A Moment of Time: Reliability in Route Choice using Stated Preference David M. Levinson Nebiyou Y. Tilahun Department of Civil Engineering University of.

Slides:



Advertisements
Similar presentations
Hypothesis Testing. To define a statistical Test we 1.Choose a statistic (called the test statistic) 2.Divide the range of possible values for the test.
Advertisements

Chapter 2 Notes Psychological Research Methods and Statistics
Hypothesis Testing When “p” is small, we reject the Ho.
Sampling Distributions
EBI Statistics 101.
Business Statistics for Managerial Decision
Copyright ©2011 Brooks/Cole, Cengage Learning Testing Hypotheses about Means Chapter 13.
Significance Testing Chapter 13 Victor Katch Kinesiology.
QUALITATIVE AND LIMITED DEPENDENT VARIABLE MODELS.
Risk Attitude Reversals in Drivers ’ Route Choice When Range of Travel Time Information is Provided Jin-Yong Sung Hamid Hussain.
2015/6/161 Traffic Flow Theory 2. Traffic Stream Characteristics.
Distribution of the sample mean and the central limit theorem
Evaluating Hypotheses
1 The Economics of Congestion Brian Gregor PSU Transportation Seminar 3/12/04.
Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 9-1 Chapter 9 Fundamentals of Hypothesis Testing: One-Sample Tests Basic Business Statistics.
BCOR 1020 Business Statistics Lecture 21 – April 8, 2008.
Estimating Congestion Costs Using a Transportation Demand Model of Edmonton, Canada C.R. Blaschuk Institute for Advanced Policy Research University of.
Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Statistics for Business and Economics 7 th Edition Chapter 9 Hypothesis Testing: Single.
BCOR 1020 Business Statistics Lecture 20 – April 3, 2008.
Measuing Preferences, Establishing Values, The Empirical Basis for Understanding Behavior David Levinson.
InMoSion: Science Shop for Innovative Mobility Solutions for Mobility Challenged Europeans 3rd INTERNATIONAL MEETING ANKARA, TURKEY Partnering: Civil Engineering.
Data Analysis Statistics. Levels of Measurement Nominal – Categorical; no implied rankings among the categories. Also includes written observations and.
BCOR 1020 Business Statistics
AN INTRODUCTION TO PORTFOLIO MANAGEMENT
Chi-Square Tests and the F-Distribution
Chapter 5: Descriptive Research Describe patterns of behavior, thoughts, and emotions among a group of individuals. Provide information about characteristics.
May 2009 Evaluation of Time-of- Day Fare Changes for Washington State Ferries Prepared for: TRB Transportation Planning Applications Conference.
Recent Evidence on Mass Transit Demand Ian Savage Northwestern University.
Chapter 10 Hypothesis Testing
Transit Estimation and Mode Split CE 451/551 Source: NHI course on Travel Demand Forecasting (152054A) Session 7.
Effects of practice, age, and task demands, on interference from a phone task while driving Author: David Shinar, Noam Tractinsky, Richard Compton Accident.
Lecture Presentation Software to accompany Investment Analysis and Portfolio Management Seventh Edition by Frank K. Reilly & Keith C. Brown Chapter 7.
Analyzing Reliability and Validity in Outcomes Assessment (Part 1) Robert W. Lingard and Deborah K. van Alphen California State University, Northridge.
Estimation and Confidence Intervals
Using the Margins Command to Estimate and Interpret Adjusted Predictions and Marginal Effects Richard Williams
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Section 9-2 Inferences About Two Proportions.
Introduction To Biological Research. Step-by-step analysis of biological data The statistical analysis of a biological experiment may be broken down into.
LECTURE 19 THURSDAY, 14 April STA 291 Spring
Research & Statistics Looking for Conclusions. Statistics Mathematics is used to organize, summarize, and interpret mathematical data 2 types of statistics.
Hypothesis Testing with Two Samples
Introduction to Probability and Statistics Thirteenth Edition Chapter 9 Large-Sample Tests of Hypotheses.
Copyright © 2009 Pearson Education, Inc LEARNING GOAL Interpret and carry out hypothesis tests for independence of variables with data organized.
1 Estimation From Sample Data Chapter 08. Chapter 8 - Learning Objectives Explain the difference between a point and an interval estimate. Construct and.
Business Statistics for Managerial Decision Farideh Dehkordi-Vakil.
AP Statistics Section 13.1 A. Which of two popular drugs, Lipitor or Pravachol, helps lower bad cholesterol more? 4000 people with heart disease were.
Copyright © 2014, 2011 Pearson Education, Inc. 1 Chapter 18 Inference for Counts.
Decision making Under Risk & Uncertainty. PAWAN MADUSHANKA MADUSHAN WIJEMANNA.
Introduction  Populations are described by their probability distributions and parameters. For quantitative populations, the location and shape are described.
Measures of Central Tendency: The Mean, Median, and Mode
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 8-1 Chapter 8 Fundamentals of Hypothesis Testing: One-Sample Tests Statistics.
Introduction Disordered eating continues to be a significant health concern for college women. Recent research shows it is on the rise among men. Media.
Issues concerning the interpretation of statistical significance tests.
Lecture 17 Dustin Lueker.  A way of statistically testing a hypothesis by comparing the data to values predicted by the hypothesis ◦ Data that fall far.
Fall 2002Biostat Statistical Inference - Confidence Intervals General (1 -  ) Confidence Intervals: a random interval that will include a fixed.
Uncertainty and Consumer Behavior Chapter 5. Uncertainty and Consumer Behavior 1.In order to compare the riskiness of alternative choices, we need to.
Introduction Suppose that a pharmaceutical company is concerned that the mean potency  of an antibiotic meet the minimum government potency standards.
Business Statistics for Managerial Decision Farideh Dehkordi-Vakil.
Value of Time for Commercial Vehicle Operators in Minnesota by David Levinson and Brian Smalkoski University of Minnesota.
Exercise - 1 A package-filling process at a Cement company fills bags of cement to an average weight of µ but µ changes from time to time. The standard.
URBDP 591 I Lecture 4: Research Question Objectives How do we define a research question? What is a testable hypothesis? How do we test an hypothesis?
Chapter 4 Statistical Inference  Estimation -Confidence interval estimation for mean and proportion -Determining sample size  Hypothesis Testing -Test.
Estimating the Benefits of Bicycle Facilities Stated Preference and Revealed Preference Approaches Kevin J. Krizek Assistant Professor Director, Active.
Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Statistics for Business and Economics 8 th Edition Chapter 9 Hypothesis Testing: Single.
BUS 308 Entire Course (Ash Course) For more course tutorials visit BUS 308 Week 1 Assignment Problems 1.2, 1.17, 3.3 & 3.22 BUS 308.
SECTION 1 TEST OF A SINGLE PROPORTION
Copyright © 2009 Pearson Education, Inc LEARNING GOAL Interpret and carry out hypothesis tests for independence of variables with data organized.
Weighting Waiting: Evaluating the Perception of In- Vehicle Travel Time Under Moving and Stopped Conditions David Levinson Kathleen Harder John Bloomfield.
Sexual Imagery & Thinking About Sex
Transportation Engineering Wrap-up of planning February 2, 2011
STA 291 Spring 2008 Lecture 17 Dustin Lueker.
Presentation transcript:

