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Congestion Alleviation Scheduling TechniqueCongestion Alleviation Scheduling Techniquefor Car Drivers Based onfor Car Drivers Based on
Prediction of Future Congestion on Roads and Prediction of Future Congestion on Roads and SpotsSpots
Hisaka Kuriyama, Yoshihiro Murata, Naoki Shibata*, Keiichi Yasumoto, Minoru Ito
Nara Institute of Science and Technology *Shiga University
October 3, 2007 ITSC 2007 H. Kuriyama et al. 2
1. Background2. Proposed method
3. Experiment
4. Conclusion
Outline
October 3, 2007 ITSC 2007 H. Kuriyama et al. 3
Background:-In sightseeing tours and parcel deliveries by cars
• Each person visits multiple destinations• If many people concentrate on same route or service spot
These routes and spots will have congestion
RouteRoute Service spotService spot
These congestions impair social activities
Background
We propose:– A method for finding schedules for massively many users
by predicting congestions on both routes and spots– Make people disperse among different routes and spots
October 3, 2007 ITSC 2007 H. Kuriyama et al. 4
A method by T.Yamashita et al. [2] distributes users over routes RIS (Route Information Sharing)
• Each user transmits route information to a server• Server estimates future traffic congestion using this information and
feeds its estimate back to each user• Each user uses the estimation to re-plan their route
[2] T. Yamashita, et al., "Smooth Traffic Flow with a Cooperative Car Navigation System", AAMAS(2005)[3] T. Kataoka, et al., "Distributed Visitors Coordination System in Theme Park Problem“ , MMAS(2004)
A method by T.Kataoka et al. [3] distributes users over spots
• Each user selects the least congested spot
Server
For distributing tourists over either routes or spotsFor distributing tourists over either routes or spots
Existing Studies
October 3, 2007 ITSC 2007 H. Kuriyama et al. 5
Our method allows users to visit many spots satisfying time constraints
Final spot
Our Contribution Each user selects the least congested routes or spots
according to the situation
If a user has to reach the final spot before a specified time-The user may violate the time constraints
October 3, 2007 ITSC 2007 H. Kuriyama et al. 6
1. Background
2. Proposed method
3. Experiment
4. Conclusion
Outline
October 3, 2007 ITSC 2007 H. Kuriyama et al. 7
Our Approach Collecting users’ visiting spots and time constraints
Sending the set of all users’ schedules
Performing traffic simulation considering congestions on both routes and spots
Modifying tour of the user who violate the time constraints by removing some of the visiting spots
October 3, 2007 ITSC 2007 H. Kuriyama et al. 8
Problem Definition Inputs:
-Each user inputs:• starting spot and time• set of spots which the user wants to visit• importance degree
representing how important the spot is to visit• final spot and its importance degree• f inishing time
representing the latest time when the user wants to reach the final spot
Objective:-Finding a set of users’ schedules which maximizes the total sum of the importance degrees
