Evaluation of Impacts on Delay, Cycle-Length Optimization, Control Types, and Peak-Hour Factor with the Randomness of Traffic

Some basic concepts about traffic which are correct in theory may be misinterpreted in practice. Such misinterpretations may lead to a different direction from the ideal operation. This four-part dissertation is designated to examine fundamental concepts in traffic operation, and to validate the impact of randomness on control delays, cycle-length optimization, control types, and the peak-hour factor. Control delays experienced by drivers is a critical performance measure on interrupted-flow traffic which involves movements at slower speeds and stops on intersection approaches, as vehicles move up in the queue or slow down upstream of an intersection. Since the basic term of control delay in a signalized intersection was originally from queueing analyses within a cycle, results from such models may be inaccurate due to the neglect of inter-cycle traffic variation. Besides, traffic is rare varying on the clock. Therefore, the peak-hour factor will be inaccurate to a certain degree if peak periods are placed on the clock. All parts of this dissertation, except the first and the last, are independent papers for different professional journals, and are summarized as follows. Part II of this dissertation, “Impacts of Inter-Cycle Demand Fluctuations on Delay”, distinguishes between intra- and inter-cycle demand fluctuations and recognizes the potentially significant impact of delay underestimation when inter-cycle demand fluctuation is unaccounted for, as in all previous models. “Short or Long … which is Better? A Probabilistic Approach towards Cycle Length Optimization” in the third part of this dissertation proposes a framework to determine the optimal or near-optimal cycle length for signalized intersections based on the criterion with minimal control delays. The fourth part with title “A Trade-Off Framework for Determining the Best Control at an Intersection” in this dissertation uses the same criterion with minimal control delays to assist decision makers in the trade-off between signals and stop signs for an intersection. Part V of this dissertation, “Impacts of Misplaced Peak Intervals on PHFs”, argues about the significant difference among different ways to define the peak intervals, and distinguishes the differences between the “real” and “on the clock” peak-hour factors

Impact of Data Resolution on Peak Hour Factor Estimation for Transportation Decisions

Inductance loop detection systems serve as a primary data source to contemporary traffic information systems. Measures like 20-second or 30-second average velocity, flow, and lane occupancy can be aggregated from individual loop detector actuation sampled at 60 Hz typically. Practically, these measures would sometimes be further aggregated into a much lower, e.g. 15-minute, resolution and then the raw data were lost. Valuable traffic information like flow variation may be distorted when the lower resolution aggregation is practiced. A biased conclusion could be drawn from a data integration system consisted of this kind of distortions. Three approaches estimating a peak hour factor based on traffic volume from loop detection systems are introduced in this paper to explore such a quality issue for data integration systems. Peak hour factor is commonly used in Highway Capacity Manual for determining and evaluating future system needs. By processing the raw data with the introduced approaches, different PHFs can be determined from a same traffic dataset. It is found that 2% to 5% (about one standard deviation from the mean) reduction in PHF may have 5 to 20 seconds increase in control delay estimation. The results suggest that distortion of control delay estimation at a signalized intersection exists due to an improper aggregation. That is, data quality might not be good enough for a right decision if the data were not processed appropriately. © Versita sp. z o.o

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Validation for the Diversity of Usage in M-Commerce among Countries by ICOMP

This paper conducted the validation of diverse usage in m-commerce services among the countries. In order to determine whether there is a discrepancy between samples, this study proposed a new approach based on ICOMP to perform model selection and variables clustering. According to the ICOMP criteria, the model selection implied that there is heterogeneity between samples. The variable clustering, whatever within a group or with all twenty variables, indicated that there are differences in ranking the most relevant variables between samples. That is, whatever model selection or variables clustering, all results based on the proposed approach support that there were discrepancies in using m-commerce services among the countries. Characters of both the ICOMP approach and the grey relational analysis in distinguishing the diverse usage were discussed. In short, both approaches can conclude there are discrepancies between the samples. The grey relational analysis is good to determine the preferences within a sample; and the ICOMP approach can determine the best fitting models for samples. The contribution of this paper towards the field in tow folds: the new approach to analyze the discrepancy; and the validation