A Moment of Time: Reliability in Route Choice using Stated Preference David M. Levinson Nebiyou Y. Tilahun Department of Civil Engineering University of Minnesota 11 th International Conference on Travel Behavior Research The Expanding Sphere of Travel Behavior Research Kyoto, August 16-20,2006, JAPAN

Reliability: Measures stability of service Connectivity Reliability –Probability that network nodes remain connected. Capacity Reliability –Probability that a network can handle a specified amount of demand at a desired level of service. Travel Time Reliability –The probability that a given trip can be made in an expected amount of time.

A Moment of Time The route choice decision is a recurring problem for the traveler. In this research, we hypothesize that in addition to direct monetary cost –the mode of travel time or the most frequent experience is what drives the decision to use a particular route –the person making the decision would also take into consideration late or early arrival as a possible outcome. In other words, individuals use the mode to position their preference on a particular route and then consider how much early or how much late they can be from that position.

Methodology A Computer Administered Stated Preference survey is used to test our hypothesis. Pros –Allows us to test a variety of travel time and cost combinations that in reality are difficult to acquire for each individual. –Gives us more control over the variables of interest and overcomes many of the consistency problems that arise in revealed preference data. Cons –Possibility of unreasonable choices because of misunderstanding the problem –No real consequences on the choice. To eliminate the first of these, this study included control questions randomly placed among the route alternatives. These questions presented a clearly dominated choice alternatives for the respondents.

