49 research outputs found
A Transformer based Multi task Model for Attribute based Person Retrieval
Person retrieval is a crucial task in video surveillance. While searching for persons-of-interest based on so-called query images gains much interest in the research community, attribute-based approaches are rarely studied. Attribute-based person retrieval takes a personโs semantic attributes as input and provides a ranked list of search results that match the description. Typically, such approaches either build on a pedestrian attribute recognition approach or learn a joint feature space between attribute descriptions and image data. In this work, both approaches are combined in a multi-task model to benefit from the advantages of both procedures. Moreover, transformer modules are incorporated to increase performance further. Experimental evaluation proves the effectiveness of the approach and shows that the proposed architecture outperforms the baselines significantly
Lidar-based Gait Analysis and Activity Recognition in a 4D Surveillance System
This paper presents new approaches for gait and activity analysis based on data streams of a Rotating Multi Beam (RMB) Lidar sensor. The proposed algorithms are embedded into an integrated 4D vision and visualization system, which is able to analyze and interactively display real scenarios in natural outdoor environments with walking pedestrians. The main focus of the investigations are gait based person re-identification during tracking, and recognition of specific activity patterns such as bending, waving, making phone calls and checking the time looking at wristwatches.
The descriptors for training and recognition are observed and extracted from realistic outdoor surveillance scenarios, where multiple pedestrians are walking in the field of interest following possibly intersecting trajectories, thus the observations might often be affected by occlusions or background noise.
Since there is no public database available for such scenarios, we created and published a new Lidar-based outdoors gait and activity dataset on our website, that contains point cloud sequences of 28 different persons extracted and aggregated from 35 minutes-long measurements.
The presented results confirm that both efficient gait-based identification and activity recognition is achievable in the sparse point clouds of a single RMB Lidar sensor. After extracting the people trajectories, we synthesized a free-viewpoint video, where moving avatar models follow the trajectories of the observed pedestrians in real time, ensuring that the leg movements of the animated avatars are synchronized with the real gait cycles observed in the Lidar stream
Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports
Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports
Robust density modelling using the student's t-distribution for human action recognition
The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. ยฉ 2011 IEEE
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Object Tracking-by-Segmentation in Videos
This thesis focuses on the problem of object tracking. Given a video, the general objective of tracking is to track the location over time of one or more targets in the image sequence. This is a very challenging task as algorithms need to deal with problems such as appearance variations, non-rigid deformations, cluttered background, occlusions etc. While most existing methods use bounding boxes to represent the target, we use segmentations instead, which provide better ac- cess to target pixels and can better handle occlusions. Our first contribution, is a new tracking algorithm that given an over-segmentation of a video tracks multiple targets through interactions and occlusions. We develop a provably convergent learning algorithm for this approach, which leverages training data to improve performance. Our second contribution targets the case when an over-segmentation is not available due to poor video quality or low resolution. For this case, we develop a new algorithm that tracks coherent regions and estimates the number of target objects in each region. This count representation of a video can be used to help inform more traditional tracking techniques. Finally, we develop the first tracking-by-segmentation approach based on deep learning. We propose a novel deep network architecture and training algorithms for learning to segment and track a target object throughout a video. All of our algorithms are rigorously evaluated on challenging benchmark video collections, which demonstrate improvements over the state-of-the-art
์ด์ผ๊ธฐํ ์ค๋ช ๋ฌธ์ ํ์ฉํ ๋๊ท๋ชจ ๋น๋์ค ํ์ต ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ, 2021. 2. ๊น๊ฑดํฌ.Extensive contributions are being made to develop intelligent agents that can recognize and communicate with the world. In this sense, various video-language tasks have drawn a lot of interests in computer vision research, including image/video captioning, video retrieval and video question answering.
It can be applied to high-level computer vision tasks and various future industries such as search engines, social marketing, automated driving, and robotics support through QA / dialog generation for the surrounding environment.
However, despite these developments, video-language learning suffers from a higher degree of complexity.
This thesis investigates methodologies for learning the relationship between videos and free-formed languages, including explanations, conversations, and question-and-answers, so that the machine can easily adapt to target downstream tasks.
First, we introduce several methods to learn the relationship between long sentences and videos efficiently. We introduce the approaches for supervising human attention transfer for the video attention model, which shows the video attention mechanism can benefit from explicit human gaze labels. Next, we introduce the end-to-end semantic attention method, which further reduces the visual attention algorithm's complexity by using the representative visual concept word detected by the attention-based detector. As a follow-up study on previous methods, we introduce a JSFusion (Joint Sequence Fusion) method that enables efficient video search and QA by enabling many-to-many matching of attention model.
