Efficient use of urban micromobility resources such as bike sharing is challenging due to the unbalanced station-level demand and supply, which causes the maintenance of the bike sharing systems painstaking. Prior efforts have been made on accurate prediction of bike traffics, i.e., demand/pick-up and return/drop-off, to achieve system efficiency. However, bike station-level traffic prediction is difficult because of the spatial-temporal complexity of bike sharing systems. Moreover, such level of prediction over entire bike sharing systems is also challenging due to the large number of bike stations. To fill this gap, we propose BikeMAN, a multi-level spatio-temporal attention neural network to predict station-level bike traffic for entire bike sharing systems. The proposed network consists of an encoder and a decoder with an attention mechanism representing the spatial correlation between features of bike stations in the system and another attention mechanism describing the temporal characteristic of bike station traffic. Through experimental study on over 10 millions trips of bike sharing systems (> 700 stations) of New York City, our network showed high accuracy in predicting
Electric bikes (e-bikes), including lightweight e-bikes with pedals and e-bikes in scooter form, are gaining popularity around the world because of their convenience and affordability. At the same time, e-bike-related accidents are also on the rise and many policymakers and practitioners are debating the feasibility of building e-bike lanes in their communities. By collecting e-bikes and bikes data in Shanghai City, the study first recalibrates the capacity of the conventional bike lane based on the traffic movement characteristics of the mixed bikes flow. Then, the study evaluates the traffic safety performance of the mixed bike flow in the conventional bike lane by the observed passing events. Finally, this study proposes a comprehensive model for evaluating the feasibility of building an e-bike lane by integrating the Analytic Hierarchy Process and fuzzy mathematics by considering the three objectives: capacity, safety, and budget constraint. The proposed model, one of the first of its kind, can be used to (i) evaluate the existing road capacity and safety performance improvement of a mixed bike flow with e-bikes and human-powered bikes by analyzing the mixed bike flow arrival r
Dockless bike-sharing (DBS) users often encounter difficulties in finding available bikes at their preferred times and locations. This study examines the determinants of the users' mode shifts in the context of bike absence, using survey data from Nanjing, China. An integrated choice and latent variable based on multinomial logit was employed to investigate the impact of socio-demographic, trip characteristics, and psychological factors on travel mode choices. Mode choice models were estimated with seven mode alternatives, including bike-sharing related choices (waiting in place, picking up bikes on the way, and picking up bikes on a detour), bus, taxi, riding hailing, and walk. The findings show that under shared-bike unavailability, users prefer to pick up bikes on the way rather than take detours, with buses and walking as favored alternatives to shared bikes. Lower-educated users tend to wait in place, showing greater concern for waiting time compared to riding time. Lower-income users, commuters, and females prefer picking up bikes on the way, while non-commuters and males opt for detours. The insights gained in this study can provide ideas for solving the problems of demand e
To enhance the service quality of bikesharing programs, bike fleet relocation is widely applied to redistribute bikes from bike sufficient areas to bike shortage areas thereby making a better bike-rider balance across different areas. In this study, a network flow model is proposed to solve the optimal relocation problem of shared bikes, and is implemented with the actual dockless shared bike usage data from Yishun, Singapore, to demonstrate its effectiveness. A series of sensitivity analyses are performed to test the impact of the relocation cost, the number of bikes and truck trikes, and the usage price on bike relocation. The results reveal an apparent connection between the profitability of the system and the analyzed factors. This work offers a modeling framework to start and operate a bikesharing service by determining the number of bikes and trikes as well as price schemes. Some bikesharing regulation policies are also suggested.
