2021

Liangpeng Gao, Fujian University
Zheng Yue (zhengyue_seu@163.com), Nanjing University
Lu Ying Chil, Fujian University
Yanjie Ji, Southeast University

How to meet the daily demand for resident transport while limiting the transmission of infectious diseases is a problem of social responsibility of urban transport systems during major public health emergencies. Considering the novel coronavirus pneumonia epidemic (COVID-19), a bus timetable system based on the “if early, wait, and if late, leave soon” strategy is proposed. Based on public transport vehicle constraints in this system, the concept of reliability is introduced and a model based on an event tree is built to calculate the failure rate of urban bus timetables. Then, the public transport situation in Yixing city is used as an example to perform confirmatory analysis, and the fluctuations in the reliability of the bus timetable during the novel coronavirus pneumonia epidemic are discussed. The research results show that the method proposed in this paper can effectively avoid the external effects of the transport network topology, obtain the overall failure rate of urban bus timetable operation, and achieve an accurate evaluation of the reliability of bus timetables. During the early, middle and more recent stages of the COVID-19 outbreak, the failure rate of bus timetables in Yixing city initially decreased and then increased. The reliability of the urban bus timetable system can be improved by at least 35% during major health emergencies, such as the novel coronavirus pneumonia outbreak, and cross-infection at bus stations can be prevented. The research results verify the feasibility and reliability of the implementation of bus timetabling strategies during major health emergencies.

Sylvan Hoover, Oregon State University
J. David Porter, Oregon State University
Claudio Fuentes, Oregon State University

Transit agencies have experienced dramatic changes in service and ridership due to the COVID-19 pandemic. As communities transition to a new normal, strategic measures are needed to support continuing disease suppression efforts. This research provides actionable results to transit agencies in the form of improved transit routes. A multi-objective heuristic optimization framework employing the NSGA-II algorithm generates multiple route solutions that allow transit agencies to balance the utility of service to riders against the susceptibility of routes to enable the spread of disease in a community. This research uses origin-destination data from a sample population to assess the utility of routes to potential riders, allows vehicle capacity constraints to be varied to support social distancing efforts, and evaluates the resulting transit encounter network produced from the simulated use of transit to estimate the susceptibility of a transit system to facilitate the transmission of disease amongst its riders. A case study of transit at Oregon State University is presented with multiple transit network solutions evaluated and the resulting encounter networks investigated. The improved transit network solution with the closest number of riders (1.2% more than baseline) provides a 10.7% reduction of encounter network edges.

Michele Gazzea, Western Norway University of Applied Sciences
Alican Karaer, Florida State University
Nozhan Balafkan, Consultant
Eren Ozguven, Florida State University
Reza Arghandeh, Hogskulen pa Vestlandet

After hurricanes, roadway damage assessment is critical to emergency responders and city authorities. In this paper,  we propose an automated semi-supervised approach to identify tree debris along roadways to improve the efficiency of damage assessment and allow faster debris cleaning operations. The solution uses two high-resolution satellite images taken before and after a hurricane. The proposed methodology is an ensemble learning machine using an unsupervised autoencoder-based feature extractor, an unsupervised vegetation coverage estimator, and a weakly-supervised tree segmentation. We show that such a combination can increase precision and accuracy. Our solution has been tested with a case study on Hurricane Michael, which hit Tallahassee, the capital of Florida, in October 2018.

Brian Caulfield (brian.caulfield@tcd.ie), Trinity College, Dublin
Sarah Bowman, University of Dublin
Martina Mullin, University of Dublin
Sarah Browne, University of Dublin
Clare Kelly, University of Dublin

