Dataset Information

Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps.

ABSTRACT: In this paper, we investigate the day-to-day regularity of urban congestion patterns. We first partition link speed data every 10 min into 3D clusters that propose a parsimonious sketch of the congestion pulse. We then gather days with similar patterns and use consensus clustering methods to produce a unique global pattern that fits multiple days, uncovering the day-to-day regularity. We show that the network of Amsterdam over 35 days can be synthesized into only 4 consensual 3D speed maps with 9 clusters. This paves the way for a cutting-edge systematic method for travel time predictions in cities. By matching the current observation to historical consensual 3D speed maps, we design an efficient real-time method that successfully predicts 84% trips travel times with an error margin below 25%. The new concept of consensual 3D speed maps allows us to extract the essence out of large amounts of link speed observations and as a result reveals a global and previously mostly hidden picture of traffic dynamics at the whole city scale, which may be more regular and predictable than expected.

SUBMITTER: Lopez C

PROVIDER: S-EPMC5656590 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps.

Lopez Clélia C Leclercq Ludovic L Krishnakumari Panchamy P Chiabaut Nicolas N van Lint Hans H

Scientific reports 20171025 1

In this paper, we investigate the day-to-day regularity of urban congestion patterns. We first partition link speed data every 10 min into 3D clusters that propose a parsimonious sketch of the congestion pulse. We then gather days with similar patterns and use consensus clustering methods to produce a unique global pattern that fits multiple days, uncovering the day-to-day regularity. We show that the network of Amsterdam over 35 days can be synthesized into only 4 consensual 3D speed maps with ...[more]

PMID: 29070859

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Project description:BackgroundEvidence suggests that regular eating patterns (i.e., consistent day-to-day frequency and timing of consumption) may be favorable with respect to weight status, and breakfast may be a particularly important meal for weight maintenance. We examined the relationship between regular breakfast consumption habits and weight status among women.Materials and methodsModified Poisson regression models examined day-to-day regularity in breakfast consumption among 46,037 women in the prospective Sister Study cohort in relation to weight status. Cross-sectional outcomes included overweight (body mass index (BMI) ≥ 25.0 kg/m2) and obesity (BMI ≥ 30.0 kg/m2); waist circumference (WC) ≥ 88 cm; and waist-to-hip ratio (WHR) ≥ 0.85. Self-reported weight 5 years post-baseline was used to calculate 5 kg weight gain and incident overweight and obesity using BMI.ResultsCompared to women who reported eating breakfast 3 to 4 days/week (irregular breakfast eaters), women who ate breakfast 7 days/week were between 11% to 17% less likely to be obese as measured by WHR (prevalence ratio (PR): 0.89; 95%CI: 0.85, 0.94), WC (PR: 0.85; 95%CI: 0.82, 0.88), and BMI (PR: 0.83; 95%CI: 0.79, 0.87) after multivariable adjustment. Women who never ate breakfast were between 11% to 22% less likely to be obese as measured by WHR (PR: 0.89; 95%CI: 0.83, 0.96), WC (PR: 0.82; 95%CI: 0.78, 0.87), and BMI (PR: 0.78; 95%CI: 0.72, 0.84) compared to irregular breakfast eaters. Prospective analyses showed a 21% and 28% lower risk of 5-year incident obesity among participants who always (relative risk (RR): 0.79; 95%CI: 0.70, 0.90) or never (RR: 0.72; 95%CI: 0.59, 0.87) ate breakfast, respectively, compared to those who ate breakfast 3 to 4 days/week. No association was observed for incident 5 kg weight gain.ConclusionsResults suggest that a regular breakfast consumption habit, comprising eating breakfast every day or never, may be important for maintaining a healthy weight.

Project description:BackgroundNocturnal symptoms in Parkinson's disease are often treated after management of daytime manifestations. In order to better understand the unmet needs of nocturnal symptoms management, we analyzed the characteristics and burden of nocturnal symptoms from patients' perspectives and explored their changes over time. Overall symptoms (occurring at day or night) were collected to compare whether the unmet needs related to nocturnal symptoms and to overall symptoms are different.MethodsWe used a Social Listening big-data technique to analyze large amounts of Parkinson's disease symptoms in dialogues available from social media platforms in 2016 to 2018. These symptoms were classified as either overall symptoms or nocturnal symptoms. We used share of voice (SOV) of symptoms as a proportion of total dialogues per year to reflect the characteristics of symptoms. Negative sentiment score of symptoms was analyzed to find out their related burden.ResultsWe found the SOV for overall motor symptoms was 79% and had not increased between 2016 and 2018 (79%, p =?0.5). The SOV for non-motor symptoms was 69% and had grown by 7% in 2018 (p <? 0.01). The SOV for motor complications was 9% and had increased by 6% in 2018 (p?<? 0.01). The SOV of motor symptoms was larger than non-motor symptoms and motor complications (p <? 0.01). The SOV of non-motor symptoms was larger than motor complications (p?<? 0.01). For nocturnal symptoms, 45% of the analyzed PD population reported nocturnal symptoms in 2018, growing by 6% (p <? 0.01). The SOV for nocturnal-occurring motor symptoms was higher than most non-motor symptoms. However, non-motor symptoms had the higher increases and evoked higher negative sentiment regardless of whether they occurred during the day or night. For symptoms that can occur at either day or night, each nocturnal symptom was rated with a higher negative sentiment score than the same symptom during the day.ConclusionsThe growing SOV and the greater negative sentiment of nocturnal symptoms suggest management of nocturnal symptoms is an unmet need of patients. A greater emphasis on detecting and treating nocturnal symptoms with 24-h care is encouraged.

Project description:Traffic congestion brings not only delay and inconvenience, but other associated national concerns, such as greenhouse gases, air pollutants, road safety issues and risks. Identification, measurement, tracking, and control of urban recurrent congestion are vital for building a livable and smart community. A considerable amount of works has made contributions to tackle the problem. Several methods, such as time-based approaches and level of service, can be effective for characterizing congestion on urban streets. However, studies with systemic perspectives have been minor in congestion quantification. Resilience, on the other hand, is an emerging concept that focuses on comprehensive systemic performance and characterizes the ability of a system to cope with disturbance and to recover its functionality. In this paper, we symbolized recurrent congestion as internal disturbance and proposed a modified metric inspired by the well-applied "R4" resilience-triangle framework. We constructed the metric with generic dimensions from both resilience engineering and transport science to quantify recurrent congestion based on spatial-temporal traffic patterns and made the comparison with other two approaches in freeway and signal-controlled arterial cases. Results showed that the metric can effectively capture congestion patterns in the study area and provides a quantitative benchmark for comparison. Also, it suggested not only a good comparative performance in measuring strength of proposed metric, but also its capability of considering the discharging process in congestion. The sensitivity tests showed that proposed metric possesses robustness against parameter perturbation in Robustness Range (RR), but the number of identified congestion patterns can be influenced by the existence of ?. In addition, the Elasticity Threshold (ET) and the spatial dimension of cell-based platform differ the congestion results significantly on both the detected number and intensity. By tackling this conventional problem with emerging concept, our metric provides a systemic alternative approach and enriches the toolbox for congestion assessment. Future work will be conducted on a larger scale with multiplex scenarios in various traffic conditions.

Dataset Information

Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps.

Publications

Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps.

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