{ "query": "Please summarize the whole context. It is important that you include a summary for each file. All files should be included, so please make sure to go through the entire context", "namespace": "dd181d46-8289-4232-a163-bb7a3b9d2107", "messages": [], "stream": false, "language_level": "", "chat_channel": "", "language": "German", "tone": "neutral", "writing_style": "standard", "model": "gemini-1.5-flash", "knowledgebase": "ki-dev-large", "seed": 0, "client_id": 0, "all_context": true, "follow_up_for": null, "knowledgebase_files_count": 0, "override_command": "", "disable_clarity_check": true, "custom_primer": "", "logging": true, "query_route": "" } INITIALIZATION Knowledgebase: ki-dev-large Base Query: Please summarize the whole context. It is important that you include a summary for each file. All files should be included, so please make sure to go through the entire context Model: gemini-1.5-flash Use Curl?: ================================================== **Elapsed Time: 0.00 seconds** ================================================== ROUTING Query type: summary ================================================== **Elapsed Time: 1.77 seconds** ================================================== PRIMER Primer: You are Simon, a highly intelligent personal assistant in a system called KIOS. You are a chatbot that can read knowledgebases through the "CONTEXT" that is included in the user's chat message. Your role is to act as an expert at summarization and analysis. In your responses to enterprise users, prioritize clarity, trustworthiness, and appropriate formality. Be honest by admitting when a topic falls outside your scope of knowledge, and suggest alternative avenues for obtaining information when necessary. Make effective use of chat history to avoid redundancy and enhance response relevance, continuously adapting to integrate all necessary details in your interactions. Use as much tokens as possible to provide a detailed response. ================================================== **Elapsed Time: 0.39 seconds** ================================================== FINAL QUERY Final Query: CONTEXT: ########## File: H.pdf Page: 1 Context: We're no strangers to love You know the rules and so do I (do I) A full commitment's what I'm thinking of You wouldn't get this from any other guy I just wanna tell you how I'm feeling Gotta make you understand Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you We've known each other for so long Your heart's been aching, but you're too shy to say it (say it) Inside, we both know what's been going on (going on) We know the game and we're gonna play it And if you ask me how I'm feeling Don't tell me you're too blind to see Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you Never gonna give you up Never gonna let you down #################### File: H.pdf Page: 2 Context: # Never Gonna Give You Up Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you We’ve known each other for so long Your heart’s been #################### File: H.pdf Page: 3 Context: Never gonna say goodbye Never gonna tell a lie and hurt you #################### File: C.doc Page: 1 Context: We're no strangers to love You know the rules and so do I (do I) A full commitment's what I'm thinking of You wouldn't get this from any other guy I just wanna tell you how I'm feeling Gotta make you understand Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you We've known each other for so long Your heart's been aching, but you're too shy to say it (say it) Inside, we both know what's been going on (going on) We know the game and we're gonna play it And if you ask me how I'm feeling Don't tell me you're too blind to see Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you #################### File: C.doc Page: 2 Context: Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you We've known each other for so long Your heart's been aching, but you're too shy to say it (to say it) Inside, we both know what's been going on (going on) We know the game and we're gonna play it I just wanna tell you how I'm feeling Gotta make you understand Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you Never gonna give you up Never gonna let you down #################### File: C.doc Page: 3 Context: Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you #################### File: test.pdf Page: 1 Context: We're no strangers to love You know the rules and so do I (do I) A full commitment's what I'm thinking of You wouldn't get this from any other guy I just wanna tell you how I'm feeling Gotta make you understand Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you We've known each other for so long Your heart's been aching, but you're too shy to say it (say it) Inside, we both know what's been going on (going on) We know the game and we're gonna play it And if you ask me how I'm feeling Don't tell me you're too blind to see Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you Never gonna give you up Never gonna let you down #################### File: test.pdf Page: 2 Context: Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you We've known each other for so long Your heart's been #################### File: test.pdf Page: 3 Context: Never gonna say goodbye Never gonna tell a lie and hurt you #################### File: D.docx Page: 1 Context: We're no strangers to love You know the rules and so do I (do I) A full commitment's what I'm thinking of You wouldn't get this from any other guy I just wanna tell you how I'm feeling Gotta make you understand Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you We've known each other for so long Your heart's been aching, but you're too shy to say it (say it) Inside, we both know what's been going on (going on) We know the game and we're gonna play it And if you ask me how I'm feeling Don't tell me you're too blind to see Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you #################### File: D.docx Page: 2 Context: Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you We've known each other for so long Your heart's been aching, but you're too shy to say it (to say it) Inside, we both know what's been going on (going on) We know the game and we're gonna play it I just wanna tell you how I'm feeling Gotta make you understand Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you Never gonna give you up Never gonna let you down #################### File: D.docx Page: 3 Context: Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you #################### File: B.xlsm Page: 1 Context: | 1147210.909350933 | Preisliche Angebotsdetaillierung (Costbook) zum Hauptauftrag | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Datum: | 2024-01-18 00:00:00 | Unnamed: 8 | Projekt: | Stapel/ Belademodul Mercedes Benz Mettingen | Unnamed: 11 | Unnamed: 12 | Unnamed: 13 | Unnamed: 14 | Unnamed: 15 | Unnamed: 16 | Unnamed: 17 | Unnamed: 18 | Unnamed: 19 | Unnamed: 20 | Unnamed: 21 | Unnamed: 22 | Unnamed: 23 | Unnamed: 24 | Unnamed: 25 | Unnamed: 26 | |:--------------------|:---------------------------------------------------------------|:----------------------------------------|:------------------|:------------------|:-------------------|:-------------------|:----------------------|:-------------|:--------------------------------------------------|:----------------------------------------------|:-------------------------------------------|:-------------------|:------------------------------------------|:----------------|:-----------------------------------------------------|:--------------------------|:-------------------|:----------------------------|:--------------|:--------------|:--------------|:--------------|:--------------|:--------------|:-----------------------|:-------------------| | 1147210.909350933 | gemäß Lastenheft Powertrain | | Bestellung: | | 1010xxxxxx | | | | | Kaufteilezuschlag | | 20 | Fremdleistungszuschlag: | | | 20 | | | | | | | | | | | | Pos. | OP/Stat/ | Bezeichnung | Konstruktion | | | Fertigung | Rohmaterial | Kaufteile | Aufbau | Inbetrieb-nahme | Programmierung | | Programmierung | | Aufbau | Inbetrieb-nahme | Einzelpreis (€) | Mengen-einheit | Bereich 1 | | Bereich 2 | | | | Übergeordneter Bereich | | | | Step … | | | | | | Halbzeuge | | | | CNC Roboter/SPS | | CNC Roboter/SPS | | | | | | | | | | | | | | | | | | mechanisch/ | elektrisch | Dokumentation | mechanisch/ | | mechanisch/ | Beim | Beim | Beim | Beim | Bei | Bei | Bei Daimler mechanisch/ hydraulisch/ pneumatisch | Bei Daimler elektrisch | ohne | z. B. Stück, Meter, kg etc. | Stapelmodul | | Belademodul | | | | (Var. 1 oder 2) | | | | | | hydraulisch/ | | | elektrisch | | hydraulisch/ | Lieferanten mechanisch/ hydraulisch/ pneumatisch | Lieferanten elektrisch | Lieferanten Konstruktion/ Programmierung | Lieferanten IBN | Daimler Konstruktion/ Programmierung | Daimler IBN | | | Nachlass | | Var. 1 | | | | | | | | | | | | pneumatisch | | | | | pneumat./ | | | | | | | | | | | | | | | | | | | | | | | | | | | | elektrisch | | | | | | | | | | | | | | | | | | | #################### File: B.xlsm Page: 1 Context: | | | Angabe der Stundensätze in Euro/Std. | 132.05 | 132.05 | 121.57 | 108.99 | 0 | 0 | 108.99 | 132.05 | 108.99 | 132.05 | 1630.69 | 1630.69 | 1630.69 | 1630.69 | | | Menge | (€) | Menge | (€) | Menge | (€) | Menge | (€) | | 1 | | Mercedes Benz Berlin Rotorfertigung | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | | Übergeordnete Kosten | | | | | | | | | | | | | | | | | | | | | | | | | | 1.1 | | Engineering Projektmanagement | 67451.02941176471 | | | | | | | | | | | | | | 67451.02941176471 | Stk | 0 | 0 | 0 | 0 | | 0 | 1 | 67451.02941176471 | | 1.2 | | Engineering Teil Projektleiter Mechanik | | 132.05 | | | | | | | | | | | | | 132.05 | Stk | 0 | 0 | 0 | 0 | | 0 | 0 | 0 | | 1.3 | | Engineering Teil Projektleiter Elektrik | | 143.0779411764706 | | | | | | | | | | | | | 143.0779411764706 | Stk | 0 | 0 | 0 | 0 | | 0 | 0 | 0 | | 1.4 | | Konstruktion Mechanik | 18395.73529411765 | | | | | | | | | | | | | | 18395.73529411765 | Stk | 0 | 0 | 0 | 0 | | 0 | 1 | 18395.73529411765 | | 1.13 | | Dokumentation/ CE | | | 10901.176470588238 | | | | | | | | | | | | 10901.176470588238 | Stk | 0 | 0 | 0 | 0 | | 0 | 1 | 10901.176470588238 | | 1.14 | | Dokumentation Mechanik | | | 16351.764705882355 | | | | | | | | | | | | 16351.764705882355 | Stk | 0 | 0 | 0 | 0 | | 0 | 1 | 16351.764705882355 | | 1.15 | | Dokumentation Elektrik | | | | | | | | | | | | | | | 0 | Stk | 0 | 0 | 0 | 0 | | 0 | 0 | 0 | #################### File: B.xlsm Page: 1 Context: | 1.21 | | Fracht und Verpackung | | | | 1177.3270588235296 | | | 318.8594117647059 | | | | | | | | 1496.1864705882354 | Stk | 0 | 0 | 0 | 0 | | 0 | 6 | 8977.118823529412 | #################### File: B.xlsm Page: 1 Context: | Unnamed: 24 | Unnamed: 25 | Unnamed: 26 | Unnamed: 27 | Unnamed: 28 | Unnamed: 29 | Unnamed: 30 | Unnamed: 31 | Unnamed: 32 | Unnamed: 33 | Unnamed: 34 | Unnamed: 35 | Unnamed: 36 | Unnamed: 37 | Unnamed: 38 | Unnamed: 39 | |:--------------|:-----------------------|:-------------------|:--------------|:-------------------|:-------------------|:------------------------------------|:-------------------|:-----------------------------------|:-------------------|:-----------------------------------|:------------------|:-------------------|:-------------------|:-------------------|:-----------------------------------------------------------------------------| | | | | | Nachlass: | 0 | | | | | | | | Nachlass: | 0 | | | | Übergeordneter Bereich | | 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|:----------------------------------------------------------|-------------:|-------------:|-------------:|----------:| | Flächenportal AGR-4 | 0 | 0 | 0 | 0 | | Stütze mit Befestigungselementen | 38.3244 | 0 | 0 | 4231.97 | | für Stützenabstand bis 6m | | | | | | Stütze mit Querstrebe für Stützenabstand von max. 6 - 8 m | 91.9787 | 0 | 0 | 5209.81 | | Fundamentplatte | 61.3191 | 0 | 0 | 778.167 | | Flächenportal AGR-4 (X- Achse 6.000mm; Y- Achse ~5.000mm) | 59.7861 | 0 | 0 | 50512 | | I- Laufwagen Z-Hub ~1.500mm | 1508.45 | 0 | 0 | 17512.7 | | H- Laufwagen Z-Hub ~1.500mm | 3139.54 | 0 | 0 | 28340.3 | #################### File: B.xlsm Page: 1 Context: | Unnamed: 8 | Projekt: | Stapel/ Belademodul Mercedes Benz Mettingen | |-------------:|-----------:|----------------------------------------------:| | 0 | 0 | 0 | | 0 | 23.9564 | 0 | | 0 | 39.9273 | 0 | | 0 | 39.9273 | 0 | | 0 | 260.885 | 0 | | 0 | 4067.87 | 0 | | 0 | 7845.87 | 0 | #################### File: B.xlsm Page: 1 Context: | Unnamed: 12 | |--------------:| | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | #################### File: B.xlsm Page: 1 Context: | 1147210.909350933 | |:--------------------| | 2.1.8 | | 2.1.9 | | 2.1.10 | | 2.1.11 | #################### File: B.xlsm Page: 1 Context: | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Datum: | |:--------------------------------------------------------------------------------------------------|:-------------------|:-------------------|:------------------|:-------------------| | Haltebremse, Z-Achse Fabikat Sitema | 0.0 | 0.0 | 0.0 | 1743.615923529412 | | Manuelle Absteckeinrichtung für Vertikalachse Z , elektrisch abgefragt | 0.0 | 0.0 | 0.0 | 806.1692529411765 | | Pneumatik, Wartungseinheit | 367.91470588235296 | 0.0 | 0.0 | 2138.7154382352946 | | Zentralschmierung | 367.91470588235296 | 0.0 | 0.0 | 2841.7429640422065 | | | 0.0 | 0.0 | 0.0 | 0.0 | | | 0.0 | 0.0 | 0.0 | 0.0 | | Roboter | 0.0 | 0.0 | 0.0 | 0.0 | | Aufwendungen für beigestellen Kuka KR 210 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9.6 | | 9.7 | #################### File: B.xlsm Page: 1 Context: | 1147210.909350933 | |--------------------:| | 13 | | 13.6 | | 13.7 | | 13.9 | | 13.7 | | 13.8 | | 13.9 | | 13.1 | | 13.11 | | 13.12 | | 13.13 | | 13.14 | | 13.15 | #################### File: s44196-022-00177-3.pdf Page: 1 Context: # STTF: An Efficient Transformer Model for Traffic Congestion Prediction **Xing Wang¹, Ruihao Zeng², Fumin Zou¹, Lyuchao Liao³, Faliang Huang⁴** **Received:** 21 October 2022 / **Accepted:** 18 December 2022 © The Author(s) 2023 ## Abstract With the rapid development of the economy, the sharp increase in the number of urban cars and the backwardness of urban road construction lead to serious traffic congestion of urban roads. Many scholars have tried their best to solve this problem by predicting traffic congestion. Some traditional models such as linear models and nonlinear models have proven to have a good prediction effect. However, with the increasing complexity of urban traffic networks, these models can no longer meet the higher demand of congestion prediction without considering more complex comprehensive factors, such as the spatial-temporal correlation information between roads. In this paper, we propose a traffic congestion index and devise a new traffic congestion prediction model spatio-temporal transformer (STTF) based on transformer, a deep learning model. The model comprehensively considers the traffic space of road segments, road network structure, the spatio-temporal correlation between road sections and so on. We embed temporal and spatial information into the model through the embedding layer for learning, and use the spatio-temporal attention module to mine the hidden spatio-temporal information within the data to improve the accuracy of traffic congestion prediction. Experimental results based on real-world datasets demonstrate that the proposed model significantly outperforms state-of-the-art approaches. **Keywords**: Traffic congestion prediction · Free-stream velocity · Road network structure · Spatio-temporal information · Transformer ## Abbreviations | Abbreviation | Meaning | |--------------|--------------------------------------------------| | STTF | Spatio-temporal transformer | | TCI | Traffic Congestion Index | | TCS | Traffic Congestion Score | | ARIMA | Autoregressive integrated moving average | | EMD | Empirical mode decomposition | | MM | Markov models | | HMM | Hidden Markov models | | KNN | K-nearest neighbor | | DBSCAN | Density-based spatial clustering of | | | applications with noise | | SVM | Support vector machine | | AMSVM | Adaptive multi-kernel SVM | | KELM | Kernel extreme learning machine | | S-ELM-Cluster| Symmetric-extreme learning machine cluster | | DTM | Decision tree models | | RF | Random forest | | BN | Bayesian network | | CNN | Convolutional neural network | | PCNN | Convolution-based deep neural network modeling | | | periodic traffic data | | ASA-RGCNN | Analogous self-attention-residual gated CNN | | SG-CNN | Road segment group-CNN | | STFS | Spatio-temporal feature selection | | LSTM | Long short-term memory | | RNN | Recurrent neural network | ## Author Information Xing Wang and Ruihao Zeng contributed equally to this work. 1. Laboratory of Automotive Electronic and Electrical Drive Technology, Fujian Normal University, Fuzhou 350108, China 2. Digital Fujian Institute of Big Data Security Technology, Fuzhou 350108, China 3. School of Civil Engineering, The University of Sydney, Sydney 2006, Australia 4. School of Computer and Information Engineering, Nanning Normal University, Nanning 530001, Guangxi, China **Published online:** 05 January 2023 **International Journal of Computational Intelligence Systems** (2023) 16:2 [https://doi.org/10.1007/s44196-022-00177-3](https://doi.org/10.1007/s44196-022-00177-3) Image Analysis: ### Comprehensive Examination of Attached Visual Content #### 1. Localization and Attribution - The content is a single-page document from a research article. - There is one primary image on the page which is the research article itself. #### 2. Object Detection and Classification - **Image 1**: - **Objects Detected**: - Title - Author Information - Abstract Section - Keywords Section - Abbreviations Section - Institutions and Contact Information - Publication Date - Journal Information #### 3. Scene and Activity Analysis - **Image 1**: - **Scene Description**: The scene is a well-structured research article page. - **Activities**: Presentation of the research findings through the text. #### 4. Text Analysis - **Image 1**: - **Text Detected**: - **Title**: "STTF: An Efficient Transformer Model for Traffic Congestion Prediction" - **Authors**: Xing Wang, Ruihao Zeng, Fumin Zou, Luychao Liao, Faliang Huang - **Abstract**: Brief summary about the research focused on using Spatio-temporal transformer (STTF) for predicting traffic congestion. - **Keywords Section**: Traffic congestion prediction, Free-stream velocity, Road network structure, Spatio-temporal information, Transformer - **Abbreviations**: Provides a list of abbreviations used in the paper, such as STTF (Spatio-temporal transformer) and TCI (Traffic Congestion Index). - **Institutional Affiliations**: Information regarding the universities and departments of the authors. - **Publication Information**: Published online: 05 January 2023, International Journal of Computational Intelligence Systems. - **Text Significance**: - The abstract summarizes the importance and findings of the study, mentioning the novel approach of leveraging an efficient transformer model to improve traffic congestion prediction. - Keywords help in identifying the research focus areas. - Abbreviations offer quick references for complex terminologies used in the paper. #### 9. Perspective and Composition - **Image 1**: - **Perspective**: Front-facing, standard document layout. - **Composition**: - The title is prominently