service reliability
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Improving travel time prediction for public transit effectively enhances service reliability, optimizes travel structure, and alleviates traffic problems. Its greater time-variance and uncertainty make predictions for short travel times (≤35min) more subject to be influenced by random factors. It requires higher precision and is more complicated than long-term predictions. Effectively extracting and mining real-time, accurate, reliable, and low-cost multi-source data such as GPS, AFC, and IC can provide data support for travel time prediction. Kalman filter model has high accuracy in one-step prediction and can be used to calculate a large amount of data. This paper adopts the Kalman filter as a travel time prediction model for a single bus based on single-line detection: including the travel time prediction model of route (RTM) and the stop dwell time prediction model (DTM); the evaluation criteria and indexes of the models are given. The error analysis of the prediction results is carried out based on AVL data by case study. Results show that under the precondition of multi-source data, the public transportation prediction model can meet the accuracy requirement for travel time prediction and the prediction effect of the whole route is superior to that of the route segment between stops.

Abstract. The aim of the paper is to justify the decision on cooperation with entities in the transport service market for foreign trade operations based on the assessment of logistics chain reliability and shipping time on different routes. The research findings will enable cargo owners and other stakeholders to make management decisions at the delivery planning stage optimizing all processes related to customs and logistics services. Methods. We assess the reliability of four logistics chain types based on a simulation model developed in the GPSS software environment using the method of statistical testing. Results. Customs and logistics service users challenge entities in the transport service market with the task of performing foreign trade operations in compliance with the highest level of quality and service reliability minimizing the cost of delivery and shipping time. The proposed method allows determining the probability of timely delivery of goods in different transportation directions as well as the reliability of the studied process. Scientific novelty. The obtained simulation results will make it possible to work out proposals and recommendations for customs and logistics service users on the choice of an optimal logistics chain type, taking into account its impact on shipping time and reliability of goods delivery. Practical value. The practical value of the research is that the proposed method will allow foreign economic entities to form logistics chains, taking into account available resources and the need to use the services of other companies in international goods delivery. In this case, the efficiency of a logistics chain can be further assessed by the shipping time and reliability for export/import operations.

The popularity of online vehicular video has caused enormous information requests in Internet of vehicles (IoV), which brings huge challenges to cellular networks. To alleviate the pressure of base station (BS), Roadside Units (RSUs) and vehicle peers are introduced to collaboratively provide broadcast services to vehicle requesters where vehicles act as both service providers and service requesters. In this paper, we propose an efficient framework leveraging scalable video coding (SVC) technique to improve quality of experience (QoE) from two perspectives: (1) maximizing the data volume received by all requesters and (2) determining buffer action based on playback fluency and average playback quality. For (1), potential providers cooperate to determine the precached video content and delivery policy with the consideration of vehicular mobility and wireless channel status. If one provider fails, other sources will complement to provide requested content delivery. Therefore, their cooperation can improve the QoE and enhance the service reliability. For (2), according to buffer occupancy status, vehicle requesters manage buffer action whether to buffer new segments or upgrade the enhancement level of unplayed segment. Furthermore, the optimization of the data volume is formulated as an integer nonlinear programming (INLP) problem, which can be converted into some linear integer programming subproblems through McCormick envelope method and Lagrange relaxation. Numerical simulation results show that our algorithm is effective in improving total data throughput and QoE.

The introduction of customized bus (CB) service intends to expand and elevate existing transit service, which offers an efficient and sustainable alternative to serve commuters. A probabilistic model is proposed to optimize CB service with mixed vehicle sizes in an urban setting considering stochastic bus arrival time and spatiotemporal demand, which minimizes total cost subject to bus capacity and time window constraints. The studied optimization problem is combinatorial with many decision variables including vehicle assignment, bus routes, timetables, and fleet size. A heuristic algorithm is developed, which integrates a hybrid genetic algorithm (HGA) and adaptive destroy-and-repair (ADAR) method. The efficiency of HGA-ADAR is demonstrated through numerical comparisons to the solutions obtained by LINGO and HGA. Numerical instances are carried out, and the results suggested that the probabilistic model considering stochastic bus arrival time is valuable and can dramatically reduce the total cost and early and late arrival penalties. A case study is conducted in which the proposed model is applied to optimize a real-world CB service in Xi’an, China. The relationship between decision variables and model parameters is explored. The impacts of time window and variance of bus arrival time, which significantly affect service reliability, are analysed.

Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (Self-Monitoring, Analysis, and Reporting Technology) hard drive metrics with other system metrics such as CPU utilisation. Therefore, we propose a combined metrics approach for failure prediction based on Artificial Intelligence to improve reliability. We tested over 100 cloud servers’ data and four AI algorithms: Random Forest, Gradient Boosting, Long-Short-Term Memory, and Gated Recurrent Unit. Our experimental result shows the benefits of combining metrics, outperforming state-of-the-art.

