Passenger Perceptions, Information Preferences, and Usability of Crowding Visualizations on Public Displays in Transit Stations and Vehicles

Large crowds in public transit stations and vehicles introduce obstacles for wayfinding, hygiene, and physical distancing. Public displays that currently provide on-site transit information could also provide critical crowdedness information. Therefore, we examined people's crowd perceptions and information preferences before and during the pandemic, and designs for visualizing crowdedness to passengers. We first report survey results with public transit users (n = 303), including the usability results of three crowdedness visualization concepts. Then, we present two animated crowd simulations on public displays that we evaluated in a field study (n = 44). We found that passengers react very positively to crowding information, especially before boarding a vehicle. Visualizing the exact physical spaces occupied on transit vehicles was most useful for avoiding crowded areas. However, visualizing the overall fullness of vehicles was the easiest to understand. We discuss design implications for communicating crowding information to support decision-making and promote a sense of safety. © 2023 ACM.

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.

Optimization of multistage timeliness transit consolidation problem using adaptive-weighted genetic algorithm.

Cargo consolidation is becoming a crucial part of international transportation and changing the customer consumption patterns of the international community. Poor connections between different operations and the delay of international express have motivated sellers and logistics organizers to put timeliness first in international multimodal transport, especially during the COVID-19 epidemic. However, for cargo with small quality and multiple batches, designing an efficient consolidation network presents a set of unique challenges, including the coupling of multiple origins and destinations (ODs), and fully utilizing the capacity of the container. We defined a multistage timeliness transit consolidation problem to decouple the multiple ODs of the logistics resource. By solving this problem, we can increase the connectivity between different phases and make full use of the container. To make this systematic multistage transit consolidation more flexible, we proposed a two-stage adaptive-weighted genetic algorithm that mainly focuses on the edge area of the Pareto front space and the diversity of the population. Computational experiments indicate that the correlation between parameters has certain regular trends, and appropriate parameter settings can lead to more satisfactory results. We also confirm that the pandemic has a giant influence on the market share of different transportation modes. Moreover, the comparison with other approaches demonstrates the feasibility and effectiveness of the proposed method.

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.

Understanding post-pandemic metro commuting ridership by considering the built environment: A quasi-natural experiment in Wuhan, China

The COVID-19 pandemic has had a significant impact on metro commuting ridership. However, the exact magnitude and spatial and temporal characteristics of the impact remain unclear. In this study, we explored the impact of the COVID-19 outbreak on metro commuting ridership in Wuhan, where the novel virus was first reported. The results of interrupted time-series (ITS) analysis showed that metro commuting ridership sharply dropped in the short term under the impact of the outbreak in the epicenter, rebounded rapidly as the pandemic eased, and it returned to pre-pandemic levels in six months. Furthermore, there was a noticeable spatial heterogeneity in the rebound. Urban centers, especially employment centers, recovered faster than other areas. In addition, the number of residents, number of bus stops, number of enterprises around a metro station and being a transfer station had a positive effect on metro ridership, while street length, number of restaurants, and number of metro exits had a negative effect. These findings may help local governments and metro managers develop sustainable metro operations and infection prevention policies to better cope with the impact of the pandemic and beyond.

Potential of Bike Sharing During Disruptive Public Health Crises: A Review of COVID-19 Impacts

With public transport (PT) continuing to be negatively affected by the coronavirus pandemic and private car usage surging, alternative modes need to be considered. In this study, we review the available evidence (from academic and gray literature sources) on the performance of bike sharing systems (BSSs) during COVID-19 around the world, with the goal of assessing their potential contribution to improving the resilience of transport systems during pandemics and similar disruptive events. We found BSS usage followed a decrease-rebound pattern, with BSSs overall sustaining lower ridership declines and faster recoveries compared with PT. During lockdowns especially, the average duration of BSS trips increased, following a rise in casual users and leisure trips, while commuting trips decreased. Evidence has also been found for a possible modal shift from some PT users to BSSs, with a decline in the share of multimodal trips conducted between PT and BSSs. Bike sharing is perceived as safer than other shared modes (e.g., PT, taxis, and ride-hailing/sharing) but as having a higher infection risk than personal modes (e.g., private car, walking, and personal bike). Moreover, the BSS was an important transport alternative for essential workers, with several operators providing waivers especially to healthcare staff, leading to ridership increases near healthcare facilities and in deprived neighborhoods. Findings from this research support policies for promoting bike sharing, namely through fee reductions, system expansions, and symbiotic integration with PT, as BSSs can increase the sustainability and resilience of transport systems during disruptive public health events like COVID-19.

