Calculating errors for measures derived from choice modelling estimates

Daly, Andrew; Hess, Stephane; Jong, Gerard de

doi:10.1016/j.trb.2011.10.008

Cited by 121 publications

(64 citation statements)

References 8 publications

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“…Even if the covariance estimates agree, there is still the problem of producing confidence regions for complex functions of the model parameters. Daly, Hess, and de Jong (2012) show that it is quite complicated to get valid confidence intervals for relatively simple functions of the underlying parameters such as willingness to pay measures. Their methods would be very difficult to implement for the policy simulations in Tables 3 and 4 or the predictions in Tables 6 and 7.…”

Section: Modelmentioning

confidence: 99%

A vehicle ownership and utilization choice model with endogenous residential density

Brownstone

Fang²

2014

JTLU

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This paper explores the impact of residential density on households' vehicle type and usage choices using the 2001 National Household Travel Survey (NHTS). Attempts to quantify the effect of urban form on households' vehicle choice and utilization often encounter the problem of sample selectivity. Household characteristics that are unobservable to the researchers might determine simultaneously where to live, what vehicles to choose, and how much to drive them. Unless this simultaneity is modeled, any relationship between residential density and vehicle choice may be biased. This paper extends the Bayesian multivariate ordered probit and tobit model developed in Fang (2008) to treat local residential density as endogenous. The model includes equations for vehicle ownership and usage in terms of number of cars, number of trucks (vans, sports utility vehicles, and pickup trucks), miles traveled by cars, and miles traveled by trucks. We carry out policy simulations that show that an increase in residential density has a negligible effect on car choice and utilization, but slightly reduces truck choice and utilization. The largest impact we find is a -.4 arc elasticity of truck fuel use with respect to density. We also perform an out-of-sample forecast using a holdout sample to test the robustness of the model.

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Section: Modelmentioning

confidence: 99%

A vehicle ownership and utilization choice model with endogenous residential density

Brownstone

Fang²

2014

JTLU

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“…The latter two measures were calculated using the Delta method, which, as discussed by Daly et al (2010), is an exact method rather than an approximation. The use of effects coding for some attributes has consequences for their associated monetary valuations.…”

Section: Monetary Valuationsmentioning

confidence: 99%

Interactive stated choice surveys: a study of air travel behaviour

2011

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“…Given the estimated values of the βs, the value of congestion was calculated as the ratio between the estimates of the parameters β congt and β fft , while, for comparison, reliability was calculated as the ratio between the estimates of the parameters β trel and β fft and the ratio between the estimate of β trel and the combination of β fft and β congt for the average level of congestion conglev [i.e., (1 − conglev) · (β fft + conglev β congt )], where conglev is the percentage of congested travel time with respect to the total travel time. Because the values were distributed, the mean, standard deviations, and confidence intervals were calculated analytically, as illustrated by Daly et al (23).…”

Section: Route Choice Modelmentioning

confidence: 99%

Estimating Value of Congestion and of Reliability from Observation of Route Choice Behavior of Car Drivers

Prato

Rasmussen

Nielsen

2014

Transportation Research Record: Journal of the Transportation R

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In recent years, a consensus has been reached about the relevance of calculating the value of congestion and the value of reliability for better understanding and therefore better prediction of travel behavior. The current study proposed a revealed preference approach that used a large amount of GPS data from probe vehicles to provide insight into actual behavior in choosing a route. Mixed path size correction logit models were estimated from samples of 5,759 observations in the peak period and 7,964 observations in the off-peak period, while a mean-variance model was specified to consider both congestion and reliability terms. Results illustrated that the value of time and the value of congestion were significantly higher in the peak period because of possible higher penalties for drivers being late and consequently possible higher time pressure. Moreover, results showed that the marginal rate of substitution between travel time reliability and total travel time did not vary across periods and traffic conditions, with the obvious caveat that the absolute values were significantly higher for the peak period. Last, results showed the immense potential of exploiting the growing availability of large amounts of data from cheap and enhanced technology to obtain estimates of the monetary value of different travel time components from the observation of actual behavior, with arguably potential significant impact on the realism of large-scale models.Travel demand studies place a significant emphasis on the estimation of the value of time (VOT), namely the monetary value assigned by travelers to their travel time savings. Theoretical foundations of the VOT concept have been thoroughly reviewed in the recent past (1, 2), and empirical estimates of the VOT have been extensively provided by researchers and practitioners and meticulously reviewed (3-6).Recently, consensus has been reached about the importance of calculating two values that are related to the VOT: (a) the value of congestion, as the VOT varies with traffic conditions and relates to the complexity of driving conditions and the emergence of feelings of frustration and danger when more vehicles are present on the road [see, e.g., Fosgerau et al. (7) and Wardman and Ibañez (8)], and (b) the value of reliability, as the VOT relates to the predictability of travel time and the repeatability of the travel experience without additional costs that are attributable to uncertainty [see, e.g., Li et al. (9) and Carrion and Levinson (10)].Evidence of the value of congestion dates back about 40 years according to a report that "the estimated coefficient of auto congestion time was about thirty percent larger (in magnitude) than that of auto non-congestion time" (11). About 10 years later, a stated preference (SP) study in the United Kingdom provided the first estimates of the value of congestion, which ranged between 1.28 and 1.46 for different trip purposes (12). Then, a large variety of studies estimated the value of congestion, with values ranging from about 1.00 to mor...

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Calculating errors for measures derived from choice modelling estimates

Cited by 121 publications

References 8 publications

A vehicle ownership and utilization choice model with endogenous residential density

A vehicle ownership and utilization choice model with endogenous residential density

Interactive stated choice surveys: a study of air travel behaviour

Estimating Value of Congestion and of Reliability from Observation of Route Choice Behavior of Car Drivers

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