Forecasting Model of PeakPeriod StationtoStation OriginDestination Matrix in Urban Rail Transit Systems

Abstract:

Urban mass transit is playing a significant role in supporting and promoting urban development. An important indicator for the planning and design of urban rail transit may be succinctly summarized by passenger flow models within a peak hour; one important feature of the model is the maximum singledirection flow. To determine this feature, it is necessary to forecast passengers’ departure time and route choice during a peak period. As the basis of this process, the peakperiod stationtostation origindestination (OD) matrix reflects passengers’ travel needs. This paper tests the traditional gravity models to find the pattern that forecasts the peakperiod stationtostation OD matrix in urban rail transit. A realworld case study of Chongqing, China, is used as a model performance measure. To alleviate its overestimation when the effect of the deterrence function between two stations is too small, the gravitymodelbased peak period coefficient (PPC) model is introduced. By comparing the PPC and gravity models using the same dataset, the results indicate that the PPC model is superior to the gravity model. The standard deviation of the PPC model is 12.90 passengers, which is 56.02% lower than that of the gravity model, which is 29.33 passengers.