The Impact of Stop-Making and Travel Time Reliability on Commute Mode Choice

By Chandra R. Bhat, R. Sardesai. 2006

361 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Transportation

View Paper: https://www.semanticscholar.org/paper/e2c267ebfa8d556407e6345d9b9ff49ff6f8bbb9

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Citations in the corpus

Travel through time: the story of research on travel time reliability (2013, background): “See Bhat and Sardesi (2006), Bogers et al. (2006), De Palma and Picard (2005), Devarasetty, Burriss, and Douglass-Shaw (2012), Noland and Polak (2002), Small, Winston, and Yan (2005) and Tilahun and Levinson (2010) for examples of SP studies.”
Value of Time by Time of Day: A Stated-Preference Study (2008, methodology): “For model stability, cost parameters were treated as non-random in our mixed logit models (see also Revelt and Train, 1998; Bhat and Sardesai, 2006).”
Value of Travel Time Reliability: A Review of Current Evidence (2010, methodology): “…(2001) SP and RP 7 1.17 0.91 1.47 Yan (2002) SP and RP 19 1.47 0.91 1.95 Liu et al. (2004) RP 1 1.73 – – Small et al. (2005) SP and RP 2 0.65 0.26 1.04 Bhat and Sardesai (2006) SP and RP 1 0.26 – – Hollander (2006) SP 1 0.10 – – Liu et al. (2007) RP 5 1.30 0.71 2.39 De Jong et al. (2007) SP 3…”
Identifying public preferences using multi-criteria decision making for assessing the shift of urban commuters from private to public transport: A case study of Delhi (2014, background): “Carreno, Rye, and Noble (2007), Bhat and Sardesai (2006), Stradling et al. (2005), Hensher et al. (2003) and Friman and Gärling (2001) Air conditioning”
Providing quantified evidence to policy makers for promoting bike-sharing in heavily air-polluted cities: A mode choice model and policy simulation for Taiyuan-China (2018, methodology): “Thus, combing SP data with RP data as a way to reduce such bias has become a popular practice in choice modeling (Hensher and Bradley, 1993; Ben-Akiva et al., 1994; Bradley and Daly, 1997; Bhat and Sardesai, 2006; Cherchi and Ortúzar, 2011; Lavasani et al., 2017).”
A simulation evaluation of the maximum approximate composite marginal likelihood (MACML) estimator for mixed multinomial probit models (2011, background): “…of panel data, and when the random coefficients have both an intra-individual and inter-individual random component (see Bhat and Castelar, 2002; Bhat and Sardesai, 2006, Hess and Rose, 2009), the utility structure may be written as: , qitqitU ε+′= qitqt xβ (5) where qtqqt βββ ~ += , Ω),(~ bβq…”
Latent class structures: taste heterogeneity and beyond (2014, background): “In a different direction, there has in recent years been growing interest in allowing for intra-agent heterogeneity in addition to inter-agent heterogeneity (Bhat and Sardesai, 2006; Hess and Rose, 2009) making use of a specification such as:”
Hail a cab or ride a bike? A travel time comparison of taxi and bicycle-sharing systems in New York City (2017, methodology): “Towards this purpose, our study defines a spatio-temporal dependent variable - BSS station-station competitiveness by time period - with three levels: (1) auto is faster, (2) bicycle is faster and (3) both alternatives are competitive.”
Crowding in public transport systems: Effects on users, operation and implications for the estimation of demand (2013, background): “…time, because a higher variability in travel times is negatively valued by travellers as shown by the growing body of research on the valuation of travel time variability and reliability (e.g., Senna, 1994; Bates et al., 2001; Bhat and Sardesai, 2006; Li et al., 2010; Börjesson et al., 2012).”
An analysis of bicycle route choice preferences in Texas, US (2009, background): “…Table 4 are obtained in a straightforward manner from the model coefficients, while the money values are obtained by applying a money value of time of $12.19 per hour (as obtained in Bhat and Sardesai (2006) for the Austin, Texas commute context) to the time value of each bicycle route attribute.”
The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models (2011, background): “…(a) continuous mixing factor error structures to accommodate unobserved taste variation across choice occasions through random coefficients (see Bhat and Sardesai, 2006), (b) individual-specific (time-stable or time-dissipating) error terms that generate error dependencies across the choice…”
Autonomous Driving: Technical, Legal and Social Aspects (2016, methodology): “Thomas Winkle furthers the discussion on the potential safety benefits of assisted, partially, and fully automated vehicles [4].”