Search arXiv⌕ Search

arXiv · 2610.05218

How Long Is the Journey to Work? One-Way Commuting Time in the United States from American Community Survey Data, 2006-2024, with Travel-Diary, European and English Comparisons

Abstract

The mean travel time to work is one of the most cited statistics of the American Community Survey (ACS), yet its distribution, its dependence on mode and departure time and its relation to other measurement bases are rarely set out together. Using ACS 1-year tables, we report that US workers aged 16 and over who did not work from home averaged 27.2 minutes one way in 2024, against 25.0 minutes in 2006, a peak of 27.6 in 2019 and 25.6 in 2021. Dividing published aggregate minutes by workers, commuter rail, long-distance train and ferry riders averaged 71.0 minutes, 2.68 times the 26.5 minutes of solo drivers, and all public transportation 49.5 minutes. In 2024, 9.3% of commuters, about 13.3 million people, travelled 60 minutes or more each way and 3.0% 90 minutes or more; commuters leaving between 5:00 and 5:29 a.m. averaged 35.1 minutes. State means ranged from 33.2 minutes in New York to 17.5 in North Dakota, and big-metro means from 36.5 minutes in New York to 21.5 in Buffalo. Working from home rose from 5.7% of workers in 2019 to 17.9% in 2021 and stood at 13.3% in 2024. The 2022 National Household Travel Survey recorded an average commute trip of 13.43 miles at 25.51 mph. On different definitions, the 2019 EU labour force survey module gave an EU average of 25 minutes and 30 minutes for the United Kingdom, and England's travel diary a commuting trip of 31.5 minutes in 2025 against 26.5 in 2002. The three bases are reported side by side and never pooled. All inputs are public.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Marcus Oyelaran, Daniel R. Whitcombe. 2026-10-04. How Long Is the Journey to Work? One-Way Commuting Time in the United States from American Community Survey Data, 2006-2024, with Travel-Diary, European and English Comparisons. https://arxiv.org/abs/2610.05218

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Addressing the Between-Group Comparison Problem: Detecting Differences Between Correlation Matrix Populations due to Single-variable Perturbations for Resting State fMRI

Resting-state fMRI has been known for decades as a promising method for evaluating cognitive and mental states, both in health and especially in disease, due to its ease of implementation as a short, standard MRI protocol. In clinical settings, a group of patients with a given disorder is typically compared to a group of healthy controls. This poses an inherent challenge of between-group comparison. We propose a new efficient model for characterizing changes to the temporal synchronization of brain activity measured using RS-fMRI between groups, summarized as individual correlation matrices. Our model posits that the between-group differences are the product of single-region effects describing the increase or decay of synchronization with the rest of the brain. This parsimonious model pools the correlation coefficients of each region with all others, and therefore can detect differences between groups even in small samples. Inference for this model accounts for the variability in individual correlation matrices, the within-group differences across individuals, and for the approximation error of the single-region model. This results in per-region estimates and confidence intervals for the parameters governing the difference between groups. In simulations, our model shows increased power to detect model-aligned alternatives compared with competing approaches. To demonstrate feasibility of the method in a clinical application, we use the model to analyze RS-fMRI correlation matrices in patients with transient global amnesia and healthy controls. Our model detects significant decreases in synchronization for the patient population in the amygdala after multiplicity correction as well as borderline decreases in memory-related brain regions that were not detected using mass-univariate tests without prior knowledge, suggesting its usefulness in the application of RS-fMRI in clinical settings.

stat.AP↗

Evaluating cross-encoders for semantic similarity assessment in psychological questionnaires

Correlations between rating scales are commonly interpreted as evidence of convergent or discriminant validity, yet prior studies suggest that part of these associations may be attributable to semantic similarity between item wordings rather than to genuine construct overlap alone. Building on this evidence, largely derived from bi-encoders, the present study explores whether cross-encoders, which jointly encode item pairs, offer a suitable technique for detecting semantic overlapping between questionnaire items. Using response data from the NEO-FFI and the PID5BF+M (N = 502, Labek et al., 2024), we examined whether cross-encoder-derived semantic similarity estimates are associated with empirical item correlations, and whether cross-encoders offer a systematic advantage over bi-encoders. Across twelve cross-encoder models, semantic distance was consistently negatively associated with absolute item correlations, reaching statistical significance in two-thirds of the models, with R2 values of up to .37. However, cross-encoders did not consistently outperform bi-encoders based on the same base models. These findings extend prior evidence for semantic components in scale intercorrelations to cross-encoder architectures, while indicating that predictive value depends more on model-specific training characteristics than on encoder architecture itself.

stat.AP↗

Spatio-Temporal Stochastic Interventions for Causal Inference in Climate Science

Estimating causal effects in climate science, such as the effect of anthropogenic warming on crop loss, is challenging because of complex spatio-temporal dependence and the high-dimensional nature of the treatment. To address this dependence and the resulting poor overlap between observed and counterfactual scenarios, we develop a spatio-temporal stochastic-intervention framework for estimating causal effects from climate observations. We introduce a regularized estimator of the stochastic-intervention treatment effect that trades a controlled bias for a reduction in the weight variance caused by poor overlap. Simulation studies show that this estimator attains lower mean squared error than alternative weighting estimators and removes the confounding bias of an unadjusted estimator. We apply the framework to estimate the effect of historical warming on vapor-pressure deficit, a driver of crop stress, adjusting for precipitation, which confounds the effect by affecting both temperature and humidity. In GISS-E2-1-G climate-model simulations, the global effect is distinguishable from zero in every year from 1995 onward, and omitting the precipitation adjustment inflates the global estimate by 47%. Adjustment reverses the sign of the estimate over 8% of global cropland (125 million hectares), where an unadjusted analysis could misdirect adaptation between heat-focused and moisture-focused measures.

stat.AP↗