## 1 Introduction  
The \"commuting time paradox\" posits that the average commuting time remains constant across different time periods [1, 2]. The stability of the commuting time has valuable practical implications for transportation planning and policy analysis, including transport improvements impact analysis, traffic congestion relief, mode shifting, and mitigating transport externalities. Numerous theoretical and empirical studies have focused on the stability of total travel time and commuting time [2, 3, 4, 5]. The results of these empirical analyses are mixed: some studies suggest that the commuting time remains stable over time [2, 4, 5], while others have found contrary evidence [6, 7, 8, 9]. Several issues potentially contribute to these mixed results. Firstly, the data collection methods may have changed over different periods within a study. For example, travel surveys have evolved from face-to-face retrospective interviews to prospective computer-assisted telephone interviews [10]. Additionally, there is a difference between activity diaries and travel diaries, which some argue might explain some, if not all, of the variations in the reported travel time expenditure across various studies [10]. Secondly, previous studies typically employed one of two categories of analysis: aggregate or disaggregate. Aggregate analyses use large geographic units, such as cities, states, nations, or population groups, whereas disaggregate methods analyze data at the household or individual level. While aggregate studies primarily utilize descriptive analysis techniques, disaggregate studies employ methodologies such as linear regression, structural equation modeling, and survival analysis [11; 12; 13]. These two types of studies often yield contradictory results: aggregate studies tend to show stability, whereas disaggregate studies fail to confirm this concept [10]. To determine if the average commuting time remains constant across different time periods, this study conducts both aggregate and disaggregate analyses of historical comprehensive multimodal transportation data. We use two travel surveys of the Portland region, conducted in 1994 and 2011, respectively. Seventeen years apart, these surveys were collected using the same method and an identical activity diary format. We employ descriptive analysis and \(t\)-tests to compare the aggregate commuting times of the two years and use regression models to explore factors affecting the disaggregate commuting time at the individual trip level after substantial changes to the transportation and land use systems in Portland, OR. Another question of particular interest is the following: does the commuting time remain stable after the transportation and land use systems go through substantial changes like those in Portland, OR, between 1994 and 2011? The commuting time is subjected to significant transportation and land use changes, as well as significant demographic changes [14; 15; 16]. Between 1994 and 2011, the Portland region underwent significant changes to its land use and transportation systems. Households in the region grew from slightly fewer than 600,000 to more than 800,000, while employment declined slightly from 947,000 to 916,000. Expansion of the transit system nearly doubled the public transit passenger miles from 258.9 million to 499.3 million. As shown in Figure 1, the Travel Time Index (TTI), a measure of congestion, has increased drastically since the 1980s. The TTI is the ratio between the time taken to travel during peak hours and the time taken to complete the same journey at free flow [17]. Similarly, the Public Transit Passenger Miles (PTPMs) and Public Transit Unlinked Passenger Trips (PTUPTs) show a similar trend [17]. In particular, this study tests the \"commuting paradox\" [2; 18] and \"rational locator\" [4] hypotheses. These hypotheses postulate that households and firms would adjust locations to maintain a tolerable commuting time. This aligns with the concept of the Travel Time Budget [10; 11].