Analysis of Urban Travel Times: Hazard-Based Approach to Random Parameters

The traditional approach to the analysis of urban travel includes a detailed and complex modeling system that considers activity and trip generation (including time-of-day of trip) and destination, mode, and route choices. Although the insights that one can gain from such a comprehensive approach are undeniable, a more simplistic approach that focuses on travel time alone (which by its nature implicitly includes the complex decision making related to destination, route, activity, and time-of-day choices) can also provide valuable information for policy makers on the determinants of congestion, the behavior and destination choices of travelers, and so on. However, in such an approach, the unobserved heterogeneity that is introduced by simplifying a complex decision-making process must be addressed. In this paper, the determinants of travel time to destination for an urban area are studied while unobserved heterogeneity is explicitly accounted for with hazard-based duration models with random parameters. With an extensive geocoded trip data set from Athens, Greece, time-to-destination model estimation results indicate that travel time duration is affected significantly by a number of factors, such as sociodemographic and trip characteristics, travel mode, frequency of trip, and time of day of the trip. In addition, the effect of many of these factors was found to vary across the population and thus underscore the need for a random-parameters formulation in the study of urban travel times with this approach.

[1]  Venkataraman N. Shankar,et al.  Activity Duration and Travel Time: Alternative Formulations , 2007 .

[2]  Michael G. McNally,et al.  On the Structure of Weekly Activity/Travel Patterns , 2003 .

[3]  Fred L Mannering,et al.  Highway accident severities and the mixed logit model: an exploratory empirical analysis. , 2008, Accident; analysis and prevention.

[4]  Y. Zahavi,et al.  Travel time “budgets” in developing countries , 1981 .

[5]  Simon Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis (2nd Edition) , 2010 .

[6]  Fred Mannering,et al.  Dynamic Traffic Equilibrium with Discrete/Continuous Econometric Models , 1990, Transp. Sci..

[7]  Samer Madanat,et al.  Using Duration Models to Analyze Experimental Pavement Failure Data , 2000 .

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

[9]  Panagiotis Ch. Anastasopoulos,et al.  Exploratory State-Level Empirical Assessment of Pavement Performance , 2011 .

[10]  Fred L. Mannering,et al.  Panel Data and Activity Duration Models: Econometric Alternatives and Applications , 1997 .

[11]  C. Bhat.  Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences , 2003 .

[12]  Chandra R. Bhat,et al.  A continuous-time model of departure time choice for urban shopping trips , 2002 .

[13]  Richard B. Davies,et al.  A joint trip timing store-type choice model for grocery shopping, including inventory effects and nonparametric control for omitted variables , 1987 .

[14]  John E. Haddock,et al.  Household Automobile and Motorcycle Ownership Analyzed with Random Parameters Bivariate Ordered Probit Model , 2012 .

[15]  Yuhong Wang,et al.  Survival Analysis of Fatigue Cracking for Flexible Pavements Based on Long-Term Pavement Performance Data , 2005 .

[16]  Fred L Mannering,et al.  An empirical assessment of fixed and random parameter logit models using crash- and non-crash-specific injury data. , 2011, Accident; analysis and prevention.

[17]  Fred L Mannering,et al.  A study of factors affecting highway accident rates using the random-parameters tobit model. , 2012, Accident; analysis and prevention.

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

[19]  John E. Haddock,et al.  Random Parameters Seemingly Unrelated Equations Approach to the Postrehabilitation Performance of Pavements , 2012 .

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

[21]  Thomas M. Cook,et al.  Estimating urban travel times: A comparative study , 1980 .

[22]  A. Chin.  Influences on commuter trip departure time decisions in Singapore , 1990 .

[23]  C. Gouriéroux,et al.  PSEUDO MAXIMUM LIKELIHOOD METHODS: THEORY , 1984 .

[24]  J. Heckman,et al.  A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data , 1984 .

[25]  Jidong Yang.  Estimation of Pavement Lifespan Using Stochastic Duration Models , 2007 .

[26]  P. Mokhtarian,et al.  Modeling the structural relationships among short-distance travel amounts, perceptions, affections, and desires , 2009 .

[27]  M. Dijst,et al.  Travel-time ratios for visits to the workplace: the relationship between commuting time and work duration , 2002 .

[28]  Samuel Labi,et al.  Empirical Assessment of the Likelihood and Duration of Highway Project Time Delays , 2012 .

[29]  Timothy C. Coburn,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2004, Technometrics.

[30]  Kenneth E. Train,et al.  Discrete Choice Methods with Simulation , 2016 .

[31]  Debbie A. Niemeier,et al.  Duration of trip-making activities by men and women , 1996 .

[32]  David M Levinson,et al.  Contacts and Meetings: Location, Duration and Distance Traveled , 2009 .

[33]  Panagiotis Ch. Anastasopoulos.  Infrastructure asset management: A case study on pavement rehabilitation , 2009 .

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

[35]  Darcy M. Bullock,et al.  Analysis of Freeway Travel Time Variability Using Bluetooth Detection , 2011 .

[36]  Jianxiong Yu,et al.  Pavement Service Life Estimation and Condition Prediction , 2010 .