A Detection System for Comfortable Locations Based on Facial Expression Analysis While Riding Bicycles

In recent years, the use of bicycle as a healthy and economical means of transportation has been promoted worldwide. In addition, with the increase in bicycle commuting due to the COVID-19, the use of bicycles are attracting attention as a last-mile means of transportation in Mobility as a Service(MaaS). To help ensure a safe and comfortable ride using a smartphone mounted on a bicycle, this study focuses on analyzing facial expressions while riding to determine potential comfort along the route with the surrounding environment and to provide a map that users can explicitly feedback(FB) after riding. Combining the emotions of facial expressions while riding and FB, we annotate comfort to different locations. Afterwards, we verify the relationship between locations with high level of comfort based on the acquired data and the surrounding environment of those locations using Google Street View(GSV). © 2023 Owner/Author.

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.

Active Transportation, For Who?

Our world has drastically changed since COVID-19 hit in March. Half of the department stores anchoring retail districts have closed permanently. Hotels are projecting occupancies below 20 percent. Student housing, multi-family, and senior housing demand have experienced uncharacteristic, disproportional demand reductions. Increased work from home has reduced the need for office space. Major event venues are closed. Active transportation depends upon people's need to travel. Here, McCourt examines what happens when near-term events etch indelible change in how and what people find a need to travel for.

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.

Impacts of the COVID-19 Pandemic on Bikeshare Usage by Rider Membership Status Across Selected U.S. Cities

Bikesharing is a popular transportation mode for people to commute, for leisurely travel, or for recreation purposes in their daily tasks. Throughout 2020, the COVID-19 pandemic had significant impacts on bikeshare usage in the United States. Previous studies show that the pandemic negatively affected bikeshare activity patterns. To examine the effects of the pandemic on bikeshare behavior across membership types, this study investigated trip volume-and trip duration patterns of both members and nonmembers of five bikeshare systems across the United States. The results showed that member ridership significantly decreased throughout the pandemic, but nonmember ridership tended to be stable. It was also found that trip durations increased across both groups throughout the pandemic. Additionally, inferences were made to determine the level of support for a reversion to prepandemic normality as the pandemic progressed and reopening occurred in phases. The findings from this study could benefit bikeshare agencies in developing postpandemic recovery strategies. © National Academy of Sciences: Transportation Research Board 2021.

Impacts of the COVID-19 Pandemic on Pedestrian Push-Button Utilization and Pedestrian Volume Model Accuracy in Utah.

This work investigated the impacts of COVID-19 on pedestrian behavior, answering two research questions using pedestrian push-button data from Utah traffic signals: How did push-button utilization change during the early pandemic, owing to concerns over disease spread through high-touch surfaces? How did the accuracy of pedestrian volume estimation models (developed pre-COVID based on push-button traffic signal data) change during the early pandemic? To answer these questions, we first recorded videos, counted pedestrians, and collected push-button data from traffic signal controllers at 11 intersections in Utah in 2019 and 2020. We then compared changes in push-button presses per pedestrian (to measure utilization), as well as model prediction errors (to measure accuracy), between the two years. Our first hypothesis of decreased push-button utilization was partially supported. The changes in utilization at most (seven) signals were not statistically significant; yet, the aggregate results (using 10 of 11 signals) saw a decrease from 2.1 to 1.5 presses per person. Our second hypothesis of no degradation of model accuracy was supported. There was no statistically significant change in accuracy when aggregating across nine signals, and the models were actually more accurate in 2020 for the other two signals. Overall, we concluded that COVID-19 did not significantly deter people from using push-buttons at most signals in Utah, and that the pedestrian volume estimation methods developed in 2019 probably do not need to be recalibrated to work for COVID conditions. This information may be useful for public health actions, signal operations, and pedestrian planning.

Pedestrians and the Built Environment during the COVID-19 Pandemic: Changing Relationships by the Pandemic Phases in Salt Lake County, Utah, U.S.A.

