126 6.2.2.1 Valuation of travel time savings
MVA et al. (1987) and Accent Marketing & Research (1996) provided an early summarization of the UK evidence on VTTS. Both market studies, using cross sectional data, proposed a more general model form for the allocation of monetary and time resources, rejecting a simple proportionality between VTTS and income, and indicating that VTTS would grow over travel time. Additionally, there is a large body of literature which attempts to estimate VTTS in various contexts by using revealed or stated preference methods. To better compare VTTS estimates in the existing literature, a sample of relevant studies are listed in Table 6.1.31 It is evident that empirical estimates of VTTS vary significantly across the literature. This is not in itself surprising, given that respondents from various regions are likely to exhibit different acceptable levels of the valuation of travel time savings. However, even on the same corridor, notable differences were observed in terms of VTTS.
If we pay particular attention to a series of studies on the SR91 road, given that this dataset is of special interest to us, there are three crucial studies Small et al. (1999); Lam and Small (2001) and Small et al. (2005b), which here are referred to simply as SR1, SR2 and SR3. Each of these studies published their estimated result for the value of time based on data collected from the SR91 corridor. The first observation is that SR3 and SR2 generate a much higher VTTS than SR1. Since the main difference between SR1 and SR2 is that the former used SP data whereas the latter used RP data, it would be sensible to conclude that the type of dataset has a direct influence on VTTS. Indeed, evidence for a similar effect can be found in other literature, for instance, Ghosh (2001) demonstrated that the estimated VTTS from RP data is almost four times higher than the VTTS from their SP dataset ($40/h vs $13/h). This suggestion can be convincingly supported by setting out the VTTS estimated across a large body of literature, as in Figure 6.1.
31 It should be noted that some of the EUT and non-EUT studies illustrated in Table 6.1 do not explicitly demonstrate VTTS, such as Michea and Polak (2006), De palma and Picard (2005), and De Lapparent (2010).
We estimated their VTTS based on their estimation results.
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Study Data Year Mode VTTS VOR RR VSDE VSDL
Risky choice model
Utility Decision weight
Bates et al. (2001) SP 1999 Rail NA NA NA $52.42/h $106.39/h EVT NA NA
Small et al. (1999)
(SR1) SP 1995 Car $3.9/h $12.6/h 3.23 NA $18.6/h NA NA NA
Hensher (2001) SP 1999 Car $8.7/h $5/h 0.57 NA NA NA NA NA
Tilahun and
Levinson (2010) SP NR Multi
modes $7.82/h $6.93/h 0.89 $0.41/h $7.11/h NA NA NA
Polak et al. (2008) SP 1999 Car & rail $8.81/h NA NA $2.15/h $17.61/h EVT NA NA
Lam and Small
(2001) (SR2) RP 1997-
1998 Car $24/h $12/h (male)
$30/h (female) 0.5-1.3 NA NA NA NA NA
Liu et al. (2004) RP/SP
1999-2000 Car $12.8/h $20.6/h 1.61 NA NA NA NA NA
Small et al. (2005)
(SR3) RP
1999-2000 Car $27.44/h $24.31/h 0.89 NA NA NA NA NA
Small et al. (2005) SP
1999-2000 Car $11.99/h $5.54/incident NA NA NA NA NA NA
Brownstone and
Small (2005) RP/SP
1999-2000 Car $12.55/h $5.02/h 0.4 NA NA NA NA NA
Noland et al.
