Analysis of urban travel time and travel distance: A fully parametric bivariate hazard-based duration modelling approach with correlated grouped random parameters

[1]  M. M. Hamed,et al.  Forecasting annual electric power consumption using a random parameters model with heterogeneity in means and variances , 2022, Energy.

[2]  Irina Benedyk,et al.  A Temporal Instability Analysis of Environmental Factors Affecting Accident Occurrences During Snow Events: The Random Parameters Hazard-Based Duration Model with Means and Variances Heterogeneity , 2022, Analytic Methods in Accident Research.

[3]  Grigorios Fountas,et al.  Addressing unobserved heterogeneity in the analysis of bicycle crash injuries in Scotland: A correlated random parameters ordered probit approach with heterogeneity in means , 2021 .

[4]  W. Guan,et al.  Ground access behaviour of air-rail passengers: A case study of Dalian ARIS , 2021, Travel Behaviour and Society.

[5]  N. Sze,et al.  Red light running behavior of bicyclists in urban area: Effects of bicycle type and bicycle group size , 2020 .

[6]  Ashish Bhaskar,et al.  Is an informed driver a better decision maker? A grouped random parameters with heterogeneity-in-means approach to investigate the impact of the connected environment on driving behaviour in safety-critical situations , 2020, Analytic Methods in Accident Research.

[7]  Panagiotis Ch. Anastasopoulos,et al.  Personal and societal impacts of motorcycle ban policy on motorcyclists’ home-to-work morning commute in China , 2020, Travel Behaviour and Society.

[8]  S. Peeta,et al.  Impacts of personalized accessibility information on residential location choice and travel behavior , 2020, Travel Behaviour and Society.

[9]  Panagiotis Ch. Anastasopoulos,et al.  An exploratory investigation of public perceptions towards key benefits and concerns from the future use of flying cars , 2020 .

[10]  Peter A. Rogerson,et al.  Identifying elderly travel time disparities using a correlated grouped random parameters hazard-based duration approach , 2019, Research in Transportation Business & Management.

[11]  Yu Shen,et al.  Semiparametric model for bivariate survival data subject to biased sampling , 2019, Journal of the Royal Statistical Society. Series B, Statistical methodology.

[12]  Srinivas Peeta,et al.  Impacts of internal migration, household registration system, and family planning policy on travel mode choice in China , 2018, Travel Behaviour and Society.

[13]  H. Mahmassani,et al.  Riders on the storm: Exploring weather and seasonality effects on commute mode choice in Chicago , 2018, Travel Behaviour and Society.

[14]  Alan Blatt,et al.  Analysis of stationary and dynamic factors affecting highway accident occurrence: A dynamic correlated grouped random parameters binary logit approach. , 2018, Accident; analysis and prevention.

[15]  Grigorios Fountas,et al.  Simultaneous estimation of discrete outcome and continuous dependent variable equations: A bivariate random effects modeling approach with unrestricted instruments , 2017 .

[16]  Adel W. Sadek,et al.  Transport habits of travelers using new energy type modes: A random parameters hazard-based approach of travel distance , 2017 .

[17]  Chuan Ding,et al.  Influences of built environment characteristics and individual factors on commuting distance: A multilevel mixture hazard modeling approach , 2017 .

[18]  Chandra R. Bhat,et al.  Unobserved heterogeneity and the statistical analysis of highway accident data , 2016 .

[19]  Fred L. Mannering,et al.  Analysis of Pavement Overlay and Replacement Performance Using Random Parameters Hazard-Based Duration Models , 2015 .

[20]  Debopam Bhattacharya.  Nonparametric Welfare Analysis for Discrete Choice , 2014 .

[21]  Satish V. Ukkusuri,et al.  A random-parameter hazard-based model to understand household evacuation timing behavior , 2013 .

[22]  J. Powell,et al.  Identification and Estimation of Average Partial Effects in “Irregular” Correlated Random Coefficient Panel Data Models , 2012 .

[23]  Gerrit-Jan Knaap,et al.  The American Way of Land Use , 2012 .

[24]  Panagiotis Ch. Anastasopoulos,et al.  Hazard-Based Analysis of Travel Distance in Urban Environments: Longitudinal Data Approach , 2012 .

[25]  John E. Haddock,et al.  Analysis of Urban Travel Times: Hazard-Based Approach to Random Parameters , 2012 .

[26]  Peter S. Arcidiacono,et al.  Conditional Choice Probability Estimation of Dynamic Discrete Choice Models With Unobserved Heterogeneity , 2011 .

[27]  Robert McMillan,et al.  A Dynamic Model of Demand for Houses and Neighborhoods , 2011 .

