•Shared e-scooters have emerged as a transportation mode.•Using data from Austin, Texas, e-scooter usage patterns are analyzed.•Spatial Lag, Spatial Durbin and geographically weighted regressions are ...estimated.•Student populations are a major source of trips.•The impact of median income varies throughout the city.
In this study, we explore the usage of e-scooter sharing services in Austin, Texas over about a six-month period. The study is based on trip records of all the shared e-scooter operators in Austin and includes trip start and end locations. We use both analysis of trip patterns and spatial regression techniques to examine how the built environment, land use, and demographics affect e-scooter trip generation. Our findings show that people use e-scooters almost exclusively in central Austin. Commuting does not seem to be the main trip purpose, and usage of e-scooters is associated with areas with high employment rates, and in areas with bicycle infrastructure. People use e-scooter sharing regardless of the affluence of the neighborhood, although less affluent areas with high usage rates have large student populations, suggesting that students use this mode of travel. Implications for planners suggest that better bicycle infrastructure will facilitate e-scooter usage, college towns are a ready market for e-scooter sharing services, and e-scooters may be a substitute for some short non-work trips, reducing car usage, and benefiting the environment.
The mediation of work practices by information and communication technologies enables knowledge workers to telework from remote non-office locations such as their homes, or to work nomadically from ...multiple locations in a day. This paper uses data from the American Time Use Survey to explore the relationship between daily work locations and travel in the United States from 2003 to 2017. Outcome variables include travel duration and travel during peak periods. Home is by far the most common non-office work location, but working from other people’s homes, cafés/libraries, vehicles, and combinations of multiple locations are also measured. Findings show that working from home only on a day (full-day telework) decreases daily travel duration and increases the likelihood of avoiding peak hour travel for both work and non-work related travel. However, for homeworkers who also conduct work from their workplace on the same day (part-day telework), there is no reduction in daily travel time, and avoiding peak hour travel is limited to work-related travel. Working from other locations such as cafés/libraries or vehicles increases the likelihood of not traveling at peak hours. Findings also indicate that morning peak periods are more affected by work location decisions than evening peak periods. A survival analysis of daily departure times for both full-day and part-day homeworkers provides insight into this mechanism. We conclude on the basis of these findings that demand management policies and peak avoidance incentives would be more effective if they encourage both temporal and spatial flexibility for employees when partnering with regional employers.
Bikeshare trip generation in New York City Noland, Robert B.; Smart, Michael J.; Guo, Ziye
Transportation research. Part A, Policy and practice,
12/2016, Volume:
94
Journal Article
Peer reviewed
•A spatial model is developed to examine bikeshare trip generation in New York City.•Estimates are developed for different months, work days versus weekends, and type of user.•Results show that more ...trips are generated near subway stations, as well as in areas with more population and employment.•Bicycle lanes and paths are associated with more casual bikeshare trips.•The model does not perform well when attempting to forecast trip generation at new stations.
Cities around the world and in the US are implementing bikesharing systems, which allow users to access shared bicycles for short trips, typically in the urban core. Yet few scholars have examined the determinants of bikeshare station usage using a fine-grained approach. We estimate a series of Bayesian regression models of trip generation at stations, examining the effects bicycle infrastructure, population and employment, land use mix, and transit access separately by season of the year, weekday/weekend, and user type (subscriber versus casual). We find that bikeshare stations located near busy subway stations and bicycle infrastructure see greater utilization, and that greater population and employment generally predict greater usage. Our findings are nuanced, however; for instance, those areas with more residential population are associated with more trips by subscribers and on both weekdays and non-working days; however, the effect is much stronger on non-working days. Additional nuances can be found in how various land use variables affect bikeshare usage. We use our models, based on 2014 data, to forecast the trips generated at new stations opened in 2015. Results suggest there is large variation in predictive power, partly caused by variation in weather, but also by other factors that cannot be predicted. This leads us to the conclusion that the nuances we find in our inferential analysis are more useful for transportation planners.
We document the falling socioeconomic status of American households without private vehicles and the continuing financial burden that cars present for low-income households that own them. We tie both ...these trends to the auto-orientation of America’s built environment, which forces people to either spend heavily on cars or risk being locked out of the economy. We first show that vehicle access remains difficult for low-income households and vehicle operating costs remain high and volatile. Using data from the Panel Study of Income Dynamics, Survey of Consumer Finances, and Census Public Use Microdata, we then show that in the last fifty years households without vehicles have lost income, both in absolute terms and relative to households with vehicles. We link these trends to the built environment by examining the fortunes of carless households in New York City, and particularly in Manhattan. Most of New York’s built environment did not change to accommodate cars, and in New York the fortunes of the carless did not fall. Our results suggest that planners should see vehicles, in most of the United States, as essential infrastructure, and work to close gaps in vehicle access.
