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Jiaxing Xu, Weihua Sun, Naoki Shibata and Minoru Ito : "GreenSwirl: Combining Traffic Signal Control and Route Guidance for Reducing Traffic Congestion," in Proc. of IEEE Vehicular Networking Conference 2014 (IEEE VNC 2014), pp. 179-186. Serious traffic congestion is a major social problem in large cities. Inefficient setting of traffic signal cycles, especially, is one of the main causes of congestion. GreenWave is a method for controlling traffic signals which allows one-way traffic to pass through a series of intersections without being stopped by a red light. GreenWave was tested in several cities around the world, but the results were not satisfactory. Two of the problems with GreenWave are that it still stops the crossing traffic, and it forms congestion in the traffic turning into or out of the crossing streets. To solve these problems, we propose a method of controlling traffic signals, GreenSwirl, in combination with a route guidance method, GreenDrive. GreenSwirl controls traffic signals to enable a smooth flow of traffic through signals times to turn green in succession and through non-stop circular routes through the city. The GreenWave technology is extended thereby. We also use navigation systems to optimize the overall control of the city's traffic. We did a simulation using the traffic simulator SUMO and the road network of Manhattan Island in New York. We confirmed that our method shortens the average travel time by 10%-60%, even when not all cars on the road are equipped to use this system.
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Jiaxing Xu†1 , Weihua Sun†2, Naoki Shibata†1, Minoru Ito†1
†1 Nara Institute of Science and Technology, Japan
†2 Shiga University, Japan
We combine Traffic Signal Control and Route
Guidance so that
Reduce traffic congestion
Vehicles likely encounter green lights
Minimize the travel time of vehicles
Proposed method consists of two parts
GreenSwirl: traffic signal control method
▪ Enable smooth-flowing traffic on many circular routes
GreenDrive: route guidance method
▪ Guide of the shortest time path for individual drivers
2
The whole traffic
Vehicles
Background and Related Study
Proposed Method
Evaluation and Results
Conclusion and Future Work
3
Serious traffic congestion
The lack of carefully planned traffic
signals is one of the major reasons
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Air pollution caused by vehicle
emissions
25% of PM2.5 pollution in Beijing comes
from exhaust gas from vehicles
In order to reduce traffic congestion
Traffic Signal Control GreenWave
Dynamic Route Guidance
Coordinating a series of traffic lights to
enable continuous traffic flow over several
intersections in one main direction
Principle: control the time period for green lights by
predicting the arrival time of vehicles
GreenWave Direction 5
GreenWave cannot be created at crossing traffic
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GreenWave
GreenWave
GreenWave
GreenWave
Often red light at opposite lane
10 traffic lights were tested in
Nanjing City, China (May 19, 2014)
GreenWave
GreenWave
GreenWave
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4km
GreenWave Direction : 4 minutes
Opposite Direction: 9 minutes
Gre
en
Wav
e
Entry
Exit
Exit
Ma
in R
oad
congestion
congestion
congestion
congestion
co
ng
estio
n
Main
Ro
ad
8
Entry
9
Control traffic signals on many direction
roads
Control the traffic and guide the vehicles to
the optimal route
Priority in a single direction
Low capacity of roads connecting to main
roads Easily causing congestion
Entering excessive vehicles Gridlock
Background and Related Study
Proposed Method
Evaluation and Results
Conclusion and Future Work
10
Traffic Signal Control
GreenSwirl
Route Guidance
GreenDrive
To reduce traffic congestion
To minimize the travel time of vehicles
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GreenWave is formed on many roads like huge swirls throughout one city
A swirl is a single loop of the multiple GreenWaves formed by GreenSwirl
Vehicles can run at the legal speed limit on a swirl without stopping unless there is traffic congestion
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Priority Direction
Prio
rity D
irectio
n
Priority Direction
Prio
rity D
irectio
n
Swirls
No Stopping
City
Center
Smoothly running
even in crossing road
Control the entering
traffic
Control the entering
traffic
Optimize each
vehicle
Guiding each vehicle to the shortest time path
Estimate the travel time of each road segment
Guide some vehicles to the swirls for dispersing traffic
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Pri
ori
ty D
ire
cti
on
Priority Direction
Prio
rity D
irec
tion
Priority Direction
Pri
ori
ty D
ire
cti
on
Priority Direction
Prio
rity D
irec
tion
Priority Direction
90s
130s
Resolve the problem in opposite lane
160s
1. Set the initial travel time for all road segments
2. Guide each vehicle to the shortest time path
