1 / 16

Paper Review: AmI Technology Helps To Sustain Speed While Merging

Paper Review: AmI Technology Helps To Sustain Speed While Merging. A Data Driven Simulation Study on Madrid Motorway Ring M30. Objectives & Results. To determine whether or not vehicle speed can be sustained while merging onto a motorway, leading to more harmonious integration of merging cars

vine
Download Presentation

Paper Review: AmI Technology Helps To Sustain Speed While Merging

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Paper Review:AmI Technology Helps To Sustain Speed While Merging A Data Driven Simulation Study on Madrid Motorway Ring M30

  2. Objectives & Results To determine whether or not vehicle speed can be sustained while merging onto a motorway, leading to more harmonious integration of merging cars Simulations showed that with Ambient Intelligence technology: • Increase in road throughput – 14% increase • Reduced variance in traffic flow

  3. Outline • Motivation • Object of Study: The Madrid Motorway • Achievement of Traffic Merging • CA-Based Simulation • Conclusions

  4. Outline: Motivation Merges on crowded motorways are a major influence on traffic fluidity and on road safety • Safety rules are disregarded • Related work: no general solution for merging • Research Hypothesis • To reduce merging delays Ambient Intelligence (AmI) technology was applied as an assistance system to help drivers make decisions

  5. Outline: Motivation Cellular Automata (CA) based simulation model, operating data driven and continually evaluating lane change possibilities - Discrete model used in computability theory, mathematics, etc.

  6. Outline: Object of Study – The Madrid Motorway M30 Motorway • Built in 1970 • 32.5km (20.2 miles) • 300,000 vehicles/day • 500,000 passengers/day

  7. Outline: Object of Study – The Madrid Motorway Intelligent Transport System (ITS) controls the open air and buried tunneled roads 2km -segment used for real traffic flow analysis • Peak: 7,290 veh/hr • 87,800 veh/workday • Traffic shapes repeat each day • Lower & upper main road: 3 lanes

  8. Outline: Object of Study – The Madrid Motorway

  9. Outline: Object of Study – The Madrid Motorway

  10. Outline: Achievement of Traffic Merging Real traffic flow analysis: • During high traffic situations, driver is highly focused on safely steering car from ramp onto main road • General optimization problem: • Drivers often keep rightmost lane instead of changing the lane early • Range of visibility: 100’s of meters (normal conditions), shortened in dense traffic/bad weather AmI Solution: • Vibro-tactile notification integrated into seat • Extending range of perception: • Inform driver of upcoming merging section • Overrule driver’s decision to keep on driving on current lane

  11. Outline: CA-Based Simulation Data Driven Simulation • 14% increase in throughput Control Experiment • Up to 9% increase in throughput from lower to higher perception coupled with decreasing inter-car lane changing distance

  12. Details Throughput vs inter-car gaps. Range of perception of driver has almost no effect on throughput for high density traffic For observed throughput of 5,270 vehicles/hour with average spacing of 50.94m, simulated throughput was 6,000veh/h with inter-car spacing of 52.20m

  13. Details Throughput vs inter-car gaps for artificial traffic at a rate of 6,000veh/h Generated one of hour constant traffic for upper and lower at a rate of 3000 veh/h and 6000veh/h. Up to a 9% increase in throughput

  14. Details

  15. Iner-Car Spacing=50m Details Iner-Car Spacing=25m Through put at logging point (L16) for perception range 100m over a 10-hour time period Iner-Car Spacing=10m

More Related