Most transportation research in the United States uses cross-sectional, “snapshot” data to understand levels of car access. Might this cross-sectional approach mask considerable variation over time ...and within households? We use a panel dataset, the Panel Study of Income Dynamics (PSID), for the years 1999–2011 to test this question. We find that for most families, being “carless” is a temporary condition. While 13 % of families in the US are carless in any given year, only 5 % of families are carless for all seven waves of data we examine in the PSID. We also find that poor families, immigrants, and people of color (particularly, blacks) are considerably more likely to transition into and out car ownership frequently and are less likely to have a car in any survey year than are non-poor families, the US-born, and whites.
The multinationalization of corporate investment in recent years has given rise to a number of international tax avoidance schemes that may be eroding tax revenues in industrialized countries, but ...which may also reduce tax burdens on mobile capital and so facilitate investment. Both the welfare effects of and the optimal response to international tax planning are therefore ambiguous. Evaluating these factors in a simple general equilibrium model, we find that citizens of high-tax countries benefit from (some) tax planning. Paradoxically, if tax rates are not too high, an increase in tax planning activity causes a rise in optimal corporate tax rates, and a decline in multinational investment. Thus fears of a “race to the bottom” in corporate tax rates may be misplaced.
We examine the relationship between transportation access on the one hand and individuals’ employment and labor earnings on the other. We improve on existing studies by bringing a large national ...panel data set to bear on this question, attempting to disentangle the mechanisms by which individuals improve their economic standing and, finally, comparing the economic benefits to the direct costs of car ownership. To do this, we use nine waves from the Panel Study of Income Dynamics from 1999 to 2015. We find that access to a car is a strong predictor of future economic benefit for individuals, and that at very high levels of transit access, carless individuals can also fare equally well. Access to an automobile is strongly associated with employment, job retention, and earning more money over time. Though having a car is associated with economic benefits, owning and operating a car is expensive; yet, our findings suggest that the benefits may outweigh the costs for most people living outside neighborhoods with truly excellent transit service.
Immigrants to the United States walk, bicycle, and use transit and carpools more than U.S.-born residents do. These differences persist over time and across income groups. The differences appear ...strongest when immigrants reside in immigrant neighborhoods with high concentrations of other immigrants. This analysis uses a large, geocoded national dataset to analyze these differences and finds that living in an immigrant neighborhood has a strong influence on mode choice for immigrant residents and a much weaker effect on non-immigrant residents of immigrant neighborhoods. These effects are strongest for walking and bicycling, and particularly for shopping-related travel, and they persist after controlling for a number of variables. That these effects are considerably stronger for immigrants than for their U.S.-born neighbors suggests that social factors of the neighborhoods may play a role in structuring travel decisions.
Canadian Adverse Driving Conditions dataset Pitropov, Matthew; Garcia, Danson Evan; Rebello, Jason ...
The International journal of robotics research,
04/2021, Volume:
40, Issue:
4-5
Journal Article
Peer reviewed
The Canadian Adverse Driving Conditions (CADC) dataset was collected with the Autonomoose autonomous vehicle platform, based on a modified Lincoln MKZ. The dataset, collected during winter within the ...Region of Waterloo, Canada, is the first autonomous driving dataset that focuses on adverse driving conditions specifically. It contains 7,000 frames of annotated data from 8 cameras (Ximea MQ013CG-E2), lidar (VLP-32C), and a GNSS+INS system (Novatel OEM638), collected through a variety of winter weather conditions. The sensors are time synchronized and calibrated with the intrinsic and extrinsic calibrations included in the dataset. Lidar frame annotations that represent ground truth for 3D object detection and tracking have been provided by Scale AI.
Americans are driving less. The changes are most pronounced among Millennials, those born in the 1980s and 1990s. Much ink has been spilled debating whether these changes in travel behavior are due ...to changing preferences or economic circumstances. In this paper, we use eight waves of data from the Panel Study of Income Dynamics (PSID) to examine recent changes in auto ownership among US families with a particular focus on Millennials. We find that today's young adults do own fewer cars than previous generations did when they were young. However, when we control for whether young adults have become economically independent from their parents, i.e. left the nest, we find that economically independent young adults own slightly more cars than we would expect, given their low incomes and wealth. We caution planners to temper their enthusiasm about “peak car,” as this may largely be a manifestation of economic factors that could reverse in coming years.
•We use a panel dataset to examine changes in car ownership over time.•We find that Millennials own fewer cars than previous cohorts.•We differentiate between economically dependent and independent Millennials.•Economic factors account for much of the differences in car ownership.•Independent Millennials own more cars than expected given their economic status.
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