Joint Decisions in Residential Choice

<p>Economic agents frequently make joint decisions, which often require a compromise by some or all of the participants. We propose an econometric model in which groups of agents make a joint decision; each agent has preferences modelled using a combination of multi-nominal logit and conditional logit parts. We combine these marginal preferences to create a joint set of probabilities of the group making a particular choice, which enables parameter estimation by maximum likelihood. We can also make the weight applied to an individual agents preferences depend on characteristics of the agent or group. To demonstrate the use of the model, data is obtained from the New Zealand Household Travel Survey. We estimate our model to show how households might make the joint decision of where to live, given that different household members have different work locations.</p>

Joint Decisions in Residential Choice

<p>Economic agents frequently make joint decisions, which often require a compromise by some or all of the participants. We propose an econometric model in which groups of agents make a joint decision; each agent has preferences modelled using a combination of multi-nominal logit and conditional logit parts. We combine these marginal preferences to create a joint set of probabilities of the group making a particular choice, which enables parameter estimation by maximum likelihood. We can also make the weight applied to an individual agents preferences depend on characteristics of the agent or group. To demonstrate the use of the model, data is obtained from the New Zealand Household Travel Survey. We estimate our model to show how households might make the joint decision of where to live, given that different household members have different work locations.</p>

Association between network characteristics and bicycle ridership across a large metropolitan region

Background: Numerous studies have explored associations between bicycle network characteristics and bicycle ridership. However, the majority of these studies have been conducted in inner metropolitan regions and as such, there is limited knowledge on how various characteristics of bicycle networks relate to bicycle trips within and across entire metropolitan regions, and how the size and composition of study regions impact on the association between bicycle network characteristics and bicycle ridership.Methods: We conducted a retrospective analysis of household travel survey data and bicycle infrastructure in the Greater Melbourne region, Australia. Seven network metrics were calculated and Bayesian spatial models were used to explore the association between these network characteristics and bicycle ridership (measured as counts of the number of trips, and the proportion of all trips that were made by bike). Results: We demonstrated that bicycle ridership was associated with several network characteristics, and that these characteristics varied according to the outcome (count of the number of trips made by bike or the proportion of trips made by bike) and the size and characteristics of the study region.Conclusions: These findings challenge the utility of approaches based on spatially modelling network characteristics and bicycle ridership when informing the monitoring and evaluation of bicycle networks. There is a need to progress the science of measuring safe and connected bicycle networks for people of all ages and abilities.

What Counts as Commute Travel? Identification and Resolution of Key Issues around Measuring Complex Commutes in the National Household Travel Survey

We use travel diary data from the 2017 National Household Travel Survey (NHTS) Georgia subsample to address critical issues associated with analyzing complex work journeys. To define the work journey, we discuss the importance of defining commute anchors by both purpose and location. We then compare two alternate measures for determining what portion of each journey should be counted as commute distance: the last leg of the journey (the NHTS default), and a modeled counterfactual simple commute to estimate the distance that would have been traveled had no stops been made. The average complex commute distance obtained using the counterfactual method was 63% higher than the estimate based on using the last leg alone. Using the last-leg method may understate Georgia’s annual commute distance by 2.6 billion miles (10% of the total, including both simple and complex commutes). We argue that the last-leg method is not an accurate gauge of work travel, particularly among populations such as women, who are more likely to trip chain on their commutes.

Glimpse of the Future: Simulating Life with Personally Owned Autonomous Vehicles and Their Implications on Travel Behaviors

To explore potential travel behavior shifts induced by personally owned, fully autonomous vehicles (AVs), we ran an experiment that provided personal chauffeurs to 43 households in the Sacramento region to simulate life with an AV. Like an advanced AV, the chauffeurs took over driving duties. Households were recruited from the 2018 Sacramento household travel survey sample. Sampling was stratified by weekly vehicle miles traveled (VMT), and households were selected to be diverse by demographics, modal preferences, mobility barriers, and residential location. Thirty-four households received 60 h of chauffeur service for 1 week, and nine households received 60 h per week for 2 weeks. Smartphone-based travel diaries were recorded for the chauffeur week(s), 1 week before, and 1 week after. During the chauffeur week, the overall systemwide VMT (summing across all sampled households) increased by 60%, over half of which came from “zero-occupancy vehicle” (ZOV) trips (when the chauffeur was the only occupant). The number of trips made in the system increased by 25%, with ZOV trips accounting for 85% of these additional trips. There was a shift away from transit, ridehailing, biking, and walking trips, which dropped by 70%, 55%, 38%, and 10%, respectively. Households with mobility barriers and those with less auto dependency had the greatest percent increase in VMT, whereas higher VMT households and families with children had the lowest. The results highlight how AVs can enhance mobility, but also caution against the potential detrimental effects on the transportation system and the need to regulate AVs and ZOVs.

