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Weinberg, Bella Hass. "The history of stoplists: lists of words not indexed". Indexer 42, n.º 1 (marzo de 2024): 53–63. http://dx.doi.org/10.3828/index.2023.56.
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The concept of the stoplist – a list of the words not indexed – is related to indexable matter, to the arrangement of subentries in indexes, and to search engine optimization. Stoplists were thought to have been developed in the 1950s in conjunction with automatic indexing, but a fifteenth-century Hebrew concordance to the Bible, Me’ir Nativ , contains a stoplist that is very similar to modern ones, consisting of function words such as prepositions. Since the Hebrew concordance was modeled on the Latin biblical concordance of Arlottus, which does not contain a stoplist, Me’ir Nativ must be regarded as the first example of this structure. Although stopwords were omitted from the earliest Latin concordances (without listing them), subsequent theological debate about the meanings of Latin function words led to the compilation of special concordances of such words. Current research by the author focuses on the importance of similar small words to the Masoretes, who were concerned about preserving an accurate text of the Hebrew Bible, and may possibly have had an index of such words.
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Donner, Amy. "Photoactive channels obey stoplights". Nature Methods 6, n.º 2 (febrero de 2009): 120. http://dx.doi.org/10.1038/nmeth0209-120.
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Rintyarna, Bagus Setya, Wahyu Nurkholis Hadi Syahputra, Triawan Adi Cahyanto y Riska Nur Maulida. "Assessing Technique For Mapping Public Response To DKI Jakarta Governor Policy In Handling COVID-19 Pandemic Using SVM BASED Sentiment Analysis". International Applied Science 1, n.º 1 (30 de enero de 2022): 57–66. http://dx.doi.org/10.32528/ias.v1i1.50.
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Since the coronavirus outbreak or known as COVID-19 spread throughout the world, especially in Indonesia. The Governor of DKI Jakarta issued several policies to deal with the spread of COVID-19. However, this policy has become a conversation on social media such as Youtube. Through audience interaction in the comments column, giving lots of positive and negative sentiment comments, the audience response is classified using the sentiment analysis technique of comments to find out which sentiments are positive, negative, and neutral for each comment. In this study, the data were taken from news video comments. The method used is the Support Vector Machine and the selection feature uses the Term Frequency-Inverse Document Frequency (TF-IDF). The data used amounted to 945 Indonesian language comments. Accurate results obtained by using the addition of a stoplist at the preprocessing stage a.
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Granrud, C. E., H. R. Haynes, A. L. Juhl, C. J. Miller y C. D. Sandbach. "Perceived size of stoplights: Further investigations into a failure of size constancy". Journal of Vision 5, n.º 8 (17 de marzo de 2010): 520. http://dx.doi.org/10.1167/5.8.520.
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Wootton, K. I., J. L. Sharp y C. E. Granrud. "Perceived size of traffic stoplights: Effects of assumed size on observers' size estimates". Journal of Vision 4, n.º 8 (1 de agosto de 2004): 885. http://dx.doi.org/10.1167/4.8.885.
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Mhamed, Mustafa, Richard Sutcliffe, Xia Sun, Jun Feng, Eiad Almekhlafi y Ephrem Afele Retta. "Improving Arabic Sentiment Analysis Using CNN-Based Architectures and Text Preprocessing". Computational Intelligence and Neuroscience 2021 (6 de septiembre de 2021): 1–12. http://dx.doi.org/10.1155/2021/5538791.
