Transport Network Reliability Research Articles

An unsupervised machine learning approach for real-time damage detection in bridges

The bridge network is progressively aging, with an alarming proportion of bridges over 100 years. This situation engenders substantial risks to the overall reliability of transportation networks, requiring innovative methods for efficient management. Monitoring can provide a direct source of information about structural behavior generating alerts when changes occur. Real-time alerts enable effective infrastructure management and decision-making during damage or anomalous situations. However, monitoring can result in a large amount of data that is often difficult to convert into valuable information in real time. This paper presents an approach for real-time detection of abrupt damage occurrence in bridges using unsupervised anomaly detection algorithms and strain/acceleration measurements. The approach incorporates the separation of measurements into events having the same loading nature and the construction of three feature matrices based on statistical features, time-frequency features, and wavelet spectrum features. It includes the evaluation of five anomaly detection algorithms including Isolation Forest, One-Class Support Vector Machine, Robust Random Cut Forest, Local Outlier Factor, and Mahalanobis Distance. The approach is illustrated with a case study of a steel-bascule-railway bridge, that has experienced a brittle cracking event during monitoring. Results highlight the robustness of One-Class Support Vector Machine, Isolation Forest, and Local Outlier Factor algorithms in promptly detecting abrupt changes across different features. The separation of strain and acceleration data into loading-based events, coupled with the comparison of previous and new event features, provides robust feature matrices for effective damage detection. Enhanced detection and higher scores are particularly attributed to time-frequency domain features during damage occurrence. The presented approach can be used as a base on how to perform real-time anomaly detection within the context of bridge monitoring.

An AHP GIS-based Methodology for the Stability Assessment of the Djebel El Ouahch Collapsees on the Numidian Flysh Formation in Northeast Algeria's Constantine Region

This study presents a comprehensive investigation into the factors underlying the collapse incident that occurred in the Jebel El Ouahch Tunnel in the Numidian flysch, Constantine region, Northeast Algeria. This methodology focuses on evaluating landslide susceptibility through the application of the Analytic Hierarchy Process (AHP) along the reconfigured path of the collapsed A1 highway tunnel section in the Constantine region of northeastern Algeria. Various influential factors contributing to landslides were analyzed, including lithofacies, slope gradient, slope aspect, elevations, fault density, plan curvature, distance from streams, and distance from roads. Utilizing a Geographic Information System (GIS), these eight causative factors were prepared for assessment. The findings indicate that slope gradient and lithology play pivotal roles as primary controlling factors in landslide susceptibility. The model exhibited a commendable success rate of 93% in predicting landslide susceptibility, as demonstrated by the area under the curve (AUC) plot generated from the landslides susceptibility map. Most of the new road falls within the highly susceptible area to landslides. This validated model can serve as an effective tool for mapping landslide susceptibility zones along the newly established road path following the collapsed tunnel. Moreover, its applicability extends to similar environments, showcasing its potential as a valuable resource for hazard assessment and planning in comparable terrains. The deviation road, as well as tunnel T01 of the A1 highway, is in a state of proven instability. It is certain that they will experience continuous, recurrent, and intense landslides. A radical solution to all the geotechnical issues plaguing this section of the highway is to reroute it far away from the Subnumidian formations. A deeper comprehension of the geological and geotechnical intricacies in challenging terrains can significantly enhance the safety and reliability of transportation networks in these demanding contexts.

НЕОПРЕДЕЛЕННОСТЬ ИНФОРМАЦИИ В АНАЛИЗЕ НАДЕЖНОСТИ ТРАНСПОРТНЫХ СЕТЕЙ

The tasks of creating a mathematical model for analyzing the reliability of transport networks are considered. Among the proposed tasks are highlighted: analysis of factors affecting reliability; creating stochastic and cognitive models for describing the probabilistic state of the network structure; removal of information uncertainty; construction of bifurcation events and processes with failures and restoration of network equipment; building an algorithm for assessing the state of a network, selecting an effective structure and predicting an evolutionary process of a transport network. To implement the tasks, information tools are indicated.

