D-RIDE General Findings
•Wide range of technologies employed, and with more mobile and internet-based technologies
•Fear of strangers often cited as reason for not sharing rides, so focus on making participants more comfortable
•Providing familiar meeting points and drop-off locations in addition to users’ homes way to make participants more comfortable
•Marketing necessary to draw initial crowd
•Incentives can draw more users
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Tables-based DSS Systems (e.g. Toronto COMPASS, Georgia NaviGAtor, etc.).  These technologies and/or methodologies are data tables with predefined response plan recommendations and require little to no processing, modeling or analysis.  Some may include basic logic to analyze data in the tables while others are purely lookup tables.
Knowledge-Driven DSS, which includes:
Expert System DSS Systems (Caltrans ATMS, KC Scout, St Louis Gateway Guide, etc.).  This DSS requires an expert system engine to generate recommendations for response plans based on a set of pre-defined rules.
Custom Rules-based DSS Systems (ODOT Transport, GDOT NaviGAtor I, PACE TODSS).  Similar to the Expert System DSS, the Custom Rules DSS uses specific rules to determine response plans.  The difference is that the rules are custom built rather than having an expert system engine.
Event Scenario Matrix (Lake County Passage, Michigan ATMS, New Jersey ATMS, etc.).  Planned or unplanned events are identified on the roadway using map coordinates such as latitude/longitude or another plane coordinate systems and users are able to respond to the events using the predefined ITS field devices along the roadway.
Model-Driven DSS which incorporate on-line simulation tool integration (Singapore – Green LInk DEtermination (GLIDE) Traffic Control System, Madrid, Beijing, Milan).
Transit Operations Decision Support Systems (TODSS), Core Requirements Prototype Development Case Study, 2010 FTA-IL-26-7009-2009.1
Data Driven DSS, which is a form of support system that will focus on the provision of internal and sometimes external data to aid in the decision making.  Sometimes this comes in the form of a data warehouse, e.g. a database designed to store data in such a way as to allow for its querying and analysis by users.
Hybrids of the above.
Assessment of Emerging Opportunities for Real-Time, Multimodal Decision Support Systems in Transportation Operations.  Concept Definition and Current Practice Report. Science Applications International Corporation, report No FHWA