Dublin, Ireland, like most other large cities has undergone lockdown due to the outbreak of the coronavirus.  Ongoing social distancing requirements, as cities reopen, has resulted in a collapse to transit’s capacity and ability to meet demand. For Dublin, campaigns have focused on encouraging those who can walk or cycle to use active transport options if they are close enough to their place of employment. This is to ensure adequate physical distancing can occur while alleviating the pressure on our public transport systems, thereby freeing up space for those who are unable to commute via active transport and for those beyond a range in which it is feasible. Trinity College Dublin, the University of Dublin (TCD) is located in the city center of Ireland’s capital. Less than 1% of staff drives to our campus and students are not permitted to park on our campus, the University community has been working with Dublin City Council (DCC) to examine ways to safely return to work and education. A survey was conducted to determine how staff and students would like to travel to TCD, from September, it identifies which factors influence their mode choice and choice of working locations.  Our University campus makes for an interesting case study as it allows us to understand how the reopening of a major employment, educational and cultural site within an urban area, which is primarily served by transit and active transport, can address physical distancing restrictions and decreased capacity of public transport.

Ruijie Bian (Ruijie.Bian@la.gov), Louisiana Transportation Research Center (LTRC)
Pamela Murray-Tuite, Clemson University
Brian Wolshon, Louisiana State University

While non-essential travel was canceled during the Coronavirus infectious disease (COVID-19) pandemic, grocery shopping was essential to households. The objectives of this study were to: 1) examine how grocery store visits changed during the early outbreak of COVID-19 and 2) estimate a model to predict the change of grocery store visits in the future. The study period was from 2/15 to 5/31/2020, which covered the outbreak and phase-one re-opening. Six counties/states in the U.S. were selected as the study areas. Grocery store visits (in-store or curbside pickup) increased over 20% when the national emergency was declared on 3/13 and then decreased below the baseline within a week. Grocery store visits on weekends were affected more significantly than those on workdays before late-April. Grocery store visits in some states (including California, Louisiana, New York, and Texas) started returning to normal by the end of May but that was not the case for some of the counties (including those with the cities of Los Angeles and New Orleans). With the data from Google Mobility Reports, this study used a Long Short-Term Memory network to predict the change of grocery store visits from the baseline in the future. The networks trained with the national data or the county data performed well in predicting the general trend of each county. The results from this study could help understand mobility patterns of grocery store visits during the pandemic and predict the process of returning to normal.

Karl Kim, University of Hawaii
Eric Yamashita, University of Hawaii
Jiwnath Ghimire, University of Hawaii

In the absence of a vaccine, non-pharmaceutical interventions such as social distancing and travel reductions have become the only strategies for slowing the spread of the COVID-19 pandemic.  Using survey data from Hawaii (n = 22,200) collected in March through May of 2020 at the onset of the pandemic, the differences between traveler spreaders, who brought the disease into the state and community spreaders are investigated.  In addition to describing the demographic attributes of these two groups and comparing them to others vulnerable to COVID-19disease, using logistic and multivariate regression models, characteristics, travel behaviors, and transport modes are examined. Traveler spreaders are likely to be male, younger, and likely to be returning students (not classified as employed) while community spreaders are also more likely to be male, essential workers, first responders, and medical personnel at the highest risk of exposure. A risk of infection score is also derived and analyzed to identify and assess attributes of individuals most likely to contract the disease. Using spatial statistics, hotspots, and clusters of locations of high-risk individuals are mapped and analyzed. The analysis supports efforts to better understand, respond, and slow the spread of the pandemic. Transportation researchers provide critical analytical capabilities and experience with relevant databases on mobility and the spread of infectious diseases.

Jun Zhao (zjunnju@gmail.com)
Minha Lee
Sepehr Ghader, University of Maryland, College Park
Hannah Younes, University of Maryland, College Park
Aref Darzi, University of Maryland, College Park
Chenfeng Xiong
Lei Zhang, University of Maryland, College Park

