameras for two days (12 h each day). The road user trajectories and rear-end conflicts were extracted using an automated artificial intelligence-based algorithm utilizing state-of-the-art Computer Vision methods. The base models developed in an earlier study were then transferred to the target sites using the two transferability approaches and the local data from the target sites. Results show that the threshold calibration approach provides the most accurate and precise predictions of crash frequency-by-severity for target sites. Thus, for peak-over threshold models, the threshold parameter is the most important, and its calibration improves the performance of the base models. The complete re-estimation of models for in idual target sites yields inferior fits and less precise crash estimates than the two transferability approaches since they utilize fewer traffic conflict extremes in their development than the larger dataset utilized in base model development. Therefore, the study results can significantly advance the applicability of traffic conflict models for crash risk estimation at transport facilities.
Publisher: Elsevier BV
Date: 12-2020
Publisher: Elsevier BV
Date: 08-2021
Publisher: Informa UK Limited
Date: 13-12-2021
Publisher: Elsevier BV
Date: 03-2023
Publisher: Elsevier BV
Date: 12-2020
Publisher: Springer Science and Business Media LLC
Date: 12-1993
DOI: 10.1038/366725A0
Publisher: Springer International Publishing
Date: 15-06-2017
Publisher: Informa UK Limited
Date: 23-03-2017
DOI: 10.1080/15389588.2017.1278628
Abstract: 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. 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 sychological characteristics with a system of seemingly unrelated equations (SURE) accounting for potential correlations due to repeated measures experiment design. 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. 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.
Publisher: Elsevier BV
Date: 03-2022
Publisher: Hindawi Limited
Date: 18-07-2022
DOI: 10.1155/2022/3260945
Abstract: In developing countries with limited or no availability of traffic sensors, theoretical delay models are the most commonly used tool to estimate control delay at intersections. The traffic conditions in such countries are characterised by a large mix of vehicle types and limited or no lane discipline (Heterogeneous, Less Lane-Disciplined (HLLD) traffic conditions), resulting in significantly different traffic dynamics. This research develops a queueing theory-based theoretical delay model that explicitly incorporates HLLD traffic conditions’ characteristic features like lack of lane discipline, violation of the First-In-First-Out rule, and a large mix of vehicle types. A new saturation flow-based Passenger Car Equivalent (PCE) estimation methodology to address heterogeneity and a virtual lane estimation approach to address lack of lane-discipline are proposed. The developed model shows 64% lesser error in average control delay estimation compared to the in-practice delay estimation models under HLLD traffic conditions. The developed model is used for signal optimisation under HLLD traffic conditions and reductions of up to 24% in control delay in comparison to the in-practice signal timing approach are observed. The study also highlights the significance of knowing the variation of delay in addition to average delay and presents a simple approach to capture the variation in delay.
Publisher: Elsevier BV
Date: 06-2018
Publisher: Elsevier BV
Date: 06-2021
Publisher: Elsevier BV
Date: 10-2022
DOI: 10.1016/J.AAP.2022.106795
Abstract: The segmentation of highways is a fundamental step in estimating crash frequency models and conducting a before-after evaluation of engineering treatments, but the effects of segmentation approaches on the engineering treatment evaluations are not known very well. This study examined the effects of segmentation approaches on the before-after evaluation of engineering treatments. In particular, this study evaluated four segmentation approaches by applying the Empirical Bayes technique to a dataset for which the ground truth was known. Four segmentation approaches included Highway Safety Manual (HSM), Fixed (kilometre post), Fisher's, and K-means segmentation. This study utilized a 440 km stretch of rural two-lane two-way highway in Queensland, Australia, to prepare a dataset with known ground truth. The treatment under evaluation was a hypothetical treatment, which should yield a crash modification factor (CMF) of 1. For assigning hypothetical treatment, a total of fifteen datasets were prepared, including ten datasets based on the random assignment and five datasets based on the hotspot identification method. Following the before-after evaluation using the Empirical Bayes technique, the results showed that HSM and Fixed segmentation approaches predict the ground truth in both dataset types. From random assignment datasets, the estimated CMFs using HSM, Fixed, Fisher's, and K-means segmentation approaches deviated from the true CMF (i.e., 1) by 2.32 %, 5.30 %, 6.08 %, and 8.62 %, respectively. In the case of hotspots, the corresponding deviations of CMFs were 8.57 %, 9.37 %, 28.84 %, and 35.43 %, respectively. Overall, HSM segmentation best identified the actual treatment effect, followed by the Fixed segmentation. If the variables to define homogeneity for HSM segmentation are limited, then Fixed segmentation can yield reliable crash modification factors from the before-after treatment evaluations than the crash-based segmentation approaches.
