Concurrent Progression of Through and Turning Movements for Arterials Experiencing Heavy Turning Flows and Bay-Length Constraints

2019 ◽  
Vol 2673 (9) ◽  
pp. 525-537
Author(s):  
Yen-Hsiang Chen ◽  
Yao Cheng ◽  
Gang-Len Chang
Keyword(s):  
State Of The Art ◽  
Negative Impact ◽  
Arterial Segment ◽  
Simulation Experiments ◽  
Average Delay ◽  
Left Turn ◽  
Signal Coordination ◽  
Priority Task ◽  
Proposed Model ◽  
The Impact

Contending with congestion on major urban arterials by providing progression bands has long been a priority task for the traffic community. However, on an arterial experiencing heavy left-turn volumes at major intersections, the left-turn queue may spill back rapidly and further degrade the effectiveness of the through progression band if the left-turn volume and the limited bay length have not been accounted for in the optimization of signal coordination plan. Such negative impact from left-turn queues also justifies the need to take into account the concurrent progression of through and left-turn flows on major arterials. To address these two issues, this paper presents a three-staged signal optimization model that can circumvent or minimize the impact of left-turn spillback to the through movements and concurrently minimize the delay of left-turn flows. The proposed model firstly obtains an initial maximized bandwidth from an existing state-of-the-art method and then maximizes the portion of through bandwidth not impeded by the left-turn overflows. The delay of left-turn flows at each intersection will also be minimized under the obtained effective through bandwidth. The results from the numerical analyses have confirmed the benefits and need of including the left-turn volume and its bay length in the design of dual progression for through and left-turn movements. The simulation experiments further show a reduction in the average delay and the number of stops, by 6.4% and 5.5%, respectively, for vehicles traversing an arterial segment of six intersections, compared with the state-of-the-art model, MULTIBAND.

scholarly journals A Credibility Score Algorithm for Malicious Data Detection in Urban Vehicular Networks

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 496
Author(s):  
Bartłomiej Płaczek ◽  
Marcin Bernas ◽  
Marcin Cholewa
Keyword(s):  
Vehicular Networks ◽  
State Of The Art ◽  
Negative Impact ◽  
Green Light ◽  
Traffic Signals ◽  
Signalized Intersection ◽  
Traffic Signal Control ◽  
Data Detection ◽  
Simulation Experiments ◽  
Improved Accuracy

This paper introduces a method to detect malicious data in urban vehicular networks, where vehicles report their locations to road-side units controlling traffic signals at intersections. The malicious data can be injected by a selfish vehicle approaching a signalized intersection to get the green light immediately. Another source of malicious data are vehicles with malfunctioning sensors. Detection of the malicious data is conducted using a traffic model based on cellular automata, which determines intervals representing possible positions of vehicles. A credibility score algorithm is introduced to decide if positions reported by particular vehicles are reliable and should be taken into account for controlling traffic signals. Extensive simulation experiments were conducted to verify effectiveness of the proposed approach in realistic scenarios. The experimental results show that the proposed method detects the malicious data with higher accuracy than compared state-of-the-art methods. The improved accuracy of detecting malicious data has enabled mitigation of their negative impact on the performance of traffic signal control.

scholarly journals Effects of COVID-19 on trade flows: Measuring their impact through government policy responses

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258356
Author(s):  
Javier Barbero ◽  
Juan José de Lucio ◽  
Ernesto Rodríguez-Crespo
Keyword(s):  
Bilateral Trade ◽  
State Of The Art ◽  
Negative Impact ◽  
Trade Agreements ◽  
Trade Flows ◽  
Policy Responses ◽  
Negative Effect ◽  
Bilateral Trade Flows ◽  
Gravity Model Of Trade ◽  
The Impact

This paper examines the impact of COVID-19 on bilateral trade flows using a state-of-the-art gravity model of trade. Using the monthly trade data of 68 countries exporting across 222 destinations between January 2019 and October 2020, our results are threefold. First, we find a greater negative impact of COVID-19 on bilateral trade for those countries that were members of regional trade agreements before the pandemic. Second, we find that the impact of COVID-19 is negative and significant when we consider indicators related to governmental actions. Finally, this negative effect is more intense when exporter and importer country share identical income levels. In the latter case, the highest negative impact is found for exports between high-income countries.