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Sentiment analysis is an essential process which is important to many natural language applications. In this paper, we apply two models for Arabic sentiment analysis to the ASTD and ATDFS datasets, in both 2-class and multiclass forms. Model MC1 is a 2-layer CNN with global average pooling, followed by a dense layer. MC2 is a 2-layer CNN with max pooling, followed by a BiGRU and a dense layer. On the difficult ASTD 4-class task, we achieve 73.17%, compared to 65.58% reported by Attia et al., 2018. For the easier 2-class task, we achieve 90.06% with MC1 compared to 85.58% reported by Kwaik et al., 2019. We carry out experiments on various data splits, to match those used by other researchers. We also pay close attention to Arabic preprocessing and include novel steps not reported in other works. In an ablation study, we investigate the effect of two steps in particular, the processing of emoticons and the use of a custom stoplist. On the 4-class task, these can make a difference of up to 4.27% and 5.48%, respectively. On the 2-class task, the maximum improvements are 2.95% and 3.87%.
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Wong, Robert, Jack White, Sumanjit Gill y Shahab Tayeb. "Virtual Traffic Light Implementation on a Roadside Unit over 802.11p Wireless Access in Vehicular Environments". Sensors 22, n.º 20 (11 de octubre de 2022): 7699. http://dx.doi.org/10.3390/s22207699.
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Blind intersections have high accident rates due to the poor visibility of oncoming traffic, high traffic speeds, and lack of infrastructure (e.g., stoplights). These intersections are more commonplace in rural areas, where traffic infrastructure is less developed. The Internet of Vehicles (IoV) aims to address such safety concerns through a network of connected and autonomous vehicles (CAVs) that intercommunicate. This paper proposes a Road-Side Unit-based Virtual Intersection Management (RSU-VIM) over 802.11p system consisting of a Field-Programmable Gate Array (FPGA) lightweight RSU that is solar power-based and tailored to rural areas. The RSU utilizes the proposed RSU-VIM algorithm adapted from existing virtual traffic light methodologies to communicate with vehicles over IEEE 802.11p and facilitate intersection traffic, minimizing visibility issues. The implementation of the proposed system has a simulated cloud delay of 0.0841 s and an overall system delay of 0.4067 s with 98.611% reliability.
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Zadobrischi, Eduard. "The Concept regarding Vehicular Communications Based on Visible Light Communication and the IoT". Electronics 12, n.º 6 (12 de marzo de 2023): 1359. http://dx.doi.org/10.3390/electronics12061359.
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The most controversial technology—visible light communication—is becoming increasingly promising in the field of wireless networks, being ideal for many indoor and outdoor applications. This article proposes VLC methods and architectures capable of providing high security in vehicles and in their communications with the environment or other cars in traffic. The architectures proposed involve the inclusion of ambient lighting equipment and systems and indoor and outdoor lighting systems, such as headlights, traffic lights, and stoplights. Securing data within vehicular networks and validating them through multiple layers of filtering at the level of the physical PHY layer would drastically strengthen the position of VLC. They are the only source of information through which direct contact is maintained with the other entities in the network. The evaluations and proposals presented here are highly viable and deserve future consideration in light of the results obtained in the practical steps carried out in the research process.
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Sujjada, Alun y Anggun Fergina. "IMPLEMENTASI METODE VECTOR SPACE MODEL UNTUK DETEKSI EMOSI MENGGUNAKAN DATA TEKS TWITTER". Jurnal RESTIKOM : Riset Teknik Informatika dan Komputer 3, n.º 3 (30 de junio de 2022): 116–29. http://dx.doi.org/10.52005/restikom.v3i3.89.
