scholarly journals Intercity Online Car-Hailing Travel Demand Prediction via a Spatiotemporal Transformer Method

2021 ◽  
Vol 11 (24) ◽  
pp. 11750
Author(s):  
Hongbo Li ◽  
Jincheng Wang ◽  
Yilong Ren ◽  
Feng Mao
Keyword(s):  
Travel Demand ◽  
Traffic Prediction ◽  
Time Interval ◽  
Learning Models ◽  
Critical Aspect ◽  
Study Region ◽  
Demand Prediction ◽  
Spatiotemporal Characteristics ◽  
Intercity Travel ◽  
Transformer Model

Traffic prediction is a critical aspect of many real-world scenarios that requires accurate traffic status predictions, such as travel demand prediction. The emergence of online car-hailing activities has given people greater mobility and makes intercity travel more frequent. The increase in online car-hailing demand has often led to a supply–demand imbalance where there is a mismatch between the immediate availability of car-hailing services and the number of passengers in certain areas. Accurate prediction of online car-hailing demand promotes efficiencies and minimizes resources and time waste. However, many prior related studies often fail to fully utilize spatiotemporal characteristics. With the development of newer deep-learning models, this paper aims to solve online car-hailing problems with an ST-transformer model. The spatiotemporal characteristics of online car-hailing data are analyzed and extracted. The study region is divided into subareas, and the demand for each subarea is summed at a specific time interval. Historical demand of the areas is used to predict future demand. The results of the ST-transformer outperformed other baseline models, namely, VAR, SVR, LSTM, LSTNet, and transformers. The validated results suggest that the ST-transformer is more capable of capturing spatiotemporal characteristics compared to the other models. Additionally, compared to others, the model is less affected by data sparsity.

scholarly journals Travel Demand Prediction during COVID-19 Pandemic: Educational and Working Trips at the University of Padova

2021 ◽  
Vol 13 (12) ◽  
pp. 6596
Author(s):  
Riccardo Ceccato ◽  
Riccardo Rossi ◽  
Massimiliano Gastaldi
Keyword(s):  
Travel Demand ◽  
Risk Mitigation ◽  
Great Part ◽  
Mitigation Measures ◽  
Sustainable Mobility ◽  
New Normal ◽  
Logistic Regression Models ◽  
Demand Prediction ◽  
Travel Decisions ◽  
The University

The diffusion of the COVID-19 pandemic has induced fundamental changes in travel habits. Although many previous authors have analysed factors affecting observed variations in travel demand, only a few works have focused on predictions of future new normal conditions when people will be allowed to decide whether to travel or not, although risk mitigation measures will still be enforced on vehicles, and innovative mobility services will be implemented. In addition, few authors have considered future mandatory trips of students that constitute a great part of everyday travels and are fundamental for the development of society. In this paper, logistic regression models were calibrated by using data from a revealed and stated-preferences mobility survey administered to students and employees at the University of Padova (Italy), to predict variables impacting on their decisions to perform educational and working trips in the new normal phase. Results highlighted that these factors are different between students and employees; furthermore, available travel alternatives and specific risk mitigation measures on vehicles were found to be significant. Moreover, the promotion of the use of bikes, as well as bike sharing, car pooling and micro mobility among students can effectively foster sustainable mobility habits. On the other hand, countermeasures on studying/working places resulted in a slight effect on travel decisions.

scholarly journals Identification of Precursory Anomalies before Moderate-strong Earthquakes in Junction of Shanxi-Hebei-Inner Mongolia Area

2020 ◽  
Vol 206 ◽  
pp. 01011
Author(s):  
Li Hong
Keyword(s):  
Standard Deviation ◽  
Inner Mongolia ◽  
Strong Earthquake ◽  
Earthquake Magnitude ◽  
Parameter Analysis ◽  
Time Interval ◽  
Study Region ◽  
Strong Earthquakes ◽  
Spectrum Method ◽  
Prediction Efficiency

In this paper, we take the Junction of Shanxi-Hebei-Inner Mongolia area as study region using earthquake corresponding relevancy spectrum method (ECRS method) to identify comprehensive precursory anomalies before moderate-strong earthquake. On base of single-parameter relevancy spectrum database with target earthquake magnitude as Ms4.7 and initial earthquake magnitude as Ms1, we carry on multi-parameter analysis and find that result with time interval of 9 months and anomaly threshold with 0.40 times standard deviation has better prediction efficiency. Its anomaly corresponding rate and earthquake corresponding rate are 6/10 and 9/9 respectively.

scholarly journals The value of the post-captopril aldosterone/renin ratio for the diagnosis of primary aldosteronism and the influential factors: A meta-analysis

2020 ◽  
Vol 21 (4) ◽  
pp. 147032032097203
Author(s):  
Qiao Xiang ◽  
Wen Wang ◽  
Tao Chen ◽  
Kai Yu ◽  
Qianrui Li ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
Primary Aldosteronism ◽  
Meta Analysis ◽  
Challenge Test ◽  
Influential Factors ◽  
Time Interval ◽  
Study Region ◽  
Subgroup Analyses ◽  
Study Population ◽  
Meta Regression