Next, we introduce the CiSIN(Character in Story Identification Network), which uses Attention to increase the performance of character grounding and character re-identification in the movie. Finally, we introduce Transitional Adaptation, which promotes the caption generation models to generates coherent narratives for long videos.
In summary, this thesis presents a novel approaches for automatic video description generation/retrieval and shows the benefits of extracting linguistic knowledge for object and motion in the video as well as the advantage of multimodal audio-visual learning for understanding videos. Since the proposed methods are easily adapted to any video-language tasks, it is expected to be applied to the latest models, bringing additional performance improvements.
Moving forward, we plan to design an unsupervised video learning framework that can solve many challenges in the industry by integrating an unlimited amount of video, audio, and free-formed language data from the web.์๊ฐ-์ธ์ด ํ์ต์ ์ด๋ฏธ์ง/๋น๋์ค ์บก์
(Image/Video captioning), ์๊ฐ ์ง์์๋ต(Visual Question and Answering), ๋น๋์ค ๊ฒ์(Video Retrieval), ์ฅ๋ฉด ์ดํด(scene understanding), ์ด๋ฒคํธ ์ธ์(event detection) ๋ฑ ๊ณ ์ฐจ์์ ์ปดํจํฐ ๋น์ ํ์คํฌ(task)๋ฟ๋ง ์๋๋ผ ์ฃผ๋ณ ํ๊ฒฝ์ ๋ํ ์ง์ ์๋ต ๋ฐ ๋ํ ์์ฑ(Dialogue Generation)์ผ๋ก ์ธํฐ๋ท ๊ฒ์ ๋ฟ๋ง ์๋๋ผ ์ต๊ทผ ํ๋ฐํ ์์
๋ง์ผํ
(Social Marketing) ์์จ ์ฃผํ(Automated Driving), ๋ก๋ณดํฑ์ค(Robotics)์ ๋ณด์กฐํ๋ ๋ฑ ์ฌ๋ฌ ๋ฏธ๋ ์ฐ์
์ ์ ์ฉ๋ ์ ์์ด ํ๋ฐํ ์ฐ๊ตฌ๋๊ณ ์๋ ์ค์ํ ๋ถ์ผ์ด๋ค.
์ปดํจํฐ ๋น์ ผ๊ณผ ์์ฐ์ด ์ฒ๋ฆฌ๋ ์ด๋ฌํ ์ค์์ฑ์ ๋ฐํ์ผ๋ก ๊ฐ์ ๊ณ ์ ํ ์์ญ์์ ๋ฐ์ ์ ๊ฑฐ๋ญํด ์์ผ๋, ์ต๊ทผ ๋ฅ๋ฌ๋์ ๋ฑ์ฅ๊ณผ ํจ๊ป ๋๋ถ์๊ฒ ๋ฐ์ ํ๋ฉด์ ์๋ก๋ฅผ ๋ณด์ํ๋ฉฐ ํ์ต ๊ฒฐ๊ณผ๋ฅผ ํฅ์์ํค๋ ๋ฑ ํฐ ์๋์ง ํจ๊ณผ๋ฅผ ๋ฐํํ๊ฒ ๋์๋ค.
ํ์ง๋ง ์ด๋ฐ ๋ฐ์ ์๋ ๋ถ๊ตฌํ๊ณ , ๋น๋์ค-์ธ์ด๊ฐ ํ์ต์ ๋ฌธ์ ์ ๋ณต์ก๋๊ฐ ํ์ธต ๋์ ์ด๋ ค์์ ๊ฒช๊ฒ ๋๋ ๊ฒฝ์ฐ๊ฐ ๋ง๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ ๋น๋์ค์ ์ด์ ๋์ํ๋ ์ค๋ช
, ๋ํ, ์ง์ ์๋ต ๋ฑ ๋ ๋์๊ฐ ์์ ํํ์ ์ธ์ด (Free-formed language)๊ฐ์ ๊ด๊ณ๋ฅผ ๋์ฑ ํจ์จ์ ์ผ๋ก ํ์ตํ๊ณ , ๋ชฉํ ์๋ฌด์ ์ ๋์ํ ์ ์๋๋ก ๊ฐ์ ํ๋ ๊ฒ์ ๋ชฉํ๋ก ํ๋ค.
