Public Bicycle Research Articles

An integrated model with a solution algorithm for improving an existing public bicycle sharing system

This study uses a time–space network flow technique combined with a mathematical programming method to construct a holistic model for improving an existing bicycle sharing system. The model considers bicycle rental station site selection, expansion of station capacity, expansion of bicycle fleet size, and bicycle deployment and relocation issues in the same integrated framework. The model can be formulated as an integer network flow problem with side constraints. Since the problem is NP-hard, we design a heuristic algorithm to solve the problem efficiently and effectively. Finally, numerical tests are conducted in order to perform a preliminary evaluation of the model and heuristic algorithm with data from an existing public bicycle sharing system. The results indicate that the model and heuristic algorithm could be helpful to planners to improve their bike-sharing system operations.

The ecological footprint of physicians: A survey of physicians in Canada, India, and USA.

Combating climate change may be the greatest public health opportunity of the 21st century. While physicians play an important role in addressing climate change, given their affluence in society, they may be an important source of greenhouse gas emissions themselves. We sought to examine the size and nature of the ecological footprint of physicians and medical students. We conducted an online survey from December 2021-May 2022 examining resource consumption, changes in consumption patterns over time, and beliefs about climate change. Participants were medical students, residents, and staff physicians in Canada, India, or USA. Only 20 out of 162 valid respondents had a low ecological footprint (12%), defined as meat intake ≤2 times per week, living in an apartment or condominium, and using public transport, bicycle, motorcycle or walking to work. 14 of these 20 participants were from India. 91% of participants were open to reducing their own ecological footprint, though only 40% had made changes in that regard. 49% participants who discussed climate change at work and at home had decreased their ecological footprint, compared to 29% of participants who rarely engaged in such conversations (OR 2.39, 95% CI 1.24-4.63, P = 0.01). We conclude that physicians have a large ecological footprint, especially those from Canada and USA. A majority of physicians are interested in reducing their ecological footprint, and those who engage in conversations around climate change are more likely to have done so. Talking frequently about climate change, at work and at home, will likely increase climate change action amongst physicians.

The Novel Application of Deep Reinforcement to Solve the Rebalancing Problem of Bicycle Sharing Systems with Spatiotemporal Features

Facing the Bicycle Rebalancing Problem (BRP), we established a Rebalancing Incentive System (BRIS). In BRIS, the bicycle operator proposes the method of financial compensation to encourage cylclists to detour some specific stations where the number of bikes is excessive or insufficient and access suitable sations. BRIS mainly includes two objects: the Bike Gym imitating the bicycle environment, and the Spatiotemporal Rebalancing Pricing Algorithm (STRPA) determining the amount of money which is given to the cyclist depending on time. STRPA is a deep reinforcement learning model based on the actor–critic structure, which is the core concept of this paper. In STRPA, the hierarchical principle is introduced to solve the dimensional disaster, and the graph matrix A is introduced to solve the complex node relationship. In addition, the traffic data including the bicycle have strong temporal and spatial characteristics. The gated recurrent unit (GRU), the sub-module of STRPA, can extract the temporal characteristics well, and the graph convolution network (GCN), also a sub-module of STRPA, can extract the spatial characteristic. Finally, our model is superior to the baseline model when verified on the the public bicycle data of Nanjing.

How do walking environments along the connecting path affect the disparity in public bicycle usage among multiple docking stations around tube stations?

The integration of public bicycles with tube systems has the potential to enhance urban transportation sustainability and efficiency. However, the utilization of public bicycles can significantly differ among various docking stations, even when they fall within the coverage area of the same tube station. Understanding the factors that attract or discourage individuals from using public bicycles in different locations is essential for informed decision-making regarding infrastructure and policy enhancements. This study examines the disparity in public bicycle usage, with a specific focus on the micro-level walking environments along connecting paths between docking stations and tube stations. Transaction data collected from London public bicycle and tube systems are utilized, alongside micro-level walking environment and macro-level built environment and socio-economic factors. A generalized additive mixed model is employed to capture nonlinear effects and spatial autocorrelations in docking station usage.The results reveal significant effects of walking environments along connecting paths on docking station usage. Specifically, positive associations are observed between bicycle station usage and footpaths quality, presence of pedestrian crossing, and points of interests (e.g., green space) along the connecting path, as well as tube ridership, cycle facilities, and land-use mixture. Conversely, negative relationships are observed with walking distance, physical barriers, road crossed, and bus stops. Moreover, proximity to transit, distance to the city center, and deprivation level demonstrate threshold effects on bicycle station usage. This study provides several practical implications for improving the multi-mode transportation system and the planning of docking stations around tube station, aiming to create cycling-friendly and transit-oriented environments.

