Exploring Influential Factors of Free-Floating Bike-Sharing Usage Frequency before and after COVID-19

In order to better understand the impact of COVID-19 on the free-floating bike-sharing (FFBS) system and the potential role of FFBS played in the pandemic period, this study explores the impact mechanism of travel frequency of FFBS users before and after the pandemic. Using the online questionnaire collected in Nanjing, China, we first analyze the changes of travel frequency, travel distance, and travel duration in these two periods. Then, two ordered logit models are applied to explore the contributing factors of the weekly trip frequency of FFBS users before and after COVID-19. The results show that: (1) While the overall travel duration and travel distance of FFBS users decreased after the pandemic, the trip frequency of FFBS users increased as the travel duration increased. (2) Since COVID-19, attitude perception variables of the comfort level and the low travel price have had significantly positive impacts on the weekly trip frequency of FFBS users. (3) Respondents who use FFBS as a substitution for public transport are more likely to travel frequently in a week after the outbreak of COVID-19. (4) The travel time in off-peak hours of working days, weekends, and holidays has a significantly positive correlation with the trip frequency of FFBS users. Finally, several relevant policy recommendations and management strategies are proposed for the operation and development of FFBS during the similar disruptive public health crisis.

Tracking the Transit Divide: A Multilevel Modelling Approach of Urban Inequalities and Train Ridership Disparities in Chicago

Using a multilevel modelling approach, this study investigates the impact of urban inequalities on changes to rail ridership across Chicago's "L” stations during the pandemic, the mass vaccination rollout, and the full reopening of the city. Initially believed to have an equal impact, COVID-19 disproportionally impacted the ability of lower socioeconomic status (SES) neighbourhoods' to adhere to non-pharmaceutical interventions: working-from-home and social distancing. We find that "L” stations in predominately Black or African American and Hispanic or Latino neighbourhoods with high industrial land-use recorded the smallest behavioural change. The maintenance of higher public transport use at these stations is likely to have exacerbated existing health inequalities, worsening disparities in users' risk of exposure, infection rates, and mortality rates. This study also finds that the vaccination rollout and city reopening did not significantly increase the number of users at stations in higher vaccinated, higher private vehicle ownership neighbourhoods, even after a year into the pandemic. A better understanding of the spatial and socioeconomic determinants of changes in ridership behaviour is crucial for policymakers in adjusting service routes and frequencies that will sustain reliant neighbourhoods' access to essential services, and to encourage trips at stations which are the most impacted to revert the trend of declining public transport use.

Can Destination Cards Help to Shape Areas' Sustainability?

This paper examines whether destination cards can simultaneously serve tourists' needs and sustainability goals. It provides useful insights for tourism authorities and policymakers in designing a smart tourist card that meets the needs of tourists while preserving and supporting areas' wellbeing. Taking Thessaloniki city as a case study, a tourist survey, designed based on the key features of European destination cards, was implemented to identify needs and motivations. Interesting insight was revealed: tourists want to self-explore the city, are coming with their families, are history-lovers and gastronomy-keen, and are strongly willing to be provided with a destination card offering unlimited access to public transport. The latter reveals an opportunity for the city;the tourists are willing to use sustainable mobility options, which means that a base of sustainable travelling exists. The proposed Thessaloniki smart card can bring together tourists' needs with the city's sustainability goals;the development of tourist packages, including sustainable mobility provisions, walking-talking tours, and bike rentals, should be the backbone of the card. The next challenge for the city is to build a cooperation network to support this smart destination card implementation and promotion.