Cities around the world face significant barriers to grow urban cycling, including competing budgetary priorities and car-centric streets. Thus, when making decisions regarding the installation of bicycle infrastructure, it is crucial to understand if and to what extent different bicycle-lane types increase bicycle ridership. However, associations between bicycle infrastructure and bicycle ridership have primarily been studied in the context of individual lanes and corridors, or when analyzed at the scale of entire cities, generalized across different bike-lane types. Drawing upon 72 million bikeshare trips from Citi Bike in New York, we demonstrate that there is an approximately 18% increase in bikeshare trips at adjacent stations in the 12 months following the installation of protected bike lanes (those with a physical barrier between cyclists and automobile traffic) and a 14% increase associated with painted bike lanes (where a line of pavement marking is present) and `sharrows' (where a normal traffic lane is marked with a bike stencil). However, using a difference-in-differences analysis, we detect a causal effect on bikeshare ridership only following the installation of prote
Bike Sharing Systems (BSSs) play a crucial role in promoting sustainable urban mobility by facilitating short-range trips and connecting with other transport modes. Traditionally, most BSS fleets have consisted of mechanical bikes (m-bikes), but electric bikes (e-bikes) are being progressively introduced due to their ability to cover longer distances and appeal to a wider range of users. However, the charging requirements of e-bikes often hinder their deployment and optimal functioning. This study examines the spatiotemporal variations in battery levels of Barcelona's BSS, revealing that bikes stationed near the city centre tend to have shorter rest periods and lower average battery levels. Additionally, to improve the management of e-bike fleets, a Markov-chain approach is developed to predict both bike availability and battery levels. This research offers a unique perspective on the dynamics of e-bike battery levels and provides a practical tool to overcome the main operational challenges in their implementation.
Bike-sharing is becoming increasingly popular as an urban traffic mode while increasing the affordability, flexibility, and reliability of interconnected public transportation systems (i.e., interconnected light rail, buses, micro-mobility, and ride-sharing modes of transportation). From the consumers perspective, 1) finding a bike station in convenient locations where demand usually occurs and 2) the availability of bikes at rush hours with a lesser probability of encountering empty docks (for fixed-station bike-share systems) are two key concerns. Some stations are more likely to be empty or full, reflecting an imbalance in bike supply and demand. Accordingly, it is essential to understand a bike-share system's demand pattern to select the optimal locations and reallocate bikes to the right stations to increase the utilization rate and reduce the number of unserved customers (i.e., potential demand). The Capital Bikeshare in the Washington DC Metropolitan Area is one of the prominent bike-share systems in the USA - with more than 4,300 bikes available at 654 stations across seven jurisdictions. This study provides a systematic analysis of a bike-sharing system's Capital Bikeshare
In this paper, machine learning techniques are used to forecast the difference between bike returns and withdrawals at each station of a bike sharing system. The forecasts are integrated into a simulation framework that is used to support long-term decisions and model the daily dynamics, including the relocation of bikes. We assess the quality of the machine learning-based forecasts in two ways. Firstly, we compare the forecasts with alternative prediction methods. Secondly, we analyze the impact of the forecasts on the quality of the output of the simulation framework. The evaluation is based on real-world data of the bike sharing system currently operating in Brescia, Italy.
Bike-sharing systems have emerged as a significant element of urban mobility, providing an environmentally friendly transportation alternative. With the increasing integration of electric bikes alongside mechanical bikes, it is crucial to illuminate distinct usage patterns and their impact on maintenance. Accordingly, this research aims to develop a comprehensive understanding of mobility dynamics, distinguishing between different mobility modes, and introducing a novel predictive maintenance system tailored for bikes. By utilising a combination of trip information and maintenance data from Barcelona's bike-sharing system, Bicing, this study conducts an extensive analysis of mobility patterns and their relationship to failures of bike components. To accurately predict maintenance needs for essential bike parts, this research delves into various mobility metrics and applies statistical and machine learning survival models, including deep learning models. Due to their complexity, and with the objective of bolstering confidence in the system's predictions, interpretability techniques explain the main predictors of maintenance needs. The analysis reveals marked differences in the usage
Combining the advantages of standard bicycles and electrified vehicles, electric bikes (e-Bikes) are promising vehicles to reduce emission and traffic. The current literature on e-Bikes ranges from works on the energy management to the vehicle control to properly govern the human-vehicle interaction. This last point is fundamental in chain-less series bikes, where the link between the human and the vehicle behavior is only given by a control law. In this work, we address this problem in a series-parallel bike. In particular, we provide an extension of the virtual-chain concept, born for series bikes, and then we improve it developing a virtual-bike framework. Experimental results are used to validate the effectiveness of the solutions, when the cyclist is actually riding the bike.