The COVID-19 pandemic has dramatically altered people's travel behavior, in particular outdoor activities, including walking. Their behavior changes may have prolonged effects after the pandemic, and such changes vary by the context and are related to the characteristics of the built environment. But empirical studies about the relationships between pedestrians and the built environment during the pandemic are lacking. This study explores how COVID-19 and related travel restrictions have affected the relationship between pedestrian traffic volume and the built environment. We estimate daily pedestrian volumes for all signalized intersections in Salt Lake County, Utah, U.S.A., from pedestrian push-button log data between January 2019 and October 2020. Multilevel spatial filtering models show that the COVID-19 pandemic has altered the relationship between pedestrian traffic volume and the built environment. During the pandemic, the higher the number of COVID-19 cases, the less (or more negative) the effects of density, street connectivity, and destination accessibility on pedestrian volume being observed. The exception is access to urban parks, as it became more significant in increasing pedestrian activities during the pandemic. The models also highlight the negative impacts of the pandemic in economically disadvantaged areas. Our findings could help urban and transportation planners find effective interventions to promote active transportation and physical activity amid the global pandemic.

On the Effect of COVID-19 on Drivers' Behavior: A Survey Study.

COVID-19 had a disruptive effect on the global community. This study looks at the effects that the stringent lockdown measures enacted in March 2020 had on motorists' driving patterns. In particular, given the greater portability of remote working associated with the drastic decline in personal mobility, it is hypothesized that these may have served as accelerators for distracted and aggressive driving. To answer these questions, an online survey was conducted in which 103 respondents were asked to report on their own and other drivers' driving behavior. While respondents agreed they drove less frequently, they also indicated that they were not prone to more aggressive driving or engaging in potentially distracting activities whether for work or personal purposes. When asked to report on other motorists' behavior, however, respondents indicated they had witnessed more aggressive and distracting drivers on the road after March 2020 relative to the time before the pandemic. These findings are reconciled with the existing literature on self-monitoring and self-enhancement bias, and the existing literature on the effect of comparable large-scale, disruptive events on traffic patterns is used to discuss the hypothesis on how driving patterns may change after the pandemic.

Energy Sufficiency in the Passenger Transport of Lithuania

This paper aims to understand the significance of energy sufficiency (ES) in passenger transport for the long-term resolution of energy, climate, and sustainable development issues in Lithuania. It computes related indicators, by fixing the passenger-kilometres (pkm) travelled by various modes of transportation and applying a scenario analysis with the MESSAGE model. The findings indicated that the country's final energy consumption (FEC) in transportation could be reduced by 21.8% by 2050 due to slowing growth rate of distances travelled by passenger car but increasing use of public transport and bicycles. This would result in a decrease in the growth rate of primary energy consumption (PEC) by half (to 0.3% a year), an increase in the use of renewable energy sources (RES) to 67.2% in the PEC structure, savings of oil products by 6.4 TWh, and savings of new electricity generation capacity by 550 MW. Furthermore, 20 MtCO2eq. in greenhouse gas (GHG) emission reductions could be realised between 2021 and 2050. To take advantage of the potential of ES, the policy measures of passenger car demand containment and a shift to non-motorised and less polluting modes of transportation should be implemented. Furthermore, priority should be given to policy measures that encourage use of public transportation.

A machine learning approach to predicting bicycle demand during the COVID-19 pandemic

Count-based bicycle demand models have traditionally focused on estimation rather than prediction and have been criticized for lacking a direct causal relationship between significant variables and the activity being modeled. Because they are not choice-based models, they are doubted for their ability to forecast well. The rise of machine learning techniques has given researchers tools to build better predictive models, and the tools to evaluate predictiveness. Extensive previous work in the statistics and machine learning field has shown that the best predictive model is not synonymous with the most true (or explanatory) model. The non-motorized demand modeling community could leverage these lessons learned to develop better count-based predictive models. The rise of the COVID-19 pandemic has clearly affected travel patterns, and the broad data collection has opened-up an opportunity to leverage machine learning techniques to build a predictive bicycle demand model. This study uses bicycle count data, COVID-19 data, and weather data to develop a LASSO regression model for three facilities in Austin, TX. The COVID-19 variables included both state- and local-level data between March 15, 2020, and January 31, 2021. The final model selects six variables out of 28 variables and reveals that the increase of statewide COVID-19 fatalities, statewide molecular positivity rate, and local precipitation cause a decrease in bike ridership, meanwhile maximum temperature causes an increase. The LASSO model also has a lower prediction MSE during cross validation compared to the full model. This paper aims to bring to light that our present-day demand and volume forecasting efforts would benefit tremendously from a predictive modeling approach rather than valuing the most explanatory models as the only strong forecasters of demand. In the end, modelers can use this approach to improve the forecasting ability of any count-based bicycle demand model. © 2023