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Makin maraknya penggunaan jejaring sosial dalam hal ini adalah Twitter, maka menjadikan aplikasi tersebut sebagai big data atau kumpulan data yang sangat besar. Secara tidak langsung penggunaan Twitter akan memberikan dampak, baik itu dampak positif maupun negatif. Salah satu dampak negatif yang terjadi adalah penyalahgunaan dari pengguna, dimana yang awal mulanya digunakan untuk ajang saling berbagi aktivitas keseharian, sekarang berganti untuk promosi kegiatan prostitusi yang berakhir dengan sebuah kejahatan pembunuhan, sehingga muncul penelitian tentang Twitter Forensic untuk menyelidiki kasus seseorang berdasarkan data-data yang terdapat pada Twitter. Penelitian ini adalah sangat penting, karena dengan mengetahui emosi seseorang berdasarkan data posting yang ada di akun Twitter nya, sehingga dapat menambahkan data penunjang untuk kegiatan Twitter Forensic yang memang belum dilakukan sampai saat ini. Beberapa jenis emosi yang dipergunakan pada penelitian ini adalah senang, sedih, dan marah. Salah satu cara untuk mengetahui emosi adalah dengan memanfaatkan kumpulan data Twitter sebagai data latih dan data uji untuk mengklasifikasi emosi pengguna yang dilihat dari status atau tweet yang dipublikasikan menggunakan text mining. Sistem dibuat untuk mengklasifikasikan tweet berdasarkan jenis emosinya. Sebelum mempelajari tweet untuk membentuk model pembelajaran, tweet melalui tahap pemrosesan awal (preproccessing) yang terdiri dari case folding (pengubahan huruf besar menjadi kecil), tokenizing (pemisahan per-kata), dan filtering (karakter dan stoplist). Kemudian menghitung bobot tiap kata dengan metode TF-IDF(Term Frequency- Inverse Document Frequency), serta mengukur tingkat similaritas menggunakan metode VSM (Vector Space Model). Tiga kali pengujian dilakukan dengan total data latih 225 tweet, sebanyak 45 data latih pada pengujian tahap pertama, 75 data latih tahap kedua, dan 105 data latih untuk tahap ketiga. Hasilnya menunjukkan akurasi klasifikasi emosi tweet berkisar antara 65% – 75%.
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Ehrenberg, Rachel. "Technology: The tao of traffic: Go with the flow: If stoplights bend to cars, a study says, the way grows clearer". Science News 178, n.º 9 (22 de octubre de 2010): 8. http://dx.doi.org/10.1002/scin.5591780906.
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Rahutomo, Faisal y Ariadi Retno Tri Hayati Ririd. "Evaluasi Daftar Stopword Bahasa Indonesia". Jurnal Teknologi Informasi dan Ilmu Komputer 6, n.º 1 (16 de enero de 2019): 41. http://dx.doi.org/10.25126/jtiik.2019611226.
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<p class="Abstrak">Pada sistem temu kembali informasi berbentuk teks maupun <em>text mining</em>, terdapat proses pengindeksan. Teks diproses dengan tujuan mengintisarikan informasi berbentuk teks tersebut. Salah satu proses yang dilakukan adalah <em>stopword filtering</em>,<em> </em> beberapa kata yang tidak layak diindeks diabaikan berdasar sebuah daftar. Di dalam sistem berbahasa Indonesia, terdapat beberapa versi daftar <em>stopword</em> yang tersedia bebas. Penelitian ini bertujuan mengevaluasi daftar yang telah tersedia tersebut. Tujuan akhir dari penelitian ini adalah telaah daftar yang tersedia berdasarkan tata bahasa Indonesia, cara penyusunan, dan kebiasaan perambah internet. Dari hasil telaah diperoleh fakta bahwa daftar yang tersedia dibangun dengan analisis frekuensi kemunculan kata pada sebuah korpus (<em>corpus</em>) teks, tanpa memperhatikan jenis kata ataupun kebiasaan pengguna internet. Hasil lain penelitian ini adalah beberapa rekomendasi lebih lanjut bagi para peneliti di bidang ini ketika membutuhkan daftar <em>stopword </em>bahasa Indonesia, yaitu daftar yang memperhatikan jenis kata dan kebiasaan pengguna internet melalui mesin perambah yang tersedia.</p><p class="Abstract"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Most of text-based information retrieval system uses indexing process. The system processes the texts in order to obtain the information essence. One of the process is stopword filtering, several words are being ignored based on a stopword list. Several Indonesian stopword list are available openly. Therefore, this paper evaluates the available lists based on Indonesian formal grammar, its preparation technique, and internet surfer habit. The results show all of the list are developed by term frequency analysis based on a text corpus. This paper also provides several recommendations for researcher both in text mining and text-based information retrieval field, developing stoplist by the word type and internet surfer habit.</em></p>
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Thornhill, D. A., A. E. Williams, T. B. Onasch, E. Wood, S. C. Herndon, C. E. Kolb, W. B. Knighton, M. Zavala, L. T. Molina y L. C. Marr. "Application of positive matrix factorization to on-road measurements for source apportionment of diesel- and gasoline-powered vehicle emissions in Mexico City". Atmospheric Chemistry and Physics Discussions 9, n.º 6 (21 de diciembre de 2009): 27571–609. http://dx.doi.org/10.5194/acpd-9-27571-2009.
