Search results for: road traffic noise

Authors: Scott R. Butler

Abstract:

Ontario, Canada’s largest province, is in the midst of an interesting experiment in mandated asset management planning for local governments. At the beginning of 2021, Ontario’s 444 municipalities were responsible for the management of 302,864 lane kilometers of roads that have a replacement cost of $97.545 billion CDN. Roadways are by far the most complex, expensive, and extensive assets that a municipality is responsible for overseeing. Since adopting Ontario Regulation 588/47: Asset Management Planning for Municipal Infrastructure in 2017, the provincial government has established prescriptions for local road authorities regarding asset category and levels of service being provided. This provincial regulation further stipulates that asset data such as extent, condition, and life cycle costing are to be captured in manner compliant with qualitative descriptions and technical metrics. The Ontario Good Roads Association undertook an exercise to aggregate the road-related data contained within the 444 asset management plans that municipalities have filed with the provincial government. This analysis concluded that collectively Ontario municipal roadways have a $34.7 billion CDN in deferred maintenance. The ill-state of repair of Ontario municipal roads has lasting implications for province’s economic competitiveness and has garnered considerable political attention. Municipal efforts to address the maintenance backlog are stymied by the extremely limited fiscal parameters municipalities must operate within in Ontario. Further exacerbating the program are provincially designed programs that are ineffective, administratively burdensome, and not necessarily aligned with local priorities or strategies. This paper addresses how municipal asset management plans – and more specifically, the data contained in these plans – can be used to design innovative policy frameworks, flexible funding programs, and new levels of service that respond to these funding challenges, as well as emerging issues such as local economic development and climate change. To fully unlock the potential that Ontario Regulation 588/17 has imposed will require a resolute commitment to data standardization and horizontal collaboration between municipalities within regions.

Keywords: transportation, municipal asset management, subnational policy design, subnational funding program design

Procedia PDF Downloads 90

Authors: Tanya Kaur Bedi, Shankha Pratim Bhattacharya

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The US Environmental Protection Agency defines indoor air quality as “The air quality within and around buildings, especially as it relates to the health and comfort of building occupants”. According to the 2021 report by the Energy Policy Institute at Chicago, Indian residents, a country which is home to the highest levels of air pollution in the world, lose about 5.9 years from life expectancy due to poor air quality and yet has numerous dwellings dependent on natural ventilation. Currently the urban population spends 90% of the time indoors, this scenario raises a concern for occupant health and well-being. The built environment can affect health directly and indirectly through immediate or long-term exposure to indoor air pollutants. Health effects associated with indoor air pollutants include eye/nose/throat irritation, respiratory diseases, heart disease, and even cancer. This study attempts to demonstrate the causal relationship between the indoor air quality and its determining aspects. Detailed indoor air quality audits were conducted in residential buildings located in Kolkata, India in the months of December and January 2021. According to the air pollution knowledge assessment city program in India, Kolkata is also the second most polluted mega-city after Delhi. Although the air pollution levels are alarming year-long, the winter months are most crucial due to the unfavorable environmental conditions. While emissions remain typically constant throughout the year, cold air is denser and moves slower than warm air, trapping the pollution in place for much longer and consequently is breathed in at a higher rate than the summers. The air pollution monitoring period was selected considering environmental factors and major pollution contributors like traffic and road dust. This study focuses on the relationship between the built environment and the spatial-temporal distribution of air pollutants in and around it. The measured parameters include, temperature, relative humidity, air velocity, particulate matter, volatile organic compounds, formaldehyde, and benzene. A total of 56 rooms were audited, selectively targeting the most dominant middle-income group. The data-collection was conducted using a set of instruments positioned in the human breathing-zone. The study assesses indoor air quality based on factors determining natural ventilation and air pollution dispersion such as surrounding environment, dominant wind, openable window to floor area ratio, windward or leeward side openings, and natural ventilation type in the room: single side or cross-ventilation, floor height, residents cleaning habits, etc.

Keywords: indoor air quality, occupant health, urban housing, air pollution, natural ventilation, architecture, urban issues

Procedia PDF Downloads 118

Authors: Srećko Favro, Dora Mužinić

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The eastern coast of the Adriatic Sea has been a significant part of the most important traffic corridors since Antiquity due to its position as the deepest indented bay of the Mediterranean and numerous bays on the coast and is-lands. The central place throughout history was occupied by the central part - Split-Dalmatia County, with its center in Antica in Salona and later in Split. Nowadays, in addition to its traffic and economic importance, this area is also important for tourism, an area where Croatia develops its economy and realizing its economic growth. Nautical tourism is the most important form of the tourist economic sector that uses the geographical features of the Croatian Adriatic water area and achieves the greatest growth based on tour-ist trends in the world (coronavirus - separation from the masses, adventure tourism - own arrangements) and thus opens up the possibility of develop-ment for other parts of the tourist economy. This will be described in the ex-ample of the business of the Split-Dalmatia County shipping company from Krilo Jesenice, which operates as a mini-cruising service provider, the lead-ing form of cruising in Croatia. The advantages that this type of tourism provides to travelers in terms of customized itineraries, high-quality services, an intimate atmosphere, and a unique experience through familiarization with local culture and tradition will be considered. Through direct primary research and analysis of available secondary research data, an attempt will be made to show how traditional Croatian mini cruisers manage to stand out in a competitive tourist environment. Their impact on the local economy, sus-tainability, and environmental protection will be considered, as well as how they are integrated into the tourist offer of other destinations in Croatia. In addition, the challenges and opportunities that arise in the maintenance and development of traditional Croatian mini cruisers will be discussed, includ-ing issues such as infrastructure, staff training, and market trends.