The development of smart cities requires innovative sensing solutions for efficient and low-cost urban environment monitoring. Bike-sharing systems, with their wide coverage, flexible mobility, and dense urban distribution, present a promising platform for pervasive sensing. At a relative early stage, research on bike-based sensing focuses on the application of data collected via passive sensing, without consideration of the optimization of data collection through sensor deployment or vehicle scheduling. To address this gap, this study integrates a binomial probability model with a mixed-integer linear programming model to optimize sensor allocation across bike stands. Additionally, an active scheduling strategy guides user bike selection to enhance the efficacy of data collection. A case study in Manhattan validates the proposed strategy, showing that equipping sensors on just 1\% of the bikes covers approximately 70\% of road segments in a day, highlighting the significant potential of bike-sharing systems for urban sensing.
The lack of cycling infrastructure in urban environments hinders the adoption of cycling as a viable mode for commuting, despite the evident benefits of (e-)bikes as sustainable, efficient, and health-promoting transportation modes. Bike network planning is a tedious process, relying on heuristic computational methods that frequently overlook the broader implications of introducing new cycling infrastructure, in particular the necessity to repurpose car lanes. In this work, we call for optimizing the trade-off between bike and car networks, effectively pushing for Pareto optimality. This shift in perspective gives rise to a novel linear programming formulation towards optimal bike network allocation. Our experiments, conducted using both real-world and synthetic data, testify the effectiveness and superiority of this optimization approach compared to heuristic methods. In particular, the framework provides stakeholders with a range of lane reallocation scenarios, illustrating potential bike network enhancements and their implications for car infrastructure. Crucially, our approach is adaptable to various bikeability and car accessibility evaluation criteria, making our tool a highl
We study an urban bike lane planning problem based on the fine-grained bike trajectory data, which is made available by smart city infrastructure such as bike-sharing systems. The key decision is where to build bike lanes in the existing road network. As bike-sharing systems become widespread in the metropolitan areas over the world, bike lanes are being planned and constructed by many municipal governments to promote cycling and protect cyclists. Traditional bike lane planning approaches often rely on surveys and heuristics. We develop a general and novel optimization framework to guide the bike lane planning from bike trajectories. We formalize the bike lane planning problem in view of the cyclists' utility functions and derive an integer optimization model to maximize the utility. To capture cyclists' route choices, we develop a bilevel program based on the Multinomial Logit model. We derive structural properties about the base model and prove that the Lagrangian dual of the bike lane planning model is polynomial-time solvable. Furthermore, we reformulate the route choice based planning model as a mixed integer linear program using a linear approximation scheme. We develop tract
Active mobility is becoming an essential component of the green transition in modern cities. However, the challenge of designing an efficient network of protected bike lanes without disrupting existing road networks for motorised vehicles remains unsolved. This paper focuses on the specific case of Milan, using a network approach that considers street widths to optimise the placement of dedicated bike lanes at the edges of the network. Unlike other network approaches in this field, our method considers the actual shapes of the streets, which introduces a realistic aspect lacking in current studies. We used these data to simulate cycling networks that maximise connectivity while minimising the impact of bike lane placement on the drivable network. Our mixed simulation strategies optimise for edge betweenness and width. Furthermore, we quantify the impact of dedicated bike lane infrastructure on the existing road network, demonstrating that it is feasible to create highly effective cycling networks with minimal disruption caused by lane width reductions. This paper illustrates how realistic cycling lanes can be simulated using road width data and discusses the challenges and benefits
Two autonomous mobile robots and a non-autonomous one, also called bike, are placed at the origin of an infinite line. The autonomous robots can travel with maximum speed $1$. When a robot rides the bike its speed increases to $v>1$, however only exactly one robot at a time can ride the bike and the bike is non-autonomous in that it cannot move on its own. An Exit is placed on the line at an unknown location and at distance $d$ from the origin. The robots have limited communication behavior; one robot is a sender (denoted by S) in that it can send information wirelessly at any distance and receive messages only in F2F (Face-to-Face), while the other robot is a receiver (denoted by R) in that it can receive information wirelessly but can send information only F2F. The bike has no communication capabilities of its own. We refer to the resulting communication model of the ensemble of the two autonomous robots and the bike as S/R. Our general goal is to understand the impact of the non-autonomous robot in assisting the evacuation of the two autonomous faulty robots. Our main contribution is to provide a new evacuation algorithm that enables both robots to evacuate from the unknown E
As more people move back into densely populated cities, bike sharing is emerging as an important mode of urban mobility. In a typical bike sharing system, riders arrive at a station and take a bike if it is available. After retrieving a bike, they ride it for a while, then return it to a station near their final destinations. Since space is limited in cities, each station has a finite capacity of docks, which cannot hold more bikes than its capacity. In this paper, we study bike sharing systems with stations having a finite capacity. By an appropriate scaling of our stochastic model, we prove a central limit theorem for an empirical process of the number of stations with $k$ bikes. The central limit theorem provides insight on the variance, and sample path dynamics of large scale bike sharing systems. We also leverage our results to estimate confidence intervals for various performance measures such as the proportion of empty stations, the proportion of full stations, and the number of bikes in circulation. These performance measures have the potential to inform the operations and design of future bike sharing systems.
