What Is Essential Travel? Socioeconomic Differences in Travel Demand in Columbus, Ohio, during the COVID-19 Lockdown

Abstract

The COVID-19 pandemic has profoundly reshaped urban mobility. During the lockdown, workers teleworked if possible and left home only for essential activities. Our study investigates the spatial patterns of essential travel and their socioeconomic differences during the COVID-19 lockdown phase in comparison with the same period in 2019. Using data from Columbus, Ohio, we categorized travelers into high, moderate, and low socioeconomic status (SES) clusters and modeled travel demand of SES clusters for both phases using spatially weighted interaction models. Then, we characterized the SES variability in essential travel based on frequently visited business activities from each cluster. Results suggest that disparities in travel across SES clusters that existed prior to COVID-19 were exacerbated during the pandemic lockdown. The diffused travel pattern of high and moderate SES clusters became localized and the preexisting localized travel pattern of low SES clusters became diffused. During the lockdown, the low and moderate SES clusters traveled mostly for work with long-and medium-distance trips, respectively, whereas the high SES cluster traveled mostly for recreational and other nonwork purposes with short-distance trips. This study draws some conclusions and implications to help researchers and practitioners plan for resilient and economically vibrant transportation systems in response to future shocks.

Comparing spatial interaction models and flow interpolation techniques for predicting “cold start” bike‐share trip demand

“…The unconstrained gravity‐type spatial interaction model (Gravity SI) described in Section 2.3 is perhaps the most widely used model for diverse types of aggregate transport flows (for several recent examples see Kar et al., 2021; Lenormand et al., 2016; Oshan, 2020; Zhou et al., 2020). It is calibrated here using a log‐linear Poisson regression with a power distance‐decay function and a set of origin/destination attributes using the spint module of the Python Spatial Analysis Library (PySAL) (Oshan, 2016).…”

“…The former group usually explains trip generation, such as labor force (Pourebrahim, Sultana, Thill, & Mohanty, 2018;Signorino et al, 2011); GDP (Zhang, Cheng, & Jin, 2019) and human activity density (Marrocu & Paci, 2013). The latter are common destination determinants of travel demand, such as amenities (e.g., school, hospitals, markets) (Botella, Gora, Sosnowska, Karsznia, & Querol, 2021;Kar et al, 2021), tourism attractions (e.g., hotel rooms) (Khadaroo & Seetanah, 2008), and land-use types (Liu, Kang, Gong, & Liu, 2016).…”

“…In this context, potential is often defined as location population or the number of job opportunities (Gao, Liu, Wang, & Ma, 2013; Krings, Calabrese, Ratti, & Blondel, 2009; Lenormand, Bassolas, & Ramasco, 2016), but can be expanded to consider a series of origin and destination attributes that may contribute toward the flow generation, each with their own parameter (Fotheringham & O’Kelly, 1989; Oshan, 2020). According to Kar, Le, and Miller (2021), two groups of variables can be identified besides population or jobs: socioeconomic and built environment attributes. The former group usually explains trip generation, such as labor force (Pourebrahim, Sultana, Thill, & Mohanty, 2018; Signorino et al., 2011); GDP (Zhang, Cheng, & Jin, 2019) and human activity density (Marrocu & Paci, 2013).…”

Accessibility and Essential Travel: Public Transport Reliance Among Senior Citizens During the COVID-19 Pandemic

“…The lockdowns and restrictions that were introduced had a differential impact on the mobility of populations, with unequal movement observed by both neighborhood deprivation and socioeconomic status (Campbell et al, 2021 ). In terms of the spatial structure of travel demand, Kar et al ( 2021 ) found in the United States that diffused travel patterns of individuals of higher and moderate socioeconomic status were found to have become more localized during the pandemic, while the pre-existing localized travel patterns of low socioeconomic status populations became diffused. The curtailing of movement through lockdowns and restrictions on travel appeared to exacerbate underlying social and spatial inequalities.…”

ANN-Based traffic volume prediction models in response to COVID-19 imposed measures

“…Several studies have identified the impact of such measures on the transport sector by analyzing the effect on traffic demand and travel behavior ( Dingil and Esztergár-Kiss, 2021 , Patra et al, 2021 ), traffic safety ( Yasin et al, 2021 , Saladié et al, 2020 , Lin et al, 2021 , Shokouhyar et al, 2021 ), travelers’ attitudes ( Ceccato et al, 2021 , De Vos, 2020 , Kar et al, 2021 , Brough et al, 2021 , Zhang et al, 2021 ), and impact on air quality and environment ( Singh and Chauhan, 2020 , Nakada and Urban, 2020 , Zangari et al, 2020 , Benchrif et al, 2021 , Polednik, 2021 , Xin et al, 2021 ). However, this study differs from the existing literature by investigating the correlation between the pandemic's response measures and urban traffic flows and then employing such a relationship in estimating traffic flows under different conditions in the future.…”