Resilience Analysis of Traffic Network under Emergencies: A Case Study of Bus Transit Network

With the continuous development of public transportation, the impact of unexpected events on the operation of bus networks has become increasingly severe due to the growing demand for public transportation and passenger volume. To accurately assess the impact of unexpected events on the operation of bus networks and scientifically evaluate their resilience, this paper proposes a framework for analyzing the resilience of bus networks. With the aim of providing scientific evidence to enhance the reliability of public transportation networks, this framework can be used to determine the resilience of bus networks to unexpected events. The main contributions of this framework include three aspects: 1. Construction of the CRITIC–entropy weighting model for screening and calculating key indicators of the resilience of the bus network; 2. Use of resilience cycle theory to construct a model for analyzing the resilience of bus routes, and design a set of resilience quantification factors to calculate the resilience of bus routes; 3. Use of complex network theory to construct a model for analyzing the resilience of the bus network, by taking the bus route resilience obtained in the second step as the edge weight to calculate the resilience of the bus network. This paper takes the Beijing public transit system as an example and uses real data to verify the accuracy, scientificity, and feasibility of the proposed framework for analyzing the resilience of public transit networks to sudden events. The resilience analysis framework constructed in this paper has improved the existing research on transportation network resilience in theoretical aspects. Furthermore, the results outputted by this framework can provide a decision-making basis for network adjustment and disaster recovery for the management departments of public transportation networks in practical applications.

Geohazard Monitoring by UAV: The Future Technology for Remote Decision Support

In Norway, as in many other parts of the world, roadside geohazards pose a serious and frequent threat to the transportation network and to road users. Roadway closures due to avalanches, landslides, and rockfalls incur high economic costs and greatly impact the reliability of critical transportation networks. To meet these challenges, road owners and operators, like the Norwegian Public Roads Administration, are adopting autonomous Uncrewed Aerial Vehicles, known as UAVs, to monitor and evaluate geohazards and to support decision-making.

Special issue on “Reliability and resilience of emerging mobility systems”, an editorial note

This Editorial Note accompanies the special issue devoted to the development of new concepts, theories and methods that address reliability and resilience related to the planning, dynamic operation, and level of service of novel mobility systems. This special issue on ‘Reliability and Resilience of Emerging Mobility Systems’ consists of seven papers which are dedicated to methodological and theoretical developments as well as advanced applications in this domain. Several of the contributions originated from works presented at INSTR2021 (the 8th International Symposium on Transport Network Reliability). In this Editorial note we reflect on the contributions made in each of the articles included in this special issue.

Machine Learning-based Seismic Fragility Curves for RC Bridge Piers

A significant part of ongoing studies in the field of earthquake engineering is directed toward the seismic risk assessment of buildings and infrastructures at a territorial scale. This task is usually accomplished by grouping the structures into homogenous classes in terms of typology, for which seismic fragility curves are then obtained for different limit states via numerical simulations or from the statistical analysis of observational data when available. Particularly, the development of typological fragility curves for bridges under earthquake is useful for assessing the reliability and resilience of transportation networks in seismic areas and can be also effective decision-making support. Within this framework, the proposed study establishes a machine learning-based paradigm for the closed-form prediction of the main statistical parameters required to obtain relevant seismic fragility curves for reinforced concrete bridge piers. Initially, a huge training dataset has been obtained by Monte Carlo simulations and displacement-based bridge pier assessments by assuming data representative of the Italian highway transportation network. Next, symbolic nonlinear regression formulae for estimating the main statistical parameters of seismic fragility curves have been generated. With the aid of those formulae, the effort of calculating the seismic fragility curves is greatly reduced since the corresponding main statistical parameters can be directly calculated from a set of commonly available attributes. Therefore, the proposed study provides a helpful tool for the rapid preliminary assessment of damage and risk level of existing highway transportation networks exposed to seismic hazards.

