1 / 61

Hisaka Kuriyama , Yoshihiro Murata, Naoki Shibata * , Keiichi Yasumoto, Minoru Ito

Congestion Alleviation Scheduling Technique for Car Drivers Based on Prediction of Future Congestion on Roads and Spots. Hisaka Kuriyama , Yoshihiro Murata, Naoki Shibata * , Keiichi Yasumoto, Minoru Ito. Nara Institute of Science and Technology * Shiga University. Outline. Background

suzuki
Download Presentation

Hisaka Kuriyama , Yoshihiro Murata, Naoki Shibata * , Keiichi Yasumoto, Minoru Ito

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Congestion Alleviation Scheduling Techniquefor Car Drivers Based onPrediction of Future Congestion on Roads and Spots Hisaka Kuriyama, Yoshihiro Murata, Naoki Shibata*, Keiichi Yasumoto, Minoru Ito • Nara Institute of Science and Technology • *Shiga University

  2. Outline • Background • Proposed method • Experiment • Conclusion ITSC 2007 H. Kuriyama et al.

  3. Background • Background: -In sightseeing tours and parcel deliveries by cars • Each person visits multiple destinations • If many people concentrate onsame route or service spot • These routes and spots will have congestion Route Service spot These congestions impair social activities • We propose: • A method for finding schedules for massively many users by predicting congestions on bothroutes and spots • Make peopledisperse among different routes and spots ITSC 2007 H. Kuriyama et al.

  4. Existing Studies For distributingtourists over either routes or spots • 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 Server • A method by T.Kataoka et al. [3] • distributes users over spots • Each user selects the least congested spot [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) ITSC 2007 H. Kuriyama et al.

  5. Our Contribution • Each user selects the least congested routes or spots according to the situation Final spot • If a user has to reach the final spot before aspecified time -The user may violate the time constraints Our method allows users to visit many spots satisfying time constraints ITSC 2007 H. Kuriyama et al.

  6. Outline • Background • Proposed method • Experiment • Conclusion ITSC 2007 H. Kuriyama et al.

  7. Our Approach • Collecting users’ visiting spots and time constraints • Performing traffic simulation considering congestions on bothroutes and spots • Modifying tour of the user who violate the time constraints by removing some of the visiting spots • Sending the set of all users’ schedules ITSC 2007 H. Kuriyama et al.

  8. AM 8:00 Imp:20 Imp:30 Imp:10 Imp:5 PM 17:00 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 • Output: -Set of all users’ schedules • Objective: -Finding a set of users’schedules which maximizes the total sum of the importance degrees ITSC 2007 H. Kuriyama et al.

  9. Algorithm for Modifying Schedules • Outline of the Scheduling algorithm: 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 scheduleby 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 ITSC 2007 H. Kuriyama et al.

  10. Explanation of Our Algorithm • We explain our method in case of 3 users Users ITSC 2007 H. Kuriyama et al.

  11. Finding Schedule (1/4) • Find the schedule for each user which minimizes the total distance of movement to go through all the requested spots ITSC 2007 H. Kuriyama et al.

  12. Finding Schedule (1/4) • Suppose, the first user’s schedule is set like this First user Our system ITSC 2007 H. Kuriyama et al.

  13. Finding Schedule (1/4) • The second user’s schedule is set similarly Second user Our system ITSC 2007 H. Kuriyama et al.

  14. Finding Schedule (1/4) Third user Our system ITSC 2007 H. Kuriyama et al.

  15. Performing Simulation (2/4) • The system performs traffic simulation -During the simulation, each user… • uses RISto choose routes to their next spots • consumes some time to wait and/or receive services at spots Our system ITSC 2007 H. Kuriyama et al.

  16. Performing Simulation (2/4) • During simulation • If many users converge on the same road • They need to require more time to finish the movement • If many users converge on the same service spot • They need to require more time to receive the service ITSC 2007 H. Kuriyama et al.

  17. Performing Simulation (2/4) • During simulation In this result, a user cannot reach the final spot by the finishing time • The system modifies user’s visiting spots ITSC 2007 H. Kuriyama et al.

  18. Modifying Schedule (3/4) • The system chooses one spot to remove under the situations to minimize loss of importance degree Imp : 30 Imp :35 Imp : 10 Imp :25 Imp : 30 Imp : 35 Imp : 10 Imp : 25 ITSC 2007 H. Kuriyama et al.

  19. Modifying Schedule (3/4) • The system changes the schedule based on new set Our system ITSC 2007 H. Kuriyama et al.

  20. Performing Simulation (2/4) • The system performs simulation based on the recalculated schedules • Avoiding congestion • Meeting the finishing time Our system The user avoids congestion and returns before the finishing time ITSC 2007 H. Kuriyama et al.

