Bikeshare Paradigms, User Perceptions, and the Urban Experience: A Comparative Analysis of Mobike Shanghai & Citi Bike NYC

Bicycle sharing schemes have gone global and the number of shared bicycles in use has ostensibly more than doubled over the past two years. Two general paradigms of bikeshare are currently competing for resources as well as hearts and minds of potential customers. Dockless bikeshare (best represented by Mobike Shanghai) and docked bikeshare (best represented by Citi Bike New York) are opposites in many regards, but their goals tend to dovetail. This study addresses the similarities and differences of these bikeshare paradigms, investigating their impacts on users' experiences of their respective urban milieus and the antecedents and correlates of user perceptions, based on survey questionnaires deployed in Shanghai and New York. This study finds that dockless bikeshare shows advantages over docked bikeshare, manifested in a greater diversity of ridership among participants, more substantive perceived effects on users' relationships with their urban environment, and an overarching importance of convenience to user perceptions. The projected expansion of bikeshare, especially dockless bikeshare underscored the timeliness of this study. By offering future directions for researchers and policymakers, this study will thus aid interested parties in decision-making process with regard to bikeshare support and implementation

Dynamic Repositioning For Bikesharing Systems

Bikesharing systems’ popularity has continuously been rising during the past years due to technological advancements. Managing and maintaining these emerging systems are indispensable parts of these systems and are necessary for their sustainable growth and successful implementation. One of the challenges that operators of these systems are facing is the uneven distribution of bikes due to users’ activities. These imbalances in the system can result in a lack of bikes or docks and consequently cause user dissatisfaction. A dynamic repositioning model that integrates prediction and routing is proposed to address this challenge. This operational model includes prediction, optimization, and simulation modules and can assist the operators of these systems in maintaining an effective system during peak periods with less number of unmet demands. It also can provide insights for planners by preparing development plans with the ultimate goal of more efficient systems. Developing a reliable prediction module that has the ability to predict future station-level demands can help system operators cope with the rebalancing needs more effectively. In this research, we utilize the expressive power of neural networks for predicting station-level demands (number of pick-ups and drop-offs) of bikeshare systems over multiple future time intervals. We examine the possibility of improving predictions by taking into account new sources of information about these systems, namely membership type and status of stations. A mathematical formulation is then developed for repositioning the bikes in the system with the goal of minimizing the number of unmet demands. The proposed module is a dynamic multi-period model with a rolling horizon which accounts for demands in the future time intervals. The performance of the optimization module and its assumptions are evaluated using discrete event simulation. Also, a three-step heuristic method is developed for solving large-size problems in a reasonable time. Finally, the integrated model is tested on several case studies from Capital Bikeshare, the District of Columbia’s bikeshare program

Using a Machine Learning Approach to Implement and Evaluate Product Line Features

Bike-sharing systems are a means of smart transportation in urban environments with the benefit of a positive impact on urban mobility. In this paper we are interested in studying and modeling the behavior of features that permit the end user to access, with her/his web browser, the status of the Bike-Sharing system. In particular, we address features able to make a prediction on the system state. We propose to use a machine learning approach to analyze usage patterns and learn computational models of such features from logs of system usage. On the one hand, machine learning methodologies provide a powerful and general means to implement a wide choice of predictive features. On the other hand, trained machine learning models are provided with a measure of predictive performance that can be used as a metric to assess the cost-performance trade-off of the feature. This provides a principled way to assess the runtime behavior of different components before putting them into operation.Comment: In Proceedings WWV 2015, arXiv:1508.0338

Rebalancing shared mobility systems by user incentive scheme via reinforcement learning