The Survey Prior to starting the survey, subjects were given a short tutorial to help them understand distributions as they relate to hypothetical travel time distributions. The tutorial covers interpreting frequencies, mean and variance identification and what these imply in comparing alternatives. Each respondent got the question in one of three randomized orders, in half of which alternatives were placed top and bottom and in half side by side.

Example (Question)

Subjects Subjects for the survey were recruited via from the University of Minnesota’s employee database. A target of 200 was set. Invitations were sent out to 2500 randomly selected non-faculty, non-student employees who had not participated in previous transportation studies conducted by the authors. 187 participated. $15 for participation. Participants came to a central testing station, where the survey was being administered. Of these ten were dropped from the analysis –Eight of those because they made irrational choices on control questions indicating possible misunderstanding. –Two more because they failed to provide demographic information that were used in the model fitting.

A Moment of Time E= Average Early from Mode E L t p L= Average Late from Mode T = Mode of Distribution of Time t i < T for E t i >T for L T The mode (T), the average late (L) or average early (E) from the most frequent experience is a representative way of getting together the possible range and frequencies experienced.

A Moment of Time Binomial Logit Model P = Probability of choosing a route (alternative 1). T = Most frequent travel time C = Toll cost of the trip L = On Average how late a traveler can be from T E = On Average how early a traveler can be from T A = Age S = Sex (0 = Female, 1 = Male) D = Education (0 = Below College, 1 = College Educated) I = Personal income O = Reported commute time M = Usual mode of travel (1 = Car, 0 = Other)

Model: A Moment of Time

Results The results of the model support the hypothesis that all but variable E are important determinants of choice. Individuals are making a choice based on whether the mode of travel time is small, whether the average lateness expected from a particular route is small, and how much toll is paid on a particular route. There is no evidence to suggest that the possibility of early arrival has any bearing on the decision to pick a particular route. Of these three determinants, the strongest disincentive from choosing a particular route is the toll, followed by the mode of travel time and finally the magnitude of average late arrival on that route.

Results A one minute increase in the mode (T) results in a 17% reduction in the odds of choosing a particular route, A one minute increase in the average late time (L) reduces the odds of choice by about 16 %. A one dollar increase on the toll reduces the odds by close to 78%. There is a considerable overlap between T (CI (-0.219,-0.160)) and L (CI (-0.259,-0.09)) which indicates that the mode travel time and the possibility of lateness are valued very close to one another.

Results The value of time based on the mode T is $7.43 per hour, while the lateness penalty L $6.91 per hour. The Marginal Rate of Substitution of between L and T is 0.93 which indicates that other things the same, for every one minute increase in L, one could reduce T by 0.93 minutes and remain at the same utility. This finding suggests that an approximately equal magnitude of utility can be derived on average by increasing reliability as can be from reducing the usual travel time users experience.

Results In the estimation of the model, the route whose probability of choice is being calculated always provided the more reliable travel time. All other things equal, we find that: –Older individuals are more likely to tolerate the unreliable alternative. For each additional increase in age there is a 1.5% decline in odds of choosing the more reliable alternative. –More dramatically men demand less reliability than women. We find that there is on average a 39% decrease in the odds of choosing the more reliable alternative when the choice maker is male as compared to female for the same set of alternatives.

Results Education and Income both have a negative sign indicating that more educated subjects as well as wealthier subjects are more tolerant of lower levels of reliability all other things equal. People that have longer commutes were more likely to choose the reliable alternative as compared to those with shorter commute times. Car users were also found to be more tolerant of the less reliable choice as compared to those that use public transport or bicycles.

Summary The paradigm adopted in this study considers the mode travel time as the important indicator of a road’s performance to the user rather than the average. In addition it is hypothesized that users trade off the extent to which one route may serve better than another, by using the average time late and average time early from the mode. The results do not provide evidence that early arrival is important in decision making. Both the mode and average time late are important contributors to route choice. The results further indicate that the mode and lateness are valued very close to one another.

Questions

Literature –Gaver (1968) shows that travelers leave earlier than they otherwise would, had there been no variability in travel time. –Jackson and Jucker (1981) tried to explicitly address tradeoff between travel time and its variability. –Small (1982) empirically estimates a model for scheduling work trips that explicitly considers early and late arrival probabilities. –Black and Towriss (1993) estimate models with travel time and standard deviation. Similar models are estimated by Noland et. al. (1998) Small et. al. ( 1999).

Example (Tutorial)