(1998) SP 1994 NR NR NR 1.27 NA NA EVT NR NR
Black et al. (1993) SP NR Multi
modes $7.24/h $5.08/h 0.7 NA NA NA NA NA
128 Bhat and Sardesai
(2006) RP/SP 2003 Multi
modes $12.2/h $3.3/h (flex);
$6.1/h (infle) 0.2-0.5 NA NA NA NA NA
Ghosh (2001) RP
1998-1999 Route $40/h NA NA NA NA NA NA NA
Ghosh (2001) SP
1998-1999 Route $13/h NA NA NA NA NA NA NA
Carrion and
Levinson (2010) RP
2008-2009 Route $9.15/h $5.99 /h 0.91 NA NA NA NA NA
Asensio and Matas
(2008) SP 2005 Car $18.89/h NA NA $12.06/h $28.27-
$68.47 /h NA NA NA
Hollander (2006) SP 2004 Bus $6.47/h $0.62/h 0.1 $4.81/h $13.48/h NA NA NA
Senna (1994) SP NR Car & bus
$0.22/h-$1.23/h
$1.08/h-
$2.00/h 0.8-1.6 NA NA EUT Mixedc NA
Liu and Polak
(2007) SP 1999 Rail NA NA NA £8.25/h £22.3/h EUT Risk
aversion NA
Polak et al. (2008) SP 1999 Car & rail $3.14/h NA NA $1.17/h $6.49/h EUT Risk
aversion NA Hensher and Li
(2012) SP 2008 Car $21.13/h NA NA NA NA RDEU Risk
proneness
Risk aversion Hensher et al.
(2011) SP 2008 Car
$6.30/h-$9.65/h NA NA NA NA SEU Risk
proneness NR
129
Li et al. (2009) SP 2008 Car $19.1/h NA NA NA NA SEU Risk
aversion NR Michea and Polak
(2006) SP 1999 Rail NA NA NA $34.96/h $70.53/h RDEV NA Mixed
De Palma and
(2010) RP 2002 Flight $59.50/h NA NA NA NA RDEU Risk
neutrality
Risk proneness NA: not applicable; NR: not reported
For the purposes of comparison, all the values have been converted into US dollars based on the exchange rates current at 20th February 2013.
a Here only commuters' WTP and WTA w.s.t free flow travel time is recorded. Further details are provided in Hess, Ross and Hensher (2008) b Here only commuters' WTP and WTA w.s.t free flow travel time in WTP/WTA space is considered. Preference space is provided.
c Risk prone for commuters with a fixed arrival time, and risk aversion for the others d 66% risk aversion and risk neutrality; 33% risk proneness
Table 6.1: Summary of empirical studies on VTTS
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Figure 6.1: Illustration of estimated VTTS from selected studies
Based on the VTTS shown in Figure 6.1, it seems that the estimated VTTS from RP studies is slightly higher than the estimate from SP studies, which is most likely due to the systematic effects of SP and RP methodologies. Alhough we cannot conclude which method is more suitable for VTTS studies, there are at least two reasons why SP studies appear to underestimate VTTS. Firstly, as noted by Brownstone and Small (2005), respondents on SR91 reported perceived travel time savings were almost twice that of the real travel time savings using a toll facility. This perception bias magnifies the benefits of using a toll road,32 and may potentially lead to drivers having higher estimates of VTTS in the RP context.
Secondly, respondents usually fail to take into account the actual situations in real travelling experience when they answer hypothetical questions in SP surveys, and therefore, they tend to display inconsistency compared to actual behaviour. For instance, in a real situation, drivers may be impatient about serious congestion, which result in an intensified feeling of regret or loss. This negative attitude towards travel time delay may also serve to enlarge VTTS, whereas respondents in an SP survey, which is more remote from the event, are unlikely to feel the same as they do in real congestion. However, it should be noted that most studies used in this comparison are from the US, and the empirical studies in the UK are different. For instance, Abrantes and Wardman (2011) conducted a meta-analysis on VTTS studies undertaken in the UK, and concluded that the valuation of SP is not greatly different from RP values. Consequently, given the regional variation, empirical practices should be conducted to accurately estimate VTTS in differnt cases.
32 Ghosh (2001) found out that drivers with bigger perception errors tend to be more likely to use toll roads.
0
131
To mitigate the effect of methodological differences between SP and RP, we compare SR2 and SR3 using an RP sample, and SR1 and SR3 using an SP sample. Given the same type of data, VTTS of SR3 is still higher than the estimates of SR1 ($11.99/h vs $3.9/h) and SR2 (($27.44/h vs $24/h). There are at least two reasons for this continuing difference. The first explanation is from a model perspective, given the fact that SR1, SR2 and SR3 employ MNL, Nested Logit, and MMNL model structure respectively. Similar findings are obtained by Hensher and Li (2012) who obtained higher VTTS from their MMNL model ($21.13/h vs
$17.92/h). This empirical evidence would suggest that the MMNL model specification tends to deliver higher estimates of VTTS when respondents’ heterogeneity is taken into account.