Keywords: croatia, adriatic, cruising, nautical tourism, mini cruise

Procedia PDF Downloads 59

Authors: Getaneh Berie Tarekegn, Li-Chia Tai

Abstract:

With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarms, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

Procedia PDF Downloads 36

Authors: Srikanth Pandurangi, Basheer Mohammed, Nezar Al Sayegh

Abstract:

In order to meet the increasing demand for housing care for Kuwaiti citizens, the government authorities in Kuwait are undertaking a series of projects in the form of new large cities, outside the current urban area. Al Mutlaa City located to the north-west of the Kuwait Metropolitan Area is one such project out of the 15 planned new cities. The city accommodates a wide variety of residential developments, employment opportunities, commercial, recreational, health care and institutional uses. This paper examines the application of comprehensive transportation demand modeling works undertaken in VISUM platform to understand the future intracity and intercity travel distribution patterns in Kuwait. The scope of models developed varied in levels of detail: strategic model update, sub-area models representing future demand of Al Mutlaa City, sub-area models built to estimate the demand in the residential neighborhoods of the city. This paper aims at offering model update framework that facilitates easy integration between sub-area models and strategic national models for unified traffic forecasts. This paper presents the transportation demand modeling results utilized in informing the planning of multi-modal transportation system for Al Mutlaa City. This paper also presents the household survey data collection efforts undertaken using GPS devices (first time in Kuwait) and notebook computer based digital survey forms for interviewing representative sample of citizens and residents. The survey results formed the basis of estimating trip generation rates and trip distribution coefficients used in the strategic base year model calibration and validation process.

Keywords: innovative methods in transportation data collection, integrated public transportation system, traffic forecasts, transportation modeling, travel behavior

Procedia PDF Downloads 219

Authors: Lara Bettinelli, Bernhard Glatz, Josef Fink

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Planning engineers and researchers use various calculation models with different levels of complexity, calculation efficiency and accuracy in dynamic calculations of railway bridges under high-speed traffic. When choosing a vehicle model to depict the dynamic loading on the bridge structure caused by passing high-speed trains, different goals are pursued: On the one hand, the selected vehicle models should allow the calculation of a bridge’s vibrations as realistic as possible. On the other hand, the computational efficiency and manageability of the models should be preferably high to enable a wide range of applications. The commonly adopted and straightforward vehicle model is the moving load model (MLM), which simplifies the train to a sequence of static axle loads moving at a constant speed over the structure. However, the MLM can significantly overestimate the structure vibrations, especially when resonance events occur. More complex vehicle models, which depict the train as a system of oscillating and coupled masses, can reproduce the interaction dynamics between the vehicle and the bridge superstructure to some extent and enable the calculation of more realistic bridge accelerations. At the same time, such multi-body models require significantly greater processing capacities and precise knowledge of various vehicle properties. The European standards allow for applying the so-called additional damping method when simple load models, such as the MLM, are used in dynamic calculations. An additional damping factor depending on the bridge span, which should take into account the vibration-reducing benefits of the vehicle-bridge interaction, is assigned to the supporting structure in the calculations. However, numerous studies show that when the current standard specifications are applied, the calculation results for the bridge accelerations are in many cases still too high compared to the measured bridge accelerations, while in other cases, they are not on the safe side. A proposal to calculate the additional damping based on extensive dynamic calculations for a parametric field of simply supported bridges with a ballasted track was developed to address this issue. In this contribution, several different approaches to determine the additional damping of the supporting structure considering the vehicle-bridge interaction when using the MLM are compared with one another. Besides the standard specifications, this includes the approach mentioned above and two additional recently published alternative formulations derived from analytical approaches. For a bridge catalogue of 65 existing bridges in Austria in steel, concrete or composite construction, calculations are carried out with the MLM for two different high-speed trains and the different approaches for additional damping. The results are compared with the calculation results obtained by applying a more sophisticated multi-body model of the trains used. The evaluation and comparison of the results allow assessing the benefits of different calculation concepts for the additional damping regarding their accuracy and possible applications. The evaluation shows that by applying one of the recently published redesigned additional damping methods, the calculation results can reflect the influence of the vehicle-bridge interaction on the design-relevant structural accelerations considerably more reliable than by using normative specifications.

Keywords: Additional Damping Method, Bridge Dynamics, High-Speed Railway Traffic, Vehicle-Bridge-Interaction

Procedia PDF Downloads 159

Authors: Fadhl Alakwaa

Abstract:

One of the sustainable goals of the system biology is understanding gene-gene interactions. Hence, gene regulatory networks (GRN) need to be constructed for understanding the disease ontology and to reduce the cost of drug development. To construct gene regulatory from gene expression we need to overcome many challenges such as data denoising and dimensionality. In this paper, we develop an integrated system to reduce data dimension and remove the noise. The generated network from our system was validated via available interaction databases and was compared to previous methods. The result revealed the performance of our proposed method.