Spatio-temporal dynamics of aerosol distribution in an urban environment recorded in situ by means of a bike based monitoring system

Aerosol pollution in urban areas is highly variable due to numerous single emission sources such as automobiles, industrial and commercial activities as well as domestic heating, but also due to complex building structures redirecting air mass flows, producing leeward and windward turbulences and resuspension effects. In this publication, it is shown that one or even few aerosol monitoring sites are not able to reflect these complex patterns. In summer 2019, aerosol pollution was recorded in high spatial resolution during six night and daytime tours with a mobile sensor platform on a trailer pulled by a bicycle. Particle mass loadings showed a high variability with PM10 values ranging from 1.3 to 221 µg m-3 and PM2.5 values from 0.7 to 69.0 µg m-3. Geostatistics were used to calculate respective models of the spatial distributions of PM2.5 and PM10. The resulting maps depict the variability of aerosol concentrations within the urban space. These spatial distribution models delineate the distributions without cutting out the built-up structures. Elsewise, the overall spatial patterns do not become visible because of being sharply interrupted by those outcuts in the resulting maps. Thus, the spatial maps allow to identify most affected urban areas and are not restricted to the street space. Furthermore, this method provides an insight to potentially affected areas, and thus can be used to develop counter measures. It is evident that the spatial aerosol patterns cannot be directly derived from the main wind direction, but result far more from an interplay between main wind direction, built-up patterns and distribution of pollution sources. Not all pollution sources are directly obvious and more research has to be carried out to explain the micro-scale variations of spatial aerosol distribution patterns. In addition, since aerosol load in the atmosphere is a severe issue for health and well-being of city residents more attention has to be paid to these local inhomogeneities.

Activation of the Groundswell in the Segment of Bicycle Manufacturers

In addition to the massive home office, the COVID-19 pandemic has brought an enormous increase in the popularity of outdoor activities. This was also reflected in the very high demand for bicycles and accessories for cyclists, which led to the groundswell effect and an increase in fan interaction and engagement within the social media profiles of bicycle manufacturers. The research design in the present paper contains a consistent and synergistically balanced share of qualitative and quantitative methods. Within the theoretical background, methods of analysis of sources from leading authors are used, especially from articles based on leading scientific journals and proceedings. The practical part uses quantitative methods in the form of data collection through the tools Zoomsphere and Socialblade. The selection of assessed business entities consisted of a ranking of profitability and evaluation according to the leading portal designed for the segment of cyclists. The findings point to the content structure of profiles on social media in the segment of bicycle manufacturers. They also point to the content structure of the best contributions on these social media and to the recommendations in the form of categories for the bicycle manufacturers segment. The authors also define the best types of posts for future content in that segment. Domestic and global businesses in this segment require knowledge of the laws on social media in the form of user behavior and the groundswell effect. The limits of the findings are in the selection of business entities, which were selected on the basis of profitability and evaluation according to the leading cycling portal, also within the limits of social media analysis and management tools. Despite the above facts, the added value exceeds the limits within the author's contribution. The originality of the paper is based mainly on the fact that the selected segment from the point of view of the groundswell effect is unexplored. It is important to examine this segment, especially due to the high demand for products in this segment and the relentless interest in the form of user interactions. © The Authors, (2022). All Rights Reserved. No reproduction, copy or transmission may be made without written permission from the individual authors.