The second reason is from a data perspective. For RP studies, SR2 extracted travel time distribution is from loop detector data, while SR3’s travel time data is based on their floating car dataset. For SP studies, SR1 verbally presents five arrival time scenarios for each alternative to represent travel time variability, whilst the SP survey in SR3 describes travel time variability as the frequency of 10 minutes or more delay for each alternative. It has been found that the different formats and presentations of SP questionnaires may have an impact on respondents’ judgements, in particular on travel time and travel time variability.
According to Tseng et al. (2009), it is easier for respondents to understand travel time distribution when it is described verbally. From his point of view, SR1 is preferred.
Another argument may be that different data collection periods appear to have an influence on VTTS, given that the data was collected in 1995, 1997 and 1999 for SR1, SR2 and SR3 respectively. It is almost certainly the case that more recent data is capable of generating higher VTTS if the other factors are constant, due to the increase due to inflation.
This is also supported by the increasing trend shown in Figure 6.1.
Additionally, it should be noted that other factors potentially play significant roles in determining VTTS, such as choice type, trip purpose, gender and regional difference, etc. For instance, the highest VTTS shown in Table 6.1 is obtained by De Lapparent (2010) focusing on air route choice, with the value of almost 60 dollars per hour. It is very likely that flight passengers tend to spend more money on saving journey time than the other passengers, say train passengers, given that a flight ticket is much higher than a train ticket. Finally, having understood the possible impact of survey and discrete choice models on VTTS, further research should attempt to address whether EUT and non-EUT approaches affect the estimates of VTTS.
132 6.2.2.2 Valuation of travel time reliability
We have also found out different estimates of VOR, in a range from $0.62/h to $30/h. One possible explanation for such a wide variation is that different representations of travel time variability are related to final VOR. For instance, SR1, SR2 and SR3 employ standard deviation, dmp90 and dmp8033 of travel time to characterize travel time variability, and correspondingly they obtained different VOR. Similar to the comparison of VTTS, comparing VOR across different time periods may also be affected by inflation and exchange rate. To eliminate this impact, it is sensible to adopt the reliability ratio (RR) as the index for comparison. It should be noted that another merit of using RR is that it reflects attitude to risk, since this ratio indicates whether the cost of travel time variability is more or less undesirable than the cost of travel time for decision making. Specifically, travellers are risk averse if RR>1 (travel time variability is more undesirable), risk prone if RR<1(travel time is more undesirable), and risk neutral if RR=1 (indifferent between travel time and travel time variability). In fact, as shown in Figure 6.2, our observations from 15 empirical studies are mixed, with the mean estimated RR=0.95.
Figure 6.2: Distribution of reliability ratio (RR) based on selected studies
In scheduling models, the relationship between different valuations appears to be relatively stable. It has consistently been found that , which reflects travellers more
33 Dmp90 means the 90th percentile of travel time minus median travel time, and dmp80 is the 80th percentile of travel time minus median travel time.
0.00 0.05 0.10 0.15 0.20 0.25
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
RR
Objective probability
133
negative taste to SDL than SDE. This is intuitively plausible, since there may be an additional penalty for late arrival, especially for workers. Indeed, as noted by Bates et al. (2001), punctuality is highly valued by respondents, and VSDL is almost twice that of VSDE. Noland et al. (1998) compared the performances between the mean-variance model and the scheduling model, concluding that the scheduling model provides the best goodness of fit and acceptable estimators, with the relationship being . Hollander (2006) conducted a similar study showing .
In summary, a large body of literature has applied a single index to valuate travel time variability such as VOR, VSDL and VSDE. It should be noted that all the indexes are constrained to specific models (mean-variance model and scheduling model), and they cannot deal with different definitions of travel time variability. In this current research, however, we focus on the travel time distribution per se rather than its moment. As a result, we can apply EUT and non-EUT models to modelling the distribution perceived by travellers, and then estimate the VTTS which is embedded within the information on travellers’ attitudes toward risk.