Joshua Auld
Biographic Data
| ID | 8886744 |
|---|---|
| NAME | Joshua Auld |
| GIVEN NAMES | Joshua |
| FAMILY NAME | Auld |
| SIGNATURE | AULD J |
| AFFILIATIONS | Argonne National Laboratory |
| ORCID | 0000-0002-2492-0093 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2013 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Analyzing users’ preferences between personal and pooled rideshare services using a mixed logit modeling approach
Ridesharing has become an increasingly popular transportation method over the past decade. Transportation network companies such as Uber and Lyft generally provide two types of rideshare services: personal rideshare, in which users ride alone or with individuals they know, and pooled rideshare, in which users ride with passengers they do not know but share similar routes. Pooled rideshare is capable of reducing energy consumption and traffic in t…
Time-use behaviour in the United Kingdom: A comparative analysis of pre-Covid19 and during Covid19
The COVID-19 pandemic triggered profound shifts in daily activity patterns and time allocation, providing a unique opportunity to study behavioural adaptations during unprecedented disruptions. This paper examines changes in time-use behaviour in the United Kingdom by comparing pre-pandemic and pandemic periods using data from the UK Time Use Survey (UKTUS) for 2014–2015 and 2020–2021. A Multiple Discrete-Continuous Extreme Value (MDCEV) model is…
GPS-supported smartphone app-based integrated travel diary and time-use data collection: Challenges and lessons learned
Travel behaviour and time-use data are two vital data sources for travel demand modelling. Travel behaviour is traditionally collected through household travel surveys, enhanced by using GPS-supported smartphone apps for passive location data collection. However, recruiting individuals willing to install these apps with sustained motivation to continue participation has been a critical challenge. This paper shares insights from a travel and time-…
The co-determination of home and workplace relocation durations using survival copula analysis
A comparison between residential relocation timing of Sydney and Chicago residents: A Bayesian survival analysis
Inter-personal interactions and constraints in travel behavior within households and social networks
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Inter-personal interactions and constraints in travel behavior within households and social networks
A comparison between residential relocation timing of Sydney and Chicago residents: A Bayesian survival analysis
The co-determination of home and workplace relocation durations using survival copula analysis
GPS-supported smartphone app-based integrated travel diary and time-use data collection: Challenges and lessons learned
Travel behaviour and time-use data are two vital data sources for travel demand modelling. Travel behaviour is traditionally collected through household travel surveys, enhanced by using GPS-supported smartphone apps for passive location data collection. However, recruiting individuals willing to install these apps with sustained motivation to continue participation has been a critical challenge. This paper shares insights from a travel and time-…
Analyzing users’ preferences between personal and pooled rideshare services using a mixed logit modeling approach
Ridesharing has become an increasingly popular transportation method over the past decade. Transportation network companies such as Uber and Lyft generally provide two types of rideshare services: personal rideshare, in which users ride alone or with individuals they know, and pooled rideshare, in which users ride with passengers they do not know but share similar routes. Pooled rideshare is capable of reducing energy consumption and traffic in t…
Time-use behaviour in the United Kingdom: A comparative analysis of pre-Covid19 and during Covid19
The COVID-19 pandemic triggered profound shifts in daily activity patterns and time allocation, providing a unique opportunity to study behavioural adaptations during unprecedented disruptions. This paper examines changes in time-use behaviour in the United Kingdom by comparing pre-pandemic and pandemic periods using data from the UK Time Use Survey (UKTUS) for 2014–2015 and 2020–2021. A Multiple Discrete-Continuous Extreme Value (MDCEV) model is…
Urban Transport and Accessibility (5 works) · Computer Science (3 works) · Economics (3 works) · Engineering (3 works) · Transport engineering (3 works) · Transportation Planning and Optimization (3 works) · Travel behavior (3 works) · Demographic economics (2 works) · Econometrics (2 works) · Relocation (2 works)