Publisher: Elsevier BV
Date: 03-2020
Publisher: Elsevier BV
Date: 04-2014
Publisher: Elsevier BV
Date: 03-2018
Publisher: Elsevier BV
Date: 2008
DOI: 10.1016/J.AAP.2007.04.002
Abstract: Most crash severity studies ignored severity correlations between driver-vehicle units involved in the same crashes. Models without accounting for these within-crash correlations will result in biased estimates in the factor effects. This study developed a Bayesian hierarchical binomial logistic model to identify the significant factors affecting the severity level of driver injury and vehicle damage in traffic crashes at signalized intersections. Crash data in Singapore were employed to calibrate the model. Model fitness assessment and comparison using intra-class correlation coefficient (ICC) and deviance information criterion (DIC) ensured the suitability of introducing the crash-level random effects. Crashes occurring in peak time and in good street-lighting condition as well as those involving pedestrian injuries tend to be less severe. But crashes that occur in night time, at T/Y type intersections, and on right-most lane, as well as those that occur in intersections where red light cameras are installed tend to be more severe. Moreover, heavy vehicles have a better resistance on severe crash and thus induce less severe injuries, while crashes involving two-wheel vehicles, young or aged drivers, and the involvement of offending party are more likely to result in severe injuries.
Publisher: Elsevier BV
Date: 10-2020
Publisher: Elsevier BV
Date: 05-2015
DOI: 10.1016/J.AAP.2015.02.011
Abstract: Understanding pedestrian crash causes and contributing factors in developing countries is critically important as they account for about 55% of all traffic crashes. Not surprisingly, considerable attention in the literature has been paid to road traffic crash prediction models and methodologies in developing countries of late. Despite this interest, there are significant challenges confronting safety managers in developing countries. For ex le, in spite of the prominence of pedestrian crashes occurring on two-way two-lane rural roads, it has proven difficult to develop pedestrian crash prediction models due to a lack of both traffic and pedestrian exposure data. This general lack of available data has further h ered identification of pedestrian crash causes and subsequent estimation of pedestrian safety performance functions. The challenges are similar across developing nations, where little is known about the relationship between pedestrian crashes, traffic flow, and road environment variables on rural two-way roads, and where unique predictor variables may be needed to capture the unique crash risk circumstances. This paper describes pedestrian crash safety performance functions for two-way two-lane rural roads in Ethiopia as a function of traffic flow, pedestrian flows, and road geometry characteristics. In particular, random parameter negative binomial model was used to investigate pedestrian crashes. The models and their interpretations make important contributions to road crash analysis and prevention in developing countries. They also assist in the identification of the contributing factors to pedestrian crashes, with the intent to identify potential design and operational improvements.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Publisher: Elsevier BV
Date: 08-2019
DOI: 10.1016/J.AAP.2019.04.023
Abstract: 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 s le sizes. The means and standard deviations of posterior parameter estimates as well as SPFs goodness of fit were compared between the models across different s le 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 s le size is relatively small.
