Traveler satisfaction in rapid rail systems: the case of Istanbul Metro

Multi-faceted characteristics of urban travel have an impact on the passengers' overall satisfaction with the transport system. In this study, we investigate the interrelationships among traveler satisfaction, travel and traveler characteristics, and service performance in a multimodal network that comprises of a trunk line and its feeder lines. We analyze the factors influencing the choices of access to rail transit stations and the satisfaction of transit travelers with the rapid rail transit systems. We quantitatively study these relationships and demonstrate the complexity of evaluating transit service performance. Since the interrelationships among variables affecting this system are mainly stochastic, we analyze the satisfaction with transit system problem using a Bayesian Belief Network (BBN), which helps capture the causality among variables with inherent uncertainty. Using the case of Istanbul, we employ the BBN as a decision support tool for policy makers to analyze the rapid rail transit services and determine policies for improving the quality and the level of service to increase the satisfaction with transit system. In the case study, satisfaction with accessibility and access mode variables are found to be more effective variables than total travel time for travel time satisfaction, confirming the significant role of access in multimodal travels

A risk assessment approach to improve the resilience of a seaport system using Bayesian networks

Over the years, many efforts have been focused on developing methods to design seaport systems, yet disruption still occur because of various human, technical and random natural events. Much of the available data to design these systems are highly uncertain and difficult to obtain due to the number of events with vague and imprecise parameters that need to be modelled. A systematic approach that handles both quantitative and qualitative data, as well as means of updating existing information when new knowledge becomes available is required. Resilience, which is the ability of complex systems to recover quickly after severe disruptions, has been recognised as an important characteristic of maritime operations. This paper presents a modelling approach that employs Bayesian belief networks to model various influencing variables in a seaport system. The use of Bayesian belief networks allows the influencing variables to be represented in a hierarchical structure for collaborative design and modelling of the system. Fuzzy Analytical Hierarchy Process (FAHP) is utilised to evaluate the relative influence of each influencing variable. It is envisaged that the proposed methodology could provide safety analysts with a flexible tool to implement strategies that would contribute to the resilience of maritime systems

Identification of Causal Paths and Prediction of Runway Incursion Risk using Bayesian Belief Networks

In the U.S. and worldwide, runway incursions are widely acknowledged as a critical concern for aviation safety. However, despite widespread attempts to reduce the frequency of runway incursions, the rate at which these events occur in the U.S. has steadily risen over the past several years. Attempts to analyze runway incursion causation have been made, but these methods are often limited to investigations of discrete events and do not address the dynamic interactions that lead to breaches of runway safety. While the generally static nature of runway incursion research is understandable given that data are often sparsely available, the unmitigated rate at which runway incursions take place indicates a need for more comprehensive risk models that extend currently available research. This dissertation summarizes the existing literature, emphasizing the need for cross-domain methods of causation analysis applied to runway incursions in the U.S. and reviewing probabilistic methodologies for reasoning under uncertainty. A holistic modeling technique using Bayesian Belief Networks as a means of interpreting causation even in the presence of sparse data is outlined in three phases: causal factor identification, model development, and expert elicitation, with intended application at the systems or regulatory agency level. Further, the importance of investigating runway incursions probabilistically and incorporating information from human factors, technological, and organizational perspectives is supported. A method for structuring a Bayesian network using quantitative and qualitative event analysis in conjunction with structured expert probability estimation is outlined and results are presented for propagation of evidence through the model as well as for causal analysis. In this research, advances in the aggregation of runway incursion data are outlined, and a means of combining quantitative and qualitative information is developed. Building upon these data, a method for developing and validating a Bayesian network while maintaining operational transferability is also presented. Further, the body of knowledge is extended with respect to structured expert judgment, as operationalization is combined with elicitation of expert data to create a technique for gathering expert assessments of probability in a computationally compact manner while preserving mathematical accuracy in rank correlation and dependence structure. The model developed in this study is shown to produce accurate results within the U.S. aviation system, and to provide a dynamic, inferential platform for future evaluation of runway incursion causation. These results in part confirm what is known about runway incursion causation, but more importantly they shed more light on multifaceted causal interactions and do so in a modeling space that allows for causal inference and evaluation of changes to the system in a dynamic setting. Suggestions for future research are also discussed, most prominent of which is that this model allows for robust and flexible assessment of mitigation strategies within a holistic model of runway safety

Participatory modelling for stakeholder involvement in the development of flood risk management intervention options

