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Federal Highway Administration, United States Department of Transportation, FHWA-HEP-15-006, Washington, DC.\nRuhm, 2000, Are recessions good for your health?, Quarterly Journal of Economics, 115, 617, 10.1162\u002F003355300554872\nSavolainen, 2011, The statistical analysis of crash-injury severities: a review and assessment of methodological alternatives, Accident Analysis and Prevention, 43, 1666, 10.1016\u002Fj.aap.2011.03.025\nSeraneeprakarn, 2017, Occupant injury severities in hybrid-vehicle involved crashes: a random parameters approach with heterogeneity in means and variances, Analytic Methods in Accident Research, 15, 41, 10.1016\u002Fj.amar.2017.05.003\nSumer, 2003, Personality and behavioral predictors of traffic accidents: testing a contextual mediated model, Accident Analysis and Prevention, 35, 949, 10.1016\u002FS0001-4575(02)00103-3\nSvenson, 1981, Are we all less risky and more skillful than our fellow drivers?, Acta Psychologica, 94, 143, 10.1016\u002F0001-6918(81)90005-6\nTversky, 1974, Judgement under uncertainty: heuristics and biases, Science, 185, 1124, 10.1126\u002Fscience.185.4157.1124\nUlleberg, 2003, Personality, attitudes and risk perception as predictors of risky driving behavior among young drivers, Safety Science, 41, 427, 10.1016\u002FS0925-7535(01)00077-7\nVenkataraman, 2016, Transferability analysis of heterogeneous overdispersion parameter negative binomial crash models, Transportation Research Record, 2583, 99, 10.3141\u002F2583-13\nWashington, 2011\nWinston, 2006, An exploration of the offset hypothesis using disaggregate data: the case of airbags and antilock brakes, Journal of Risk and Uncertainty, 32, 83, 10.1007\u002Fs11166-006-8288-7\nWorld Health Organization, 2015\nXie, 2007, Predicting motor vehicle collisions using Bayesian neural networks: an empirical analysis, Accident Analysis and Prevention, 39, 922, 10.1016\u002Fj.aap.2006.12.014\nXiong, 2014, The analysis of vehicle crash injury-severity data: a Markov switching approach with road-segment heterogeneity, Transportation Research Part B, 67, 109, 10.1016\u002Fj.trb.2014.04.007\nYu, 2013, Utilizing support vector machine in real-time crash evaluation, Accident Analysis and Prevention, 51, 252, 10.1016\u002Fj.aap.2012.11.027",{"EN":115},"Temporal instability and the analysis of highway accident data",{"VOID":117},"10.1016\u002Fj.amar.2017.10.002","PUBLICATION","VERIFIED","Auto Verify","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2213665717300271",[123],{"id":124,"sortIndex":19,"researcher":18,"roles":125,"affiliations":127,"properties":136},"c2855ca5-1ad2-4d6b-b27c-96040e9039b1",[126],"AUTHOR",[128],{"id":18,"sortIndex":19,"affiliation":129,"properties":18},{"id":130,"createTime":131,"updateTime":131,"relativeEntities":132,"slug":18,"properties":133,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"8c65fdfc-0281-425f-8c96-14014f8a1aa3","2024-01-03T07:38:53.281+00:00",[],{"title":134},{"VI":135},"College of Engineering, Civil and Environmental Engineering, University of South Florida, 4202 E. 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10.1016\u002Fj.trb.2013.01.002\nXiong, 2014, The analysis of vehicle crash injury-severity data: a Markov switching approach with road-segment heterogeneity, Transportation Research Part B, 67, 109, 10.1016\u002Fj.trb.2014.04.007\nXu, 2015, Modeling crash spatial heterogeneity: random parameter versus geographically weighting, Accident Analysis and Prevention, 75, 16, 10.1016\u002Fj.aap.2014.10.020\nXu, 2014, Sensitivity analysis in the context of regional safety modeling: Identifying and assessing the modifiable areal unit problem, Accident Analysis and Prevention, 70, 110, 10.1016\u002Fj.aap.2014.02.012\nYannis, 2008, Impact of enforcement on traffic accidents and fatalities: a multivariate multilevel analysis, Safety Science, 46, 738, 10.1016\u002Fj.ssci.2007.01.014\nYasmin, 2013, Evaluating alternate discrete outcome frameworks for modeling crash injury severity, Accident Analysis and Prevention, 59, 506, 10.1016\u002Fj.aap.2013.06.040\nYasmin, 2014, A latent segmentation based 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2020, Analysis of safety benefits and security concerns from the use of autonomous