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Apeldoorn, Netherlands

Moiseeva A.,Urban Planning Group | Timmermans H.,Urban Planning Group | Choi J.,Kyung Hee University | Joh C.-H.,Kyung Hee University
Transportation Research Record | Year: 2014

Variability of activity travel patterns has long been an important issue in transportation research. Such variability has been typically explained in relation to covariance with a set of sociodemographic characteristics of travelers. However, variability also stems from differences in knowledge about the environment, which changes over time. To improve understanding of the contribution of different sources to variability in observed activity travel patterns, this paper applies sequence alignment to investigate different sources of variability in longitudinal patterns. The data on activity travel patterns were collected in 2010 for 3 months from newcomers to the city of Eindhoven, Netherlands. GPS technology was used to obtain traces that were processed with TraceAnnotator to impute activities and trips. A set of activity travel sequences for 8 weeks for 27 respondents was used in the analysis. The results show that (a) interpersonal variability is significantly higher than intrapersonal variability, although intrapersonal variability is yet substantial and should not be ignored; (b) intrapersonal variability reflecting different speeds of learning the new environment substantially changes over time; and (c) both interpersonal and intrapersonal variability are affected by sociodemographic characteristics such as gender and country of origin. The paper also discusses the implications of these findings for future research. Source

Khademi E.,Urban Planning Group | Timmermans H.,Urban Planning Group | Borgers A.,Urban Planning Group
Transportation Research Record | Year: 2014

Several reward scheme-based projects implemented in the Netherlands have stimulated car users to avoid using certain links of the network during peak hours. This paper reports the findings of a model that was formulated to analyze temporal effects of the Dutch SpitsScoren reward scheme. On one hand, one might expect that reward schemes lose their effectiveness over time as individuals tend to return to their old habits. On the other hand, by changing their routines, individuals may enjoy their new travel experience, and that enjoyment may in turn lead to positive reinforcement and ultimately to new habitual behavior. On balance, the impact of these opposite processes may conclude differently for different segments of travelers. To disentangle these effects, a panel-effects mixed logit model that predicted the probability of applying different adaptation strategies, including the option of no change, was estimated. Because the various strategies may be correlated, the model also allowed for covariance between the options. Results indicated that socioeconomic and situational variables strongly affected travelers' adaptation strategies. Moreover, the effectiveness of the reward scheme changed over time and affected the various options differently. The estimated model also showed evidence of significant covariances between adaptation strategies. Source

Zheng Z.,Urban Planning Group | Rasouli S.,Urban Planning Group | Timmermans H.,Urban Planning Group
Geographical Analysis | Year: 2015

Research on complex systems has identified various aggregate relationships in phenomena that describe these systems. Travel length has been characterized by negative power distributions. Controversy, however, exists over whether mobility patterns can be modeled in terms of a simple power law (Lévy flight model) or that more complicated power laws (exponential power law, truncated Pareto) are required. This study concentrates on two issues: testing the validity of exponential power laws and truncated Pareto distributions in urban systems to describe aggregate mobility patterns, and examining differences in mobility patterns for different travel purposes. The article describes the results of an analysis of Global Positioning System (GPS) taxi trajectory data, collected in Guangzhou, China, to identify mobility patterns in the city. The least squares statistic is used to estimate the parameters of the distribution functions. Results suggest that a fusion of functions, based on an exponential power law and a truncated Pareto distribution, represents the travel time distribution best. Moreover, the findings of this study indicate different mobility patterns to exist for different travel purposes. © 2015 The Ohio State University. Source

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