Output:-Set of all users’ schedules
AM 8:00
Imp:30Imp:10
Imp:20
Imp:5
PM 17:00
October 3, 2007 ITSC 2007 H. Kuriyama et al. 9
Algorithm for Modifying Schedules
1. Finding schedule -Find schedule for each user with the minimum distance to go through all the requested spots
2. Performing simulation-Perform simulation based on the routes generated by step 1.
3. Modifying schedule -Modify schedule by decreasing/increasing the number of spots
4. Iterating steps 2. to 3.-Repeat from step 2. until all users can reach the final spot no later than the finishing time OR the predetermined time expires
Outline of the Scheduling algorithm:
October 3, 2007 ITSC 2007 H. Kuriyama et al. 10
Users
Explanation of Our Algorithm We explain our method in case of 3 users
October 3, 2007 ITSC 2007 H. Kuriyama et al. 11
Find the schedule for each user which minimizes the total distance of movement to go through all the requested spots
Finding Schedule (1/4)
October 3, 2007 ITSC 2007 H. Kuriyama et al. 12
Our system
First user
Finding Schedule (1/4) Suppose, the first user’s schedule is set like this
October 3, 2007 ITSC 2007 H. Kuriyama et al. 13
Our system
Second user
Finding Schedule (1/4) The second user’s schedule is set similarly
October 3, 2007 ITSC 2007 H. Kuriyama et al. 14
Our system
Third user
Finding Schedule (1/4)
October 3, 2007 ITSC 2007 H. Kuriyama et al. 15
Our system
Performing Simulation (2/4) The system performs traffic simulation
-During the simulation, each user…• uses RIS to choose routes to their next spots• consumes some time to wait and/or receive services at spots
October 3, 2007 ITSC 2007 H. Kuriyama et al. 16
During simulation
Performing Simulation (2/4)
• If many users converge on the same service spot They need to require more time to receive the service
• If many users converge on the same road They need to require more time to finish the movement
October 3, 2007 ITSC 2007 H. Kuriyama et al. 17
The system modifies user’s visit ing spots
During simulation
Performing Simulation (2/4)
In this result, a user cannot reach the final spot by the finishing timeIn this result, a user cannot reach the final spot by the finishing time
October 3, 2007 ITSC 2007 H. Kuriyama et al. 18
Imp : 30 Imp : 35 Imp : 10 Imp : 25
Imp : 30 Imp : 35 Imp : 10 Imp : 25
Modifying Schedule (3/4) The system chooses one spot to remove under the situations
to minimize loss of importance degree
October 3, 2007 ITSC 2007 H. Kuriyama et al. 19
Our system
Modifying Schedule (3/4) The system changes the schedule based on new set
October 3, 2007 ITSC 2007 H. Kuriyama et al. 20
• Avoiding congestion• Meeting the finishing time
Our system
Performing Simulation (2/4)
The user avoids congestion and returns before the finishing timeThe user avoids congestion and returns before the finishing time
The system performs simulation based on the recalculated schedules
October 3, 2007 ITSC 2007 H. Kuriyama et al. 21
If a user can reach the final spot within the finishing timeThe system adds the once removed spots again
Imp : 30 Imp : 35 Imp : 10 Imp : 25
Imp : 30 Imp : 35 Imp : 10 Imp : 25
Modifying Schedule (3/4) During the rescheduling
Each user changes the visiting spots Congestion situations tend to be changed Congestion of certain routes or spots may be alleviated
October 3, 2007 ITSC 2007 H. Kuriyama et al. 22
Iterating Steps 2. to 3. (4/4)
Our system
The system repeats these procedures
October 3, 2007 ITSC 2007 H. Kuriyama et al. 23
With our method, the schedules might not converge
We use a tabu list to improve convergenceWe use a tabu list to improve convergence
Avoiding Unnecessary Repeats
Removing
Adding
Violating the finishing time
October 3, 2007 ITSC 2007 H. Kuriyama et al. 24
For each user, if the system repeats adding and removing the spot a predetermined number of times
This spot is added to the tabu list for the user
Adding to tabu list
We can stop the repetition of changing for a short timeWe can stop the repetition of changing for a short time
Removing
The spot will never be added
Avoiding Unnecessary Repeats
Adding Removing
RemovingAdding Adding
October 3, 2007 ITSC 2007 H. Kuriyama et al. 25
Outline
• Background• Proposed method
• Experiment• Conclusion
October 3, 2007 ITSC 2007 H. Kuriyama et al. 26
Purpose of Experiment-To evaluate performance of our method, we compare it with existing method
Experiment
Evaluation Metrics 1. Satisfaction degree 2. Incentive for users to follow the computed schedules 3. Tolerance
• Some users do not use our method• New users are incrementally added on the road
October 3, 2007 ITSC 2007 H. Kuriyama et al. 27
Road Network used for Simulation
Each User’s behavior-Visiting 4 spots-Finally returning to the starting spot
Number of spots 32
Number of roads 56
Total length of map 59.6 [km]