How the COVID-19 pandemic changed travel behaviour? A case study on public bikes in Seoul

This study aims to examine how travel behaviour has changed after the COVID-19 outbreak, specifically focusing on the use of public bikes in Seoul. In January 2020, the Korean government reported its first COVID-19 case and implemented several levels of social distancing measures excluding movement restrictions. These measures, coupled with the fear of infection, resulted in reduced social interactions and, further, changed travel behaviour. Transit usage in Seoul has experienced a significant decline of approximately 25% in 2020 and 2021 compared to the previous two years. However, the use of public bikes continued to rise even during the pandemic, unaffected by the prevalence of the disease. Moreover, there was a doubling of public bike usage during commute hours, implying the potential of public bikes as an alternative travel mode for commute purposes. Our findings suggest that COVID-19 acted as a disruptive shock that prompted changes in travel behaviour. Policymakers should seize this opportunity to promote the wider adoption of cycling, potentially through the development of more efficient and safer infrastructure as well as the implementation of various incentives.

Linking residential mobility with daily mobility: A three-wave cross-lagged panel analysis of travel mode choices and preferences pre–post residential relocation in the Netherlands

The causal impact of the built environment on travel behaviours is a subject of debate. This debate especially concerns the independent effect of the built environment on the observed travel patterns after taking into account residential self-selection arising from pre-existing travel-related attitude. This study argues that travel attitude varies over time, and thus, is also reshaped by residential built environment and interrelated with residents’ travel behaviours. Focusing on the event of residential relocation in the Netherlands, this study longitudinally investigated the interrelations between travel mode choices and preferences before, immediately after and a year after the relocation. Results from the random-intercept cross-lagged panel models substantiated the residential self-selection based on the pre-relocation preferences for motorised means of transport, including cars and public transport. Moreover, travel mode preferences varied to a greater extent than travel mode use pre–post relocation, and especially, frequent use of public transport or bicycles stimulated by the new place of residence had a one-year lagged effect on developing the mode preference. Therefore, the structural role of residential built environment manifests as (re)shaping travel mode choices as well as mode-specific preferences in the process of residential relocation.

Influence of real and video-based experiences on stated acceptance of connected public transportation and autonomous vehicles in a transit mall: A hybrid choice modeling approach

Innovative technologies such as autonomous and connected vehicles can create a better transit mall, a street where only public transit vehicles, bicycles, and pedestrians are allowed. However, successful introduction of such technologies depends on user acceptance. To assess the potential acceptance of these technologies in the context of a new transit mall environment, it is crucial to provide an opportunity to experience them before they roll out. This study explores how real and video-based experiences of new technologies can affect public acceptance of new transit mall options in Hiroshima City, Japan, where trams and buses are significant modes of public transportation. We conducted a survey on the stated acceptance of new transit mall options with three groups of respondents: real experience, video-based experience, and no experience groups. The real-experience group self-selected themselves for the experiment, which potentially caused bias. Thus, we used a hybrid choice model with attitudinal variables (ethical, legal, and social concerns regarding emerging technologies, as well as risk-related factors) introduced to control for this bias. We found that both real and video-based experiences improved overall acceptance of new transit mall options, but there were differences in preferences between those who had only video-based experiences and those who had real experiences. We also found that prior knowledge of new transport services and infrastructure had a considerable influence on stated acceptance.

Public Bicycle Sharing in India: Lessons Learned from Implementation in Three Cities

This working paper examines the growth and challenges of public bicycle-sharing (PBS) systems in India, within the context of global efforts towards low-carbon transport. While many Indian cities initially adopted rental-based PBS systems with limited bicycle availability, Mysuru, Bhopal, and Pune emerged as pioneers, introducing innovative models based on public-private partnerships, smart technology, and quality service incentives.