placed at the top. - Authors and their affiliations are listed under the title. - The abstract, keywords, and abbreviations sections are sequentially arranged, providing a clear and systematic presentation of information. #### 10. Contextual Significance - **Image 1**: - **Context Contribution**: The image serves to showcase a scholarly research article that contributes to the field of traffic congestion prediction using advanced machine learning models, specifically transformers. It provides context to researchers, scholars, and practitioners interested in computational intelligence and urban traffic management. #### Additional Aspect: Ablaufprozesse (Process Flows) - **Process Descriptions**: - The research article describes the process of embedding temporal and spatial information into the transformer model to enhance traffic congestion prediction accuracy. ### Summary The research article presents a significant contribution to traffic congestion prediction using a novel Spatio-temporal transformer model. The systematic layout, clear abstracts, and keywords provide a comprehensive understanding of the study's approach and findings. Further sections like abbreviations and authors' affiliations offer necessary context and quick references for readers. #################### File: s44196-022-00177-3.pdf Page: 2 Context: # PreCrt Predictor for position congestion tensor # GCN Graph convolutional network # NSGIM Next Generation SLImodel # ALB Annotated Jarge-Bear # NLP Natural language processing # CV Computer visual # DCRNN Diffusion convolutional RNN # ST-GCN ST-graph convolution network # MAE Mean absolute error # RMSE Root mean squared error # MAPE Mean absolute percentage error ## 1 Introduction In the past decade, with the rapid growth of global population and the acceleration of urbanization, cities have become more and more crowded, and urban road traffic is inevitably facing the problem of traffic congestion. Traffic congestion not only leads to inefficient transportation, but also increases the time and money spent by travelers. The environmental pollution problems are also aggravated by the increasing emissions of vehicles. Therefore, it is considered to be one of the important tasks for municipal management to solve the traffic congestion problem efficiently. Current research on traffic congestion prediction can be mainly divided into three directions: linear models [9–15], nonlinear models [16–22] and neural network models [23–38]. Among them, linear models usually consider the traffic prediction values in a probability distribution model and make predictions by calculating the variation pattern of the predicted values on the timeline, for example, in literature [12, 15]. However, this type of models does not consider the spatio-temporal correlation between roads at all. The quantified road congestion is not a simple flow-prediction problem. Considering that the generational activity habits of most residents are regular, and roads may show the same changes at different times or different roads may show the same changes at the same time, all these potential relationships may help us to make better congestion prediction. Nonlinear models are mainly based on clustering and classification models, where researchers work to simplify complex flow changes into several different types of patterns and use them as a benchmark, such as in literature [16, 19]. But again, such type of models suffers from a lack of applicability. Considering the unsupervised nature of clustering, the optimal clustering criteria may also be completely different in areas with very different traffic conditions. Neural network models are widely used in congestion prediction because of their strong learning and in-depth mining ability for large-scale datasets, for example, in literature [24, 30]. But since traffic flow and road network structure are two completely different types of information, it is difficult for the neural network to learn both features at the same time. Of course, some scholars have tried, for example, in literature [49, 50], to integrate the road network structure information into the graph network and learn it at the same time. However, the prediction accuracy still needs to be further improved. The model also needs to consider more critical impact factors, such as traffic flow, speed, running time, spatial and temporal correlation between road segments, etc. Based on the above problems in traffic congestion prediction, we propose a new traffic congestion index with the introduced free-stream velocity of the road segment to reflect the road capacity and devise our prediction model "spatio-temporal Transformer" (STTF). Although there are many traffic data, such as traffic volume, vehicle speed and travel time that can reflect traffic congestion to some extent, the reason why we use free-stream velocity instead of traffic volume is that there are large gaps between main roads and non-main roads on traffic conditions in cities (especially large and densely populated cities). For example, traffic volume and speed are closely related to the geographical location of the road and the capacity of the road. A high traffic volume may only mean that the road segment is busy and does not necessarily indicate congestion. A low traffic volume may not necessarily indicate congestion if it is surrounded by residences or schools which has complex road conditions or has speed limits. Only traffic volume or speed does not accurately reflect the congestion of the road. Thus, we introduce the free-stream velocity to reflect the capacity of the road and then propose a new traffic congestion index. Besides, we deeply excavate the relationship among road network structure, correlation between road segments and road itself from spatio-temporal perspective. We take the construction of road network structure as the starting point and use the improved transformer to gradually retain the “spatio-temporal information” of roads. The main contributions of this paper are summarized as follows: - We propose a new traffic congestion index, which can accurately reflect the congestion degree of the road section according to the different traffic capacity and daily traffic conditions of each road. - We devise an effective STTF model for traffic congestion prediction based on the Transformer model, which can learn both the spatio-temporal information and road network structure information. - We introduce an embedding learning module to learn the spatial and temporal information of the road network. - On top of that, we encode and decode these two parts of information separately in the training phase to ensure the model can obtain the spatio-temporal relationship of the data. #################### File: s44196-022-00177-3.pdf Page: 3 Context: # International Journal of Computational Intelligence Systems (2023) 16:2 In experiments with real-world datasets, our model has superior performance and accuracy compared with both classical and state-of-the-art models. The remainder of paper is organized as follows. Section 2 mainly introduces the state-of-the-art research on traffic congestion prediction. Sections 3 and 4 mainly introduce the notations used in this paper and present our proposed model. In Sect. 5, we verify the superiority of STTF model by experiments. Finally, this paper is concluded in Sect. 6. ## 2 Related Work Traffic congestion prediction can usually be viewed as a complex series prediction problem. Considering the rich variety of data in the traffic domain and referring to Akhtar et al. [1] for an overview of research in this area, we can classify the research directions into direct and indirect types based on the type of data. Among them, the direct type of methods uses data that may affect traffic conditions, such as weather conditions [2] and emergencies [3], which often give direct information about the traffic status and facilitate drivers' judgments. Some data that reflect the public state can also directly reflect the congestion, such as the diversion structure of roads [4], public opinion reports [5], and electricity consumption [6]. ### 2.1 Linear Models Linear model-based approaches usually consider traffic data to satisfy a particular distribution. Such approaches include traditional mathematical statistical models and state-space models. Traditional statistical models were first used for traffic state prediction by Nikolov et al. [9] who used spectral analysis to find the interconnections of data in the dimension. Later, Yang et al. [10] used root occupancy, He et al. [11] used speed performance index to mine road congestion probability are similar reasoning. Besides, in recent years, Autoregressive Integrated Moving Average (ARIMA) model is also widely used in the research. For example, Alghamdi et al. [12] used ARIMA model to study the factors affecting traffic congestion and proposed a short-term prediction model for non-Gaussian distributed data. Wang et al. [13] combined ARIMA with Empirical Mode Decomposition (EMD), based on which the hybrid framework has better short-term prediction than similar methods. In addition, methods based on Markov Models (MM) and Hidden Markov Models (HMM) are also widely used. For example, Zakii et al. [14] used HMM to build a suitable Neuro-Fuzzy prediction network for congestion at a specific period, while Al-Eidan et al. [15] used HMM to construct a two-dimensional space based on average speed and contrast and used it to capture the changing patterns of traffic conditions. This type of linear time-series-based prediction models usually utilizes only the temporal characteristics of the data and does not consider other additional information. So, it is only suitable for road data with strong