  21. 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 bealleviated • If a user can reach the final spot within the finishing time • The system adds theonce removedspotsagain Imp : 30 Imp : 35 Imp : 10 Imp : 25 Imp : 30 Imp :35 Imp :10 Imp :25 ITSC 2007 H. Kuriyama et al.

  22. Iterating Steps 2. to 3. (4/4) • The system repeats these procedures Our system ITSC 2007 H. Kuriyama et al.

  23. Adding Removing Violating the finishing time We use a tabu list to improve convergence Avoiding Unnecessary Repeats • With our method, • the schedules might not converge ITSC 2007 H. Kuriyama et al.

  24. Avoiding Unnecessary Repeats • For each user, if the system repeats adding and removing the spot a predetermined number of times Removing Adding Removing • This spot is added to the tabu list for the user Adding Adding Removing The spotwill never be added Adding to tabu list We can stop the repetition of changing for a short time ITSC 2007 H. Kuriyama et al.

  25. Outline • Background • Proposed method • Experiment • Conclusion ITSC 2007 H. Kuriyama et al.

  26. Experiment • Purpose of Experiment -To evaluate performance of our method, we compare it with existing method • 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 ITSC 2007 H. Kuriyama et al.

  27. Simulation Configuration • Road Network used for Simulation Road Spot • Each User’s behavior -Visiting 4 spots -Finally returning to the starting spot • Each User’s input data -Random 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 ITSC 2007 H. Kuriyama et al.

  28. 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 Configuration of Existing Methods • Existing studies -Only treating congestion either in route or service spot • Extended version of existing studies named E-RIS • For the baseline to evaluate the usefulness of our method ITSC 2007 H. Kuriyama et al.

  29. Score Configuration • Importance degrees of spots • A user specifies different importance degrees for each spot • To keep fairness among users • We assume that each userhas the same points Imp : 30 Imp :35 Imp :10 Imp :25 Sum : 100 ITSC 2007 H. Kuriyama et al.

  30. Score Configuration • When each user receives the service at a spot • The user can obtain the score equal to the importance degree specified for that spot Score : 10 • If the service does not finish before his/her finishing time • The user does not obtain the score for the spot Score : 0 • If the user visited all inputted spots by the finishing time Total Score : 100 ITSC 2007 H. Kuriyama et al.

  31. Experiment 1 : Satisfaction Degree • Simulation Configuration • All users use the same algorithm (E-RIS or our method) All users are set at the same time They start to move at the same time ITSC 2007 H. Kuriyama et al.

  32. Result1 : Satisfaction Degree • Simulation Result Figure.2 Figure.1 100 250 E-RIS E-RIS Our method Excess users 200 80 Ave. score Our method 150 60 100 40 50 20 0 0 500 users 1,000 users 500 users 1,000 users ITSC 2007 H. Kuriyama et al.

  33. Result1 : Satisfaction Degree • Simulation Result Figure.2 Figure.1 100 250 E-RIS E-RIS Our method Excess users 200 80 Ave. score Our method 150 60 100 40 50 20 0 0 500 users 1,000 users 500 users 1,000 users The average score of all users ITSC 2007 H. Kuriyama et al.

  34. Result1 : Satisfaction Degree • Simulation Result Figure.2 Figure.1 100 250 E-RIS E-RIS Our method Excess users 200 80 Ave. score Our method 150 60 100 40 50 20 0 0 500 users 1,000 users 500 users 1,000 users The number of users who exceeded the finishing time ITSC 2007 H. Kuriyama et al.

  35. Result1 : Satisfaction Degree • Simulation Result Figure.2 Figure.1 100 250 E-RIS E-RIS Our method Excess users 200 80 Ave. score Our method 150 60 100 40 50 20 0 0 500 users 1,000 users 500 users 1,000 users • Our method • 20-30% higher average score • Much less excess users *Computation time of our method : 4 minutes ITSC 2007 H. Kuriyama et al.

  36. Experiment 2 : Evaluation of Incentive • Assumption -Users follow the schedules computed by our algorithm • If users outwit the algorithm and obtain better results • They would ignore the computed schedules We evaluate… The users who follow our method have enough incentive or not • Simulation Configuration -Some users ignore the computed schedules and force their original tour plans We define ignoring users asoutwitters ITSC 2007 H. Kuriyama et al.

  37. Result 2 : Evaluation of Incentive • Simulation Result The ratio of outwitters who have disadvantage(%) The ratio of outwitters (%) ITSC 2007 H. Kuriyama et al.

  38. Result 2 : Evaluation of Incentive • Simulation Result The ratio of outwitters who have disadvantage(%) Ratio of outwitters who could not improve score nor reach the final spot before the finishing time to all the outwitters The ratio of outwitters (%) ITSC 2007 H. Kuriyama et al.

