COVID-19: Modeling Out-of-Home and In-Home Activity Participation during the Pandemic.

Understanding the interaction between in-home and out-of-home activity participation decisions is important, particularly at a time when opportunities for out-of-home activities such as shopping, entertainment, and so forth are limited because of the COVID-19 pandemic. The travel restrictions imposed as a result of the pandemic have had a massive impact on out-of-home activities and have changed in-home activities as well. This study investigates in-home and out-of-home activity participation during the COVID-19 pandemic. Data comes from the COVID-19 Survey for assessing Travel impact (COST), conducted from March to May in 2020. This study uses data for the Okanagan region of British Columbia, Canada to develop the following two models: a random parameter multinomial logit (RPMNL) model for out-of-home activity participation and a hazard-based random parameter duration (HRPD) model for in-home activity participation. The model results suggest that significant interactions exist between out-of-home and in-home activities. For example, a higher frequency of out-of-home work-related travel is more likely to result in a shorter duration of in-home work activities. Similarly, a longer duration of in-home leisure activities might yield a lower likelihood for recreational travel. Health care workers are more likely to engage in work-related travel and less likely to participate in personal and household maintenance activities at home. The model confirms heterogeneity among the individuals. For instance, a shorter duration of in-home online shopping yields a higher probability for participation in out-of-home shopping activity. This variable shows significant heterogeneity with a large standard deviation, which reveals that sizable variation exists for this variable.

How residential energy consumption has changed due to COVID-19 pandemic? An agent-based model.

Integrating occupant behavior with residential energy use for detailed energy quantification has attracted research attention. However, many of the available models fail to capture unseen behavior, especially in unprecedented situations such as COVID-19 lockdowns. In this study, we adopted a hybrid approach consisting of agent-based simulation, machine learning and energy simulation techniques to simulate the urban energy consumption considering the occupants' behavior. An agent-based model is developed to simulate the in-home and out-of-home activities of individuals. Separate models were developed to recognize physical characteristics of residential dwellings, including heating equipment, source of energy, and thermostat setpoints. The developed modeling framework was implemented as a case study for the Central Okanagan region of British Columbia, where alternative COVID-19 scenarios were tested. The results suggested that during the pandemic, the daily average in-home-activity duration (IHD) increased by approximately 80%, causing the energy consumption to increase by around 29%. After the pandemic, the average daily IHD is expected to be higher by approximately 32% compared with the pre-pandemic situation, which translates to an approximately 12% increase in energy consumption. The results of this study can help us understand the implications of the imposed COVID-19 lockdown with respect to energy usage in residential locations.

COVID-19: Are you satisfied with traveling during the pandemic?

The outbreak of COVID-19 and preventive measures to limit the spread of the virus has significantly impacted our daily activities. This study aims to investigate the effect of daily activity engagement including travel activity and sociodemographic characteristics on travel satisfaction during COVID-19. This study develops a latent segmentation-based ordered logit (LSOL) model using data from the 2020 COVID-19 Survey for Assessing Travel Impact (COST), for the Kelowna region of British Columbia, Canada. The LSOL model accommodates the ordinal nature of the satisfaction level and captures heterogeneity by allocating individuals into discrete latent segments. The model results suggest that the two-segment LSOL model fits the data best. Segment one is more likely to be younger and older high-income workers; whereas, segment two includes middle-aged lower-income, unemployed individuals. The model results suggest that daily activity engagement and sociodemographic attributes significantly affect travel satisfaction. For example, participation in travel for routine shopping, recreational activity, and household errands has a positive effect on travel satisfaction. The use of transportation modes like bike/walk depicted a higher probability to yield travel satisfaction. The model confirms the existence of significant heterogeneity. For instance, travel for work showed a negative relationship in segment one; whereas, a positive relationship is found in segment two. Access to higher household vehicle yield lower satisfaction in segment one; in contrast, a positive relationship is found in segment two. The findings of this study provide important insights towards maintaining the health and well-being of the population during this and any future pandemic crisis.

Traffic crashes in Dhaka, Bangladesh: analysing crashes involving unconventional modes, pedestrians and public transit.

Road safety is a global concern; particularly, in developing countries due to the significantly high collision occurrences and subsequent deaths. This study presents a spatial and temporal analysis of collision frequency and injury severity of crashes in Dhaka, Bangladesh. The focus is to understand the spatio-temporal trend of collisions involving pedestrians, public transit and unconventional modes, which are the key collision factors in Dhaka. This research utilizes the police-reported collision record for Dhaka for the years 2011-2015. In temporal analysis, temporal trends (monthly, daily and hourly) of injury severity of different vehicle occupants (pedestrians, public transit and unconventional modes) have been explored using descriptive analytics. Daily distribution suggests that a higher share of severe injuries involving pedestrians (16.6%) and unconventional modes (20.5%) occur on Fridays and Thursdays, respectively. The hourly distribution suggests that pedestrians are more vulnerable from 11:00am to 12:00pm on weekends. Unconventional mode users are vulnerable from 7:00am to 8:00am on weekdays. Spatial analysis is performed adopting a Kernel density estimation (KDE) technique. The results suggest that the major activity locations of Dhaka such as central business district (CBD), airport and ferry terminals are collision prone areas. Interestingly, the density of public transit collisions is skewed around the major transit hubs of the city.

COVID-19 and Travel: How Our Out-of-home Travel Activity, In-home Activity, and Long-Distance Travel Have Changed.

COVID-19 has made unprecedented impacts on our daily life. This paper investigates individuals' immediate response to COVID-19, exploring out-of-home activities, in-home activities, and long-distance travel. Data for the Kelowna region of Canada comes from a web-based COVID-19 Survey for assessing Travel impact (COST). In addition to analyzing the survey, this research models adjustments in travel decisions by developing ordered logit models for in-home and out-of-home activities, and a binomial logit model for long-distance travel. Data analysis suggests a reduction of about 50% out-of-home activities/day/person during COVID-19 compared to the pre-pandemic period, with the only exception being picking up online orders which significantly increased in frequency. Individuals were engaged in longer duration of in-home activities; the average duration of teleworking, online shopping for groceries and other goods at-home was around 5.5 h/day/person, 32 min/day/person, and 26 min/day/person respectively. The out-of-home activity model results suggest that higher income, younger and middle aged individuals, and full-time workers are more likely to decrease their out-of-home activity; whereas, males, lower income groups, health care professionals, and picking up online orders are more likely to increase. The in-home activity model suggests that older and younger adults, higher and lower income, full-time workers, and highly educated individuals are most likely to increase their in-home activity frequency; in contrast, health care professionals are likely to decrease. Long-distance travel model results reveal that seniors, students, and airline travelers are more likely to reschedule; whereas, trips to visit friends and family are more likely to be cancelled.