arXiv subjects

Giancarlos Parady

Publications and source records attributed to Giancarlos Parady.

7 recordsLinked to original sources

arXiv2026

Evaluating Transit Accessibility to Education and Effects of Operational Delays in Japanese Regional Cities: A Case Study of Matsumoto City

Realistic assessments of school commuting accessibility in areas with infrequent public transport services require accounting for operational delays; however, the impact of these delays has not been sufficiently examined. This study evaluates high-school accessibility in Matsumoto City, a regional city in Japan, using GTFS data representing both scheduled timetables and actual operating conditions. Accessibility levels are assessed under scheduled operations, while the effects of delays are examined through a comparative analysis based on actual delay measurements over a five-day workweek. Furthermore, a sensitivity analysis of travel-time thresholds was conducted. Results show that, when walking, cycling to stations, and public transport use are allowed, 78% of children under 15 can reach at least one high school within a 90-minute round trip, and 67% within a 60-minute round trip. Extending the threshold to 120 minutes enables access to nearly all schools in the city center, but the overall proportion increases only marginally to 81%. Delay impacts are particularly pronounced along bus routes connecting the central station with suburban areas, while in some areas, delays generate idiosyncratic events, where irregular transfers and reduced waiting times result in improved accessibility. Results underscore the need for both short-term measures,such as adjusting school start times, prioritizing buses, and introducing dedicated school routes, and long-term strategies, such as incorporating public transport accessibility into school consolidation decisions, to guarantee fair access to education opportunities without relying on private vehicles.

cs.CE↗

arXiv2026

Can large language models interpret unstructured chat data on dynamic group decision-making processes? Evidence on joint destination choice

Social activities result from complex joint activity-travel decisions between group members. While observing the decision-making process of these activities is difficult via traditional travel surveys, the advent of new types of data, such as unstructured chat data, can help shed some light on these complex processes. However, interpreting these decision-making processes requires inferring both explicit and implicit factors. This typically involves the labor-intensive task of manually annotating dialogues to capture context-dependent meanings shaped by the social and cultural norms. This study evaluates the potential of Large Language Models (LLMs) to automate and complement human annotation in interpreting decision-making processes from group chats, using data on joint eating-out activities in Japan as a case study. We designed a prompting framework inspired by the knowledge acquisition process, which sequentially extracts key decision-making factors, including the group-level restaurant choice set and outcome, individual preferences of each alternative, and the specific attributes driving those preferences. This structured process guides the LLM to interpret group chat data, converting unstructured dialogues into structured tabular data describing decision-making factors. To evaluate LLM-driven outputs, we conduct a quantitative analysis using a human-annotated ground truth dataset and a qualitative error analysis to examine model limitations. Results show that while the LLM reliably captures explicit decision-making factors, it struggles to identify nuanced implicit factors that human annotators readily identified. We pinpoint specific contexts when LLM-based extraction can be trusted versus when human oversight remains essential. These findings highlight both the potential and limitations of LLM-based analysis for incorporating non-traditional data sources on social activities.

cs.CL↗

arXiv2023

Text-aided Group Decision-making Process Observation Method (x-GDP): A novel methodology for observing the joint decision-making process of travel choices

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.

cs.SI↗

arXiv2023

Modeling joint eating-out destination choices incorporating group-level impedance: A case study of the Greater Tokyo Area

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.

cs.SI↗

arXiv2022

Evaluating the Impact of Automated Vehicles on Residential Location Distribution using Activity-based Accessibility: A Case Study of Japanese Regional Areas

Automated Vehicles (AVs) are expected to disrupt the transport sector in the future. Extensive research efforts have been dedicated to studying its potential implications. However, the existing literature is yet limited regarding the long-term impacts. To fill this gap, this paper estimates and validates a residential location choice model to evaluate the impacts of AVs on residential location distributions in a context of Japanese regional area. Activity-based accessibility is used to reflect the changes from AVs in transport costs. The year 2040 is set as the backdrop for the analyses, where the effects of the decreased population are reflected in the scenario settings, along with some other variables to accommodate the uncertainties in the characteristics of AVs. The simulation results confirm the potential of urban expansion. The results demonstrate that, compared to Base Scenario, the median distances between the residences and the closest Dwelling Attraction Areas expand by 7.2% and 41.6% for two AV scenarios, respectively. Two hypothetical policy mandates are then applied to alleviate the problem. The results suggest that providing a 20% subsidy to the land price is effective for the scenario with relatively conservative AV settings, as the median distance indicator can be resumed to the level of Base Scenario.

stat.AP↗

arXiv2022

The effectiveness of using Google Maps Location History data to detect joint activities in social networks

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.

stat.AP↗

arXiv2022

Size Matters: The Use and Misuse of Statistical Significance in Discrete Choice Models in the Transportation Academic Literature

In this paper we review the academic transportation literature published between 2014 and 2018 to evaluate where the field stands regarding the use and misuse of statistical significance in empirical analysis, with a focus on discrete choice models. Our results show that 39% of studies explained model results exclusively based on the sign of the coefficient, 67% of studies did not distinguish statistical significance from economic, policy or scientific significance in their conclusions, and none of the reviewed studies considered the statistical power of the tests. Based on these results we put forth a set of recommendations aimed at shifting the focus away from statistical significance towards proper and comprehensive assessment of effect magnitudes and other policy relevant quantities.

stat.AP↗