scholarly journals Excess demand prediction for bike sharing systems

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252894
Author(s):  
Xin Liu ◽  
Konstantinos Pelechrinis
Keyword(s):  
Real Data ◽  
Transportation Systems ◽  
Excess Demand ◽  
Demand And Supply ◽  
Demand Prediction ◽  
Bike Sharing ◽  
Demand Rates ◽  
The Difference ◽  
Supply Analysis ◽  
Demand Patterns

One of the most crucial elements for the long-term success of shared transportation systems (bikes, cars etc.) is their ubiquitous availability. To achieve this, and avoid having stations with no available vehicle, service operators rely on rebalancing. While different operators have different approaches to this functionality, overall it requires a demand-supply analysis of the various stations. While trip data can be used for this task, the existing methods in the literature only capture the observed demand and supply rates. However, the excess demand rates (e.g., how many customers attempted to rent a bike from an empty station) are not recorded in these data, but they are important for the in-depth understanding of the systems’ demand patterns that ultimately can inform operations like rebalancing. In this work we propose a method to estimate the excess demand and supply rates from trip and station availability data. Key to our approach is identifying what we term as excess demand pulse (EDP) in availability data as a signal for the existence of excess demand. We then proceed to build a Skellam regression model that is able to predict the difference between the total demand and supply at a given station during a specific time period. Our experiments with real data further validate the accuracy of our proposed method.

scholarly journals Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1250
Author(s):  
Daniel Medina ◽  
Haoqing Li ◽  
Jordi Vilà-Valls ◽  
Pau Closas
Keyword(s):  
Intelligent Transportation Systems ◽  
Robust Statistics ◽  
Real Data ◽  
Transportation Systems ◽  
Global Navigation Satellite Systems ◽  
Mixed Integer ◽  
Robust Filtering ◽  
Precise Positioning ◽  
The Impact ◽  
Filtering Techniques

Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. Exploiting carrier phase observations allows for precise positioning solutions at the complexity cost of resolving integer phase ambiguities, a procedure that is particularly affected by non-nominal conditions. This limits the applicability of conventional filtering techniques in challenging scenarios, and new robust solutions must be accounted for. This contribution deals with real-time kinematic (RTK) positioning and the design of robust filtering solutions for the associated mixed integer- and real-valued estimation problem. Families of Kalman filter (KF) approaches based on robust statistics and variational inference are explored, such as the generalized M-based KF or the variational-based KF, aiming to mitigate the impact of outliers or non-nominal measurement behaviors. The performance assessment under harsh propagation conditions is realized using a simulated scenario and real data from a measurement campaign. The proposed robust filtering solutions are shown to offer excellent resilience against outlying observations, with the variational-based KF showcasing the overall best performance in terms of Gaussian efficiency and robustness.

scholarly journals Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivity

Biometrika ◽  
2020 ◽  
Author(s):  
S Na ◽  
M Kolar ◽  
O Koyejo
Keyword(s):  
Graphical Models ◽  
Latent Variable ◽  
Graphical Model ◽  
Brain Connectivity ◽  
Statistical Error ◽  
Real Data ◽  
Low Rank ◽  
Conditional Dependence ◽  
Gradient Descent Algorithm ◽  
The Difference

Abstract Differential graphical models are designed to represent the difference between the conditional dependence structures of two groups, thus are of particular interest for scientific investigation. Motivated by modern applications, this manuscript considers an extended setting where each group is generated by a latent variable Gaussian graphical model. Due to the existence of latent factors, the differential network is decomposed into sparse and low-rank components, both of which are symmetric indefinite matrices. We estimate these two components simultaneously using a two-stage procedure: (i) an initialization stage, which computes a simple, consistent estimator, and (ii) a convergence stage, implemented using a projected alternating gradient descent algorithm applied to a nonconvex objective, initialized using the output of the first stage. We prove that given the initialization, the estimator converges linearly with a nontrivial, minimax optimal statistical error. Experiments on synthetic and real data illustrate that the proposed nonconvex procedure outperforms existing methods.

scholarly journals A Robust Rerank Approach for Feature Selection and Its Application to Pooling-Based GWA Studies

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jia-Rou Liu ◽  
Po-Hsiu Kuo ◽  
Hung Hung
Keyword(s):  
Characteristic Curve ◽  
Real Data ◽  
Tuning Parameter ◽  
Practical Implementation ◽  
Tuning Parameters ◽  
Genome Wide ◽  
Feature Based ◽  
The Difference ◽  
Gwa Studies ◽  
Selection Of