Keywords: gene regulatory network, biclustering, denoising, system biology

Procedia PDF Downloads 229

Authors: Ahmet Karagoz, Irfan Karagoz

Abstract:

Synthetic Aperture Radar (SAR) is a radar mechanism that can be integrated into manned and unmanned aerial vehicles to create high-resolution images in all weather conditions, regardless of day and night. In this study, SAR images of military vehicles with different azimuth and descent angles are pre-processed at the first stage. The main purpose here is to reduce the high speckle noise found in SAR images. For this, the Wiener adaptive filter, the mean filter, and the median filters are used to reduce the amount of speckle noise in the images without causing loss of data. During the image segmentation phase, pixel values are ordered so that the target vehicle region is separated from other regions containing unnecessary information. The target image is parsed with the brightest 20% pixel value of 255 and the other pixel values of 0. In addition, by using appropriate parameters of statistical region merging algorithm, segmentation comparison is performed. In the step of feature extraction, the feature vectors belonging to the vehicles are obtained by using Gabor filters with different orientation, frequency and angle values. A number of Gabor filters are created by changing the orientation, frequency and angle parameters of the Gabor filters to extract important features of the images that form the distinctive parts. Finally, images are classified by sparse representation method. In the study, l₁ norm analysis of sparse representation is used. A joint database of the feature vectors generated by the target images of military vehicle types is obtained side by side and this database is transformed into the matrix form. In order to classify the vehicles in a similar way, the test images of each vehicle is converted to the vector form and l₁ norm analysis of the sparse representation method is applied through the existing database matrix form. As a result, correct recognition has been performed by matching the target images of military vehicles with the test images by means of the sparse representation method. 97% classification success of SAR images of different military vehicle types is obtained.

Keywords: automatic target recognition, sparse representation, image classification, SAR images

Procedia PDF Downloads 359

Authors: Getaneh Berie Tarekegn, Li-Chia Tai

Abstract:

With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

Procedia PDF Downloads 40

Authors: Sara Alshehri, Bayan Alenzi, Atheer Alshehri, Samia Chelloug, Zainab Almry, Hussah Albugmai

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This paper concerns body area networks sensor that collect signals around a human body. The black hole attacks are the main security challenging problem because the data traffic can be dropped at any node. The focus of our proposed solution is to efficiently route data packets while detecting black hole nodes.

Keywords: body sensor networks, security, black hole, routing, broadcasting, OMNeT++

Procedia PDF Downloads 636

Authors: Arcady Ponosov., Ramazan Kadiev

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The work studies the global moment stability of solutions of systems of nonlinear differential Itô equations with delays. A modified regularization method (W-method) for the analysis of various types of stability of such systems, based on the choice of the auxiliaryequations and applications of the theory of positive invertible matrices, is proposed and justified. Development of this method for deterministic functional differential equations is due to N.V. Azbelev and his students. Sufficient conditions for the moment stability of solutions in terms of the coefficients for sufficiently general as well as specific classes of Itô equations are given.

Keywords: asymptotic stability, delay equations, operator methods, stochastic noise

Procedia PDF Downloads 218

Authors: Maria Amelia V. C. Araujo, Billy J. Araujo, Brian Greenwood

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The flow over a backward-facing step is characterized by the presence of flow separation, recirculation and reattachment, for a simple geometry. This type of fluid behaviour takes place in many practical engineering applications, hence the reason for being investigated. Historically, fluid flows over a backward-facing step have been examined in many experiments using a variety of measuring techniques such as laser Doppler velocimetry (LDV), hot-wire anemometry, particle image velocimetry or hot-film sensors. However, some of these techniques cannot conveniently be used in separated flows or are too complicated and expensive. In this work, the applicability of the acoustic Doppler velocimetry (ADV) technique is investigated to such type of flows, at various Reynolds numbers corresponding to different flow regimes. The use of this measuring technique in separated flows is very difficult to find in literature. Besides, most of the situations where the Reynolds number effect is evaluated in separated flows are in numerical modelling. The ADV technique has the advantage in providing nearly non-invasive measurements, which is important in resolving turbulence. The ADV Nortek Vectrino+ was used to characterize the flow, in a recirculating laboratory flume, at various Reynolds Numbers (Reh = 3738, 5452, 7908 and 17388) based on the step height (h), in order to capture different flow regimes, and the results compared to those obtained using other measuring techniques. To compare results with other researchers, the step height, expansion ratio and the positions upstream and downstream the step were reproduced. The post-processing of the AVD records was performed using a customized numerical code, which implements several filtering techniques. Subsequently, the Vectrino noise level was evaluated by computing the power spectral density for the stream-wise horizontal velocity component. The normalized mean stream-wise velocity profiles, skin-friction coefficients and reattachment lengths were obtained for each Reh. Turbulent kinetic energy, Reynolds shear stresses and normal Reynolds stresses were determined for Reh = 7908. An uncertainty analysis was carried out, for the measured variables, using the moving block bootstrap technique. Low noise levels were obtained after implementing the post-processing techniques, showing their effectiveness. Besides, the errors obtained in the uncertainty analysis were relatively low, in general. For Reh = 7908, the normalized mean stream-wise velocity and turbulence profiles were compared directly with those acquired by other researchers using the LDV technique and a good agreement was found. The ADV technique proved to be able to characterize the flow properly over a backward-facing step, although additional caution should be taken for measurements very close to the bottom. The ADV measurements showed reliable results regarding: a) the stream-wise velocity profiles; b) the turbulent shear stress; c) the reattachment length; d) the identification of the transition from transitional to turbulent flows. Despite being a relatively inexpensive technique, acoustic Doppler velocimetry can be used with confidence in separated flows and thus very useful for numerical model validation. However, it is very important to perform adequate post-processing of the acquired data, to obtain low noise levels, thus decreasing the uncertainty.