Publisher: Elsevier BV
Date: 12-2019
Publisher: SAGE Publications
Date: 2010
DOI: 10.3141/2194-10
Abstract: Motorcycles are particularly vulnerable in right-angle crashes at signalized intersections. The objective of this study is to explore how variations in roadway characteristics, environmental factors, traffic factors, maneuver types, human factors, and driver demographics influence the right-angle crash vulnerability of motorcycles at intersections. The problem is modeled by using a mixed logit model with a binary choice category formulation to differentiate how an at-fault vehicle collides with a not-at-fault motorcycle in comparison with other collision types. The mixed logit formulation allows randomness in the parameters and hence takes into account the underlying heterogeneities potentially inherent in driver behavior and other unobserved variables. A likelihood ratio test reveals that the mixed logit model is indeed better than the standard logit model. Nighttime riding shows a positive association with the vulnerability of motorcyclists. Moreover, motorcyclists are particularly vulnerable on single-lane roads, on the curb and median lanes of multilane roads, and on one-way and two-way roads relative to ided highways. Drivers who deliberately run red lights and those who are careless toward motorcyclists, especially when turning at intersections, increase the vulnerability of motorcyclists. Drivers appear more restrained when there is a passenger onboard, and this factor has decreased the crash potential for motorcyclists. The presence of red light cameras also significantly decreases right-angle crash vulnerabilities of motorcyclists. The findings of this study would be helpful in developing more targeted countermeasures for traffic enforcement, driver or rider training or education, and safety awareness programs to reduce the vulnerability of motorcyclists.
Publisher: Elsevier BV
Date: 12-2021
Publisher: MDPI AG
Date: 14-07-2016
Publisher: Elsevier BV
Date: 12-2022
Publisher: Elsevier BV
Date: 12-2021
Publisher: Elsevier BV
Date: 12-2021
Publisher: Elsevier
Date: 2021
Publisher: Wiley
Date: 24-09-2010
DOI: 10.1002/ATR.145
Publisher: Elsevier BV
Date: 08-2023
Publisher: Elsevier BV
Date: 11-2016
DOI: 10.1016/J.AAP.2015.03.042
Abstract: Driving on an approach to a signalized intersection while distracted is relatively risky, as potential vehicular conflicts and resulting angle collisions tend to be relatively more severe compared to other locations. Given the prevalence and importance of this particular scenario, the objective of this study was to examine the decisions and actions of distracted drivers during the onset of yellow lights. Driving simulator data were obtained from a s le of 69 drivers under baseline and handheld cell phone conditions at the University of Iowa - National Advanced Driving Simulator. Explanatory variables included age, gender, cell phone use, distance to stop-line, and speed. Although there is extensive research on drivers' responses to yellow traffic signals, the examinations have been conducted from a traditional regression-based approach, which do not necessary provide the underlying relations and patterns among the s led data. In this paper, we exploit the benefits of both classical statistical inference and data mining techniques to identify the a priori relationships among main effects, non-linearities, and interaction effects. Results suggest that the probability of yellow light running increases with the increase in driving speed at the onset of yellow. Both young (18-25 years) and middle-aged (30-45 years) drivers reveal reduced propensity for yellow light running whilst distracted across the entire speed range, exhibiting possible risk compensation during this critical driving situation. The propensity for yellow light running for both distracted male and female older (50-60 years) drivers is significantly higher. Driver experience captured by age interacts with distraction, resulting in their combined effect having slower physiological response and being distracted particularly risky.
Publisher: Elsevier BV
Date: 12-0012
Publisher: Informa UK Limited
Date: 17-06-2016
Publisher: Elsevier BV
Date: 11-2020
Publisher: Elsevier BV
Date: 06-2023
Publisher: Elsevier BV
Date: 10-2018
DOI: 10.1016/J.AAP.2018.07.006
Abstract: 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.
