Bus systems have a large passenger capacity when compared to personal vehicles and thus have the potential to improve urban mobility. However, buses that operate in mixed vehicle traffic can undermine the effectiveness of the road system as they travel at lower speeds, take longer to accelerate and stop frequently to board and alight passengers. In traffic flow theory, buses are known as slow-moving bottlenecks that have the potential to create queue-spillbacks and thus increase the probability of gridlock. Currently, traditional metropolitan transportation planning models do not account for these negative effects on roadway capacity. Also, research methods that study multimodal operations are often simulated or algorithmic which can only provide specific results for defined inputs.
The objective of this research is to model and understand the effects of bus operations (e.g., headway, number of stops, number of routes) on system performance (e.g. urban corridor and network vehicular capacity) using a parsimonious analytical approach with a few parameters.The models are built using the Macroscopic Fundamental Diagram (MFD) of traffic which provides aggregate measures of vehicle density and flow. Existing MFD theory, which accounts for corridors with only one vehicle class are extended to include network roadway systems and bus operations. The results indicate that buses have two major effects on corridors: the moving bottleneck and the bus short-block effect. Also, these corridor effects are expanded to urban networks through a vehicle density-weighted average. The models have the potential to transform urban multimodal operations and management as they provide a simple tool to capture aggregate performance of transportation systems.
Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/54349 |
Date | 07 January 2016 |
Creators | Castrillon, Felipe |
Contributors | Laval, Jorge |
Publisher | Georgia Institute of Technology |
Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
Language | en_US |
Detected Language | English |
Type | Dissertation |
Format | application/pdf |
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