vehicles: A grouped random parameters bivariate probit approach with heterogeneity in means, Analytic Methods in Accident Research, 28, 100134, 10.1016\u002Fj.amar.2020.100134\nAhmed, 2021, A correlated random parameters with heterogeneity in means approach of deer-vehicle collisions and resulting injury-severities, Analytic Methods in Accident Research, 30, 100160, 10.1016\u002Fj.amar.2021.100160\nAl-Bdairi, 2020, Temporal stability of driver injury severities in animal-vehicle collisions: A random parameters with heterogeneity in means (and variances) approach, Analytic Methods in Accident Research, 26, 100120, 10.1016\u002Fj.amar.2020.100120\nAnastasopoulos, 2016, The effect of speed limits on drivers’ choice of speed: a random parameters seemingly unrelated equations approach, Analytic Methods in Accident Research, 10, 1, 10.1016\u002Fj.amar.2016.03.001\nBalusu, 2018, Non-decreasing threshold 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Prevention, 59, 506, 10.1016\u002Fj.aap.2013.06.040\nYasmin, 2014, A latent segmentation based generalized ordered logit model to examine factors influencing driver injury severity, Analytic Methods in Accident Research, 1, 23, 10.1016\u002Fj.amar.2013.10.002\nYasmin, 2015, Pooling data from fatality analysis reporting system (FARS) and generalized estimates system (GES) to explore the continuum of injury severity spectrum, Accident Analysis and Prevention, 84, 112, 10.1016\u002Fj.aap.2015.08.009\nYoung\nYu, 2021, Temporal stability of driver injury severity in single-vehicle roadway departure crashes: a random thresholds random parameters hierarchical ordered probit approach, Analytic Methods in Accident Research, 29, 100144, 10.1016\u002Fj.amar.2020.100144\nZheng, 2021, Modeling traffic conflicts for use in road safety analysis: A review of analytic methods and future directions, Analytic Methods in Accident Research, 29, 100142, 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Using UTAUT to understand public acceptance of automated road transport systems, Transportation Research Part F, 50, 55, 10.1016\u002Fj.trf.2017.07.007\nMalyshkina, 2009, Markov switching multinomial logit model: An application to accident-injury severities, Accident Analysis and Prevention, 41, 829, 10.1016\u002Fj.aap.2009.04.006\nMalyshkina, 2010, Zero-state Markov switching count-data models: An empirical assessment, Accident Analysis and Prevention, 42, 122, 10.1016\u002Fj.aap.2009.07.012\nMannering, 2018, Temporal instability and the analysis of highway accident data, Analytic Methods in Accident Research, 17, 1, 10.1016\u002Fj.amar.2017.10.002\nMcIlroy, 2019, Vulnerable road users in low-, middle-, and high-income countries: validation of a pedestrian behaviour questionnaire, Accident Analysis and Prevention, 131, 80, 10.1016\u002Fj.aap.2019.05.027\nMillard-Ball, 2018, Pedestrians, autonomous vehicles, and cities, Journal of Planning Education and Research, 38, 6, 10.1177\u002F0739456X16675674\nMoody, 2020, Public perceptions of autonomous vehicle safety: An international comparison, Safety Science, 121, 634, 10.1016\u002Fj.ssci.2019.07.022\nNoy, 2018, Automated driving: Safety blind spots, Safety Science, 102, 68, 10.1016\u002Fj.ssci.2017.07.018\nOviedo-Trespalacios, 2020, A hierarchical Bayesian multivariate ordered model of distracted drivers’ decision to initiate risk-compensating behaviour, Analytic Methods in Accident Research, 26, 10.1016\u002Fj.amar.2020.100121\nOviedo-Trespalacios, O., Watson, B. 2021. 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Injury Prevention, 27(2), 103-103.\nPalmeiro, 2018, Interaction between pedestrians and automated vehicles: A Wizard of Oz experiment, Transportation Research Part F, 58, 1005, 10.1016\u002Fj.trf.2018.07.020\nPapadimitriou, 2013, Patterns of pedestrian attitudes, perceptions and behaviour in Europe, Safety Science, 53, 114, 10.1016\u002Fj.ssci.2012.09.008\nRazmi Rad, 2020, Pedestrians’ road crossing behaviour in front of automated vehicles: Results from a pedestrian simulation experiment using agent-based modelling, Transportation Research Part F, 69, 101, 10.1016\u002Fj.trf.2020.01.014\nReady, 1995, Statistical approaches to the fat tail problem for dichotomous choice contingent valuation, Land Economics, 491, 10.2307\u002F3146713\nReig, 