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Abstract. The goal of this research is to quantify diesel- and gasoline-powered motor vehicle emissions within the Mexico City Metropolitan Area (MCMA) using on-road measurements captured by a mobile laboratory combined with positive matrix factorization (PMF) receptor modeling. During the MCMA-2006 ground-based component of the MILAGRO field campaign, the Aerodyne Mobile Laboratory (AML) measured many gaseous and particulate pollutants, including carbon dioxide, carbon monoxide (CO), nitrogen oxides (NOx), benzene, toluene, alkylated aromatics, formaldehyde, acetaldehyde, acetone, ammonia, particle number, fine particulate mass (PM2.5), and black carbon (BC). These serve as inputs to the receptor model, which is able to resolve three factors corresponding to gasoline engine exhaust, diesel engine exhaust, and the urban background. Using the source profiles, we calculate fuel-based emission factors for each type of exhaust. The MCMA's gasoline-powered vehicles are considerably dirtier, on average, than those in the US with respect to CO and aldehydes. Its diesel-powered vehicles have similar emission factors of NOx and higher emission factors of aldehydes, particle number, and BC. In the fleet sampled during AML driving, gasoline-powered vehicles are responsible for 97% of mobile source emissions of CO, 22% of NOx, 95–97% of aromatics, 72–85% of carbonyls, 74% of ammonia, negligible amounts of particle number, 26% of PM2.5, and 2% of BC; diesel-powered vehicles account for the balance. Because the mobile lab spent 17% of its time waiting at stoplights, the results may overemphasize idling conditions, possibly resulting in an underestimate of NOx and overestimate of CO emissions. On the other hand, estimates of the inventory that do not correctly account for emissions during idling are likely to produce bias in the opposite direction. Nevertheless, the fuel-based inventory suggests that mobile source emissions of CO and NOx are overstated in the official inventory while emissions of VOCs may be understated. For NOx, the fuel-based inventory is lower for gasoline-powered vehicles but higher for diesel-powered ones compared to the official inventory.
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Thornhill, D. A., A. E. Williams, T. B. Onasch, E. Wood, S. C. Herndon, C. E. Kolb, W. B. Knighton, M. Zavala, L. T. Molina y L. C. Marr. "Application of positive matrix factorization to on-road measurements for source apportionment of diesel- and gasoline-powered vehicle emissions in Mexico City". Atmospheric Chemistry and Physics 10, n.º 8 (20 de abril de 2010): 3629–44. http://dx.doi.org/10.5194/acp-10-3629-2010.