Seismic reliability assessment of bridge networks considering travel time and connectivity reliabilities

AbstractTransportation networks play a vital role in transporting goods and casualties immediately after an earthquake. Thus, identifying the most reliable and shortest path is critical for ensuring efficient emergency response and recovery after an earthquake; consequently, it is important to compute the reliability of transportation networks by simultaneously accounting for connectivity and travel time. In this regard, this paper proposes a novel seismic reliability indicator termed as the travel time and connectivity reliability (TTCR), which combines the travel time and connectivity reliabilities. Additionally, a novel framework for computing the TTCR of transportation networks is proposed. The proposed methodology is applied to a bridge network in California. Furthermore, a retrofit plan based on the TTCR is proposed to help risk managers make appropriate and timely decisions for reducing the seismic risk of transportation networks.

Reliability of Certain Class of Transportation Networks

The reliabilityof a special class of technical objects - tree-like transportation networks is considered. It is argued that the indicators traditionally used to assess reliability in relation to such objects are not very informative and are ambiguously interpreted from a physical point of view. As a quantitative measure, an indicator of operational reliability is proposed and an engineering method for its calculation has been developed. It uses the features of the object, is based on an analogue of the Y-shaped structure-forming fragment of the network and is reduced to a repeating step-by-step computational procedure, when at each step the results of the calculation are obtained at the previous stage and used as initial data. Relationships are derived that allow extending this approach to the case when at the junction point of network elements not two, but, in general, n elements are combined. The advantages of the introduced indicator of the operational reliability of tree-like transportation networks are discussed and the ways of its further generalization and use are outlined

Rail transport network reliability with train arrival delay: A reference indicator for a travel agency in tour planning

As a tour agency plans a tour schedule with rail transport, it is necessary to evaluate the capacity of the rail transport network to determine the number of passengers in the tour group that can be served. This study develops a stochastic rail transport network (SRTN) according to the train timetable and tour schedule, such that each available train’s loading capacity (that is, the number of cars) may be fully and partially reserved for other requests. Under the consideration of a tour group including q passengers, the network reliability accordingly means the probability that the q-passenger group can successfully depart from a source station for a sink station via the SRTN. In addition, the passengers might not punctually change trains at a transfer station owing to train arrival delay. Because the travel agency cannot control the situation of train arrival delay while planning a tour schedule, the uncertainty of train arrival delay results in an acceptable delay probability that the passengers traveling by non-direct trains can successfully change trains and this must be considered for the network reliability evaluation. An algorithm integrating the minimal paths and the modified Recursive Sum of Disjoint Product are integrated to solve the addressed problem. A real case of a rail transport network in Taiwan is adopted to demonstrate the applicability of the proposed approach. The travel agency can view the network reliability as a reference indicator to evaluate the number of passengers in the group that can be served.

Assess reliability of a tourism transport network considering limited-budget and late arrivals

Tourism transport is becoming a crucial part of the tourism industry. Determining the reliability of tourism transport networks is required by travel agencies and other practitioners. However, much of recent reliability assessments cannot utilize directly for tourism transport networks. Moreover, there exists a lack of passengers-oriented reliability evaluation that involves the impacts of latency. Therefore, this study assesses reliability regarding carrying the required number of passengers to their destination under budget and time constraints when considering late arrivals. For reliability assessment, an algorithm, which combines the concept of the minimal path adding a searching procedure with the Recursive Sum of Disjoint Products method, is proposed. An illustrative example is adopted to provide travel agencies with an intuitive visualization of the proposed algorithm. Reliability analysis suggests improving the performance of tourism transport networks.

Short-term prediction of outbound truck traffic from the exchange of information in logistics hubs: A case study for the port of Rotterdam

Short-term traffic prediction is an important component of traffic management systems. Around logistics hubs such as seaports, truck flows can have a major impact on the surrounding motorways. Hence, their prediction is important to help manage traffic operations. However, The link between short-term dynamics of logistics activities and the generation of truck traffic has not yet been properly explored. This paper aims to develop a model that predicts short-term changes in truck volumes, generated from major container terminals in maritime ports. We develop, test, and demonstrate the model for the port of Rotterdam. Our input data are derived from exchanges of operational logistics messages between terminal operators, carriers and shippers, via the local Port Community System. We propose a feed-forward neural network to predict the next one hour of outbound truck traffic. To extract hidden features from the input data and select a model with appropriate features, we employ an evolutionary algorithm in accordance with the neural network model. Our model predicts outbound truck volumes with high accuracy. We formulate 2 scenarios to evaluate the forecasting abilities of the model. The model predicts lag and non-proportional responses of truck flows to changes in container turnover at terminals. The findings are relevant for traffic management agencies to help improve the efficiency and reliability of transport networks, in particular around major freight hubs.