Publisher: Elsevier BV
Date: 03-2021
Publisher: Elsevier BV
Date: 06-2022
DOI: 10.1016/J.AAP.2022.106663
Abstract: Right-turn movements (equivalent to left turn movements for countries that drive on the right) at intersections are among the most complex driving maneuvers and require a high level of attention for turning across (potentially) oncoming traffic by accepting a safe gap. Not surprisingly, right-turn-involved crashes are one of the most frequent collision types at intersections (e.g., 42% of all signalised intersection crashes in Queensland, Australia). Unfortunately, the causes and contributing factors to right-turn crashes are not well understood, particularly the effect of right-turn signal strategies on the crash risk. In the safety literature, signal strategies are coarsely considered in two generic categories-protected right-turns and permitted right-turns. In reality, right-turn signal strategies could be of various types (usually 5) based on the level of intersection complexity and potential traffic conflicts. The effects of these signal strategies, along with the geometric and traffic factors, have not been well studied. To fill this gap, this study investigates the effects of right-turn signal strategies, intersection geometry and traffic operations factors on right-turn crashes at signalised intersections. To achieve this aim, crash frequency models were estimated using crash data from 221 signalised intersections in Queensland from the years spanning 2012 to 2018. Hierarchical Poisson Regression Models (random intercept models) were employed to capture the hierarchical structure of influences on crashes, with upper-level capturing intersection characteristics and lower-level capturing approach characteristics. The hierarchical model structure, disaggregate exposure variables, and signal strategies examined in this study give rise to an entirely unique study in the literature.
Publisher: Elsevier BV
Date: 12-2021
Publisher: Wiley
Date: 03-2016
DOI: 10.1002/ATR.1371
Publisher: Elsevier BV
Date: 06-2023
Publisher: Elsevier BV
Date: 09-2020
Publisher: Elsevier BV
Date: 2014
DOI: 10.1016/J.AAP.2013.09.010
Abstract: The use of mobile phones while driving is more prevalent among young drivers-a less experienced cohort with elevated crash risk. The objective of this study was to examine and better understand the reaction times of young drivers to a traffic event originating in their peripheral vision whilst engaged in a mobile phone conversation. The CARRS-Q advanced driving simulator was used to test a s le of young drivers on various simulated driving tasks, including an event that originated within the driver's peripheral vision, whereby a pedestrian enters a zebra crossing from a sidewalk. Thirty-two licensed drivers drove the simulator in three phone conditions: baseline (no phone conversation), hands-free and handheld. In addition to driving the simulator each participant completed questionnaires related to driver demographics, driving history, usage of mobile phones while driving, and general mobile phone usage history. The participants were 21-26 years old and split evenly by gender. Drivers' reaction times to a pedestrian in the zebra crossing were modelled using a parametric accelerated failure time (AFT) duration model with a Weibull distribution. Also tested where two different model specifications to account for the structured heterogeneity arising from the repeated measures experimental design. The Weibull AFT model with gamma heterogeneity was found to be the best fitting model and identified four significant variables influencing the reaction times, including phone condition, driver's age, license type (provisional license holder or not), and self-reported frequency of usage of handheld phones while driving. The reaction times of drivers were more than 40% longer in the distracted condition compared to baseline (not distracted). Moreover, the impairment of reaction times due to mobile phone conversations was almost double for provisional compared to open license holders. A reduction in the ability to detect traffic events in the periphery whilst distracted presents a significant and measurable safety concern that will undoubtedly persist unless mitigated.