Service time of each spot 600 -1,800 [sec]
Capacity of each spot 10-30 [users]
Number of users 500 or 1,000 Spot
Road
Each User’s input data-Random
Simulation Configuration
These values are determined so that each user would have to wait for a while before receiving the service if 500 users are distributed evenly among all spots
These values are determined so that each user would have to wait for a while before receiving the service if 500 users are distributed evenly among all spots
October 3, 2007 ITSC 2007 H. Kuriyama et al. 28
Existing studies-Only treating congestion either in route or service spot
Configuration of Existing Methods
Extended version of existing studies named E-RIS − For the baseline to evaluate the usefulness of our method
Extended version of existing studies named E-RIS − For the baseline to evaluate the usefulness of our method
E-RIS-Using RIS algorithm between two spots
-Selecting the spot where total necessary time of movement and stay is the smallest as a next destination
If the user may overrun the finishing time Giving up visiting further spots and return to the final spot
October 3, 2007 ITSC 2007 H. Kuriyama et al. 29
Sum : 100
Importance degrees of spots• A user specifies different importance degrees for each spot
• To keep fairness among users We assume that each user has the same points
Imp : 30 Imp : 35 Imp : 10 Imp : 25
Score Configuration
October 3, 2007 ITSC 2007 H. Kuriyama et al. 30
When each user receives the service at a spot• The user can obtain the score equal to the importance degree
specified for that spot
If the service does not finish before his/her finishing time• The user does not obtain the score for the spot
Score : 10
Total Score : 100
Total Score : 100
If the user visited all inputted spots by the finishing time
Score Configuration
Score : 0
October 3, 2007 ITSC 2007 H. Kuriyama et al. 31
Simulation Configuration All users use the same algorithm (E-RIS or our method)
Experiment 1 : Satisfaction Degree
They start to move at the same time
They start to move at the same time
All users are set at the same time
All users are set at the same time
October 3, 2007 ITSC 2007 H. Kuriyama et al. 32
Ave
. score
Exce
ss users
0
20
40
60
80
100
500 users 1,000 users
E-RISOur method
0
50
100
150
200
250
500 users 1,000 users
E-RISOur method
Result 1 : Satisfaction Degree Simulation Result
Figure.1 Figure.2
October 3, 2007 ITSC 2007 H. Kuriyama et al. 33
Ave
. score
Exce
ss users
The average score of all usersThe average score of all users
0
20
40
60
80
100 E-RISOur method
0
50
100
150
200
250E-RISOur method
Figure.1 Figure.2
500 users 1,000 users 500 users 1,000 users
Result 1 : Satisfaction Degree Simulation Result
October 3, 2007 ITSC 2007 H. Kuriyama et al. 34
Ave
. score
Exce
ss users
The number of users who exceeded the finishing timeThe number of users who exceeded the finishing time
0
20
40
60
80
100 E-RISOur method
0
50
100
150
200
250E-RISOur method
Figure.1 Figure.2
500 users 1,000 users 500 users 1,000 users
Result 1 : Satisfaction Degree Simulation Result
October 3, 2007 ITSC 2007 H. Kuriyama et al. 35
Simulation Result
*Computation time of our method : 4 minutes
Ave
. score
Exce
ss users Our method
• 20-30% higher average score• Much less excess users
Our method• 20-30% higher average score• Much less excess users
0
20
40
60
80
100 E-RISOur method
0
50
100
150
200
250E-RISOur method
Figure.1 Figure.2
500 users 1,000 users 500 users 1,000 users
Result 1 : Satisfaction Degree
October 3, 2007 ITSC 2007 H. Kuriyama et al. 36
Assumption-Users follow the schedules computed by our algorithm
The users who follow our method have enough incentive or not The users who follow our method have enough incentive or not
Experiment 2 : Evaluation of Incentive
Simulation Configuration-Some users ignore the computed schedules and force their original tour plans
We define ignoring users as outwittersWe define ignoring users as outwitters
We evaluate…
If users outwit the algorithm and obtain better results They would ignore the computed schedules
October 3, 2007 ITSC 2007 H. Kuriyama et al. 37
Result 2 : Evaluation of Incentive Simulation Result
The ratio of outwitters (%)
The ratio of outw
itters w
ho have disadvantage(%)
October 3, 2007 ITSC 2007 H. Kuriyama et al. 38
The ratio of outw
itters w
ho have disadvantage(%)
The ratio of outwitters (%)
Ratio of outwitters who could not improve score nor reach the final spot before the finishing time to all the outwitters
Result 2 : Evaluation of Incentive Simulation Result
October 3, 2007 ITSC 2007 H. Kuriyama et al. 39
Result 2 : Evaluation of Incentive Simulation Result
The ratio of outwitters (%)
The ratio of outw
itters w
ho have disadvantage(%)
Most outwitters (over 70%) have disadvantage Our method should give users the motivation to follow