Bridge fragilities to network fragilities in seismic scenarios: An integrated approach

As significant uncertainties are associated with the factors governing the operational characteristics of bridges following earthquakes, it is important to compute the reliability of transportation networks as a function of bridge fragility. A novel framework for the computation of transportation network fragilities following earthquakes is proposed herein, and the effectiveness of the proposed methodology is demonstrated using a hypothetical bridge network in California, USA. The proposed methodology affords the likelihood of functionality of bridge routes as a function of ground motion intensity. The network fragilities identified by this methodology can aid emergency responders and bridge owners in assessing the availability of possible routes after a seismic event based on the likelihoods of functionality of the routes.

Reliability assessment of a stochastic air transport network with late arrivals

With the rapid development of low-fare airlines with cheaper fares, the demand for safe and fast air-travel is growing. However, weather shocks, unpredictable situations, or mismatched interactions between related players sometimes impair the on-time landing of air transport networks. This work considers late arrivals and assesses the reliability of an air transport network and its performance indicator in terms of meeting the requested number of passengers under the time constraint. The available number of seats in each flight is regarded as stochastic owing to the number of seats that have been reserved by both travel agents and individual customers. An air transport network is thus formulated as a stochastic-flow air transport network with late arrivals, and all minimal itineraries feasible under the constraint of time are found through a depth-first search based approach. The reliability is derived from all flow vectors such that its capacity and tardiness-related valid conditions are not bounded while fulfilling the given number of passengers. Evaluating the reliability with considering late arrivals implies handling the rapid increase in flights for each tardiness level and facing the influence on the validity of each minimal itinerary. This study also contributes towards potential improvements in the reliability of air transport networks, which can benefit travel agents.

A New Bit Repair Fast Reroute Mechanism for Smart Sensors IoT Network Infrastructure.

Today’s IP networks are experiencing a high increase in used and connected Internet of Things (IoT) devices and related deployed critical services. This puts increased demands on the reliability of underlayer transport networks. Therefore, modern networks must meet specific qualitative and quantitative parameters to satisfy customer service demands in line with the most common requirements of network fault tolerance and minimal packet loss. After a router or link failure within the transport network, the network convergence process begins. This process can take an unpredictable amount of time, usually depending on the size, the design of the network and the routing protocol used. Several solutions have been developed to address these issues, where one of which is the group of so-called Fast ReRoute (FRR) mechanisms. A general feature of these mechanisms is the fact that the resilience to network connectivity failures is addressed by calculating a pre-prepared alternative path. The path serves as a backup in the event of a network failure. This paper presents a new Bit Repair (B-REP) FRR mechanism that uses a special BIER header field (Bit-String) to explicitly indicate an alternative path used to route the packet. B-REP calculates an alternative path in advance as a majority of existing FRR solutions. The advantage of B-REP is the ability to define an alternative hop-by-hop path with full repair coverage throughout the network, where, unlike other solutions, we propose the use of a standardized solution for this purpose. The area of the B-REP application is communication networks working on the principle of packet switching, which use some link-state routing protocol. Therefore, B-REP can be successfully used in the IoT solutions especially in the field of ensuring communication from sensors in order to guarantee a minimum packet loss during data transmission.