Objective: The procedure for the captopril challenge test (CCT) in diagnosing primary aldosteronism (PA) is not standardized. We performed a meta-analysis to evaluate the controversial diagnostic value and influential factors of the post-captopril aldosterone/renin ratio (ARR). Methods: We searched literature in databases for eligible studies (until October 1, 2020). We extracted information regarding study and patient characteristics, CCT methods, outcome data. We pooled studies using the random-effect model. We performed meta-regression and six pre-specified subgroup analyses to explore heterogeneity. Results: Nineteen studies involving 4568 subjects were included. The pooled sensitivity and specificity were 0.825 (95% CI 0.804–0.844) and 0.919 (95% CI 0.908–0.928). The area under the summary receiver operating characteristic curve was 0.9487 (95% CI 0.9207–0.9767). Meta-regression revealed that heterogeneity might derive from time interval ( p = 0.0117) and study population ( p = 0.0033). Subgroup analyses showed significant differences between the subgroups stratified by the dose, posture, study region, time interval, cut-off value and study population for sensitivity and/or specificity ( p < 0.05). Conclusion: Post-captopril ARR is comparably valuable for diagnosing PA at cut-offs from 12.0 to 50.0. Conducting the CCT in the supine position with 25 mg of captopril may attain greater sensitivity. Conducting the CCT in the seated position with 50 mg of captopril may attain greater specificity. A 90-min time interval may perform best in both the sensitivity and specificity.

scholarly journals Incorporating Graph Attention and Recurrent Architectures for City-Wide Taxi Demand Prediction

2019 ◽  
Vol 8 (9) ◽  
pp. 414 ◽  
Author(s):  
Ying Xu ◽  
Dongsheng Li
Keyword(s):  
State Of The Art ◽  
Sequence Data ◽  
Building Blocks ◽  
Time Interval ◽  
Mean Square ◽  
Key Factors ◽  
Demand Prediction ◽  
Traffic Efficiency ◽  
Temporal Correlations ◽  
Spatial Dependencies

Taxi demand prediction is one of the key factors in making online taxi hailing services more successful and more popular. Accurate taxi demand prediction can bring various advantages including, but not limited to, enhancing user experience, increasing taxi utilization, and optimizing traffic efficiency. However, the task is challenging because of complex spatial and temporal dependencies of taxi demand. In addition, relationships between non-adjacent regions are also critical for accurate taxi demand prediction, whereas they are largely ignored by existing approaches. To this end, we propose a novel graph and time-series learning model for city-wide taxi demand prediction in this paper. It has two main building blocks, the first one utilize a graph network with attention mechanism to effectively learn spatial dependencies of taxi demand in a broader perspective of the entire city, and the output at each time interval is then transferred to the second block. In the graph network, the edge is defined by an Origin–Destination relation to capture non-adjacent impacts. The second one uses a neural network which is adept with processing sequence data to capture the temporal correlations of city-wide taxi demand. Using a large, real-world dataset and three metrics, we conduct an extensive experimental study and find that our model outperforms state-of-the-art baselines by 9.3% in terms of the root-mean-square error.

Transferability of aggregate intercity total demand models

1995 ◽  
Vol 22 (2) ◽  
pp. 283-291
Author(s):  
Amal S. Kumarage ◽  
S. C. Wirasinghe
Keyword(s):  
Sri Lanka ◽  
Public Transportation ◽  
Travel Demand ◽  
Contextual Factors ◽  
Demand Model ◽  
Aggregate Model ◽  
Demand Models ◽  
Intercity Travel ◽  
Model Transfer ◽  
Contextual Characteristics

Over the last 15 years, extensive research has been done on the transferability of travel demand models. However, much of this work has been concentrated towards investigating the transferability of disaggregate mode choice models. The transferability of an aggregate total demand model for intercity travel is examined. Model transfer is possible only when a number of preconditions for transferability are satisfied. One of the principal obstacles to the successful transfer of intercity demand models is the inability to overcome the contextual differences between calibration and application. Here, the components of the intercity total demand model are separated into exogenous and intrinsic (contextual) factors. The latter is thereafter classified as being either transferable or nontransferable. It is shown that transferable attributes can accompany a model during transfer. Nontransferable attributes, on the other hand, will free the model of city or city-pair specific contextual characteristics which should not be transferred to other city pairs. The issues involved in transferring an aggregate model are also investigated. Aggregate data on interdistrict travel by public transportation in Sri Lanka have been used to successfully calibrate a total demand model with a number of transferable and nontransferable attributes that represent both temporal and spatial contextual factors. It is shown that the forecasting ability of this model is far superior to a counterpart model without the intrinsic variables. Key words: travel demand, aggregate, forecasting, transferability, intercity, Sri Lanka.

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