What do commuters think travel time reliability is worth? Calculating economic value of reducing the frequency and extent of unexpected delays

The measurement of transportation system reliability has become one of the central topics of travel demand studies. A growing literature concerns the measurement of value of travel time reliability which provides a monetary cost of avoiding unpredictable travel time. The goal of this study is to measure commuters’ sensitivities to travel time reliability and their willingness to pay (WTP) to avoid unreliable routes. The preferences are elicited through a pivoted stated preference survey technique. To circumvent the issue of presenting numerical distributions and statistical terms to day-to-day commuters, we use the frequency of delay days as a means of measuring traveler’s sensitivities to travel time reliability. The advantage of using simplified measures to elicit traveler preferences for travel time reliability is that these methods simply compare days with high delay to days with usual travel time. It was found that travelers are not only averse to the amount of unexpected delay but also to the frequency of days with unexpected delays. The paper presents WTP findings for three measures: travel time, frequency embedded travel time, and travel time reliability. The ‘reliability’ increase in WTP for travel time is found to be nearly proportional to the frequency of experiencing unexpected delays. For example, the WTP for mean travel time is calculated at $6.98/h; however, reliability adds $3.27 (about 50 % of $6.98) to avoid unexpected delays ‘5 out of 10 days’. The results of the study would provide valuable inputs to cost-benefit analyses and traffic and revenue studies required for road tolling investment projects.     

The value of travel time and reliability-evidence from a stated preference survey and actual usage

This research examined travel behavior of Managed Lane (ML) users to better understand the value travelers place on travel time savings and travel time reliability. We also highlight the importance of survey design techniques. These objectives were accomplished through a stated preference survey of Houston’s Katy Freeway travelers. Three stated choice experiment survey design techniques were tested in this study: Bayesian (D_b) efficient, random level attribute generation, and an adaptive random approach. Mixed logit models were developed from responses using each of those designs. The value of travel time savings (VTTSs) estimates do vary across the design strategies, with the VTTS estimates based on the D_b-efficient design being approximately half the estimates from the other two designs. However, among the three design strategies, the value of travel time reliability (VOR) was only significant in the D_b-efficient design.The estimated VTTS from actual Katy Freeway usage (as measured using actual tolls paid and travel time saved on the managed lanes) is $51/h. This likely also includes any value that travelers place on travel time reliability. In comparison, our combined estimate of VTTS and VOR based on the SP survey (D_b-efficient design) was $50/h, which is remarkably close to the estimate from the actual usage data. Based on our dataset, the D_b-efficient design technique proved superior to the other two techniques. Finally, this research also supports the importance of including both travel time and travel time reliability parameters when estimating the willingness to pay for, and therefore benefits derived from, ML travel.     

Drivers’ willingness-to-pay to reduce travel time: evidence from the San Diego I-15 congestion pricing project

The adoption of congestion pricing depends fundamentally upon drivers’ willingness to pay to reduce travel time during the congested morning peak period. Using revealed preference data from a congestion pricing demonstration project in San Diego, we estimate that willingness to pay to reduce congested travel time is higher than previous stated preference results. Our estimate of median willingness to pay to reduce commute time is roughly $30 per hour, although this may be biased upward by drivers’ perception that the toll facility provides safer driving conditions. Drivers also use the posted toll as an indicator of abnormal congestion and increase their usage of the toll facility when tolls are higher than normal.     

Value of leisure time based on individuals' mode choice behavior

Values of time have been defined in various forms such as value of leisure time (shadow price of time), value of travel time, and value of saving time, and are mostly measured based on individuals' travel choice behavior. The main purpose of this study is to estimate the value of leisure time by general mode choice models. The estimated level can be used to evaluate the benefits from the increasing leisure time gained by people in Taiwan after the government has practiced a series of policies to shorten employee's working hours in the last few years. To justify the application, this study reviews and reinterprets the theoretical results of some major works on value of time derivations. Then to practically estimate the value of leisure time, it suggests a method of combining revealed preference and stated preference data for application. Finally, it conducts an empirical study on travelers' mode choices behavior in Taiwan to carry out the method suggested. The value of leisure time is estimated at 56NT$ per hour (around 1.65US$/hr), which is even lower than the minimum wage rate regulated by Taiwan government.     

A stated choice approach to developing multi-faceted models of activity behavior

Most existing activity-based models have been developed from revealed preference data. This paper introduces an approach to developing activity-based models from stated preference data. We focus on activity behavior as a multi-facet choice process to decide where and in what sequence to conduct activities, i.e., choice of destination and choice of stop pattern. A design strategy is developed to generate choice experiments that allow the estimation of multi-facet models of activity behavior. The results of an empirical application are reported. The experience and results obtained indicate that the proposed approach does provide a stated preference alternative to the revealed preference approach in developing multi-facet models of activity behavior.     