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.

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.

Non-Stationary Time Series Model for Station-Based Subway Ridership During COVID-19 Pandemic: Case Study of New York City.

The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City (NYC), U.S. Understanding the temporal pattern of subway ridership through statistical models is crucial during such shocks. However, many existing statistical frameworks may not be a good fit to analyze the ridership data sets during the pandemic, since some of the modeling assumptions might be violated during this time. In this paper, utilizing change point detection procedures, a piecewise stationary time series model is proposed to capture the nonstationary structure of subway ridership. Specifically, the model consists of several independent station based autoregressive integrated moving average (ARIMA) models concatenated together at certain time points. Further, data-driven algorithms are utilized to detect the changes of ridership patterns as well as to estimate the model parameters before and during the COVID-19 pandemic. The data sets of focus are daily ridership of subway stations in NYC for randomly selected stations. Fitting the proposed model to these data sets enhances understanding of ridership changes during external shocks, both in relation to mean (average) changes and the temporal correlations.

Application of Protection Motivation Theory to Quantify the Impact of Pandemic Fear on Anticipated Postpandemic Transit Usage.

The COVID-19 pandemic had an unprecedented impact on transit usage, primarily owing to the fear of infection. Social distancing measures, moreover, could alter habitual travel behavior, for example, using transit for commuting. This study explored the relationships among pandemic fear, the adoption of protective measures, changes in travel behavior, and anticipated transit usage in the post-COVID era, through the lens of protection motivation theory. Data containing multidimensional attitudinal responses about transit usage at several pandemic stages were utilized for the investigation. They were collected through a web-based survey in the Greater Toronto Area, Canada. Two structural equation models were estimated to examine the factors influencing anticipated postpandemic transit usage behavior. The results revealed that people taking relatively higher protective measures were comfortable taking a cautious approach such as complying with transit safety policies (TSP) and getting vaccinated to make transit trips. However, the intention to use transit on vaccine availability was found to be lower than in the case of TSP implementation. Conversely, those who were uncomfortable taking transit with caution and who were inclined to avoid travel and rely on e-shopping were most unlikely to return to transit in the future. A similar finding was observed for females, those with vehicle access, and middle-income individuals. However, frequent transit users during the pre-COVID period were more likely to continue to use transit after the pandemic. The study's findings also indicated that some travelers might be avoiding transit specifically because of the pandemic, implying they are likely to return in the future.

Station-Level Effects of the COVID-19 Pandemic on Subway Ridership in the Seoul Metropolitan Area.

This paper investigates the station-level impacts of the coronavirus disease (COVID-19) pandemic on subway ridership in the Seoul Metropolitan Area. Spatial econometric models are constructed to examine the association between ridership reduction caused by the pandemic and station-level characteristics during the pandemic years 2020 and 2021. The results reveal unequal effects on station-level ridership, based on the pandemic waves, the demographics, and the economic features of pedestrian catchment areas. First, the subway system was severely disrupted by the pandemic, with significant decreases in ridership-by about 27% for each of the pandemic years-compared with the pre-pandemic year (2019). Second, the ridership reduction was sensitive to the three waves in 2020 and responded accordingly; however, it became less sensitive to the waves in 2021, indicating that subway usage was less responsive to pandemic waves during the second year of the pandemic. Third, pedestrian catchment areas with higher numbers of younger residents (in their 20s) and older residents (65 years and older), those with more businesses requiring face-to-face interactions with consumers, and stations located in the employment centers were hit the hardest in ridership reduction caused by the pandemic.