scholarly journals Impact of Guideline Markings on Saturation Flow Rate at Signalized Intersections

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Zhengtao Qin ◽  
Jing Zhao ◽  
Shidong Liang ◽  
Jiao Yao
Keyword(s):  
Flow Rate ◽  
Signalized Intersections ◽  
Estimation Model ◽  
Left Turn ◽  
Proposed Model ◽  
Research Goal ◽  
Saturation Flow Rate ◽  
Saturation Flow ◽  
The Impact

Many intersections around the world are irregular crossings where the approach and exit lanes are offset or the two roads cross at oblique angles. These irregular intersections often confuse drivers and greatly affect operational efficiency. Although guideline markings are recommended in many design manuals and codes on traffic signs and markings to address these problems, the effectiveness and application conditions are ambiguous. The research goal was to analyze the impact of guideline markings on the saturation flow rate at signalized intersections. An adjustment estimation model was established based on field data collected at 33 intersections in Shanghai, China. The proposed model was validated using a before–after case study. The underlying reasons for the impact of intersection guideline markings on the saturation flow rate are discussed. The results reveal that the improvement in the saturation flow rate obtained from painting guide line markings is positively correlated with the number of traffic lanes, offset of through movement, and turning angle of left-turns. On average, improvements of 7.0% and 10.3% can be obtained for through and left-turn movements, respectively.

scholarly journals End-to-end speech emotion recognition using a novel context-stacking dilated convolution neural network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Duowei Tang ◽  
Peter Kuppens ◽  
Luc Geurts ◽  
Toon van Waterschoot
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Speech Signal ◽  
State Of The Art ◽  
Speech Emotion Recognition ◽  
Model Parameters ◽  
Proposed Model ◽  
End To End ◽  
The Impact ◽  
Temporal Dependencies

AbstractAmongst the various characteristics of a speech signal, the expression of emotion is one of the characteristics that exhibits the slowest temporal dynamics. Hence, a performant speech emotion recognition (SER) system requires a predictive model that is capable of learning sufficiently long temporal dependencies in the analysed speech signal. Therefore, in this work, we propose a novel end-to-end neural network architecture based on the concept of dilated causal convolution with context stacking. Firstly, the proposed model consists only of parallelisable layers and is hence suitable for parallel processing, while avoiding the inherent lack of parallelisability occurring with recurrent neural network (RNN) layers. Secondly, the design of a dedicated dilated causal convolution block allows the model to have a receptive field as large as the input sequence length, while maintaining a reasonably low computational cost. Thirdly, by introducing a context stacking structure, the proposed model is capable of exploiting long-term temporal dependencies hence providing an alternative to the use of RNN layers. We evaluate the proposed model in SER regression and classification tasks and provide a comparison with a state-of-the-art end-to-end SER model. Experimental results indicate that the proposed model requires only 1/3 of the number of model parameters used in the state-of-the-art model, while also significantly improving SER performance. Further experiments are reported to understand the impact of using various types of input representations (i.e. raw audio samples vs log mel-spectrograms) and to illustrate the benefits of an end-to-end approach over the use of hand-crafted audio features. Moreover, we show that the proposed model can efficiently learn intermediate embeddings preserving speech emotion information.

scholarly journals Data-Driven Mitigation of Energy Scheduling Inaccuracy in Renewable-Penetrated Grids: Summerside Electric Use Case

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2228
Author(s):  
Mostafa Farrokhabadi
Keyword(s):  
Time Series ◽  
Energy Storage ◽  
Energy Cost ◽  
Prince Edward Island ◽  
State Of The Art ◽  
Negative Impact ◽  
Probabilistic Method ◽  
Data Driven ◽  
Renewable Energy Resources ◽  
The Impact