U.S. Public Transportation Ridership Analysis and Prediction based on COVID-19

In this paper, a research was conducted to analyse and predict the impacts of COVID-19 on public transportation ridership in the U.S. and 5 most populous cities of the U.S. (New York City, Los Angeles, Chicago, Houston, Philadelphia). The paper aims to exploit the correlation between COVID-19 and public transportation ridership in the U.S. and make the reasonable prediction by machine learning models, including ARIMA and Prophet, to help the local governments improve the rationality of their policy implementation. After correlation analyses, high level of significant and negative correlations between monthly growth rate of COVID-19 infections and monthly growth rate of public transportation ridership are decidedly validated in the total U.S., and New York City, Los Angeles, Chicago, Philadelphia, except Houston. To analyse the errors of Houston, we consult the literature and made a discussion of Influencing factors. We find that the level of public transportation in quantity and utilization is terribly low in Houston. In addition, the factors, such as the lack of planning law and estimation of urban expressways, the high level of citizens' dependence on private cars and pride of owning cars play a considerable roll in the errors. And the impacts can be predicted to a certain extent through two forecasting models (ARIMA and Prophet), although the precision of our models is not enough to make a precise forecast due to the limitations of model tuning and model design. According to the comparison of the two models, ARIMA models' forecasting accuracy is between 6% and 10%, and Prophet's forecasting accuracy is between 8%-12%, depending on the city. Since the insufficient stationarity, periodicity, seasonality of time series, the Prophet models are hard be more refined. © 2023 SPIE.

Three's a crowd? Examining evolving public transit crowding standards amidst the COVID-19 pandemic

The COVID-19 pandemic dramatically affected public transit systems around the globe. Because transit systems typically move many people closely together on buses and trains, public health guidance demanded that riders should keep a distance of about two meters to others changed the definition of "crowding” on transit in 2020. Accordingly, this research examines how U.S. public transit agencies responded to public health guidance that directly conflicted with their business model. To do this, we examined published crowding standards before the COVID-19 pandemic for a representative sample of 200 transit systems, including whether they started or changed their published standards during the pandemic, as well as the reasons whether agencies publicize such standards at all. We present both descriptive statistics and regression model results to shed light on the factors associated with agency crowding standards. We find that 56% of the agencies surveyed published crowding standards before the pandemic, while only 46% published COVID-19-specific crowding standards. Regression analyses suggest that larger agencies were more likely to publish crowding standards before and during the COVID-19 pandemic, likely because they are more apt to experience crowding. Pandemic-specific crowding standards, by contrast, were associated with a more complex set of factors. We conclude that the relative lack of pandemic standards reflects the uncertainty and fluidity of the public health crisis, inconsistent and at times conflicting with the guidance from public health officials, and, in the U.S., a lack national or transit industry consensus on appropriate crowding standards during the first year of the pandemic.

Comparing COVID spread to mask-wearing rates in public transportation using AI detection

The spread of COVID-19 has resulted in several changes worldwide. In particular, border closures and economic stagnation have significantly affected societies. Although the implementation of preventive measures has improved the pandemic scenario in several countries, the effectiveness of vaccines has decreased with the emergence of mutant viruses. With this background, the use of masks is considered the best method for preventing the spread of the virus. Notably, public transportation is closely related to socioeconomic activities, and the spread of infectious diseases is more likely in closed, dense, and congested areas. Moreover, the probability of infection during public transportation also depends on the proportion of commuters wearing masks. Based on the closed-circuit television footage of various public transportation spaces, the number of mask wearers can be analysed using artificial intelligence deep learning, and the probability of COVID-19 spread can be predicted by determining the proportion of mask wearers among the commuters. With this background, in this study, the importance of masks in controlling the spread of the virus is confirmed. In conclusion, appropriate measures can be implemented by determining the probability of infection according to the mask-wearing rate in public transportation spaces. © 2023 ICE Publishing: All rights reserved.

Investigating changes in longitudinal associations between declining bus ridership, bus service, and neighborhood characteristics

Amid the recent transit ridership decline, gaining an understanding of the factors affecting ridership becomes crucial for transit agencies to utilize limited resources effectively. I use generalized linear multilevel negative binomial models to investigate the longitudinal relationship and changes in the associations between neigh-borhood-level bus ridership and a series of socio-economic and bus service factors in Philadelphia between 2014 and 2018. Data come from passenger boarding at bus stops in Philadelphia. Results show that the associations between bus ridership, population and the number of jobs, and the percent of zero-car households are positive, but weakened over time. The associations between ridership and bus service supply are inelastic. The findings have implications on transit agencies' resource allocation and service adjustments as they recover from the ridership and revenue losses during the COVID-19 pandemic while facing competition from new travel options such as Uber and Lyft.