Nonlinear Associations of the Built Environment with Cycling Frequency among Older Adults in Zhongshan, China

The health and welfare of older adults have raised increasing attention due to global aging. Cycling is a physical activity and mode of transportation to enhance the mobility and quality of life among older adults. Nevertheless, the planning strategies to promote cycling among older adults are underutilized. Therefore, this paper describes the nonlinear associations of the built environment with cycling frequency among older adults. The data were collected from the Zhongshan Household Travel Survey (ZHTS) in 2012. The modeling approach was the eXtreme Gradient Boosting (XGBoost) model. The findings demonstrated that nonlinear relationships exist among all the selected built environment attributes. Within specific intervals, the population density, the land-use mixture, the distance from home to the nearest bus stop, and the distance from home to CBD are positively correlated to the cycling among older adults. Additionally, an inverse “U”-shaped relationship appears in the percentage of green space land use among all land uses. Moreover, the intersection density is inversely related to the cycling frequency among older adults. These findings provide nuanced and appropriate guidance for establishing age-friendly neighborhoods.

Spatial variation in bicycling: a retrospective review of travel survey data from Greater Melbourne, Australia

Understanding spatial variation in bicycling within cities is necessary to identify and address inequities. We aimed to explore spatial variation in bicycling and explore how bicycling rates vary across population sub-groups. We conducted a retrospective analysis of household travel survey data in Greater Melbourne, Australia. We present a descriptive analysis of bicycling behaviour across local government areas (LGAs; n=31), with a focus on quantifying spatial variation in the number and proportion of trips made by bike, and by age, sex and trip distance. Associations between the proportion of infrastructure that had provision for biking and the proportion of all trips made by bike were analysed using linear regression. Overall, 1.7% of all trips were made by bike. While more than half (53.2%) of all trips were less than 5km, only 2% of these trips were by bike. Across LGAs, there was considerable variation in the proportion of trips made by bike (range: 0.1% to 5.7%). Mode share by females was 35.0%, and this varied across LGAs from 0% to 49%. Tor each percentage increase in the proportion of infrastructure that had provision for biking, there was an associated 0.2% increase in the proportion of trips made by bike (coefficient = 0.20; SE = 0.05; adjusted R2 = 0.38). While we observed a low bicycle mode share, more than half of all trips were less than 5 km, demonstrating substantial opportunity to increase the number of trips taken by bike.

Purpose-Based Walking Trips by Duration, Distance, and Select Characteristics, 2017 National Household Travel Survey

Background: New or enhanced activity-friendly routes to everyday destinations is an evidence-based approach for increasing physical activity. Although national estimates for some infrastructure features surrounding where one lives and the types of nearby destinations are available, less is known about the places where individuals walk. Methods: A total of 5 types of walking trips (N = 54,034) were defined by whether they began or ended at home (home based [HB]) and trip purpose (HB work, HB shopping, HB social/recreation, HB other, and not HB trip) (2017 National Household Travel Survey). Differences and trends by subgroups in the proportion of each purpose-oriented trip were tested using pairwise comparisons and polynomial contrasts. Results: About 14% of U.S. adults reported ≥1 walking trip on a given day. About 64% of trips were HB trips. There were few differences in prevalence for each purpose by subgroup. For example, prevalence of trips that were not HB decreased significantly with increasing age and increased with increasing education and household income. Conclusions: Given age-related and socioeconomic differences in walking trips by purpose, planners and other professionals may want to consider trip origin and destination purposes when prioritizing investments for the creation of activity-friendly routes to everyday destinations where people live, work, and play.