2018, A field study of pedestrians and autonomous vehicles, 198\nRothenbücher, 2016, Ghost driver: A field study investigating the interaction between pedestrians and driverless vehicles, 795\nSahebi, 2019, Incorporating car owner preferences for the introduction of economic incentives for speed limit enforcement, Transportation Research Part F, 64, 509, 10.1016\u002Fj.trf.2019.05.014\nSheela, 2020, The effect of information on changing opinions towards autonomous vehicle adoption: An exploratory analysis, International Journal of Sustainable Transportation, 14, 475, 10.1080\u002F15568318.2019.1573389\nTipping, 1999, Probabilistic principal component analysis, Journal of the Royal Statistical Society, 61, 611, 10.1111\u002F1467-9868.00196\nVan Loon, 2015, Automated driving and its effect on the safety ecosystem: How do compatibility issues affect the transition period?, Procedia Manufacturing, 3, 3280, 10.1016\u002Fj.promfg.2015.07.401\nVelasco, 2019, Studying pedestrians’ crossing behavior when interacting with automated vehicles using virtual reality, Transportation Research Part F, 66, 1, 10.1016\u002Fj.trf.2019.08.015\nVenkatesh, 2012, Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology, MIS Quarterly, 157, 10.2307\u002F41410412\nWang, 2012, Speed modeling and travel time estimation based on truncated normal and lognormal distributions, Transportation Research Records, 2315, 66, 10.3141\u002F2315-07\nWashington, 2020\nWeng, 2016, Probability distribution-based model for work zone capacity prediction, Journal of Advanced Transportation, 50, 165, 10.1002\u002Fatr.1310\nWoldeamanuel, 2018, Perceived benefits and concerns of autonomous vehicles: An exploratory study of millennials' sentiments of an emerging market, Research in Transportation Economics, 71, 44, 10.1016\u002Fj.retrec.2018.06.006\nXiong, 2014, The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity, Transportation Research Part B, 67, 109, 10.1016\u002Fj.trb.2014.04.007\nYu, 2019, A marginalized random effects hurdle negative binomial model for analyzing refined-scale crash frequency data, Analytic Methods in Accident Research, 22, 10.1016\u002Fj.amar.2019.100092",{"EN":527},"How much should a pedestrian be fined for intentionally blocking a fully automated vehicle? 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2020, Analysis of safety benefits and security concerns from the use of autonomous vehicles: A grouped random parameters bivariate probit approach with heterogeneity in means, Analytic Methods in Accident Research, 28, 10.1016\u002Fj.amar.2020.100134\nAlnawmasi, 2019, A statistical assessment of temporal instability in the factors determining motorcyclist injury severities, Analytic Methods in Accident Research, 22, 10.1016\u002Fj.amar.2019.100090\nAnastasopoulos, 2016, Random parameters multivariate tobit and zero-inflated count data models: Addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis, Analytic Methods in Accident Research, 11, 17, 10.1016\u002Fj.amar.2016.06.001\nAnastasopoulos, 2009, A note on modeling vehicle accident frequencies with random-parameters count models, Accident Analysis and Prevention, 41, 153, 10.1016\u002Fj.aap.2008.10.005\nAnastasopoulos, 2011, An empirical assessment of fixed and random parameter logit models using crash-and non-crash-specific injury data, Accident Analysis and Prevention, 43, 1140, 10.1016\u002Fj.aap.2010.12.024\nAnastasopoulos, 2012, A study of factors affecting highway accident rates using the random-parameters tobit model, Accident Analysis and Prevention, 45, 628, 10.1016\u002Fj.aap.2011.09.015\nBehnood, 2017, The effect of passengers on driver-injury severities in single-vehicle crashes: A random parameters heterogeneity-in-means approach, Analytic Methods in Accident Research, 14, 41, 10.1016\u002Fj.amar.2017.04.001\nBehnood, 2017, Determinants of bicyclist injury severities in bicycle-vehicle crashes: A random parameters approach with heterogeneity in means and variances, Analytic Methods in Accident Research, 16, 35, 10.1016\u002Fj.amar.2017.08.001\nBehnood, 2019, Time-of-day variations and temporal instability of factors affecting injury severities in large-truck crashes, Analytic Methods in Accident Research, 23, 10.1016\u002Fj.amar.2019.100102\nCaliendo, 2019, Analysis