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• Autonomous vehicles' attitudes are analyzed with repeated cross-sectional data. • Interest in AVs has not changed over time while concerns have increased across time. • Educational campaigns are needed to alter public attitudes about safety. A growing number of research attempts have been made to enhance our knowledge about the characteristics of the potential early Autonomous Vehicle (AV) adopters. However, little is known about whether the public attitudes towards AVs change over time and how. With a multiyear cross-sectional travel survey data of the Puget Sound Region that encompasses the Seattle metropolitan area, we analyzed the fractions of population with various levels of interest and concerns regarding AVs. A two-part model combining a binary logit model and a partial proportional odds model was utilized to investigate the change of individuals' positions on AVs over time, controlling for their socio-demographic characteristics, travel behavior characteristics, and built environment attributes. We find that the percentage of population unfamiliar with AVs has declined over the years, which is probably due to a greater exposure to the information about AVs. All other variables being equal, individuals' interest in AVs has not changed over time while their concerns have increased across time. The findings suggest that information campaigns or educational programs that introduce the advantages of AV adoption with a focus on the safety aspects of AVs could potentially alter public attitudes, which could help achieve greater market penetration. [ABSTRACT FROM AUTHOR]

Movement is manifested through a series of patterns at multiple spatial and temporal scales. Movement data today are becoming available at increasingly fine-grained temporal granularity. These observations often represent multiple behavioral modes and complex patterns along the movement path. However, the relationships between the observation scale of movement data and the analysis scales at which movement patterns are captured remain understudied. This article aims at investigating the role of temporal scale in movement data analytics. It takes up an important question of "how do decisions surrounding the scale of movement data and analyses impact our inferences about movement patterns?" Through a set of computational experiments in the context of human movement, we take a systematic look at the impact of varying temporal scales on common movement analytics techniques including trajectory analytics to calculate movement parameters (e.g., speed, path tortuosity), estimation of individual space usage, and interactions analysis to detect potential contacts between multiple mobile individuals. [ABSTRACT FROM AUTHOR]

This paper demonstrates the use of motif and sequence analysis in tandem to analyse differences and commonalities between telecommuters and usual commuters. In terms of substantive findings, telecommuters are by far more diverse in their allocation of time to places, activities, and travel. Approximately 20% of telecommuters stay at home all day during a workday, while only 8% of commuters do. Telecommuters that have at least one trip during their workday accrue more vehicle miles travelled and number of trips than their commuter counterparts. However, they travel less driving alone and tend to have more complex schedules visiting more locations. Within telecommuters and commuters, however, we have substantial variation in activity participation and travel captured by the combination of motifs and sequence analysis. As expected, a substantial proportion of commuters display morning and afternoon peaks of arriving at and departing from work, and telecommuters do not show this pattern. In addition, telecommuters do not only perform work tasks from home. Instead, during a day a high percentage travel to a variety of locations to either visit customers and/or use their spatio-temporal schedule flexibility to perform work tasks from locations other than home. In contrast, more than 80% of commuters perform work at their workplace. In addition, a slightly higher proportion of telecommuters function as the designated driver escorting other people to their activity locations. [ABSTRACT FROM AUTHOR]

• A new pattern recognition method combining motif with activity sequence analysis. • Apply motif to identify human mobility patterns in California travel survey. • Explore the relationship between motifs and people's characteristics. • Conduct an activity sequence analysis to reveal heterogeneity in time allocation. In this paper, we develop a new joint pattern recognition method that combines network motif-based analysis with activity sequence-based analysis. We use the advantages of both methods in creating groups of patterns that have within them distinct pattern homogeneity and across-pattern heterogeneity. The first portion of the analysis here applies a more traditional approach to identify unique network motifs, with 16 of them capturing 83.05% of the 2017 NHTS-California workday data. Multivariate analysis of grouped motifs data shows different preference of motifs for students, part-time workers, retirees, telecommuters, drivers, women, and younger adults. In the second portion of the analysis, motifs are grouped into categories based on the number of locations a person visits in a day and their correlation with time use and travel is explored. Time use and travel are analyzed based on minute-by-minute time allocation pattern identification using sequence analysis and hierarchical clustering. The correlation between motifs group and sequence analysis finds substantial heterogeneity within the motif groups. The within motif group clusters of activity-based sequences show typical commuting, going to school, and resting patterns. We also find seven patterns that are not typical but have similarities across motifs in their temporal footprint and the variety of activities in each sequence. The paper provides a summary of the analytical steps and findings as well as next steps. [ABSTRACT FROM AUTHOR]

In this study using data from the 2017 National Household Travel Survey in California from 26,078 survey participants, sequence analysis is used to estimate a fragmentation indicator of people's daily schedules. Then, spatial clustering is used to find groups of observations with similarly high or low fragmentation using the longitude and latitude of their residential locations. This is followed by a hierarchical sequence clustering within each spatial cluster to identify distinct patterns of time allocation. Using the Local Indicator of Spatial Association (LISA) we find a large portion (approximately 30%) of the sample with significant spatial clustering of fragmentation. We also find systematic and significant differences in membership to these clusters based on land use, county of residence, household and personal characteristics, and travel modes used. Sequence analysis pattern recognition within LISA spatial clusters shows systematically repeating time allocation patterns that include typical work and school schedules as well as staying at home patterns. However, each spatial LISA cluster is composed of different time allocation clusters. All this analysis taken together points out substantial and measurable heterogeneity in spatial clustering of fragmentation and the need for customized policy actions in different geographies. [ABSTRACT FROM AUTHOR]

Population ageing has been a thorny issue in many countries. One of the challenges is how to improve and change transportation design and transport policy development to adapt to the dramatic changes in the composition of our population. In this paper, we apply a network-based approach of human mobility measurement called "motif" to investigate the distinct patterns in daily travel for seniors (age 60 and above) in California using the 2017 National Household Travel Survey (California-NHTS) data. Motifs are networks of distinct locations visited in a day and the directional movements between them. Using patterns of motifs, we correlate the diverse daily mobility patterns with socio-demographic characteristics as well as built environment factors. We find that 15 distinct motifs can capture approximately 82% and 86% of the total senior respondents on workdays and non-workdays, respectively. Seniors are more likely to have simple motifs with three or fewer distinct locations on non-workdays, while they present more complex motifs during workdays. Given 65% of the included seniors are retired, a large number of seniors present diverse and complex daily mobility patterns instead of staying at home all day. In addition, given the similarity between the urban core, urban district, and urban neighborhood in function and spatial proximity, there is significant heterogeneity in the daily mobility patterns among seniors living in these areas. Furthermore, we find that seniors living in areas with higher percentages of single-family housing units are most likely to stay at home on workdays. [ABSTRACT FROM AUTHOR]