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Joint travel decisions, particularly related to social activities remain poorly explained in traditional behavioral models. A key reason for this is the lack of empirical data, and the difficulties associated with collecting such data in the first place. To address this problem, we propose Text-aided Group Decision-making Process Observation Method (x-GDP), a novel survey methodology to collect data on joint leisure activities, from all members of a given clique. Through this method we are able to observe not only the outcome (i.e., the joint activity location chosen) but also the decision-making process itself, including the alternatives that compose the choice set, individual and clique characteristics that might affect the choice process, as well as the discussion behind the choice via texts. Observing such a process will allow researchers to gain a deeper understanding of the joint decision-making process, including how alternatives are weighted, how members interact with each other, and finally how joint choices are made. In this paper we introduce the results of a x-GDP survey implementation focusing on joint eating-out activities in the Greater Tokyo Area, giving a detailed overview of the survey components, execution logistics and initial insights on the data. This is to the best of our knowledge the first attempt to observe group joint travel decisions in real time through a zoom-moderated experiment., Comment: 19 pages, 8 figures

Individuals undertake both solo and joint activities as part of their overall activity-travel patterns. Compared to work and maintenance activities, social and leisure activities differ in that they exhibit high levels of temporal and spatial flexibility. In this study we used data from an ego-centric social networks survey in the Greater Tokyo Area and follow-up group activity survey to estimate a joint eating-out destination choice model explicitly incorporating group-level impedance. Consistent with the literature, travel time has a large impact on destination choice as measured by its elasticity; however, the elasticities of group-level maximum, average and median travel times are larger than individual-level travel times. Furthermore, we show that incorporating group-level impedance increases model performance up to 49% against the ego-level impedance model, a substantial increase that underscores the need to incorporate group-level characteristics in travel behavior models., Comment: 19 pages, 8 figures

This study evaluates the effectiveness of using Google Maps Location History data to identify joint activities in social networks. To do so, an experiment was conducted where participants were asked to execute daily schedules designed to simulate daily travel incorporating joint activities. For Android devices, detection rates for 4-person group activities ranged from 22% under the strictest spatiotemporal accuracy criteria to 60% under less strict yet still operational criteria. The performance of iPhones was markedly worse than Android devices, irrespective of accuracy criteria. In addition, logit models were estimated to evaluate factors affecting activity detection given different spatiotemporal accuracy thresholds. In terms of effect magnitudes, non-trivial effects on joint activity detection probability were found for floor area ratio (FAR) at location, activity duration, Android device ratio, device model ratio, whether the destination was an open space or not, and group size. Although current activity detection rates are not ideal, these levels must be weighed against the potential of observing travel behavior over long periods of time, and that Google Maps Location History data could potentially be used in conjunction with other data-gathering methodologies to compensate for some of its limitations., Comment: 26 pages, 8 tables, 7 figures

To analyze the influence of introducing the High-Speed Railway (HSR) system on business and non-business travel behavior, this study develops an integrated inter-city travel demand model to represent trip generations, destination choice, and travel mode choice behavior. The accessibility calculated from the RP/SP (Revealed Preference/Stated Preference) combined nested logit model of destination and mode choices is used as an explanatory variable in the trip frequency models. One of the important findings is that additional travel would be induced by introducing HSR. Our simulation analyses also reveal that HSR and conventional airlines will be the main modes for middle distances and long distances, respectively. The development of zones may highly influence the destination choices for business purposes, while prices of HSR and Low-Cost Carriers affect choices for non-business purposes. Finally, the research reveals that people on non-business trips are more sensitive to changes in travel time, travel cost and regional attributes than people on business trips., Comment: 20 pages

It is one of hottest topics in Vietnam whether to construct a High Speed Rail (HSR) system or not in near future. To analyze the impacts of introducing the HSR on the intercity travel behavior, this research develops an integrated intercity demand forecasting model to represent trip generation and frequency, destination choice and travel mode choice behavior. For this purpose, a comprehensive questionnaire survey with both Revealed Preference (RP) information (an inter-city trip diary) and Stated Preference (SP) information was conducted in Hanoi in 2011. In the SP part, not only HSR, but also Low Cost Carrier is included in the choice set, together with other existing inter-city travel modes. To make full use of the advantages of each type of data and to overcome their disadvantages, RP and SP data are combined to describe the destination choice and mode choice behavior, while trip generation and frequency are represented by using the RP data. The model estimation results show the inter-relationship between trip generation and frequency, destination choice and travel mode choice, and confirm that those components should not dealt with separately., Comment: 13 pages, 2 figures, 6 tables. Proceedings of The 45th Conference of Infrastructure Planning and Management, Japan Society of Civil Engineers