Towards better traffic volume estimation: Tackling both underdetermined and non-equilibrium problems via a correlation-adaptive graph convolution network

Traffic volume is an indispensable ingredient to provide fine-grained information for traffic management and control. However, due to limited deployment of traffic sensors, obtaining full-scale volume information is far from easy. Existing works on this topic primarily focus on improving the overall estimation accuracy of a particular method and ignore the underlying challenges of volume estimation, thereby having inferior performances on some critical tasks. This paper studies two key problems with regard to traffic volume estimation: (1) underdetermined traffic flows caused by undetected movements, and (2) non-equilibrium traffic flows arise from congestion propagation. Here we demonstrate a graph-based deep learning method that can offer a data-driven, model-free and correlation adaptive approach to tackle the above issues and perform accurate network-wide traffic volume estimation. Particularly, in order to quantify the dynamic and nonlinear relationships between traffic speed and volume for the estimation of underdetermined flows, a speed patternadaptive adjacent matrix based on graph attention is developed and integrated into the graph convolution process, to capture non-local correlations between sensors. To measure the impacts of non-equilibrium flows, a temporal masked and clipped attention combined with a gated temporal convolution layer is customized to capture time-asynchronous correlations between upstream and downstream sensors. We then evaluate our model on a real-world highway traffic volume dataset and compare it with several benchmark models. It is demonstrated that the proposed model achieves high estimation accuracy even under 20% sensor coverage rate and outperforms other baselines significantly, especially on underdetermined and non-equilibrium flow locations. Furthermore, comprehensive quantitative model analysis are also carried out to justify the model designs

Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach

Traffic speed is central to characterizing the fluidity of the road network. Many transportation applications rely on it, such as real-time navigation, dynamic route planning, and congestion management. Rapid advances in sensing and communication techniques make traffic speed detection easier than ever. However, due to sparse deployment of static sensors or low penetration of mobile sensors, speeds detected are incomplete and far from network-wide use. In addition, sensors are prone to error or missing data due to various kinds of reasons, speeds from these sensors can become highly noisy. These drawbacks call for effective techniques to recover credible estimates from the incomplete data. In this work, we first identify the issue as a spatiotemporal kriging problem and propose a Laplacian enhanced low-rank tensor completion (LETC) framework featuring both lowrankness and multi-dimensional correlations for large-scale traffic speed kriging under limited observations. To be specific, three types of speed correlation including temporal continuity, temporal periodicity, and spatial proximity are carefully chosen and simultaneously modeled by three different forms of graph Laplacian, named temporal graph Fourier transform, generalized temporal consistency regularization, and diffusion graph regularization. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging. By performing experiments on two public million-level traffic speed datasets, we finally draw the conclusion and find our proposed LETC achieves the state-of-the-art kriging performance even under low observation rates, while at the same time saving more than half computing time compared with baseline methods. Some insights into spatiotemporal traffic data modeling and kriging at the network level are provided as well

Nexus sine qua non: Essentially connected neural networks for spatial-temporal forecasting of multivariate time series

Modeling and forecasting multivariate time series not only facilitates the decision making of practitioners, but also deepens our scientific understanding of the underlying dynamical systems. Spatial-temporal graph neural networks (STGNNs) are emerged as powerful predictors and have become the de facto models for learning spatiotemporal representations in recent years. However, existing architectures of STGNNs tend to be complicated by stacking a series of fancy layers. The designed models could be either redundant or enigmatic, which pose great challenges on their complexity and scalability. Such concerns prompt us to re-examine the designs of modern STGNNs and identify core principles that contribute to a powerful and efficient neural predictor. Here we present a compact predictive model that is fully defined by a dense encoder-decoder and a message-passing layer, powered by node identifications, without any complex sequential modules, e.g., TCNs, RNNs, and Transformers. Empirical results demonstrate how a simple and elegant model with proper inductive basis can compare favorably w.r.t. the state of the art with elaborate designs, while being much more interpretable and computationally efficient for spatial-temporal forecasting problem. We hope our findings would open new horizons for future studies to revisit the design of more concise neural forecasting architectures

A Macro-Micro Approach to Reconstructing Vehicle Trajectories on Multi-Lane Freeways with Lane Changing

Vehicle trajectories can offer the most precise and detailed depiction of traffic flow and serve as a critical component in traffic management and control applications. Various technologies have been applied to reconstruct vehicle trajectories from sparse fixed and mobile detection data. However, existing methods predominantly concentrate on single-lane scenarios and neglect lane-changing (LC) behaviors that occur across multiple lanes, which limit their applicability in practical traffic systems. To address this research gap, we propose a macro-micro approach for reconstructing complete vehicle trajectories on multi-lane freeways, wherein the macro traffic state information and micro driving models are integrated to overcome the restrictions imposed by lane boundary. Particularly, the macroscopic velocity contour maps are established for each lane to regulate the movement of vehicle platoons, meanwhile the velocity difference between adjacent lanes provide valuable criteria for guiding LC behaviors. Simultaneously, the car-following models are extended from micro perspective to supply lane-based candidate trajectories and define the plausible range for LC positions. Later, a two-stage trajectory fusion algorithm is proposed to jointly infer both the car-following and LC behaviors, in which the optimal LC positions is identified and candidate trajectories are adjusted according to their weights. The proposed framework was evaluated using NGSIM dataset, and the results indicated a remarkable enhancement in both the accuracy and smoothness of reconstructed trajectories, with performance indicators reduced by over 30% compared to two representative reconstruction methods. Furthermore, the reconstruction process effectively reproduced LC behaviors across contiguous lanes, adding to the framework's comprehensiveness and realism

Is the COVID-19 epidemic affecting the body mass of Chinese teenagers? – A longitudinal follow-up study