By the emergence of the novel coronavirus disease (COVID-19) in Wuhan, China, and its rapid outbreak worldwide, the infectious illness has changed our everyday travel patterns. In this research, our team investigated the changes in the daily mobility pattern of people during the pandemic by utilizing an integrated data panel. To incorporate various aspects of human mobility, the team focused on the Social Distancing Index (SDI) which was calculated based on five basic mobility measures. The SDI patterns showed a plateau stage in the beginning of April that lasted for about two weeks. This phenomenon then followed by a universal decline of SDI, increased number of trips and reduction in percentage of people staying at home. We called the observation Quarantine Fatigue. The Rate of Change (ROC) method was employed to trace back the start date of quarantine fatigue which was indicated to be April 15th. Our analysis showed that despite the existence of state-to-state variations, most states started experiencing a quarantine fatigue phenomenon during the same period. This observation became more important by knowing that none of the states had officially announced the reopening until late April showing that people decided to loosen up their social distancing practices before the official reopening announcement. Moreover, our analysis indicated that official reopening led to a rapid decline in SDI, raising the concern of a second wave of outbreak. The synchronized trend among states also emphasizes the importance of a more nationwide decision-making attitude for the future as the condition of each state depends on the nationwide behavior.

Ioannis Tsouros, University of the Aegean
Athena Tsirimpa, University of the Aegean
Ioanna Pagoni, University of the Aegean
Amalia Polydoropoulou, University of the Aegean

The COVID-19 pandemic shocked the global society and caused significant disruptions on various levels of economic and social activity, apart from the purely humanitarian and health perspective. International community and national goverments introduced a series of restrictions and other measures to minimize the spread of the virus. This paper provides insight from Greece, focusing on activities and time-use, statements towards mental health and overall wellbeing of citizens during the spring lockdown period of 2020. The analysis is based on data from more than 400 individuals collected through an online survey, which included psychometric attitudes and mental health scales, activity participation and time-use, as well as socio-economic variables and reactions to COVID-19 measures and overall situation. We present results and a respondent segmentation (based on k-means clustering) which provides useful insight into the mental health, wellbeing of individuals during the restrictions period and information regarding the activities of the various segments of the population before and during the lockdown. Main findings include the identification of three distinct clusters of the respondents, “Breezeless”, “Phobic” and “At peace” which demonstrated heterogenous time-use allocation and activity patterns during the lockdown. This is one of the first papers to present activity and time-use data for the 2020 lockdown period in Greece by developing a segmentation approach of the participants based on mental health scales and indicators. Such exploratory efforts are useful in identifying different population segments that may react to government restrictions in a heterogenous way and may exhibit varying mental health statuses.

Xinyuan Wang, Tongji University
Jian Li (jianli@tongji.edu.cn), Tongji University
Ruijie Bian, Louisiana Transportation Research Center (LTRC)
Yuyang Zhou, Beijing University of Technology

The novel coronavirus (COVID-19) pandemic has had a significant impact on people and communities around the world. Previous studies have investigated the influence of natural disasters and road construction on commuting behavior, but few studies have explored the impacts of pandemics. This paper focuses on the change in commuting behavior during the outbreak of COVID-19. An online survey was conducted to understand the changes in the commuting frequency and mode choice due to COVID-19 in Shanghai, China. The 378 responses showed that 82.31% of transit users switched to private modes or telecommuting during the pandemic. A nested logit model was then employed to determine the factors that affected the mode choice of transit users during the pandemic. The job, age, and commuting time significantly affected the commuting mode choice during the pandemic. This research enriches our knowledge of commuting behavior during the outbreak of a pandemic disease in a metropolitan area, and the findings provide a reference for traffic management during a pandemic.

Maria Rojas, Recinto Universitario de Mayaguez Universidad de Puerto Rico
Didier Valdés, Recinto Universitario de Mayaguez Universidad de Puerto Rico
Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)

After massive disasters and catastrophes, the infrastructure and communication systems of a community can be severely affected to the point that their function could be nonexistent. Furthermore, the affected areas are supplied with significant amounts of donations, which need to be optimally inventoried, stored, and distributed to benefit the community while minimizing logistics costs. In these events, it is vital to have disaster response plans in place as well as readily available trained personnel who can reach and support the affected areas with critical supplies in time to prevent the loss of lives and properties. In 2017, Puerto Rico was devastated by Hurricane Maria, a category five hurricane. Non-Established Relief Groups (NERGs) formed immediately after the disaster to contribute to reducing the distress in the considerably affected population. This research presents a conceptual framework with the key factors to improve the operation of NERGs when participating in the relief efforts after a catastrophic event. The proposed framework considers the required steps for an efficient relief effort, and it is intended to support a well-organized emergency response process by NERGs. Also, simulation tools were implemented to assess the operations performed by these groups for the management of supplies. Various layouts for the space usage distribution and factors that affect the material convergence phenomenon were evaluated. Recommendations are provided for NERGs to improve the efficiency of their activities and increase the benefits offered to the affected communities.