Advancing stakeholder participation beyond consultation offers a range of benefits for local flood risk management, particularly as responsibilities are increasingly devolved to local levels. This paper details the design and implementation of a participatory approach to identify intervention options for managing local flood risk. Within this approach, Bayesian networks were used to generate a conceptual model of the local flood risk system, with a particular focus on how different interventions might achieve each of nine participant objectives. The model was co-constructed by flood risk experts and local stakeholders. The study employs a novel evaluative framework, examining both the process and its outcomes (short-term substantive and longer-term social benefits). It concludes that participatory modelling techniques can facilitate the identification of intervention options by a wide range of stakeholders, and prioritise a subset for further investigation. They can help support a broader move towards active stakeholder participation in local flood risk management

The propositional nature of human associative learning

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends oil high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Ports’ congestion factors: Applying risk analysis as a problem identification tool to figure out the interrelated complex factors that contribute to the problem by assigning weights and probabilities to each factor

Ports’ congestion is a recurring problem that is caused by several factors. There are several past attempts to resolve ports’ congestion by applying governing and constructional reforms. Due to divergence and instability of congestion causal factors, the available studies and solutions are specific to individual ports. The main objective of this master thesis is to apply risk analysis as a problem identifier to figure out the interrelated complex factors that contribute to the congestion problem by assigning weights and probabilities to each factor. The research is based on qualitative data from secondary sources to gather all available information about the causal factors for ports’ congestion. A structured questionnaire was carried out and sent to various ports’ managers to figure out the most effective causal factors globally, as a means of validation for the secondary data and to ensure that the data reflect the current congestions causing factors from the port’s users themselves. Congestion’s factors can be human, technical, or organizational with different magnitudes based on the port’s features and capabilities. They are vulnerable to sudden and quick changes due to their interrelated and complex structure. Bayesian network (BN) is a risk analysis tool that fits the complex and changing scenarios of the congestion problem. It can incorporate the newly received information into the pre-established network of causal factors for port congestion. BN managed to reflect the cause-and-effect relationship between the causal factors and by means of appropriate software, the effect of any new event on congestion occurrence is visualized. Furthermore, the application of BN needs to be integrated into the port information management system as a permanent warning system that predicts the congestion and virtually shows the results of applying suggested solutions before applying it. Keywords: port congestion, congestion factors, Bayesian network, port productivit

A Bayesian Abduction Model For Sensemaking

This research develops a Bayesian Abduction Model for Sensemaking Support (BAMSS) for information fusion in sensemaking tasks. Two methods are investigated. The first is the classical Bayesian information fusion with belief updating (using Bayesian clustering algorithm) and abductive inference. The second method uses a Genetic Algorithm (BAMSS-GA) to search for the k-best most probable explanation (MPE) in the network. Using various data from recent Iraq and Afghanistan conflicts, experimental simulations were conducted to compare the methods using posterior probability values which can be used to give insightful information for prospective sensemaking. The inference results demonstrate the utility of BAMSS as a computational model for sensemaking. The major results obtained are: (1) The inference results from BAMSS-GA gave average posterior probabilities that were 103 better than those produced by BAMSS; (2) BAMSS-GA gave more consistent posterior probabilities as measured by variances; and (3) BAMSS was able to give an MPE while BAMSS-GA was able to identify the optimal values for kMPEs. In the experiments, out of 20 MPEs generated by BAMSS, BAMSS-GA was able to identify 7 plausible network solutions resulting in less amount of information needed for sensemaking and reducing the inference search space by 7/20 (35%). The results reveal that GA can be used successfully in Bayesian information fusion as a search technique to identify those significant posterior probabilities useful for sensemaking. BAMSS-GA was also more robust in overcoming the problem of bounded search that is a constraint to Bayesian clustering and inference state space in BAMSS

Project portfolio resource risk assessment considering project interdependency by the fuzzy Bayesian network

Resource risk caused by specific resource sharing or competition among projects due to resource constraints is a major issue in project portfolio management, which challenges the application of risk analysis methods effectively. This paper presents a methodology by using a fuzzy Bayesian network to assess the project portfolio resource risk, determine critical resource risk factors, and propose risk-reduction strategies. In this method, the project portfolio resource risk factors are first identified by taking project interdependency into consideration, and then the Bayesian network model is developed to analyze the risk level of the identified risk factors in which expert judgments and fuzzy set theory are integrated to determine the probabilities of all risk factors to deal with incomplete risk data and information. To reduce the subjectivity of expert judgments, the expert weights are determined by combining experts’ background and reliability degree of expert judgments. A numerical analysis is used to demonstrate the application of the proposed methodology. The results show that project portfolio resource risks can be analyzed effectively and efficiently. Furthermore, “poor communication and cooperation among projects,” “capital difficulty,” and “lack of sharing technology among projects” are considered the leading factors of the project portfolio resource risk. Risk-reduction strategic decisions based on the results of risk assessment can be made, which provide project managers with a useful method or tool to manage project risks