Bioprotection of Transportation and Facilities from SARS-CoV-2 (COVID-19).

The recent COVID-19 pandemic has led to a nearly world-wide shelter-in-place strategy. This raises several natural concerns about the safe relaxing of current restrictions. This article focuses on the design and operation of heating ventilation and air conditioning (HVAC) systems in the context of transportation. Do HVAC systems have a role in limiting viral spread? During shelter-in-place, can the HVAC system in a dwelling or a vehicle help limit spread of the virus? After the shelter-in-place strategy ends, can typical workplace and transportation HVAC systems limit spread of the virus? This article directly addresses these and other questions. In addition, it also summarizes simplifying assumptions needed to make meaningful predictions. This article derives new results using transform methods first given in Ginsberg and Bui. These new results describe viral spread through an HVAC system and estimate the aggregate dose of virus inhaled by an uninfected building or vehicle occupant when an infected occupant is present within the same building or vehicle. Central to these results is the derivation of a quantity called the "protection factor"-a term-of-art borrowed from the design of gas masks. Older results that rely on numerical approximations to these differential equations have long been lab validated. This article gives the exact solutions in fixed infrastructure for the first time. These solutions, therefore, retain the same lab validation of the older methods of approximation. Further, these exact solutions yield valuable insights into HVAC systems used in transportation.

Continuous physiological signal measurement over 24-hour periods to assess the impact of work-related stress and workplace violence.

Work-related stress has long been recognized as an essential factor affecting employees' health and wellbeing. Repeated exposure to acute occupational stressors puts workers at high risk for depression, obesity, hypertension, and early death. Assessment of the effects of acute stress on workers' wellbeing usually relies on subjective self-reports, questionnaires, or measuring biometric and biochemical markers in long-cycle time intervals. This study aimed to develop and validate the use of a multiparameter wearable armband for continuous non-invasive monitoring of physiological states. Two worker populations were monitored 24 h/day: six loggers for one day and six ICU nurses working 12-hr shifts for one week. Stress responses in nurses were highly correlated with changes in heart rate variability (HRV) and pulse transit time (PTT). A rise in the low-to high-frequency (LF/LH) ratio in HRV was also coincident with stress responses. HRV on workdays decreased compared to non-work days, and PTT also exhibited a persistent decrease reflecting increased blood pressure. Compared to loggers, nurses were involved in high-intensity work activities 45% more often but were less active on non-work days. The wearable technology was well accepted by all worker participants and yielded high signal quality, critical factors for long-term non-invasive occupational health monitoring.

Effectiveness Analysis of Public Transit Pandemic Prevention Strategy Considering Traveler Risk Perception

Since the outbreak of COVID-19, there have been hundreds of millions of confirmed cases in the world, and people can strongly perceive the risk of infection with the virus in their daily lives, which has seriously affected people's life and travel, thus hindering the development of all sectors of society, especially the transportation sector. Taking China as an example, since the outbreak of the pandemic, China's overall public transportation passenger volume has decreased by about 37%, seriously affecting the normal running of the public transit. Therefore, the ways of ensuring the normal running of the public transport system during the pandemic has become the focus of this paper. In order to solve this problem, this paper constructed a SEM model based on pandemic risk perception, analyzed the impact of public transit pandemic prevention strategies (TPS) on risk perception (RP) and travel mode use according to the personal trip survey data in Harbin, China during the pandemic. The results showed that people's risk perception had a significant negative impact on car usage and transit usage. In other words, people's risk perception of virus infection had a great impact on travel, especially on the use of public transit. The transit pandemic prevention strategy had a significant negative impact on risk perception, and had a significant positive impact on people's use of transit. This showed that in the current pandemic outbreak period, the transit pandemic prevention strategy proposed by the Harbin authorities cannot effectively reduce transit usage, and can provide proven and effective transit pandemic prevention strategies. This provided an important support for ensuring the normal running of the public transit system and guiding the sustainable development of public transit during the outbreak of the pandemic.

