Delay Propagation in Large Railway Networks with Data-Driven Bayesian Modeling

2021 ◽  
pp. 036119812110184
Author(s):  
Boyu Li ◽  
Ting Guo ◽  
Ruimin Li ◽  
Yang Wang ◽  
Yuming Ou ◽  
...  
Keyword(s):  
Evaluation Criteria ◽  
Capacity Utilization ◽  
Markov Property ◽  
Data Driven ◽  
Probability Models ◽  
Railway Network ◽  
Delay Propagation ◽  
Propagation Modeling ◽  
Railway Networks ◽  
Temporal Dimensions

Reliability and punctuality are the key evaluation criteria in railway service for both passengers and operators. Delays spanning over spatial and temporal dimensions significantly affect the reliability and punctuality level of train operation. The optimization of capacity utilization and timetable design requires the prediction of the reliability and punctuality level of train operations, which is determined by train delays and delay propagation. To predict the punctuality level of train operations, the distributions of arrival and departure delays must be estimated as realistically as possible by taking into account the complex railway network structure and different types of delays caused by route conflict and connected trips. This paper aims to predict the propagation of delays on the railway network in the Greater Sydney area by developing a conditional Bayesian model. In the model, the propagation satisfies the Markov property if one can predict future delay propagation in the network based solely on its present state just as well as one could knowing the process’s full history, so that it is independent of such historical procedures. Meanwhile, we consider the throughput estimation for the cases of delay caused by interchange line conflicts and train connection in this model. To the best of the authors’ knowledge, this is the first work of data-driven delay propagation modeling that examines both spatial and temporal dimensions under four different scenarios for railway networks. Implementation on real-world railway network operation data shows the feasibility and accuracy of the proposed model compared with traditional probability models.

scholarly journals Ranking the critical sections of railway networks

2020 ◽  
pp. 095440972092568
Author(s):  
Ratthaphong Meesit ◽  
John Andrews
Keyword(s):  
Network Flow ◽  
Topological Analysis ◽  
Preventive Strategy ◽  
Railway Network ◽  
Macroscopic Models ◽  
Railway Systems ◽  
Railway Networks ◽  
Network Simulation Model ◽  
The Impact ◽  
Critical Sections

Railway systems are now facing an increasing number of threats such as aging infrastructures and climate changes. The identification of critical network sections provides infrastructure managers with the ability to understand the impact of a disruption and creates a suitable preventive strategy to counter such threats. To this end, various vulnerability analysis methods have been proposed for railway networks. Two main types of methods, network topological analysis and network flow-based analysis, have been developed. Both approaches are constructed based on macroscopic models, which take only some railway properties such as network structure, train and passenger flow into account. Thus, the results obtained are high level approximations. This study proposes a new analysis method, which is developed based on the stochastic-microscopic railway network simulation model. The method can be applied to identify the critical sections of a railway network. The effect of impact levels and occurrence times of a disruption on the network section criticality is presented. An application of the proposed model is demonstrated using the Liverpool railway network in the UK.

scholarly journals Development of a method for modelling delay propagation in railway networks using epidemiological SIR models

2020 ◽  
Vol 6 (3 (108)) ◽  
pp. 6-13
Author(s):  
Dmytro Gurin ◽  
Andrii Prokhorchenko ◽  
Mykhailo Kravchenko ◽  
Ganna Shapoval
Keyword(s):  
Delay Propagation ◽  
Railway Networks

scholarly journals A Data-Driven Evaluation Approach for Assessing Student Nurse Training Effectiveness in Clinical Practice Using A Fuzzy Mathematics Model

2020 ◽  
Author(s):  
Yanmei Liu ◽  
Yuwen Chen
Keyword(s):  
Evaluation Criteria ◽  
Student Nurses ◽  
Quality Of Healthcare ◽  
Data Driven ◽  
Fuzzy Mathematics ◽  
Mathematical Tool ◽  
Nurse Training ◽  
Treatment Practice ◽  
Evaluation Approach ◽  
Overall Performance

ABSTRACTThe overall performance of student nurses during training and subsequent medical treatment practice has a direct effect on the quality of healthcare they provide in hospitals. The evaluation of student nurses’ overall performance is usually not straightforward, as the evaluation criteria includes many aspects and it’s difficult to develop a generic metric. Fuzzy mathematics provides a mathematical tool for processing data with fuzziness. Using fuzzy mathematics theory enables data-driven evaluation of the overall performance of student nurses after their training program.

