A Forecasting Model of the Proportion of PPR in Urban Mass Transit System: A Case Study of Chongqing

Cheng, Yanwei; Ye, Xiafei; Yu, Qingxing; Zhou, Lifeng

doi:10.1061/9780784480915.401

Cited by 1 publication

(2 citation statements)

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“…(3) if j � i then //in this case e is a leaf node. (4) Peak(e) � Valley L (e) � Valley R (e) � p i (5) else (6) let the first and second child nodes of e be, respectively, e 1 and e 2 (7) if y(Peak (e 1 )) < y(Peak (e 2 )) then (8) Peak…”

Section: Computation Time and Spacementioning

confidence: 99%

“…Peak-hour congestion is also a problem in many suburban areas. Traffic peaks can be used to compute congestion indices, helping individuals and traffic management units to avoid traffic jam and minimize travel time [1][2][3][4]. By monitoring traffic peaks, transportation administrators can make better decisions to optimize the traffic networks and therefore enhance the performance of transportation systems [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Detection and Optimization of Traffic Networks Based on Voronoi Diagram

Tao

Liu²,

Song

et al. 2021

Discrete Dynamics in Nature and Society

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Traffic peak is an important parameter of modern transport systems. It can be used to calculate the indices of road congestion, which has become a common problem worldwide. With accurate information about traffic peaks, transportation administrators can make better decisions to optimize the traffic networks and therefore enhance the performance of transportation systems. We present a traffic peak detection method, which constructs the Voronoi diagram of the input traffic flow data and computes the prominence of candidate peak points using the diagram. Salient peaks are selected based on the prominence. The algorithm takes O(n log n) time and linear space, where n is the size of the input time series. As compared with the existing algorithms, our approach works directly on noisy data and detects salient peaks without a smoothing prestep and thus avoids the dilemma in choosing an appropriate smoothing scale and prevents the occurrence of removing/degrading real peaks during smoothing step. The prominence of candidate peaks offers the subsequent analysis the flexibility to choose peaks at any scale. Experiments illustrated that the proposed method outperforms the existing smoothing-based methods in sensitivity, positive predictivity, and accuracy.

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