๋จผ์ , ์๊ฐ์ ๋ณต์ก๋๊ฐ ์ด๋ฏธ์ง๋ณด๋ค ๋์ ๋น๋์ค์ ๊ธด ๋ฌธ์ฅ ์ฌ์ด์ ๊ด๊ณ๋ฅผ ํจ์จ์ ์ผ๋ก ํ์ตํ๊ธฐ ์ํ ์ฌ๋ฌ ๋ฐฉ๋ฒ๋ค์ ์๊ฐํ๋ค. ์ธ๊ฐ์ ์ฃผ์ ์ธ์(Attention) ๋ชจ๋ธ์ ๋น๋์ค-์ธ์ด ๋ชจ๋ธ์ ์ง๋ ํ์ต ํ๋ ๋ฐฉ๋ฒ์ ์๊ฐํ๊ณ , ์ด์ด์ ๋น๋์ค์์ ์ฐ์ ๊ฒ์ถ๋ ๋ํ ์๊ฐ ๋จ์ด๋ฅผ ๋งค๊ฐ๋ก ํ์ฌ ์ฃผ์ ์ธ์(Attention) ์๊ณ ๋ฆฌ์ฆ์ ๋ณต์ก๋๋ฅผ ๋์ฑ ์ค์ด๋ ์๋ฏธ ์ค์ฌ ์ฃผ์ ์ธ์ (Semantic Attention) ๋ฐฉ๋ฒ, ์ดํ
์
๋ชจ๋ธ์ ๋ค๋๋ค ๋งค์นญ์ ๊ธฐ๋ฐ์ผ๋ก ํจ์จ์ ์ธ ๋น๋์ค ๊ฒ์ ๋ฐ ์ง์์๋ต์ ๊ฐ๋ฅ์ผ ํ๋ ๋น๋์ค-์ธ์ด๊ฐ ์ตํฉ (Joint Sequence Fusion) ๋ฐฉ๋ฒ ๋ฑ ๋น๋์ค ์ฃผ์ ์ธ์์ ํจ์จ์ ์ผ๋ก ํ์ต์ํฌ ์ ์๋ ๋ฐฉ๋ฒ๋ค์ ์ ์ํ๋ค.
๋ค์์ผ๋ก๋, ์ฃผ์ ์ธ์(Attention) ๋ชจ๋ธ์ด ๋ฌผ์ฒด-๋จ์ด ๊ฐ ๊ด๊ณ๋ฅผ ๋์ด ๋น๋์ค ์์์ ์ธ๋ฌผ ๊ฒ์ (Person Searching) ๊ทธ๋ฆฌ๊ณ ์ธ๋ฌผ ์ฌ ์๋ณ (Person Re-Identification)์ ๋์์ ์ํํ๋ฉฐ ์์น์์ฉ์ ์ผ์ผํค๋ ์คํ ๋ฆฌ ์ ์บ๋ฆญํฐ ์ธ์ ์ ๊ฒฝ๋ง (Character in Story Identification Network) ์ ์๊ฐํ๋ฉฐ, ๋ง์ง๋ง์ผ๋ก ์๊ธฐ ์ง๋ ํ์ต(Self-supervised Learning)์ ํตํด ์ฃผ์ ์ธ์(Attention) ๊ธฐ๋ฐ ์ธ์ด ๋ชจ๋ธ์ด ๊ธด ๋น๋์ค์ ๋ํ ์ค๋ช
์ ์ฐ๊ด์ฑ ์๊ฒ ์ ์์ฑํ ์ ์๋๋ก ์ ๋ํ๋ ๋ฐฉ๋ฒ์ ์๊ฐํ๋ค.