Abstract As the subsea well intervention sector ascends from a double-dip downturn, operators are motivated to execute projects with increased agility. By adopting new rationale and supported by strategic partnerships, an enhanced focus on operating efficiency is targeted. This paper discusses how operators can –deliver predictable operational expenditure (opex) results, safeguarding life-of-field economics through routine interventions and robust risk mitigation–align objectives with service providers through technology, strategy, and process–realize value of integrated alliances in the subsea intervention sector. Reliability is the cornerstone on which operational integrity is established. Repeatability is the foundation on which partnerships are formed, forging a synchronized philosophy; pledging operational efficiency as commanded in a dynamic environment; bringing to market a holistic, multileveled, multiskilled solution to subsea interventions; and uniting product and operational experts to evaluate the range of scope requirements selecting fit-for-purpose solutions, delivered with synchronized processes. Here we describe how this approach, coupled with repeatable field-proven product and service reliability and driven by true customer-centric thinking, minimizes operating risk to deliver predictable outcomes. This ethos is demonstrated in action via three case studies that discuss the benefits of combining customer expertise with product and operational specialists during the initiation and planning phases of subsea well intervention projects. The shared examples demonstrate how inculcating a continuous improvement mindset and applying lessons learned to product design, process, and procedures can drive operating efficiency to new levels. Campaigns executed in 2020 by the supplier delivered best-in-class results, exceeding customer expectations. The campaign represented the first fully synchronized service operation on the Q5000 intervention vessel at 90% operating efficiency. In addition, they demonstrated outstanding versatility by outperforming budget on the first deployment of the 15,000 psi intervention riser system (IRS) from the Q4000 intervention vessel. Less than 1% operating nonproductive time (NPT) was attributed to the well access package for the campaign. Sustaining this level of success via collaborative performance management is the next step on the roadmap to ensure this becomes the new normal. Conclusions drawn from customer feedback indicated expectations were surpassed, and that synchronicity is the key that unlocks the door to sustainability in the subsea deepwater intervention sector.

<p>Car use is engrained in our culture. Changing behaviour towards using more sustainable travel modes such as public transport is notoriously difficult, despite the increasing awareness of environmental problems caused by car use. Integrated ticketing is a policy measure more recently used in strategies towards achieving integrated and sustainable transport systems. It allows a passenger to travel with one public transport ticket throughout a region. This research uses a mixed method approach to assess how integrated ticketing may affect public transport use in Greater Wellington. The psychological constructs determining decisions to use public transport are tested using an integrated environmental behaviour model proposed by Bamberg and Möser (2007). The results support the integrated modelling approach. Intentions to use public transport are indirectly affected by awareness of environmental problems caused by car use mediated through social norms, guilt, perceived behavioural control and attitude. The intention to use public transport explains 56% of the variance in public transport behaviour. Integrated ticketing presents an opportunity to increase the ease and convenience of travel, shown to be important in the model. The majority of survey respondents perceived that they would use integrated ticketing in Greater Wellington and that it was important both on a regional and national scale. Achieving an effective integrated ticketing system in Greater Wellington will be conditional on firstly improving public transport service reliability and stakeholder communication. Integrating fares across the region and across modes will also be crucial to the success of the system.</p>

<p>Car use is engrained in our culture. Changing behaviour towards using more sustainable travel modes such as public transport is notoriously difficult, despite the increasing awareness of environmental problems caused by car use. Integrated ticketing is a policy measure more recently used in strategies towards achieving integrated and sustainable transport systems. It allows a passenger to travel with one public transport ticket throughout a region. This research uses a mixed method approach to assess how integrated ticketing may affect public transport use in Greater Wellington. The psychological constructs determining decisions to use public transport are tested using an integrated environmental behaviour model proposed by Bamberg and Möser (2007). The results support the integrated modelling approach. Intentions to use public transport are indirectly affected by awareness of environmental problems caused by car use mediated through social norms, guilt, perceived behavioural control and attitude. The intention to use public transport explains 56% of the variance in public transport behaviour. Integrated ticketing presents an opportunity to increase the ease and convenience of travel, shown to be important in the model. The majority of survey respondents perceived that they would use integrated ticketing in Greater Wellington and that it was important both on a regional and national scale. Achieving an effective integrated ticketing system in Greater Wellington will be conditional on firstly improving public transport service reliability and stakeholder communication. Integrating fares across the region and across modes will also be crucial to the success of the system.</p>

Abstract We can say with some clarity that the Internet of Things (IoT) can be made up of a set of embedded devices such as light detection sensors, ranging (LiDAR) sensors, and millimetre-wave (mmWave) sensors. These sensors generate a massive amount of data in which limited communication capacity is available to share a massive amount of data to Fog-based Roadside Units (RSU) for data process and analysis service. Fog-based RSU has become an emerging paradigm in intelligent transportation but needs research attention to design intelligent decision-making methods for data communication and computation at Fog-based RSU. To address these issues, we design a two-level Quantum based D2D Computation, Communication ($QDC^{2}$) approach. First, design a bandwidth allocation strategy based on spatial importance score factors to resolve embedded devices' data transmission issues. Second, design an adaptive equilibrium service offloading strategy based on device-centric measurements to assess the computation capacity and performance rate for resolving Fog-node computation consistency issues. Additionally, Fog-based RSU is interconnected with LAN helps to optimize service latency. Simulation results show that our approach achieved a high service reliability rate (79.56\%), low error rate (0.9\%), and an execution delay of 22.5s for 15 devices than state-of-art approaches.