Valuation of travel time reliability from a GPS-based experimental design

In the Minneapolis–St. Paul region (Twin Cities), the Minnesota Department of Transportation (MnDOT) converted the Interstate 394 High Occupancy Vehicle (HOV) lanes to High Occupancy Toll (HOT) lanes (or MnPASS Express Lanes). These lanes allow single occupancy vehicles (SOVs) to access the HOV lanes by paying a fee. This fee is adjusted according to a dynamic pricing system that varies with the current demand. This paper estimates the value placed by the travelers on the HOT lanes because of improvements in travel time reliability. This value depends on how the travelers regard a route with predictable travel times (or small travel time variability) in comparison to another with unpredictable travel times (or high travel time variability). For this purpose, commuters are recruited and equipped with Global Positioning System (GPS) devices and instructed to commute for two weeks on each of three plausible alternatives between their home in the western suburbs of Minneapolis eastbound to work in downtown or the University of Minnesota: I-394 HOT lanes, I-394 General Purpose lanes (untolled), and signalized arterials close to the I-394 corridor. They are then given the opportunity to travel on their preferred route after experiencing each alternative. This revealed preference data is then analyzed using discrete choice models of route. Three measures of reliability are explored and incorporated in the estimation of the models: standard deviation (a classical measure in the research literature); shortened right range (typically found in departure time choice models); and interquartile range (75th–25th percentile). Each of these measures represents distinct ways about how travelers deal with different sections of reliability. In all the models, it was found that reliability was valued highly (and statistically significantly), but differently according to how it was defined. The estimated value of reliability in each of the models indicates that commuters are willing to pay a fee for a reliable route depending on how they value their reliability savings.

[1]  Peter R. Stopher,et al.  Defining the perceived attributes of travel modes for urban work trips , 1981 .

[2]  K. Small,et al.  The economics of urban transportation , 2007 .

[3]  R. Noland,et al.  Travel time variability: A review of theoretical and empirical issues , 2002 .

[4]  Manouchehr Vaziri,et al.  PERCEIVED FACTORS AFFECTING DRIVER ROUTE DECISIONS , 1983 .

[5]  J. Jucker,et al.  An Empirical Study of Travel Time Variability and Travel Choice Behavior , 1982 .

[6]  Dynamic travel time estimation on highway networks , 1973 .

[7]  Gerard J. Blaauw,et al.  Driving Experience and Task Demands in Simulator and Instrumented Car: A Validation Study , 1982 .

[8]  David M Levinson,et al.  Waiting Tolerance: Ramp Delay Vs. Freeway Congestion , 2006 .

[9]  David M Levinson,et al.  Unexpected delay and the cost of lateness on I-394 high occupancy/toll lanes , 2008 .

[10]  D. Bartram,et al.  Comprehending spatial information: the relative efficiency of different methods of presenting information about bus routes. , 1980, The Journal of applied psychology.

[11]  M. Bierlaire,et al.  ESTIMATION OF VALUE OF TRAVEL-TIME SAVINGS USING MIXED LOGIT MODELS , 2005 .

[12]  Alfonso Orro Arcay Modelos de elección discreta en transportes con coeficientes aleatorios , 2005 .

[13]  P. Bovy,et al.  ROUTE CHOICE: WAYFINDING IN TRANSPORT NETWORKS , 1990 .

[14]  Hainan Li Investigating Morning Commute Route Choice Behavior Using Global , 2004 .

[15]  C. Winston,et al.  Uncovering the Distribution of Motorists' Preferences for Travel Time and Reliability , 2005 .

[16]  Hani S. Mahmassani,et al.  Interactive Experiments for the Study of Trip maker Behaviour Dynamics in Congested Commuting Systems , 1990 .

[17]  Eliahu Stern,et al.  Determinants of subjective time estimates in simulated urban driving , 1988 .

[18]  Juan de Dios Ortúzar,et al.  Willingness-to-Pay Estimation with Mixed Logit Models: Some New Evidence , 2005 .

[19]  P. Thorndyke,et al.  Simulating Navigation for Spatial Knowledge Acquisition , 1982 .

[20]  Mohamed Abdel-Aty,et al.  USING STATED PREFERENCE DATA FOR STUDYING THE EFFECT OF ADVANCED TRAFFIC INFORMATION ON DRIVERS' ROUTE CHOICE , 1997 .