The one meter rail station Implementing physical distanciation at Paris Nord and North Shenzen railway stations

Since the pandemy, the carrying capacity of major railway stations has been restricted to respect the distance. In Asian metropolises, a tech-driven flow management system reinforces the surveillance of travellers and help the maintenance of a top affluence in station's buildings. In France, an attempt is made to space out travelers with less intrusive processes, such as signage and boarding protocol adaptation. A comparison between these two responses leads us to question what physical distancing does, here and there, to the practices and places of transit and to the economic injonction to densify flows in and around stations until now. Articulating the approaches of crowding science and health regulation in transit environments, the article presents a transcontinental comparison, and then questions the status of major station as post pandemic urbanism showroom.

Influence of ride-sourcing on the public transport demand among university students in the Metropolitan Area of Recife in the Covid-19 pandemic context

This article analyzes the influence of ride-sourcing in reducing demand on public transportation systems among university students in the Metropolitan Region of Recife before and during the COVID-19 pandemic. The literature points out that, depending on the local context, the relationship between public transport and ride-sourcing can be one of competition or complementation. Because the pandemic's reality has had a significant impact on urban mobility, we sought to comprehend its implications in this context. The model chosen was logistic regression. A questionnaire via the web obtained 404 valid interviews. The results indicate that there was a reduction in the use of both modes during the pandemic, with the public system being considerably higher. The main reason for the use of public transport shifted from study to work, while in ride-sourcing it was no longer leisure. Higher education, the availability of a driving license, and higher income reduce the chances of using public transportation. Being formally employed and attending class personally positively influences the use of ride-sourcing. Public transportation was the most cited as a substitute in the case of unavailability of ride-sourcing and vice-versa. If ride-sharing transportation did not lead to the desired destination, public transportation was the preferred complementary mode. It was concluded that the effects of competition were stronger.

Sustainability, Scalability, and Resiliency of the Town of Innisfil Mobility-on-Demand Experiment: Preliminary Results, Analyses, and Lessons Learned

In 2017, the town of Innisfil, Ontario, launched Innisfil Transit in partnership with Uber, a transportation network company, to provide a subsidized on-demand public mobility service as an alternative to investing in a new fixed-route bus service. The performance of Innisfil Transit is documented in a 2021 Ryerson University report by Sweet, Mitra, and Benaroya, which shows greater cost effectiveness of the mobility provided over the proposed bus alternative. This paper expands on those findings by assessing Innisfil Transit with respect to sustainability, scalability, and resiliency. First, we quantify the energy and emissions of this program relative to traditional transit and driving alone across varying powertrains. We then characterize a conservative firstorder estimate of the percentage of US communities that fall within a similar spatial-demographic tier as Innisfil. Replicability also hinges on service cost and performance in comparison to average values for low-density transit in the US. Lastly, most transit agencies experienced a significant drop in demand (as much as 90%) with slowly rebounding ridership since the onset of the COVID-19 pandemic. The resiliency of the Innisfil program to the pressures induced by the pandemic is examined in comparison to other transit operations. The lessons learned across these three dimensions complement prior work to better understand the efficiency and sustainability of on-demand public mobility service for low-density communities like Innisfil.

Challenges and opportunities regarding the COVID-19 pandemic on urban mobility in Constanta, Romania

