United States Department of Transportation
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Incentive Systems for New Mobility Services [Supporting Dataset]
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2021-12-30
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Abstract:With rapid population growth and urban development, traffic congestion has become an inescapable issue in large metropolitan regions. Research studies have proposed different strategies to control traffic, ranging from roadway expansion to transportation demand management programs. Among these strategies, congestion pricing and incentive offering schemes have been widely studied as reinforcements for traffic control in traditional traffic networks where each driver is a “player” in the network. In such a network, the “selfish” behavior of individual drivers prevents the entire network to reach a socially optimal operation point. In future mobility services, on the other hand, a large portion of drivers/vehicles may be controlled by a small number of companies/organizations. In such a system, offering incentives to organizations can potentially be much more effective in reducing traffic congestion rather than offering incentives directly to drivers. This research project studies the problem of offering incentives to organizations to change the behavior of their individual drivers (or individuals using their organization’s services). The incentives are offered to each organization based on their aggregated travel time loss across all their drivers. This step requires solving a large-scale optimization problem to minimize the system-level travel time. We propose an efficient algorithm for solving this optimization problem. To evaluate the performance of the proposed algorithm, multiple experiments are conducted by Los Angeles traffic data. Our experiments show that the proposed algorithm can decrease the system-level travel time by up to 6.9%. Moreover, our experiments demonstrate that incentivizing organizations can be up to 8 times more efficient than incentivizing individual drivers in terms of incentivization monetary cost. The total size of the described zip file is 1.55 MB. A file that ends in .m are files written in Objective-C. They help initialize variables and functions from other Objective-C source files. They can be viewed with Apple Xcode or using an iOS simulator. This file is used in conjunction with the .MAT file, which is a binary data container format used by MATLAB, an open source program. Python Files hold python project. They can be opened using open source software such as PyCharm. Text files can be view in notepad or any document reading software. Batch Files execute commands in Microsoft Command Prompt. These files can be run using the computer's command executer or be viewed using the open source program Notepad. The .csv, Comma Separated Value, file is a simple format that is designed for a database table and supported by many applications. The .csv file is often used for moving tabular data between two different computer programs, due to its open format. Any text editor or spreadsheet program will open .csv files. Pickle files are Python object files that have been serialized into a binary file. This file can be opened as an object in open source Python programs such as PyCharm. YAML files are markup language files independent of a specific programming language. These files can be used to specify settings in different application sand programs. These files can be opened using open source text readers such as Notepad.
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Content Notes:National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT's Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. The current level of dataset documentation is the responsibility of the dataset creator. NTL staff last accessed this dataset at its repository URL on 2022-11-11. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
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