Most outwitters (over 70%) have disadvantage Our method should give users the motivation to follow
October 3, 2007 ITSC 2007 H. Kuriyama et al. 40
New users are added to random positions every 600 seconds When new users are added, all users using our method re-calculate schedules
(1) Some users use our method and the others use E-RIS(2) New users are incrementally added on the road network
(1) Some users use our method and the others use E-RIS(2) New users are incrementally added on the road network
Using our method
Using E-RISNew users are incrementally added on the road network
Experiment 3 : Evaluation of Tolerance Simulation Configuration
October 3, 2007 ITSC 2007 H. Kuriyama et al. 41
020406080
100120140160180200
100 90 80 70 60 50 40 30 20 10 0
Our method E-RIS
0102030405060708090
100
100 90 80 70 60 50 40 30 20 10 0
Our method E-RIS
100 users are added at once until the number of users exceeds 1,000
Ave. score
Excess users
The ratio of users who use our method (%) The ratio of users who use our method (%)
Figure.1 Figure.2
Result 3 : Evaluation of Tolerance
October 3, 2007 ITSC 2007 H. Kuriyama et al. 42
Result 3 : Evaluation of Tolerance
020406080
100120140160180200
100 90 80 70 60 50 40 30 20 10 00
102030405060708090
100
100 90 80 70 60 50 40 30 20 10 0E
xcess usersThe ratio of users who use our method (%) The ratio of users who use our method (%)
Figure.1 Figure.2
Ave. score
The average score of all usersThe average score of all users
Our method E-RIS
Our method E-RIS
100 users are added at once until the number of users exceeds 1,000
October 3, 2007 ITSC 2007 H. Kuriyama et al. 43
Result 3 : Evaluation of Tolerance
020406080
100120140160180200
100 90 80 70 60 50 40 30 20 10 00
102030405060708090
100
100 90 80 70 60 50 40 30 20 10 0E
xcess usersThe ratio of users who use our method (%) The ratio of users who use our method (%)
Figure.1 Figure.2
Ave. score
The number of users who exceeded the finishing timeThe number of users who exceeded the finishing time
Our method E-RIS
Our method E-RIS
100 users are added at once until the number of users exceeds 1,000
October 3, 2007 ITSC 2007 H. Kuriyama et al. 44
The ratio of users who use our method
We changed ratio of users who use our method from 100% to 0%
The ratio of users who use our method
We changed ratio of users who use our method from 100% to 0%
Result 3 : Evaluation of Tolerance
020406080
100120140160180200
100 90 80 70 60 50 40 30 20 10 00
102030405060708090
100
100 90 80 70 60 50 40 30 20 10 0E
xcess usersThe ratio of users who use our method (%) The ratio of users who use our method (%)
Figure.1 Figure.2
Ave. score
Our method E-RIS
Our method E-RIS
100 users are added at once until the number of users exceeds 1,000
October 3, 2007 ITSC 2007 H. Kuriyama et al. 45
Our method is better than the existing methodOur method is better than the existing method
Result 3 : Evaluation of Tolerance
020406080
100120140160180200
100 90 80 70 60 50 40 30 20 10 00
102030405060708090
100
100 90 80 70 60 50 40 30 20 10 0E
xcess usersThe ratio of users who use our method (%) The ratio of users who use our method (%)
Figure.1 Figure.2
Ave. score
Our method E-RIS
Our method E-RIS
100 users are added at once until the number of users exceeds 1,000
October 3, 2007 ITSC 2007 H. Kuriyama et al. 46
200 users are added at once until the number of users exceeds 2,000
• Advantageous of our method becomes small, due to chronic congestion• Most of users using our method can reach the final spot within their finishing time
• Advantageous of our method becomes small, due to chronic congestion• Most of users using our method can reach the final spot within their finishing time
Ave. score
Excess users
The ratio of users who use our method (%) The ratio of users who use our method (%)
Figure.1 Figure.2
Result 3 : Evaluation of Tolerance
0102030405060708090
100
100 90 80 70 60 50 40 30 20 10 0
Our method E-RIS
0306090
120150180210240270300
100 90 80 70 60 50 40 30 20 10 0
Our method E-RIS
October 3, 2007 ITSC 2007 H. Kuriyama et al. 47
We proposed a method for scheduling visits for several thousands of users-Our method’s advantage:
• Higher satisfaction degree• Much Less excess users• Incentive to use our method• Tolerance for the case that some users do not utilize the method
or new users are incrementally added
Future Work-We are planning to Implement more practical and accurate traffic cases and models
Conclusion
October 3, 2007 ITSC 2007 H. Kuriyama et al. 48
Kuriyama, H., Murata, Y., Shibata, N., Yasumoto, K. and Ito, M.: Congestion Alleviation Scheduling Technique for Car Drivers Based on Prediction of Future Congestion on Roads and Spots, Proc. of 10th IEEE Int'l. Conf. on Intelligent Transportation Systems (ITSC'07), pp. 910-915.DOI:10.1109/ITSC.2007.4357704 [ PDF ]