์์ฝํ์๋ฉด, ์ด ํ์ ๋
ผ๋ฌธ์์ ์ ์ํ ์๋ก์ด ๋ฐฉ๋ฒ๋ก ๋ค์ ๋น๋์ค-์ธ์ด ํ์ต์ ํด๋นํ๋ ๋น๋์ค ์บก์
(Video captioning), ๋น๋์ค ๊ฒ์(Video Retrieval), ์๊ฐ ์ง์์๋ต(Video Question and Answering)๋ฑ์ ํด๊ฒฐํ ์ ์๋ ๊ธฐ์ ์ ๋๋ค๋์ด ๋๋ฉฐ, ๋น๋์ค ์บก์
ํ์ต์ ํตํด ํ์ต๋ ์ฃผ์ ์ธ์ ๋ชจ๋์ ๊ฒ์ ๋ฐ ์ง์์๋ต, ์ธ๋ฌผ ๊ฒ์ ๋ฑ ๊ฐ ๋คํธ์ํฌ์ ์ด์๋๋ฉด์ ์๋ก์ด ๋ฌธ์ ๋ค์ ๋ํด ๋์์ ์ต๊ณ ์์ค(State-of-the-art)์ ์ฑ๋ฅ์ ๋ฌ์ฑํ์๋ค. ์ด๋ฅผ ํตํด ๋น๋์ค-์ธ์ด ํ์ต์ผ๋ก ์ป์ ์ธ์ด ์ง์์ ์ด์ ์ ์๊ฐ-์ฒญ๊ฐ์ ์์ฐ๋ฅด๋ ๋น๋์ค ๋ฉํฐ๋ชจ๋ฌ ํ์ต์ ํฐ ๋์์ด ๋๋ ๊ฒ์ ์คํ์ ์ผ๋ก ๋ณด์ฌ์ค๋ค. ํฅํ ์์
๋ฐฉํฅ (Future Work)์ผ๋ก๋ ์์ ์ฐ๊ตฌํ ๋ด์ฉ๋ค์ ๊ธฐ๋ฐ์ผ๋ก ์น ์์ ์กด์ฌํ๋ ๋๊ท๋ชจ์ ์ธ์ด, ๋น๋์ค, ์ค๋์ค ๋ฐ์ดํฐ๋ฅผ ํตํฉํด ํ์ต์ ํ์ฉํ์ฌ ์ฐ์
๊ณ์ ๋ง์ ๋์ ๋ฅผ ํด๊ฒฐํ ์ ์๋ ๋น์ง๋ ํ์ต ๋ชจ๋ธ์ ๋ง๋ค๊ณ ์ ํ๋ค.Chapter 1
Introduction
1.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4
1.2 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . .8
Chapter 2
Related Work
2.1 Video Captioning . . . . . . . . . . . . . . . . . . . . . . . . . . .9
2.2 Video Retrieval with Natural Language . . . . . . . . . . . . . . 12
2.3 Video Question and Answering . . . . . . . . . . . . . . . . . . . 13
2.4 Cross-modal Representation Learning for Vision and LanguageTasks . . . . 15
Chapter 3 Human Attention Transfer for Video Captioning18
3.1 Introduction
3.2 Video Datasets for Caption and Gaze . . . . . . . . . . . . . . . 21
3.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3.1 Video Pre-processing and Description . . . . . . . . . . . 22
3.3.2The Recurrent Gaze Prediction (RGP) Model . . . . . . . 23
3.3.3Construction of Visual Feature Pools . . . . . . . . . . . . 24
3.3.4The Decoder for Caption Generation . . . . . . . . . . . . 26
3.3.5Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.4.1Evaluation of Gaze Prediction . . . . . . . . . . . . . . . . 29
3.4.2Evaluation of Video Captioning . . . . . . . . . . . . . . . 32
3.4.3Human Evaluation via AMT . . . . . . . . . . . . . . . . 35
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Chapter 4 Semantic Word Attention for Video QA and VideoCaptioning
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.1.1Related Work . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.1.2Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.2.1Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.2.2An Attention Model for Concept Detection . . . . . . . . 42
4.2.3Video-to-Language Models . . . . . . . . . . . . . . . . . 45
4.2.4A Model for Description . . . . . . . . . . . . . . . . . . . 45
4.2.5A Model for Fill-in-the-Blank . . . . . . . . . . . . . . . . 48
4.2.6A Model for Multiple-Choice Test . . . . . . . . . . . . . 50
4.2.7A Model for Retrieval . . . . . . . . . . . . . . . . . . . . 51
4.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.3.1The LSMDC Dataset and Tasks . . . . . . . . . . . . . . 52
4.3.2Quantitative Results . . . . . . . . . . . . . . . . . . . . . 54
4.3.3Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 56
4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Chapter 5 Joint Sequnece Fusion Attention for Multimodal Sequence Data
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.3.1Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.3.2The Joint Semantic Tensor . . . . . . . . . . . . . . . . . 65
5.3.3The Convolutional Hierarchical Decoder . . . . . . . . . . 66
5.3.4An Illustrative Example of How the JSFusion Model Works 68
5.3.5Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.3.6Implementation of Video-Language Models . . . . . . . . 69
5.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.4.1LSMDC Dataset and Tasks . . . . . . . . . . . . . . . . . 71
5.4.2MSR-VTT-(RET/MC) Dataset and Tasks . . . . . . . . . 73
5.4.3Quantitative Results . . . . . . . . . . . . . . . . . . . . . 74
5.4.4Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 76
5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
Chapter 6 Character Re-Identification and Character Ground-ing for Movie Understanding
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
6.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.3.1Video Preprocessing . . . . . . . . . . . . . . . . . . . . . 84
6.3.2Visual Track Embedding . . . . . . . . . . . . . . . . . . . 85
6.3.3Textual Character Embedding . . . . . . . . . . . . . . . 86
6.3.4Character Grounding . . . . . . . . . . . . . . . . . . . . 87
6.3.5Re-Identification . . . . . . . . . . . . . . . . . . . . . . . 88
