The document presents a congestion alleviation scheduling technique for car drivers aimed at improving traffic flow by predicting future congestion on routes and service spots. It outlines a method that allows users to select less congested routes while adhering to time constraints, utilizing traffic simulation and an iterative scheduling algorithm. Experimental results indicate that the proposed method significantly enhances user satisfaction and reduces the likelihood of exceeding time limits compared to existing methods.

1. Congestion Alleviation Scheduling TechniqueCongestion Alleviation Scheduling Technique
for Car Drivers Based onfor Car Drivers Based on
Prediction of Future Congestion on Roads andPrediction 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

2. October 3, 2007 ITSC 2007 H. Kuriyama et al. 2
1. Background
2. Proposed method
3. Experiment
4. Conclusion
Outline

3. 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

4. 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

5. 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

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1. Background
2. Proposed
method
3. Experiment
4. Conclusion
Outline

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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

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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
• finishing 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:30
Imp:10
Imp:20
Imp:5
PM 17:00

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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:

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Users
Explanation of Our Algorithm
 We explain our method in case of 3 users

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 Find the schedule for each user
which minimizes the total distance of movement
to go through all the requested spots
Finding Schedule (1/4)

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Our system
First user
Finding Schedule (1/4)
 Suppose, the first user’s schedule is set like this

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Our system
Second user
Finding Schedule (1/4)
 The second user’s schedule is set similarly

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Our system
Third user
Finding Schedule (1/4)

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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

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 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

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 The system modifies user’s visiting 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

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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

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Our system
Modifying Schedule (3/4)
 The system changes the schedule based on new set

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• 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

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 If a user can reach the final spot within the finishing time
The 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

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Iterating Steps 2. to 3. (4/4)
Our system
 The system repeats these procedures

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 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

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 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

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Outline
• Background
• Proposed method
• Experiment
• Conclusion

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 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

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 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

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 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

29. 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

30. 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

31. 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

32. October 3, 2007 ITSC 2007 H. Kuriyama et al. 32
Ave.score
Excessusers
0
20
40
60
80
100
500 users 1,000 users
E-RIS
Our method
0
50
100
150
200
250
500 users 1,000 users
E-RIS
Our method
Result 1 : Satisfaction Degree
 Simulation Result
Figure.1 Figure.2

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Ave.score
Excessusers
The average score of all usersThe average score of all users
0
20
40
60
80
100
E-RIS
Our method
0
50
100
150
200
250
E-RIS
Our method
Figure.1 Figure.2
500 users 1,000 users 500 users 1,000 users
Result 1 : Satisfaction Degree
 Simulation Result

34. October 3, 2007 ITSC 2007 H. Kuriyama et al. 34
Ave.score
Excessusers
The number of users who exceeded the finishing timeThe number of users who exceeded the finishing time
0
20
40
60
80
100
E-RIS
Our method
0
50
100
150
200
250
E-RIS
Our method
Figure.1 Figure.2
500 users 1,000 users 500 users 1,000 users
Result 1 : Satisfaction Degree
 Simulation Result

35. October 3, 2007 ITSC 2007 H. Kuriyama et al. 35
 Simulation Result
*Computation time of our method : 4 minutes
Ave.score
Excessusers
 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-RIS
Our method
0
50
100
150
200
250
E-RIS
Our method
Figure.1 Figure.2
500 users 1,000 users 500 users 1,000 users
Result 1 : Satisfaction Degree

36. 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 notThe 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

37. October 3, 2007 ITSC 2007 H. Kuriyama et al. 37
Result 2 : Evaluation of Incentive
 Simulation Result
The ratio of outwitters (%)
Theratioofoutwitters
whohavedisadvantag

38. October 3, 2007 ITSC 2007 H. Kuriyama et al. 38
Theratioofoutwitters
whohavedisadvantag
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

39. October 3, 2007 ITSC 2007 H. Kuriyama et al. 39
Result 2 : Evaluation of Incentive
 Simulation Result
The ratio of outwitters (%)
Theratioofoutwitters
whohavedisadvantag
 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

40. 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-RIS
New users are incrementally
added on the road network
Experiment 3 : Evaluation of Tolerance
 Simulation Configuration

41. October 3, 2007 ITSC 2007 H. Kuriyama et al. 41
0
20
40
60
80
100
120
140
160
180
200
100 90 80 70 60 50 40 30 20 10 0
Our method E-RIS
0
10
20
30
40
50
60
70
80
90
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
Excessusers
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

42. October 3, 2007 ITSC 2007 H. Kuriyama et al. 42
Result 3 : Evaluation of Tolerance
0
20
40
60
80
100
120
140
160
180
200
100 90 80 70 60 50 40 30 20 10 0
0
10
20
30
40
50
60
70
80
90
100
100 90 80 70 60 50 40 30 20 10 0 Excessusers
The 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

43. October 3, 2007 ITSC 2007 H. Kuriyama et al. 43
Result 3 : Evaluation of Tolerance
0
20
40
60
80
100
120
140
160
180
200
100 90 80 70 60 50 40 30 20 10 0
0
10
20
30
40
50
60
70
80
90
100
100 90 80 70 60 50 40 30 20 10 0 Excessusers
The 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

44. 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
0
20
40
60
80
100
120
140
160
180
200
100 90 80 70 60 50 40 30 20 10 0
0
10
20
30
40
50
60
70
80
90
100
100 90 80 70 60 50 40 30 20 10 0 Excessusers
The 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

45. 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
0
20
40
60
80
100
120
140
160
180
200
100 90 80 70 60 50 40 30 20 10 0
0
10
20
30
40
50
60
70
80
90
100
100 90 80 70 60 50 40 30 20 10 0 Excessusers
The 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

46. 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
Excessusers
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
0
10
20
30
40
50
60
70
80
90
100
100 90 80 70 60 50 40 30 20 10 0
Our method E-RIS
0
30
60
90
120
150
180
210
240
270
300
100 90 80 70 60 50 40 30 20 10 0
Our method E-RIS

47. 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

48. 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 ]