This paper presents findings on mitigating the negative impact of renewable energy resources variability on the energy scheduling problem, in particular for island grids and microgrids. The methods and findings presented in this paper are twofold. First, data obtained from the City of Summerside in the province of Prince Edward Island, Canada, is leveraged to demonstrate the effectiveness of state-of-the-art time series predictors in mitigating energy scheduling inaccuracy. Second, the outcome of the time series prediction analysis is used to propose a novel data-driven battery energy storage system (BESS) sizing study for energy scheduling purposes. The proposed probabilistic method accounts for intra-interval variations of generation and demand, thus mitigating the trade-off between time resolution of the problem formulation and the solution accuracy. In addition, as part of the sizing study, a BESS management strategy is proposed to minimize energy scheduling inaccuracies, and is then used to obtain the optimal BESS size. Finally, the paper presents quantitative analyses of the impact of both the energy predictors and the BESS on the supplied energy cost using the actual data of the Summerside Electric grid. The paper reveals the significant potential for reducing energy cost in renewable-penetrated grids and microgrids through state-of-the-art predictors combined with applications of properly-sized energy storage systems.

scholarly journals The Audit Quality and Audit Delay: Evidence from Indonesia

2020 ◽  
Vol 2 (3) ◽  
pp. 22-32
Author(s):  
T. Husain ◽  
I Gusti Ayu Intan Saputra Rini
Keyword(s):  
Audit Quality ◽  
Negative Impact ◽  
Stock Exchange ◽  
Manufacturing Sector ◽  
Manufacturing Firms ◽  
Average Delay ◽  
Data Set ◽  
Significant Negative Impact ◽  
The Impact ◽  
Insight Into

Purpose: This study specifically aims to identify the impact of Audit Quality on Audit Delay. Audit Quality is measured by the proxy log natural fee audit (LNFE). Methods: This is a causal research with quantitative analysis. This study involves six companies listed in the sub-sectors of Cable under the manufacturing sector in the Indonesia Stock Exchange for the period of 2013-2019. It applied panel data set in a regression model using STATA MP - Parallel Edition Ver14.00 application. Results: The findings show that the Audit quality has a significant negative impact on the Audit Delay with an average delay of 83.62 days. Implications: This study could be extended further by considering all manufacturing firms of IDX which may provide more insight into the audit quality with other proxies.

scholarly journals Real and false goals of artificial intelligence

2021 ◽  
pp. 43-48
Author(s):  
Oleg Figovsky ◽  
◽  
Oleg Penskiy ◽  
Keyword(s):  
Artificial Intelligence ◽  
Mathematical Model ◽  
Negative Impact ◽  
Modern Society ◽  
Modern World ◽  
Human Psychology ◽  
Proposed Model ◽  
Numerical Assessment ◽  
Human Thinking ◽  
The Impact

The paper describes and justifies the possible dangers of artificial intelligence to human psychology. The manifestations of this danger in the modern world are illustrated by examples. Authors formulated and proved the hypothesis that under the influence of artificial intelligence on a person some changes in the ways of human thinking are possible. A mathematical model for calculating the influence of artificial intelligence on the psychological parameters of a person is proposed. In order to control the influence of artificial intelligence on society authors suggested to formulate specific goals for the integration of artificial intelligence into society, taking into account the negative impact of this intelligence on human psychology. Based on the formulated goals, a simple mathematical model is offered. This model allows for a quick numerical assessment of the impact society on the "psychology" of the robot and vice versa. Simple example of calculating this influence in modern society demonstrates the work of the proposed model.

Incorporating Delay Minimization in Design of the Optimized Arterial Signal Progression

2021 ◽  
pp. 036119812110642
Author(s):  
Yen-Hsiang Chen ◽  
Yao Cheng ◽  
Gang-Len Chang
Keyword(s):  
Numerical Analyses ◽  
Traffic Flows ◽  
Network Delay ◽  
Simulation Experiments ◽  
Average Delay ◽  
Delay Reduction ◽  
Total Signal ◽  
Delay Minimization ◽  
The Impact