The impact of Covid-19 pandemic on public transit use: Case study of Konya city

Since March 2019, Turkey has been enforcing various measures on the policies based on the trends in the COVID-19 cases. To restrain the spread of virus, policies limiting the mobility of people (i.e. lock downs, remote working and travel bans) were applied as in many other countries. Furthermore, social distancing calls for health concern directly caused a major reduction in public transit (PT) use. However, economic activities and new normal conditions required return of a part of the commute travels, which brought the issue of use of PT modes. This study focuses on the comparison of the PT mobility during the month of April in the 2019 (pre-pandemic), in 2020 during restrictions and in 2021 under new normal condition using the Smart Card (SC) data in Konya, Turkey. Monthly, daily and hourly distribution of ridership patterns are compared as well as usage patterns and characteristics of different bus lines are examined in detail. The results suggested that during the restrictions, the ridership was about one eight of the pre-pandemic periods, while it increased to 2.5 million ridership in 2021 which is still very low. Daily ridership in 2020 showed no PT mobility due to lockdowns, while during weekdays, hourly ridership distributions were changed parallel to changes in the work/education activity schedules. Evaluation of the bus lines having highest ridership in 2019 with 2020 and 2021 showed some of the bus lines were cancelled during the pandemic and routes/frequencies changed. The results showed the importance of PT management during pandemic which is very challenging due to economic loss and fear of infection by public. However, it should be emphasized the importance of continuation of public transportation in terms of accessibility and equity for all. © 2023 The Authors. Published by ELSEVIER B.V.

A New Big Data Approach to Understanding General Traffic Impacts on Bus Passenger Delays

This paper presents a new method to quantify the potential user time savings if the urban bus is given preferential treatment, changing from mixed traffic to an exclusive bus lane, using a big data approach. The main advantage of the proposal is the use of the high amount of information that is automatically collected by sensors and management systems in many different situations with a high degree of spatial and temporal detail. These data allow ready adjustment of calculations to the specific reality measured in each case. In this way, we propose a novel methodology of general application to estimate the potential passenger savings instead of using simulation or analytical methods already present in the literature. For that purpose, in the first place, a travel time prediction model per vehicle trip has been developed. It has been calibrated and validated with a historical series of observations in real-world situations. This model is based on multiple linear regression. The estimated bus delay is obtained by comparing the estimated bus travel time with the bus travel time under free-flow conditions. Finally, estimated bus passenger time savings would be obtained if an exclusive bus lane had been implemented. An estimation of the passenger's route in each vehicle trip is considered to avoid average value simplifications in this calculation. A case study is conducted in A Coruña, Spain, to prove the methodology's applicability. The results showed that 18.7% of the analyzed bus trips underwent a delay exceeding 3 min in a 2,448 m long corridor, and more than 33,000 h per year could have been saved with an exclusive bus lane. Understanding the impact of different factors on transit and the benefits of a priority bus system on passengers can help city councils and transit agencies to know which investments to prioritize given their limited budget.

Public Preferences of Using Public Transport During the Covid-19 Pandemic and the Implementation of Level 3 Community Activity Restrictions Policy in Padang City

The Covid-19 pandemic has had a significant influence on the usage of public transport, in this case, a decrease in the use of public transport services. The decreased use of public transport was because the government created a new social system called social distance, followed by the Enforcement of Restrictions on Community Activities (PPKM). This study aims to perform utility and probability modelling and determine the most significant factor in influencing the preferences of the community in using public transport during the Covid-19 pandemic and the implementation of the PPKM level 3 policy in Padang City. Primary data was collected using online and offline questionnaires. The questionnaire used the expressed preference technique on eight alternatives with three attributes and two levels. The attributes include the difference in travel time, the cost of travel, and health protocols. The best model was Y(AUK-AUO) = 0.656 + (- 0.038) X1 + (- 0.056) X2 + 0.644 X3 with R-Square = 0.174 referring to all characteristics as a whole influence 17.4% of Y. According to the sensitivity test results, the travel cost difference is the most influencing variable in people's decisions to use traditional public transportation during the covid-19 pandemic and the implementation of the PPKM level 3 policy.