of crash frequency in motorway tunnels based on a correlated random-parameters approach, Tunnelling and Underground Space Technology, 85, 243, 10.1016\u002Fj.tust.2018.12.012\nCai, 2018, Developing a grouped random parameters multivariate spatial model to explore zonal effects for segment and intersection crash modeling, Analytic Methods in Accident Research, 19, 1, 10.1016\u002Fj.amar.2018.05.001\nCerwick, 2014, A comparison of the mixed logit and latent class methods for crash severity analysis, Analytic Methods in Accident Research, 3–4, 11, 10.1016\u002Fj.amar.2014.09.002\nChand, 2018, Application of Fractal theory for crash rate prediction: Insights from random parameters and latent class tobit models, Accident Analysis & Prevention, 112, 30, 10.1016\u002Fj.aap.2017.12.023\nChen, 2017, Impact of road-surface condition on rural highway safety: a multivariate random parameters negative binomial approach, Analytic Methods in Accident Research, 11, 75, 10.1016\u002Fj.amar.2017.09.001\nChen, 2014, Modeling safety of highway work zones with random parameters and random effects models, Analytic Methods in Accident Research, 1, 86, 10.1016\u002Fj.amar.2013.10.003\nCoruh, 2015, Accident analysis with aggregated data: the random parameters negative binomial panel count data model, Analytic Methods in Accident Research, 7, 37, 10.1016\u002Fj.amar.2015.07.001\nEker, 2019, An exploratory investigation of public perceptions towards safety and security from the future use of flying cars in the United States, Analytic Methods in Accident Research, 23, 10.1016\u002Fj.amar.2019.100103\nEluru, 2012, A latent class modeling approach for identifying vehicle driver injury severity factors at highway-railway crossings, Accident Analysis and Prevention, 47, 119, 10.1016\u002Fj.aap.2012.01.027\nFountas, 2018, Analysis of accident injury-severities using a correlated random parameters ordered probit approach with time variant covariates, Analytic Methods in Accident Research, 18, 57, 10.1016\u002Fj.amar.2018.04.003\nFountas, 2018, Analysis of vehicle accident-injury severities: A comparison of segment-versus accident-based latent class ordered probit models with class-probability functions, Analytic Methods in Accident Research, 18, 15, 10.1016\u002Fj.amar.2018.03.003\nFountas, 2019, The effects of driver fatigue, gender, and distracted driving on perceived and observed aggressive driving behavior: A correlated grouped random parameters bivariate probit approach, Analytic Methods in Accident Research, 22, 10.1016\u002Fj.amar.2019.100091\nFountas, 2018, Analysis of stationary and dynamic factors affecting highway accident occurrence: a dynamic correlated random parameters binary logit approach, Accident Analysis and Prevention, 113, 330, 10.1016\u002Fj.aap.2017.05.018\nGarnowski, 2011, On factors related to car accidents on German Autobahn connectors, Accident Analysis and Prevention, 43, 1864, 10.1016\u002Fj.aap.2011.04.026\nGreene, 2016\nGuo, 2019, Modeling correlation and heterogeneity in crash rates by collision types using full Bayesian random parameters multivariate tobit model, Accident Analysis & Prevention, 128, 164, 10.1016\u002Fj.aap.2019.04.013\nHamed, 2020, An exploratory analysis of traffic accidents and vehicle ownership decisions using a random parameters logit model with heterogeneity in means, Analytic Methods in Accident Research, 25, 10.1016\u002Fj.amar.2020.100116\nHan, 2018, Investigating varying effect of road-level factors on crash frequency across regions: a Bayesian hierarchical random parameter modeling approach, Analytic Methods in Accident Research, 20, 81, 10.1016\u002Fj.amar.2018.10.002\nHeydari, 2018, Benchmarking regions using a heteroskedastic grouped random parameters model with heterogeneity in mean and variance: Applications to grade crossing safety analysis, Analytic Methods in Accident Research, 19, 33, 10.1016\u002Fj.amar.2018.06.003\nHou, 2019, Examination of driver injury severity in freeway single-vehicle crashes using a mixed logit model with heterogeneity-in-means, Physica A: Statistical Mechanics and its Applications, 531, 10.1016\u002Fj.physa.2019.121760\nHou, 2020, A correlated random parameters tobit model to analyze the safety effects and temporal instability of factors affecting crash