BackgroundAfter the spread and outbreak of COVID-19 worldwide, the learning, lifestyle, and health level of young individuals have been immensely impacted. With regard to the existing studies, the development trend of adolescents’ body shape in the late COVID-19 period is not sufficiently analyzed, and relevant targeted investigation is lacking. This study aimed to explore the body mass index (BMI) changes of 6–14 years-old adolescents before and after the COVID-19 epidemic, and to provide a reference for promoting the continuous enhancement of adolescent health.MethodsThe BMI and related data pertaining to 93,046 individuals from 2019 to 2022 were collected by cluster sampling, and changes in the BMI Z score and detection rate of overweight and obese adolescents before and after the epidemic were analyzed. Furthermore, the trend of obesity rates among adolescents in Jinan from 2019 to 2022 was analyzed using a logistic regression analysis model.ResultsThe one-way ANOVA models indicated that the BMI Z score of 6–14 years-old adolescents in 2020 significantly increased compared to 2019 (p < 0.01), and decreased in 2021 and 2022; in 2020, the obesity rate of adolescents exhibited a significant increase; however, the rate decreased after being controlled in 2021 and 2022. The normal-body size proportion continued to rise (p < 0.01), and adolescents of different age groups and genders exhibited similar development trends; the results of the logistic regression analysis indicate that there was a significant increase in obesity rates in 2020, adolescents of different age groups and genders exhibited similar development trends (p < 0.05).ConclusionThis study demonstrated that the COVID-19 epidemic impacts the BMI and obesity detection rate of adolescents. Adolescents from different age groups and genders exhibited similar development trends

Table_1_Association between BMI and health-related physical fitness: A cross-sectional study in Chinese high school students.DOCX

BackgroundExisting studies reporting on the levels of physical fitness among high school students use relatively few fitness tests for indicators of physical fitness, thus, incomprehensively evaluating the levels of physical fitness. Therefore, this study investigated the relationship between body mass index (BMI) and physical fitness index (PFI) by investigating five physical fitness indicators and calculating PHI.MethodAnthropometric measurements and indicators from five measures of physical fitness (50-m sprint, sit and reach, standing long jump, 800/1,000-m run, pull-up/bent-leg sit-up) were assessed. BMI was calculated to classify individuals into underweight, normal weight, overweight, and obese categories. Z-scores based on sex-specific mean and standard deviation were calculated, and the sum of Z-scores from the six fitness tests indicated the PFI. The findings were fitted to a linear regression model to elucidate the potential relationship between BMI and PFI.ResultsIn total, 176,655 high school students (male: 88,243, female: 88,412, age: 17.1 ± 1.05 years, height: 168.87 ± 11.1 cm, weight: 62.54 ± 15.15 kg) in Jinan, China, completed the physical fitness tests between 2020 and 2021. The one-way ANOVA models showed that PFI in the normal category was significantly higher as compared to all the other BMI categories within both male and female groups (p ConclusionsThis study demonstrated that BMI affects the PFI in both males and females. As compared to the obese and overweight categories based on BMI, significantly higher scores of PFI were observed for males and females.</p

Quantifying the Impact of Rainfall on Taxi Hailing and Operation

Adverse weathers are well-known to impact the operation of transportation systems, including taxis. This paper utilizes taxi GPS waypoint data to investigate the quantitative impact of rainfall on taxi hailing and taxi operations to help improve service quality on rainy days. Through statistical analysis, the study proves that it is more difficult to hail taxis on rainy days, especially during morning peak hours. By modelling the difference value of factors for rainfall and nonrainfall conditions in a multivariate regression model and attaining the significance and elasticity of each factor, passenger demand, taxi supply, search time and velocity are proved to be the significant factors that lower the taxis’ level of service on rainy days. Among them, the number of passengers and taxis are two factors that have the greatest impact. It is also shown that there is no significant difference in the total taxi supply and passenger demand between rainfall and nonrainfall conditions, but a dramatic change in the spatial distribution is discovered. The results suggest that instead of simply providing more taxis on rainy days, optimally dispatching taxicabs to high demand regions can be a more effective solution

Prenatal Diagnosis of Congenital Cataract: Sonographic Features and Perinatal Outcome in 41 Cases

Purpose To describe the prenatal ultrasonographic characteristics and perinatal outcomes of congenital cataract. Materials and Methods We analyzed congenital cataract diagnosed prenatally at four referral centers between August 2004 and February 2019. The diagnosis was confirmed by postnatal ophthalmologic evaluation of liveborn infants or autopsy for terminated cases. Maternal demographics, genetic testing results, prenatal ultrasound images, and perinatal outcomes were abstracted. Results Total of 41 cases of congenital cataract diagnosed prenatally among 788751 women undergoing anatomic survey. Based on the sonographic characteristics, 16/41 (39.0%) had a dense echogenic structure, 15/41 (36.6%) had a hyperechogenic spot and 10/41 (24.4%) had the double ring sign. 17/41 (41.5%) were isolated, and 24/41 (58.5%) had associated intraocular and extraocular findings. Microphthalmia, cardiac abnormalities, and central nervous system abnormalities were the most common associated abnormalities. Regarding potential etiology, 6 cases had a known family history of congenital cataract, 4 cases had confirmed congenital rubella infection, and 2 cases had aneuploidy. 31/41 (75.6%) elected termination and 10/41 (24.4%) elected to continue their pregnancy. Among the 10 cases, one case died, one case was lost to follow-up, and the remaining 8 cases were referred for ophthalmologist follow-up and postnatal surgery. Conclusion Once fetal cataracts are detected, a detailed fetal anatomy survey to rule out associated abnormalities and a workup to identify the potential etiology are recommended. Prenatal diagnosis of congenital cataracts provides vital information for counseling and subsequent management