Increasing physical activity and active transportation in an arid city: Slow Streets and the COVID-19 pandemic

Slow Streets promote walkability and provide safe spaces for active travel and recreation by minimizing vehicle traffic on roads. Their effectiveness was tested when the City of Tucson implemented Slow Streets by temporarily closing certain neighbourhood streets to all but local traffic, giving people more space to safely walk, run, and bicycle. Using a quasi-experimental research design, it was possible to measure differences in walking and bicycling between Slow Streets and control streets. Results show Slow Streets are effective in increasing the number of people walking and bicycling on neighbourhood streets, especially while the temporary traffic barriers were in place.

The influence of personal and public health and smart applications on biking behavior in South Korea

Bicycling is an important form of active transport that contributes to sustainability mobility as a result of its role in personal and public health and emissions reduction. The significance of which has grown since the COVID‐19 pandemic outbreak. However, biking studies have neglected, in theoretical terms, developing an understanding of why consumers bike. Therefore, this research designs and verifies an extended theory of planned behavior adding personal and public health and a moderator of perceived smart application usage to help explain such consumer behavior. This study is based on a digital survey of South Koreans who biked for leisure, tourism, and/or work, utilizing partial least squares‐structural equation modeling with multi‐group analysis and Fuzzy‐set Qualitative Comparative Analysis. Results revealed that personal health is most important to cyclists, followed by public health, attitude, and subjective norm. Interestingly, people with perceived high usage of smart applications for biking show stronger relationships between public health and attitude and perceived behavioral control and behavioral intention than low users. In contrast, individuals with perceived low usage of smart applications for biking reveal a stronger relationship between attitude and behavioral intention than high users. The high and low user groups of smart applications also distinctively differ in levels of cycling behavior. Consequently, this work offers several theoretical and managerial implications for research and practice.

Optimizing the Cleaning and Disinfection Scheme for Dockless Shared Bikes

Shared bikes can help cities achieve carbon neutrality goals. Cleaning and disinfection are vital procedures of the maintenance of shared bikes, especially during the COVID-19 pandemic because shared bikes could be a transmission intermediary of viruses. This study proposes an optimization model of the cleaning and disinfection scheme of the dockless shared bikes. The disinfection is assumed to be performed at night, when the usage is lowest. By regarding the disinfection staff as traveling salesmen, the model is formulated as an extension of the Multidepot Multiple Traveling Salesman Problem (MDMTSP). The objective function is to minimize the total cost;which consists of the cost associated with the working time and per-capita cost of the disinfection staff. A heuristic algorithm combining k -means clustering and genetic algorithm (K-GA) is adopted to find the lower bound solution. Then, the K-GA-adjustment algorithm has been adopted to find the solutions that satisfy the constraints. To reduce the computing time needed, an approximate function for the lower bound of the optimal number of disinfection staff is obtained by constructing a Continuous Approximation (CA) model. A case study based on real location data of shared bikes in Chengdu, China, is performed to show how the maintenance department could adopt the optimization framework to design an efficient scheme to clean and disinfect the shared bikes. [ABSTRACT FROM AUTHOR] Copyright of Journal of Advanced Transportation is the property of Hindawi Limited 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather

To help related operators to allocate and dispatch the number of bike-sharing and provide good guidance for setting up electronic fences, this paper proposes a spatiotemporal graph convolution network prediction model (SGCNPM) with multiple factors to enhance the accuracy of predicting the demand for bike-sharing. First, we consider time, built environment, and weather. We use a multigraph convolution network (GCN) to model the built environment, utilize a long short-term memory (LSTM) network to extract temporal features, and utilize a fully connected network (FCN) to model weather influence. We construct SGCNPM which can effectively fuse GCN, LSTM, and FCN, thus creating a prediction method considering the influence of multiple factors. The results of the real case in Tianjin, China, show that the proposed model can perform well in improving prediction accuracy. Also, we analyze the influence of factors on model prediction results in different periods.