  39. Result 2 : Evaluation of Incentive • Simulation Result The ratio of outwitters who have disadvantage(%) The ratio of outwitters (%) • Most outwitters (over 70%) have disadvantage • Our method should give users the motivation to follow ITSC 2007 H. Kuriyama et al.

  40. Experiment 3 : Evaluation of Tolerance • Simulation Configuration (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 • New users are added to random positions every 600 seconds • When new users are added, all users using our method re-calculate schedules ITSC 2007 H. Kuriyama et al.

  41. Result 3 : Evaluation of Tolerance • 100users are added at once until the number of users exceeds1,000 Figure.2 Figure.1 Ave. score Excess users 200 100 180 90 Our method E-RIS 160 80 140 70 120 60 100 50 80 40 60 30 40 20 Our method E-RIS 20 10 0 0 100 90 80 70 60 50 40 30 20 10 0 100 90 80 70 60 50 40 30 20 10 0 The ratio of users who use our method (%) The ratio of users who use our method (%) ITSC 2007 H. Kuriyama et al.

  42. Result 3 : Evaluation of Tolerance • 100users are added at once until the number of users exceeds1,000 Figure.2 Figure.1 Ave. score Excess users 200 100 180 90 Our method E-RIS 160 80 140 70 120 60 100 50 80 40 60 30 40 20 Our method E-RIS 20 10 0 0 100 90 80 70 60 50 40 30 20 10 0 100 90 80 70 60 50 40 30 20 10 0 The ratio of users who use our method (%) The ratio of users who use our method (%) The average score of all users ITSC 2007 H. Kuriyama et al.

  43. Result 3 : Evaluation of Tolerance • 100users are added at once until the number of users exceeds1,000 Figure.2 Figure.1 Ave. score Excess users 200 100 180 90 Our method E-RIS 160 80 140 70 120 60 100 50 80 40 60 30 40 20 Our method E-RIS 20 10 0 0 100 90 80 70 60 50 40 30 20 10 0 100 90 80 70 60 50 40 30 20 10 0 The ratio of users who use our method (%) The ratio of users who use our method (%) The number of users who exceeded the finishing time ITSC 2007 H. Kuriyama et al.

  44. Result 3 : Evaluation of Tolerance • 100users are added at once until the number of users exceeds1,000 Figure.2 Figure.1 Ave. score Excess users 200 100 180 90 Our method E-RIS 160 80 140 70 120 60 100 50 80 40 60 30 40 20 Our method E-RIS 20 10 0 0 100 90 80 70 60 50 40 30 20 10 0 100 90 80 70 60 50 40 30 20 10 0 The ratio of users who use our method (%) The ratio of users who use our method (%) • The ratio of users who use our method • We changed ratio of users who use our method from 100% to 0% ITSC 2007 H. Kuriyama et al.

  45. Result 3 : Evaluation of Tolerance • 100users are added at once until the number of users exceeds1,000 Figure.2 Figure.1 Ave. score Excess users 200 100 180 90 Our method E-RIS 160 80 140 70 120 60 100 50 80 40 60 30 40 20 Our method E-RIS 20 10 0 0 100 90 80 70 60 50 40 30 20 10 0 100 90 80 70 60 50 40 30 20 10 0 The ratio of users who use our method (%) The ratio of users who use our method (%) Our method is better than the existing method ITSC 2007 H. Kuriyama et al.

  46. Result 3 : Evaluation of Tolerance • 200users are added at once until the number of users exceeds2,000 Figure.2 Figure.1 Ave. score 100 300 90 270 Our method E-RIS Excess users 80 240 70 210 60 180 50 150 40 120 30 90 20 60 Our method E-RIS 10 30 0 0 100 90 80 70 60 50 40 30 20 10 0 100 90 80 70 60 50 40 30 20 10 0 The ratio of users who use our method (%) The ratio of users who use our method (%) • 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 ITSC 2007 H. Kuriyama et al.

  47. Conclusion • 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 ITSC 2007 H. Kuriyama et al.

  48. Thank you for your attention Please speak slowly during Question ITSC 2007 H. Kuriyama et al.

  49. ITSC 2007 H. Kuriyama et al.

  50. RIS Algorithm Illustration 1 1 1/6 5/6 3/4 1/4 2/6 1 4/6 3/6 2/4 4/5 3/5 1 1 1 1 1/5+1/6 11/30 89/30 5/6 1/4 2/5+2/6+3/4 5/6 1/4 1 1 4/5+4/6 3/5+3/6+2/4 5/3 16/5 ITSC 2007 H. Kuriyama et al.

More Related