Accommodating flexible daily temporal constraint on a continuous choice model of departure time for urban shopping travel

Ramli, Muhammad Isran; Oeda, Yoshinao; Sumi, Tomonori; Matsunaga, Chiaka

doi:10.1080/12265934.2011.635879

Cited by 5 publications

(3 citation statements)

References 23 publications

(27 reference statements)

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“…All this means that, in some areas, these detailed databases are not available or updated. This lack of reliable or specific (non-work related travel) information (mainly in rural areas), which has been argued in abundant studies, has forced scholars to use other ways to collect information different than the household travel surveys (such as internet survey techniques as employed by Bernard, Kostelecký, & Patočková, 2014;Verhoeven, Arentze, Timmermans, & van der Waerden, 2007, or interview surveys as Brown & O'Hara, 2003;or Ramli, Oeda, Sumi, & Matsunaga, 2011; or a combination of both as Sioui, Morency, & Trapanier, 2013) and to limit their analyses to a few number of surveys and places. Overall, as in many similar mobility studies (see Appendix 1), to collect information about the commuting 15 and business 16 travel patterns of the CLM working population, the data were based on two original surveys.…”

Section: Sampling and Data Collectionmentioning

confidence: 99%

The influence of education level and job type on work-related travel patterns within rural metro-adjacent regions: The case of Castilla-La Mancha, Spain

Sanz

Ureña

Solís³

2019

JTLU

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Contemporary functional linkages and their relationships with the underlying settlement structure have been widely explored within polycentric urban configurations, but little attention has been paid to their adjacent rural regions. This paper examines the spatial patterns of commuting versus business travel in rural metro-adjacent regions to explain their reconfigured urban structures. These travel patterns are compared by considering workers’ education levels and occupations to investigate how rural metro-adjacent regions offer different opportunities for highly and non-highly skilled workers. Based on two surveys conducted by the authors in 2012, this work focuses on Castilla-La Mancha (CLM, Spain), a rural region under the influence of Madrid. The empirical results demonstrate the effectiveness of considering different functional linkages when explaining the underlying urban network. In particular, the results reinforce the idea of consolidating the polycentric spatial organization of urban centers in CLM, although this concentration is greater for commuting travel purposes and for highly skilled professionals. Conversely, the openness of CLM to other Spanish regions (including the adjacent metropolitan region) is greater for business travel than for commuting. The results also illustrate that the level of engagement, both in commuting and in business travel, increases with education. Finally, the results show that business travel occurs over longer distances than commuting does for all workers residing in rural metro-adjacent regions, regardless of education.

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Section: Sampling and Data Collectionmentioning

confidence: 99%

The influence of education level and job type on work-related travel patterns within rural metro-adjacent regions: The case of Castilla-La Mancha, Spain

Sanz

Ureña

Solís³

2019

JTLU

View full text Add to dashboard Cite

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“…The paper therefore analyzes only the trips for non-work and flexible activities including shopping, personal business, and leisure, recreation, and visiting relatives. These trips tend to be determined and implemented in-between or after the fixed, mandatory activities that affect the choice of flexible activities (Ramli et al, 2011). The rapid increase of singleperson households also affects the choice of leisure and shopping trips as well as trip frequency and transport modes (Fan et al, 2010).…”

Section: Travel Datamentioning

confidence: 99%

An examination of the accuracy of an activity-based travel simulation against smartcard and navigation device data

Hwang

Kim

Cho

et al. 2017

Travel Behaviour and Society

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Activity-based travel simulators have been experiencing difficulty obtaining high quality activity-travel data and network information, which limits the applicability of the simulator to real world problems. For example, accurate information regarding travel time, link traffic volume and trip distribution is essential for sensitivity analysis using an activity-based travel simulator. Survey data, which relies on respondents' memories, is typically inaccurate. The recent development of big data engineering has enabled us to use passively collected big data such as from smartcards and navigation devices; their travel time and spatial information is highly accurate. Activity-based travel simulation based on the household travel survey (HTS) can therefore identify inaccuracies in simulated travels by comparing smartcard and navigation device data. This paper aims to examine the accuracy of journeys simulated by an activity-based travel simulator, FEATHERS Seoul (FS), against smartcard and car navigation device data collected in Seoul. The analysis found that the activity-based simulator performs well and reproduces individual travel decisions, as reflected by the overall trip frequency and distance, but it partly fails to correctly reproduce geographical distributions in flexible, non-work trip destinations. The results imply that an activity-based travel simulator needs to improve its incorporation of geographical characteristics using big data engineering to enhance the simulated travel accuracy.

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“…Shopping trip has big proportion for urban trips, especially during peak period. The journey has more individual temporal flexibility than travel journeys and provides more congestion and some types of environmental problems in the downtown zone [1].…”

Section: Introductionmentioning

confidence: 99%

Trip attraction model of central market in Poso City based on multiple linier regression model

2018

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Analysis of trip attraction in shopping centers is one of the most important aspects of travel demand management (TDM) in developing countries. This research aims to describe significant factors that influence people in obtaining their choice of frequency. Variables used in this research are family’s socio-demographic variables, properties of trip to shopping centers, nature of selecting trip time, and ways to travel. This research adopts multinomial logit model by modeling or building causal relationship between significant variables that influence trip frequency. The findings show that on holiday, no variable has significant influence towards the trip attraction of visitor movement at the Poso Central Market, while on workday, shopping cost (Χ16) has become the most influential variable. Based on the regression equation, the trip attraction of visitor movement model at the Poso Central Market on holiday is Y Χ003D; 2.076 Χ002B;0.997 Χ6Χ002B;0.276 Χ13 Χ002B; 0.605 Χ15 Χ002B; 0.643 Χ16, where R2 Χ003D; 0.004 below 5% or tend to close to 0, so it can be concluded that the ability of independent variables in eΧplaining variation of variables is very limited. Meanwhile, the regression equation on the trip attraction of visitor movement model at the Poso Central Market on workday is Y Χ003D; 3.090 Χ002B; 0.250 Χ2 Χ002B;0.158 Χ6 Χ002B; 0.628 Χ15 Χ002B;0.050 Χ16 Χ002B;0.662 Χ17, where R2 Χ003D; 0.030 below 5% or tend to approach to close to 0, so it can be concluded that the ability of independent variables in in eΧplaining variation of variables is also very limited.

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Accommodating flexible daily temporal constraint on a continuous choice model of departure time for urban shopping travel

Cited by 5 publications

References 23 publications

The influence of education level and job type on work-related travel patterns within rural metro-adjacent regions: The case of Castilla-La Mancha, Spain

The influence of education level and job type on work-related travel patterns within rural metro-adjacent regions: The case of Castilla-La Mancha, Spain

An examination of the accuracy of an activity-based travel simulation against smartcard and navigation device data

Trip attraction model of central market in Poso City based on multiple linier regression model

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