Estimating Travel Frequency of Public Bikes in Seoul Considering Intermediate Stops

Estimating Travel Frequency of Public Bikes in Seoul Considering Intermediate Stops

The impacts of Covid-19 pandemic on the sustainable mobility of university members in Turkey

The global Covid-19 pandemic had a devastating impact on our lives. The pandemic led to sudden and momentous changes in mobility styles and travel habits. Many users started preferring to travel via private vehicles, which is contrary to sustainability policies. Strict measures were implemented against the Covid-19 pandemic in Turkey during this process, as was the case all over the world. Taking into consideration these realities, the aim of the present study was to examine the impacts of the Covid-19 pandemic on the travel modes (public transportation, walking, and bicycle), anxiety and stress perceptions, and working conditions of individuals. A survey was conducted to measure the impacts of the pandemic and the measures taken. The sample of this study consists of people who regularly attended Suleyman Demirel University in Isparta and Akdeniz University in Antalya, Turkey, before the pandemic. An online survey was conducted for the case study during the May–June 2021 semester, and the survey was representative of the study population with a total of 556 participants. The travel time for both different university students decreased during the pandemic period. The percentage of those traveling to the university with their own cars increased to 77 % in AU and 66.8 % in SDU. The use of public transportation dropped to 6.1 % in AU and 11.8 % in SDU. 56.97 % of AU participants and 51.15 % of SDU participants reported that their walking habits decreased. It was reported that 52.73 % of the respondents in AU and 55.75 % of the respondents in SDU did not change their bicycle usage frequency. 64.24 % of AU and 74.42 % of SDU reported that their anxiety levels increased during the pandemic period. And for each of these analyses, there was no statistical difference between the two different university students.

Changes in public bike usage after the COVID-19 outbreak: A survey of Seoul public bike sharing users

When the COVID-19 pandemic swept across the world, people tended to seek more individualized and viable transportation modes, such as a bicycle. In this study, we examined the factors influencing changes in public bike sharing (PBS) in Seoul, to assess this trend post-pandemic. We conducted an online survey of 1,590 Seoul PBS users between July 30 and August 7, 2020. Using a difference-in-differences analysis, we found that participants who were affected by the pandemic used PBS 44.6 h more than unaffected individuals throughout the year. In addition, we used a multinomial logistic regression analysis to identify the factors affecting changes in PBS usage. In this analysis, the discrete dependent variables of increased, unchanged, and decreased were considered, representing the changes in PBS usage after the COVID-19 outbreak. Results revealed that PBS usage increased among female participants during weekday trips such as commuting to work and when there were perceived health benefits of using PBS. Conversely, PBS usage tended to decrease when the weekday trip purpose was for leisure or working out. Our findings offer insight into PBS user behaviors within the context of the COVID-19 pandemic and present policy implications to revitalize PBS usage.

Impacts of COVID-19 on bike-share usage: The case of Daejeon, Korea

After COVID-19 began spreading through fecal-oral routes, crowded cities introduced social distancing policies. Mobility patterns in urban also changed because of the pandemic and the policies to reduce the infection of it. This study investigates the impact of COVID-19 and related policies such as social-distancing by comparing bike-share demand in Daejeon, Korea. By using big data analytics and data visualization, the study measures differences in bike-sharing demand between 2018 and 19, before the pandemic, and 2020–21, during the pandemic. According to results, (1) bike-share users tend to travel long distances and cycle more than before the pandemic, (2) bike users choose cycling not for commuting but for transportation during the pandemic, and (3) the pandemic has broadened the spatial borders bike-usages. These results provide meaningful implications for urban planners and policymakers by identifying differences in the ways people use public bikes during the pandemic era.

