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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Exploring in-store and e-shopping against disruptive events: A cross-lagged panel SEM</title>
      <link>https://trid.trb.org/View/2550330</link>
      <description><![CDATA[This paper addresses a key gap in the literature by examining the dynamic and bidirectional relationship between in-store and e-shopping frequency during different stages of the COVID-19 pandemic. Previous studies primarily rely on cross-sectional data which fail to capture the temporal evolution and bidirectional nature of these behaviours. To overcome these limitations, this study implements a Random Intercept Cross-lagged Structural Equation Modelling (RI-CLPM) approach using three waves of panel data. Taking Luxembourg as the case study, the paper investigates the modifications in in-store shopping-related travel behaviour by evaluating shifts in trip frequency for three periods: pre-pandemic, post-peak, and relaxed measures phase. The results showed a significant shift in shopping frequency between the pre-pandemic and post-peak phase, evidencing substitution and complementarity effects both on individual as well as group level. Moreover, ANOVA and chi-square tests suggested that age and gender significantly influence in-store shopping frequency for these periods. However, no significant differences in e-shopping and in-store shopping frequencies were observed between the post-peak and the relaxed measures period. These findings provide critical insights for understanding shopping behaviour transitions and offer valuable guidance for transport policymaking. The paper closes by discussing how RI-CLPM models may improve transport policymaking, in the context of future disruptions, considering their potential for: (i) isolating policy impacts amid individual differences, (ii) addressing stable and dynamic shopping behaviours, and (iii) dealing with longitudinal data that allows for adaptive policy design.]]></description>
      <pubDate>Thu, 26 Jun 2025 11:42:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2550330</guid>
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      <title>Shopping Travel Behaviour Trade-Offs Between Physical Stores and Online Deliveries: Post-COVID Scenario in New Delhi, India</title>
      <link>https://trid.trb.org/View/2364482</link>
      <description><![CDATA[This research focuses on understanding the impact of the type of shopping activity (general shopping goods, grocery, prepared meals) on shopping channel (in-person vs. online shopping) preferences. To better understand consumer decisions in the post-COVID era, this study used a large-scale consumer behaviour survey in New Delhi, India, with 1798 respondents to develop multivariate ordered probit models (MORP) involving in-person shopping travel frequencies (INFs) and online delivery frequencies (ONFs). Considering the online and physical shopping decision counterparts in a joint modelling framework enabled us to quantify the determinants of online shopping and in-person shopping frequencies and how they vary across consumption categories. The influences of demographic characteristics (e.g., car ownership, income, mode choice) and attitudes (e.g., tech-savviness, attitude towards perceived risks of online shopping) were delineated in the analysis by treating them as exogenous predictors. The model estimation results and discussions in this study are expected to help advance the understanding of how the emergence of online shopping and delivery-based services are influencing activity-travel patterns and choices in the aftermath of the pandemic.]]></description>
      <pubDate>Wed, 17 Apr 2024 11:29:43 GMT</pubDate>
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      <title>Modelling the effects of COVID-19 on travel mode choice behaviour in India</title>
      <link>https://trid.trb.org/View/1756064</link>
      <description><![CDATA[The COVID-19 pandemic has resulted in unprecedented changes in the activity patterns and travel behaviour around the world. Some of these behavioural changes are in response to restrictive measures imposed by the Government (e.g. full or partial lock-downs), while others are driven by perceptions of own safety and/or commitment to slow down the spread (e.g. during the preceding and following period of a lock-down). Travel behaviour amidst the stricter of these measures is quite straightforward to predict as people have very limited choices, but it is more challenging to predict the behavioural changes in the absence of restrictive measures. The limited research so far has demonstrated that different socio-demographic groups of different countries have changed travel behaviour in response to COVID-19 in different ways. However, no studies to date have either (a) investigated the changes in travel behaviour in the context of the Global South, or (b) modelled the relationship between changes in transport mode usage and traveller characteristics in order to quantify the associated heterogeneity. In this paper, the authors address these two gaps by developing mathematical models to quantify the effect of the socio-demographic characteristics of the travellers on the mode-specific trip frequencies before (January 2020) and during the early stages of COVID-19 spread in India (March 2020). Primary data collected from 498 respondents participating in online surveys have been used to estimate multiple discrete choice extreme value (MDCEV) models in this regard. Results indicate – a) significant inertia to continue using the pre-COVID modes, and b) high propensity to shift to virtual (e.g. work from home, online shopping, etc.) and private modes (e.g. car, motorcycle) from shared ones (e.g. bus and ride-share options). The extent of inertia varies with the trip purpose (commute and discretionary) and trip lengths. The results also demonstrate significant heterogeneity based on age, income, and working status of the respondents. The findings will be directly useful for planners and policy-makers in India as well as some other countries of the Global South in better predicting the mode-specific demand levels and subsequently, making better investment and operational decisions during similar disruptions.]]></description>