Assume that $m \geq 1$ autonomous mobile agents and $0 \leq b \leq m$ single-agent transportation devices (called {\em bikes}) are initially placed at the left endpoint $0$ of the unit interval $[0,1]$. The agents are identical in capability and can move at speed one. The bikes cannot move on their own, but any agent riding bike $i$ can move at speed $v_i > 1$. An agent may ride at most one bike at a time. The agents can cooperate by sharing the bikes; an agent can ride a bike for a time, then drop it to be used by another agent, and possibly switch to a different bike. We study two problems. In the \BS problem, we require all agents and bikes starting at the left endpoint of the interval to reach the end of the interval as soon as possible. In the \RBS problem, we aim to minimize the arrival time of the agents; the bikes can be used to increase the average speed of the agents, but are not required to reach the end of the interval. Our main result is the construction of a polynomial time algorithm for the \BS problem that creates an arrival-time optimal schedule for travellers and bikes to travel across the interval. For the \RBS problem, we give an algorithm that gives an optim
Bit Flipping Key Encapsulation (BIKE) is a code-based cryptosystem that was considered in Round 4 of the NIST Post-Quantum Cryptography Standardization process. It is based on quasi-cyclic moderate-density parity-check (QC-MDPC) codes paired with an iterative decoder. While (low-density) parity-check codes have been shown to perform well in practice, their capabilities are governed by the code's graphical representation and the choice of decoder rather than the traditional code parameters, making it difficult to determine the decoder failure rate (DFR). Moreover, decoding failures have been demonstrated to lead to attacks that recover the BIKE private key. In this paper, we demonstrate a strong correlation between weak keys and $4$-cycles in their associated Tanner graphs. We give concrete ways to enumerate the number of 4-cycles in a BIKE key and use these results to present a filtering algorithm that will filter BIKE keys with large numbers of 4-cycles. These results also apply to more general parity check codes.
A free-floating bike-sharing system (FFBSS) is a dockless rental system where an individual can borrow a bike and returns it anywhere, within the service area. To improve the rental service, available bikes should be distributed over the entire service area: a customer leaving from any position is then more likely to find a near bike and then to use the service. Moreover, spreading bikes among the entire service area increases urban spatial equity since the benefits of FFBSS are not a prerogative of just a few zones. For guaranteeing such distribution, the FFBSS operator can use vans to manually relocate bikes, but it incurs high economic and environmental costs. We propose a novel approach that exploits the existing bike flows generated by customers to distribute bikes. More specifically, by envisioning the problem as an Influence Maximization problem, we show that it is possible to position batches of bikes on a small number of zones, and then the daily use of FFBSS will efficiently spread these bikes on a large area. We show that detecting these zones is NP-complete, but there exists a simple and efficient $1-1/e$ approximation algorithm; our approach is then evaluated on a data
In 2018, the City of Kelowna entered into a license agreement with Dropbike to operate a dockless bike-share pilot in and around the downtown core. The bikes were tracked by the user's cell phone GPS through the Dropbike app. The City's Active Transportation team recognized that this GPS data could help understand the routes used by cyclists which would then inform decision-making for infrastructure improvements. Using OSMnx and NetworkX, the map of Kelowna was converted into a graph network to map inaccurate, infrequent GPS points to the nearest street intersection, calculate the potential paths taken by cyclists and count the number of trips by street segment though the comparison of different path-finding models. Combined with the data from four counters around downtown, a mixed effects statistical model and a least squares optimization were used to estimate a relationship between the different traffic patterns of the bike-share and counter data. Using this relationship based on sparse data input from physical counting stations and bike share data, estimations and visualizations of the annual daily bicycle volume in downtown Kelowna were produced. The analysis, modelling and vis