Keywords: ADV, experimental data, multiple Reynolds number, post-processing

Procedia PDF Downloads 138

Authors: Ben Soltane Cheima, Ittansa Yonas Kelbesa

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Speaker Identification (SI) is the task of establishing identity of an individual based on his/her voice characteristics. The SI task is typically achieved by two-stage signal processing: training and testing. The training process calculates speaker specific feature parameters from the speech and generates speaker models accordingly. In the testing phase, speech samples from unknown speakers are compared with the models and classified. Even though performance of speaker identification systems has improved due to recent advances in speech processing techniques, there is still need of improvement. In this paper, a Closed-Set Tex-Independent Speaker Identification System (CISI) based on a Multiple Classifier System (MCS) is proposed, using Mel Frequency Cepstrum Coefficient (MFCC) as feature extraction and suitable combination of vector quantization (VQ) and Gaussian Mixture Model (GMM) together with Expectation Maximization algorithm (EM) for speaker modeling. The use of Voice Activity Detector (VAD) with a hybrid approach based on Short Time Energy (STE) and Statistical Modeling of Background Noise in the pre-processing step of the feature extraction yields a better and more robust automatic speaker identification system. Also investigation of Linde-Buzo-Gray (LBG) clustering algorithm for initialization of GMM, for estimating the underlying parameters, in the EM step improved the convergence rate and systems performance. It also uses relative index as confidence measures in case of contradiction in identification process by GMM and VQ as well. Simulation results carried out on voxforge.org speech database using MATLAB highlight the efficacy of the proposed method compared to earlier work.

Keywords: feature extraction, speaker modeling, feature matching, Mel frequency cepstrum coefficient (MFCC), Gaussian mixture model (GMM), vector quantization (VQ), Linde-Buzo-Gray (LBG), expectation maximization (EM), pre-processing, voice activity detection (VAD), short time energy (STE), background noise statistical modeling, closed-set tex-independent speaker identification system (CISI)

Procedia PDF Downloads 306

Authors: C. Fernandez-Plaza, I. Abad, E. Diaz, I. Diaz

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Births occurring before the 37th week of gestation are considered preterm births. A threat of preterm is defined as the beginning of regular uterine contractions, dilation and cervical effacement between 23 and 36 gestation weeks. To author's best knowledge, the factors that determine the beginning of the birth are not completely defined yet. In particular, the incidence of sleep habits on preterm births is weekly studied. The aim of this study is to develop a model to predict the factors affecting premature delivery on pregnancy, based on the above potential risk factors, including those derived from sleep habits and light exposure at night (introduced as 12 variables obtained by a telephone survey using two questionnaires previously used by other authors). Thus, three groups of variables were included in the study (maternal, fetal and sleep habits). The study was approved by Research Ethics Committee of the Principado of Asturias (Spain). An observational, retrospective and descriptive study was performed with 481 births between January 1, 2015 and May 10, 2016 in the University Central Hospital of Asturias (Spain). A statistical analysis using SPSS was carried out to compare qualitative and quantitative variables between preterm and term delivery. Chi-square test qualitative variable and t-test for quantitative variables were applied. Statistically significant differences (p < 0.05) between preterm vs. term births were found for primiparity, multi-parity, kind of conception, place of residence or premature rupture of membranes and interruption during nights. In addition to the statistical analysis, machine learning methods to look for a prediction model were tested. In particular, tree based models were applied as the trade-off between performance and interpretability is especially suitable for this study. C5.0, recursive partitioning, random forest and tree bag models were analysed using caret R-package. Cross validation with 10-folds and parameter tuning to optimize the methods were applied. In addition, different noise reduction methods were applied to the initial data using NoiseFiltersR package. The best performance was obtained by C5.0 method with Accuracy 0.91, Sensitivity 0.93, Specificity 0.89 and Precision 0.91. Some well known preterm birth factors were identified: Cervix Dilation, maternal BMI, Premature rupture of membranes or nuchal translucency analysis in the first trimester. The model also identifies other new factors related to sleep habits such as light through window, bedtime on working days, usage of electronic devices before sleeping from Mondays to Fridays or change of sleeping habits reflected in the number of hours, in the depth of sleep or in the lighting of the room. IF dilation < = 2.95 AND usage of electronic devices before sleeping from Mondays to Friday = YES and change of sleeping habits = YES, then preterm is one of the predicting rules obtained by C5.0. In this work a model for predicting preterm births is developed. It is based on machine learning together with noise reduction techniques. The method maximizing the performance is the one selected. This model shows the influence of variables related to sleep habits in preterm prediction.

