A Lane-based Predictive Model of Downstream Arrival Rates in a Queue Estimation Model Using a Long Short-Term Memory Network

In this study, we develop a mathematical framework to predict cycle-based queued vehicles at each individual lane using a deep learning method - the long short-term memory (LSTM) network. The key challenges are to decide the existence of residual queued vehicles at the end of each cycle, and to predict the lane-based downstream arrivals to calculate vertical queue lengths at individual lanes using an integrated deep learning method. The primary contribution of the proposed method is to enhance the predictive accuracy of lane-based queue lengths in the future cycles using the historical queuing patterns. A major advantage of implementing an integrated deep learning process compared to the previously Kalman-filter-based queue estimation approach (Lee et al., 2015) is that there is no need to calibrate the co-variance matrix and tune the gain values (parameters) of the estimator. In the simulation results, the proposed method perform better in only straight movements and a shared lane with left turning movements

The Effect of Valerian on Sleep Component among Menopausal Women

Abstract: Background & Aims: About 50 percent of menopausal women have sleep problems that can lead to reduced quality of life; according to the population growth in postmenopausal women today, raising the level of their health issues are deemed important. The aim of this study was to investigate the effect of valerian on sleep component among 60-50 year women. Methods: In this study, a randomized controlled trial design was employed. Participants consisted of 100 women with menopause aged 50-60, who suffered from insomnia. Instruments included demographic data form and Pittsburg sleep quality index. Descriptive and inferential statistics were used to analyze the data. Results: Prevalence of sleep disorders by Pittsburg sleep quality index in this group was 70%. A statistically significant change was reported in the six component of sleep disturbance in intervention group in comparison to the placebo group (p=0.000). But the sixth component (the mount of drugs) was not statistically different. Conclusion: The results show that valerian improves the component of sleep in women with insomnia. So, it is essential that health providers would be familiar with these herb supplements. Keywords: Menopause, Valerian, Sleep disturbanc

The effect of aphrodite on orgasm and sexual desire in menopausal women: A randomized clinical trial

Background & Aim: Lack of orgasm during intercourse and loss of libido in menopause is very common and can reduce women's quality of life. The aim of the present study was to determine the effect of Aphrodite on orgasm and sexual desire in postmenopausal women. Methods & Materials: The study design was a randomized clinical trial with a control group. Participants were comprised of 80 postmenopausal women 50-60 years old. The instruments consisted of the demographic characteristics form and the Sabbatsbergsexual function scale that a part of itevaluates orgasm and sexual desire. Descriptive statistics, paired t-test and independent t-test were used to analyze data through SPSS software v.16. Results: The mean score of orgasm before intervention in the Aphrodite group was 30.25±20.6 and in the placebo group was 29±21.9. One month after intervention, this score increased to 41.12±10.08 in the Aphrodite groupand was 29.12±29.66 in the placebo group, that the difference between the two groups was statistically significant (P=0.02). As well, for the sexual desire score, there was a significant difference between the two groups of Aphrodite and control after intervention (P=0.008). Conclusion: The use of Aphrodite can improve sexual desire and orgasm in menopausal women. So, it is essential that healthcare providers be familiar with this herbal supplement. © 2016, Tehran University of Medical Sciences (TUMS). All rights reserved

Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways

© 2019 The current paper proposes a novel stochastic procedure for modelling car-following behaviours on a multi-lane motorway. We develop an integrated multi-lane stochastic continuous car-following model where a deep learning architecture is used to estimate a probability of lane-changing (LC) manoeuvres. To the best of our knowledge, this work is among the very few papers which exploit deep learning to model driving behaviour on a multi-lane road. The objective of this study is to establish a coupled stochastic continuous multi-lane car-following model using Langevin equations to cope with probabilistic characteristics of LC manoeuvres. In particular, a stochastic volatility, derived from LC manoeuvres is introduced in a multi-lane stochastic optimal velocity model (SOVM). In additions, Convolutional Neural Network (CNN) is applied to estimate a probability of LC manoeuvres in the integrated multi-lane car-following model. Furthermore, imaged second-based trajectories of the lane-changer and surrounding vehicles are used to identify whether LC manoeuvres occur by using the CNN. Finally, the proposed method is validated using a real-world high-resolution vehicle trajectory dataset. The results indicate that the prediction of the integrated SOVM is almost identical to the observed trajectories of the lane-changers and the following vehicles in the initial and the target lane. It has been found that the proposed multi-lane SOVM can tackle the unpredictable fluctuations in the velocity of the vehicles in the acceleration/deceleration zone