stability in the time dimension. ### 2.2 Nonlinear Models With the increasing randomness and volatility of modern urban traffic, it is difficult for simple linear models to meet the requirements for congestion prediction. Therefore, researchers have started to use non-linear models to tap into traffic variations. One of the main categories of the mining of traffic patterns from the perspective of historical data using clustering models is by K-Nearest Neighbor (KNN) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). For example, Weng et al. [16] used DBSCAN to find the spatio-temporal association rules of roads and performed classification summarizing different patterns of road links to improve the prediction accuracy. Support Vector Machine (SVM) is also widely used for congestion prediction due to its non-linear regression capability. For example, Feng et al. [17] proposed an Adaptive Multi-kernel SVM (AMSVM) using Gaussian kernel and polynomial kernel to explore the stochasticity and spatio-temporal relationship of traffic flow. Xing et al. [18] proposed a Kernel Extreme Learning Machine (KELM) based on kernel functions as the replacement of hidden layer. Ban et al. [19] proposed an efficient learning method based on Symmetric Extreme Learning Machine (SEL-MC), which is able to transform large-scale data learning to different problems on small-scale datasets. In addition, Decision Tree Models (DTM) [20], Random Forest (RF) [21] and Bayesian Network (BN) [22] also have similar ideas to nonlinear methods. ### 2.3 Neural Network Models With the development of deep learning, neural network models [23] have achieved excellent results in more and... #################### File: s44196-022-00177-3.pdf Page: 4 Context: 2 Page of 16 International Journal of Computational Intelligence Systems (2023) 162 # 3 Notations In this section, we focus on some of the basic parameters that will be used in this paper. ## 3.1 Traffic Congestion Index According to the description in Sect. 1, we introduce the vehicle free-stream velocity \(v_{\text{stream}}\) and propose a new traffic congestion index \(TCI\). We denote the current time period as \(t\), and use \(t_i\) to denote the average velocity of all sampled vehicles passing through one road segment or sensor in time period \(t\). Then \(TCI\) is calculated as follows: \[ TCI = \begin{cases} \frac{v_{\text{stream}} - v_{\text{avg}}}{v_{\text{stream}}} & \text{if } v_{\text{avg}} < v_{\text{stream}} \\ 0 & \text{otherwise} \end{cases} \] where \(v_{\text{stream}}\) is the speed of a vehicle passing the road segment under ideal conditions (only one vehicle on the road and no external factors are considered), i.e., the maximum unadjusted speed of the road. By observing the vehicle speed distribution in the public dataset PEM's-Bay, Beijing and the private dataset FUZHOU we can simply assume that the vehicle speed \(v\) adheres to a normal distribution \(N = (\mu, \sigma^2)\). Then the probability density function \(f(v)\) of the speed is as follows: \[ f(v) = \frac{1}{\sqrt{2 \pi}} \exp\left(-\frac{(v - \mu)^2}{2 \sigma^2}\right) \] Then, if this assumption holds, the variables should satisfy the more efficient and improved Jarque-Bera (AJB) test [27], i.e., \[ AJB = \frac{(VS^2)(V_s)}{(VS_Y)} \] where \(VS\) is velocity skewness, which is used to measure the direction and degree of skewness of the velocity sample data distribution. \(V_s\) is represented by the third-order standard matrix of the velocity variable \(v\). The \(V_k\) is velocity kurtosis, which is used to indicate the sharpness of the peak of the velocity sample data distribution. \(V_k\) is represented by the fourth-order standard matrix of the velocity variable \(v\). The two variables are calculated as follows: \[ VS = E\left[\left(\frac{v - \sigma}{\sigma}\right)^3\right] \] \[ VK = E\left[\left(\frac{v - \sigma}{\sigma}\right)^4\right] \] In the case of satisfying the AJB test we can express the free speed \(v_{\text{stream}}\) of vehicle in terms of the total overall expectation, i.e., \[ v_{\text{stream}} = \int v \cdot f(v) \, dv \] ## 3.2 Road Network Structure Graph We first denote the selected road network as a weighted directed graph \(G = (V, E)\). Among it, \(V\) is the set of nodes in the network and the number of vertices \(|V|\) is #################### File: s44196-022-00177-3.pdf Page: 5 Context: # International Journal of Computational Intelligence Systems (2023) 16:2 5 of 16 The set \(\mathcal{V}\) is the set of link states in the network, and \(\mathcal{W}\) is the set of weights between nodes, which can be regarded as a weight matrix and \(\mathcal{W} \in \mathbb{R}^{n \times n}\). Therefore, \(\mathcal{W}_{ij}\) denotes the link weights between nodes \(i\) and \(j\). The exact calculation method will be given later. In the traffic road network, each node represents a personal road segment (or sensor), while the link between nodes indicates the connected relationship between road segments (or sensors). The link weights represent the degree of association between connected road segments (or sensors). Then the time variables are defined. We define historical time steps as \(h\) and future time steps as \(s\). Then the Traffic Condition Index (TCI) in the current time period can be represented by the matrix \(\mathbf{X}_t \in \mathbb{R}^{n \times r}\). Therefore, the TCI of the road network \(\mathcal{V}\) in the past time period \(t\) is denoted as \(\mathbf{X} = \{\mathbf{X}_{t-h}, \mathbf{X}_{t-h+1}, \ldots, \mathbf{X}_t\}\) and \(\mathbf{Y} \in \mathbb{R}^{n \times s}\). The ground truth of the TCI for the future time steps can be written as \(\mathcal{L} = (\mathbf{U}_{t+1}, \mathbf{U}_{t+2}, \ldots, \mathbf{U}_{t+s})\). In that case, we give a definition of the link weight coefficient \(W\) so that it reflects the actual distance between two interconnected road segments and the correlation between the two road segments. Then we have the following equation: \[ W_{i,j} = \begin{cases} \exp\left( -\frac{d_{i,j}}{d_{avg}} \right), & \text{if } d_{i,j} < \epsilon \\ \exp\left( -\frac{d_{i,j}}{d_{i,j}} \right), & \text{otherwise} \end{cases} \] where \(d_{i,j}\) denotes the actual distance between the center points of the road segments. \(\gamma_{i,j} \) denotes the Pearson Correlation Coefficient of the traffic of nodes \(i\) and \(j\). Here, \(\epsilon\) is introduced as the adjustment factor to make \(d_{i,j}\) and \(\gamma_{i,j}\) comparable, which is taken as \(\epsilon = 1000\), and \(\epsilon = 0.05\). ## 4 STTF Model In this section, we introduce the structure of the proposed STTF model and the functions of each part. ### 4.1 The STTF Model Structure The Transformer model was first proposed by Vaswani et al. [39]. The Attention mechanism, encoder and decoder in the model together form the black box, which is the core structure of this model. The complex nature of its parallelized computation dictates that it is better than RNN in terms of accuracy and performance. Transformer has previously been widely used in the Natural Language Processing (NLP) [40] and Computer Visual (CV) [41] fields. Lin et al. [42] also used it to mine the temporal dimensional features of time series data, but studies using Transformer to mine spatiotemporal patterns are less common. ![Structure of STTF Model](fig1.png) | Layer Type | Description | Input | Output | |--------------------|------------------------------------------------------------|------------------------|---------------------| | **Embedding Layer**| Initial layer for embedding inputs | \(\mathbf{X}_1\) | \(\mathbf{X}_1\) | | **Spatial Layer** | Applies spatial attention | \(\mathbf{X}_1\) | \(\mathbf{Z}_1\) | | **Feed Forward** | Fully connected layers | \(\mathbf{Z}_1\) | \(\tilde{\mathbf{Z}}_1\)| | **Final Layer** | Final adjustments for output before decoding | \(\tilde{\mathbf{Z}}_1\)| \(\mathbf{Y}\) | The input sequence is denoted by \(\mathbf{X}_1, \mathbf{X}_2, \mathbf{X}_3, \ldots, \mathbf{X}_t\) with a start token included, leading to \(\text{start token} \ \mathbf{X}_{t+1}, \mathbf{X}_{t+2}, \ldots\). --- This representation is now formatted correctly according to Markdown syntax and captures the original message's intent. #################### File: s44196-022-00177-3.pdf Page: 6 Context: Based on the classical Transformer, we propose a new Spatio-Temporal Transformer (STTF) model. The complete structure is shown in Fig. 1. The Transformer framework mostly consists of encoder, decoder and embedding module, which contains the new given ST-Embedding layer (Spatial Embed block & Temporal Embed block), the new given ST-Attention layers (Spatial Att Layer & Temporal Att Layer) and other classical structures. The input of the Transformer is the TCI data \(X^{t}\) at time steps in the future. Each module is set to output a D-dimensional vector to facilitate the connection of the modules in each layer. ## 4.2 ST-Embedding Layer ST-Embedding layer is the number 1 module in Fig. 1. ### Spatial Embed Block Considering that the road network structure graph \(G\) is a directed acyclic graph with weights, to transform it into variables that the Transformer can learn and retain the structural information, we need to transform the network nodes into vector form represented in the vector space. Here we use the LINE algorithm proposed by Tang et al. [43]. The input structure graph \(G\) is vectorially represented and a feedforward neural network with GRLU activation function is added after the output to transform it into a D-dimensional vector. Then the final output is noted as \(s_{e,t}\), where \(s_{e} \in \mathbb{R}^{D},\; e \in V\). ### Temporal Embed Block Spatial Embed block provides structural information of road data, while Temporal Embed block is also