2019
DOI: 10.1016/j.physa.2018.09.090
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Trip destination prediction based on multi-day GPS data

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Cited by 68 publications
(34 citation statements)
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“…More details can be found in [36]. The Apriori algorithm assumes that the nonempty subsets of a frequent itemset are frequent itemsets, which greatly reduces the model computational consumption, [29,[37][38][39]. Several itemset types of PSC inspection data are null (i.e., the ship deficiency information is deliberately discarded due to data sensitivity).…”
Section: Dqcpea Algorithm For Identifying Ship Deficiencymentioning
confidence: 99%
“…More details can be found in [36]. The Apriori algorithm assumes that the nonempty subsets of a frequent itemset are frequent itemsets, which greatly reduces the model computational consumption, [29,[37][38][39]. Several itemset types of PSC inspection data are null (i.e., the ship deficiency information is deliberately discarded due to data sensitivity).…”
Section: Dqcpea Algorithm For Identifying Ship Deficiencymentioning
confidence: 99%
“…Thus, future behavior is likely to be similar to previous behavior if the (latent) inertia is strong. Habit and inertia have also been extensively studied in the transport literature within a microeconomic approach, in the context of mode of mode (Bamberg et al, 2003;Cantillo et al, 2007;Chatterjee, 2011;Cherchi et al, 2014;Cherchi and Manca, 2011;Gardner, 2009;Golob et al, 1997;Gärling and Axhausen, 2003;Sharmeen and Timmermans, 2014;Srinivasan and Bhargavi, 2007;Yáñez et al, 2009), vehicle purchase (Bauer, 2018;Jansson et al, 2009), car engine type (Valeri and Cherchi, 2016), destination (Zong et al, 2019), route (Bogers et al, 2005;He et al, 2014;Prato et al, 2012), residential location (Ralph and Brown, 2017) and parking choice (van der Waerden et al, 2015). Cherchi et al (2014) have studied the role of habitual behavior in mode choice, using a hybrid approach that assumes that inertia is revealed by past behavior but recognizes that past behavior is only an indicator of habitual behavior, the true process behind the formation of habitual behavior being latent.…”
Section: Introductionmentioning
confidence: 99%
“…For example, a spatial econometric model is widely used to explore the relationships between travel demand and a set of factors [16][17][18][19][20][21]. From the temporal perspective, applying time series models or dividing time into several periods is the most commonly used approach to understand the variations of travel demand at different times, such as weekdays and weekends, the morning peak, the evening peak and late at night [22,23].…”
Section: Introductionmentioning
confidence: 99%
“…In addition, many spatio-temporal data can be expressed as time series data. Therefore, the GWR can also be used to clarify the spatio-temporal variations by combining the technique of time series mining [22]. As a result, certain patterns and regularities of travel demand in different zones at different time periods can be explored.…”
Section: Introductionmentioning
confidence: 99%