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Kyun Queue: A Sensor Network System to Monitor Road Traffic Queues 2012.09.24 Junction

Kyun Queue: A Sensor Network System to Monitor Road Traffic Queues 2012.09.24 Junction. Rijurekha Sen , Abhinav Maurya , Bhaskaran Raman, Rupesh Mehta, Ramakrishnan Kalyanaraman , Nagamanoj Vankadhara , Swaroop Roy, Prashima Sharma India Institute of Technology, Bombay. Abstract.

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Kyun Queue: A Sensor Network System to Monitor Road Traffic Queues 2012.09.24 Junction

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  1. Kyun Queue: A Sensor Network System to Monitor Road Traffic Queues2012.09.24 Junction RijurekhaSen, AbhinavMaurya, Bhaskaran Raman, Rupesh Mehta, RamakrishnanKalyanaraman, NagamanojVankadhara, Swaroop Roy, Prashima Sharma India Institute of Technology, Bombay

  2. Abstract • A SWN system for real-time traffic queue monitoring. • Works in chaotic traffic • Doesn’t interrupt traffic flow during its installation and maintenance • Incurs low cost

  3. Introduction • Road congestion/traffic queue • Unpredictable travel times • Fuel inefficiency • Non-lane base and highly heterogeneous

  4. Contribution • Propose a new mechanism to sense road occupancy based on variation in RF link characteristics, when line of sight between a transmitter-receiver pair is obstructed • Algorithms to classify traffic states into congested or free-flowing at time scales of 20 seconds with above 90% accuracy • This network can correlate the traffic state classification decisions of individual sensors, to detect multiple levels of traffic congestion or traffic queue length on a given stretch of road, in real time. • Deployment of our system on a Mumbai road, after careful consideration of issues like localization and interference, gives correct estimates of traffic queue lengths, validated against 9 hours of image based ground truth.

  5. Other solutions Infrared sensors: overly sensitive to even small obstacles

  6. Traffic sensing • 802.15.4 compliant Telosb motes • Tx sends 25 pkt/sec, payload 100bytes at -25dBm power 25m

  7. Real-time classification of traffic states • Window size = 5 minutes? • Adaptive traffic light control would intuitively need faster inputs • 1 minute cycle time • Very low time windows give noisy predictions • the inherent stochastic nature of wireless links which causes link quality to be intermittently bad though the tx-rx are in perfect LOS • the instantaneous traffic condition between the tx-rx are contrary to the actual traffic state

  8. Labeled data-set for evaluation • 16 hours data-set from • 25m wide AdiShankaracharyaMarg (wide) • 13676 seconds: free-flow • 14992 seconds: congested • 8m wide henceforth (narrow) • 13486 seconds: free-flow • 16678 seconds: congested • Labeling • Larger time-scale of 5 minutes to reduce manual overhead • Traffic states did not toggle within 5 minutes

  9. Classification Algorithm • Machine learning • Binary traffic state classification • (a) congested traffic • (b) free-flowing traffic • Trade-off between • (a) accuracy of classification • (b) implementability on a low end embedded platform • (c) complexity of the classifier models • (d) overhead of model training • Two possible solutions • (a) FeatureClassifier (FC) algorithm • (b) SignalClassifier(SC) algorithm

  10. Which combination? • FeatureClassifier (FC) algorithm • Feature vector comprising 9 features (RSSI percentile) • SignalClassifier (SC) algorithm • Voting with one RSSI • SVM, K-means

  11. Choice of features • LQI (link quality index), RSSI, PRR • Only use RSSI is better • Non-intuitive observation : RSSI is much more strongly correlated with line-of-sight than LQI or PRR

  12. Choice of features • There are several IEEE 802.15.4 compliant radios like XBEE, which do not report LQI value. RSSI shows good difference between the two traffic states even for small d’’.