Publisher: Elsevier BV
Date: 03-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: Emerald Publishing Limited
Date: 09-04-2018
Publisher: Elsevier BV
Date: 03-2023
Publisher: No publisher found
Date: 2015
DOI: 10.1016/J.AAP.2015.05.001
Abstract: Multitasking, such as the concurrent use of a mobile phone and operating a motor vehicle, is a significant distraction that impairs driving performance and is becoming a leading cause of motor vehicle crashes. This study investigates the impact of mobile phone conversations on car-following behaviour. The CARRS-Q Advanced Driving Simulator was used to test a group of young Australian drivers aged 18-26 years on a car-following task in three randomised phone conditions: baseline (no phone conversation), hands-free and handheld. Repeated measure ANOVA was applied to examine the effect of mobile phone distraction on selected car-following variables such as driving speed, spacing, and time headway. Overall, drivers tended to select slower driving speeds, larger vehicle spacings, and longer time headways when they were engaged in either hands-free or handheld phone conversations, suggesting possible risk compensatory behaviour. In addition, phone conversations while driving influenced car-following behaviour such that variability was increased in driving speeds, vehicle spacings, and acceleration and decelerations. To further investigate car-following behaviour of distracted drivers, driver time headways were modelled using Generalized Estimation Equation (GEE). After controlling for various exogenous factors, the model predicts an increase of 0.33s in time headway when a driver is engaged in hands-free phone conversation and a 0.75s increase for handheld phone conversation. The findings will improve the collective understanding of distraction on driving performance, in particular car following behaviour which is most critical in the determination of rear-end crashes.
Publisher: Wiley
Date: 26-07-2018
DOI: 10.1111/JACE.15927
Publisher: Elsevier BV
Date: 11-2017
Publisher: Elsevier BV
Date: 08-2018
Publisher: Elsevier BV
Date: 04-2021
Publisher: No publisher found
Date: 2018
DOI: 10.1080/15389588.2018.1482537
Abstract: Traffic crashes along mountainous highways may lead to injuries and fatalities more often than along highways on plain topography however, research focusing on the injury outcome of such crashes is relatively scant. The objective of this study was to investigate the factors affecting the likelihood that traffic crashes along rural mountainous highways result in injuries. This study proposes a combination of decision tree and logistic regression techniques to model crash severity (injury vs. noninjury), because the combined approach allows the specification of nonlinearities and interactions in addition to main effects. Both a scobit model and a random parameters logit model, respectively accounting for an imbalance response variable and unobserved heterogeneities, are tested and compared. The study data set contains a total of 5 years of crash data (2008-2012) on selected mountainous highways in Malaysia. To enrich the data quality, an extensive field survey was conducted to collect detailed information on horizontal alignment, longitudinal grades, cross-section elements, and roadside features. In addition, weather condition data from the meteorology department were merged using the time st and proximity measures in AutoCAD-Geolocation. The random parameters logit model is found to outperform both the standard logit and scobit models, suggesting the importance of accounting for unobserved heterogeneity in crash severity models. Results suggest that proportion of segment lengths with simple curves, presence of horizontal curves along steep gradients, highway segments with unsealed shoulders, and highway segments with cliffs along both sides are positively associated with injury-producing crashes along rural mountainous highways. Interestingly, crashes during rainy conditions are associated with crashes that are less likely to involve injury. It is also found that the likelihood of injury-producing crashes decreases for rear-end collisions but increases for head-on collisions and crashes involving heavy vehicles. A higher order interaction suggests that single-vehicle crashes involving light and medium-sized vehicles are less severe along straight sections compared to road sections with horizontal curves. One the other hand, crash severity is higher when heavy vehicles are involved in crashes as single vehicles traveling along straight segments of rural mountainous highways. In addition to unobserved heterogeneity, it is important to account for higher order interactions to have a better understanding of factors that influence crash severity. A proper understanding of these factors will help develop targeted countermeasures to improve road safety along rural mountainous highways.
Publisher: SAGE Publications
Date: 2016
DOI: 10.3141/2602-06
Abstract: 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.
Publisher: Elsevier BV
Date: 12-2022
Publisher: Elsevier BV
Date: 03-2022
Publisher: Elsevier BV
Date: 08-2023
Publisher: Elsevier BV
Date: 12-2022
Publisher: Elsevier BV
Date: 06-2023
Publisher: Elsevier BV
Date: 12-2023
Publisher: Elsevier BV
Date: 06-2023
Publisher: Elsevier BV
Date: 12-2023
Publisher: Wiley
Date: 29-05-2018
DOI: 10.1111/RISA.13119
Abstract: 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' in idual differences were evaluated as relevant in idual predictors within the behavioral adaptation framework. An innovative questionnaire, which includes randomized textual and visual scenarios, was administered to collect data from a s le 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.