needed to provide temporal feature information for Transformer. Referring to the nonlinear method to learn the distribution pattern of data in time dimension by historical data, the historical data is also used here for embedding encoding. Considering the uniqueness of each time dimension, one-hot encoding [44] is used here to encode the time in the past \(h\) steps. We encode the number of days in a week into the vector space of \(7\) and the time period in a day into the vector space of \(24\). Finally, the two encodings are transformed into the feature vector of \(\mathbb{R}^{2D}\) by concatenation operation, and a feedforward neural network with GRLU activation function is also added to the output to transform it into a D-dimensional vector. In this case, we can encode the temporal features of the past \(h\) time steps and write the vector of the neural network output as \(t_{e}\), while \(t_{e} \in \mathbb{R}^{D},\; t \in \{t_{1}, \ldots, t_{h}\}\). After gathering feature information of temporal embedding and spatial embedding respectively, we need to integrate the two parameters of the same dimensions. Here we introduce the new embedding coefficients \(s_{e,t}\) and \(t_{e,t}\), and we can get the following embedding coefficient in \(i\) steps of node \(v\): \[ s_{e,t} = exp\left(-\left( s_{e} + t_{e} \right)\right) \] We denote this operation as O. Then the ST-Embedding layer structure diagram is shown below in Fig. 2. ## 4.3 Encoder Architecture Encoder is the number 2 module in Fig. 1. A total of \(L\) encoders are included in STTF model. Each encoder consists of three consecutive layers: Spatial Att Layer (number 3 module in Fig. 1), Temporal Att Layer (number 4 module in Fig. 1), and Feed Forward layer (where Spatial Att layer and Temporal Att layer together form the ST-Attention layer). The first two attention structures have a skip-connection structure used to skip inter-layer connections (indicated by dashed lines). To improve the generalization ability, each attention operation is employed the normalization and dropout. The Feed Forward layer is mainly designed to integrate high-dimensional transportation information and consists of two fully connected neural networks with ReLU activation functions. After feeding the feature vector sequence \(X\) to the first encoder, the ST-Embedding layer finally outputs the hidden representation vector of the encoder to the decoder's attention layer \(L - 1\) encoder's attention operation. Referring to the design of the attention layer in the classical Transformer structure [39], we propose a new ST-Attention layer structure consisting of Spatial Att layer and Temporal Att layer. Each encoder and decoder has one ST-Attention layer, then we can note that in \(i\)-th ST-Attention layer in encoder or decoder, the output of Spatial Att layer is \(s_{a}^{i}\), and the output of Temporal Att layer is \(t_{a}^{i}\). Then there are \(h\)-th ST-Attention layer whose input is \(s_{a}^{h}\) and output is \(s_{a}^{h}\). \[ G = (V, E, W) \] \[ \{t_{e_{1}}, t_{e_{2}}, \ldots, t_{e_{h}}\} \] ![](Fig2) #################### File: s44196-022-00177-3.pdf Page: 7 Context: # Spatial Att layer To fully consider the influence of each road link on the specified road segment in the road network structure, we calculate the effect of each node in the \(l\)-th Spatial Att layer on the node \(v\) in the \((l-1)\)-th layer, i.e., assigning different weights to each node at different time periods, which is shown in Fig. 3. The output hidden representation vector of this layer is calculated below, \[ s_{v, l}^{(i)} = \sum_{u} \alpha_{u \to v}^{(i-1)} \] where \(\alpha_{u \to v}^{(i)}\) denotes the normalized attention coefficient. Noting its pre-normalization state as \(s_{uv}^{(i)}\), which is directly used to represent the correlation coefficient between each node \(v\) of upper layer and the given node \(u\) of current layer. According to the classical Transformer structure [39], we choose to use scaled dot-product approach to represent the correlation between the two nodes. Then we can obtain the following equation, \[ s_{uv}^{(i)} = \frac{1}{\sqrt{d}} \left[ \text{Concat}(s_{u}^{(i-1)}, \text{ste}_{u}), \text{Concat}(s_{v}^{(i-1)}, \text{ste}_{v}) \right] \] where \([a, b]\) denotes the calculation of the inner product of \(a\) and \(b\) denotes the dimension of the vector after the concatenation operation is performed. Thus, we normalize \(s_{uv}\), using the softmax function to obtain \(\alpha_{u \to v}\), \[ \alpha_{u \to v}^{(i)} = \frac{\exp(s_{uv}^{(i)})}{\sum_{u} \exp(s_{uv}^{(i)})} \] Finally, to improve the efficiency and expand the capacity of the network through parallel computation, we introduce the multi-head attention mechanism [39]. We set the number of attention heads to \(Q\), i.e., use different, learnable linear projections to project each parameter linearly \(Q\) times to the corresponding dimension. The attention function of each projection is computed in parallel, and the concatenation operation is performed after each computation. In each case, we denote the projection operation as \(p\). Then, \(p(\cdot)\) is the linear projection function, which is calculated below, \[ \hat{p}(\cdot) = \text{sigmoid}(Mx + n) \] where both \(M\) and \(n\) denote learnable variable parameters. \(\hat{p}_{\text{h}}\) denotes the projection function with different parameters. Then these can be obtained that at the \(q\)-th projection, \[ \alpha_{u \to v}^{(q)} = \left[ \text{Concate}\left( \alpha_{u \to v}^{(i)}, p_{\text{h}}^{(q)} \right) \right] \] \[ s_{v, l}^{(q)} = \text{Concat}\left( \sum_{u \in \text{rel}} \left( \alpha_{u \to v}^{(q)} \cdot p_{\text{h}}^{(q)}\left( s_{u, l}^{(i)} \right) \right) \right) \] ## Temporal Att layer To fully explore the hidden temporal patterns in the historical data of the same road segment, Temporal Att layer is introduced in accord, whose input is the output \(s_{v, l}^{(d)}\) of Spatial Att layer of the same ST-Attention layer. We calculate the influence of past and future moment of node \(v\) in each Temporal Att layer on the present moment, which is shown in Fig. 4. Using the same computational model and time vector as in the Spatial Att layer, the hidden representation vector of the layer output is noted as \(t_{u, l}\), the attention coefficient in the latter is denoted by \(\beta_{u, l}\), and its representation follows: \[ s_{v, l}^{(d)} = \text{Concat}\left(\sum_{t_k \in T} \left(\alpha_{t_j} \cdot p_{h}(t_k)\right)\right) \] #################### File: s44196-022-00177-3.pdf Page: 8 Context: # 4 Principle of Temporal Attention State before normalization is denoted as \( r_{t,j} \), which indicates the impact of \( t \) time step on the current step \( j \) of same road segment. The \( \mathcal{H}_j \) is the set of all time steps before and after the step \( j \) (including the current step \( t_j \)). Finally, the multi-head attention mechanism, \( \rho_{j}^h \), is introduced to denote the projection function with different parameters. Then we have the following equations: \[ r_{t,j} = \frac{1}{\sqrt{d}} \left[ \text{Concat} \left( \psi^{h}_{t,j}, \text{ste}_{j} \right) \right] \] (16) \[ \beta_{j} = \frac{\exp(r_{j})}{\sum_{k} \exp(r_{k}) } \] (17) \[ \theta^{h}_{t,j} = \sum_{n \in \mathcal{H}_{j}} \beta_{j} \cdot \left( \rho^{h}_{j}, \text{ste}_{j} \right) \] (18) Then these can be obtained that at the \( q \)-th linear projection. \[ u^{h}_{t,j} = \left[ \psi^{h} \left( \text{Concat} \left( \psi^{h}_{t,j}, \text{ste}_{j} \right) \right) \cdots \right] \frac{1}{\sqrt{d}} \] (19) \[ \theta^{h}_{j} = \frac{\exp(u^{h}_{j})}{\sum_{k} \exp(u^{h}_{k}) } \] (20) ## 4.4 Decoder Architecture Encoder is the number 5 module in Fig. 1. A total of \( L \) decoders are included in the STT model. The overall structure of each decoder is similar to that of the encoder, including an identical Spatial Att layer, an amended Masked-Temporal Att layer, a classical E-D Att layer for Decoder-Decoder Attention layer, number 7 module in Fig. 1 [39], and an identical Feed Forward layer. Among them, the E-D Att layer extracts feature information using encoder and Masked-Temporal Att layer’s encoding vectors. Each node's embedding vector \( \text{ste}_{j} \) at future time steps and \( \text{ste}_{j} \) at historical time steps. #################### File: s44196-022-00177-3.pdf Page: 9 Context: # 5 Experiments To test the practical effectiveness of our model, we conduct experiments on two real-world large-scale datasets, respectively. ## 5.1 Datasets Considering that FUZHOU is vehicle GPS data and PeMS-Bay is sensor data, we first use the IVMM algorithm [15] to do map matching for the vehicles data in FUZHOU. After that, we count the speed data in both datasets in every 5, 10, and 15 minutes and fill the missing data with 0 values as well as normalized the data in the way of Li et al. [46]. ### FUZHOU This private traffic dataset is collected by the Department of Transport of Fujian Province. The dataset contains speed data for 2 months ranging from May 1 to June 31 in 2018, gathered from part of urban roads in Fuzhou City, Fujian Province. The distribution of road sections is shown in Fig. 6a. ### PeMS-Bay This public traffic dataset is collected by California Transportation Agencies (CalTrans) Performance Measurement System (PeMS). The dataset contains speed data for 6 months ranging from January 1 to May 31 in 2017 from 325 sensors, gathered from highway in Bay Area, Los Angeles. The distribution of the sensors is shown in Fig. 6b. Among them, considering the complexity of urban road links, we consider that the information complexity of FUZHOU dataset is higher than that of PeMS-Bay dataset. Image Analysis: ### Analysis of the Attached Visual Content #### Localization and Attribution: - **Image 1**: Located at the top of the page, labeled as "Fig. 5". - **Image 2**: Located on the lower half of the page, labeled as "Fig. 6". #### Object Detection and Classification: - **Image 1** (Fig. 5): - **Objects Detected**: - Nodes represented by circles. - Arrows connecting the nodes. - Text labels adjacent to nodes and arrows. - **Classification**: - This image is a diagram illustrating a masked temporal attention mechanism. - **Key Features**: - Nodes labeled as \( t_a \) and \( t_{\alpha} \). - Connections symbolizing the interaction between the temporal sequences (arrows). - **Image 2** (Fig. 6): - **Objects Detected**: - Two maps. - Indications of sensor locations marked by red points. - **Classification**: - The image is a comparative visualization of sensor distributions in two different areas. - **Key Features**: - Map a labeled "FUZHOU". - Map b labeled "PeMS". - Red points indicating sensor locations on the maps. #### Scene and Activity Analysis: - **Image 1** (Fig. 5): - **Scene Description**: A conceptual diagram detailing the principle of masked temporal attention. - **Activities**: The interaction between different temporal elements (\( t_a \), \( t_{\alpha} \)) through a series of transformations (\( \beta_{a,t} \)). - **Image 2** (Fig. 6): - **Scene Description**: Two geographical maps showing the distribution of sensors in FUZHOU and PeMS-Bay. - **Activities**: The visual comparison of sensor locations in the urban road networks of the two regions. #### Text Analysis: - **Image 1** (Fig. 5): - **Detected Text**: - Labels on nodes and arrows such as \( t_a^{(1)} \), \( t_\alpha \), \( \beta_{a,t} \), \( t_{j+1} \), etc. - **Content Significance**: - This represents the interactions and information flow in a temporal attention model, key for understanding the model's architecture. - **Image 2** (Fig. 6): - **Detected Text**: - "a-FUZHOU" for the left map. - "b-PeMS" for the right map. - **Content Significance**: - Identifies the regions being compared, providing context to the sensor distributions. #### Diagram and Chart Analysis: - **Image 1** (Fig. 5): - **Axes**: - Horizontal axis labeled \( t \), indicating the time flow. - **Explanation**: - The diagram illustrates masked temporal attention, showing how the attention mechanism focuses on different time steps. #### Product Analysis: - **Image 2** (Fig. 6): - **Description**: - Two types of urban datasets are depicted: FUZHOU, with a denser and possibly more complex urban network, and PeMS, which is more spread out. - **Visual Differences**: - FUZHOU shows a densely connected network of sensors, while PeMS appears less dense with sensors scattered over a larger area. #### Anomaly Detection: - **Image 1** (Fig. 5): - **Possible Anomalies**: None - **Image 2** (Fig. 6): - **Possible Anomalies**: None #### Color Analysis: - **Image 1** (Fig. 5): - **Dominant Colors**: Yellow nodes and black arrows. - **Image 2** (Fig. 6): - **Dominant Colors**: Red points (sensors), white background for the maps. #### Perspective and Composition: - **Image 1** (Fig. 5): - **Perspective**: 2D diagrammatic view. - **Composition**: Sequential arrangement of nodes and arrows illustrating temporal flow. - **Image 2** (Fig. 6): - **Perspective**: Overhead map view. - **Composition**: Comparison layout with FUZHOU on the left and PeMS on the right. #### Contextual Significance: - **Image 1** (Fig. 5): - **Significance**: Helps in understanding the concept of masked temporal attention as applied in computational intelligence systems. - **Image 2** (Fig. 6): - **Significance**: Facilitates a comparative study of urban sensor distributions, crucial for the experiments described in the text. #### Textual Context: - The text provides context on the datasets and experiments conducted, pertinent to the accompanying visual data representations. ### Conclusion: These images serve to illustrate specific technical concepts and datasets relevant to computational intelligence system research, aiding in the reader's understanding of experimental setups and data-driven insights. #################### File: s44196-022-00177-3.pdf Page: 10 Context: # 5.2 Experimental Configuration According to the method of Li et al. [46], we set a standard time step of 5 minutes. Thus, the historical time periods \( h = 12 \) time steps and the future time periods \( f = 12 \) time steps, i.e., both are one hour. For the use of optimizer, we chose Adam-warmup optimizer [47] and set the initial learning rate as 0.01, warmup step size and batch size as 4000 and 2, respectively. In STTF model, there are three hyperparameters, namely, the number of layers of Encoder and Decoder, the number of attention heads \( Q \) in the multi-head attention mechanism, and the vector dimension \( D \) of the output of each module. After several experiments and referring to the setting of the classic transformer structure, we selected the hyperparameter with the better performance, i.e., \( L = 4, Q = 8, \) and \( D = 64. \) In addition, we set the dropout rate to 0.3 and initialized the parameters of the network using Xavier weight initialization [48]. # 5.3 Baselines and Measures We select five benchmark models for comparative experiments, including some basic models in the prediction problem and some state-of-the-art deep learning models. These five baselines are ARIMA [12], PrePCT [3], DRCNN (Diffusion Convolutional RNN) [46], ST-GCN (ST-Graph Convolution Network) [49], and Graph WaveNet [50]. Among them, ARIMA is the representative work in the linear model, PrePCT and DRCNN are the state-of-the-art convolutional neural network models, and the remaining two models are the state-of-the-art graph neural network models. Considering the different training mechanisms and the lack of labels, a comparison with the non-linear model is not made here. The codes of all the above models are publicly available by the authors, so we can all experiment with our own datasets. In our experiments, we measure the accuracy of the models by three widely used metrics, namely, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). For a more visual comparison of values, all MAE and RMSE values are artificially expanded by a factor of 50. # 5.4 Experimental Results and Discussion The main purpose of our experiments is to explore the prediction accuracy, the generalization ability for different road conditions, the robustness under different time intervals and values, and the computational efficiency of the model. Therefore, we designed several experiments to test STTF model by varying the time variables and road conditions. Moreover, the prediction time step indicates the time period of the model prediction results; the standard time step denotes the time period used in model learning, and the interval indicates the time period of integrating data during processing. We first test the prediction accuracy of the model under different prediction time step. In Fuzhou dataset, complex road network structure data can better verify the prediction ability of each model. Then all six models are made to predict the change value of \( T_{7} \) every 30 minutes during the main weekly period (June 4, 2018, Monday, 00:00-00:30). The visualization results are shown in Fig. 7 below, where ground truth is bolded. We can find that the STTF model has significantly stronger accuracy compared to the ARIMA and PrePCT models, especially for peak values and moments with large change rates that STTF is better fitted to the ground truth. To better compare quantitatively with the remaining deep neural network models, we calculate the MAE, RMSE, and MAPE values of the six models for the given time periods in the Fuzhou dataset under different prediction ranges. The results are shown in Table 1. From the results we can see that ARIMA performs the worst under the same prediction range because of its singularity of temporal characteristics. The prediction ability of PrePCT differs more from its authors’ experimental results, probably because it is more suitable for road network prediction with a smaller number of nodes. The better performance of #################### File: s44196-022-00177-3.pdf Page: 11 Context: # International Journal of Computational Intelligence Systems (2023) 16(2) 162 Page 11 of 16 ## Table 1: Performance of each model in FUZHOU database under different prediction time steps (predictions are made every 30 minutes and the optimal values under the same time step are bold) | Prediction time steps | Metrics | ARIMA | PrePCT | DCRNN | ST-GCN | Graph WaveNet | STTF | |-----------------------|---------|-------|--------|-------|--------|---------------|------| | FUZHOU | 15 min | MAE (x50) | 3.577 | 2.877 | 2.312 | 2.276 | 2.253 | 2.225 | | | RMSE (x50) | 4.027 | 3.339 | 2.460 | 2.452 | 2.421 | 2.349 | | | MAPE | 7.578 | 6.165 | 5.795 | 5.595 | 5.575 | 5.525 | | | 30 min | MAE (x50) | 3.974 | 3.111 | 2.407 | 2.397 | 2.416 | 2.358 | | | RMSE (x50) | 4.895 | 3.442 | 2.517 | 2.518 | 2.463 | 2.382 | | | MAPE | 7.756 | 6.606 | 5.582 | 6.252 | 5.563 | 5.582 | | | 60 min | MAE (x50) | 4.339 | 3.247 | 2.258 | 2.252 | 2.297 | 2.497 | | | RMSE (x50) | 5.401 | 3.485 | 2.559 | 2.568 | 2.553 | 2.566 | | | MAPE | 12.25 | 10.25 | 7.58 | 8.48 | 7.28 | 7.24 | ## Table 2: Performance of each model in PeMS-BAY database under different prediction time steps (predictions are made every 30 minutes and the optimal values under the same time step are bold) | Prediction time steps | Metrics | ARIMA | PrePCT | DCRNN | ST-GCN | Graph WaveNet | STTF | |-----------------------|---------|-------|--------|-------|--------|---------------|------| | PeMS-Bay | 15 min | MAE (x50) | 1.573 | 0.874 | 0.305 | 0.278 | 0.271 | 0.263 | | | RMSE (x50) | 2.063 | 