Despite the abundance of studies on signal progression for arterial roads, most existing models for bandwidth maximization cannot concurrently ensure that the resulting delays will be at a desirable level, especially for urban arterials accommodating high turning volume at some major intersections or constrained by limited turning bay length. Extending from those models that aim to address delay minimization in the progression design, this study provides two enhanced progression maximization models for arterials with high turning volumes. The first model aims to select the signal plan that can produce the lowest total signal delays for all movements from the set of non-inferior offsets produced by MAXBAND. Failing to address the impact of potential turning bay spillback at some critical intersections under such a design may significantly degrade the quality of through progression and increase the overall delay. For this reason, the second model proposed in this study offers the flexibility to trade the progression bandwidths within a pre-specified level for the target delay reduction, especially for turning traffic. The evaluation results from both numerical analyses and simulation experiments have shown that both proposed models can produce the desirable level of performance when compared with the two benchmark models, MAXBAND and TRANSYT 16. The second model yielded the lowest average network delay of 117.2 seconds per vehicle (s/veh), compared with 121.7 s/veh with TRANSYT. Moreover, even its average delay of 141.8 s/veh for through vehicles is comparable with that of 141.2 s/veh by MAXBAND, which is designed mainly to benefit through-traffic flows.

A Capacity Estimation Model for a Contraflow Left-Turn Pocket Lane at Signalized Intersections

2018 ◽  
Vol 2672 (17) ◽  
pp. 22-34 ◽  
Author(s):  
Yi Zhao ◽  
Rachel M. James ◽  
Lin Xiao ◽  
Joe Bared
Keyword(s):  
Signalized Intersections ◽  
Signalized Intersection ◽  
Traffic Operations ◽  
Estimation Model ◽  
Capacity Estimation ◽  
Average Delay ◽  
Left Turn ◽  
Traffic Demand ◽  
Key Characteristics ◽  
The Impact

Alternative intersection designs are increasingly proposed and adopted by different agencies to meet the needs of growing traffic demand and constrained transportation resources. The left turn (LT) is one of the most critical movements at signalized intersections from both a safety and operations perspective. Heavy LT volumes are especially impactful to the operational efficiency of a signalized intersection and often result in queue spillback. A contraflow left-turn pocket lane (CLPL) is proposed to mitigate congestion caused by heavy LT demand and has been shown in simulation to greatly mitigate the impact of queue spillback. The CLPL dynamically uses the opposing through lane (OTL) as an additional LT lane within the signal cycle on a temporary basis when the OTL is not occupied by through traffic. While geometric design schematics and analytical procedures for estimating delay have been proposed and discussed in existing literature, methodologies for estimating capacity benefits and traffic operations are not yet well defined. This paper has three primary contributions to the literature: development of a probabilistic capacity estimation model, exploration of the impact of key characteristics (e.g., cycle length, LT demand, lane selection preference) on estimated intersection capacity, and recommendations for the real-world implementation of a CLPL. The simulation results indicate that the CLPL treatment can increase a signalized intersection’s throughput up to 25% and decrease the intersection’s average delay by 35%.

scholarly journals Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies

2019 ◽  
Vol 9 (22) ◽  
pp. 4963 ◽  
Author(s):  
Samee Ullah Khan ◽  
Ijaz Ul Haq ◽  
Seungmin Rho ◽  
Sung Wook Baik ◽  
Mi Young Lee
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Negative Impact ◽  
Learning Approach ◽  
Detection Scheme ◽  
Aged People ◽  
Violence Detection ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Deep Learning Model

Movies have become one of the major sources of entertainment in the current era, which are based on diverse ideas. Action movies have received the most attention in last few years, which contain violent scenes, because it is one of the undesirable features for some individuals that is used to create charm and fantasy. However, these violent scenes have had a negative impact on kids, and they are not comfortable even for mature age people. The best way to stop under aged people from watching violent scenes in movies is to eliminate these scenes. In this paper, we proposed a violence detection scheme for movies that is comprised of three steps. First, the entire movie is segmented into shots, and then a representative frame from each shot is selected based on the level of saliency. Next, these selected frames are passed from a light-weight deep learning model, which is fine-tuned using a transfer learning approach to classify violence and non-violence shots in a movie. Finally, all the non-violence scenes are merged in a sequence to generate a violence-free movie that can be watched by children and as well violence paranoid people. The proposed model is evaluated on three violence benchmark datasets, and it is experimentally proved that the proposed scheme provides a fast and accurate detection of violent scenes in movies compared to the state-of-the-art methods.

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