      <pubDate>Mon, 08 Feb 2021 11:16:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/1756064</guid>
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    <item>
      <title>The interactions between online shopping and personal activity travel behavior: an analysis with a GPS-based activity travel diary</title>
      <link>https://trid.trb.org/View/1455219</link>
      <description><![CDATA[Accompanying the widespread use of the Internet, the popularity of e-commerce is growing in developing countries such as China. Online shopping has significant effects on in-store shopping and on other personal activity travel behavior such as leisure activities and trip chaining behavior. Using data collected from a GPS-based activity travel diary in the Shangdi area of Beijing, this paper investigates the relationships between online shopping, in-store shopping and other dimensions of activity travel behavior using a structural equation modelling framework. Our results show that online buying frequency has positive effects on the frequencies of both in-store shopping and online searching, and in-store shopping frequency positively affects the frequency of online searching. Frequent online purchasers tend to shop in stores on weekends rather than weekdays. We also found a negative effect of online buying on the frequency of leisure activities, indicating that online shopping may reduce out-of-home leisure trips.]]></description>
      <pubDate>Mon, 27 Feb 2017 09:27:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1455219</guid>
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    <item>
      <title>Shopping Online and/or In-store? A Structural Equation Model of the Relationships Between E-shopping and In-store Shopping</title>
      <link>https://trid.trb.org/View/795257</link>
      <description><![CDATA[Searching product information and buying goods online are becoming increasingly popular activities, which would seem likely to affect shopping trips. However, little empirical evidence about the relationships between e-shopping and in-store shopping is available. The aim of this study is to describe how the frequencies of online searching, online buying, and non-daily shopping trips relate to each other, and how they are influenced by such factors as attitudes, behaviour, and land use features. Questionnaire data were collected from 826 respondents residing in four municipalities (one urban, three suburban) in the centre of the Netherlands. Structural equation modelling was used to examine the variables' multiple and complex relationships. The results show that searching online positively affects the frequency of shopping trips, which in its turn positively influences buying online. An indirect positive effect of time-pressure on online buying was found and an indirect negative effect of online searching on shopping duration. These findings suggest that, for some people, e-shopping could be task-oriented (a time-saving strategy), and leisure-oriented for others. Urban residents shop online more often than suburban residents, because they tend to have a faster Internet connection. The more shopping opportunities one can reach within 10 min by bicycle, the less often one searches online. (A) "Reprinted with permission from Elsevier".]]></description>
      <pubDate>Tue, 19 Dec 2006 10:27:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/795257</guid>
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      <title>SHOPPING TRAVEL PATTERNS IN TYNE AND WEAR: A "BEFORE-METRO" PROFILE</title>
      <link>https://trid.trb.org/View/186744</link>
      <description><![CDATA[This report describes the travel patterns of shoppers in Tyne and Wear before the introduction of the Metro, based on in-street shopping surveys carried out in five district centres and in Newcastle city centre.  The work described was part of the Tyne and Wear Public Transport impact study and was designed specifically to examine some of the changes in activity levels (secondary effects) which may occur after the introduction of the metro.  The travel patterns of shoppers are discussed in terms of modal split, trip lengths, trip origins, trip frequencies and types of shopping.  About 55 per cent of people in Newcastle and Gateshead and 25 to 40 per cent in the other centres went shopping by bus.  The average trip lengths for shoppers travelling on foot and by bus and car to the district centres were 0.74, 2.94 and 4.01 kilometres respectively. Eighty-nine per cent of shoppers travelled 5 kilometres or less to their district centre.  The potential use of the metro by shoppers was estimated by counting the trips which started within 1 kilometer of a metro station and by modelling changes in accessibility by public transport.]]></description>
      <pubDate>Mon, 31 Jan 1983 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/186744</guid>
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      <title>ALTERNATIVE SPECIFICATIONS OF THE RANDOM DISTURBANCES FOR TRIP FREQUENCY MODELS</title>
      <link>https://trid.trb.org/View/181792</link>
      <description><![CDATA[The purpose of this paper is to present alternative functional specifications for models of shopping trip frequency and to illustrate the influence of the modelling assumptions on the interpretation of the determinants of trip frequency.  The data used for this analysis is a 23-day diary of shopping travel by able bodied elderly individuals in Lawrence, Massachussetts.  The alternative models are, in addition to ordinary least squares, an integer dependent variable model, and an error component model of a time of cross-sections.  The findings suggest that, when models are developed that consider explicitly the discrete nature of the daily trip generation variable (i.e. the number of trips taken by an individual on a given day), forecasts which are not significantly different from the ordinary least square forecasts are obtained.  (Author/TRRL)]]></description>
      <pubDate>Thu, 30 Dec 1982 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/181792</guid>
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