| 1. |
- Liu, Bing, et al.
(författare)
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Passenger flow anomaly detection in urban rail transit networks with graph convolution network-informer and Gaussian Bayes models
- 2023
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Ingår i: Philosophical Transactions. Series A. - : The Royal Society. - 1364-503X .- 1471-2962. ; 381:2254
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Tidskriftsartikel (refereegranskat)abstract
- Passenger flow anomaly detection in urban rail transit networks (URTNs) is critical in managing surging demand and informing effective operations planning and controls in the network. Existing studies have primarily focused on identifying the source of anomalies at a single station by analysing the time-series characteristics of passenger flow. However, they ignored the high-dimensional and complex spatial features of passenger flow and the dynamic behaviours of passengers in URTNs during anomaly detection. This article proposes a novel anomaly detection methodology based on a deep learning framework consisting of a graph convolution network (GCN)-informer model and a Gaussian naive Bayes model. The GCN-informer model is used to capture the spatial and temporal features of inbound and outbound passenger flows, and it is trained on normal datasets. The Gaussian naive Bayes model is used to construct a binary classifier for anomaly detection, and its parameters are estimated by feeding the normal and abnormal test data into the trained GCN-informer model. Experiments are conducted on a real-world URTN passenger flow dataset from Beijing. The results show that the proposed framework has superior performance compared to existing anomaly detection algorithms in detecting network-level passenger flow anomalies. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
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| 2. |
- Liu, Zhengke, et al.
(författare)
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Integrated optimization of timetable, bus formation, and vehicle scheduling in autonomous modular public transport systems
- 2023
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Ingår i: Transportation Research Part C. - : Elsevier BV. - 0968-090X .- 1879-2359. ; 155
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a joint optimization of the timetable, bus formation, and vehicle scheduling in a flexible public transport (PT) system that utilizes autonomous modular vehicles (AMVs). In this system, AMVs have the capability to detach or join with each other at intermediate stops along the route to dynamically adjust the bus formation (capacity). To increase vehicle utilization, a flexible scheduling strategy is proposed that allows AMVs to detach from one modular bus and join another modular bus in either direction of a bidirectional line. In particular, the penalty cost for each detachment or joining operation, as well as the limited number of available AMVs is explicitly considered. We formulate a unified model for the integrated optimization of the modular bus service (timetable and bus formation) and vehicle scheduling by introducing two types of decision variables. The objective is to minimize overall system costs, including passenger waiting time cost, operational costs, and detachment/joining penalty costs. The two types of decision variables are coupled by a vehicle resource consistency constraint, ensuring the conformity of the modular bus service and vehicle scheduling decisions. To tackle the complexity of our model, the Alternating Direction Method of Multipliers (ADMM) is employed to decompose it into two subproblems, which can be efficiently solved using a customized forward dynamic programming algorithm and a commercial solver. The model is validated using illustrative examples and a real-world instance from the Beijing Public Transport system, and it is compared with two benchmark models. Our results demonstrate the efficiency of the ADMM-based solution framework for solving the integrated optimization model. Furthermore, our findings indicate that the use of AMVs in PT systems can lead to reduced overall system costs and increased vehicle utilization.
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| 3. |
- Tan, Erlong, et al.
(författare)
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MVOPFAD : Multiview Online Passenger Flow Anomaly Detection
- 2024
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Ingår i: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 24:9, s. 14668-14681
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Tidskriftsartikel (refereegranskat)abstract
- Prompt and accurate identification of anomalies in passenger flow within metro systems is crucial for safety, security, and operational efficiency. However, traditional anomaly detection methods often struggle to achieve high accuracy and low latency when constrained by limited labeled data for online applications. This study presents a straightforward yet effective online anomaly detection framework, termed multiview online passenger flow anomaly detection (MVOPFAD), to address these difficulties in a data-driven manner. Specifically, to reduce the computational burden and meet online requirements, we particularly propose an elastic extreme studentized deviate (EESD) model accounting for the characteristic of abnormal passenger flow. Concurrently, an improved shifted wavelet tree (ISWT) is employed to effectively capture various passenger flow features. It is joined by the implementation of ensemble learning techniques and EESD to further enhance the accuracy and robustness of our detection model. To evaluate the performance of our proposed framework, we conducted a comprehensive series of experiments utilizing data collected from the automated fare collection (AFC) system integrated into the Beijing Metro network. Our proposed MVOPFAD demonstrates significant superiority over the other three types of methods across all evaluation metrics. In particular, it yields a 15.49% increase in precision and a 9.71% rise in the F2-score compared to the second-best model for detecting outbound passenger flow anomalies. Additionally, our model incurs lower computational cost than deep learning models and machine learning models. The experimental results strongly suggest the feasibility of online implementation, thereby demonstrating the practicality and effectiveness of our proposed model.