Can you ever be certain? Reducing hypothetical bias in stated choice experiments via respondent reported choice certainty

Stated choice experiments are a preeminent method for researchers and practitioners who seek to examine the behavior of consumers. However, the extent to which these experiments can replicate real markets continues to be debated in the literature, with particular reference to the potential for biased estimates as a result of the hypothetical nature of such experiments. In this paper, a first in the transportation literature, we compare stated choice responses to revealed preference behavior and examine three methods proposed in the literature for calibrating choice experiments via reported choice certainty. In doing so we provide evidence that the incorrect calibration of responses can produce stated choice results that are more biased than doing nothing at all, however we show that by jointly estimating choice and choice certainty there is a significant reduction in hypothetical bias such that stated choice responses more directly replicate real behavior.     

Determining the Monetary Value of Quality Attributes in Freight Transportation Using a Stated Preference Approach

It is commonly accepted that the modal choice of a shipper is influenced not only by the pure economic attributes of transportation – time and cost – but also by more qualitative factors. These quality attributes relate to frequency, reliability, flexibility, transport duration and risk of loss or damage; they are usually difficult to quantify in monetary terms. Different techniques exist that help to understand better how these different quality attributes of freight transportation influence modal choice. In this paper we apply a stated preference design. Using real business data, the aim is then to derive partial utility functions that allow us to calculate monetary values for these different quality attributes.     

Understanding suburban travel demand: Flexible modelling with revealed and stated choice data

We analyse the choice of mode in suburban corridors using nested logit specifications with revealed and stated preference data. The latter were obtained from a choice experiment between car and bus, which allowed for interactions among the main policy variables: travel cost, travel time and frequency. The experiment also included parking cost and comfort attributes. The attribute levels in the experiment were adapted to travellers’ experience using their revealed preference information. Different model specifications were tested accounting for the presence of income effect, systematic taste variation, and incorporating the effect of latent variables. We also derived willingness-to-pay measures, such as the subjective value of time, that vary among individuals as well as elasticity values. Finally, we analysed the demand response to various policy scenarios that favour public transport use by considering improvements in level-of-service, fare reductions and/or increases in parking costs. In general, demand was shown to be more sensitive to policies that penalise the private car than those improving public transport.     

Bayesian imputation of non‐chosen attribute values in revealed preference surveys

Obtaining attribute values of non‐chosen alternatives in a revealed preference context is challenging because non‐chosen alternative attributes are unobserved by choosers, chooser perceptions of attribute values may not reflect reality, existing methods for imputing these values suffer from shortcomings, and obtaining non‐chosen attribute values is resource intensive. This paper presents a unique Bayesian (multiple) Imputation Multinomial Logit model that imputes unobserved travel times and distances of non‐chosen travel modes based on random draws from the conditional posterior distribution of missing values. The calibrated Bayesian (multiple) Imputation Multinomial Logit model imputes non‐chosen time and distance values that convincingly replicate observed choice behavior. Although network skims were used for calibration, more realistic data such as supplemental geographically referenced surveys or stated preference data may be preferred. The model is ideally suited for imputing variation in intrazonal non‐chosen mode attributes and for assessing the marginal impacts of travel policies, programs, or prices within traffic analysis zones. Copyright © 2012 John Wiley & Sons, Ltd.     

Enhanced traffic information dissemination to facilitate toll road utilization: a nested logit model of a stated preference survey in Texas

This paper investigates the effects of the provision of traffic information on toll road usage based on a stated preference survey conducted in central Texas. Although many researchers have studied congestion pricing and traffic information dissemination extensively, most of them focused on the effects that these instruments individually produce on transportation system performance. Few studies have been conducted to elaborate on the impacts of traffic information dissemination on toll road utilization. In this study, 716 individuals completed a survey to measure representative public opinions and preferences for toll road usage in support of various traffic information dissemination classified by different modes, contents, and timeliness categories. A nested logit model was developed and estimated to identify the significant attributes of traffic information dissemination, traveler commuting patterns, routing behavior, and demographic characteristics, and analyze their impacts on toll road utilization. The results revealed that the travelers using dynamic message sign systems as their primary mode of receiving traffic information are more likely to choose toll roads. The potential toll road users also indicated their desire to obtain traffic information via internet. Information regarding accident locations, road hazard warnings, and congested roads is frequently sought by travelers. Furthermore, high-quality congested road information dissemination can significantly enhance travelers’ preferences of toll road usage. Specifically the study found that travelers anticipated an average travel time saving of about 11.3 min from better information; this is about 30 % of travelers’ average one-way commuting time. The mean value of the time savings was found to be about $11.82 per hour, close to ½ of the average Austin wage rate. The model specifications and result analyses provide in-depth insights in interpreting travelers’ behavioral tendencies of toll road utilization in support of traffic information. The results are also helpful to shape and develop future transportation toll system and transportation policy.     

