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Enhance Routing Efficiency and Semantics through Participatory Sensing Liviu Iftode/Ruilin Liu Dept. of Computer Science, Rutgers University Oct. 29, 2014.

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Presentation on theme: "Enhance Routing Efficiency and Semantics through Participatory Sensing Liviu Iftode/Ruilin Liu Dept. of Computer Science, Rutgers University Oct. 29, 2014."— Presentation transcript:

1 Enhance Routing Efficiency and Semantics through Participatory Sensing Liviu Iftode/Ruilin Liu Dept. of Computer Science, Rutgers University Oct. 29, 2014

2 Routing of connected vehicles Connected vehicles vs. traditional way of traffic routing –Real-time traffic based route planning (Themis) –No way to evaluate the route choice (DoppelDriver) –Weak semantics sensible/acceptable by drivers (NaviTweet) “Congestio n up ahead” “What would have happened if I took the other route” “I’m taking route 66” 2

3 Themis: A Participatory Navigation System for Balanced Traffic Routing R. Liu, H. Liu, D. Kwak, Y. Xiangy, C. Borcea, B. Nath and L. Iftode, "Themis: A Participatory Navigation System For Balanced Traffic Routing," accepted at the 2014 IEEE Vehicular Networking Conference (VNC). 3

4 Balanced routing: the concept 4

5 Balanced routing solution Routing request 5  Participatory Sensing Algorithm  Traffic Flow Estimation Algorithm  Balanced Routing Algorithm

6 Participatory traffic sensing 6 1. Pre-processing 2. Hidden Markov Map Matching 3. Travel Time Allocation & Aggregation

7 Flow Estimation  Total Traffic = controlled traffic + background traffic  Extrapolate the controlled traffic into the total traffic ▪dynamic ratio ▪sample roads have ground truth ▪Estimate the ratio based on similarity to the sample roads Aslam, J., et al. "City-scale traffic estimation from a roving sensor network." ACM Sensys,

8 Balanced routing algorithm Modified cost: 8

9 City-scale synthetic experiment  Synthetic (Real Data + Simulation) ▪26,000 fleet of taxis in three consecutive Tuesdays ▪Generate traffic demand for 7%, 20%, and 40% penetration rate ▪Traffic simulation based on traffic-delay model 9

10 DoppelDriver: Counterfactual Actual Travel Times D. Kwak, D. Kim, R. Liu, B. Nath and L. Iftode, “DoppelDriver: Counterfactual Actual Travel Times for Alternative Routes," submitted for peer review. 10

11 What if I have taken the other route? Problem –no way to compare and assess a route decision –no systematic way to learn from them to make to make more efficient decisions in the future How to integrate drivers’ experience into route decision  sharing the actual travel times drivers experienced  logging daily trips of actual travel times for chosen and non- chosen routes into a personal trip diary 11

12 Counterfactual Thinking in Routing Taken Route ATA Counterfactual Route ATA You are here You would be here if you chose this route Star t Destination What if your navigation could tell you where you currently are on and how long it took if you had taken the other non-chosen routes to your destination? 12

13 Counterfactual route decomposition Determining Counterfactual Route Travel Time via crowdsensing of Actual Travel Times 13

14 DoppelDriver algorithm Comparison of Travel Times (Black vs. ∑ (Blue, Red, Green) 14

15 Feasibility study over taxi dataset 15

16 NaviTweet: Social Vehicle Navigation W. Sha, D. Kwak, B. Nath and L. Iftode,“Social Vehicle Navigation: Integrating Shared Driving Experience into Vehicle Navigation,” in Proc. 14th International Workshop on Mobile Computing Systems and Applications (HotMobile’13), Feb D. Kwak, D. Kim, R. Liu, B. Nath and L. Iftode,“Tweeting Traffic Image Reports on the Road,” in Proc. Sixth International Conference on Mobile Computing, Applications and Services (MobiCASE’14), Nov

17 Social Vehicle Navigation Share traffic reports using voice and image (NaviTweet) Traffic Digest sent to interested drivers → complements factors such as ETA in route choice → provides semantically richer information Route 66 or Route 22?? 17

18 Social Vehicle Navigation - NaviTweet O & D input Route selection Traffic report view Navigation and Tweet participants are included in our pilot study 30% of drivers change their route choices The weight of tweet information over the route choice is 3.9/5.0 compared with 3.97 of ETA 67% of users thought this system would be useful

19 Conclusion & future work  Changing the route planning from one-directional style to two-way participatory style  Providing more semantics towards route choice is urgent required by drivers  Incentivizing a community is the key to success  Incorporate multiple data sources and sensing platforms to diversify the routing planning 19

20 Questions? Thanks for listening! 20 For additional information and other related projects, please refer to:


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