Abstract
Origin-destination (OD) forecasting is a difficult task in urban rail transit because many random factors can influence outcomes such as numerous OD pairs with few or even no flows. Therefore, treating all OD pairs equally, which is the method adopted by all existing studies, may not only increase model complexity and computation, but also negatively influence forecasting results. Therefore, in this study, we propose an indicator called OD attraction degree (ODAD) to address this problem in the field of OD forecasting. First, we introduce the ODAD indicator and five ODAD levels to describe the attraction between OD pairs. Based on the ODAD, an OD matrix pre-processing method is presented to prepare data for the LSTM model. Second, we use the mature long short-term memory (LSTM) network model to examine the effects of the introduction of ODAD. The LSTM model's advantage of dealing with variable-length sequences by leveraging the masking layer is creatively utilized. Finally, nine cases under different time granularities and different ODAD levels are thoroughly studied to explore their optimal combination. Based on this analysis, we recommend a time granularity of 30 min and an ODAD level of “Low” for actual subway operation. In this case, the root mean squared error, mean absolute error, and weighted mean absolute percentage error are 2.31%, 0.66%, and 27.28%, respectively, for a network of nearly 300 subway stations. The introduction of ODAD can provide critical insights for subway operators to conduct short-term OD forecasting.
Highlights
With the rapid development of urban rail transit (URT), additional features such as improved service levels, punctuality, and real-time passenger flow monitoring have been implemented
We introduce an OD attraction degree (ODAD) indicator and ODAD levels to capture the importance of OD pairs in the entire URT network
weighted mean absolute percentage error (WMAPE) increases to 46.9% when a time granularities (TGs) of 15 min and ODAD level of ‘‘Lowest’’ is adopted, signifying that the model performance is slightly worse in this setting
Summary
With the rapid development of urban rail transit (URT), additional features such as improved service levels, punctuality, and real-time passenger flow monitoring have been implemented. Yang et al [21], [22] established a bi-level programming approach based on the least squares method and equilibrium traffic assignment using link counts data They introduced a concept of maximum possible relative error to evaluate the reliability of the estimated OD matrix [23]. Using the historical OD matrix and travel time distribution, Yao et al [27] built a state space model and used the Kalman filtering algorithm to solve the model He introduced a GLS model based on a moving-average strategy using the average OD flows in several previous time periods [28]. Treating all OD pairs which is the method adopted by all existing studies, may increase model complexity and computation, and negatively influence forecasting results To compensate for these shortcomings, we have introduced an OD attraction degree (ODAD) indicator.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