Influence of the weather on mode choice in corridors with time-varying congestion: a mixed data study

Studies that link human behaviour to the influence of weather have historically been conducted in such fields as tourism, marketing and leisure. In most studies that jointly examine weather and the mode of transport, only open-air transportation has been considered (for example, bicycle, motorcycle or walking). This focus, together with the habitual use of data collected with automatic devices and a lack of studies that analyse this issue using stated preference data, are the main reasons motivating this paper. This paper aims to analyse the influence of weather and the density of traffic on the choice of transport mode. A case study is conducted in an access/egress corridor located in the city of Barcelona (Spain). Two data sources were used: revealed preference and stated preference data. Modelling techniques using mixed data enabled the stronger features from both data sources to be captured. Finally, we discuss how the selection of different alternative specific constants in models estimated using mixed data could generate unrealistic forecasting results if environmental changes are expected in the actual market.     

Estimation of switching models from revealed preferences and stated intentions

This paper proposes a methodology for modeling switching behavior using simultaneously cross-sectional revealed preference (RP) data and stated intentions, a type of stated preference (SP) data. With explicit consideration of biases and random errors potentially contained in SP data, combined estimation with RP and SP data can exploit the advantages of both data sources. An empirical analysis of commuters' mode choice shows that the stated intention data have predictive validity if their biases and errors are properly corrected.     

Door-to-door travel times in RP departure time choice models: An approximation method using GPS data

A common way to determine values of travel time and schedule delay is to estimate departure time choice models, using stated preference (SP) or revealed preference (RP) data. The latter are used less frequently, mainly because of the difficulties to collect the data required for the model estimation. One main requirement is knowledge of the (expected) travel times for both chosen and unchosen departure time alternatives. As the availability of such data is limited, most RP-based scheduling models only take into account travel times on trip segments rather than door-to-door travel times, or use very rough measures of door-to-door travel times. We show that ignoring the temporal and spatial variation of travel times, and, in particular, the correlation of travel times across links may lead to biased estimates of the value of time (VOT). To approximate door-to-door travel times for which no complete measurement is possible, we develop a method that relates travel times on links with continuous speed measurements to travel times on links where relatively infrequent GPS-based speed measurements are available. We use geographically weighted regression to estimate the location-specific relation between the speeds on these two types of links, which is then used for travel time prediction at different locations, days, and times of the day. This method is not only useful for the approximation of door-to-door travel times in departure time choice models, but is generally relevant for predicting travel times in situations where continuous speed measurements can be enriched with GPS data.     

Willingness to accept commuting time within the household: stated preference evidence

In this study, stated preference data is used to derive estimated values of commuting time (VOCT). Both spouses in two-earner households are individually making trade-offs between commuting time and wage; both with regard to their own commuting time and wage only, as well as when both their own commuting time and wage and their spouse’s commuting time and wage are simultaneously changed. Thus, we are able to compare how male spouses and female spouses value each other’s commuting time. When only ones own commuting time and wage are attributes, the empirical results show that the estimated VOCT is plausible with a tendency towards high values compared to other studies, and that VOCT does not differ significantly between men and women. When decisions affecting commuting time and wage of both spouses are analyzed, both spouses value the commuting time of the wife highest. Further analysis show that this result is driven by households where the man has the highest income. If VOCT were to be gender specific in policy implications, the value might be higher for women than for men in two-earner households.     

Modelling travellers’ risky choice in a revealed preference context: a comparison of EUT and non-EUT approaches

Recent work on risky choice modelling has sought to address the shortcomings of expected utility theory (EUT) by using non-expected utility theoretic (non-EUT) approaches. However, to date these approaches have been merely tested on stated choice data which is flexible and cheap. In this study, we empirically investigate the feasibility and validity of non-EUT approaches in a revealed preference (RP) context in which travel time distribution is extracted from observed historical travel time data, and subsequently present systematic comparisons between EUT, weighted utility theory, rank-dependent expected utility theory, and prospect theory (PT). The empirical evidence indicates that each non-EUT model has important behavioural insights to offer, moreover, EUT as well as non-EUT models can be applied to the RP context. However, the EUT and non-EUT model fits are generally similar with only PT providing a marginally improved model fit over EUT. The key findings presented in this study reinforce the importance of exploring non-EUT models within a revealed preference context before they can be applied reliably to modelling risky choices in the real world.     