Research on Affective Interaction in Mini Public Transport Based on IPA-FMEA

In the promotion of sustainable modes of transport, especially public transport, reasonable failure risk assessment at the critical moment in the process of service provider touch with users can improve the service quality to a certain extent. This study presents a product service touch point evaluation approach based on the importance–performance analysis (IPA) of user and failure mode and effect analysis (FMEA). Firstly, the authors capture service product service touch points in the process of user interaction with the product by observing the user behavior in a speculative design experiment, and perform the correlation analysis of the service product service touch point. Second, the authors use the IPA analysis method to evaluate and classify the product service touch points and identify the key product service touch points. Thirdly, the authors propose to analyze the failure of key product service touch points based on user-perceived affective interaction and clarify the priority of each key touch point. Finally, reluctant interpersonal communication, as the key failure caused by high risk, is derived according to the evaluation report, which leads to establishing new product service touch points and improving the overall user experience to promote sustainable transports with similar forms and characteristics.

A Comprehensive Analysis of Clustering Public Utility Bus Passenger's Behavior during the COVID-19 Pandemic: Utilization of Machine Learning with Metaheuristic Algorithm

Public utility bus (PUB) systems and passenger behaviors drastically changed during the COVID-19 pandemic. This study assessed the clustered behavior of 505 PUB passengers using feature selection, K-means clustering, and particle swarm optimization (PSO). The wrapper method was seen to be the best among the six feature selection techniques through recursive feature selection with a 90% training set and a 10% testing set. It was revealed that this technique produced 26 optimal feature subsets. These features were then fed into K-means clustering and PSO to find PUB passengers' clusters. The algorithm was tested using 12 different parameter settings to find the best outcome. As a result, the optimal parameter combination produced 23 clusters. Utilizing the Pareto analysis, the study only considered the vital clusters. Specifically, five vital clusters were found to have comprehensive similarities in demographics and feature responses. The PUB stakeholders could use the cluster findings as a benchmark to improve the current system.

Effects of COVID-19 on transportation services and travel behaviour in Malaysia

Movement restrictions implementation due the COVID-19 pandemic affected people's mobility and and transportation sectors globally. Tourism, retail, transportation, and hospitality also suffered significant consequences that indirectly impacted a country's economic stability. Hence, this paper presents the Malaysian experience with the effects of the pandemic on transportation services and travel behaviour. Online sources, secondary data and a questionnaire survey were employed to collect the relevant data. These data were further analysed and presented in this paper. In general, transportation sectors involving air, rail and public bus showed decreased demand and services. A severe drop in services was observed in air transportation which is more than 97%. Most people prefer not to use public transportation to avoid the risk of COVID-19 infection. Noticeable changes in travel behaviour and mode preference during and pre-pandemic are observed, particularly on trips related to work/school. Key pandemic factors concerning transportation were fear of infection and social distance. Hence, most people prefer to use a private vehicle. The changes in transportation service and travel behaviour result in severe losses in the transportation sector in Malaysia.

No Place for Hate California: A Model for Community-Centered Policymaking

These are just three of more than 11,000 reports of hate against Asian Americans and Pacific Islanders (AAPIs) shared with the Stop AAPI Hate coalition during the first two years of the COVID-19 pandemic. Many more acts continue to go unreported, making the actual number much higher-potentially in the millions. Reports of anti-AAPI hate come from all fifty states and the District of Columbia, with nearly 40 percent from California. In response to the rise in hate against AAPI communities, Stop AAPI Hate introduced No Place for Hate California, a package of first-in-the-nation, state-level policy proposals. Together, these proposals take a gender-based, public health, and civil rights approach to addressing the racialized and sexualized verbal harassment experienced by AAPIs (especially AAPI women) in public, which comprise a majority of the reports submitted to Stop AAPI Hate. Stop AAPI Hate partnered with state legislators and mobilized a coalition of over fifty community-based organizations.

