Analyzing Traffic Impacts of Planned Major Events

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.

Efficient Original-Destination Bandwidth: A Novel Model for Arterial Traffic Signal Coordination

This paper proposes an efficient origin-estimation bandwidth (OD band) model, which provides dedicated progression bands for arterial traffic based on the real-time dynamic matrix of their estimated OD pairs. The innovations of the OD band model are as follows: First, the dynamics of through and turning-in/out traffics are analyzed based on the matrix of their estimated OD pairs, and used to generate the traffic movement sequence at continuous intersections; Second, the end-time of green interval for lag-lag phase sequence at continuous intersections is determined according to the relevant constraints, the relationship between the start/end-time of green interval and the minimum/maximum green intervals; Third, the bandwidths of the two directions of the artery ware produced, after being weighted by their traffic demands. The intuitiveness, convenience, and feasibility of the OD band model were fully demonstrated through a case study. Overall, the OD band model helps to produce bi-directional progression bands for traffic with many turning movements on the artery, and enables the through and turning-in/out traffics to proceed through continuous intersections, when the signals at those intersections are green.