3. Collect travel time from all road segments and output result
4. Increase travel time by 1% for the top 1% congested road
5. Until the result ( average travel time of all vehicles) becomes
stable, output the best recommend route to vehicles
Traffic volume
balance
Congestion
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Traffic data
Speed of vehicles on road segments
Two ways to collect traffic data1. Deploy speed-detecting sensors in all road segments
2. Deploy sensors in some road segments and part of vehicles report their speed and position to the server via communication
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Static Data
GreenSwirlSwirls
Road Network
GreenDrive Server
Tra
ffic D
ata
Route
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Current Swirls are formed manually
CenterCenter Park
Main Area2
Determine main roads
High traffic capacity
Multi-Lane
Detect the area crowded with
vehicles
Main area
City center
Form the swirls
To disperse Vehicles
3
1
Background and Related Study
Proposed Method
Evaluation and Results
Conclusion and Future Work
17
Experiment Purposes
Evaluate the performance of GreenSwirl and GreenDrive
Evaluate the proposed method in scenarios where a part of road segments has sensor installed for obtaining traffic data
Simulator
SUMO (Simulation of Urban Mobility)
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Manhattan in New York
Map data is taken from OpenStreetMap
Many one-way streets
4km×20km
Number of streets : 870
Number of vehicles: 5000/10000/15000/20000
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Synchronized Method
Default signal control method implemented in SUMO
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GreenWave
GreenWave is used for main streets with a high traffic capacity
The default synchronized method is used for other streets
Same time
Shortest-Path Method
Vehicles are guided along paths of the shortest distance
DUA (dynamic user assignment) Method[1]
It works by running simulations to discover travel times and assigning alternative routes to vehicles
Vehicles are guided along routes selected according to calculated probabilities, to distribute all the traffic
Problems
▪ This method does not optimize the travel time for each user
▪ Some users are guided along detour paths
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[1] Krajzewicz, Daniel, Michael Behrisch, and Yun-Pang Wang. "Comparing performance and
quality of traffic assignment techniques for microscopic road traffic simulations." DTA2008
International Symposium on Dynamic Traffic Assignment. No. EPFL-CONF-154987. 2008.
800
1300
1800
2300
2800
5000 10000 15000 20000
Avera
ge T
ravel
Tim
e (
s)
Number of vehicles
Synchronized
GreenWave
GreenSwirl
Reduce average
travel time by 21% compared with
GreenWave
Reduce average travel time by 10% compared with GreenWave
GreenDrive
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GreenDrive 100% penetration is the best
800
1300
1800
2300
2800
3300
3800
4300
5000 10000 15000 20000
Avera
ge
Tra
vel
Tim
e (
s)
Number of vehicles
Shortest Path 100%
DUA 100%
GreenDrive 25%
GreenDrive 50%
GreenDrive 75%
GreenDrive 100%
Reduce 55%
compared to
Shortest-path
Reduce 23%
compared to
DUA
GreenSwirl
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800
1300
1800
2300
2800
3300
3800
4300
5000 10000 15000 20000
Avera
ge
Tra
vel
Tim
e (
s)
Number of vehicles
DUA 100%
GreenDrive 50%
GreenDrive 100%
GreenSwirl
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GreenDrive 100% penetration is the best
DUA 100% penetration ≈ GreenDrive 50% penetration
Sensor 30%
Traffic data can be obtained from 30% of the streets
Sensor 30% + Feedback 1%
Traffic information can be obtained from 30% of the streets and 1% of the all vehicles report their travel information
Sensor 70%
Traffic data can be obtained from 70% of the streets
Sensor 100%
Traffic data can be obtained from all the streets
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0
500
1000
1500
2000
2500
5000 10000 15000 20000
Avera
ge
Tra
vel
Tim
e (
s)
Number of vehicles
GreenDrive_Sensor30%
GreenDrive_Sensor30%+Feedback1%
GreenDrive_Sensor70%
GreenDrive_Sensor100%
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Less travel time as the ratio of sensors increases
Sensor 30% + Feedback 1% ≈ Sensor 70%
Proposed Method
GreenSwirl: traffic signal control method
GreenDrive: route guidance method
Reduce the average travel time by 10%-60% compared with the existing methods
Future works
Evaluate the proposed method for maps of other cities
Automatic generation algorithm for GreenSwirl according to traffic data
27
center
center
center
Beijing BostonVehicles:50,000
Vehicles:80,000
Jiaxing Xu, Weihua Sun, Naoki Shibata and
Minoru Ito : "GreenSwirl: Combining Traffic
Signal Control and Route Guidance for
Reducing Traffic Congestion," in Proc. of
IEEE Vehicular Networking Conference 2014
(IEEE VNC 2014)(23% acceptance rate), pp.
179-186, 2014-12-15.
DOI:10.1109/VNC.2014.7013337 [ PDF ]
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