Calculation of Urban Transportation Network Reliability Considering Correlation Among the Links Comprising a Route

Recently, the researchers in the field of urban transportation network planning have become increasingly interested in network reliability, publishing research works focused on the calculation of various types of network reliability. Accurate calculation of network reliability has led the transportation network optimizers toward new approaches . Travel time reliability is among the most important reliabilities investigated when analyzing urban transportation networks, with various approaches based on different assumptions proposed for calculating it. In the present research, the uncertainty associated with the demand for travel and the flows passing across links and also the correlations among the links comprising a route were considered to calculate the travel time for each of the network links. Moreover, it was shown that this process follows shifted log-normal distribution. These calculations are expected to serve as a basis for the employment of travel time reliability of a network within the modeling of an urban transportation system, so as to increase the accuracy and reliability of the simulations. Finally, in order to validate the model, an urban network with 12 nodes, 21 links, and 4 origin-destination pairs was subjected to the travel time reliability assessment by calculating the travel time over all forming links.

Quantifying Operational Disruptions as Measured by Transportation Network Reliability

AbstractThere is global interest by transportation planners and enterprise operators to monitor the inherent uncertainty of transportation networks. Traditional performance metrics may erroneously ...

Model and simulation analysis for the reliability of the transportation network

ABSTRACT The reliability of the transportation network is studied by simulating the attack on the important edges. The important edges are deliberately attacked, and the impedance of the attacking edges is set to infinity. Then the influence of the characteristics of single failure edge on network reliability change and the influence of failure edge number on network reliability change are studied. Two test schemes are designed to compare the changes of network reliability in three failure experiments. At last, for the transportation network ofZhengzhou-Europe International Block train, the simulation experiment of the transportation network reliability is curried out to demonstrate the proposed method. The reliability of the transportation network is studied through numerical simulation, which is helpful to improve the reliability of the transportation network and reduce the huge economic loss caused by the failure.

Reliability Analysis of the Air Transportation Network when Blocking Nodes and/or Connections Based on the Methods of Percolation Theory

The paper shows that to study the reliability and fault tolerance of air transportation networks, methods of percolation theory can be used, in which any aviation transport structure can be represented as a random non-planar, incompletely connected graph (nodes are airports, arcs are airlines). In the theory of percolation, one can consider the solution of the problems of finding the shares of blocked nodes and blocked connections for networks with various random and regular structures, in which they decompose into unconnected areas. The share of blocked nodes (in the node problem) or connections (in the connection problem), at which the conductivity between two arbitrarily selected network nodes disappears, is called the percolation (flow) threshold. For the same structure, the values of percolation thresholds for the bond problem and the node problem have different meanings. The percolation threshold value depends on the average number of connections per network node (density), and is a criterion for its reliability, i.e. determines the percentage of blocked nodes and/or communications that the network will lose the necessary level of performance ability. The dependence of the blocking threshold (percolation) on the network connection density can be expressed mathematically. Using a map of a real aviation transport network, it is possible to determine the average number of connections per one node and then calculate the threshold value of its predetermined reliability value. If the reliability threshold needs to be increased, then the necessary number of additional links can be calculated.

Predicting real-time surge pricing of ride-sourcing companies

Ride-sourcing companies such as Uber and Lyft represent a popular and growing mode of transit in cites worldwide. These companies employ surge pricing in real time to balance the needs of both drivers and riders. The prediction of surge prices in the next few minutes to hours encapsulates the complex evolution of service fleets and service demand in the short term. Surge pricing, if effectively predicted and disseminated to both drivers and riders, can be used to more efficiently allocate vehicles, save users money and time, and provide profitable insight to drivers, which ultimately helps the efficiency and reliability of transportation networks. This paper explores the spatio-temporal correlations between the urban environment, traffic flow characteristics, and surge multipliers. We propose a general framework for predicting the short-term evolution of surge multipliers in real-time using a log-linear model with L1 regularization, coupled with pattern clustering. This model is able to predict Uber surge multipliers in Pittsburgh up to two hours in advance using data from the previous hour out-performing the overall mean and the historical average in all but 3 of the 49 locations in Pittsburgh and outperforming three non-linear methods in 28 of the 49 locations. The model is able to out-perform the overall mean, historical mean, and non-linear methods on Lyft surge multipliers in Pittsburgh up to 20 min in advance. Cross-correlation of Uber and Lyft surge multipliers is also explored.