A Point Processes approach to bicycle sharing systems’ design and management

Public bicycle systems have attracted a great deal of attention in recent years. The success of this service heavily depends on the topology of the city and on the locations of bike stations in relation to potential demand. In fact, it is primarily important that users find bike stations in convenient locations, sufficiently close both to the origins of their trips and to their destinations, and that each rental station guarantees the availability both of enough bicycles and of enough empty docking slots.This paper proposes a point processes approach to the study of bike-sharing systems, allowing us to quantify and control parameters having a key role in decisions both of strategic and operational type.Differently from previous studies, the point processes approach catches both the interdependence among the stations and the links between spatial and time aspects of the problem.The application of point processes, in particular spatial mixed Poisson processes, to this field requires the statement and proof of an invariance property of such processes under stochastic dependent transformations, that may be of interest also from a theoretical point of view.

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.

Public bicycle sharing systems control and performance evaluation: A Petri net-based approach

Public bicycle sharing systems (PBSS) allow citizens to move between two points within a region using bicycles, being composed of stations where users can either pick up or return bicycles after using them. Depending on the demand for bicycles, stations may either become empty or full of bicycles. This problem is usually solved by means of a vehicle that exchanges bicycles among the stations following some balancing strategy. In this paper, we propose a model for PBSSs using generalized stochastic Petri nets with external control, a Petri net with an external agent that decides how to execute the balancing strategy. The main advantage of our model is that it allows different balancing strategies to be implemented without changing the Petri net structure. In addition, since the model is a Petri net, we can easily implement it by making a Petri net simulator built with the incidence matrix associated with the Petri net model. We also propose two balancing strategies and validate both the simulation algorithm and the balancing strategies by applying them to the performance evaluations of the model of a real system, named Integra UFRJ PBSS. The performance evaluation of the system under the balacing strategies have shown that both strategies considerably reduce the times, in average, that stations remain full or empty throughout the system operation, solving problems related to stations being empty of full; in particular, the first strategy reduced those times in such a way that no more than five percent of the users of each station had trouble returning their bicycles due to stations being full, whereas the second strategy reduced the percentage of lost users due to stations being empty to no more than five percent for each station.

Densely connected sub-Gaussian linear structural equation model learning via ℓ1- and ℓ2-regularized regressions

This paper develops a new algorithm for learning densely connected sub-Gaussian linear structural equation models (SEMs) in high-dimensional settings, where the number of nodes increases with increasing number of samples. The proposed algorithm consists of two main steps: (i) the component-wise ordering estimation using ℓ2-regularized regression and (ii) the presence of edge estimation using ℓ1-regularized regression. Hence, the proposed algorithm can recover a large degree graph with a small indegree constraint. Also proven is that the sample size n=Ω(p) is sufficient for the proposed algorithm to recover a sub-Gaussian linear SEM provided that d=O(plog⁡p), where p is the number of nodes and d is the maximum indegree. In addition, the computational complexity is polynomial, O(np2max⁡(n,p)). Therefore, the proposed algorithm is statistically consistent and computationally feasible for learning a densely connected sub-Gaussian linear SEM with large maximum degree. Numerical experiments verified that the proposed algorithm is consistent, and performs better than the state-of-the-art high-dimensional linear SEM learning HGSM, LISTEN, and TD algorithms in both sparse and dense graph settings. Also demonstrated through real data is that the proposed algorithm is well-suited to estimating the Seoul public bike usage patterns in 2019.

Study to Shift From Motorised to Non-Motorised Transport – A Case of Nashik City

With the aim to reduce the adverse environmental impact caused due to the urban transportation system, an alternative solution is identified. Non-motorized transport is the most relevant alternative for low-carbon mobility. It not only helps in the city's sustainable development but also helps reduce traffic problems and improve one’s health. In India and in foreign countries, the government is taking initiatives to promote Non Motorised Transport (NMT) and funding the project to come into operation. In most of the surveys, the solutions are given for improving NMT infrastructure, but from the literature, a gap is found that only improving the infrastructure is not important, instead at first it is important to recognize the actual need as to where we need to develop infrastructure, secondly recognizing the potential trips and then proposing infrastructure respectively. There is no study done on the potential trips that could be converted to NMT with reference to Nashik City. This research aims to study the mobility plan of the Gangapur road area of Nashik city and identify the potential trips of such area which can be shifted to non-motorised transport. The survey includes the study of daily trips taken by people for various activities, which caters to all age groups. The study also includes an understanding of the trip length, alignment of the roads, and important commercial and institutional zones in the area. A thought is also given for redesigning Public Bicycle Sharing System in the study area for the maximum usage of the system. A module is proposed after an analysis of the survey and the existing transportation plan. A plan representing the module and indicating the potential trips that can be converted to non-motorised transport is proposed.

