A planned special event (PSE), such as a sports game or a concert, can greatly affect the normal operations of a transportation system. To facilitate traffic, the road network is usually reconfigured, which could include road closures, reversed lanes, and limited access to parking facilities. For recurring PSEs, event-goers are often provided with recommended routes to designated parking areas in advance. Such network reconfiguration and route and parking recommendations are, however, often ad hoc in practice. This paper focuses on the PSE traffic planning problem. We propose to simultaneously consider parking, ridesharing, and network configuration. The problem is formulated as an optimization problem with integer decision variables. We developed a flow-based traffic simulation tool that is able to incorporate parking and lane changing (which cannot be ignored around ridesharing drop-off locations) to evaluate the objective function. We also developed effective and efficient heuristic solution algorithms. The models and algorithms are tested using the real network and traffic data from Super Bowl XLIX in 2015. The results show that our methods and approaches are able to produce an effective comprehensive traffic plan with reasonable computation time. For the Super Bowl XLIX case study, the resulting optimal plan is able to save 39.6% of the total vehicle-hours associated with default network configurations. Sensitivity analysis has also been conducted with respect to the compliance rate of travelers following recommended routes. It is found that the resulting near-optimal PSE traffic plans are able to tolerate some uncertainty in the compliance rate.
A Simulation-based Approach for Large-scale Evacuation Planning
Abstract—Evacuation planning methods aim to design routes
and schedules to relocate people to safety in the event of
natural or man-made disasters. The primary goal is to minimize
casualties which often requires the evacuation process to be
completed as soon as possible. In this paper, we present QueST, an
agent-based discrete event queuing network simulation system,
and STEERS, an iterative routing algorithm that uses QueST
for designing and evaluating large scale evacuation plans in
terms of total egress time and congestion/bottlenecks occurring
during evacuation. We use the Houston Metropolitan Area, which
consists of nine US counties and spans an area of 9,444 square
miles as a case study, and compare the performance of STEERS
with two existing route planning methods. We find that STEERS
is either better or comparable to these methods in terms of total
evacuation time and congestion faced by the evacuees. We also
analyze the large volume of data generated by the simulation
process to gain insights about the scenarios arising from following
the evacuation routes prescribed by these methods.
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- NSF-PAR ID:
- 10253401
- Date Published:
- Journal Name:
- IEEE International Conference on Big Data
- Page Range / eLocation ID:
- 1338 to 1345
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BigData50022.2020.9377794
