75 research outputs found
Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data
It is an enduring question how to combine revealed preference (RP) and stated
preference (SP) data to analyze travel behavior. This study presents a
framework of multitask learning deep neural networks (MTLDNNs) for this
question, and demonstrates that MTLDNNs are more generic than the traditional
nested logit (NL) method, due to its capacity of automatic feature learning and
soft constraints. About 1,500 MTLDNN models are designed and applied to the
survey data that was collected in Singapore and focused on the RP of four
current travel modes and the SP with autonomous vehicles (AV) as the one new
travel mode in addition to those in RP. We found that MTLDNNs consistently
outperform six benchmark models and particularly the classical NL models by
about 5% prediction accuracy in both RP and SP datasets. This performance
improvement can be mainly attributed to the soft constraints specific to
MTLDNNs, including its innovative architectural design and regularization
methods, but not much to the generic capacity of automatic feature learning
endowed by a standard feedforward DNN architecture. Besides prediction, MTLDNNs
are also interpretable. The empirical results show that AV is mainly the
substitute of driving and AV alternative-specific variables are more important
than the socio-economic variables in determining AV adoption. Overall, this
study introduces a new MTLDNN framework to combine RP and SP, and demonstrates
its theoretical flexibility and empirical power for prediction and
interpretation. Future studies can design new MTLDNN architectures to reflect
the speciality of RP and SP and extend this work to other behavioral analysis
Deep Neural Networks for Choice Analysis: Architectural Design with Alternative-Specific Utility Functions
Whereas deep neural network (DNN) is increasingly applied to choice analysis,
it is challenging to reconcile domain-specific behavioral knowledge with
generic-purpose DNN, to improve DNN's interpretability and predictive power,
and to identify effective regularization methods for specific tasks. This study
designs a particular DNN architecture with alternative-specific utility
functions (ASU-DNN) by using prior behavioral knowledge. Unlike a fully
connected DNN (F-DNN), which computes the utility value of an alternative k by
using the attributes of all the alternatives, ASU-DNN computes it by using only
k's own attributes. Theoretically, ASU-DNN can dramatically reduce the
estimation error of F-DNN because of its lighter architecture and sparser
connectivity. Empirically, ASU-DNN has 2-3% higher prediction accuracy than
F-DNN over the whole hyperparameter space in a private dataset that we
collected in Singapore and a public dataset in R mlogit package. The
alternative-specific connectivity constraint, as a domain-knowledge-based
regularization method, is more effective than the most popular generic-purpose
explicit and implicit regularization methods and architectural hyperparameters.
ASU-DNN is also more interpretable because it provides a more regular
substitution pattern of travel mode choices than F-DNN does. The comparison
between ASU-DNN and F-DNN can also aid in testing the behavioral knowledge. Our
results reveal that individuals are more likely to compute utility by using an
alternative's own attributes, supporting the long-standing practice in choice
modeling. Overall, this study demonstrates that prior behavioral knowledge
could be used to guide the architecture design of DNN, to function as an
effective domain-knowledge-based regularization method, and to improve both the
interpretability and predictive power of DNN in choice analysis
Fairness-enhancing deep learning for ride-hailing demand prediction
Short-term demand forecasting for on-demand ride-hailing services is one of
the fundamental issues in intelligent transportation systems. However, previous
travel demand forecasting research predominantly focused on improving
prediction accuracy, ignoring fairness issues such as systematic
underestimations of travel demand in disadvantaged neighborhoods. This study
investigates how to measure, evaluate, and enhance prediction fairness between
disadvantaged and privileged communities in spatial-temporal demand forecasting
of ride-hailing services. A two-pronged approach is taken to reduce the demand
prediction bias. First, we develop a novel deep learning model architecture,
named socially aware neural network (SA-Net), to integrate the
socio-demographics and ridership information for fair demand prediction through
an innovative socially-aware convolution operation. Second, we propose a
bias-mitigation regularization method to mitigate the mean percentage
prediction error gap between different groups. The experimental results,
validated on the real-world Chicago Transportation Network Company (TNC) data,
show that the de-biasing SA-Net can achieve better predictive performance in
both prediction accuracy and fairness. Specifically, the SA-Net improves
prediction accuracy for both the disadvantaged and privileged groups compared
with the state-of-the-art models. When coupled with the bias mitigation
regularization method, the de-biasing SA-Net effectively bridges the mean
percentage prediction error gap between the disadvantaged and privileged
