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URBAN RAIL CORRIDOR CONTROL THROUGH MACHINE LEARNING: AN INTELLIGENT VEHICLE-HIGHWAY SYSTEM APPROACH.
| 题名： | URBAN RAIL CORRIDOR CONTROL THROUGH MACHINE LEARNING: AN INTELLIGENT VEHICLE-HIGHWAY SYSTEM APPROACH. |
| 作者： | Khasnabis-S; Arciszewski-T; Hoda-SK; Ziarko-W |
| 关键词： | URBAN-RAIL-CORRIDORS; RAIL-TRAFFIC-CONTROL; INTELLIGENT-VEHICLE-HIGHWAY-SYSTEMS; MACHINE-LEARNING; TRAVEL-TIME; ENERGY-CONSUMPTION; PASSENGER-COMFORT; OPTIMIZATION-; COMPUTER-MODELS; ALGORITHMS-; AUTOMATED-KNOWLEDGE-ACQUISITION; DECISION-RULES |
| 摘要： | Traffic control along an urban rail corridor with closely spaced stations can be considered a sequence of decision-making stages. A train on an urban rail corridor that connects two terminal points with a number of intermediate stations can follow various regimes of moving and stopping, which identify individual driving scenarios. The execution of these regimes may result in different values of attributes that describe driving scenarios, namely, travel time, energy consumption, passenger comfort, and others. An attempt is made to demonstrate how to develop decision rules for driving scenarios along an urban rail corridor that can optimize travel time, energy consumption, and passenger comfort, using the concept of machine learning. Machine learning is a science that deals with the development and implementation of computational models of learning processes. The concept of knowledge acquisition through inductive learning as an intelligent vehicle-highway system approach is explored to establish some initial decision rules. A computer model, REGIME, was developed for the estimation of values of evaluation criteria, such as travel time, energy consumption, and passenger comfort levels for a hypothetical rail corridor for various driving scenarios. Next, a commercial learning system, ROUGH, was used in conjunction with the examples created through REGIME to develop decision rules. The learning algorithm is based on the theory of rough sets. The feasibility of machine learning in automated knowledge acquisition to develop decision rules for complex engineering problems, such as urban rail corridor control, is demonstrated. Further research is needed to verify the rules developed before these can be applied. |
| 总页数： | Transportation Research Record. 1994. (1453) pp91-97 (2 Fig., 8 Tab., 10 Ref.) |
| 报告类型： | 科技报告 |
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