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Abstract. The goal of this research is to quantify diesel- and gasoline-powered motor vehicle emissions within the Mexico City Metropolitan Area (MCMA) using on-road measurements captured by a mobile laboratory combined with positive matrix factorization (PMF) receptor modeling. During the MCMA-2006 ground-based component of the MILAGRO field campaign, the Aerodyne Mobile Laboratory (AML) measured many gaseous and particulate pollutants, including carbon dioxide, carbon monoxide (CO), nitrogen oxides (NOx), benzene, toluene, alkylated aromatics, formaldehyde, acetaldehyde, acetone, ammonia, particle number, fine particulate mass (PM2.5), and black carbon (BC). These serve as inputs to the receptor model, which is able to resolve three factors corresponding to gasoline engine exhaust, diesel engine exhaust, and the urban background. Using the source profiles, we calculate fuel-based emission factors for each type of exhaust. The MCMA's gasoline-powered vehicles are considerably dirtier, on average, than those in the US with respect to CO and aldehydes. Its diesel-powered vehicles have similar emission factors of NOx and higher emission factors of aldehydes, particle number, and BC. In the fleet sampled during AML driving, gasoline-powered vehicles are found to be responsible for 97% of total vehicular emissions of CO, 22% of NOx, 95–97% of each aromatic species, 72–85% of each carbonyl species, 74% of ammonia, negligible amounts of particle number, 26% of PM2.5, and 2% of BC; diesel-powered vehicles account for the balance. Because the mobile lab spent 17% of its time waiting at stoplights, the results may overemphasize idling conditions, possibly resulting in an underestimate of NOx and overestimate of CO emissions. On the other hand, estimates of the inventory that do not correctly account for emissions during idling are likely to produce bias in the opposite direction.The resulting fuel-based estimates of emissions are lower than in the official inventory for CO and NOx and higher for VOCs. For NOx, the fuel-based estimates are lower for gasoline-powered vehicles but higher for diesel-powered ones compared to the official inventory. While conclusions regarding the inventory should be interpreted with care because of the small sample size, 3.5 h of driving, the discrepancies with the official inventory agree with those reported in other studies.
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Blessed, Guda, Nuhu Bello Kontagora, James Agajo y Ibrahim Aliyu. "Performance Evaluation of Keyword Extraction Techniques and Stop Word Lists on Speech-To-Text Corpus". International Arab Journal of Information Technology 20, n.º 1 (2022). http://dx.doi.org/10.34028/iajit/20/1/14.
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The dawn of conversational user interfaces, through which humans communicate with computers through voice audio, has been reached. Therefore, Natural Language Processing (NLP) techniques are required to focus not only on text but also on audio speeches. Keyword Extraction is a technique to extract key phrases out of a document which can provide summaries of the document and be used in text classification. Existing keyword extraction techniques have commonly been used on only text/typed datasets. With the advent of text data from speech recognition engines which are less accurate than typed texts, the suitability of keyword extraction is questionable. This paper evaluates the suitability of conventional keyword extraction methods on a speech-to-text corpus. A new audio dataset for keyword extraction is collected using the World Wide Web (WWW) corpus. The performances of Rapid Automatic Keyword Extraction (RAKE) and TextRank are evaluated with different Stoplists on both the originally typed corpus and the corresponding Speech-To-Text (STT) corpus from the audio. Metrics of precision, recall, and F1 score was considered for the evaluation. From the obtained results, TextRank with the FOX Stoplist showed the highest performance on both the text and audio corpus, with F1 scores of 16.59% and 14.22%, respectively. Despite lagging behind text corpus, the recorded F1 score of the TextRank technique with audio corpus is significant enough for its adoption in audio conversation without much concern. However, the absence of punctuation during the STT affected the F1 score in all the techniques.
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Mastur, Mohammad, Fika Hastarita Rachman y Firdaus Solihin. "EFEKTIVITAS PENGGUNAAN STOPLIST KATA UMUM DARI DOKUMEN HASIL KLASIFIKASI PRETOPOLOGY". KONVERGENSI 13, n.º 1 (28 de agosto de 2019). http://dx.doi.org/10.30996/konv.v13i1.2746.