Large-p-small-ndatasets are commonly encountered in modern biomedical studies. To detect the difference between two groups, conventional methods would fail to apply due to the instability in estimating variances int-test and a high proportion of tied values in AUC (area under the receiver operating characteristic curve) estimates. The significance analysis of microarrays (SAM) may also not be satisfactory, since its performance is sensitive to the tuning parameter, and its selection is not straightforward. In this work, we propose a robust rerank approach to overcome the above-mentioned diffculties. In particular, we obtain a rank-based statistic for each feature based on the concept of “rank-over-variable.” Techniques of “random subset” and “rerank” are then iteratively applied to rank features, and the leading features will be selected for further studies. The proposed re-rank approach is especially applicable for large-p-small-ndatasets. Moreover, it is insensitive to the selection of tuning parameters, which is an appealing property for practical implementation. Simulation studies and real data analysis of pooling-based genome wide association (GWA) studies demonstrate the usefulness of our method.

Qualitative Reasoning Approach to a Driver’s Cognitive Mental Load

2013 ◽  
pp. 145-157
Author(s):  
Shinichiro Sega ◽  
Hirotoshi Iwasaki ◽  
Hironori Hiraishi ◽  
Fumio Mizoguchi
Keyword(s):  
Eye Movements ◽  
User Interfaces ◽  
Mental State ◽  
Intelligent Transportation Systems ◽  
Real Data ◽  
Qualitative Reasoning ◽  
Transportation Systems ◽  
Cognitive State ◽  
Mental Load ◽  
Car Driving

This paper explores applying qualitative reasoning to a driver’s mental state in real driving situations so as to develop a working load for intelligent transportation systems. The authors identify the cognitive state that determines whether a driver will be ready to operate a device in car navigation. In order to identify the driver’s cognitive state, the authors will measure eye movements during car-driving situations. Data can be acquired for the various actions of a car driver, in particular braking, acceleration, and steering angles from the experiment car. The authors constructed a driver cognitive mental load using the framework of qualitative reasoning. The response of the model was checked by qualitative simulation. The authors also verified the model using real data collected by driving an actual car. The results indicated that the model could represent the change in the cognitive mental load based on measurable data. This means that the framework of this paper will be useful for designing user interfaces for next-generation systems that actively employ user situations.

scholarly journals Empirical Study of Omnichannel Purchasing Pattern with Real Customer Data from Health and Lifestyle Company

2019 ◽  
Vol 11 (24) ◽  
pp. 7185 ◽  
Author(s):  
Jongsoo Kang ◽  
Marko Majer ◽  
Hyun-Jung Kim
Keyword(s):  
High Performance ◽  
Statistical Significance ◽  
Real Data ◽  
Usage Pattern ◽  
Customer Data ◽  
Customer Group ◽  
Trading Period ◽  
Online Channel ◽  
The Difference ◽  
Offline Channels

This study examines the effect of omnichannel usage pattern on customers’ purchasing amount by determining statistical significance of different purchasing amount occurred for online and offline channel usage pattern with empirical analysis. The data is collected from a health and lifestyle company operated by Major Pharmaceutical company in Korea, which sells health supplement and skincare products through their owned online and offline channels. The channel usage pattern of customers is categorized into four groups: Customer using online channel only, customer using offline channel only, customer first joined membership through online and use both on/offline channels and customers joined membership through offline channel and use both on/offline. Then, the trading period, total number of purchasing, average purchasing amount per transaction and total purchasing amount during trading period among the above four groups were analyzed. The result demonstrated the number of purchasing, average purchasing amount and total purchasing amount for the omnichannel customer groups who cross used on and offline showed statistical significance. However, the difference in purchasing amount between the group of customers who joined online membership and use offline channel and another customer group that joined offline membership and use online channel was not statistically significant. This study overcame the limitation of conventional studies used survey based data, by the application of empirical data from the real customers in on/offline channels, and provides meaningful insights based on empirical real data that group of customers with higher purchasing experience in both on/offline channels shows high performance.