6.3.6Joint Training . . . . . . . . . . . . . . . . . . . . . . . . 90
6.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
6.4.1Experimental Setup . . . . . . . . . . . . . . . . . . . . . 92
6.4.2Quantitative Results . . . . . . . . . . . . . . . . . . . . . 93
6.4.3Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 95
6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Chapter 7 Transitional Adaptation of Pretrained Models forVisual Storytelling
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
7.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
7.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
7.3.1The Visual Encoder . . . . . . . . . . . . . . . . . . . . . 104
7.3.2The Language Generator . . . . . . . . . . . . . . . . . . 104
7.3.3Adaptation training . . . . . . . . . . . . . . . . . . . . . 105
7.3.4The Sequential Coherence Loss . . . . . . . . . . . . . . . 105
7.3.5Training with the adaptation Loss . . . . . . . . . . . . . 107
7.3.6Fine-tuning and Inference . . . . . . . . . . . . . . . . . . 107
7.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7.4.1Experimental Setup . . . . . . . . . . . . . . . . . . . . . 109
7.4.2Quantitative Results . . . . . . . . . . . . . . . . . . . . . 112
7.4.3Further Analyses . . . . . . . . . . . . . . . . . . . . . . . 112
7.4.4Human Evaluation Results . . . . . . . . . . . . . . . . . 115
7.4.5Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 116
7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
Chapter 8 Conclusion
8.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
8.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
Bibliography ... 123
์์ฝ ... 148
Acknowledgements ... 150Docto
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Optimization and Technology-Based Strategies to Improve Public Transit Performance Accounting for Demand Distribution
Public transit is important to societies worldwide. The operation of public transit systems is generally associated with great benefits for the users, but there are also cases in which these systems demonstrate inefficient performance. Quantifying transit performance is an important area of research over the last decades. This dissertation presents models to improve transit system performance through optimization techniques and new technologies, recognizing the effects of non-uniform distribution of demand over space and time. The contributions span fixed route transit services and on-demand transit, as well as models for flexible transit operations that lie in between.
Regarding fixed route systems, a methodology is proposed to estimate the number of passengers being left-behind subway train vehicles due to overcrowding. Methods to identify appropriate time periods and locations for studying this phenomenon are presented. The effects of overcrowding on passenger waiting times are also investigated. The challenging case of transit networks where passengers tap-in only upon entrance is analyzed, adding a new methodology to a very short list of similar studies and enhancing previous work in this field.
For demand responsive systems, this dissertation focuses on optimizing the operation of paratransit services through coordination with alternative providers in order to decrease high operating costs of such a service. The analysis includes a heuristic-based method. The proposed model is more detailed than existing aggregated methods and is able to perform well in high demand levels, unlike existing exact approaches. This part of the dissertation also assists in making transportation network companies a complementary part of public transit, rather than a competitor.
Finally, flexible transit systems are studied to identify the operational and demand related characteristics of a service area that could serve as indicators of such systems\u27 efficient performance. The focus here is on route deviation flexible services. Continuous approximation is used to model this flexible system. A new optimized hybrid transit system with elements of both fixed route and flexible services is proposed. Finally, it is highlighted that the current COVID-19 pandemic has proven the need for public transit systems that could be adjusted to accommodate changes in transit demand
Open Platforms for Connected Vehicles
L'abstract รจ presente nell'allegato / the abstract is in the attachmen