Understanding human mobility in urban areas is crucial for transportation planning, operations, and online control. The availability of large-scale and diverse mobility data (e.g., smart card data, GPS data), provides valuable insights into human mobility patterns. However, organizing and analyzing such data pose significant challenges. Knowledge graph (KG), a graph-based knowledge representation method, has been successfully applied in various domains but has limited applications in urban mobility. This paper aims to address this gap by reviewing existing KG studies, introducing the concept of a mobility knowledge graph (MKG), and proposing a general learning framework to construct MKG from smart card data. The MKG represents hidden travel activities between public transport stations, with stations as nodes and their relations as edges. Two decomposition approaches, rule-based and neural network-based models, are developed to extract MKG relations from smart card data, capturing latent spatiotemporal travel dependencies. The case study is conducted using smart card data from a heavily used urban railway system to validate the effectiveness of MKG in predicting individual trip destinations. The results demonstrate the significance of establishing an MKG database, as it assists in a typical problem of predicting individual trip destinations for public transport systems with only tap-in records. Additionally, the MKG framework offers potential for efficient data management and applications such as individual mobility prediction and personalized travel recommendations.

Understanding human mobility in urban areas is important for transportation, from planning to operations and online control. This paper proposes the concept of user-station attention, which describes the user’s (or user group’s) interest in or dependency on specific stations. The concept contributes to a better understanding of human mobility (e.g., travel purposes) and facilitates downstream applications, such as individual mobility prediction and location recommendation. However, intrinsic unsupervised learning characteristics and untrustworthy observation data make it challenging to estimate the real user-station attention. We introduce the user-station attention inference problem using station visit counts data in public transport and develop a matrix decomposition method capturing simultaneously user similarity and station-station relationships using knowledge graphs. Specifically, it captures the user similarity information from the user-station visit counts matrix. It extracts the stations’ latent representation and hidden relations (activities) between stations to construct the mobility knowledge graph (MKG) from smart card data. We develop a neural network (NN)-based nonlinear decomposition approach to extract the MKG relations capturing the latent spatiotemporal travel dependencies. The case study uses both synthetic and real-world data to validate the proposed approach by comparing it with benchmark models. The results illustrate the significant value of the knowledge graph in contributing to the user-station attention inference. The model with MKG improves the estimation accuracy by 35% in MAE and 16% in RMSE. Also, the model is not sensitive to sparse data provided only positive observations are used.

To effectively manage and control public transport operations, understanding the various factors that impact bus arrival delays is crucial. However, limited research has focused on a comprehensive analysis of bus delay factors, often relying on single-step delay prediction models that are unable to account for the heterogeneous impacts of spatiotemporal factors along the bus route. To analyze the heterogeneous impact of bus arrival delay factors, the paper proposes a set of regression equations conditional on the bus location. A seemingly unrelated regression equation (SURE) model is developed to estimate the regression coefficients, accounting for potential correlations between regression residuals caused by shared unobserved factors among equations. The model is validated using bus operations data from Stockholm, Sweden. The results highlight the importance of developing stop-specific bus arrival delay models to understand the heterogeneous impact of explanatory variables. The significant factors impacting bus arrival delays are primarily associated with bus operations, such as delays at consecutive upstream stops, dwell time, scheduled travel time, recurrent congestion, and current traffic conditions. Factors like the calendar and weather have significant but marginal impacts on arrival delays. The study suggests that different bus operating management strategies, such as schedule adjustments, route optimization, and real-time monitoring and control, should be tailored to the characteristics of stop sections since the impacts of these factors vary depending on the stop location.

