Designing a Fast Adaptive Clustering Approach for Traffic Wave Simulation

Abstract

Traffic wave, also known as stop wave or traffic shockwave, is travelling disturbance in the distribution of vehicles on the highways. In this paper, we attempt to study this problem using a simulation approach. Largely inspired by an interesting observation from ant chain movement, we explore how such a vivid pattern can be mathematically modeled and whether the similar way of behavior is helpful for dealing traffic wave issue in our highway systems. Therefore, a decentralized fast-adaptive clustering approach is proposed jointly with considerations for traffic optimization. To validate the proposed approach and to better understand its mechanism in lifting traffic flow, simulation study is carried out using real-world traffic data. Results have revealed the applicability and effectiveness of the proposed approach and have also indicated that both road configuration and traffic demand affect the effectiveness of the proposed model.

Learning From the Past: Uncovering Design Process Models Using an Enriched Process Mining

“…All the algorithms integrated in the proposed process mining approach were implemented in PYTHON. The entire process mining approach was demonstrated and verified on a real case study of a university-hosted design project named "traffic wave project." It focused on designing a traffic control system to ease the traffic congestion on expressway and published the study results in a conference paper [38]. This project had eight core participants, including students and professors from two different disciplines.…”