Publisher: Elsevier BV
Date: 08-2019
DOI: 10.1016/J.AAP.2019.05.010
Abstract: 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.
Publisher: Informa UK Limited
Date: 06-01-2017
Publisher: Informa UK Limited
Date: 17-11-2018
DOI: 10.1080/15389588.2018.1509208
Abstract: 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. 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. 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 ided 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. 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.
Publisher: Elsevier BV
Date: 10-2022
Publisher: Elsevier BV
Date: 05-2022
Publisher: Informa UK Limited
Date: 08-03-2017
Publisher: Elsevier BV
Date: 08-2023
Publisher: Elsevier BV
Date: 09-2021
Publisher: Elsevier BV
Date: 12-2021
Publisher: SAGE Publications
Date: 2014
DOI: 10.3141/2451-14
Abstract: This research identifies roadway, traffic, and environmental factors that influence the injury severity of road traffic crashes in Dhaka, Bangladesh. Dhaka provides a rather unusual driving-risk environment to study because virtually anyone in Dhaka can obtain a driver's license, traffic enforcement is lax, and few fines are given when drivers violate traffic rules. To examine this city with presumed heightened crash severity risk, the authors collected police-reported crash data from 2007 to 2011 containing about 2,714 road traffic crashes. The injury severity of traffic crashes—recorded as fatal injury, serious injury, or property damage only—was modeled with an ordered probit model. Significant factors increasing the probability of fatal injuries included crashes along highways (65%), absence of a road ider (80%), crashes during night time (54%), and vehicle–pedestrian collisions (367%) two-way traffic configuration (21%) and traffic police–controlled schemes (41%) decreased the probability of fatalities. Both similarities and differences of the findings between crash risk in Dhaka and that in developed countries are discussed in policy-relevant terms.
Publisher: IEEE
Date: 03-2014
Publisher: Elsevier BV
Date: 12-2013
Publisher: Elsevier BV
Date: 2010
DOI: 10.1016/J.AAP.2009.07.022
Abstract: Motorcycles are overrepresented in road traffic crashes and particularly vulnerable at signalized intersections. The objective of this study is to identify causal factors affecting the motorcycle crashes at both four-legged and T signalized intersections. Treating the data in time-series cross-section panels, this study explores different Hierarchical Poisson models and found that the model allowing autoregressive lag-1 dependence specification in the error term is the most suitable. Results show that the number of lanes at the four-legged signalized intersections significantly increases motorcycle crashes largely because of the higher exposure resulting from higher motorcycle accumulation at the stop line. Furthermore, the presence of a wide median and an uncontrolled left-turn lane at major roadways of four-legged intersections exacerbate this potential hazard. For T signalized intersections, the presence of exclusive right-turn lane at both major and minor roadways and an uncontrolled left-turn lane at major roadways increases motorcycle crashes. Motorcycle crashes increase on high-speed roadways because they are more vulnerable and less likely to react in time during conflicts. The presence of red light cameras reduces motorcycle crashes significantly for both four-legged and T intersections. With the red light camera, motorcycles are less exposed to conflicts because it is observed that they are more disciplined in queuing at the stop line and less likely to jump start at the start of green.
Publisher: Elsevier BV
Date: 09-2023
Start Date: 02-2021
End Date: 02-2024
Amount: $368,488.00
Funder: Australian Research Council
View Funded ActivityStart Date: 02-2024
End Date: 01-2026
Amount: $126,839.00
Funder: Australian Research Council
View Funded ActivityStart Date: 03-2014
End Date: 06-2019
Amount: $171,277.00
Funder: Australian Research Council
View Funded Activity