Bus delays pose significant challenges to urban public transportation systems, impacting operational efficiency and incurring substantial costs. Understanding the causative factors behind bus delays is crucial for developing effective strategies to mitigate them. However, existing research predominantly relies on correlational analysis, which falls short of revealing the underlying causal relationships among factors. This study introduces a novel data-driven causality-based modeling approach, combining data-driven causal discovery and structure equation models (SEM), to investigate the causal relationships among various operational factors influencing bus delays. It can automatically generate a causal delay graph that elucidates the interconnectedness and influence of operational factors on bus delays, providing a deeper understanding of the causal mechanism of bus delays. We explored and evaluated the performance of different causal discovery algorithms in generating causal graph from both aspects of the data fitting and causality discovery performance. The SEM model is used to quantify the direct causal effects among factors in the causal graph. The case study is conducted to validate the causal discovery model performance using Google Transit Feed Specification (GTFS) data from high-frequency bus routes in Stockholm, Sweden. The validation results highlight potential of the data-driven causal discovery models in discovering causality relationships and automating the knowledge discovery process, particularly combining with domain knowledge. The empirical findings show the complexity of factors contributing to bus delays and emphasize the importance of integrating causality into the bus delay factor analysis. For example, a high correlation between origin delay and current arrival delay (coefficient = 0.63) doesn't necessarily indicate causation, and a strong causal link from dwell time and arrival delay also does not reflect a high correlation (coefficient = 0.12). The proof of data-driven causal discovery would facilitate the automated and informed decision-making process to optimize bus services towards better efficiency and reliability.

Buses in public transit networks often face operational delays due to dynamic conditions such as traffic congestion, which can propagate through transit routes, affecting overall system performance. Understanding the causes of bus arrival delays is crucial for effective public transport management and control. Moreover, understanding the contribution of each factor to bus delays not only aids in developing targeted strategies to mitigate delays but is also crucial for effective decision-making and planning. Traditional research primarily focuses on correlation-based analysis, lacking the ability to reveal the underlying causal mechanisms. Additionally, no studies have considered the complex causal relationships between factors when quantifying their contributions to outcomes in public transport. This study aims to analyze the factors causing bus arrival delays from a causal perspective, focusing on quantifying the causal contribution of each factor while considering their causal relationships. Quantifying a factor's causal contribution poses challenges due to computational complexity and statistical bias from the limited sample size. Using a causal discovery method, this study generates a causal graph for bus arrival delays and employs the causality-based Shapley value to quantify the contribution of each variable. The study further uses the Double Machine Learning (DML) approach to estimate the causal contributions, which provides a consistent and computationally feasible method. A case study was conducted using Google Transit Feed Specification (GTFS) data, focusing on high-frequency bus routes in Stockholm, Sweden. To validate the model, cross-validation was performed by comparing variable importance rankings with traditional models, including Linear Regression (LR) and Structural Equation Modeling (SEM). The comparison shows that results from the causality-based Shapley value significantly differ from those obtained by traditional methods in terms of importance rankings and influence magnitudes. The findings underscore the significant impact of origin delays on bus punctuality, a factor often underestimated in previous studies. Additionally, it demonstrates that employing a causal discovery model can not only infer causal relationships but also reveal direct and indirect effects, which can provide more intuitive explanations. Finally, although the causal results are mathematically and intuitively sound, it is important to further investigate the real causality impact in practice using lab experiments or A/B tests in real-world settings.

Bus transport, characterized by a complex network of routes and stops, frequently experiences delays that can affect the entire system’s reliability, passenger satisfaction, and operational efficiency. Existing research on bus delay propagation predominantly focuses on the route level. They lack a broader network-level perspective, which is essential for fully understanding the complex interactions and delay propagation. Additionally, previous studies typically rely on correlation-based analysis, which may not adequately uncover the underlying causal mechanisms of bus delay propagation. To understand bus delay propagation in the Public Transport System (PTS), this study employs a causality-based model instead of traditional correlation-based analysis to identify causal relationships between bus stops. We introduce a time-series causal discovery model that integrates temporal and spatial features of stop delays to generate a delay propagation causal graph (DPCG). Then, complex network theory and metrics are used to perform topological analysis on the DPCG and identify key bus stops. The case study is conducted using real-time GTFS data from Stockholm, Sweden. The results indicate that stops with more connections significantly influence delay propagation, and the network displays a distinct community structure with mixed connectivity. Moreover, bus stops exhibit different delay propagation patterns during various time periods. During the morning peak, delays primarily propagate to stops in the inner city due to the commuting surge. In the evening peak, however, delays are more widely distributed across central and suburban areas, reflecting the diversity of after-work travel patterns. The study also reveals that delay propagation extends beyond a single route and affects multiple routes.