  13. Classification models • Use FeatureClassifier algorithm with K-Means model • Built over 20 seconds of RSSI percentiles • Training model is built from the 8 hour dataset collected from roads

  14. Design of the Kyun system • Architecture • 3 pairs of transmitter (Ti) – receiver (Ri) perform sensing and computation to know the traffic condition. • A central controller unit (C) resides on the traffic light.C upon receiving the road occupancy observation values from Tx-Rx pairs can compute the optimal green light distribution • C can communicate with a server (S). S upon receiving road occupancy information can implement other applications like coordinated signal control, bottleneck identification and congestion mapping

  15. Which RF to use? • 802.11 a/b/g requires higher power • Bluetooth shows an issue of poor range and links are hard to establish for wide roads of 25m. • Choose 802.15.4

  16. Sensing and communication confilicts • (1) sensing links: across the road from T and R • 0.5 m from the ground level • (2) communication links: along the road, from on R to R • R is mounted on the road-side Lamp-post (30m) • Fault: alternate lamp-posts to communicate => 60 m • Experiment • T: transmit 25 pkt/sec • 30m:10m:100m • 6 am: 60m at 60% PRR • 8 am: >30m at 40% PRR

  17. Resolution • Two 802.15.4 radios in R units • XBEE radio for sensing (0.5m) • CC2520 radio for communication (2m) • Clear of pedestrians on the footpath

  18. Software protocol • Ensure that all R’s perform sensing and computation simultaneously • C centrally controls measurement cycle • No need of any explicit time synchronization mechanism • MAC protocol • Simple CSMA-CA with 4 tries C-RDY: receiver ready mode waiting for a control message C sends a control message 1. Write in micro-SD card and Compute the traffic signal schedule from the queue length information in the data message (every 30s) 2. Send to S by GPRS Compute sensing decision Decision results originated by the last R

  19. Hardware prototypes $200 $250

  20. Deployment based evaluation • Classification accuracy • 50 minutes • 149/150 correct • 99.67%

  21. Deployment based evaluation • Deployment • Nov. 17(Thu), 18(Fri), 19(Sat), 2011 6pm – 9pm • Image based manual verification scheme • Samsung Google Nexus phone to capture image ever 30 seconds • Can only cover T-R pair 1-3 • 1 person observes offline and estimates the queue length • 2nd person as consulter C R1 R2 R3 R4 R5 3

  22. Deployment based evaluation • Only cover 0-3, error up to 3 units

  23. Deployment based evaluation • The queue buildup and clearing was very rapid on that day

  24. Self localization • Without pre-defined order and program R unit • To use RSSI ranging • It is not a monotonically decreasing function of distance • Two-ray model equation: lower antenna heights seem to give less oscillations

  25. Self Localization • Vertical polarization seems to give far less oscillations than horizontal polarization • Horizontal: {2, 3, 5, 6, 4} Vertical: {2, 3, 6, 5, 4} • Reordering occur independent of antenna orientation and polarization • Vertical orientation gives worse RSSI values • Fading effect of the metal body of the lamp-post with which the vertical antenna is parallel

  26. Self Localization • Using different 802.15.4 channels, the average RSSI over the channels would have less oscillations • Useless • None of the parameters like antenna height, polarization or channel, seem to be useful to remove the RSSI oscillation

  27. Self Localization • Fig. 30: {node 2, node 5, node 4, node 6, node 3} 31 70 95 116 144 • Fig. 31: {node 2, node 5, node 6, node 4, node 3} 39 64 85 113 140 • The R recording the best RSSI is marked as the first node

  28. Discussion and future work • Interference issues • 802.15.4 has 16 orthogonal channel • Inter-network interference can still exists (Wi-Fi AP) • Interference doesn’t affect RSSI of received packets • Disable CRC • Disable CCA • Use only 802.15.4 channel 26 • Outside 802.11 spectrum

  29. Discussion and future work • Power consumption • T units: 20 XBEE tx operations per second • R units: in every measurement cycle spanning 30 seconds, • receive at most 400 XBEE packets during sensing • Perform one classification operation • Receive and transmit at most 8 CC2520 messages • C unit: • performs 1 GPRS communication • 2 SD card operations • Receive and transmit at most eight CC2520 messages

  30. Discussion and future work • Classification schemes and model training • For different kinds of roads • Semi-supervized training – balance between overhead of manual labeling in supervized learning and noise in unsupervized methods • Identify characteristics like road width and vehicle types, that affect model parameters, and subsequently see if roads similar in those characteristics can use the same model with sufficient accuracy • Extend -> muticlasses • Empty road • Fast traffic • Slow traffic • Standing traffic

  31. Discussion and future work • Other design choices • Different topologies to reduce overhead • Replace dual radio solution with single radio • …..

  32. Conclusion • Design and implement a new sensing system to detect road occupancy based on RF link quality degradation • Design and implement a sensor network to distributedly decide traffic queue length in real time • Achieve upto 96% accuracy in queue length estimation

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