Keywords: machine learning, noise reduction, preterm birth, sleep habit

Procedia PDF Downloads 141

Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder

Procedia PDF Downloads 286

Authors: Sachil Kumar, Anoop Kumar Verma, Wahid Ali, Uma Shankar Singh

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Globally approximately 55.3 million people die each year. In the India there were 95 lakh annual deaths in 2013. The number of deaths resulted from homicides, suicides and unintentional injuries in the same period was about 5.7 lakh. The ever-increasing crime rate necessitated the development of methods for determining time since death. An erroneous time of death window can lead investigators down the wrong path or possibly focus a case on an innocent suspect. In this regard a research was carried out by analyzing the temperature dependent degradation of a Cardiac Troponin-T protein (cTnT) in the myocardium postmortem as a marker for time since death. Cardiac tissue samples were collected from (n=6) medico-legal autopsies, (in the Department of Forensic Medicine and Toxicology, King George’s Medical University, Lucknow India) after informed consent from the relatives and studied post-mortem degradation by incubation of the cardiac tissue at room temperature (20±2 OC), 12 0C, 25 0C and 37 0C for different time periods ((~5, 26, 50, 84, 132, 157, 180, 205, and 230 hours). The cases included were the subjects of road traffic accidents (RTA) without any prior history of disease who died in the hospital and their exact time of death was known. The analysis involved extraction of the protein, separation by denaturing gel electrophoresis (SDS-PAGE) and visualization by Western blot using cTnT specific monoclonal antibodies. The area of the bands within a lane was quantified by scanning and digitizing the image using Gel Doc. The data shows a distinct temporal profile corresponding to the degradation of cTnT by proteases found in cardiac muscle. The disappearance of intact cTnT and the appearance of lower molecular weight bands are easily observed. Western blot data clearly showed the intact protein at 42 kDa, two major (27 kDa, 10kDa) fragments, two additional minor fragments (32 kDa) and formation of low molecular weight fragments as time increases. At 12 0C the intensity of band (intact cTnT) decreased steadily as compared to RT, 25 0C and 37 0C. Overall, both PMI and temperature had a statistically significant effect where the greatest amount of protein breakdown was observed within the first 38 h and at the highest temperature, 37 0C. The combination of high temperature (37 0C) and long Postmortem interval (105.15 hrs) had the most drastic effect on the breakdown of cTnT. If the percent intact cTnT is calculated from the total area integrated within a Western blot lane, then the percent intact cTnT shows a pseudo-first order relationship when plotted against the log of the time postmortem. These plots show a good coefficient of correlation of r = 0.95 (p=0.003) for the regression of the human heart at different temperature conditions. The data presented demonstrates that this technique can provide an extended time range during which Postmortem interval can be more accurately estimated.

Keywords: degradation, postmortem interval, proteolysis, temperature, troponin

Procedia PDF Downloads 382

Authors: Mohd Ayyub Khan, Nadeem Akhtar

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Due to recent advances in wireless communication technologies and hand-held devices, there is a huge demand for video-based applications such as video surveillance, video conferencing, remote surgery, Digital Video Broadcast (DVB), IPTV, online learning courses, YouTube, WhatsApp, Instagram, Facebook, Interactive Video Games. However, the raw videos posses very high bandwidth which makes the compression a must before its transmission over the wireless channels. The High Efficiency Video Codec (HEVC) (also called H.265) is latest state-of-the-art video coding standard developed by the Joint effort of ITU-T and ISO/IEC teams. HEVC is targeted for high resolution videos such as 4K or 8K resolutions that can fulfil the recent demands for video services. The compression ratio achieved by the HEVC is twice as compared to its predecessor H.264/AVC for same quality level. The compression efficiency is generally increased by removing more correlation between the frames/pixels using complex techniques such as extensive intra and inter prediction techniques. As more correlation is removed, the chances of interdependency among coded bits increases. Thus, bit errors may have large effect on the reconstructed video. Sometimes even single bit error can lead to catastrophic failure of the reconstructed video. In this paper, we study the performance of HEVC bitstream over additive white Gaussian noise (AWGN) channel. Moreover, HEVC over Quadrature Amplitude Modulation (QAM) combined with forward error correction (FEC) schemes are also explored over the noisy channel. The video will be encoded using HEVC, and the coded bitstream is channel coded to provide some redundancies. The channel coded bitstream is then modulated using QAM and transmitted over AWGN channel. At the receiver, the symbols are demodulated and channel decoded to obtain the video bitstream. The bitstream is then used to reconstruct the video using HEVC decoder. It is observed that as the signal to noise ratio of channel is decreased the quality of the reconstructed video decreases drastically. Using proper FEC codes, the quality of the video can be restored up to certain extent. Thus, the performance analysis of HEVC presented in this paper may assist in designing the optimized code rate of FEC such that the quality of the reconstructed video is maximized over wireless channels.

Keywords: AWGN, forward error correction, HEVC, video coding, QAM

Procedia PDF Downloads 142

Authors: Nikitha Francis, Anjana Hoode, Vinitha George, Jayashree S. Bhat

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The interaction between cognition and language, referred as cognitive-communication, is very intricate, involving several mental processes such as perception, memory, attention, lexical retrieval, decision making, motor planning, self-monitoring and knowledge. Cognitive-communication disorders are difficulties in communicative competencies that result from underlying cognitive impairments of attention, memory, organization, information processing, problem solving, and executive functions. Traumatic brain injury (TBI) is an acquired, non - progressive condition, resulting in distinct deficits of cognitive communication abilities such as naming, word-finding, self-monitoring, auditory recognition, attention, perception and memory. Cognitive-communication intervention in TBI is individualized, in order to enhance the person’s ability to process and interpret information for better functioning in their family and community life. The present case report illustrates the cognitive-communicative behaviors and the intervention outcomes of an adult with TBI, who was brought to the Department of Audiology and Speech Language Pathology, with cognitive and communicative disturbances, consequent to road traffic accident. On a detailed assessment, she showed naming deficits along with perseverations and had severe difficulty in recalling the details of the accident, her house address, places she had visited earlier, names of people known to her, as well as the activities she did each day, leading to severe breakdowns in her communicative abilities. She had difficulty in initiating, maintaining and following a conversation. She also lacked orientation to time and place. On administration of the Manipal Manual of Cognitive Linguistic Abilities (MMCLA), she exhibited poor performance on tasks related to visual and auditory perception, short term memory, working memory and executive functions. She attended 20 sessions of cognitive-communication intervention which followed a domain-general, adaptive training paradigm, with tasks relevant to everyday cognitive-communication skills. Compensatory strategies such as maintaining a dairy with reminders of her daily routine, names of people, date, time and place was also recommended. MMCLA was re-administered and her performance in the tasks showed significant improvements. Occurrence of perseverations and word retrieval difficulties reduced. She developed interests to initiate her day-to-day activities at home independently, as well as involve herself in conversations with her family members. Though she lacked awareness about her deficits, she actively involved herself in all the therapy activities. Rehabilitation of moderate to severe head injury patients can be done effectively through a holistic cognitive retraining with a focus on different cognitive-linguistic domains. Selection of goals and activities should have relevance to the functional needs of each individual with TBI, as highlighted in the present case report.