What is the Short-Term Impact of Decreasing the Speed Limit for Motor Vehicles on Bicycle Commuting? Evidence from French Cities during Covid-19 Pandemic

This article investigates the effect of a decrease in the speed limit for motor vehicles on bicycle commuting in French cities. I use a difference-in-differences event study design to measure a possible causal effect of motor vehicle speed limits on changes in bicycle traffic. I do not find any effect of the reduction of the speed limit from 50 km/h to 30 km/h on bicycle commuting. This result is important for public policy design, since increasing the number of bicycles is one of the benefits that politicians expect from decreasing the speed limit for motor vehicles.

Forced digital transformation during the COVID-19 pandemic

Due to an unprecedented threat from COVID-19, the World Health Organization recommended the introduction of physical distancing measures, such as quarantine and social isolation. These measures have severely affected some sectors of the economy, hampering the development of many economic activities, especially retail. In this context, this article investigated strategies for the dissemination, commercialisation and distribution of goods during the COVID-19 pandemic, particularly related to small companies that were not yet fully integrated with digital technologies. We perceived a forced digital transformation, disclosure on social networks, negotiating sales through text messaging applications, electronic payments and motorcycle and bicycle delivery service. The combination of different technologies has supported small businesses in times of pandemic, since commercialisation in digital media has been one of the main solutions for the prevention of bankruptcy, particularly for physical companies.

Complete Street strategies in cycleways applied in district with high delivery demand to reduce travel costs

Currently, the implementation of bicycle lanes is one of the main ways to improve the dense traffic existing in the main cities of each country. This is because they take the bicycle and use it as a sustainable means of transportation that, in addition to caring for the environment, helps to reduce the vehicular load directly. However, many cities have started with a distribution of their streets that initially focused only on motorized vehicles and did not consider the consequences that this would entail. This, and with the advent of COVID-19, many of the bicycle lanes have been implemented in an improvised manner, sacrificing aspects such as safety, connectivity and the level of impact on other modes of transport. Faced with this problem, the study proposes an analysis from a different point of view than the conventional one, applying "Complete Street"(CS) strategies, seeking a new proposal that satisfies not only the needs of cyclists, but also the balance between all users who interact with the streets. This article will take the current scenario of an area with a high demand for delivery in the city of Lima, Peru;an area that includes 5 districts of Lima with a high population rate, will compare it with a proposal for a regularized bicycle lane layout under the criteria of CS, and will analyze with the software Grafos under the Graph Theory the trip times between the main points of demand and supply. The effectiveness of the proposal was validated through projected user surveys, meeting all the criteria that make up the CS and the acceptance of all stakeholders. The results obtained through the graph compared the travel times for both the current scenario and the proposed bicycle lanes, which managed to demonstrate an existing improvement in travel times (minutes) between the main generating and attracting points, reducing them by 75% and 60% compared to the current scenario. © 2022 IEEE.

Hazard exposure of bicyclists at urban intersections

Traditionally, mobility problems and automobile traffic congestion have increased in cities around the world due to the urban development process, especially in the city of Metropolitan Lima. For this reason, the government of the Peruvian capital has established temporary detours in certain sections of the arterial roads of the network, to invite new cyclists, due to the effects of Covid-19. Today, Lima has a network of 294 km of bicycle lanes, which have been implemented without adequate planning. In view of this, we evaluated the risk of poor planning on the vulnerable user (the cyclist) at an intersection of this road network, with a high rate of motorized congestion. The main objective of this study has been to propose corrective actions to avoid the exposure to danger on the users of the bicycle lane (countermeasures);due to lack of safety at the intersection of La Marina Ave. and Universitaria Ave. In this sense, a risk matrix was developed with the most concurrent factors that occur at this intersection;to then obtain a risk level and take actions in each of them, to mitigate the impact. The result obtained in the analysis of this study for the intersection is classified as a level 2 risk: Important risk, which means that it presents several important danger factors. Finally, in addition to the analyses developed, a treatment scheme was proposed for the intersection to provide greater safety to the users of the bicycle lane, avoiding fatal and non-fatal accidents. © 2022 IEEE.