1.337 | 0.443 | 0.442 | 0.389 | 0.362 | | | MAPE | 3.425 | 2.367 | 2.87 | 2.91 | 2.79 | 2.717 | | | 30 min | MAE (x50) | 1.969 | 1.127 | 0.392 | 0.411 | 0.388 | 0.357 | | | RMSE (x50) | 2.883 | 1.432 | 0.512 | 0.504 | 0.447 | 0.404 | | | MAPE | 5.645 | 4.75 | 3.94 | 3.951 | 3.875 | 3.597 | | | 60 min | MAE (x50) | 2.334 | 1.276 | 0.539 | 0.514 | 0.529 | 0.537 | | | RMSE (x50) | 3.418 | 1.508 | 0.580 | 0.574 | 0.554 | 0.537 | | | MAPE | 8.87 | 7.18 | 4.77 | 5.5 | 4.32 | 4.996 | Graph-based deep learning models such as DCRNN illustrate that current deep learning methods are better than most traditional linear methods, and that neural networks based on graph structures are more likely to perform better than traditional time series networks. The STTF model outperforms all benchmark models, which proves that ST-Attention layer can better mine hidden information and is more efficient compared to short-term serial prediction methods. Secondly, we need to consider the performance of the models in road network structures with different levels of complexity, where the change of road complexity is reflected in the difference of data collection locations. Therefore, we test the prediction performance of TC for each model in the selected time periods (March 6, 2017, Monday, 6:00-20:00) of the PeMS-Bay dataset in different prediction ranges. The results are presented in Table 2. Combining Tables 1 and 2 can see that the STTF model outperforms most of the benchmark models, and its predictions are more stable for complex road networks. It only loses to Graph WaveNet in predicting TC values for 60 min. Such a situation may be explained by the following: in the complex road networks, each road segment has more neighboring road segments. One road segment may affect other road segments. More valid information is created when we consider the impact of all road segments on the specific road segment in the structure. When there is a relatively simple road structure, a road segment may only affect some neighboring road segments; however, in more complex networks, this can eventually leads to inferior performance as the network, which is ill suited to the road networks of different complexity. In addition, the traffic conditions in the same time period may have exceptions during peak/off-peak hours and weekdays/weekends, we further validate the STTF model's ability to cope with these exceptions. Firstly, we consider traffic volumes to have different patterns or variations at different times of the day, i.e., what we generally consider as morning peak, evening peak, and off-peak periods. This directly results in different peaks and different rates of change of TC values for each time period. Therefore, we test the generalization. #################### File: s44196-022-00177-3.pdf Page: 12 Context: Table 3: Performance of each model with different time intervals and same prediction time steps at different times of the day (predictions are made every 30 minutes and the optimal values within the same time period are bolded). | Model | Time periods | 15 min MAE (x50) | RMSE (x50) | MAPE (%) | 30 min MAE (x50) | RMSE (x50) | MAPE (%) | 60 min MAE (x50) | RMSE (x50) | MAPE (%) | |--------------|--------------|------------------|------------|----------|------------------|------------|----------|------------------|------------|----------| | DCNN (4-12) | | 2.343 | 2.574 | 5.82 | 2.532 | 2.959 | 7.78 | 2.691 | 2.951 | 7.35 | | ST-GCN (4-12)| | 2.741 | 2.422 | 5.93 | 2.536 | 6.18 | 2.671 | 2.483 | 6.261 | 7.95 | | Graph WaveNet (4-12) | | 2.294 | 2.573 | 5.56 | 2.354 | 5.69 | 2.692 | 2.967 | 2.848 | 7.35 | | STFT (1-2) | | 2.134 | 2.154 | 5.80 | 2.499 | 2.561 | 6.47 | 2.347 | 2.533 | 7.11 | | DCNN (12-20) | | 2.187 | 2.934 | 5.60 | 2.393 | 2.431 | 6.82 | 2.516 | 2.561 | 7.46 | | ST-GCN (12-20) | | 2.166 | 2.377 | 5.88 | 2.506 | 6.03 | 2.422 | 2.496 | 3.808 | 8.00 | | Graph WaveNet (12-20) | | 2.170 | 2.138 | 5.52 | 2.253 | 2.899 | 5.49 | 2.506 | 2.362 | 7.06 | | STTF (1-2) | | 2.239 | 2.166 | 5.51 | 2.237 | 2.232 | 5.48 | 2.332 | 2.363 | 7.05 | | DCNN (20-4) | | 2.249 | 2.507 | 5.67 | 2.356 | 5.85 | 5.89 | 2.519 | 2.568 | 7.67 | | ST-GCN (20-4)| | 2.871 | 2.402 | 5.97 | 2.377 | 2.572 | 6.25 | 2.555 | 2.578 | 8.47 | | Graph WaveNet (20-4) | | 2.685 | 2.322 | 5.52 | 2.366 | 6.30 | 5.36 | 2.506 | 2.491 | 7.96 | | STTF (20-4) | | 2.188 | 2.377 | 5.51 | 2.324 | 3.46 | 5.49 | 2.445 | 2.536 | 7.25 | Table 4: Performance of each model in weekdays and weekends with different time intervals and same prediction time steps (predictions are made every 30 minutes and the optimal values in the same day are bolded). | Model | Days | 15 min MAE (x50) | RMSE (x50) | MAPE (%) | 30 min MAE (x50) | RMSE (x50) | MAPE (%) | 60 min MAE (x50) | RMSE (x50) | MAPE (%) | |--------------|--------------|------------------|------------|----------|------------------|------------|----------|------------------|------------|----------| | DCNN (6-4) | | 2.221 | 2.365 | 5.61 | 2.353 | 2.466 | 5.71 | 2.475 | 2.504 | 7.42 | | ST-GCN (6-4) | | 2.176 | 2.399 | 5.83 | 2.318 | 6.10 | 2.463 | 2.437 | 2.507 | 8.90 | | Graph WaveNet (6-4) | | 2.199 | 2.390 | 5.48 | 2.332 | 2.561 | 5.85 | 2.465 | 2.438 | 7.12 | | STTF (6-4) | | 2.196 | 2.337 | 5.45 | 2.292 | 5.22 | 6.25 | 2.494 | 2.393 | 7.28 | | DCNN (6-9) | | 2.372 | 2.448 | 5.75 | 2.497 | 5.678 | 5.62 | 2.572 | 2.537 | 7.48 | | ST-GCN (6-9) | | 2.392 | 2.505 | 5.86 | 2.464 | 2.534 | 6.27 | 2.507 | 2.557 | 8.05 | | Graph WaveNet (6-9) | | 2.301 | 2.338 | 5.45 | 2.513 | 2.464 | 5.95 | 2.641 | 2.509 | 7.92 | | STTF (6-9) | | 2.204 | 2.423 | 5.49 | 2.313 | 2.413 | 5.35 | 2.524 | 2.474 | 7.26 | Table 5: Performance of STTF model with different time step. | Standard time steps | Metrics | STTF | |---------------------|----------|---------| | FUZHOU | 5 min | MAE (x50) | 2.497 | | | | RMSE (x50) | 2.505 | | | | MAPE (%) | 2.584 | | | 10 min | MAE (x50) | 2.610 | | | | RMSE (x50) | 2.696 | | | | MAPE (%) | 2.832 | | | 15 min | MAE (x50) | 2.685 | | | | RMSE (x50) | 2.660 | | | | MAPE (%) | 2.967 | | | | | | | | | PM-SAY | 5 min | | | | MAE (x50) | 0.629 | | | | RMSE (x50) | 0.603 | | | | MAPE (%) | 0.797 | | | 10 min | MAE (x50) | 0.629 | | | | RMSE (x50) | 0.683 | | | | MAPE (%) | 0.672 | | | 15 min | MAE (x50) | 0.737 | | | | RMSE (x50) | 0.781 | | | | MAPE (%) | 0.770 | #################### File: s44196-022-00177-3.pdf Page: 13 Context: # International Journal of Computational Intelligence Systems (2023) 16:162 ## Page 13 of 16 Have time to go out and relax in the weekends' evening, so the roads may be more congested at night. Therefore, we re-select weekday hours (June 4, 2018, Monday, 00:00-24:00) and weekend hours (June 9, 2018, Saturday, 00:00-24:00) for the FUZHOU dataset to compare the generalization ability of the four graph-based learning models, respectively. From the results in Table 4, it is noticeable that the accuracy of all models for predicting weekend data is not significantly different from that of weekdays, and the STTF model has a marked predictive advantage. Also, we need to consider the robustness of the model, i.e., to examine whether the model still maintains a comparable accuracy when the standard time step changes. Therefore, for the specific time of the FUZHOU dataset (Monday, June 4, 2018, 6:00-20:00) and the specific time of the PeMS-Bay dataset (Monday, March 6, 2017, 6:00-20:00), we use different standard time steps (5 min, 10 min, and 15 min) to predict the TC7 value using the STTF model with a prediction range of 60 min and calculate MAE and RMSE, as shown in Table 5. Comparing Table 5 with Tables 1 and 2, it can be seen that the prediction accuracy of the STTF model decreases as the step length becomes longer, although it still has a competitive prediction ability under the variation of the standard step length. This is also due to its reduced amount of learning for temporal information. In the case of longer standard steps, the performance may be affected. ### Figures #### Fig. 8. Ablation experiments (display with MAE×50) ```plaintext +-------------------+---------------------+-------------------+ | Time Step | FUZZHOU-a | PeMS-a | | | MAE | MAE | | | | | | | | | | | | | +-------------------+---------------------+-------------------+ | 1 | 2.1 | 0.1 | | 2 | 2.5 | 0.2 | | 3 | 2.8 | 0.3 | | 4 | 3.0 | 0.4 | | 5 | 3.3 | 0.5 | +-------------------+---------------------+-------------------+ +-------------------+---------------------+-------------------+ | Time Step | FUZZHOU-b | PeMS-b | | | MAE | MAE | | | | | | | | | | | | | +-------------------+---------------------+-------------------+ | 1 | 1.2 | 0.6 | | 2 | 1.6 | 0.8 | | 3 | 1.7 | 0.9 | | 4 | 1.8 | 1.0 | | 5 | 2.0 | 1.2 | +-------------------+---------------------+-------------------+ ``` #################### File: s44196-022-00177-3.pdf Page: 14 Context: # Table 1: Training and predicting efficiency of each model | Model | Training time (s) | Inference time (s) | |--------------|--------------------|---------------------| | ARIMA | 3.94 | 28.84 | | PFCFT | 698.04 | 20.43 | | DCRNN | 834.89 | 17.93 | | ST-GCN | 7.86 | 10.20 | | Graph WaveNet| 2198.98 | 9.37 | | STTF | 243.93 | 12.52 | ## 6 Conclusion We propose a new traffic congestion index and devise a STTF model based on data spatio-temporal information for predicting congestion on the road network. Specifically, we describe a new information embedding learning module that transforms both road network structure information and temporal information into feature vectors that can be learned by the network. The embedding vectors are learned by a new spatial attention module and a temporal attention module with different learning directions. The model has better prediction accuracy and relatively high efficiency compared with the state-of-the-art algorithm under real-world data. **Acknowledgements**: The authors would like to thank the anonymous reviewers for providing helpful comments. **Author Contributions**: R.Z. and J.W. completed the writing of the thesis. F.Z. conducted the guidance of the thesis, L.L. conducted part of the experiment of the thesis. **Funding**: This research was funded by the National Science Foundation of China (Grant No. 61925308), the Foreign Cooperation Project of Fujian Provincial Department of Science and Technology (Grant No. 20120014), and in part by the Guggenheim Teaching Fund for Innovation and Research (Grant No. 2019-79). **Availability of Data and Materials**: The FUZHOU datasets analyzed during the current study are not publicly available due to the confidentiality of private data of cities. The PeMS-Bay datasets used is publicly available at https://guhong6.com/SANDog/PeMS-Datasets. **Declarations** **Conflict of Interests**: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. **Ethics Approval**: Not applicable. **Consent to Participate**: Not applicable. **Consent to Publication**: Not applicable. **Open Access**: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit to the material. 