Keywords: cognitive-communication, executive functions, memory, traumatic brain injury

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Authors: Thiago Brito Bezerra, Mauro Lopes de Freitas, Waldir Sabino da Silva Júnior

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The Sigma-Delta A/D converters have been proposed as a practical application for A/D conversion at high rates because of its simplicity and robustness to imperfections in the circuit, also because the traditional converters are more difficult to implement in VLSI technology. These difficulties with conventional conversion methods need precise analog components in their filters and conversion circuits, and are more vulnerable to noise and interference. This paper aims to analyze the architecture, function and application of Analog-Digital converters (A/D) Sigma-Delta to overcome these difficulties, showing some simulations using the Simulink software and Multisim.

Keywords: analysis, oversampling modulator, A/D converters, sigma-delta

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Authors: Said Elkassimi, Said Safi, B. Manaut

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This paper presented a study of three algorithms, the equalization algorithm to equalize the transmission channel with ZF and MMSE criteria, application of channel Bran A, and adaptive filtering algorithms LMS and RLS to estimate the parameters of the equalizer filter, i.e. move to the channel estimation and therefore reflect the temporal variations of the channel, and reduce the error in the transmitted signal. So far the performance of the algorithm equalizer with ZF and MMSE criteria both in the case without noise, a comparison of performance of the LMS and RLS algorithm.

Keywords: adaptive filtering second equalizer, LMS, RLS Bran A, Proakis (B) MMSE, ZF

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Authors: Zachary Huffman, Joana Rocha

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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.

Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations

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Authors: Zuzanna Miodonska, Michal Krecichwost, Pawel Badura

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Sigmatism (lisping) is a speech disorder in which sibilant consonants are mispronounced. The diagnosis of this phenomenon is usually based on the auditory assessment. However, the progress in speech analysis techniques creates a possibility of developing computer-aided sigmatism diagnosis tools. The aim of the study is to statistically verify whether specific acoustic features of sibilant sounds may be related to pronunciation correctness. Such knowledge can be of great importance while implementing classifiers and designing novel tools for automatic sibilants pronunciation evaluation. The study covers analysis of various speech signal measures, including features proposed in the literature for the description of normative sibilants realization. Amplitudes and frequencies of three fricative formants (FF) are extracted based on local spectral maxima of the friction noise. Skewness, kurtosis, four normalized spectral moments (SM) and 13 mel-frequency cepstral coefficients (MFCC) with their 1st and 2nd derivatives (13 Delta and 13 Delta-Delta MFCC) are included in the analysis as well. The resulting feature vector contains 51 measures. The experiments are performed on the speech corpus containing words with selected sibilant sounds (/ʃ, ʒ/) pronounced by 60 preschool children with proper pronunciation or with natural pathologies. In total, 224 /ʃ/ segments and 191 /ʒ/ segments are employed in the study. The Mann-Whitney U test is employed for the analysis of stigmatism and normative pronunciation. Statistically, significant differences are obtained in most of the proposed features in children divided into these two groups at p < 0.05. All spectral moments and fricative formants appear to be distinctive between pathology and proper pronunciation. These metrics describe the friction noise characteristic for sibilants, which makes them particularly promising for the use in sibilants evaluation tools. Correspondences found between phoneme feature values and an expert evaluation of the pronunciation correctness encourage to involve speech analysis tools in diagnosis and therapy of sigmatism. Proposed feature extraction methods could be used in a computer-assisted stigmatism diagnosis or therapy systems.

Keywords: computer-aided pronunciation evaluation, sigmatism diagnosis, speech signal analysis, statistical verification

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Authors: A.R. Eskandari, M.R. Eskandari

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A 2D complete identification algorithm for dielectric and multiple objects immersed in air is presented. The employed technique consists of initially retrieving the shape and position of the scattering object using a linear sampling method and then determining the electric permittivity and conductivity of the scatterer using adjoint sensitivity analysis. This inversion algorithm results in high computational speed and efficiency, and it can be generalized for any scatterer structure. Also, this method is robust with respect to noise. The numerical results clearly show that this hybrid approach provides accurate reconstructions of various objects.

Keywords: inverse scattering, microwave imaging, two-dimensional objects, Linear Sampling Method (LSM)

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Authors: Rutej R. Mehta, Michael A. Chappell