Drone-Based Bathymetry Modeling for Mountainous Shallow Rivers in Taiwan Using Machine Learning

The river cross-section elevation data are an essential parameter for river engineering. However, due to the difficulty of mountainous river cross-section surveys, the existing bathymetry investigation techniques cannot be easily applied in a narrow and shallow field. Therefore, this study aimed to establish a model suitable for mountainous river areas utilizing an unmanned aerial vehicle (UAV) equipped with a multispectral camera and machine learning-based gene-expression programming (GEP) algorithm. The obtained images were combined with a total of 171 water depth measurements (0.01–1.53 m) for bathymetry modeling. The results show that the coefficient of determination (R2) of GEP is 0.801, the mean absolute error (MAE) is 0.154 m, and root mean square error (RMSE) is 0.195 m. The model performance of GEP model has increased by 16.3% in MAE, compared to conventional simple linear regression (REG) algorithm, and also has a lower bathymetry retrieval error both in shallow (0.8 m). The GEP bathymetry retrieval model has a considerable degree of accuracy and could be applied to shallow rivers or near-shore areas under similar conditions of this study

Geological significance of Early Triassic porphyry Cu mineralization in the eastern Xar Moron–Changchun Metallogenic Belt, Northeast China: a case study of the newly-discovered Guokuidingzi Cu deposit

The Guokuidingzi porphyry copper deposit is a poorly studied deposit located in the eastern Xar Moron-Changchun Mo–Cu Metallogenic Belt (XCMB) of the northern margin of the North China Craton. The Cu mineralization occurs mainly within the porphyritic granodiorite. Discovery of the Guokuidingzi deposit in the eastern XCMB is of great significance for understanding the regional porphyry Mo–Cu mineralization and metallogeny. To better understand the genetic relationship between Cu mineralization and associated magmatism, we in present study documented molybdenite Re–Os, zircon U–Pb geochronology, geochemical and in-situ zircon Hf isotope data in the Guokuidingzi deposit. The ore-related porphyritic granodiorite from the alteration center yields zircon U–Pb age of 251.2 ± 2.1 Ma (MSWD = 0.25) that is similar to zircon U–Pb age of 250.7 ± 2.0 Ma (MSWD = 0.56) yielded by the fresh porphyritic granodiorite. Both ages are consistent with molybdenite weighted average age of 250.0 ± 1.5 Ma (MSWD = 0.26). These similar ages constrain the timing of emplacement and associated Cu mineralization in the Guokuidingzi Cu deposit to the Early Triassic, and indicate a close temporal and genetic relationship between Cu mineralization and the porphyritic granodiorite. The Guokuidingzi porphyritic granodiorite is high in SiO2 Al2O3, K2O, Sr, Mg#, Sr/Y, LREE and LILE, and low in Y, Yb, Cr, Co, Ni, HREE and HFSE, as well as a weak negative Eu anomaly and positive εHf(t) values (+8.1 to +11.1), suggesting a geochemical affinity to high-K adakite-like rocks. The ore-forming porphyritic granodiorite was most likely formed by partial melting of thickened juvenile mafic lower-crust, which previously underwent underplating of mantle-derived components/or melts at the base of the crust. The geology, geochemistry, geochronology and tectonic setting of the Guokuidingzi Cu deposit in the eastern XCMB are similar to the Early to Middle Triassic porphyry deposits in the western XCMB (EW-trending), but differ from the Middle Jurassic porphyry deposits in the southern Lesser Xing’an–Zhangguangcai Ranges Metallogenic Belt (NS-trending). This important finding suggests that the XCMB extends eastward to the Jilin, and even to the Yanbian area, which indicates the need for further research on the Early to Middle Triassic porphyry mineralization potential and prospecting in the eastern XCMB. We propose that the Early Triassic Guokuidingzi and porphyry deposits in the XCMB were formed during ition from syn-collisional compression to post-collisional extension after the final closure of the Paleo-Asian Ocean