TRAFFIC congestions and accidents are major concerns in today’s transportation systems. This thesis investigates how to improve traffic throughput by reducing or eliminating bottlenecks on highways, in particular for merging situations such as intersections where a ramp leads onto the highway. In our work, cars are equipped with sensors that can measure distance to neighboring cars, and communicate their velocity and acceleration readings with one another. Sensor-enabled cars can locally exchange sensed information about the traffic and adapt their behavior much earlier than regular cars. / We propose proactive algorithms for merging different streams of sensor-enabled cars into a single stream. A proactive merging algorithm decouples the decision point from the actual merging point. Sensor-enabled cars allow us to decide where and when a car merges before it arrives at the actual merging point. This leads to a significant improvement in traffic flow as velocities can be adjusted appropriately. We compare proactive merging algorithms against the conventional priority-based merging algorithm in a controlled simulation environment. Experimental results show that proactive merging algorithms outperform the priority-based merging algorithm in terms of flow and delay. / More importantly, the imprecise information (errors in sensor measurements) is a major challenge for merging algorithms, because inaccuracies can potentially lead to unsafe merging behaviors. In this thesis, we investigate how the accuracy of sensors impacts merging algorithms, and design robust merging algorithms that tolerate sensor errors. Experimental results show that one of our proposed merging algorithms, which is based on the theory of time geography, is able to guarantee safe merging while tolerating two to four times more imprecise positioning information, and can double the road capacity and increase the traffic flow by 25%.
| Identifer | oai:union.ndltd.org:ADTP/273563 |
| Date | January 2009 |
| Creators | Wang, Ziyuan |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
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