[21]  J R Duffell,et al.  EMPIRICAL STUDIES OF CAR DRIVER ROUTE CHOICE IN HERTFORDSHIRE , 1988 .

[22]  Stephane Hess,et al.  Advanced discrete choice models with applications to transport demand , 2005 .

[23]  Alan Nicholson,et al.  ASSESSING TRANSPORT RELIABILITY: MALEVOLENCE AND USER KNOWLEDGE , 2003 .

[24]  David Levinson,et al.  A Moment of Time: Reliability in Route Choice Using Stated Preference , 2010, J. Intell. Transp. Syst..

[25]  Cheng Hsiao,et al.  Analysis of Panel Data , 1987 .

[26]  C. Winston,et al.  Differentiated Road Pricing, Express Lanes and Carpools: Exploiting Heterogeneous Preferences in Policy Design , 2006 .

[27]  Donald P. Gaver,et al.  Headstart Strategies for Combating Congestion , 1968 .

[28]  R. Herman,et al.  An Attempt to Characterize Traffic in Metropolitan Areas , 1978 .

[29]  Joseph N. Prashker,et al.  Direct analysis of the perceived importance of attributes of reliability of travel modes in urban travel , 1979 .

[30]  Richard L. Lieber,et al.  Statistical significance and statistical power in hypothesis testing , 1990, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[31]  Kenneth A. Small,et al.  THE SCHEDULING OF CONSUMER ACTIVITIES: WORK TRIPS , 1982 .

[32]  Arne Risa Hole,et al.  A comparison of approaches to estimating confidence intervals for willingness to pay measures. , 2007, Health economics.

[33]  David M Levinson,et al.  Determinants of Route Choice and Value of Traveler Information , 2006 .

[34]  I. Krinsky,et al.  On Approximating the Statistical Properties of Elasticities , 1986 .

[35]  Henry X. Liu,et al.  Estimation of the Time-dependency of Values of Travel Time and Its Reliability from Loop Detector Data , 2007 .

[36]  T MIYAGI,et al.  [ON THE PRESCRIPTION OF NEW DRUGS]. , 1964, Nihon Ishikai zasshi. Journal of the Japan Medical Association.

[37]  Alan Nicholson,et al.  Degradable transportation systems: An integrated equilibrium model , 1997 .

[38]  Thomas J Triggs,et al.  Driving simulator validation for speed research. , 2002, Accident; analysis and prevention.

[39]  J. Bates,et al.  The valuation of reliability for personal travel , 2001 .

[40]  A. Stated preference analysis of travel choices: the state of practice , 2004 .

[41]  David M Levinson,et al.  Weighting Waiting: Evaluating Perception of In-Vehicle Travel Time Under Moving and Stopped Conditions , 2004 .

[42]  D. Hensher,et al.  Willingness to pay for travel time reliability in passenger transport: A review and some new empirical evidence , 2010 .

[43]  Randall Guensler,et al.  Using Global Positioning System Data to Understand Day-to-Day Dynamics of Morning Commute Behavior , 2004 .

[44]  I. Krinsky,et al.  On Approximating the Statistical Properties of Elasticities: A Correction , 1990 .

[45]  Robert B. Noland,et al.  Travel-time uncertainty, departure time choice, and the cost of morning commutes , 1995 .

[46]  A. Cameron,et al.  Microeconometrics: Methods and Applications , 2005 .

[47]  Randall Guensler,et al.  Analysis of Morning Commute Route Choice Patterns Using Global Positioning System–Based Vehicle Activity Data , 2005 .

[48]  David C. Ribar,et al.  Analysis of panel data: Second Edition, Cheng Hsiao, Cambridge University Press, Cambridge, United Kingdom, 2003, ISBN 0-521-81855-9, 382 pages, [UK pound]21.95 , 2004 .

[49]  F. Koppelman,et al.  Stated preferences for investigating commuters' diversion propensity , 1993 .

[50]  D. Hensher,et al.  Stated Choice Methods: Analysis and Applications , 2000 .

[51]  Anirban Pal Modeling of Commuters' Route Choice Behavior , 2004 .

[52]  Jan-Dirk Schmöcker,et al.  Assessing Transport Reliability , 2003 .

[53]  Moshe Ben-Akiva,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1985 .

[54]  A. Hole Fitting Mixed Logit Models by Using Maximum Simulated Likelihood , 2007 .