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| 4. |
- Yan, Haoyang, et al.
(författare)
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Improving multi-modal transportation recommendation systems through contrastive De-biased heterogenous graph neural networks
- 2024
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Ingår i: Transportation Research Part C. - : Elsevier Ltd. - 0968-090X .- 1879-2359. ; 164
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Tidskriftsartikel (refereegranskat)abstract
- Conventional uni-modal transportation recommendation systems focused on single modes of transportation are limited in providing satisfactory solutions since passengers often undertake journeys involving multiple modes. Multi-modal transportation recommendation systems are becoming increasingly popular within navigation applications. However, these systems face challenges from biased raw data, data sparsity and long-tail distribution, as well as complexities in representing large-scale graph structures, which collectively hinder their optimal performance. This study introduces a novel approach for enhancing multi-modal transportation recommendation systems: the Contrastive De-biased Heterogeneous Graph Neural Network (CDHGNN). By integrating contrastive learning, the model generates augmented samples to mitigate bias and overcome the data-skewing problem. The heterogeneous graph neural network adaptively captures temporal and spatial patterns among users and locations, as well as spatial adjacency and attribute relations, leading to enhanced representations of nodes, and consequently, improved model performance. The proposed method was evaluated using real-world data from over 300,000 users’ records in Beijing over two months in 2018. The extensive experiments demonstrate that the approach outperforms four contemporary state-of-the-art methods. The results underscore the potential of the CDHGNN in large-scale city-level problems in practical applications, revealing a promising advancement for multi-modal transportation recommendation systems.
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| 5. |
- Zhang, Qi, et al.
(författare)
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User-station attention inference using smart card data : a knowledge graph assisted matrix decomposition model
- 2023
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Ingår i: Applied intelligence (Boston). - : Springer Nature. - 0924-669X .- 1573-7497. ; 53:19, s. 21944-21960
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Chen, Haoye, et al.
(författare)
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Mixed Integer Formulation with Linear Constraints forIntegrated Service Operations and Traveler Choices inMultimodal Mobility Systems
- 2023
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Konferensbidrag (refereegranskat)abstract
- Multimodal mobility systems provide seamless travel by integrating different types of transportation modes. Most existing studies model service operations and travelers’ choices independently or limited in multimodal travel options. We propose a choice-based optimization model for optimal operations of multimodal mobility systems with embedded travelers’ choices using a multinomial logit (MNL) model. We derive a mixed-integer linear formulation for the problem by linearizing transformed MNL constraints with bounded errors. The preliminary experimental test for a small mobility on demand and public transport network shows the model provides a good solution quality.
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| 7. |
- Chen, X., et al.
