Author(s): Forscher, Teddy; Deakin, Elizabeth, PhD; Walker, Joan, PhD; Shaheen, Susan, PhD | Abstract: A significant portion of the population stayed, and continue to stay, at home due to the COVID-19 pandemic. With more people staying home, online shopping increased along with trips related to pickups and deliveries. To gain a better understanding of the change in retail purchases and related travel, UC Berkeley researchers compared pre-pandemic shopping to pandemic-related shifts in consumer purchases in the greater Sacramento area for nine types of essential and non-essential commodities (e.g., groceries, meals, clothing, paper products, cleaning supplies). In May 2020, the research team resampled 327 respondents that participated in the 2018 Sacramento Area Council of Governments (SACOG) household travel survey. The 2018 SACOG survey collected responses over a rolling six-week period from April to May 2018 and asked residents about their motivations for, attitudes toward, and ease of use of online shopping. They were also were asked about the number of e-commerce purchases made, and the number of deliveries and pickups made from those e-commerce purchases for each commodity type. In addition, respondents also reported changes (less or more) in their behavior from a typical week in January or February 2020 (prior to the COVID-19 pandemic) for: 1) tripmaking, e-commerce purchases, and delivery and pick up frequencies; 2) purchase sizes; 3) distances traveled; and 4) modes used for in-person trips. This brief highlights findings from an analysis on changes in frequency of purchases, deliveries and pickups, and order sizes.

Bicycle level of service (LOS) measures are essential tools for transportation agencies to monitor and prioritize improvements to infrastructure for cyclists. While it is apparent that different types of cyclists have varying preferences for the facilities on which they ride, in current research and practice, measures are used that are either insufficiently quantitative and empirical or lack cyclist segmentation. In this study, we conducted a detailed survey on cyclist habits, preferences, and user experience, capturing responses to videos of a bicycle traveling on road segments in the San Francisco Bay Area. The survey provided rich behavioral data, which invited both quantitative and qualitative exploration. We compared facility preferences from the survey to scores from two common measures, NCHRP bicycle level of service (NCHRP BLOS), and level of traffic stress (LTS); and we examined the responses to open-ended questions to gain insights about heterogeneity of preferences among cyclists. Finally, we applied behavioral analysis tools as a proof of concept for a new bicycle level of service measure that accounts for the segmentation of cyclist types via a latent class choice model. Combining statistics and behavioral analysis, we can improve the quality of bicycle level of service measures to make decisions driven by empirically measured cyclist preferences.

Current travel demand models are unable to predict long-range trends in travel behavior as they do not entail a mechanism that projects membership and market share of new modes of transport (Uber, Lyft, etc). We propose integrating discrete choice and technology adoption models to address the aforementioned issue. In order to do so, we build on the formulation of discrete mixture models and specifically Latent Class Choice Models (LCCMs), which were integrated with a network effect model. The network effect model quantifies the impact of the spatial/network effect of the new technology on the utility of adoption. We adopted a confirmatory approach to estimating our dynamic LCCM based on findings from the technology diffusion literature that focus on defining two distinct types of adopters: innovator/early adopters and imitators. LCCMs allow for heterogeneity in the utility of adoption for the various market segments i.e. innovators/early adopters, imitators and non-adopters. We make use of revealed preference (RP) time series data from a one-way carsharing system in a major city in the United States to estimate model parameters. The data entails a complete set of member enrollment for the carsharing service for a time period of 2.5 years after being launched. Consistent with the technology diffusion literature, our model identifies three latent classes whose utility of adoption have a well-defined set of preferences that are significant and behaviorally consistent. The technology adoption model predicts the probability that a certain individual will adopt the service at a certain time period, and is explained by social influences, network effect, socio-demographics and level-of-service attributes. Finally, the model was calibrated and then used to forecast adoption of the carsharing system for potential investment strategy scenarios.

Activity-based travel demand models can be useful tools for understanding the individual level equity impacts of transportation plans, because of their ability to generate disaggregate transportation measures. However, these capabilities have yet to be fully explored in public practice. In this paper we first discuss a general framework for performing transportation equity analysis using activity-based travel demand models, distributional comparisons, and incorporating equity standards. In addition, we demonstrate the advantages of distributional comparisons, relative to average measures. This demonstration uses the 2000 Bay Area Travel Survey and (activity-based) mode choice model. The findings show that distributional comparisons are capable of clearly revealing the winners and losers that result from transportation improvements, in comparison with average measures. The use of these results will likely result in different conclusions on transportation investments.

Latent modal preferences, or modality styles, are defined as behavioral predispositions characterized by a certain travel mode or set of travel modes that an individual habitually uses. They are reflective of higher-level orientations, or lifestyles, that are hypothesized to influence all dimensions of an individual's travel and activity behavior. The objectives of this paper are to understand and quantify different modality styles, and to show how the modality styles construct can be operationalized within the context of traditional models of travel mode choice. We employ the six-week MOBIDRIVE travel diary and estimate behavioral mixture models in which the modality style provides a behavioral rationale to the way in which unobserved heterogeneity is specified in the travel model. Our analysis consists of two stages: First, we explore the presence and types of modality styles suggested by the data through the means of a descriptive analysis. Next, we develop a model that captures the influence of modality styles on two dimensions of an individual's travel behavior: travel mode choice for work tours and travel mode choice for non-work tours. The modality styles are specified as latent classes; heterogeneity across modality styles include both the modes considered (choice set) and the values of taste parameters. The modality style of an individual then influences all of his/her travel mode choice decisions for work and non-work tours. In addition, error components capture unobserved correlation across travel mode choice decisions made by the same individual. Results indicate the presence of habitual drivers who display a strong bias for using the automobile and multimodal individuals who exhibit variation in their modal preferences. Multimodal behavior is further distinguished by those who appear to be sensitive to travel times and those who appear to be insensitive. Estimation results further find that modality styles are strongly correlated with more long-term travel decisions and life-cycle characteristics. (C) 2013 Elsevier Ltd. All rights reserved.

