Proactive vehicle re-routing strategies for congestion avoidance
description
Transcript of Proactive vehicle re-routing strategies for congestion avoidance
Proactive vehicle re-routing strategies for congestion
avoidanceJuan (Susan) Pan*, Mohammad A. Khan*, Iulian
Sandu Popa+, Karine Zeitouni+ and Cristian Borcea* *New Jersey Institute of Technology, USA
+University of Versailles Saint-Quentin-en-Yvelines, France
DCOSS 2012
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Traffic congestion: an ever-increasing problem
In 2010, congestion caused urban Americans a cost of $101 billions
By 2015, this cost will rise to $133 billion and the amount of wasted fuel will jump to 2.5 billion gallons
Increase road capacity? Optimize traffic signal control? Provide traffic guidance to drivers?
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Congestion avoidance using mobile sensing and actuation (1)
Smart phones (mobile sensors) & road-side sensors monitor traffic at fine granularity Mobile sensors can be vehicular embedded systems Road-side sensors: loop detectors, cameras, etc Demonstrated by other researchers
Traffic management service (TMS) collects data and estimates congestion in real-time
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Congestion avoidance using mobile sensing and actuation (2)
TMS provides real-time, proactive, individually-tailored re-routing guidance to drivers to prevent congestion Drivers provide their origin-destination
information Guidance is pushed to drivers’ smart phones
when signs of congestion are observed on their current route
Drivers may or may not follow the guidance The main focus of our research
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Comparison with existing work (1)
Google, Microsoft & Inrix: real time traffic info to compute traffic-aware shortest routes Reactively provide same
guidance for all drivers Problem: move congestion from
one spot to another▪ Similar to route oscillation in
computer networks
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Comparison with existing work (2)
Transportation researchers proposed dynamic traffic assignment models Iteratively calculate shortest paths and assign
routes to each driver to achieve the user/system equilibrium
Example of systems: DynaMIT, Contram Problems ▪ Tractability at scale (providing real-time guidance)▪ Ability to work when not all drivers are part of the
system▪ Robustness to drivers who ignore the guidance
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Outline
Motivation & related work Our 3 proactive re-routing strategies Simulation results Conclusion and future work
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The 4 phases of re-routing Road network represented as graph, with estimated
travel time as edge weight1. Travel time estimation
Greenshield’s model for travel time estimation
2. Traffic congestion estimation Density greater than threshold (δ=0.7)
3. Selection of candidate vehicles for re-routing4. Re-routing: alternative route computation and
assignment to drivers
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Selection of candidate vehicles for re-routing
Step 1: Detect road segments with signs of congestion
Blue: 1st level incoming segmentsGreen: 2nd level incoming segments
Step 2: Recursively select incoming segments to “congested” segment until depth L
Step3: Select vehicles on these road segments
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Our 3 re-routing strategies
Dijkstra’s Shortest Path (DSP) Computes one single shortest path for each driver Potential to switch congestion from one spot to
another Random K Shortest Paths (RkSP)
Compute k shortest paths for each driver and randomly pick one
Solves DSP problem, but could be far from optimal Entropy Balanced K Shortest Paths (EBkSP)
Prioritize candidate vehicles Compute k shortest paths for each driver and pick
the one with least popularity Improves on RkSP by choosing better paths
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EBkSP popularity entropy
Let (p1,…, pk) be the set of k paths that will be assign next
Let (r1,…, rn) be the union of all segments of (p1,…, pk), and (fc1,…,fcn) be the set of weighted footprint counters
Def: Entropy(pj) is the entropy of pj and is computed as
Def:
Def: the weighted footprint counter fci of a road segment i is: fci =ni х wi ni is the number of vehicles that are assigned to paths that include this segment, and wi is the weight of the road segment
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EBkSP example
a b c d e
f g h i j
k
V2, assigned path (fg, gh, hi, ij )V1, assigned path (ab, bg,gh, hi, ij )V3, assigned path (ch, hk )P1 (ab, bg, gh, hi, ij) fc(P1)(1,1,2,2,2) entropy=1.49P2 (ab, bc, ch, hi, ij) fc(P2)(1,0,1,2,2) entropy=1.16P3 (ab, bc, cd, di, ij) fc(P3)(1,0,0,0,2) entropy=0.58
?
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Evaluation Goals
Effectiveness average travel time Users’ experience number of re-routings Robustness average travel time as function of driver
compliance rate & system penetration rate Real-time CPU time
Simulation setup SUMO-0.13 microscopic simulator, open source, car
following model TRACI python library send commands to SUMO to assign
new paths
Brooklyn (1000 vehicles)Newark (908 vehicles)
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Average travel time
All strategies improve average travel time significantly EBkSP has the best performance
Compared to “no-reroute” reduces the travel time by up to 81% and 104%
Compared to DSP, it is better by 15% and 25%
Brooklyn(L=3,δ=0.7)
Brooklyn(L=4,δ=0.7)
13001500170019002100230025002700 noreroute DSP RkSP EBkSP
Aver
age
trav
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(sec
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Newark(L=3,δ=0.9)
Newark(L=4,δ=0.9)
1500
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2500
3000
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4000
Aver
agte
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Average number of re-routings
EBkSP has least number of re-routings Less distraction to drivers
Newark(L=3,δ=0.9)
Newark(L=4,δ=0.9)
1.71.92.12.32.52.72.93.13.33.5 DSP RkSP EBkSP
Aver
age
num
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Brooklyn(L=3,δ=0.7)
Brooklyn(L=4,δ=0.7)
1.31.51.71.92.12.32.5 DSP RkSP EBkSP
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erou
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Average travel time vs. compliance and penetration rate
130014001500160017001800
DSP RkSP EBkSP
Compliance rate
Aver
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0.2 0.313001500170019002100230025002700 noreroute DSP RkSP EBkSP
Compliance rateAv
erag
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cond
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Performance increases with compliance rate up to a point Low compliance still much better than “no-reroute”
1400160018002000220024002600
DSPRkSPEBkSPnoreroute
Penetration rate (no road-side sensors)
Aver
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trav
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cond
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1400160018002000220024002600
DSPRkSPEBkSPnoreroute
Penetration rate(with road-side senors)
Aver
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trav
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time(
seco
nds) Performance increases with penetration rate
With road-side sensors help to detect vehicular density, low penetration rate still much better than “no-reroute”
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CPU time
DSP lowest CPU time Dijkstra lower complexity than k shortest path O(E + V log(V )) vs. O(kV (E+V log(V )))
EBkSP better than RkSP Fewer origin-destination pairs due to better re-routing
Brooklyn(L=3,δ=0.7)
Brooklyn(L=4,δ=0.7)
01020304050607080
DSP RkSP EBkSP
Cpu
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Brooklyn(L=3,δ=0.7)
Brooklyn(L=4,δ=0.7)
1000
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1500 DSP RkSP EBkSP
Tota
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D pa
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Conclusion & future work
All re-routing strategies decrease significantly the average travel time EBkSP is the best—careful path selection ▪ 104% improvement compared to the “no rerouting” baseline▪ Lowers with 34% the number of re-routings
Good improvements are observed even for relatively low penetration/compliance rates
To improve scalability and real-time response, we plan to work on hybrid system architectures Offload part of computation to mobile nodes Use ad hoc communication in addition to Internet
communication
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Thank you !Acknowledgment: NSF Grant CNS-0831753
http://www.njit.edu/~borcea/invent/