60 research outputs found
Multi-Step Subway Passenger Flow Prediction under Large Events Using Website Data
An accurate and reliable forecasting method of the subway passenger flow provides the operators with more valuable reference to make decisions, especially in reducing energy consumption and controlling potential risks. However, due to the non-recurrence and inconsistency of large events (such as sports games, concerts or urban marathons), predicting passenger flow under large events has become a very challenging task. This paper proposes a method for extracting event-related information from websites and constructing a multi-step station-level passenger flow prediction model called DeepSPE (Deep Learning for Subway Passenger Flow Forecasting under Events). Experiments on the actual data set of the Beijing subway prove the superiority of the model and the effectiveness of website data in subway passenger flow forecasting under events
Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction
Traffic prediction plays an important role in the realization of traffic
control and scheduling tasks in intelligent transportation systems. With the
diversification of data sources, reasonably using rich traffic data to model
the complex spatial-temporal dependence and nonlinear characteristics in
traffic flow are the key challenge for intelligent transportation system. In
addition, clearly evaluating the importance of spatial-temporal features
extracted from different data becomes a challenge. A Double Layer - Spatial
Temporal Feature Extraction and Evaluation (DL-STFEE) model is proposed. The
lower layer of DL-STFEE is spatial-temporal feature extraction layer. The
spatial and temporal features in traffic data are extracted by multi-graph
graph convolution and attention mechanism, and different combinations of
spatial and temporal features are generated. The upper layer of DL-STFEE is the
spatial-temporal feature evaluation layer. Through the attention score matrix
generated by the high-dimensional self-attention mechanism, the
spatial-temporal features combinations are fused and evaluated, so as to get
the impact of different combinations on prediction effect. Three sets of
experiments are performed on actual traffic datasets to show that DL-STFEE can
effectively capture the spatial-temporal features and evaluate the importance
of different spatial-temporal feature combinations.Comment: 39 pages, 14 figures, 5 table
How machine learning informs ride-hailing services: A survey
In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed
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