Apparent diffusion time of oxygen from blood to tissue in rat cerebral cortex: implication for tissue oxygen dynamics during brain functions

2007 ◽  
Vol 103 (4) ◽  
pp. 1352-1358 ◽  
Author(s):  
Kazuto Masamoto ◽  
Jeff Kershaw ◽  
Masakatsu Ureshi ◽  
Naosada Takizawa ◽  
Hirosuke Kobayashi ◽  
...  
Keyword(s):  
Time Course ◽  
Oxygen Demand ◽  
Excess Demand ◽  
Tissue Oxygen ◽  
Doppler Flowmetry ◽  
Demand And Supply ◽  
Brain Functions ◽  
Apparent Diffusion ◽  
Time Required ◽  
Stimulus Length

To investigate the dynamics of tissue oxygen demand and supply during brain functions, we simultaneously recorded Po2 and local cerebral blood flow (LCBF) with an oxygen microelectrode and laser Doppler flowmetry, respectively, in rat somatosensory cortex. Electrical hindlimb stimuli were applied for 1, 2, and 5 s to vary the duration of evoked cerebral metabolic rate of oxygen (CMRO2). The electrical stimulation induced a robust increase in Po2 (4–9 Torr at peak) after an increase in LCBF (14–26% at peak). A consistent lag of ∼1.2 s (0.6–2.3 s for individual animals) in the Po2 relative to LCBF was found, irrespective of stimulus length. It is argued that the lag in Po2 was predominantly caused by the time required for oxygen to diffuse through tissue. During brain functions, the supply of fresh oxygen further lagged because of the latency of LCBF onset (∼0.4 s). The results indicate that the tissue oxygen supports excess demand until the arrival of fresh oxygen. However, a large drop in Po2 was not observed, indicating that the evoked neural activity demands little extra oxygen or that the time course of excess demand is as slow as the increase in supply. Thus the dynamics of Po2 during brain functions predominantly depend on the time course of LCBF. Possible factors influencing the lag between demand and supply are discussed, including vascular spacing, reactivity of the vessels, and diffusivity of oxygen.

scholarly journals Bayesian Inference for the Difference of Two Proportion Parameters in Over-Reported Two-Sample Binomial Data Using the Doubly Sample

Stats ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 111-120 ◽  
Author(s):  
Dewi Rahardja
Keyword(s):  
Bayesian Inference ◽  
Closed Form ◽  
Bayesian Approach ◽  
Interval Estimation ◽  
Real Data ◽  
Simulation Studies ◽  
Posterior Distributions ◽  
Binomial Data ◽  
The Difference ◽  
Data Subject

We construct a point and interval estimation using a Bayesian approach for the difference of two population proportion parameters based on two independent samples of binomial data subject to one type of misclassification. Specifically, we derive an easy-to-implement closed-form algorithm for drawing from the posterior distributions. For illustration, we applied our algorithm to a real data example. Finally, we conduct simulation studies to demonstrate the efficiency of our algorithm for Bayesian inference.

scholarly journals Fairness-Aware Demand Prediction for New Mobility

2020 ◽  
Vol 34 (01) ◽  
pp. 1079-1087
Author(s):  
An Yan ◽  
Bill Howe
Keyword(s):  
Continuous Distribution ◽  
Urban Mobility ◽  
Demographic Information ◽  
Demographic Groups ◽  
Demand Prediction ◽  
Geographic Patterns ◽  
Transportation Modes ◽  
Bike Sharing ◽  
Space Constant ◽  
Socioeconomic Inequity

Emerging transportation modes, including car-sharing, bike-sharing, and ride-hailing, are transforming urban mobility yet have been shown to reinforce socioeconomic inequity. These services rely on accurate demand prediction, but the demand data on which these models are trained reflect biases around demographics, socioeconomic conditions, and entrenched geographic patterns. To address these biases and improve fairness, we present FairST, a fairness-aware demand prediction model for spatiotemporal urban applications, with emphasis on new mobility. We use 1D (time-varying, space-constant), 2D (space-varying, time-constant) and 3D (both time- and space-varying) convolutional branches to integrate heterogeneous features, while including fairness metrics as a form of regularization to improve equity across demographic groups. We propose two spatiotemporal fairness metrics, region-based fairness gap (RFG), applicable when demographic information is provided as a constant for a region, and individual-based fairness gap (IFG), applicable when a continuous distribution of demographic information is available. Experimental results on bike share and ride share datasets show that FairST can reduce inequity in demand prediction for multiple sensitive attributes (i.e. race, age, and education level), while achieving better accuracy than even state-of-the-art fairness-oblivious methods.

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