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Enhancing Traffic Intersection Control with “Intelligent” ObjectsRudi Ball and NarankerDulay{r.ball,n.dulay}@imperial.ac.u...
Traffic delays were estimated to cost the United States US$75 billion in 2007. Congestion commonly caused by bottlenecked ...
Tokyo Traffic Control Centre: data is collected from cameras, helicopters, police, citizen reports, and over 17,000 vehicl...
If we make objects “intelligent”, could we do intersection control better?<br />
If we make objects “intelligent”, could we do intersection control better?<br />X<br />X<br />X<br />X<br />X<br />
If we make objects “intelligent”, could we do intersection control better?<br />X<br />X<br />X<br />X<br />X<br />20<br /...
Problem<br />REQUIREMENTS<br />Primary<br />Avoid collisions (safety critical);<br />Reduce delay and improve throughput;<...
 Weather
 Road surface conditions
 Emergency overrides
 Badly behaving drivers</li></ul>Elements of the system importantly depend and combine human actors, mobile devices, vehic...
Related Work: Timed Traffic Control<br />Timed<br />[General Electric]<br />[Morgan]<br />[Potts]<br />Simple, collisions ...
Related Work: Distributed Approach<br />Distributed <br />[Balmer et al.]<br />[Gradinescu et al.]<br />[Lammer and Helbin...
Our Approach<br />Intersection Control Protocols (ICP) family – architecture<br />Remove the physical traffic lights; <br ...
Vehicle Back-Off Protocol<br />Filtering Phase<br />Collision Avoidance<br />Phase<br />Sharing<br />Phase<br />
Results: Delay and Throughput<br />DELAY<br />THROUGHPUT<br />Parameters: communication range of 200 meters.<br />VBP redu...
Contributions<br />We have presented a distributed software architecture and VBP protocol for cooperative intersection con...
Future Work<br />The Vehicle Back-Off Protocol<br />Ranking Conditions: who has precedence (various factors given specific...
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Enhancing Traffic Intersection Control with Intelligent Objects

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Presented at the Urban Internet of Things 2010 - Tokyo, Japan. 28th November 2010.

Abstract: Traffic control is an old and ever growing problem in cities throughout the world. Within many cities, intersections represent bottlenecks in the flow of traffic. Evaluating intersections control is complex and difficult. Given this, intersection management is both costly and time consuming. This paper considers the potential benefits of enhancing the traffic intersection with the use of intelligent objects in vehicles. We present, compare and demonstrate a novel Vehicle Back-Off Protocol against a classical Timed Traffic Control system. Our protocol uses ad-hoc messaging, collision avoidance and shared journey plans as a means by which to reduce delay, adapt a journey and maximize the efficient usage of a traffic intersection. We use simulation to model and evaluate intersection control.

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Enhancing Traffic Intersection Control with Intelligent Objects

  1. 1. Enhancing Traffic Intersection Control with “Intelligent” ObjectsRudi Ball and NarankerDulay{r.ball,n.dulay}@imperial.ac.ukImperial College London<br />UIOT<br />29th November 2010<br />AEDUS2<br />
  2. 2. Traffic delays were estimated to cost the United States US$75 billion in 2007. Congestion commonly caused by bottlenecked traffic flowing across intersections.<br />Source: <br />Texas Transport Institute: Congestion Data 2007 http://mobility.tamu.edu/ums/congestion_data/tables/national/table_2.pdf<br />Flickr Trey Ratcliff<br />
  3. 3. Tokyo Traffic Control Centre: data is collected from cameras, helicopters, police, citizen reports, and over 17,000 vehicle detectors across the city. The Central Display Board highlights more than 1,000 intersections, and has 15,154 traffic signals in the system. Decision making made by a team of supervisors. <br />Source: C Scout Japan http://www.cscoutjapan.com/<br />
  4. 4. If we make objects “intelligent”, could we do intersection control better?<br />
  5. 5. If we make objects “intelligent”, could we do intersection control better?<br />X<br />X<br />X<br />X<br />X<br />
  6. 6. If we make objects “intelligent”, could we do intersection control better?<br />X<br />X<br />X<br />X<br />X<br />20<br />18.7m<br />
  7. 7. Problem<br />REQUIREMENTS<br />Primary<br />Avoid collisions (safety critical);<br />Reduce delay and improve throughput;<br />“Fairly”allocate usage of the intersection<br />Encourage best practice<br />Assume: Intelligent objects are capable of adapting or influencing mobility. Vehicles are constrained but capable of speeding up or slowing down. <br />Effects not included in models:<br /><ul><li> Pedestrians / cyclists (other objects)
  8. 8. Weather
  9. 9. Road surface conditions
  10. 10. Emergency overrides
  11. 11. Badly behaving drivers</li></ul>Elements of the system importantly depend and combine human actors, mobile devices, vehicles and road rules.<br />(Objects have some combined intelligence, both computer and human, concerning context)<br />
  12. 12. Related Work: Timed Traffic Control<br />Timed<br />[General Electric]<br />[Morgan]<br />[Potts]<br />Simple, collisions avoided using road rules;<br />Does not dynamically adapt to vehicle flows, failure of controller forces overall failure.<br />
  13. 13. Related Work: Distributed Approach<br />Distributed <br />[Balmer et al.]<br />[Gradinescu et al.]<br />[Lammer and Helbing]<br />Timing based on requests made by vehicles, purports lower deployment and maintenance cost.<br />Seeks to replicate operation of timed approach with demand improvements, requires collaboration of system components<br />
  14. 14. Our Approach<br />Intersection Control Protocols (ICP) family – architecture<br />Remove the physical traffic lights; <br />Provide a richer customised “virtual” traffic light to each driver;<br />Use sensory inputs, shared journey plans, inter-vehicle messaging and behavioural adaptation to reduce both total delay and improve vehicle flow through intersections; <br />Collisions are predicted and behaviour is adapted to reduce the effect of total delay experienced by vehicles; ad-hoc control by vehicles is feasible within bounds (non-deadlock scenes).<br />
  15. 15. Vehicle Back-Off Protocol<br />Filtering Phase<br />Collision Avoidance<br />Phase<br />Sharing<br />Phase<br />
  16. 16. Results: Delay and Throughput<br />DELAY<br />THROUGHPUT<br />Parameters: communication range of 200 meters.<br />VBP reduces total system delay and increases throughput.<br />
  17. 17. Contributions<br />We have presented a distributed software architecture and VBP protocol for cooperative intersection control using journey plans, messaging, collision avoidance, feedback and sensory inputs;<br />VBP represents one protocol of a family of protocols for traffic intersection control;<br />
  18. 18. Future Work<br />The Vehicle Back-Off Protocol<br />Ranking Conditions: who has precedence (various factors given specific scenarios – seek non-conflicting rankings);<br />Robustness: operation with incomplete or erroneous data – consensus and fault tolerance;<br />Scale: scaling the system to include multiple intersections;<br />Other Objects: pedestrians, cyclists, etc<br />Multi-hop Messaging: share data concerning multi-hop neighbouring vehicle plans e.g. Friend of a friend of a friend;<br />Extended Applications<br />
  19. 19. Thank you.<br />Questions?<br />
  20. 20. Visualisation<br />
  21. 21. Visualisation<br />
  22. 22. Visualisation<br />

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