Shared E-Scooter Trajectory Analysis During the COVID-19 Pandemic in Austin, Texas.

By March of 2020, most cities worldwide had enacted stay-at-home public health orders to slow the spread of COVID-19. Restrictions on nonessential travel had extensive impacts across the transportation sector in the short term. This study explores the effects of COVID-19 on shared e-scooters by analyzing route trajectory data in the pre- and during-pandemic periods in Austin, TX, from a single provider. Although total shared e-scooter trips decreased during the pandemic, partially owing to vendors pulling out of the market, this study found average trip length increased, and temporal patterns of this mode did not meaningfully change. A count model of average daily trips by road segment found more trips on segments with sidewalks and bus stops during the pandemic than beforehand. More trips were observed on roads with lower vehicle miles traveled and fewer lanes, which might suggest more cautious travel behavior since there were fewer trips in residential neighborhoods. Stay-at-home orders and vendor e-scooter rebalancing operations inherently influence and can limit trip demand, but the unique trajectory data set and analysis provide cities with information on the road design preferences of vulnerable road users.

Quantitative Framework for Establishing Low-Risk Inter-District Travel Corridors During COVID-19.

Aspirations to slow down the spread of novel Coronavirus (COVID-19) resulted in unprecedented restrictions on personal and work-related travels in various nations across the globe in 2020. As a consequence, economic activities within and across the countries were almost halted. As restrictions loosen and cities start to resume public and private transport to revamp the economy, it becomes critical to assess the commuters' travel-related risk in light of the ongoing pandemic. The paper develops a generalizable quantitative framework to evaluate the commute-related risk arising from inter-district and intra-district travel by combining nonparametric data envelopment analysis for vulnerability assessment with transportation network analysis. It demonstrates the application of the proposed model for establishing travel corridors within and across Gujarat and Maharashtra, two Indian states that have reported many COVID-19 cases since early April 2020. The findings suggest that establishing travel corridors between a pair of districts solely based on the health vulnerability indices of the origin and destination discards the en-route travel risks from the prevalent pandemic, underestimating the threat. For example, while the resultant of social and health vulnerabilities of Narmada and Vadodara districts is relatively moderate, the en-route travel risk exacerbates the overall travel risk of travel between them. The study provides a quantitative framework to identify the alternate path with the least risk and hence establish low-risk travel corridors within and across states while accounting for social and health vulnerabilities in addition to transit-time related risks.

Mitigating Increased Driving after the COVID-19 Pandemic: An Analysis on Mode Share, Travel Demand, and Public Transport Capacity.

Reduced transit capacity to accommodate social distancing during the COVID-19 pandemic was a sudden constraint that along with a large reduction in total travel volume and a shift in activity patterns contributed to abrupt changes in transportation mode shares across cities worldwide. There are major concerns that as the total travel demand rises back toward prepandemic levels, the overall transport system capacity with transit constraints will be insufficient for the increasing demand. This paper uses city-level scenario analysis to examine the potential increase in post-COVID-19 car use and the feasibility of shifting to active transportation, based on prepandemic mode shares and varying levels of reduction in transit capacity. An application of the analysis to a sample of cities in Europe and North America is presented. Mitigating an increase in driving requires a substantial increase in active transportation mode share, particularly in cities with high pre-COVID-19 transit ridership; however, such a shift may be possible based on the high percentage of short-distance motorized trips. The results highlight the importance of making active transportation attractive and reinforce the value of multimodal transportation systems as a strategy for urban resilience. This paper provides a strategic planning tool for policy makers facing challenging transportation system decisions in the aftermath of the COVID-19 pandemic.

Equity Implications of Ride-Hail Travel during COVID-19 in California.

