Investigation of representation of accidents in car-following models

This is the term project for the CEE 551 Traffic Science course at the University of Michigan, taught by Prof. Henry Liu. Together with collaborator Zhaoming Zeng, we investigated the representation of accidents by different car-following models and the performance of different car-following models in the evaluation of traffic safety.

Project Report Abstract

It has become more commonplace for researchers and industries to utilize microscopic traffic simulators to evaluate the efficiency and safety of road designs and assisted or autonomous vehicle control systems. As a result, it is also ever more important for the underlying car- following models of such simulators to accurately represent the behavior of both autonomous vehicles and human-driven ones, especially under abnormal, near-accident situations. However, even given the increasing complexity of rules-based, analytical models and the emergence of novel machine learning and data-based car-following algorithms, not all such models may be up to this task of accurately reflecting traffic crash probabilities and replicating near-crash scenarios in the real world.

We will investigate this issue from the following different perspectives. Firstly, we shall demonstrate that even car-following models that do not account for driver miscues or high- risk scenarios (“accident-free”) can also produce accidents in simulations, using arithmetic calculations with the Intelligent Driver Model (IDM). We shall then review various ways in the literature to modify traditional, rules-based, accident-free models to account for high-risk or crash situations, as well as how to calibrate such modifications, using the Gipps model as an example. Lastly, we will turn to more novel, data-based models that utilize machine learning and neural networks to predict vehicle trajectories. We shall evaluate their primary characteristics and drawback in modeling high-risk or accident riving scenarios using the example of a Long Short-Term Memory (LSTM) model, and provide suggestions on improving the capabilities of data-driven models in the safety evaluation of car-following behavior.

Please reach out to me if you are interested in reading the full report.