Predictions of short-term traffic volume support proactive transportation management and traveler information services. To address the periodicity, nonlinearity, uncertainty, and complexity of short-term traffic forecasting, seasonal autoregressive integrated moving average (SARIMA) and support vector machine (SVM) models are often used separately to forecast the time series of traffic flow. SARIMA can discover intrinsic relations (correlations) in time series data, especially fit for modeling seasonal, stochastic time series. In contrast, SVM has a strong nonlinear mapping ability for input and output, appropriate for solving complex, nonlinear problems. The few combined schemes that have been presented previously tend to be complicated. A simple and effective novel hybrid methodology that combines the SARIMA and SVM models is proposed to take advantage of the individual strengths of the two models. Two key issues in building a hybrid model process are discussed: identification of SVM input dimensions via SARIMA and parameter optimization of the hybrid model with particle swarm optimization. Experimental results with real-world data sets indicated that the hybrid model was superior to the individual SARIMA or SVM model in terms of root mean square error and mean absolute percentage error for the prediction of the short-term traffic flow.

Reliable and accurate estimates of metro demand can provide metro authorities with insightful information for the planning of route alignment and station locations. Many existing studies focus on metro demand from daily or annual ridership profiles, but only a few concern the variation in hourly ridership. In this paper, a geographically and temporally weighted regression (GTWR) model was used to examine the spatial and temporal variation in the relationship between hourly ridership and factors related to the built environment and topological structure. Taking Nanjing, China as a case study, an empirical study was conducted with automatic fare collection (AFC) data in three weeks. With an analysis of variance (ANOVA), it was found that the GTWR model produced the best fit for hourly ridership data compared with traditional regression models. Four built-environment factors, namely residence, commerce, scenery, and parking, and two topological-structure factors, namely degree centrality and closeness centrality, were proven to be significantly related to station-level ridership. The spatial distribution pattern and temporal nonstationarity of these six variables were further analyzed. The result of this study confirmed that the GTWR model can provide more realistic and useful information by capturing spatiotemporal heterogeneity.

Forecasting for short-term ridership is the foundation of metro operation and management. A prediction model is necessary to seize the weekly periodicity and nonlinearity characteristics of short-term ridership in real-time. First, this research captures the inherent periodicity of ridership via seasonal autoregressive integrated moving average model (SARIMA) and proposes a support vector machine overall online model (SVMOOL) which insets the weekly periodic characteristics and trains the updated data day by day. Then, this research captures the nonlinear characteristics of the ridership via successive ridership value inputs and proposes a support vector machine partial online model (SVMPOL) which insets the nonlinear characteristics and trains the updated data of the predicted day by time interval (such as 5-min). Afterwards, to avoid the drawbacks and to take advantages of the strengths of the two individual online models, this research takes the average predicted values of two models as the final predicted values, which are called support vector machine combined online model (SVMCOL). Finally, this research uses the 5-min ridership at Zhujianglu and Sanshanjie Stations of Nanjing Metro to compare the SVMCOL model with three well-known prediction models including SARIMA, back-propagation neural network (BPNN), and SVM models. The resultant performance comparisons suggest that SARIMA is superior for the stable weekday ridership to other models. Yet the SVMCOL model is the best performer for the unstable weekend ridership and holiday ridership. It shows that for metro operation manager that gear toward timely response to real-world unstable and abnormal situations, the SVMCOL may be a better tool than the three well-known models.

With reliable and accurate predictions of short-term passenger flow, metro agencies can assign proper trains and crews, optimize schedules, and operate efficiently. This paper presents a hybrid model, which is intended for forecasting short-term passenger flow. The forecasting model aggregates SVR-based sub-models developed with subsets extracted by combining bootstrap sampling with dimensions subsampling. The sub-models consider not only the data from the reconstructed chaos attractor but also the periodic historical data. Passenger flow data collected from four different metro stations in Nanjing are used for model implementation and performance evaluation. Results confirm that the proposed EICSVR model was able to significantly improve predictive performance and generalization. The excellent accuracy and stability obtained in the empirical study indicate that the proposed model has good development potential for forecasting short-term passenger flow.

Due to different functions and land use types, there are significant differences in ridership patterns among different urban rail transit stations. Considering the characteristics of different ridership and coping with the uncertainty, periodical and stochastic natures of short-term passenger flow, and this paper proposes a novel hybrid methodology that combines the autoregressive integrated moving average (ARIMA) model and wavelet decomposition, which has strong strengths in signal processing, to short-term ridership forecasting. The seasonal ARIMA is used to represent the relatively stable and regular ridership patterns while the wavelet decomposition is used to capture the stochastic or sometimes drastic changing characteristics of ridership patterns. The inclusion of wavelet decomposition and reconstruction provides the hybrid model with a unique strength in capturing sudden change in ridership patterns associated with certain rail stations. The case study is carried out by analyzing real ridership data of Metro Line 1 in Nanjing, China. The experimental results indicate that the hybrid method is superior to the individual ARIMA model for all ridership patterns, but particularly advantageous in predicting ridership at stations often associated with sudden pattern changes due to special events.

This paper investigates pedestrians’ choices between escalator and stairway inside urban rail transit stations during peak hours. After the review of mode choice models, a logit model based on the utility maximization theory is selected. Binary logit models for the passenger flow distribution are established with walking time, walking distance, gender and age of the passenger as the main influencing factors. The influences of these factors are quantitatively analyzed through data collected from three rail transit stations with different arrangement forms of vertical walking facilities in Nanjing, China. The developed models are verified to produce a high accuracy in predicting passengers’ choices of vertical walking facilities. With developed models, sensitivity analyses for selecting escalator for upward and downward pedestrians in the three types of stations are also conducted and compared to further reveal the differences in pedestrians’ choices of using vertical walking facilities. The modeling procedure and results can provide a theoretical reference for operation decisions, such as the planning of vertical walking facilities and passenger flow organization in busy rail transit stations.