groups, and also protects the disadvantaged regions against systematic
underestimation of TNC demand. Our proposed de-biasing method can be adopted in
many existing short-term travel demand estimation models, and can be utilized
for various other spatial-temporal prediction tasks such as crime incidents
predictions
Mapping the concentration changes during the dynamic processes of crevice corrosion by digital
The dynamic process of crevice corrosion during anodic dissolution of a crevice electrode in a 5.0 mmol dm-3 NaCl solution has been studied by digital holographic reconstruction. Digital holographic reconstruction has been proved to be an effective and in situ technique to detect the changes in the solution concentration because useful and direct information can be obtained from the three-dimensional images. It provides a valuable method for a better understanding of the mechanism of crevice corrosion by studying the dynamic processes of changes in the solution concentration at the interface of crevice corrosion
Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction
Predicting traffic incident risks at granular spatiotemporal levels is
challenging. The datasets predominantly feature zero values, indicating no
incidents, with sporadic high-risk values for severe incidents. Notably, a
majority of current models, especially deep learning methods, focus solely on
estimating risk values, overlooking the uncertainties arising from the
inherently unpredictable nature of incidents. To tackle this challenge, we
introduce the Spatiotemporal Zero-Inflated Tweedie Graph Neural Networks
(STZITD-GNNs). Our model merges the reliability of traditional statistical
models with the flexibility of graph neural networks, aiming to precisely
quantify uncertainties associated with road-level traffic incident risks. This
model strategically employs a compound model from the Tweedie family, as a
Poisson distribution to model risk frequency and a Gamma distribution to
account for incident severity. Furthermore, a zero-inflated component helps to
identify the non-incident risk scenarios. As a result, the STZITD-GNNs
effectively capture the dataset's skewed distribution, placing emphasis on
infrequent but impactful severe incidents. Empirical tests using real-world
traffic data from London, UK, demonstrate that our model excels beyond current
benchmarks. The forte of STZITD-GNN resides not only in its accuracy but also
in its adeptness at curtailing uncertainties, delivering robust predictions
over short (7 days) and extended (14 days) timeframes
Dynamic Sensing of Localized Corrosion at the Metal/Solution Interface
A Mach-Zehnder interferometer is employed to detect localized corrosion at the metal/solution interface in the potentiodynamic sweep of the iron electrode in solutions. During the electrochemical reactions, local variations of the electrolyte's refractive index, which correlate with the concentration of dissolved species, change the optical path length (OPL) of the object beam when the beam passes through the electrolyte. The distribution of the OPL difference was obtained to present the concentration change of the metal ions visually, which enable direct evidence of corrosion processes. The OPL difference distribution shows localized and general corrosion during the anodic dissolution of the iron electrode in solutions with and without chloride ions, respectively. This method provides an approach for dynamic detection of localized corrosion at the metal/solution interface
FWAlgaeDB, an integrated genome database of freshwater algae
Algal genomics research contributes to a deeper understanding of algal evolution and provides useful genomics inferences correlated with various functions. Published algal genome sequences are very limited owing to genome assembly challenges. Because genome data of freshwater algae are rapidly increasing with the recent boom in next-generation sequencing and bioinformatics, an interface to store, interlink, and display these data is needed. To provide a substantial genomic resource specifically for freshwater algae, we developed the Freshwater Algae Database (FWAlgaeDB), a user-friendly, constantly updated online repository for integrating genomic data and annotation information. This database, which includes information on 204 freshwater algae, allows easy access to gene repertoires and gene clusters of interest and facilitates potential applications. Three functional modules are integrated into FWAlgaeDB: a Basic Local Alignment Search Tool tool for similarity analyses, a Search tool for rapid data retrieval, and a Download function for data downloads. This database tool is freely available at http://www.fwalagedb.com/#/home. To demonstrate the utility of FWAlgaeDB, we also individually mapped metagenomic sequencing reads of 10 water samples to FWAlgaeDB and Nt algae databases we constructed to obtain taxonomic composition information. According to the mapping results, FWAlgaeDB may be a better choice for identifying algal species in freshwater samples, with fewer potential false positives because of its focus on freshwater algal species. FWAlgaeDB can therefore serve as an open-access, sustained platform to provide genomic data and molecular analysis tools specifically for freshwater algae
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