scholarly journals Review of Existing Energy Retrofit Decision Tools for Homeowners

2021 ◽  
Vol 13 (18) ◽  
pp. 10189
Author(s):  
Mohammed Seddiki ◽  
Amar Bennadji ◽  
Richard Laing ◽  
David Gray ◽  
Jamal M. Alabid
Keyword(s):  
Financial Support ◽  
Current Knowledge ◽  
Evaluation Criteria ◽  
Data Driven ◽  
Energy Calculation ◽  
Calculation Methods ◽  
Decision Tools ◽  
Energy Retrofit ◽  
The Impact

Energy retrofit tools are considered by many countries as one of the strongest incentives to encourage homeowners to invest in energy renovation. These tools help homeowners to get an initial overview of suitable retrofit measures. Although a large number of energy retrofit tools have been developed to inspire and educate homeowners, energy renovation by individual homeowners is still lagging and the impact of current tools is insufficient as awareness and information issues remain one of main obstacles that hinder the uptake of energy retrofitting schemes. This research extends the current knowledge by analysing the characteristics of 19 tools from 10 different countries. The selected tools were analysed in terms of energy calculation methods, features, generation and range of retrofit measures, evaluation criteria, and indications on financial support. The review indicates that: (1) most toolkits use empirical data-driven methods, pre-simulated databases, and normative calculation methods; (2) few tools generate long-term integrated renovation packages; (3) technological, social, and aesthetic aspects are rarely taken into consideration; (4) the generation of funding options varies between the existing tools; (5) most toolkits do not suggest specific retrofit solutions adapted to traditional buildings; and (6) preferences of homeowners in terms of evaluation criteria are often neglected.

scholarly journals Evaluation of Cracks in Metallic Material Using a Self-Organized Data-Driven Model of Acoustic Echo-Signal

2018 ◽  
Vol 9 (1) ◽  
pp. 95 ◽  
Author(s):  
Xudong Teng ◽  
Xin Zhang ◽  
Yuantao Fan ◽  
Dong Zhang
Keyword(s):  
Crack Growth ◽  
Statistical Significance ◽  
Distance Matrix ◽  
Strong Relationship ◽  
Data Driven ◽  
P Value ◽  
Probability Models ◽  
Statistical Parameters ◽  
Frequency Spectra ◽  
Statistical Probability

Non-linear acoustic technique is an attractive approach in evaluating early fatigue as well as cracks in material. However, its accuracy is greatly restricted by external non-linearities of ultra-sonic measurement systems. In this work, an acoustical data-driven deviation detection method, called the consensus self-organizing models (COSMO) based on statistical probability models, was introduced to study the evolution of localized crack growth. By using pitch-catch technique, frequency spectra of acoustic echoes collected from different locations of a specimen were compared, resulting in a Hellinger distance matrix to construct statistical parameters such as z-score, p-value and T-value. It is shown that statistical significance p-value of COSMO method has a strong relationship with the crack growth. Particularly, T-values, logarithm transformed p-value, increases proportionally with the growth of cracks, which thus can be applied to locate the position of cracks and monitor the deterioration of materials.

Evaluation of Single Track Timetables Using Simulation

2014 ◽  
Author(s):  
Hans Sipilä
Keyword(s):  
High Speed ◽  
High Capacity ◽  
Capacity Utilization ◽  
Single Track ◽  
Traffic Operations ◽  
Traffic Demand ◽  
Passenger Train ◽  
Delay Propagation ◽  
Track Line

One way to model train operations and make predictions of future outcome is to use simulation. Many lines and networks connecting major cities have a high capacity utilization, meaning that running additional trains leads to an even more strained situation and delays are likely to increase. The mix of average train speeds is also related to capacity and delay propagation. Considering one line or several lines connected in a network a requested train traffic can consist of different train categories and departure frequencies. There are usually several possible timetables satisfying this traffic demand. The infrastructure often implies limitations on the type and volume of traffic that can be handled. Additionally constraints introduced by requests for regular intervals, minimum headways, passenger transfers between trains etc. can reduce the number of acceptable timetables. This paper presents an approach using combinatorial train initiations and simulation to generate conflict-free timetables. These can then be simulated with random variations in departure and dwell times. This is implemented on a fictive single track line with high speed passenger train traffic. The objective is to study outcome by varying allowance times and delays. Simulations are carried out in RailSys, a software using synchronous simulation to model train traffic operations.

Sign in / Sign up

Export Citation Format

Share Document