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Dokumen teks bahasa Indonesia sangat melimpah dan setiap waktu bertambah. Dalam proses pencarian, banyak dokumen yang dihasilkan menjadi kurang relevan jika tidak sesuai dengan keinginan pengguna. Stoplist merupakan kumpulan kata yang “tidak relevanâ€, namun sering muncul dalam dokumen. Kata umum juga sering digunakan pada bidang tertentu sehingga dimungkinkan untuk dokumen sebidang akan ada kata umum yang sering muncul. Pada sistem temu kembali informasi, asumsi yang ada adalah dengan menghapus stoplist, maka mengurangi jumlah kata yang akan diproses. Adanya kata umum bidang, memungkinkan adanya mengurangan jumlah kata yang akan diproses juga. Dalam penelitian ini dilakukan ekstraksi kata umum dari dokumen hasil klasifikasi dan melakukan perbandingan efektifitas antara aplikasi pencarian-1 menggunakan penghapusan stoplist dengan aplikasi pencarian-2 menggunakan penghapusan stoplist dan kata umum. Hal ini dilakukan untuk mengetahui model pencarian dengan tingkat relevansi dan waktu proses pencarian dokumen yang lebih tinggi. Hasil uji coba klasifikasi pretopology dengan 25 dokumen teknik, 25 ekonomi dan 25 pertanian diperoleh nilai rata-rata recall dan precision sebesar 90% dan 76%. Dan uji coba pencarian dengan 6 query terhadap 746 dokumen pada aplikasi pencarian-1 diperoleh nilai rata-rata f-measure dan waktu proses adalah 30.6% dan 0.239 detik. Sedangkan aplikasi pencarian-2 dengan threshold kata umum 1% adalah 76.5% dan 0.098 detik. Sehingga dapat dikatakan bahwa aplikasi pencarian-2 (dengan menggunakan penghapusan stoplist dan kata umum) lebih efektif dari pada aplikasi pencarian-1.Kata kunci: Sistem temu kembali informasi, Stoplist, Klasifikasi Pretopology, Kata Umum.
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Egerer, Herbert. "Litigation on a Collapsed Slurry Wall as Viewed by a Product Design Engineer". Journal of the National Academy of Forensic Engineers 5, n.º 2 (1 de enero de 1988). http://dx.doi.org/10.51501/jotnafe.v5i2.432.
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On Saturday, January 19, 1974, part of a slurry wall of a government office building, which was being constructed in downtown Omaha, Nebraska, collapsed. Miraculously, there were no personal injuries, only property damage, which was subsequently assessed at approximately 1.5 million dollars. Had the accident occurred 24 hours earlier, when approximately 30 men were working in the collapse sight and a dozen cars which on the average were stopped at the stoplights of the adjacent intersection, which collapsed with the slurry wall, this accident would have been a major catastrophe. I was originally retained to assist the attorney for the architect who designed the building, to gather as much physical data as possible and to document the location of collapsed portions of the wall, and to assist in identifying and cataloging material as it was being removed from the collapse site. But as I became more and more involved in my task, it became obvious that I would eventually be able to
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Abd-Almohdi Hazzaimeh, Yousef, Norita Md Norwawi y Najm Abdul Rahman Khalaf. "Generating Arabic Stop-Word for Hadith". Malaysian Journal of Science Health & Technology, 1 de noviembre de 2019. http://dx.doi.org/10.33102/mjosht.v4i.86.
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Stop-words or (function words) play a great role in performing various functions in sentences, but are still typically inadequate to use for retrieval. They consist of several elements such as common nouns, pronouns and prepositions. With that, there are several Arabic particles available in the online Khoja and Abu EL-Khair stoplist taken from various websites. Arabic stop-words’ main problem lies in accepting the prefixes and suffixes’ attachment. In the current paper, a new methodology for generating a general stop-word list has been proposed and applied on hadiths. In detail, hadith is defined as words, acts, deeds, traditions, silent approvals and character of Prophet Muhammad S.A.W. (peace be upon him). The current paper aims at examining the effect of removing stopwords from verification of hadiths. The problem is that the previously generated stop-word lists have been on Modern Standard Arabic (MSA), which is the most commonly used language in hadiths. A stop-word list of Hadith and a corpus-based list has been created to be used in the process of hadith verification. The effectiveness and success of Hadith verification when using the newly generated lists along with earlier generated lists of MSA, combining the Hadith lists have been compared with the MSA lists. The Hadith verification has been performed using sequential pattern mining. Lastly, the experiments have demonstrated that the general lists comprising hadith words showed a better performance compared to using the lists of MSA stopwords.