A model predictive perimeter control with real-time partitions

Previous studies through simulation and empirical data have shown that a Network Macroscopic Fundamental Diagram (NMFD) exists and can be used for designing network optimal perimeter control strategies. These control strategies rely on well defined NMFDs, which highly depend on the homogeneity of the traffic condition in the network. However, it is known that traffic dynamics change drastically during the day in different zones in a large-scale network, and different control strategies might lead to heterogeneous traffic distribution across the urban network. One potential direction is re-partitioning the network to maintain the well defined NMFDs. However, re-partitioning the network changes each sub network’s size, such that it makes the well-defined NMFDs unpredictable. This paper provides a model predictive controlbased optimization approach for perimeter control using real-time partitioning to avoid this problem and utilize re-partitioning techniques. Results show that the proposed method can be used in a heterogeneous network to improve control performance by redistributing accumulations via re-partitioning over time. Our results, which are compared to no control and the traditional model predictive control, yield that the proposed method is superior to the others

Evaluation of ACSL

This report evaluates the advanced Continuous Simulation Language ( ACSL). It describes the language structure and assesses its features. The user-computer interaction, ease of use of the package and its capabilities in result analysis are discussed. The report also considers the ways to improve the software....

Effect of Aphrodit capsule on somatic symptoms of postmenopausal women

Background and Objective: Somatic and psychological altrations during menopause have negative impact on quality of life. This study was done to evaluate the effect of Aphrodit on somatic symptoms in postmenopausal women. Methods: In this clinical trial study 63 menopausal women were randomly divided into intervention and control groups. Subjects in interventional group were received Aphrodit capsule (40 mg of Tribulus terrestris fruits, 12.27 mg ginger, 33 mg saffron and 11 mg of cinnamon) for four weeks. Somatic symptoms of menopause (including hot flashes, night sweats and tachycardia) and sleep disorder and muscluskeletal disorder were evaluated using Menopause Rating Scale. Results: After intervention, the mean of hot flash score in interventional and control groups was 1.29±0.1 and 3.1±0.6 (P<0.05).The mean of sleep disorder score in interventional group and controls was 1.82±0.2 and 2.82±3.1 (P<0.05). The mean of muscluskeletal disorder score in interventional group and controls was 1.03±0.1 and 2.81±1.2 (P<0.05).There was no diference in the heart problem score between interventional and control groups. Conclusion: Consumption of Aphrodit capsule reduces hot flash, sleep disorder and muscluskeletal disorder in postmenopausal women

An advanced deep learning approach to real-time estimation of lane-based queue lengths at a signalized junction

© 2019 Elsevier Ltd In this study, we develop a real-time and novel estimation method of lane-based queue lengths using two deep learning processes, which include of a Convolutional Neural Network (CNN) into a Long Short-Term Memory (LSTM). This approach not only outperforms the recently developed real-time estimation of lane-based queue lengths but also captures the spatiotemporal attributes of traffic. There are three primary challenges to design a deep learning based queue estimation model. First, the CNN and the LSTM are integrated to estimate lane-based queue lengths minimizing accumulative counting errors. Furthermore, short-term arrival patterns and long-term traffic demand trends are captured by the LSTM to improve the accuracy of estimates of cycle-based proportional lane-uses. In addition, imaged second-based occupancy rates and impulse memories are used to identify whether vehicular queues are remained at the end of each cycle by using the CNN. In numerical examples and case study, the integrated CNN – LSTM method shows excellent performance to estimate queue lengths in individual lanes in seconds compared to the other approaches applied in this paper. This work paves the way for the applicability of the deep learning to estimate traffic quantities in real-time for lane-based adaptive traffic control systems (ATCS). Furthermore, we will introduce offset in a signal plan and lane-based turning proportion on the proposed framework to explain vehicular spillbacks in an individual lane and a grid lock for pursuing coordinated traffic movements along arterials and in signalized urban networks

Langevin method for a continuous stochastic car-following model and its stability conditions

In car-following models, the driver reacts according to his physical and psychological abilities which may change over time. However, most car-following models are deterministic and do not capture the stochastic nature of human perception. It is expected that purely deterministic traffic models may produce unrealistic results due to the stochastic driving behaviors of drivers. This paper is devoted to the development of a distinct car-following model where a stochastic process is adopted to describe the time-varying random acceleration which essentially reflects the random individual perception of driver behavior with respect to the leading vehicle over time. In particular, we apply coupled Langevin equations to model complex human driver behavior. In the proposed model, an extended Cox-Ingersoll-Ross (CIR) stochastic process will be used to describe the stochastic speed of the follower in response to the stimulus of the leader. An important property of the extended CIR process is to enhance the non-negative properties of the stochastic traffic variables (e.g. non-negative speed) for any arbitrary model parameters. Based on stochastic process theories, we derive stochastic linear stability conditions which, for the first time, theoretically capture the effect of the random parameter on traffic instabilities. Our stability results conform to the empirical results that the traffic instability is related to the stochastic nature of traffic flow at the low speed conditions, even when traffic is deemed to be stable from deterministic models