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Introduction: Arterial spin labelling (ASL) is an increasingly popular perfusion MRI technique, in which arterial blood water is magnetically labelled in the neck before flowing into the brain, providing a non-invasive measure of cerebral blood flow (CBF). The accuracy of ASL CBF measurements, however, is hampered by dispersion effects; the distortion of the ASL labelled bolus during its transit through the vasculature. In spite of this, the current recommended implementation of ASL – the white paper (Alsop et al., MRM, 73.1 (2015): 102-116) – does not account for dispersion, which leads to the introduction of errors in CBF. Given that the transport time from the labelling region to the tissue – the arterial transit time (ATT) – depends on the region of the brain and the condition of the patient, it is likely that these errors will also vary with the ATT. In this study, various dispersion models are assessed in comparison with the white paper (WP) formula for CBF quantification, enabling the errors introduced by the WP to be quantified. Additionally, this study examines the relationship between the errors associated with the WP and the ATT – and how this is influenced by dispersion. Methods: Data were simulated using the standard model for pseudo-continuous ASL, along with various dispersion models, and then quantified using the formula in the WP. The ATT was varied from 0.5s-1.3s, and the errors associated with noise artefacts were computed in order to define the concept of significant error. The instantaneous slope of the error was also computed as an indicator of the sensitivity of the error with fluctuations in ATT. Finally, a regression analysis was performed to obtain the mean error against ATT. Results: An error of 20.9% was found to be comparable to that introduced by typical measurement noise. The WP formula was shown to introduce errors exceeding 20.9% for ATTs beyond 1.25s even when dispersion effects were ignored. Using a Gaussian dispersion model, a mean error of 16% was introduced by using the WP, and a dispersion threshold of σ=0.6 was determined, beyond which the error was found to increase considerably with ATT. The mean error ranged from 44.5% to 73.5% when other physiologically plausible dispersion models were implemented, and the instantaneous slope varied from 35 to 75 as dispersion levels were varied. Conclusion: It has been shown that the WP quantification formula holds only within an ATT window of 0.5 to 1.25s, and that this window gets narrower as dispersion occurs. Provided that the dispersion levels fall below the threshold evaluated in this study, however, the WP can measure CBF with reasonable accuracy if dispersion is correctly modelled by the Gaussian model. However, substantial errors were observed with other common models for dispersion with dispersion levels similar to those that have been observed in literature.

Keywords: arterial spin labelling, dispersion, MRI, perfusion

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Authors: Kangrij Roeksiripat

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Street vending is one of the informal economy activities which considered significance to Thai people in the economic and the day-to-day social life. It had been believed that the street vendor is a group of the poor and uneducated people. With the increasing numbers of the street vendor occupying space on public sidewalks especially in central business districts, it becomes unclear whether street vending continues as a solution to unemployment for access labors. This research attempts to study and analyze types of street vendors in Bangkok under the informal economy framework. The debate on the heterogeneous informal economy has categorized into four schools; the dualism, the structuralism, the legalism and the voluntarism. The examination also embodies with market concept with Porter’s Five Forces of Competitive Position Model analysis and the interviews with the street vendors in three case study areas: Inner zone (Pathumwan district - the sidewalk on the opposite side of Siam Paragon mall), Middle zone (Ramkhamhaeng district - the sidewalk on the opposite side of Ramkhamhaeng University) and Outer zone (Minburi district- the sidewalk of Sriburanukit Road). The result indicates that most of street vendors in Siam square are voluntarily choose to make a living in vending on a sidewalk and tend to take it as a long-term occupation even though they can be in formal wage employment. Moreover, average income and positive attitude towards self-employed are the important factors that drive them to operate street vending businesses. Meanwhile, street vending is often a family enterprise in Ramkhamhaeng area and most vendors do not wish to transform their businesses into the formal sectors. Whereas the survey conducted in Sriburankit Road reveals that almost all of street vendors migrated from other provinces and were previously paid as the unskilled workers in formal sectors. They moved to informal trades because of the uncertainty of employment in the mainstream sectors and the inconsistent income with knowledge support of friends and relatives from the same hometown. In particular, the result reveals a common pattern that street vending is the very first occupation of some group of vendors and they will continue to engage in this activity. Thus, it is important for the government to design optimal policy which not only integrating informal workers into the formal economy but also monitoring the enforcement of regulations on the modern informal economy.

Keywords: informal economy, sidewalks, street vendors, occupation

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Authors: Muhammad Ahnaf Zahin, Yaw Adu-Gyamfi

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Bus transit is a crucial component of transportation networks, especially in urban areas. Any intelligent transportation system must have accurate real-time information on bus travel times since it minimizes waiting times for passengers at different stations along a route, improves service reliability, and significantly optimizes travel patterns. Bus agencies must enhance the quality of their information service to serve their passengers better and draw in more travelers since people waiting at bus stops are frequently anxious about when the bus will arrive at their starting point and when it will reach their destination. For solving this issue, different models have been developed for predicting bus travel times recently, but most of them are focused on smaller road networks due to their relatively subpar performance in high-density urban areas on a vast network. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. In this study, Gated Recurrent Unit (GRU) neural network was followed to predict the mean vehicle travel times for different hours of the day for multiple stations along multiple routes. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. The spatial and temporal information and the historical average travel times were captured from the dataset for model input parameters. As adjacency matrices for the spatial input parameters, the station distances and sequence numbers were used, and the time of day (hour) was considered for the temporal inputs. Other inputs, including volatility information such as standard deviation and variance of journey durations, were also included in the model to make it more robust. The model's performance was evaluated based on a metric called mean absolute percentage error (MAPE). The observed prediction errors for various routes, trips, and stations remained consistent throughout the day. The results showed that the developed model could predict travel times more accurately during peak traffic hours, having a MAPE of around 14%, and performed less accurately during the latter part of the day. In the context of a complicated transportation network in high-density urban areas, the model showed its applicability for real-time travel time prediction of public transportation and ensured the high quality of the predictions generated by the model.

Keywords: gated recurrent unit, mean absolute percentage error, single-step forecasting, travel time prediction.

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Authors: Zhijie Ma, Qinglin Zhao, Hongning Dai, Huan Zhang

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This paper proposes an APPLE scheme that aims at providing absolute and proportional throughput guarantees, and maximizing system throughput simultaneously for wireless LANs with homogeneous and heterogenous traffic. We formulate our objectives as an optimization problem, present its exact and approximate solutions, and prove the existence and uniqueness of the approximate solution. Simulations validate that APPLE scheme is accurate, and the approximate solution can well achieve the desired objectives already.

