19 research outputs found

    Indiana SR-9 Development: Traffic Signal Performance Measures Case Study

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    Detector-Free Optimization of Traffic Signal Offsets with Connected Vehicle Data

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    It has recently been shown that signal offset optimization is feasible using vehicle trajectory data at low levels of market penetration. This study performs offset optimization on two corridors using this type ofdata. Six weeks oftrajectory splines were processed for two corridors including 25 signalized intersections, in order to create vehicle arrival profiles, using a proposed procedure called virtual detection. After processing and filtering the data, penetration rates between 0.09-0.80% were observed, varying by approach. The arrival profiles were statistically compared against those measured with physical detectors, with the majority of the approaches showing statistically significant goodness-of-fit at a 90% confidence level. Finally, the virtual detection arrival profiles were used to optimize offsets, and compared against a solution derived from physical detector arrival profiles. The results demonstrate that virtual detection can produce good quality offsets with current market penetration rates of probe data. The study also includes a sensitivity analysis to the sample period, which shows that two weeks of data may be sufficient for data collection at current penetration rates

    Using Connected Vehicle Data to Reassess Dilemma Zone Performance of Heavy Vehicles

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    The rate of fatalities at signalized intersections involving heavy vehicles is nearly five times higher than for passenger vehicles in the US. Previous studies in the US have found that heavy vehicles are twice as likely to violate a red light compared with passenger vehicles. Current technologies leverage setback detection to extend green time for a particular phase and are based upon typical deceleration rates for passenger cars. Furthermore, dilemma zone detectors are not effective when the max out time expires and forces the onset of yellow. This study proposes the use of connected vehicle (CV) technology to trigger force gap out (FGO) before a vehicle is expected to arrive within the dilemma zone limit at max out time. The method leverages position data from basic safety messages (BSMs) to map-match virtual waypoints located up to 1,050 ft in advance of the stop bar. For a 55 mph approach, field tests determined that using a 6 ft waypoint radius at 50 ft spacings would be sufficient to match 95% of BSM data within a 5% lag threshold of 0.59 s. The study estimates that FGOs reduce dilemma zone incursions by 34% for one approach and had no impact for the other. For both approaches, the total dilemma zone incursions decreased from 310 to 225. Although virtual waypoints were used for evaluating FGO, the study concludes by recommending that trajectory-based processing logic be incorporated into controllers for more robust support of dilemma zone and other emerging CV applications

    Next Generation Traffic Signal Performance Measures: Leveraging Connected Vehicle Data

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    High-resolution connected vehicle (CV) trajectory and event data has recently become commercially available. With over 500 billion vehicle position records generated each month in the United States, these data sets provide unique opportunities to build on and expand previous advances on traffic signal performance measures and safety evaluation. This report is a synthesis of research focused on the development of CV-based performance measures. A discussion is provided on data requirements, such as acquisition, storage, and access. Subsequently, techniques to reference vehicle trajectories to relevant roadways and movements are presented. This allows for performance analyses that can range from the movement- to the system-level. A comprehensive suite of methodologies to evaluate signal performance using vehicle trajectories is then provided. Finally, uses of CV hard-braking and hard-acceleration event data to assess safety and driver behavior are discussed. To evaluate scalability and test the proposed techniques, performance measures for over 4,700 traffic signals were estimated using more than 910 million vehicle trajectories and 14 billion GPS points in all 50 states and Washington, D.C. The contents of this report will help the industry transition towards a hybrid blend of detector- and CV-based signal performance measures with rigorously defined performance measures that have been peer-reviewed by both academics and industry leaders

    Male breast cancer in BRCA1 and BRCA2 mutation carriers : pathology data from the Consortium of Investigators of Modifiers of BRCA1/2