The global COVID-19 pandemic has had a significant impact on urban mobility in Constanta, Romania and other cities around the world. This has resulted in reduced travel, increased bicycle and pedestrian trips, fewer taxicabs, and shorter public transport hours. It has also generated increased challenges for local authorities regarding safety, hygiene, air quality, road network management and public trust in public transit systems. On the other hand, the pandemic presents many opportunities for cities to rethink their urban mobility plans and adopt more sustainable transport solutions. This paper will look at the challenges and opportunities posed by the pandemic on urban mobility in Constanta and the measures that can be taken to mitigate the negative impacts while taking advantage of the opportunities it provides. This paper aims to provide an overview of the perception of urban mobility in the city of Constanța, Romania. It draws upon the results of a survey conducted among citizens in the city, which asked respondents to assess their perception regarding the quality, availability, and efficiency of urban mobility services in their area. The results show that most respondents feel that the availability of urban mobility services is limited and that the quality and efficiency of these services are poor. Additionally, majority of respondents feel that the lack of public transportation options and the lack of bicycle lanes impede urban mobility in Constanța. The paper concludes by suggesting that the city should consider investing in improved public transportation options and bike lanes in order to increase its urban mobility. Additionally, public education surrounding urban mobility and transportation safety is recommended. [ FROM AUTHOR] Copyright of Technium Social Sciences Journal is the property of Technium Press Constanta and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Disparities in public transit accessibility and usage by people with mobility disabilities: An evaluation using high-resolution transit data

Many people with mobility disabilities (PwMD) rely on public transit to access crucial resources and maintain social interactions. However, they face higher barriers to accessing and using public transit, leading to disparities between people with and without mobility disabilities. In this paper, we use high-resolution public transit real-time vehicle data, passenger count data, and paratransit usage data from 2018 to 2021 to estimate and compare transit accessibility and usage of people with and without mobility disabilities. We find large disparities in powered and manual wheelchair users' accessibility relative to people without disabilities. The city center has the highest accessibility and ridership, as well as the highest disparities in accessibility. Our scenario analysis illustrates the impacts of sidewalks on accessibility disparities among the different groups. We also find that PwMD using fixed-route service are more sensitive to weather conditions and tend to ride transit in the middle of the day rather than during peak hours. Further, the spatial pattern of bus stop usage by PwMD is different than people without disabilities, suggesting their destination choices can be driven by access concerns. During the COVID-19 pandemic, accessibility disparities increased in 2020, and PwMD disproportionately avoided public transit during 2020 but used it disproportionately more during 2021 compared to riders without disabilities. This paper is the first to examine PwMD's transit experience with large high-resolution datasets and holistic analysis incorporating both accessibility and usage. The results fill in these imperative scientific gaps and provide valuable insights for future transit planning. © 2023 Elsevier Ltd

Sustainable Urban Freight for Energy-Efficient Smart Cities—Systematic Literature Review

Smart cities need energy-efficient and low-emission transportation for people and goods. Most studies focus on sustainable urban-transportation systems for passengers. Freight transportation in cities has increased significantly during the COVID-19 pandemic, leading to greenhouse gases emissions and negative externalities, such as traffic congestion. The purpose of this paper is to identify through a systematic literature review which innovations (hardware and software) applied by logistics service providers (LSPs) in sustainable urban freight (SUF) are suitable to support the transition to energy-efficient smart cities. We propose to classify the existing innovations in last-mile delivery for SUF into categories: (1) urban freight consolidation and/or trans-shipment;(2) the Consumer as a Service Provider (CaaSP);(3) choice of transportation modes. We introduce the concept of CaaSP as an innovative solution in last-mile delivery (LMD), where customers take over some transport operations with the use of smart technologies, and thus reduce the energy demand. We consider the modes of transportation, such as: drones, autonomous delivery robots, autonomous vehicles, cargo bikes (including e-cargo bikes, e-tricycles), electric vehicles (mainly vans), and combined passenger-and-cargo transportation rapid-transit systems. From the analyzed dataset, we find that energy-efficiency in smart cities can be improved by the consolidation of parcels in micro-depots, parcel lockers, and mobile depots. We analyze smart technologies (the Internet of things, big data, artificial intelligence, and digital twins), which enable energy efficiency by reducing the energy demand (fuel) of SUF, due to better operational planning and infrastructure sharing by logistics service providers. We propose a new IEE matrix as an actionable tool for the classification of innovations applied by LSPs in SUF, according to the level of their interconnectivity and energy efficiency. Additionally, this paper contributes to the theory by exploring possible future research directions for SUF in energy-efficient smart cities. © 2023 by the authors.