Generating a Spatiotemporal Dynamic Map for Traffic Analysis Using Macroscopic Fundamental Diagram

Abstract EN

Transportation simulation and analysis projects that utilize maps with inappropriate fidelity levels carry a significant risk of having poor runtime or poor prediction performance.

To address this, researchers use map abstraction method to abstract out a simplified map with fewer links and nodes based on the original full detailed map.

Traditional static abstraction methods produce analysis maps with a single fidelity across the entire planning horizon, which cannot reflect the dynamic changes of daily traffic.

This paper proposes a spatiotemporal dynamic map abstraction approach that adopts a time series clustering method to segment the analysis time horizon adaptively based on a Macroscopic Fundamental Diagram (MFD) curve, which describes network-wide dynamic traffic states.

Time periods with similar macro-performance are grouped into one subinterval.

A map with a dedicated fidelity is produced for each subinterval.

Furthermore, a simulation is run on multiple abstracted maps with different fidelities in a sequence according to their temporal order.

A numerical experiment ascertains that the proposed approach has promising results in both analysis accuracy and efficiency for resource-constrained modeling agents.