Citation

Giuliano G, Lu Y. Transp. Res. Rec. 2021; 2675(8): 432-442.

Copyright

(Copyright © 2021, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198121998710

PMID

unavailable

Abstract

Major events are a significant source of traffic congestion, especially in large metropolitan areas. This paper presents a case study of football games played at the Los Angeles Memorial Coliseum, a venue near downtown Los Angeles, California, with a capacity of about 80,000. Two teams play home games at the Coliseum: the Los Angeles Rams and the University of Southern California (USC) Trojans. These events take place in an area that has a high level of recurrent congestion. The traffic impacts of game days are analyzed by comparing game day traffic with traffic on control days on both the highway and arterial systems. The data include speed records from in-road detectors. Two sets of models are estimated to test relationships between game attributes and traffic performance. The first set is traditional regression models controlling for spatial and temporal correlation. The second set is random forest (RF), a type of machine learning estimation. RF is found to perform better, as it allows for complex non-linearities in variables. The results show that Rams and USC impacts are different. Rams fans arrive in a more concentrated time interval closer to the start time of games and, therefore, have a greater impact on the major approach routes than USC fans. The greatest impacts on highways are around nearby freeway-to-freeway interchanges. Arterial traffic is more consistently affected by distance from the venue. This case study provides the basis for better management of major planned events.

Language: en