Investigating preference heterogeneity in Value of Time (VOT) and Value of Reliability (VOR) estimation for managed lanes

Hossan, Sakoat; Asgari, Hamidreza; Jin, Xia

doi:10.1016/j.tra.2016.10.022

Cited by 20 publications

(12 citation statements)

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“…Such scenarios have been studied extensively in the United States and present travellers with a real time-money trade-off (see e.g. Brownstone and Small, 2005, Hess et al, 2011, Hossan et al, 2016, Lam and Small, 2005. However, they are not commonly used for national studies, which seek to establish a reliable long-term measure of VTT for appraisal rather than a journey specific one.…”

Section: Introductionmentioning

confidence: 99%

A critical appraisal of the use of simple time-money trade-offs for appraisal value of travel time measures

2020

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Section: Introductionmentioning

confidence: 99%

A critical appraisal of the use of simple time-money trade-offs for appraisal value of travel time measures

2020

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“…The variance of the Poisson distribution is equal to the mean. The utilized VOT stochastic distribution in the Monte Carlo simulation was calculated based on a distribution derived in a study by Hossan et al ( 28 ). The travel demand is assumed to have a fixed coefficient of variation, calculated based on real-world data in Florida and with the mean demands changing according to a uniformly distributed random annual growth rate between 0.5% and 1.5%.…”

Section: Methodolgymentioning

confidence: 99%

Monte Carlo Simulation-Based Benefit-Cost Analysis Combined with Analytical Hierarchy Process to Support ITS Investment with Consideration of Connected Vehicle Technology

Khazraeian

Hadi

2018

Transportation Research Record: Journal of the Transportation R

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Decisions to invest in alternative intelligent transportation system (ITS) technologies are expected to increase in complexity, particularly with the introduction of connected vehicles (CV) and automated vehicles (AV) in the coming years. Traditional alternative analyses based on deterministic return on investment analysis are unable to capture the risks and uncertainties associated with the investment problem. In addition, these methods cannot account for agency preferences and constraints that cannot be converted to dollar values. This study utilizes a combination of a stochastic return on investment and a multi-criteria decision analysis method referred to as the Analytical Hierarchy Process (AHP) to select between ITS deployment alternatives considering emerging technologies. The approach is applied in a case study of the selection between using CV data and point detector data to support the freeway traffic data collection and monitoring service. The four objectives specified in the AHP analysis are providing the required functions, providing the required performance, minimizing the risks and constraints, and maximizing the return on investment. A stochastic return-on-investment analysis using a Monte Carlo simulation was used to calculate the return on investment values for input to the AHP method.

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“…The most common data source for managed lane behavior modeling is SP data, as many managed lane studies investigate responses to infrastructures, policies, or both that have not yet been implemented (e.g., 32 , 33 ). SP studies generally have the limitation of hypothetical bias ( 15 , 33 ), which can be alleviated by combining SP with RP data of the existing situation ( 20 , 34 , 35 ). However, in the context of managed lanes, several studies have found that how travelers actually make choices deviates from the presumptions and conclusions of many SP studies.…”

Section: Literature Reviewmentioning

confidence: 99%

Personalized Choice Model for Managed Lane Travel Behavior

Xie

Zhang

Akkinepally

et al. 2020

Transportation Research Record: Journal of the Transportation R

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This paper presents a methodology for enhancing discrete choice models for managed lane travel behavior with personal trip history. We refer to this process as personalization and the enhanced model as a personalized choice model. With the objective of better understanding managed lane choices and improving the model’s prediction capability, personalization was carried out at two levels. First, we used each traveler’s habits and travel experiences before each trip for constructing a set of explanatory variables that could be used with any model structure. Second, under a logit mixture framework, the distribution of random parameters was updated with Bayesian inference according to personal trip history. The structure of the parameter distribution explicitly considered preference variations across individuals (interpersonal heterogeneity), as well as preference variations across trips performed by the same individual (intrapersonal heterogeneity). The proposed methodology is especially relevant for modeling revealed preference (RP) data from automatic vehicle identification sensors, for which limited socioeconomic characteristics of travelers are available. An empirical study was conducted on an operational managed lane corridor near Dallas/Fort Worth Airport in Texas. Available trip records over a 5-month period were utilized. A hierarchical Bayes estimator was adopted for efficient model estimation. The results suggest significant inter- and intrapersonal heterogeneity and that the proposed personalization method improves the model’s explanatory power and prediction capability. To the best of our knowledge, this paper represents the first introduction of personalization in managed lane choice behavior modeling and the first attempt to estimate intrapersonal heterogeneity with RP data.

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Investigating preference heterogeneity in Value of Time (VOT) and Value of Reliability (VOR) estimation for managed lanes

Cited by 20 publications

References 20 publications

A critical appraisal of the use of simple time-money trade-offs for appraisal value of travel time measures

A critical appraisal of the use of simple time-money trade-offs for appraisal value of travel time measures

Monte Carlo Simulation-Based Benefit-Cost Analysis Combined with Analytical Hierarchy Process to Support ITS Investment with Consideration of Connected Vehicle Technology

Personalized Choice Model for Managed Lane Travel Behavior

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