National incidence and trends of bicycle injury.

Bicycle utilization continues to increase annually, and this trend was recently accelerated by thecoronavirus disease of 2019pandemic. There is limited epidemiologic data, however, regarding the prevalence and nature of bicycle-related injuries. Therefore, the purpose of this study is to characterize trends in bicycle-related injuries. The National Electronic Injury Surveillance System was queried for bicycle-related injuries from 2012 to 2021. Patient demographic and injury data were collected and analyzed to describe trends in incidence, patient demographics, and injury patterns associated with an emergency department encounter for a bicycle-related injury. There were an estimated 4,666,491 (95% confidence interval: 4,661,472-4,671,510) bicycle-related injuries from 2012 to 2021. The incidence of these injuries has significantly decreased over time (R = -0.983, R2 = 0.967, p < 0.001). However, the rate of injury in elderly patients increased over time. Injuries occurred most often during summer months (36%) and on weekend days (31.9%). Males and younger patients were more commonly injured. Head injuries were the most commonly affected body part among all age groups. Fractures were the most common injury type overall. Upper extremity injuries were more common than the lower extremity. Despite increased public bicycle utilization, there is a significant downtrend in bicycle-related injuries over the last decade. Injuries among elderly patients are becoming more common, who demonstrate a high rate of fracture and head injury. Fractures and head injuries were the most common injuries among all age groups, highlighting the importance of bicycle safety initiatives and helmet-wearing regardless of age.

Behavioural Factors for Users of Bicycles as a Transport Alternative: A Case Study

Recent mobility and transportation dynamics have shaped the main sustainability problems of today, evidencing the need to potentiate alternative, flexible, environmentally friendly transportation means—such as bicycles—that significantly contribute to the health and well-being of users. However, in cities that are just beginning to implement bicycle systems or are seeking to achieve high levels of bicycle use, it is important to know which are the most relevant factors that users consider when using this type of medium. Therefore, the objective of this study was to identify the main behavioural factors among users of the public bicycle programme EnCicla in the city of Medellín, Colombia. Confirmatory factor analysis of responses to a self-administered questionnaire, elaborated based on the Theory of Planned Behaviour, was conducted using the statistical tool Statistical Product and Service Solutions (SPSS). Among the main results, the behavioural factors that further explained behavioural intention were attitude towards behaviour and perceived behavioural control, with values of 0.579 and 0.519, respectively (Cramér coefficient or Cramér’s V). The relevance of these factors lies in the implementation of dynamics that affect a better assessment by users of public bicycles in the EnCicla system, increasing satisfaction with the service and promoting greater adoption in the context of current mobility needs. The practical implications of this study are related to the possibility of designing strategies and public policies to enhance this means of transport in the cities where it is implemented, or to prepare a possible implementation in those that plan to do so.

Causal impacts of the COVID-19 pandemic on daily ridership of public bicycle sharing in Seoul

Public bicycle can be a disease-resilient travel mode during the coronavirus disease 2019 (COVID-19) pandemic. Nonetheless, its evidence on public bicycle sharing is still inconclusive. This study used Bayesian structural time series models and causal impact inference for the data on the daily ridership of public bicycles in Seoul, South Korea, for 1826 days from January 1, 2017, to December 31, 2021. The study found that the usage of public bicycles was robust against the COVID-19 pandemic even in densely populated Seoul. Compared with the pre-pandemic period, public bicycles’ usage was unaffected on days when weather conditions, such as snow, rain, and wind speed were not as severe, as well as on days with non-seasonal event factors, such as weekdays, public holidays, and traditional Korean holidays. In addition, its robustness against the pandemic became more pronounced as the number of bicycle racks increased and the intensity of social distancing increased. However, public bicycles were in demand primarily for leisure and exercise, not for travel, during the pandemic. Public bicycle sharing can be a disease-resilient travel mode. Continuous investment in infrastructure such as bicycle paths and public bicycle is required to become a more resilient travel mode against infectious diseases.