Travel-Limiting Disabilities in the United States: Why Accessibility Matters?

This study evaluates the distribution of travel-limiting disabilities across genders and geographic locations in the United States. This study aims to describe and compare the socioeconomic and demographic variables of the people with and without travel-limiting disabilities across geographic locations and gender. The study further evaluates the trip purpose and impact of Covid-19 fourth wave pandemic on the use of public transit and travel to physical workplace for the people with and without travel-limiting disabilities across gender and geographic locations. The study uses the 2022 weighted National Household Travel Survey dataset and employs descriptive statistics. Results reaffirm the findings from previous literature that there are more people with travel-limiting disabilities in urban areas and among women. Over 50 percent of people aged 65 and above have a form of travel-limiting disabilities. The most trip for people with travel-limiting disabilities is made for shopping and medical purposes. Across all categories, rural areas, urban areas, male and female for the people without travel-limiting disabilities, COVID-19 fourth wave did not change the pattern of trips made to physical workplace as pre-COVID-19 era. This pattern is also observable for the people with travel-limiting disabilities in rural and urban areas. Females with travel-limiting disabilities reported making less trips to physical workplaces while male reported doing the same as before COVID-19 era. The study concludes that the quantification of travel-limiting disabilities across geographic location and gender is vital in disability study and could drive policy implementation for improved accessibility for the vulnerable population.

Reviewing trip purpose imputation in GPS-based travel surveys

The global positioning system(GPS)has motivated rapid advances in mobility data collection.A massive amount of spatio-temporal information has made it possible to know where a person was and when,but not how and why(s)he travelled,creating the need for inference models.Compared with mode detection,purpose imputation has been insufficiently studied.However,the relative lack of attention to purpose identification does not mean that this field has not emerged.For this paper,which is the first review dedicated to inferringtrip purposes from GPS data,1162 non-duplicate papers from four databases(Scopus,Web of Science,ScienceDirect and TRID)were screened,and a corpus of 25 publications was selected for examination.Based on these papers,the purpose imputation problem is defined in the contexts of the evolution of GPS-based travel surveys and two research domains,transportation science(TS)and human geography(HG).Subsequently,three steps of the purpose detection process,namely trip end detection,input feature selection and main algorithms and validation,are surveyed.During these procedures,the differences between studies in TS and those in HG are highlighted.Finally,unresolved issues related to data and feature selection,algorithms and assessment are discussed substantially to provide potential research directions.This review may be an inform ative reference for those newly accessing the GPS-based purpose imputation field and/or intending to develop solutions to this problem.

An empirical comparison of four technology-mediated travel survey methods

The increasing demana for advanced modelling methods, which can reflect complex travel activities of individuals, requires enhanced travel data collection methods. The introduction of GPS-assisted data collection methods has provided an alternative to the conventional methods of travel data collection. GPS-assisted data collection methods improve the accu- racy of data collection and enable capturing more details of individuals＇ travel behaviour. Recent technological advancements in smartphone-based positioning technologies and communication facilities have opened up new opportunities to apply smartphones as the media of GPS-assisted data collection. Although, different GPS-assisted methods have been employed recently, their performance has not been widely evaluated in real-world experi- ments compared to traditional data collection methods. Accordingly, this paper evaluates the performance of three GPS-assisted methods, namely handheld GPS tracking, smart- phone-based GPS tracking and smartphone-based prompted-recall data collection methods, in conjunction with the web-based data collection to shed light on different aspects of GPS- assisted data collection methods. These methods are compared in terms of the quality and accuracy of the collected data, the demographic attributes of participants and the specifi- cations of labelled trips. The results show that an appropriate employment of smartphones enhances the accuracy of data collection. It is also found that putting an extra burden on participants during a travel data collection survey results in lower trip-rates and poor data quality. Finally, it is found that the application of smartphone-assisted data collection methods help reporting non-motorised trips more accurately.

The stay coefficient: a novel quantification of the relationship between stay time and travel time for urban shopping behavior analysis

Shoppers typically want to spend an amount of time at a destination that is proportional to the travel time required to arrive there;thus,the travel time can be considered the cost of their trip.This is likely to be the case across regions with different urban structures and cultures.The purpose of this study was therefore to analyze the shopping behaviors contained in travel survey data from three metropolitan areas in Japan to identify common patterns and indicators based on travel time and stay time,thereby obtaining an understanding to inform future trade area analyses.Both the travel time and stay time associated with shopping behavior were found to be log-normally distributed regardless of metropolitan area,and four shopping behavior patterns common among the metropolitan areas were identified.The“stay coeffi-cient”was then defined to express the elasticity of stay time according to travel time,and its values were similar according to shopping behavior pattern regardless of metropolitan area.The stay coefficient proposed in this study can therefore be applied to identify shopping behavior patterns in any urban area based on the relationship between travel time and stay time,realizing a novel approach to the analysis of and marketing for trade areas when planning the construction or renovation of commercial facilities.This approach can help inform the decisions of urban policy makers,marketing advisors,and commercial facility operators,and should be of interest to researchers and practitioners working with geospatial,shopping,and other human behavioral characteristics.