[28]  Eben Kenah,et al.  Nonparametric survival analysis of epidemic data , 2013 .

[29]  David R. Anderson,et al.  AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons , 2011, Behavioral Ecology and Sociobiology.

[30]  Carlos Pestana Barros,et al.  The length of stay of golf tourism: a survival analysis. , 2010 .

[31]  Jennifer Dill,et al.  Understanding and Measuring Bicycling Behavior: a Focus on Travel Time and Route Choice , 2008 .

[32]  Liang Long,et al.  What neighborhood are you in? Empirical findings of relationships between household travel and neighborhood characteristics , 2008 .

[33]  Jianwen Cai,et al.  Partially linear hazard regression with varying coefficients for multivariate survival data , 2008 .

[34]  Susan L Handy,et al.  Correlation or causality between the built environment and travel behavior? Evidence from Northern California , 2005 .

[35]  P. Mokhtarian,et al.  What if you live in the wrong neighborhood? The impact of residential neighborhood type dissonance on distance traveled , 2005 .

[36]  M. Grace,et al.  Hazard Ratio in Clinical Trials , 2004, Antimicrobial Agents and Chemotherapy.

[37]  Gerard J. van den Berg,et al.  The nonparametric identification of treatment effects in duration models , 2003 .

[38]  Brigitte S. Waldorf,et al.  Spatial Patterns and Processes in a Longitudinal Framework , 2003 .

[39]  David A. Hensher,et al.  The Mixed Logit Model: the State of Practice and Warnings for the Unwary , 2001 .

[40]  Bradley Efron,et al.  The two‐way proportional hazards model , 2002 .

[41]  J. Ibrahim,et al.  Models for survival data from cancer prevention studies , 2002 .

[42]  David Card.  Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems , 2000 .

[43]  Joel L. Horowitz,et al.  Semiparametric Estimation of a Proportional Hazard Model with Unobserved Heterogeneity , 1999 .

[44]  R. Henderson,et al.  Effect of frailty on marginal regression estimates in survival analysis , 1999 .

[45]  Fred Mannering,et al.  Modeling Travelers' Postwork Activity Involvement: Toward a New Methodology , 1993, Transp. Sci..

[46]  Zhiliang Ying,et al.  A simple nonparametric estimator of the bivariate survival function under univariate censoring , 1993 .

[47]  J. Vaupel,et al.  The impact of heterogeneity in individual frailty on the dynamics of mortality , 1979, Demography.

[48]  J. Halton.  On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals , 1960 .

[49]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .

[50]  Panagiotis Ch. Anastasopoulos,et al.  An exploratory empirical analysis of willingness to hire and pay for flying taxis and shared flying car services , 2021 .

[51]  Nazmul Arefin Khan,et al.  When do households make vehicle transactions? What type of vehicle households choose after transaction events? , 2021 .

[52]  J. V. Ommeren,et al.  Do rich households live farther away from their workplaces , 2012 .

[53]  Samuel Labi,et al.  Planning-stage estimation of highway project duration on the basis of anticipated project cost, project type, and contract type , 2011 .

[54]  Fred L Mannering,et al.  A note on modeling vehicle accident frequencies with random-parameters count models. , 2009, Accident; analysis and prevention.

[55]  Antonio Páez,et al.  Determinants of distance traveled with a focus on the elderly: a multilevel analysis in the Hamilton CMA, Canada , 2009 .

[56]  C. Wilmot,et al.  Survival Analysis–Based Dynamic Travel Demand Models for Hurricane Evacuation , 2006 .

[57]  Matthew G. Karlaftis,et al.  Prediction of Pavement Crack Initiation from In-Service Pavements , 2005 .

[58]  S. Sarmiento,et al.  HOUSEHOLD, GENDER, AND TRAVEL , 2000 .

[59]  D K Dey,et al.  A Weibull Regression Model with Gamma Frailties for Multivariate Survival Data , 1997, Lifetime data analysis.

[60]  D. Oakes.  Multivariate survival distributions , 1994 .

[61]  Fred L. Mannering,et al.  HAZARD-BASED DURATION MODELS AND THEIR APPLICATION TO TRANSPORT ANALYSIS. , 1994 .

[62]  M. Lindeboom,et al.  Heterogeneity in Models for Bivariate Survival: the Importance of the Mixing Distribution , 1994 .

[63]  Philip Hougaard,et al.  Modelling multivariate survival , 1987 .

[64]  J. Kalbfleisch.  Non‐Parametric Bayesian Analysis of Survival Time Data , 1978 .

[65]  D.,et al.  Regression Models and Life-Tables , 2022 .