An essential element of demand modeling in the airline industry is the representation of time of day demand-the demand for a given itinerary as a function of its departure or arrival times. It is an important datum that drives successful scheduling and fleet decisions. There are two key components to this problem: the distribution of the time of day demand and how preferred travel time influences itinerary choice. This paper focuses on estimating the time of day distribution. Our objective is to estimate it in a manner that is not confounded with air travel supply; is a function of the characteristics of the traveler, the trip, and the market; and accounts for potential measurement errors in self-reported travel time preferences. We employ a stated preference dataset collected by intercepting people who were booking continental US trips via an internet booking service. Respondents reported preferred travel times as well as choices from a hypothetical set of itineraries. We parameterize the time of day distribution as a mixture of normal distributions (due to the strong peaking nature of travel time preferences) and allow the mixing function to vary by individual characteristics and trip attributes. We estimate the time of day distribution and the itinerary choice model jointly in a manner that accounts for measurement error in the self-reported travel time preferences. We find that the mixture of normal distributions fits the time of day distribution well and is behaviorally intuitive. The strongest covariates of travel time preferences are party size and time zone change. The methodology employed to treat self-reported travel time preferences as potentially having error contributes to the broader transportation time of day demand literature, which either assumes that the desired travel times are known with certainty or that they are unknown. We find that the error in self-reported travel time preferences is statistically significant and impacts the inferred time of day demand distribution. (C) 2011 Elsevier Ltd. All rights reserved.

While psychologists and behavioral economists emphasize the importance of social influences, an outstanding issue is how to capture such influences in behavioral models used to inform urban planning and policy. In this paper we focus on operational models that do not require explicit knowledge of the individual networks of decision makers. We employ a field effect variable to capture social influences, which is calculated as the percent of population in the peer group that has chosen the specific alternative. We define the peer group based on socio-economic status and spatial proximity of residential location. As in behavioral economics and psychology, the concept is that one is influenced by the choices made by one's peers. However, using such a social influence variable in a behavioral model causes complications because it is likely endogenous; unobserved factors that impact the peer group also influence the decision maker, yielding correlation between the field effect variable and the error. The contribution of this paper is the use of the Berry, Levinsohn, and Pakes (BLP) method to correct the endogeneity in a choice model. The two-stage BLP introduces constants for each peer group to remove the endogeneity from the choice model (where it is difficult to deal with) and insert it into a linear regression model (where endogeneity is relatively easier to deal with). We test the method using a mode choice data set from the Netherlands and readily available software and find there is an upward bias of the field effect parameter when endogeneity is not corrected. The procedure outlined presents a practical and tractable method for incorporating social influences in choice models. (C) 2011 Elsevier Ltd. All rights reserved.

This paper aims to evaluate the effects of social capital on the willingness to use a new cable car service in Bogotá (Colombia) that connects the Bus Rapid Transit system with a vulnerable zone with low accessibility. We estimated a hybrid discrete choice model for the new cable car service considering social capital as a latent variable and a stated preference survey issued before the cable car implementation. Social capital is measured through six domains associated with membership of civic groups, social networks, interpersonal trust, institutional trust, cooperation, and empowerment. Results suggest that the social capital stock in the influence zone of the cable car depends principally on age, sex, education level, and time living in the neighborhood. Results also show that higher social capital stocks are associated with a greater willingness to use the new cable car service, suggesting that social capital is a potential determinant for mode choice. Finally, we discuss implications for social capital and policymaking.

ACCESS F N A U L M L B E R Tr a n s p o r t a t i o n R e s e a r c h a t t h e U n i v e r s i t y o f C a l i f o r n i a

A major aspect of transportation planning is understanding behavior: how to predict it and how to influence it over the long term. Behavioral models in transportation are predominantly rooted in the classic microeconomic paradigm of rationality. However, there is a long history in behavioral economics of raising serious questions about rationality. Behavioral economics has made inroads in transportation in the areas of survey design, prospect theory, and attitudinal variables. Further infusion into transportation could lead to significant benefits in terms of increased ability to both predict and influence behavior. The aim of this research is to investigate the transferability of findings in behavioral economics to transportation, with a focus on lessons regarding personalized information and social influences. Three computer experiments were designed and conducted by using University of California, Berkeley, students: one on personalized information and route choice, one on social influences and auto ownership, and one combining information and social influences and pedestrian safety. The findings suggest high transferability of lessons from behavioral economics and great potential for influencing transport behavior. It was found that person- and trip-specific information regarding greenhouse gas emissions has significant potential for increasing sustainable behavior, and it was possible to quantify this value of green at around $0.24/lb of greenhouse gas avoided. Congruent with lessons from behavioral economics, information on peer compliance with pedestrian laws was found to have a stronger influence on pedestrian safety behavior than information on the law, citation rates, or accident statistics. It was also found that social influences positively affect the decision to buy a hybrid car over a conventional car or to forgo a car altogether.