COVID-19 has shocked every system in the U.S., including transportation. In the first months of the pandemic, driving and transit use fell far below normal levels. Yet people still need to travel for essential purposes like medical appointments, buying groceries, and-for those who cannot work from home-to work. For some, the pandemic may exacerbate extant travel challenges as transit agencies reduce service hours and frequency. As travelers reevaluate modal options, it remains unclear how one mode-ride-hailing-fits into the transportation landscape during COVID-19. In particular, how does the number of ride-hail trips vary across neighborhood characteristics before versus during the pandemic? And how do patterns of essential trips pre-pandemic compare with those during COVID-19? To answer these questions, we analyzed aggregated Uber trip data before and during the first two months of the COVID-19 pandemic across four regions in California. We find that during these first months, ride-hail trips fell at levels commensurate with transit (82%), while trips serving identified essential destinations fell by less (62%). Changes in ride-hail use were unevenly distributed across neighborhoods, with higher-income areas and those with more transit commuters and higher shares of zero-car households showing steeper declines in the number of trips made during the pandemic. Conversely, neighborhoods with more older (aged 45+) residents, and a greater proportion of Black, Hispanic/Latinx, and Asian residents still appear to rely more on ride-hail during the pandemic compared with other neighborhoods. These findings further underscore the need for cities to invest in robust and redundant transportation systems to create a resilient mobility network.

Negligible risk of surface transmission of SARS-CoV-2 in public transportation.

BACKGROUND: Exposure to pathogens in public transport systems is a common means of spreading infection, mainly by inhaling aerosol or droplets from infected individuals. Such particles also contaminate surfaces, creating a potential surface-transmission pathway. METHODS: A fast acoustic biosensor with an antifouling nano-coating was introduced to detect SARS-CoV-2 on exposed surfaces in the Prague Public Transport System. Samples were measured directly without pre-treatment. Results with the sensor gave excellent agreement with parallel qRT-PCR measurements on 482 surface samples taken from actively used trams, buses, metro trains, and platforms between 7-9 April 2021, in the middle of the lineage Alpha SARS-CoV-2 epidemic wave when 1 in 240 people were COVID-19 positive in Prague. RESULTS: Only ten of the 482 surface swabs produced positive results and none of them contained virus particles capable of replication, indicating that positive samples contained inactive virus particles and/or fragments. Measurements of the rate of decay of SARS-CoV-2 on frequently touched surface materials showed that the virus did not remain viable longer than 1-4 hours. The rate of inactivation was the fastest on rubber handrails in metro escalators and the slowest on hard-plastic seats, window glasses, and stainless-steel grab rails. As a result of this study, Prague Public Transport Systems revised their cleaning protocols and the lengths of parking times during the pandemic. CONCLUSIONS: Our findings suggest that surface transmission played no or negligible role in spreading SARS-CoV-2 in Prague. The results also demonstrate the potential of the new biosensor to serve as a complementary screening tool in epidemic monitoring and prognosis.

Analysis of Road Transport Response to COVID-19 Pandemic in Nigeria and its Policy Implications.

The COVID-19 pandemic presents a serious global health challenge to humanity in recent times. It has caused fundamental disruptions to the global transportation system, supply chains, and trade. The impact on the transport sector resulting from lockdowns has led to huge losses in revenue. At the moment there are limited studies of the road transport sector response to the COVID-19 pandemic. This paper fills this gap using Nigeria as a case study area. A mixed method involving both qualitative and quantitative research was employed. Principal Component Analysis and Multiple Criteria Analysis were used to analyze the data. The results suggest that road transport operators strongly (90.7%) believe that 51 adopted new technologies/innovations, processes, and procedures will keep them and passengers safe from the COVID-19 pandemic in Nigeria. A breakdown shows that observing the lockdown directive is perceived by road transport operators as the most effective response to the pandemic. The breakdown continues in descending order thus: COVID-19 safety protocols, environmental sanitation, and promotion of hygiene, information technology, facemask, and social distancing. Others are public enlightenment, palliative, inclusion, and mass media. This indicates that non-pharmaceutical measures are very effective in the fight against the pandemic. This finding leverages support for the application of non-pharmaceutical guidelines in containing the COVID-19 pandemic in Nigeria.