TOD optimal control method of urban traffic based on clustering analysis

YAO Jiao; XU Jie-qiong; HAN Yin

Volume 14 Issue 6

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YAO Jiao, XU Jie-qiong, HAN Yin. TOD optimal control method of urban traffic based on clustering analysis[J]. Journal of Traffic and Transportation Engineering, 2014, 14(6): 110-116.

Citation:

YAO Jiao, XU Jie-qiong, HAN Yin. TOD optimal control method of urban traffic based on clustering analysis[J]. Journal of Traffic and Transportation Engineering, 2014, 14(6): 110-116.

YAO Jiao, XU Jie-qiong, HAN Yin. TOD optimal control method of urban traffic based on clustering analysis[J]. Journal of Traffic and Transportation Engineering, 2014, 14(6): 110-116.

Citation:

YAO Jiao, XU Jie-qiong, HAN Yin. TOD optimal control method of urban traffic based on clustering analysis[J]. Journal of Traffic and Transportation Engineering, 2014, 14(6): 110-116.

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TOD optimal control method of urban traffic based on clustering analysis

School of Business, University of Shanghai for Science and Technology, Shanghai 200093, China

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Author Bio:
YAO-Jiao (1982-), male, lecturer, PhD, +86-21-65710430, yaojiao@126.com
Received Date: 2014-07-13
Publish Date: 2014-12-25

Abstract

Abstract

To overcome the high failure rate of circular loop vehicle detector in real road, the loss historical data were mended by using moving average convergence divergence method.The historical traffic flow data were clustered by using Ward least square method.A clustering terminal condition was proposed to determine the optimal control plan number and switch time of TOD multi-schedule control based on the modified cubic clustering criterion.TOD optimal control method was simulated and verified by using signal timing optimization software Synchro.Verification result indicates that TOD optimal control method can provide more detailed TOD control plan, which can also respond the fluctuation of real traffic demand.The average decrement rate of each vehicle delay based on the optimal control method is 11.9%, in which the decrement rate in pre-morning peak period is 20.27%, and the values in evening low peak period, evening peak period and morning peak period are 12.99%, 8.07% and 6.25%, respectively.
- intelligent transportation system,
- urban traffic,
- clustering analysis,
- cubic clustering criterion,
- TOD optimal control method,
- average vehicle delay

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References(24)

References

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