Integrated traffic analysis and visualization for future road events

The existing traffic simulation methods are limited to specific synthetic scenarios. In addition, the natural structure of traffic and accident data requires modeling the dependent observations on multiple levels. Therefore, a system that utilizes hierarchical LMMs and GBM models are proposed which adaptively analyzes and predicts the traffic pattern based on hypothetical inputs. We developed a user-friendly interface to show the outcomes of the hybrid model. The proposed system encompasses three major components: (1) a road accident simulator and event profile to simulate an accident and predict its effects on traffic status; (2) a robust spatiotemporal traffic speed prediction model that integrates the impact of road accident with the prediction model to adaptively predict the future traffic status in response to this accident; (3) a traffic simulation tool to present the future traffic status. Our system provides satisfactory prediction results in terms of predicting with small errors, obtaining optimal hyperparameters, and less computational complexity. The hierarchical structure of the spatial component in our approach effectively captures the correlation in traffic status across different spatial points on the same road. Furthermore, computing the traffic speed at different spatial levels and how it interacts with lagged prior traffic speed over the past four periods and a day prior up-scaled the system efficiency. Evaluation is conducted to test the functionality, usability, and viability. Performance evaluation shows that the event profile model achieves small error rates with an MSE of 0.24 and an RMSE of 0.53 on the testing data, demonstrating satisfactory performance. For traffic status, the integrated model achieves high accuracy with low computational complexity. The boosted LMMs achieved high performance on the test data with an R2 of 0.9190 and an R2 of 0.9291 on the full-fitted dataset. The MAE and RMSE are 0.27 and 0.80, respectively, indicating that the fitness of our data was excellent.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Alghamdi, Taghreed

Advisor dc:contributor.advisor

• Elgazzar, Khalid

• Random effects models
• Spatiotemporal
• Event modelling
• Traffic prediction
• Traffic simulator

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1594

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1594