rates, Accident Analysis and Prevention, 134, 10.1016\u002Fj.aap.2019.105326\nHou, 2018, Investigating factors of crash frequency with random effects and random parameters models: new insights from Chinese freeway study, Accident Analysis and Prevention, 120, 1, 10.1016\u002Fj.aap.2018.07.010\nHou, 2018, Analyzing crash frequency in freeway tunnels: a correlated random parameters approach, Accident Analysis and Prevention, 111, 94, 10.1016\u002Fj.aap.2017.11.018\nHuang, 2019, Modeling unobserved heterogeneity for zonal crash frequencies: A Bayesian multivariate random-parameters model with mixture components for spatially correlated data, Analytic Methods in Accident Research, 24, 10.1016\u002Fj.amar.2019.100105\nHuo, 2020, A Correlated Random Parameters Model with Heterogeneity in Means to Account for Unobserved Heterogeneity in Crash Frequency Analysis, Transportation Research Record, 2674, 312, 10.1177\u002F0361198120922212\nHuo, 2020, Assessing the explanatory and predictive performance of a random parameters count model with heterogeneity in means and variances, Accident Analysis and Prevention, 147, 10.1016\u002Fj.aap.2020.105759\nIslam, 2017, A comparative injury severity analysis of motorcycle at-fault crashes on rural and urban roadways in Alabama, Accident Analysis and Prevention, 108, 163, 10.1016\u002Fj.aap.2017.08.016\nIslam, 2020, Unobserved heterogeneity and temporal instability in the analysis of work-zone crash-injury severities, Analytic Methods in Accident Research, 28, 10.1016\u002Fj.amar.2020.100130\nKim, 2010, A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model, Accident Analysis and Prevention, 42, 1751, 10.1016\u002Fj.aap.2010.04.016\nKim, 2013, Driver-injury severity in single-vehicle crashes in California: A mixed logit analysis of heterogeneity due to age and gender, Accident Analysis and Prevention, 50, 1073, 10.1016\u002Fj.aap.2012.08.011\nLi, 2019, Using latent class analysis and mixed logit model to explore risk factors on driver injury severity in single-vehicle crashes, Accident Analysis and Prevention, 129, 230, 10.1016\u002Fj.aap.2019.04.001\nLord, 2010, The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives, Transportation Research Part A, 44, 291\nMalyshkina, 2010, Zero-state Markov switching count-data models: an empirical assessment, Accident Analysis and Prevention, 42, 122, 10.1016\u002Fj.aap.2009.07.012\nMalyshkina, 2009, Markov switching negative binomial models: An application to vehicle accident frequencies, Accident Analysis and Prevention, 41, 217, 10.1016\u002Fj.aap.2008.11.001\nMannering, 2014, Analytic methods in accident research: Methodological frontier and future directions, Analytic Methods in Accident Research, 1, 1, 10.1016\u002Fj.amar.2013.09.001\nMannering, 2020, Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis, Analytic Methods in Accident Research, 25, 10.1016\u002Fj.amar.2020.100113\nMannering, 2016, Unobserved heterogeneity and the statistical analysis of highway accident data, Analytic Methods in Accident Research, 11, 1, 10.1016\u002Fj.amar.2016.04.001\nMatsuo, 2020, Hierarchical Bayesian modeling to evaluate the impacts of intelligent speed adaptation considering individuals’ usual speeding tendencies: A correlated random parameters approach, Analytic Methods in Accident Research, 27, 10.1016\u002Fj.amar.2020.100125\nPantangi, 2019, A preliminary investigation of the effectiveness of high visibility enforcement programs using naturalistic driving study data: A grouped random parameters approach, Analytic Methods in Accident Research, 21, 1, 10.1016\u002Fj.amar.2018.10.003\nPantangi, 2020, Do high visibility enforcement programs affect aggressive driving behavior? An empirical analysis using naturalistic driving study data, Accident Analysis & Prevention, 138, 10.1016\u002Fj.aap.2019.105361\nPeng, 2011, Application of latent class growth model to longitudinal analysis of traffic crashes, Transportation Research Record, 2236, 102, 10.3141\u002F2236-12\nRusli, 2017, Single-vehicle crashes along rural mountainous highways in Malaysia: an application of random parameters negative binomial model, Accident Analysis and Prevention, 102, 153, 10.1016\u002Fj.aap.2017.03.002\nSaeed, 2019, Analyzing road crash frequencies with uncorrelated and