Luis Abreu, City College of New York
Alison Conway, City College of New York

This paper presents a qualitative analysis of the transportation system changes that occurred in NYC from the beginning of the COVID-19 pandemic until the city began its first phase of reopening. The study was conducted by tracking publicly available transportation-related news articles and publications (1) to capture key issues and challenges and (2) to identify changes in policies, services, and infrastructure that occurred in response across five passenger transportation modes: public transit; taxis; ridesharing; personal driving; and cycling and micromobility. Result were assessed to identify common issues and interactions between modes. The paper concludes with key lessons learned from this event.

Amit Mondal, Cambridge Systematics
Xiao Yun Chang, Cambridge Systematics
David Von Stroh, Cambridge Systematics
Jason Lemp, Cambridge Systematics
Krishnan Viswanathan, Cambridge Systematics

Anonymized mobile Location-Based Services (LBS) data has gained its popularity in the realm of transportation planning for its availability at ever lower costs, spatial and temporal accuracy, and representativeness of movement and activity patterns among the population even at low levels of aggregation. Despite its capability in complementing regional travel surveys and providing more accurate portraits of typical travel patterns, there is a dearth of research on drawing insights about evacuation following disruptive, disastrous events for future planning from the mobile LBS data. This paper presents two case studies where the mobile LBS data are used in the context of climate resiliency and evacuation planning. The first case study is designed to analyze movement patterns of mobile devices before and after several large-scale natural disasters in Southern California in recent years. Baseline activities patterns are established using LBS records before the event, to be compared against activities during and after the event. The second case study focuses on the daily activity analysis in the Orlando region in Florida for the estimation and identification of population to be evacuated during an emergency. Metrics for evaluating the movement and activity patterns, including changes in device activity intensity, spatiotemporal distribution of device activities are examined to better understand transportation demand during and after a natural disaster or an emergency and guide efficient evacuation and infrastructure planning in response to climate change.

Mehrdad Arabi, University of Texas, Arlington
Kate Hyun, University of Texas, Arlington
Stephen Mattingly, University of Texas, Arlington

Freight transportation is a major economic backbone of the United States and is vital to sustaining the nation’s economic growth. Ports, as one of the primary components of freight transportation and important means of integrating into the global economic system, have experienced significant growth and increased capacity during the past two decades. The study addresses an important national freight mobility goal to enhance the resilience of the port transportation operations in the event of extreme weather events. This study develops an adaptable resilience assessment framework that evaluates the impact of a disruptive event on transportation operations. The framework identifies dynamic performance levels over an extended period of an event including five distinct phases of responses- staging, reduction, peak, restoration, and overloading.  This study applies the framework to the port complex in Houston, Texas, during a major hurricane event, Harvey, and two holiday events in 2017.  The framework evaluates proactive and reactive responses of port truck activities during the disruptions and provides a comprehensive assessment of resilience and adaptability in port truck operations. Trucks serving local facilities show stable and shorter response phases while regional operations maintain a prolonged staging or overloading phases to handle the excess demands especially for significant multi-day disruptive events. Evaluating response systems and resilience of port truck activities during severe weather events such as Hurricane Harvey represents the first step for designing plans that support a fast system recovery that minimizes the economic, social, and human impacts.