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3133.979223529412 | Stk | 1.0 | 3133.98 | 0.0 | 0 | | 0.0 | 0.0 | 436.18059823529416 | lfm | 0.0 | 0 | 0.0 | 0 | #################### File: B%281%29.xlsm Page: 1 Context: | 1147210.909350933 | |:--------------------| | 2.3 | | 2.3.1 | | 2.3.2 | | 2.3.3 | | 2.3.4 | #################### File: L.txt Page: 1 Context: We're no strangers to love You know the rules and so do I (do I) A full commitment's what I'm thinking of You wouldn't get this from any other guy I just wanna tell you how I'm feeling Gotta make you understand Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you We've known each other for so long Your heart's been aching, but you're too shy to say it (say it) Inside, we both know what's been going on (going on) We know the game and we're gonna play it And if you ask me how I'm feeling Don't tell me you're too blind to see Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you We've known each other for so long Your heart's been aching, but you're too shy to say it (to say it) Inside, we both know what's been going on (going on) We know the game and we're gonna play it I just wanna tell you how I'm feeling Gotta make you understand Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you #################### File: test%281%29.pdf Page: 3 Context: # Never Gonna Give You Up ## Lyrics Never gonna say goodbye Never gonna tell a lie and hurt you #################### File: N.xlsx Page: 1 Context: | We're no strangers to love | |:-------------------------------------------------------------------| | You know the rules and so do I (do I) | | A full commitment's what I'm thinking of | | You wouldn't get this from any other guy | | I just wanna tell you how I'm feeling | | Gotta make you understand | | Never gonna give you up | | Never gonna let you down | | Never gonna run around and desert you | | Never gonna make you cry | | Never gonna say goodbye | | Never gonna tell a lie and hurt you | | We've known each other for so long | | Your heart's been aching, but you're too shy to say it (say it) | | Inside, we both know what's been going on (going on) | | We know the game and we're gonna play it | | And if you ask me how I'm feeling | | Don't tell me you're too blind to see | | Never gonna give you up | | Never gonna let you down | | Never gonna run around and desert you | | Never gonna make you cry | | Never gonna say goodbye | | Never gonna tell a lie and hurt you | | Never gonna give you up | | Never gonna let you down | | Never gonna run around and desert you | | Never gonna make you cry | | Never gonna say goodbye | | Never gonna tell a lie and hurt you | | We've known each other for so long | | Your heart's been aching, but you're too shy to say it (to say it) | | Inside, we both know what's been going on (going on) | | We know the game and we're gonna play it | | I just wanna tell you how I'm feeling | | Gotta make you understand | | Never gonna give you up | | Never gonna let you down | | Never gonna run around and desert you | | Never gonna make you cry | | Never gonna say goodbye | | Never gonna tell a lie and hurt you | | Never gonna give you up | | Never gonna let you down | | Never gonna run around and desert you | | Never gonna make you cry | | Never gonna say goodbye | | Never gonna tell a lie and hurt you | | Never gonna give you up | | Never gonna let you down | | Never gonna run around and desert you | | Never gonna make you cry | | Never gonna say goodbye | | Never gonna tell a lie and hurt you | #################### File: D%281%29.docx Page: 1 Context: We're no strangers to love You know the rules and so do I (do I) A full commitment's what I'm thinking of You wouldn't get this from any other guy I just wanna tell you how I'm feeling Gotta make you understand Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you We've known each other for so long Your heart's been aching, but you're too shy to say it (say it) Inside, we both know what's been going on (going on) We know the game and we're gonna play it And if you ask me how I'm feeling Don't tell me you're too blind to see Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you #################### File: D%281%29.docx Page: 2 Context: Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you We've known each other for so long Your heart's been aching, but you're too shy to say it (to say it) Inside, we both know what's been going on (going on) We know the game and we're gonna play it I just wanna tell you how I'm feeling Gotta make you understand Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you Never gonna give you up Never gonna let you down #################### File: D%281%29.docx Page: 3 Context: Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you #################### File: M.xls Page: 1 Context: | We're no strangers to love | |:-------------------------------------------------------------------| | You know the rules and so do I (do I) | | A full commitment's what I'm thinking of | | You wouldn't get this from any other guy | | I just wanna tell you how I'm feeling | | Gotta make you understand | | Never gonna give you up | | Never gonna let you down | | Never gonna run around and desert you | | Never gonna make you cry | | Never gonna say goodbye | | Never gonna tell a lie and hurt you | | We've known each other for so long | | Your heart's been aching, but you're too shy to say it (say it) | | Inside, we both know what's been going on (going on) | | We know the game and we're gonna play it | | And if you ask me how I'm feeling | | Don't tell me you're too blind to see | | Never gonna give you up | | Never gonna let you down | | Never gonna run around and desert you | | Never gonna make you cry | | Never gonna say goodbye | | Never gonna tell a lie and hurt you | | Never gonna give you up | | Never gonna let you down | | Never gonna run around and desert you | | Never gonna make you cry | | Never gonna say goodbye | | Never gonna tell a lie and hurt you | | We've known each other for so long | | Your heart's been aching, but you're too shy to say it (to say it) | | Inside, we both know what's been going on (going on) | | We know the game and we're gonna play it | | I just wanna tell you how I'm feeling | | Gotta make you understand | | Never gonna give you up | | Never gonna let you down | | Never gonna run around and desert you | | Never gonna make you cry | | Never gonna say goodbye | | Never gonna tell a lie and hurt you | | Never gonna give you up | | Never gonna let you down | | Never gonna run around and desert you | | Never gonna make you cry | | Never gonna say goodbye | | Never gonna tell a lie and hurt you | | Never gonna give you up | | Never gonna let you down | | Never gonna run around and desert you | | Never gonna make you cry | | Never gonna say goodbye | | Never gonna tell a lie and hurt you | ########## """QUERY: Please summarize the whole context. It is important that you include a summary for each file. All files should be included, so please make sure to go through the entire context""" Consider the chat history for relevant information. Use all information included. Use as much tokens as needed. Important: If you find information separated by a | in the context, it is a table formatted in Markdown. Final Files Sources: H.pdf - Page 1, H.pdf - Page 2, H.pdf - Page 3, C.doc - Page 1, C.doc - Page 2, C.doc - Page 3, test.pdf - Page 1, test.pdf - Page 2, test.pdf - Page 3, D.docx - Page 1, D.docx - Page 2, D.docx - Page 3, B.xlsm - Page 1, s44196-022-00177-3.pdf - Page 1, s44196-022-00177-3.pdf - Page 2, s44196-022-00177-3.pdf - Page 3, s44196-022-00177-3.pdf - Page 4, s44196-022-00177-3.pdf - Page 5, s44196-022-00177-3.pdf - Page 6, s44196-022-00177-3.pdf - Page 7, s44196-022-00177-3.pdf - Page 8, s44196-022-00177-3.pdf - Page 9, s44196-022-00177-3.pdf - Page 10, s44196-022-00177-3.pdf - Page 11, s44196-022-00177-3.pdf - Page 12, s44196-022-00177-3.pdf - Page 13, s44196-022-00177-3.pdf - Page 14, s44196-022-00177-3.pdf - Page 15, s44196-022-00177-3.pdf - Page 16, I.ppt - Page 1, I.ppt - Page 2, B%281%29.xlsm - Page 1, L.txt - Page 1, test%281%29.pdf - Page 3, N.xlsx - Page 1, D%281%29.docx - Page 1, D%281%29.docx - Page 2, D%281%29.docx - Page 3, M.xls - Page 1 ================================================== **Elapsed Time: 25.77 seconds** ================================================== FINAL ANSWER Answer: ================================================== **Elapsed Time: 0.00 seconds** ==================================================