Keywords: IEEE 802.11e, throughput guarantee, priority, WLANs

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Authors: Afifa Aslam, Muhammad Ibrahim, Abid Mahmood, Muhammad Usman Alvi, Fariha Jabeen, Umara Tabassum

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Many factors cause air pollution in Pakistan, which poses a significant threat to human health. Diesel fuel and gasoline motor vehicles, as well as industrial companies, pollute the air in Pakistan's cities. The study's goal is to determine the level of air pollution in a Pakistani industrial city and to establish risk levels for the health of the population. We measured the intensity of air pollution by chemical characterization and examination of air samples collected at stationary remark sites. The PM10 levels observed at all sampling sites, including residential, commercial, high-traffic, and industrial areas were well above the limits imposed by Pakistan EPA, the United States EPA, and WHO. We assessed the health risk via chemical factors using a methodology approved for risk assessment. All Igeo index values greater than one were considered moderately contaminated or moderately to severely contaminated. Heavy metals have a substantial risk of acute adverse effects. In Faisalabad, Pakistan, there was an enormously high risk of chronic effects produced by a heavy metal acquaintance. Concerning specified toxic metals, intolerable levels of carcinogenic risks have been determined for the entire population. As a result, in most of the investigated areas of Faisalabad, the indices and hazard quotients for chronic and acute exposure exceeded the permissible level of 1.0. In the current study, re-suspended roadside mineral dust, anthropogenic exhaust emissions from traffic and industry, and industrial dust were identified as major emission sources of elemental particulate contents. Because of the unacceptable levels of risk in the research area, it is strongly suggested that a comprehensive study of the population's health status as a result of air pollution should be conducted for policies to be developed against these risks.

Keywords: elemental composition, particulate pollution, Igeo index, health risk assessment, hazard quotient

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Authors: Syed Imran Hussain Shah

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Air pollution rapidly impacts the Earth's climate and environmental quality, causing public health nuisances and cardio-pulmonary illnesses. Air pollution is a global issue, and all population groups in all the regions in the developed and developing parts of the world were affected by it. The promise of a reduction in deaths and diseases as per SDG No. 3 is an international commitment towards sustainable development. In that context, assessing and evaluating the ambient air quality is paramount. This article estimates the air pollution released by the vehicles on roads of Lahore, a mega city having 13.98 million populations. A survey was conducted on different fuel stations to determine the estimated fuel pumped to different types of vehicles from different fuel stations. The number of fuel stations in Lahore is around 350. Another survey was also conducted to interview the drivers to know the per-litre fuel consumption of other vehicles. Therefore, a survey was conducted on 189 fuel stations and 400 drivers using a combination of random sampling and convenience sampling methods. The sampling was done in a manner to cover all areas of the city including central commercial hubs, modern housing societies, industrial zones, main highways, old traditional population centres, etc. Mathematical equations were also used to estimate the emissions from different modes of vehicles. Due to the increase in population, the number of vehicles is increasing, and consequently, traffic emissions were rising at a higher level. Motorcycles, auto rickshaws, motor cars, and vans were the main contributors to Carbon dioxide and vehicular emissions in the air. It has been observed that vehicles that use petrol fuel produce more Carbon dioxide emissions in the air. Buses and trucks were the main contributors to NOx in the air due to the use of diesel fuel. Whereas vans, buses, and trucks produce the maximum amount of SO2. PM10 and PM2.5 were mainly produced by motorcycles and motorcycle two-stroke rickshaws. Auto rickshaws and motor cars mainly produce benzene emissions. This study may act as a major tool for traffic and vehicle policy decisions to promote better fuel quality and more fuel-efficient vehicles to reduce emissions.

Keywords: particulate matter, nitrogen dioxide, climate change, pollution control

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Authors: Meng-Cong Zheng, Yu-Sheng Chen

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The meeting behavior is a pervasive kind of interaction, which often occurs between the passenger and the shuttle. However, the meeting point set up at the Taoyuan International Airport is too far from the entry-exit, often causing passengers to stop searching near the entry-exit. When the number of people waiting for the rush hour increases, it often results in chaos in the waiting area. This study tried to find out what is the key factor to promote the rapid finding of each other between the passengers and the pick-ups. Then we implemented several design proposals to improve the meeting behavior of passengers and pick-ups based on behavior mapping and post-occupancy evaluation to enhance their meeting efficiency in unfamiliar environments. The research base is the reception hall of the second terminal of Taoyuan International Airport. Behavioral observation and mapping are implemented on the entry of inbound passengers into the welcome space, including the crowd distribution of the people who rely on the separation wall in the waiting area, the behavior of meeting and the interaction between the inbound passengers and the pick-ups. Then we redesign the space planning and signage design based on post-occupancy evaluation to verify the effectiveness of space plan and signage design. This study found that passengers ignore existing meeting-point designs which are placed on distant pillars at both ends. The position of the screen affects the area where the receiver is stranded, causing the pick-ups to block the passenger's moving line. The pick-ups prefer to wait where it is easy to watch incoming passengers and where it is closest to the mode of transport they take when leaving. Large visitors tend to gather next to landmarks, and smaller groups have a wide waiting area in the lobby. The location of the meeting point chosen by the pick-ups is related to the view of the incoming passenger. Finally, this study proposes an improved design of the meeting point, setting the traffic information in it, so that most passengers can see the traffic information when they enter the country. At the same time, we also redesigned the pick-ups desk to improve the efficiency of passenger meeting.

Keywords: meeting point design, post-occupancy evaluation, behavioral mapping, international airport

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