Yaye Keita, University of South Florida
Nikhil Menon (nikhilmenon@cutr.usf.edu), University of South Florida
Robert Bertini, Oregon State University

The novel coronavirus (COVID-19), also called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has unprecedentedly disrupted businesses, society, and daily lives. Various aspects of travel, transportation, and mobility have been severely impacted during the outbreak. Due to the challenges associated with the evolving COVID-19 pandemic, transportation is more crucial than ever to address daily needs and safely move essential workers and service providers to destinations. Consequently, further studies are necessary to document the best transportation practices with positive and healthy outcomes during this global crisis. The objective of this paper is to document notable approaches used by shared mobility organizations amid the COVID-19 pandemic. This review is intended to assist public transportation providers in mitigating evolving risks associated with COVID-19 and future outbreaks. An online search of available documents, data, and studies is used to summarize findings. Shared mobility companies have employed numerous novel strategies in response to the global crisis, including using ultraviolet (UV) light and electrostatic sprayers for deeper cleaning of vehicles and facilities, and installing anti-microbial film protection to continuously guard frequently touched areas from bacteria and germs in buses and trains. For public awareness and protection, agencies have also used floor decals and signage at stations and urged in vehicles face coverings. Other approaches include adding delivery services for ride-hailing companies, using real-time information to address service issues and crowdedness, starting rebate program to regain customers, and closing large sections of roadways and making them strictly available for micromobility users.

Edward Clay, California State Polytechnic University, Pomona
Bengang Li, California State Polytechnic University, Pomona
Yongping Zhang, California State Polytechnic University, Pomona
Mankirat Singh, California State Polytechnic University, Pomona
Wen Cheng, California State Polytechnic University, Pomona

COVID-19 has been declared a pandemic by the World Health Organization and in turn, several policies have been established to limit the spread of the virus within the general population. Many countries have enacted a Stay at Home (SAH) order, or something to this extent, which restricts travel to only what is considered essential. With restricted vehicular traffic, there has been a noticeable decrease in traffic volume. To determine the effect that COVID-19 has on overall traffic volume due to the implementation of the SAH order as well as other factors such as weather and the daily confirmed new cases of Covid-19, a study ranging three months focusing on three different freeways in Los Angeles County has been conducted using a temporal analysis via Integrated Nested Laplace Approximation (INLA) method.             With the intent to understand the effect COVID-19 and the temporal trend have on traffic volume, this paper aims to compare three different models under the INLA method. A generalized linear model with fixed parameters, with random intercepts, and both random intercepts and parameters were generated in an effort to explore these effects. Through the use of several evaluation criteria, namely the Deviance Information Criteria, Watanabe-Akaike Information Criteria, and Los Pseudo Marginal Likelihood, it is clear that using both random intercepts and random parameters alongside a linear model produces the most effective model by a significant amount within each of the criteria mentioned.

Alican Karaer, Florida State University
Mehmet Ulak, Universiteit Twente
Tarek Abichou, Florida A&M University-Florida State University College of Engineering
Reza Arghandeh, Hogskulen pa Vestlandet
Eren Ozguven, Florida A&M University-Florida State University College of Engineering

Vegetative debris has been known to be one of the most significant causes of hurricane-induced damage on property and infrastructure such as roadways. Hurricane Michael (2018), for example, crushed the Florida’s Panhandle creating more than 16 million cubic yards of debris with very strong winds. As such, identification and collection of vegetative debris is an integral part of pre-hurricane planning and post-hurricane response. This study evaluates the volume of vegetative debris collected by the City of Tallahassee, Florida after Hurricane Michael at the U.S. Census block group level. This is conducted by utilizing remote sensing indices to generalize the vegetation and other land cover/use characteristics based on images taken before and after the hurricane. Four different regression models are developed and compared using these vegetation indices (VI) as well as other variables that represent the hurricane impact (maximum wind speeds and the distances to the hurricane track) and the socioeconomics of the population impacted (population, age, race and income). Findings suggest that vegetation and other land use characteristics had more impact on the hurricane-induced vegetative debris compared to other factors. This approach can help emergency responders locate areas that are prone to generate high amount of vegetative debris to be collected.