Shared mobility systems regularly suffer from an imbalance of vehicle supply within the system, leading to users being unable to receive service. If such imbalance problems are not mitigated some users will not be serviced. There is an increasing interest in the use of reinforcement learning (RL) techniques for improving the resource supply balance and service level of systems. The goal of these techniques is to produce an effective user incentivization policy scheme to encourage users of a shared mobility system to slightly alter their travel behavior in exchange for a small monetary incentive. These slight changes in user behavior are intended to over time increase the service level of the shared mobility system and improve user experience. In this thesis, two important questions are explored: (1) What state-action representation should be used to produce an effective user incentive scheme for a shared mobility system? (2) How effective are reinforcement learning-based solutions on the rebalancing problem under varying levels of resource supply, user demand, and budget? Our extensive empirical results based on data-driven simulation show that: 1. A state space with predicted user behavior coupled with a simple action mechanism produces an effective incentive scheme under varying environment scenarios. 2. The reinforcement learning-based incentive mechanisms perform at varying degrees of effectiveness under different environmental scenarios in terms of service level

Spatio-temporal forecasts for bike availability in dockless bike sharing systems

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesForecasting bike availability is of great importance when turning the shared bike into a reliable, pleasant and uncomplicated mode of transport. Several approaches have been developed to forecast bike availability in station-based bike sharing systems. However, dockless bike sharing systems remain fairly unexplored in that sense, despite their rapid expansion over the world in recent years. To fill this gap, this thesis aims to develop a generally applicable methodology for bike availability forecasting in dockless bike sharing systems, that produces automated, fast and accurate forecasts. To balance speed and accuracy, an approach is taken in which the system area of a dockless bike sharing system is divided into spatially contiguous clusters that represent locations with the same temporal patterns in the historical data. Each cluster gets assigned a model point, for which an ARIMA(p,d,q) forecasting model is fitted to the deseasonalized data. Each individual forecast will inherit the structure and parameters of one of those pre-build models, rather than building a new model on its own. The proposed system was tested through a case study in San Francisco, California. The results showed that the proposed system outperforms simple baseline methods. However, they also highlighted the limited forecastability of dockless bike sharing data

Comparing cities’ cycling patterns using online shared bicycle maps

Bicycle sharing systems are increasingly being deployed in urban areas around the world, alongside online maps that disclose the state (i.e., location, number of bicycles/number of free parking slots) of stations in each city. Recent work has demonstrated how regularly monitoring these online maps allows for a granular analysis of a city’s cycling trends; further, the literature indicates that different cities have unique spatio-temporal patterns, reducing the generalisability of any insights or models derived from a single system. In this work, we analyse 4.5 months of online bike-sharing map data from 10 cities which, combined, have 996 stations. While an aggregate comparison supports the view of cities having unique usage patterns, results of applying unsupervised learning to the temporal data shows that, instead, only the larger systems display heterogeneous behaviour, indicating that many of these systems share intrinsic similarities. We further show how these similarities are reflected in the predictability of stations’ occupancy data via a cross-city comparison of the error that a variety of approaches achieve when forecasting the number of bicycles that a station will have in the near future.We close by discussing the impact of uncovering these similarities on how future bicycle sharing systems can be designed, built, and managed.This is the accepted manuscript. The final published version is available at http://link.springer.com/article/10.1007%2Fs11116-015-9599-9

Forecasting Bike Rental Demand Using New York Citi Bike Data

The idea of this project is from a Kaggle competition “Bike Sharing Demand”① which provides dataset of Capital Bikeshare in Washington D.C. and asked to combine historical usage patterns with weather data in order to forecast bike rental demand. This dissertation will extend this work, working with a broader range of project not only just focusing on the phrase of model building but all phases of KDD (Knowledge Discovery in Databases). This dissertation focuses on Citi Bike which is one of the biggest bike share projects in the world, collects Citi Bike data, weather data and holiday data from three different databases, and integrates the data to a model ready format. Four basic predictive models are built and compared using multiple modelling algorithms, five techniques are used to enhance the accuracy of random forest model, and the final model’s RMSLE (with 10-fold cross validation) decreases from 0.499 to 0.265. This paper learns many experience from case study of Kaggle Bike Sharing Demand, and seek to build optimize predictive model with smallest error rate. This project generally answers a question of “How many bikes will meet users’ demand in a future certain time”, the future work of this project will be to focus on each docking station’s activity. The realistic meaning of this dissertation is to provide an overview solution for bike rebalance problem, and helps to better manage Citi Bike program