A comparison of hedonic price and stated preference methods to derive monetary values for aircraft noise disturbance and annoyance

This paper compares monetary values for aircraft noise disturbance and annoyance derived using hedonic price and stated preference methods in the same sample areas and over the same sampling time periods. The hedonic price method compares house prices in residential areas across a range of aircraft noise sound levels. The statistical power can be compromised by the many other variables which have significant and often greater effects on prices and because only small fractions of the overall housing stock are bought and sold at any one time. The stated preference method on the other hand, by requiring study participants to trade hypothetical money against hypothetical noise, is entirely based on subjective opinion. It seems that neither method is entirely satisfactory, but there are strong positive correlations between the ratios of aggregated stated preference values to weighted average house prices and LAeq, 16 h and similar noise metrics suggesting sufficient underlying similarities     

The effect of travel time variability on route choice decision: a generalized linear mixed model based analysis

The effect of travel time variability (TTV) on route choice behavior is explored in this study. A stated preference survey is conducted to collect behavioral data on Shanghai drivers’ choice between a slow but stable route and a fast but unreliable route. Travel time and TTV are respectively measured by mean and standard deviation of random travel time. The generalized linear mixed model (GLMM) is applied to quantify trade-offs between travel time and TTV. The GLMM based route choice model effectively accounts for correlations among repeated observations from the same respondent, and captures heterogeneity in drivers’ values of TTV. Model estimation results show that, female drivers and drivers with rich driving experience are less likely to choose a route with high TTV; smaller expected travel time of a route increase the probability of its being chosen; all drivers have intrinsic preference for a route with smaller expected travel time, but the degree of preference may vary within the population; TTV on average has negative effects on route choice decision, but a small portion of drivers are risk-prone to choose a fast but unreliable route despite high TTV.     

Choice of parking: Stated preference approach

Over recent years, parking policy has become a key element of transport policy in many countries. Parking policy measures can affect many different dimensions of travel behaviour but are likely to be most significant in terms of travellers' choice of parking type and location. This dimension of travel choice has, to date, received comparatively little attention, yet is of vital importance if we are to properly understand and predict the effects of parking policy measures.This paper presents two studies addressing this issue carried out in the United Kingdom and Germany. Both studies used a stated preference approach in order to collect disaggregate data on travellers responses to changes in parking attributes and used these data to build simple logit models of parking type choice. The studies were designed in order to allow comparable choice models to be estimated from the two datasets. The results obtained strongly indicate the need to separately identify the costs associated with different components of the parking activity (e.g., general in-vehicle time, parking search time, egress time) and also point to the existence of significant differences in the relative valuation of these components across different journey purposes. Where possible, the results of the choice modelling exercises are also compared with existing revealed and stated preference results and are found to be generally in line with prior expectations.     

Mixed RP/SP models incorporating interaction effects

In order to analyse the impact of a new train service in Cagliari (Italy) a databank including information from a revealed preference (RP) and a stated preference (SP) survey was set up. The RP data concern choice between car, bus and train; the SP data consider the binary choice between a new train service (quicker, more frequent, with a lower fare and more stations than the current one) and the alternative currently chosen by car and bus users. Logit models allowing for correlation among RP alternatives were estimated for this mixed RP/SP data set using the artificial tree structure method. The analysis included level-of-service variables measured with an unusually high level of precision, latent or second order variables (such as comfort), inertia and interaction variables. Different specifications of the utility function were tested, including the expenditure rate model, and the effects of these specifications on modelling results are highlighted. Our results show that for a population mainly composed of fixed income workers, the expenditure rate model is superior to the traditional wage rate model, yielding lower and more significant subjective values of time. Moreover, we found that the non-linear specifications appear to be more suitable as not only better model results were obtained, but also the real distribution of the error terms was revealed (i.e. highlighting correlation among public transport options).     

Embedding risk attitude and decision weights in non-linear logit to accommodate time variability in the value of expected travel time savings

In recent years we have seen important extensions of logit models in behavioural research such as incorporation of preference and scale heterogeneity, attribute processing heuristics, and estimation of willingness to pay (WTP) in WTP space. With rare exception, however, a non-linear treatment of the parameter set to allow for behavioural reality, such as embedded risk attitude and perceptual conditioning of occurrence probabilities attached to specific attributes, is absent. This is especially relevant to the recent focus in travel behaviour research on identifying the willingness to pay for reduced travel time variability, which is the source of estimates of the value of trip reliability that has been shown to take on an increasingly important role in project appraisal. This paper incorporates, in a generalised non-linear (in parameters) logit model, alternative functional forms for perceptual conditioning (known as probability weighting) and risk attitude in the utility function to account for travel time variability, and then derives an empirical estimate of the willingness to pay for trip time variability-embedded travel time savings as an alternative to separate estimates of time savings and trip time reliability. We illustrate the richness of the approach using a stated choice data set for commuter choice between unlabelled attribute packages. Statistically significant risk attitude parameters and parameters underlying decision weights are estimated for multinomial logit and mixed multinomial logit models, along with values of expected travel time savings.