2015
DOI: 10.1371/journal.pone.0134322
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Do People Use the Shortest Path? An Empirical Test of Wardrop’s First Principle

Abstract: Most recent route choice models, following either the random utility maximization or rule-based paradigm, require explicit enumeration of feasible routes. The quality of model estimation and prediction is sensitive to the appropriateness of the consideration set. However, few empirical studies of revealed route characteristics have been reported in the literature. This study evaluates the widely applied shortest path assumption by evaluating routes followed by residents of the Minneapolis—St. Paul metropolitan… Show more

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Cited by 165 publications
(136 citation statements)
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“…Evidence from revealed route choice behavior finds after evaluating habitual routes, only 59 % of respondents from Cambridge, Massachusetts , 30 % from Boston (Ramming and Michael Scott 2001a), and 87 % from Turin, Italy (Prato et al 2006) chose paths with the shortest distance or shortest travel time. In the Minneapolis-St. Paul region, 90 % of subjects took paths one-fifth longer than average commute time (Zhu 2011;Zhu et al 2015) based on actual GPS data while more than half commute trips were at least 5 minutes longer than the shortest path based on TomTom GPS data (Tang et al 2015), and a high percentage of commuting routes were found to differ considerably from the shortest paths in Nagoya, Japan (Morikawa et al 2005 and Lexington, Kentucky (Jan et al 2000). All findings above revealed that people do not usually take the shortest paths and the utilized paths generally have higher costs than shortest ones.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Evidence from revealed route choice behavior finds after evaluating habitual routes, only 59 % of respondents from Cambridge, Massachusetts , 30 % from Boston (Ramming and Michael Scott 2001a), and 87 % from Turin, Italy (Prato et al 2006) chose paths with the shortest distance or shortest travel time. In the Minneapolis-St. Paul region, 90 % of subjects took paths one-fifth longer than average commute time (Zhu 2011;Zhu et al 2015) based on actual GPS data while more than half commute trips were at least 5 minutes longer than the shortest path based on TomTom GPS data (Tang et al 2015), and a high percentage of commuting routes were found to differ considerably from the shortest paths in Nagoya, Japan (Morikawa et al 2005 and Lexington, Kentucky (Jan et al 2000). All findings above revealed that people do not usually take the shortest paths and the utilized paths generally have higher costs than shortest ones.…”
Section: Literature Reviewmentioning
confidence: 99%
“…As a result, in the steady (equilibrium) state of the route switching process all the experienced travel costs for the same O-D pair are the same and minimum. However, in reality drivers do not always choose the least costly routes (Zhu and Levinson, 2015) due to a variety of reasons such as imperfect information and behavioural inertia. Rather, if the difference between the perceived travel cost of the current route and the cost of an alternative route is below a threshold, then drivers are not incentivised to switch to the alternative route.…”
Section: Bounded Rationalitymentioning
confidence: 99%
“…In the traffic assignment step, if the relative excess travel cost is less than 0.001, the Wardrop user equilibrium (Wardrop 1952) is considered to be satisfied. In practice, it is unlikely that traffic is actually at equilibrium (Zhu and Levinson 2015); however, this is standard practice in models, and guarantees a unique set of link flows, but not paths, and allows solutions to be systematically compared with fewer confounds.…”
Section: Travel Demandmentioning
confidence: 99%