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Mhamed, Mustafa, Richard Sutcliffe, Husam Quteineh, Xia Sun, Eiad Almekhlafi, Ephrem Afele Retta y Jun Feng. "A deep CNN architecture with novel pooling layer applied to two Sudanese Arabic sentiment data sets". Journal of Information Science, 21 de octubre de 2023. http://dx.doi.org/10.1177/01655515231188341.
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Arabic sentiment analysis has become an important research field in recent years. Initially, work focused on Modern Standard Arabic (MSA), which is the most widely used form. Since then, work has been carried out on several different dialects, including Egyptian, Levantine and Moroccan. Moreover, a number of data sets have been created to support such work. However, up until now, no work has been carried out on Sudanese Arabic, a dialect which has 32 million speakers. In this article, two new public data sets are introduced, the two-class Sudanese Sentiment Data set (SudSenti2) and the three-class Sudanese Sentiment Data set (SudSenti3). In the preparation phase, we establish a Sudanese stopword list. Furthermore, a convolutional neural network (CNN) architecture, Sentiment Convolutional MMA (SCM), is proposed, comprising five CNN layers together with a novel Mean Max Average (MMA) pooling layer, to extract the best features. This SCM model is applied to SudSenti2 and SudSenti3 and shown to be superior to the baseline models, with accuracies of 92.25% and 85.23% (Experiments 1 and 2). The performance of MMA is compared with Max, Avg and Min and shown to be better on SudSenti2, the Saudi Sentiment Data set and the MSA Hotel Arabic Review Data set by 1.00%, 0.83% and 0.74%, respectively (Experiment 3). Next, we conduct an ablation study to determine the contribution to performance of text normalisation and the Sudanese stopword list (Experiment 4). For normalisation, this makes a difference of 0.43% on two-class and 0.45% on three-class. For the custom stoplist, the differences are 0.82% and 0.72%, respectively. Finally, the model is compared with other deep learning classifiers, including transformer-based language models for Arabic, and shown to be comparable for SudSenti2 (Experiment 5).
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Saeidi-Razavi, Ray y Peter Furth. "Head Start in Time or in Space? Determining Needed Leading Pedestrian Interval Length as a Function of Intersection Layout". Transportation Research Record: Journal of the Transportation Research Board, 9 de febrero de 2023, 036119812211510. http://dx.doi.org/10.1177/03611981221151023.
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Where signalized pedestrian crossings run concurrently with vehicles, the permitted conflict between right-turning vehicles and pedestrians can be mitigated by giving pedestrians a head start. With a head start, pedestrians can establish themselves in the crosswalk before right-turning traffic can get there, reinforcing pedestrians’ priority and engendering better motorist yielding behavior. In some U.S. cities, it is becoming common to give pedestrians a head start “in time” by means of a leading pedestrian interval (LPI); however, pedestrians can also be given a head start “in space” using corner bulbouts, setback stoplines, and other protected intersection features that increase the distance from the pedestrian stopline to the vehicle stopline. A model was developed for determining the necessary LPI length as a function of an intersection’s corner geometry. Model elements include determining the path of a right-turning vehicle, defining the conflict zone, determining maximum vehicle speed, and modeling vehicle acceleration, so that vehicle and pedestrian time to reach the conflict zone can be determined. Application to a particular intersection showed that with (a) traditional corner geometry, (b) corner bulbouts added, and (c) a protected intersection layout, necessary LPI was 3.7, 1.1, and 0 s, respectively. The benefits of replacing an LPI with a head start in space included greater vehicle capacity and reduced cycle length, which in turn reduced pedestrian-, transit-, and vehicle delay and improved pedestrian compliance. To fully achieve these benefits, the Manual on Uniform Traffic Control Devices should consider eliminating its recommended minimum of 3 s for an LPI.