The impact of COVID-19 on airline passenger travel behavior: An exploratory analysis on the Chinese aviation market

2019 ◽

Vol 11 (1) ◽

pp. 108-129

Author(s):

Andrew G. Mueller ◽

Daniel J. Trujillo

Keyword(s):

Land Use ◽

Built Environment ◽

Discrete Choice ◽

Travel Behavior ◽

Choice Model ◽

Mode Choice ◽

Land Use Regulation ◽

Policy Action ◽

Development Goals ◽

The Impact

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.

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Repeat donation and deferral rates in US source plasma donors: Exploratory analysis from the IMPACT trial

Transfusion ◽

10.1111/trf.16610 ◽

2021 ◽

Author(s):

Jan Hartmann ◽

Michael J. Ragusa ◽

Elmar R. Burchardt ◽

Zorayr Manukyan ◽

Mark A. Popovsky ◽

...

Keyword(s):

Exploratory Analysis ◽

The Impact

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Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

Remote Sensing ◽

10.3390/rs13122329 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2329

Author(s):

Elżbieta Macioszek ◽

Agata Kurek

Keyword(s):

Travel Behavior ◽

Traffic Management ◽

Road Traffic ◽

Traffic Volume ◽

The Road ◽

Traffic Characteristics ◽

Annual Average Daily Traffic ◽

On The Road ◽

The Impact ◽

The City

Continuous, automatic measurements of road traffic volume allow the obtaining of information on daily, weekly or seasonal fluctuations in road traffic volume. They are the basis for calculating the annual average daily traffic volume, obtaining information about the relevant traffic volume, or calculating indicators for converting traffic volume from short-term measurements to average daily traffic volume. The covid-19 pandemic has contributed to extensive social and economic anomalies worldwide. In addition to the health consequences, the impact on travel behavior on the transport network was also sudden, extensive, and unpredictable. Changes in the transport behavior resulted in different values of traffic volume on the road and street network than before. The article presents road traffic volume analysis in the city before and during the restrictions related to covid-19. Selected traffic characteristics were compared for 2019 and 2020. This analysis made it possible to characterize the daily, weekly and annual variability of traffic volume in 2019 and 2020. Moreover, the article attempts to estimate daily traffic patterns at particular stages of the pandemic. These types of patterns were also constructed for the weeks in 2019 corresponding to these stages of the pandemic. Daily traffic volume distributions in 2020 were compared with the corresponding ones in 2019. The obtained results may be useful in terms of planning operational and strategic activities in the field of traffic management in the city and management in subsequent stages of a pandemic or subsequent pandemics.

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Using Bus Ticketing Big Data to Investigate the Behaviors of the Population Flow of Chinese Suburban Residents in the Post-COVID-19 Phase

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18116066 ◽

2021 ◽

Vol 18 (11) ◽

pp. 6066

Author(s):

Yanbing Bai ◽

Lu Sun ◽

Haoyu Liu ◽

Chao Xie

Keyword(s):

Travel Behavior ◽

Large Scale ◽

Behavior Patterns ◽

Population Movements ◽

Economic Level ◽

Passenger Travel ◽

Scale Population ◽

Travel Behaviors ◽

Railway Passenger ◽

And Control

Large-scale population movements can turn local diseases into widespread epidemics. Grasping the characteristic of the population flow in the context of the COVID-19 is of great significance for providing information to epidemiology and formulating scientific and reasonable prevention and control policies. Especially in the post-COVID-19 phase, it is essential to maintain the achievement of the fight against the epidemic. Previous research focuses on flight and railway passenger travel behavior and patterns, but China also has numerous suburban residents with a not-high economic level; investigating their travel behaviors is significant for national stability. However, estimating the impacts of the COVID-19 for suburban residents’ travel behaviors remains challenging because of lacking apposite data. Here we submit bus ticketing data including approximately 26,000,000 records from April 2020–August 2020 for 2705 stations. Our results indicate that Suburban residents in Chinese Southern regions are more likely to travel by bus, and travel frequency is higher. Associated with the economic level, we find that residents in the economically developed region more likely to travel or carry out various social activities. Considering from the perspective of the traveling crowd, we find that men and young people are easier to travel by bus; however, they are exactly the main workforce. The indication of our findings is that suburban residents’ travel behavior is affected profoundly by economy and consistent with the inherent behavior patterns before the COVID-19 outbreak. We use typical regions as verification and it is indeed the case.

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Using Deep Learning to Understand Travel Demands in Different Urban Districts

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198121994582 ◽

2021 ◽

pp. 036119812199458

Author(s):

Shunhua Bai ◽

Junfeng Jiao

Keyword(s):

Built Environment ◽

Travel Behavior ◽

Prediction Accuracy ◽

Travel Demand ◽

Short Term Memory ◽

Future Research ◽

Demand Forecast ◽

Time And Space ◽

The University ◽

The Impact

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.

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