(författare)
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Unplanned Disruption Analysis and Impact Modeling in Urban Railway Systems
- 2022
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Ingår i: Transportation Research Record. - : SAGE Publications. - 0361-1981 .- 2169-4052. ; 2676:10, s. 16-27
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Tidskriftsartikel (refereegranskat)abstract
- Unplanned disruptions bring challenges to urban railway system operations because of their impacts on safety, operation efficiency, and service quality. Identifying the contributing factors of operation delays and affected areas under unplanned disruptions is critical for agencies to make effective and informed management decisions. Despite its importance, few studies have been reported on unplanned disruption analysis in urban railway systems or they have been limited in their analysis and modeling because of the lack of disruption data. This paper collects a complete set of unplanned disruption data for 7 years in Hong Kong and explores important factors affecting operation delays and affected areas. Quantile regression (QR) models are developed to explore the causes of operation delays under unplanned disruptions. The significant factors include the time of day, weather condition, signal control system (moving/fixed block), line types (urban/suburban), line operation direction, disruption location (underground/ground/elevated), the number of affected stations, and disruption types (e.g., tracing, locomotive and rolling stock, passengers, and operation). A binary logit model is developed to explore the variables contributing to the affected areas (single or multiple stations). The results show that the affected area is significantly influenced by the signal control system, line types, line operation direction, disruption location, terminal/departure station involved or not, transfer station involved or not, and disruption types. The findings provide useful insights into unplanned disruptions and support the development of engineering and policy countermeasures to prevent and mitigate unplanned disruption effects on operations and services.
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| 8. |
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| 9. |
- Crespo Materna, Arturo, et al.
(författare)
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Use of Hybrid Methods for the Enhancement of Real-Time Railway Traffic Control (Dispatching)
- 2023
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Konferensbidrag (refereegranskat)abstract
- The execution of scheduled railway operations is characterized by continuous monitoring and systematic adjustment of the existing schedule to the occurrence of stochastic events. The adjustment of the schedule can be referred to as the “Conflict Detection & Conflict Resolution” (CDCR) process. Caused by propagating conflicts between plan adjustments and the initially planned schedule, CDCR is a highly complex process. Due to complexity, a series of decision-support tools mostly relying on heuristic methods have been developed to assist dispatchers in real-time. This article aims to identify strategic enhancement potentials for improving existing schedule adjustment approaches by integrating different methods (e.g., machine learning methods). A decomposition method is utilized to identify the processes during schedule adjustment that could benefit from applying hybrid methodologies, resulting in a much more efficient and effective search space exploration. At the outset the processes of generating a set of conflict resolution alternatives and selecting the best-fitting alternative to the actual operating situation have been early identified as potential processes that would benefit from incorporating hybrid methods (e.g., machine learning and heuristic methods). This study utilizes an actual decision-support tool applied within a real scenario to derive concrete evidence regarding the extent to which hybrid methods can be integrated and used to solve complex problems within the real-time adjustment of railway schedules by means of their actual implementation in an existing process. The knowledge and experience gained from the experimental research, acting as a proof of concept, are then translated into general guidelines for further use in improving existing approaches used in decision-support tools for the CDCR.
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| 10. |
- Cui, Shaohua, 1995, et al.
(författare)
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Joint optimal vehicle and recharging scheduling for mixed bus fleets under limited chargers
- 2023
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Ingår i: Transportation Research Part E. - : Elsevier BV. - 1366-5545 .- 1878-5794. ; 180
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Tidskriftsartikel (refereegranskat)abstract
- Owing to the high acquisition costs, maintenance expenses, and inadequate charging infrastructure associated with electric buses, achieving a complete replacement of diesel buses with electric counterparts in the short term proves challenging. A substantial number of bus operators currently find themselves in a situation where they must integrate electric buses with their existing diesel fleets. Confronted with the constraints of limited electric bus range and charging infrastructure, the primary concern for bus operators is how to effectively utilize their mixed bus fleets to adhere to pre-established bus timetables while maximizing the deployment of electric buses, known for their zero pollution and cost-effective travel. Consequently, this paper introduces the concept of the joint optimization problem for vehicle and recharging scheduling within mixed bus fleets operating under constrained charging conditions. To tackle this issue, a mixed integer linear model is formulated to optimize the coordination of bus schedules and recharging activities within the context of limited charging infrastructure. By establishing a set of feasible charging activities, the problem of electric buses queuing for charging at constrained charging stations is transformed into a linear optimization model constraint. Numerical simulations are conducted within the real transit network of the Dalian Economic Development Zone in China. The results indicate that the judicious joint optimization of vehicle and charging scheduling significantly enhances the service frequency of electric buses while reducing operational costs for bus lines. Notably, the proportion of total trips performed by electric buses rises to 80.4%.
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