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    Background: BRCA1 and, more commonly, BRCA2 mutations are associated with increased risk of male breast cancer (MBC). However, only a paucity of data exists on the pathology of breast cancers (BCs) in men with BRCA1/2 mutations. Using the largest available dataset, we determined whether MBCs arising in BRCA1/2 mutation carriers display specific pathologic features and whether these features differ from those of BRCA1/2 female BCs (FBCs). Methods: We characterised the pathologic features of 419 BRCA1/2 MBCs and, using logistic regression analysis, contrasted those with data from 9675 BRCA1/2 FBCs and with population-based data from 6351 MBCs in the Surveillance, Epidemiology, and End Results (SEER) database. Results: Among BRCA2 MBCs, grade significantly decreased with increasing age at diagnosis (P = 0.005). Compared with BRCA2 FBCs, BRCA2 MBCs were of significantly higher stage (P for trend = 2 x 10(-5)) and higher grade (P for trend = 0.005) and were more likely to be oestrogen receptor-positive [odds ratio (OR) 10.59; 95 % confidence interval (CI) 5.15-21.80] and progesterone receptor-positive (OR 5.04; 95 % CI 3.17-8.04). With the exception of grade, similar patterns of associations emerged when we compared BRCA1 MBCs and FBCs. BRCA2 MBCs also presented with higher grade than MBCs from the SEER database (P for trend = 4 x 10(-12)). Conclusions: On the basis of the largest series analysed to date, our results show that BRCA1/2 MBCs display distinct pathologic characteristics compared with BRCA1/2 FBCs, and we identified a specific BRCA2-associated MBC phenotype characterised by a variable suggesting greater biological aggressiveness (i.e., high histologic grade). These findings could lead to the development of gender-specific risk prediction models and guide clinical strategies appropriate for MBC management.Peer reviewe

    Associations of common breast cancer susceptibility alleles with risk of breast cancer subtypes in BRCA1 and BRCA2 mutation carriers

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    Introduction: More than 70 common alleles are known to be involved in breast cancer (BC) susceptibility, and several exhibit significant heterogeneity in their associations with different BC subtypes. Although there are differences in the association patterns between BRCA1 and BRCA2 mutation carriers and the general population for several loci, no study has comprehensively evaluated the associations of all known BC susceptibility alleles with risk of BC subtypes in BRCA1 and BRCA2 carriers. Methods: We used data from 15,252 BRCA1 and 8,211 BRCA2 carriers to analyze the associations between approximately 200,000 genetic variants on the iCOGS array and risk of BC subtypes defined by estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and triple-negative- (TN) status; morphologic subtypes; histological grade; and nodal involvement. Results: The estimated BC hazard ratios (HRs) for the 74 known BC alleles in BRCA1 carriers exhibited moderate correlations with the corresponding odds ratios from the general population. However, their associations with ER-positive BC in BRCA1 carriers were more consistent with the ER-positive as

    Associations of common breast cancer susceptibility alleles with risk of breast cancer subtypes in BRCA1 and BRCA2 mutation carriers

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    Associations of common breast cancer susceptibility alleles with risk of breast cancer subtypes in BRCA1 and BRCA2 mutation carriers

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    Abstract Introduction More than 70 common alleles are known to be involved in breast cancer (BC) susceptibility, and several exhibit significant heterogeneity in their associations with different BC subtypes. Although there are differences in the association patterns between BRCA1 and BRCA2 mutation carriers and the general population for several loci, no study has comprehensively evaluated the associations of all known BC susceptibility alleles with risk of BC subtypes in BRCA1 and BRCA2 carriers. Methods We used data from 15,252 BRCA1 and 8,211 BRCA2 carriers to analyze the associations between approximately 200,000 genetic variants on the iCOGS array and risk of BC subtypes defined by estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and triple-negative- (TN) status; morphologic subtypes; histological grade; and nodal involvement. Results The estimated BC hazard ratios (HRs) for the 74 known BC alleles in BRCA1 carriers exhibited moderate correlations with the corresponding odds ratios from the general population. However, their associations with ER-positive BC in BRCA1 carriers were more consistent with the ER-positive associations in the general population (intraclass correlation (ICC) = 0.61, 95% confidence interval (CI): 0.45 to 0.74), and the same was true when considering ER-negative associations in both groups (ICC = 0.59, 95% CI: 0.42 to 0.72). Similarly, there was strong correlation between the ER-positive associations for BRCA1 and BRCA2 carriers (ICC = 0.67, 95% CI: 0.52 to 0.78), whereas ER-positive associations in any one of the groups were generally inconsistent with ER-negative associations in any of the others. After stratifying by ER status in mutation carriers, additional significant associations were observed. Several previously unreported variants exhibited associations at P <10−6 in the analyses by PR status, HER2 status, TN phenotype, morphologic subtypes, histological grade and nodal involvement. Conclusions Differences in associations of common BC susceptibility alleles between BRCA1 and BRCA2 carriers and the general population are explained to a large extent by differences in the prevalence of ER-positive and ER-negative tumors. Estimates of the risks associated with these variants based on population-based studies are likely to be applicable to mutation carriers after taking ER status into account, which has implications for risk prediction
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