Nonlinear Model-Based Subway Station-Level Peak-Hour Ridership Estimation Approach in the Context of Peak Deviation

Existing techniques for estimation of subway station-level long-term peak-hour ridership (PHR) may produce underestimated PHR values that may result in stations being designed with insufficient capacity during the planning stage; this in turn may increase congestion on the platforms in actual operation. One of the reasons for this potential undesirable outcome is that peak deviation phenomena often arise between stations and lines in subway systems, which could create underestimated PHR values. The default assumption has always been that the peak hour of passenger flow of each station always overlaps with its attributed line. This paper presents a framework of a station-level long-term PHR estimation method calibrated using the peak deviation coefficient (PDC) and a nonlinear model (eXtreme Gradient Boosting). This approach can estimate the PDC values for PHR prediction, and can also quantify the relative importance of PDC associated factors, yielding an explanation of the main causes of peak deviation phenomena. Using a real-world, large-scale passenger flow dataset from Xi’an, China, the approach produces more stable and accurate predictive performance compared with conventional methods (i.e., absolute percentage error controlled within 20% versus 50%, and mean average percentage error reduced by 3.26%–8.35%). Meanwhile, it is found that the relative importance of the unimproved land use ratio ranks in the top four for all types of peak periods; this ratio is a key factor that may be used to mitigate ridership deviations between stations and line peaks. In addition, for subway networks, the influence of land use entropy increases from the morning peak hour to the evening peak hour and weakens across the route from origin to destination.