SEU Methods for Predicting the Likelihood of A Collision Paper
Project Title: Detect the likelihood of collision Problem statement: Urban traffic collisions, especially in metropolitan areas such as Los Angeles, present multifacet problems impacting people across various demographics and locations. We will attempt to delve into the patterns, behaviors, and high-risk zones associated with collision incidents. By analyzing factors such as the time, location, victim demographics, and modus operandi of collisions, the aim is to uncover underlying trends and potential preventive measures. The ultimate goal is to enhance urban safety and optimize traffic management in the city. Related work: Loukaitou-Sideris, A., Liggett, R., & Sung, H. G. (2007). Death on the crosswalk: A study of pedestrian-automobile collisions in Los Angeles. Journal of Planning Education and Research, 26(3), 338-351. The traffic flow is encoded as images through Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence Plot (RP). Next, these images merge into a three-channel image as the input of the rear components fic data, data-driven models have been gaining popularity in recent years due to their superior performance compared to traditional methods (Cai et al. 2020a, b;Zheng et al. 2021;Huakang et al. 2020;Fang et al. 2022) Initial hypothesis: How do temporal patterns, geographical zones, and victim demographics influence the frequency and severity of traffic collisions in Los Angeles? Based on preliminary data insights, it’s anticipated that certain time slots, specific urban areas, and particular demographic groups might be disproportionately affected, highlighting areas for targeted traffic safety interventions. Dataset(s): Dataset source (link and reference) Traffic Collision Data from LA City’s public safety portal. https://data.lacity.org/Public-Safety/Traffic-Collision-Datafrom -2010-to-Present/d5tf-ez2w Number of instances 597,788 Number of features 18 Not applicable Class distribution (# instances in each class, if applicable) Dataset splits Suggestion: 70% for training, 15% for validation, and 15% for testing. Preprocessing steps 1. Handle missing values (e.g., imputation or removal).2. Convert time from 24-hour format to standard format.3. Geographical clustering for areas with high collision rates.4. Encode categorical variables like Area Name, Victim Sex, and Victim Descent.5. Extract features from date (e.g., day of the week, month, year). Method(s): In order to analyze urban traffic accidents in Los Angeles, we can perform Exploratory data analysis. We plot geospatial data on a map to identify high-risk areas through Pandas for data manipulation and Matplotlib/ or Seaborn visualization. Our combined use of Spatio-Temporal Graph Convolutional Networks (ST-GCN) will be used to capture spatial dependencies between different urban areas and temporal patterns over time. The novelty lies in the integration of spatial and temporal data, which we will implement using Python’s PyTorch Geometric. EDA helps to understand and visualize the current state of the data, while ST-GCN’s spatio-temporal analysis leverages this understanding to make future predictions. This combination provides a retrospective and prospective view of traffic accidents in Los Angeles. Evaluation: To quantitatively measure the performance of the solution for traffic collision prediction, we’ll employ Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). These metrics will provide insights into the average magnitude of errors between predicted and observed collision counts. Additionally, considering the spatial nature of the problem, we’ll introduce distance-based metrics to evaluate how closely our predicted high-risk areas align with actual collision locations. Qualitatively, feedback from traffic management authorities will be sought to gauge the practical utility and interpretability of our predictions. When comparing our methods to prior work, we’ll benchmark against traditional statistical models or simpler machine learning models, focusing on both prediction accuracy and the depth of insights provided. Management plan: To effectively manage our project implementation, we’ve assigned roles based on the strengths, interests, and backgrounds of each team member. Our Project Manager( Redha) will coordinate team activities, ensuring timely contributions and overseeing the synthesis of report and presentation contents. The Software Architect(Jiayu Yuan) will lead the organization and structure of our code, managing Github activities and ensuring collaborative code quality. Our Experiment Architect(Chittesh Pandita) will design and implement the experiment protocols, focusing on method evaluation and hypothesis testing. The Data Architect(Avish Khosla) is responsible for all data-related tasks, from collection to processing. For projects with specific domain requirements, we’ll have a Domain Expert(Padmasini) to provide insights and guidance. Communication will be maintained through regular virtual meetings, and accountability will be ensured through periodic progress checks and collaborative platforms like Github. Project Title: Step 1: Summary of relevant work To identify relevant work, you should search key words related to your chosen topic in search engines such as Google Scholar. Then you will select the most relevant papers to your proposed project. The number of relevant papers will vary for each project, but most projects will probably find 8-10 key relevant papers. Once you’ve selected your list of papers, at least one member of your group should read each paper and make notes about the key points. To help you organize your notes about each paper, fill out the following template for each of the key papers (thus you will have around 8-10 of these blocks below, though the exact number of papers will depend on what is relevant for your project). Citation in ACM citation style. Brief summary: ● 1-3 bullets that concisely summarize the key innovation and results in the paper Strengths: ● 1-3 bullets that concisely summarize the key strengths of the paper Limitations: ● 1-3 bullets that concisely summarize the key limitations of the paper Here is an example: Rußwurm, M., Courty, N., Emonet, R., Lefèvre, S., Tuia, D., & Tavenard, R. (2023). End-to-end learned early classification of time series for in-season crop type mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 196, 445-456. Brief summary: ● Proposed loss function that optimizes dual objective of classification accuracy and earliness of classification ● Model outputs crop type class prediction in addition to probability that the prediction should be used at that timestep or wait for more data from later timesteps ● Demonstrated using LSTM with Sentinel-2 time series, but can be implemented for any deep learning model Strengths: ● Simple approach that would be easy to implement for any neural network architecture ● Provides information that can be used to judge reliability of predictions at a given time in the growing season (which can be used to inform end-user decision-making) Limitations: ● Poor performance for minority classes (subject to class imbalance issues) ● Poor performance for small datasets (as with many deep learning models) ● Outperformed by random forest baseline Step 2: Organization of relevant work In this section, you will organize the papers from above into groups of papers that have similar techniques, strengths, and/or limitations. For example, you might group papers by the type of methods used (e.g., deep learning vs. other techniques for classification) or by their limitations (e.g., studies that showed poor vs. strong performance on imbalance datasets). There is not a specific format for this section, as long as you clearly show how you have organized your papers from Step 1. This is meant to help you prepare to write your Related Work section in your written report. You can refer to each paper by its in-text citation (e.g., Rußwurm et al., 2023 in the earlier example). Here are some suggested resources to review to help you prepare to write a good Related Work section based on your literature review: ● ● Carnegie Mellon University pdf and video on preparing a literature review: https://www.cmu.edu/student-success/other-resources/resource-descriptions/relatedwork.html Related Work slides from Penn State University: https://www.cse.psu.edu/~pdm12/cse544/slides/cse544-relwork.pdf You can delete this page in your submission: Final project literature review Criteria This criterion is linked to a Learning Outcome Part 1: Summary of relevant work This criterion is linked to a Learning Outcome Part 2: Organizati on of relevant work Ratings 6 to >3.0 pts Concise summary of contributions and strengths/limitations of relevant prior work 5 to >3.0 pts Organized related work around sensible themes that form a compelling narrative about prior work Pts 3 to >0.0 pts Summary of prior relevant work written but does not show understanding of key points in papers 3 to >0.0 pts Organized related work into common themes but themes are superficial or do not form compelling narrative 0 pts Missing or insufficient summary of related work 0 pts Missing or incomplete organization of relevant work Total Points: 11 (we’ll scale to match 5% of final score) 6 pts 5 pts
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