correlated random-parameters count models: An empirical assessment of multilane highways, Analytic Methods in Accident Research, 23, 10.1016\u002Fj.amar.2019.100101\nSeraneeprakarn, 2017, Occupant injury severities in hybrid-vehicle involved crashes: A random parameters approach with heterogeneity in means and variances, Analytic Methods in Accident Research, 15, 41, 10.1016\u002Fj.amar.2017.05.003\nTang, 2019, 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2013, The joint analysis of injury severity of drivers in two-vehicle crashes accommodating seat belt use endogeneity, Transportation Research Part B, 50, 74, 10.1016\u002Fj.trb.2013.01.007\nAhmed, 2018, Effects of truck traffic on crash injury severity on rural highways in Wyoming using Bayesian binary logit models, Accident Analysis and Prevention, 117, 106, 10.1016\u002Fj.aap.2018.04.011\nAlnawmasi, 2019, A statistical assessment of temporal instability in the factors determining motorcyclist injury severities, Analytic Methods in Accident Research, 22, 1, 10.1016\u002Fj.amar.2019.100090\nAnastasopoulos, 2011, An empirical assessment of fixed and random parameter logit models using crash- and non-crash-specific injury data, Accident Analysis and Prevention, 43, 1140, 10.1016\u002Fj.aap.2010.12.024\nAnastasopoulos, 2016, Safety-oriented pavement performance thresholds: accounting for unobserved heterogeneity in a multi-objective optimization and goal programming approach, Analytic Methods in Accident Research, 12, 35, 10.1016\u002Fj.amar.2016.10.001\nAnderson, 2017, Roadway classifications and the accident injury severities of heavy-vehicle drivers, Analytic Methods in Accident Research, 15, 17, 10.1016\u002Fj.amar.2017.04.002\nBehnood, 2015, The temporal stability of factors affecting driver-injury severities in single-vehicle crashes: some empirical evidence, Analytic Methods in Accident Research, 8, 7, 10.1016\u002Fj.amar.2015.08.001\nBehnood, 2016, An empirical assessment of the effects of economic recessions on pedestrian-injury crashes using mixed and latent-class models, Analytic Methods in Accident Research, 12, 1, 10.1016\u002Fj.amar.2016.07.002\nBehnood, 2017, The effects of drug and alcohol consumption on driver injury severities in single-vehicle crashes, Traffic Injury Prevention, 18, 456, 10.1080\u002F15389588.2016.1262540\nBehnood, 2017, Determinants of bicyclist injury severities in bicycle-vehicle crashes: a random parameters approach with 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Washington, DC.\nUddin, 2018, Factors influencing injury severity of crashes involving HAZMAT trucks, International Journal of Transportation Science and Technology, 7, 1, 10.1016\u002Fj.ijtst.2017.06.004\nWaseem, 2019, Factors affecting motorcyclists’ injury severities: an empirical assessment using random parameters logit model with heterogeneity in means and variances, Accident Analysis and Prevention, 123, 12, 10.1016\u002Fj.aap.2018.10.022\nWashington, 2011\nXiong, 2013, The heterogeneous effects of guardian supervision on adolescent driver-injury severities: a finite-mixture random-parameters approach, Transportation Research Part B, 49, 39, 10.1016\u002Fj.trb.2013.01.002\nXiong, 2014, The analysis of vehicle crash injury-severity data: a Markov switching approach with road-segment heterogeneity, Transportation Research Part B, 67, 109, 10.1016\u002Fj.trb.2014.04.007\nYasmin, 2014, A latent segmentation based generalized ordered logit model to examine factors influencing driver injury severity, Analytic Methods in Accident Research, 1, 23, 10.1016\u002Fj.amar.2013.10.002\nZheng, 2018, Commercial truck crash injury severity analysis using gradient boosting data mining model, Journal of Safety Research, 65, 115, 10.1016\u002Fj.jsr.2018.03.002\nZhu, 2011, Modeling occupant-level injury severity: an application to large-truck crashes, Accident Analysis and Prevention, 43, 1427, 10.1016\u002Fj.aap.2011.02.021\nZou, 2017, Truck crash severity in New York city: an investigation of the spatial and the time of day effects, Accident Analysis and Prevention, 99, 249, 10.1016\u002Fj.aap.2016.11.024",{"EN":833},"Time-of-day variations and temporal instability of factors affecting injury severities in large-truck 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