As the IoT technology becomes well established and sharing economy expands worldwide, the bike-sharing system (BSS) has spread fast in the last decade. When introducing the BSS in a new city, the operator often faces many challenges: e.g., optimizing station siting (physical or electric), constructing bike lanes, and making strategies for the initial distribution and rebalancing of bikes. These challenges require an accurate interblock flow prediction before deploying the BSS. In this article, we derive blocks from the urban road network and extract blocks’ features based on the distribution and types of POI. Then, the interblock flow can be predicted based on the features of both start/end block and neighbor blocks. We propose a unified architecture with a generalized attention mechanism named the relation graph network (RGN) to extract block features and make predictions across cities. The evaluations on real-world data sets show that RGN outperforms other popular graph neural networks in multiple metrics. We further simulate the applications of station site selection and bike lane planning based on the prediction from RGN, and the result provides a noticeable increase in the BSS’s deployment/operation efficiency.

Single-Shot Echo Planar Imaging (SSEPI) based Diffusion Weighted Imaging (DWI) has shortcomings such as low resolution and severe distortions. In contrast, Multi-Shot EPI (MSEPI) provides optimal spatial resolution but increases scan time. This study proposed a Multiple b-value mOdel-based Residual Network (MORN) model to reconstruct multiple b-value high-resolution DWI from undersampled k-space data simultaneously. We incorporated Parallel Imaging (PI) into a residual U-net to reconstruct multiple b-value multi-coil data with the supervision of MUltiplexed Sensitivity-Encoding (MUSE) reconstructed Multi-Shot DWI (MSDWI). Moreover, asymmetric concatenations among different b-values and the combined loss to back propagate helped the feature transfer. After training and validation of the MORN in a dataset of 32 healthy cases, additional assessments were performed on 6 patients with different tumor types. The experimental results demonstrated that the MORN model outperformed conventional PI reconstruction (i.e. SENSE) and two state-of-the-art deep learning methods (SENSE-GAN and VSNet) in terms of PSNR (Peak Signal-to-Noise Ratio), SSIM (Structual SIMilarity) and apparent diffusion coefficient maps. In addition, using the pre-trained model under DWI, the MORN achieved consistent fractional anisotrophy and mean diffusivity reconstructed from multiple diffusion directions. Hence, the proposed method shows potential in clinical application according to the observations on tumor patients as well as images of multiple diffusion directions.

Advanced traveler information systems (ATISs) provide travelers with public transportation information to improve the quality of individual life and alleviate congestion as well as air pollution. However, existing works have not fully incorporated bike sharing systems within ATIS, providing no interaction with other modalities nor taking bike stocks into account. In addition, the uncertainty of traffic conditions and multimodal routing makes it challenging to accurately estimate the travel time. In this paper, we leverage large-scale historical data collected in London and construct a multimodal transportation network, including bus, tube, public bikes, and walking. We solve the modalities aggregation problem by practically modeling the travel time, arrival time, bike stock, and transfer time between different transport modalities. Furthermore, we propose TERP, a time-event-dependent route planner that optimizes both trip duration and reliability. We conduct experiments on extensive real-world data with over 23 million arrival records and 15 million stock records on more than 10 000 stations from transport for London platform (TfL). The results validates 14.91% reduction of actual total trip duration and 56.28% improvement in terms of route reliability in rush hours comparing with TfL.

As an innovative mobility strategy, public bike-sharing has grown dramatically worldwide. Though it provides convenient, low-cost, and environmental-friendly transportation, the unique features of bike-sharing systems give rise to problems for both users and operators. The primary issue is the uneven distribution of bikes caused by ever-changing usage and (available) supply. This imbalance necessitates efficient bike rebalancing strategies, which depends highly on bike mobility modeling and prediction. In this paper, a trace-driven simulation-based prediction approach is proposed by simultaneously taking user mobility demand and real-time status of stations into consideration. We extensively evaluate the performance of our design with the dataset from one of the world's largest public bike-sharing systems located in Hangzhou, China, which owns more than 2800 stations. The evaluation results show an 85 percentile relative error of 0.6 for checkout and 0.4 for checkin prediction. The preliminary results on how the predictions can be used for bike rebalancing are also provided. We believe that this new mobility modeling and prediction approach can improve the bike-sharing system operation algorithm design and pave the way for rapid deployment and adoption of bike-sharing systems across the globe.

The rapid development of bike-sharing systems has brought people enormous convenience during the past decade. On the other hand, high transport flexibility gives rise to problems for both users and operators. For users, dynamic distribution of shared bikes caused by uneven user demand often leads to the check in or check out service unavailable at some stations. For operators, unbalanced bike usage comes with more bike broken and growing maintenance cost. In this paper, we consider enhancing user experiences and rebalance bicycle utilization by directing users to different stations with a higher success rate of rental and return. For the first time, we devise a trip advisor that recommends bike check-in and check-out stations with joint consideration of service quality and bicycle utilization. To ensure service quality, we firstly predict the user demand of each station to obtain the success rate of rental and return in the future. Experiments indicate that the precision of our method is as much as 0.826, which has raised by 25.9 percent as compared with that of the historical average method. To rebalance bike usage, from historical data, we identify that biased bike usage is rooted from circumscribed bicycle circulation among few active stations. Therefore, with defined station activeness, we optimize the bike circulation by leading users to shift bikes between highly active stations and inactive ones. We extensively evaluate the performance of our design through real-world datasets. Evaluation results show that the percentage of frequently used bikes decreases by 33.6 percent on usage number and 28.6 percent on usage time.

Rapid development of bike-sharing systems has brought people enormous convenience during the past decade. On the other hand, high transport flexibility comes with dynamic distribution of shared bikes, leading to an unbalanced bike usage and growing maintenance cost. In this paper, we consider to rebalance bicycle utilization by means of directing users to different stations. For the first time, we devise a trip advisor that recommends bike check-in and check-out stations with joint consideration of service quality and bicycle utilization. From historical data, we firstly identify that biased bike usage is rooted from circumscribed bicycle circulation among few active stations. Therefore, with defined station activeness, we optimize the bike circulation by leading users to shift bikes between highly active stations and inactive ones. We extensively evaluate the performance of our design through real-world datasets. Evaluation results show that the percentage of frequent used bikes decreases by 33.6% on usage number and 28.6% on usage time.

As an innovative mobility strategy, public bike-sharing has grown dramatically worldwide. Though providing convenient, low-cost and environmental-friendly transportation, the unique features of bike-sharing systems give rise to problems to both users and operators. The primary issue among these problems is the uneven distribution of bicycles caused by the ever-changing usage and (available) supply. This bicycle imbalance issue necessitates efficient bike re-balancing strategies, which depends highly on bicycle mobility modeling and prediction. In this paper, for the first time, we propose a spatio-temporal bicycle mobility model based on historical bike-sharing data, and devise a traffic prediction mechanism on a per-station basis with sub-hour granularity. We extensively evaluated the performance of our design through a one-year dataset from the world's largest public bike-sharing system (BSS) with more than 2800 stations and over 103 million check in/out records. Evaluation results show an 85 percentile relative error of 0.6 for both check in and check out prediction. We believe this new mobility modeling and prediction approach can advance the bike re-balancing algorithm design and pave the way for the rapid deployment and adoption of bike-sharing systems across the globe.