Set Of Trips Research Articles

SIMULATION OF BOILER FEED PUMP USING CONTROLLER PROGRAM: A CASE STUDY OF SAPELE POWER BOILER FEED PUMP

The goal of this work is to automate the Sapele Power Plant Boiler Feed Pumps (BFPs) using CODESYS software to simulate BFP controlled by a proposed PLC system that was manually controlled. The boiler pressure, level, feed stream flow, and primary heat transport (PHT) pressure BFP were restored to their optimal levels within a few seconds after trip. This was possible for the standby program used for automation within 14 seconds when compared to the manual process that was in place. During the simulation, the BFP goes into standby mode after 14 seconds. The results obtained under automated condition shows the boiler pressure rises to 40.3 kg/cm-1s-2 and remained stable for 147 seconds as against 55.9 kg/cm-1s-2 in 25 seconds under unautomated conditions. The flow rate of the feed water shows a decrease of -0.172 m at 50 seconds. The boiler pressure rose further to 44 kg/cm-1s-2 at 175 seconds, which corresponds to a specific gravity value of 59.5 kg/cm3 at the same time. At 146 seconds, the feed water hits the trip setting of -2.85m owed to low specific gravity (SG) level when compared to the unautomated condition. Thus, a further increase reached fluctuation level of -10m at 200 seconds. The PHT was 88.5 kg/cm-1s-2 at 146 seconds contrary to the expected pressure of 105.6 kg/cm-1s-2 for unautomated state. This change after 146 seconds results from generator trip. The pressure increases to 90.2 kg/cm3 at approximately 148 seconds and subsequently decreases to 48 kg/cm3 consequently opening of the condenser steam discharge valve (CSDVs) and atmospheric steam discharge valve (ASDVs) during the simulation state.

Coordination of protective relays in the substation

<p class="IEEEAbtract"><span lang="EN-US">To make an electrical system reliable and cost-effective, its protection coordination is crucial. Protection coordination is a study to determine the trip settings of protective devices. This research proposes protection coordination for Mehran University of Engineering and Technology, Jamshoro, Sindh. This study includes the coordination of relays connected at each department to the main relay connected with the main vacuum circuit breakers (VCB) by using the time characteristic curves (TCC) and time delay setting of relays using ETAP software. This method is used for the trip of a particular circuit breaker at a proper time. Hence, if a fault occurs in any department only that department will be disconnected, and the rest of the departments will remain in a working condition. This study helps to maintain the system’s high reliability.</span></p>

Detection of driver distraction in the Australian naturalistic driving study videos using pre-trained models and transfer learning

Naturalistic driving data (NDS), collected from fitted vehicles with multiple sensors, including cameras, is a rich source to understand how drivers become distracted. However, detecting the distraction events traditionally required watching NDS videos which is expensive and time-consuming. Adopting state-of-the-art machine learning techniques to automate distraction detection using videos will save effort and money. Reducing/annotating a random sample of the videos and using it to train a machine learning model to carry out the data reduction for the rest of the dataset is possible. This paper investigates the feasibility of using pre-trained deep neural networks and random forest to detect driver distraction in the Australian Naturalistic Driving Study (ANDS) video data.The pre-trained model was fine-tuned using transfer learning; therefore, it can be used to classify (i.e. distracted/non-distracted) successive frames independently. To capture the time dependency between the consecutive images/frame during the training phase, we converted the 1-D output probability of the fine-tuned model into a higher dimension space. The distraction probability of each frame at a time is transformed into a vector by concatenating the distraction output probabilities of all frames within a time window centred at time. Moreover, the vector centred at the time was assigned the actual label of the frame at this time. Then we used Random Forest (RF) classifier to model the non-linear relationship between the probabilities in this new space and the distracted/non-distracted labels. This proposed framework was trained using some ANDS trips and tested using another set of trips. When applied to the dashboard camera, the proposed framework achieved 0.609, 0.218 and 0.325 for the true positive rate (TPR), the false positive rate (FPR), and the precision, respectively. Moreover, the face camera achieved 0.748, 0.344 and 0.651 for TPR, the FPR and the precision, respectively. These promising results suggest the proposed framework can be used routinely in many other data reduction tasks.

Minimizing Maximum Unmet Demand by Transportations Between Adjacent Nodes Characterized by Supplies and Demands

Suppose we are given a graph with nodes characterized by amounts of supplies and demands of multiple commodities. The amounts of commodities stored at nodes (supplies) are given by positive numbers while those of demands at nodes are given by negative numbers. To meet demands we send commodities from nodes to neighbors by using vehicles, one at each node, with some loading capacity moving to and from neighbors. In this paper we adopt a one-way transportation model in which we just send commodities from a node to one of its neighbors along an edge. When we choose one neighbor at each node, we have a set of trips which naturally define a graph such that each connected component has at most one cycle, which is known as a pseudoforest. We present a linear-time algorithm for deciding whether there is a set of trips that meet all demands using one-way multi-commodity transportations on a pseudoforest with node degrees bounded by a constant. Using the algorithm, we first present an efficient algorithm for finding an optimal set of one-way one-commodity trips that minimize the maximum unmet demand on a pseudoforest, and then extend the idea to a multi-commodity problem on a pseudoforest with node degrees bounded by a constant.

Same Network, Same Access to Urban Opportunities? Accessibility Via Public Transportation for Wheelchair Users

Public transport plays a fundamental role in providing access to urban opportunities. People with mobility restrictions face greater obstacles than other members of society. This article seeks to explore the obstacles faced by wheelchair users in Montevideo, Uruguay. To do so, we designed a set of trips to actual destinations for five types of activities to compare time costs between wheelchair users and people without mobility restrictions. We estimated time costs for sidewalk segments, waiting time, onboard bus travel time, and coupling time (i.e., idle time due to the lack of bus provision closer to the activity starting times) and found that, relative to people without mobility restrictions, each of these transit components poses high costs for wheelchair users. From a policymaking perspective, our findings point out the importance of expanding universal design in public transport. They also call attention to other factors that explain travel time such as sidewalks segments and the temporal organization of urban activities that may turn into schedule mismatches.

Polynomial-Time Carsharing Optimization: Linear Formulation and Large-Scale Simulations

Shared mobility services are useful for low-income people that are not satisfied with public transport and cannot comfortably afford their own vehicle. Among the low-cost shared mobility services, carsharing stands out because it avoids the cost of a driver. This work applies a polynomial-time linear programming formulation to optimize different carsharing business models for the Metropolitan Region of São Paulo. Real mobility data are used, focusing on inhabitants who are able to drive. Scenarios are evaluated varying distances that clients would be flexible to walk to get to an available vehicle or parking slot, comparing carsharing business models, and considering different rental prices. Results show that it is possible to offer a profitable low-cost carsharing service without performing vehicle relocations if clients are flexible enough to walk and if only a subset of trips is selected to be served. Results also demonstrate that trips selected to be served are similar among the different business models; are concentrated on São Paulo’s downtown region; are shorter than the average trip, but otherwise behave in a similar way as compared to the complete set of trips; and the lack of parking slots may be a risk to the carsharing company.

A Petri net model for vehicle scheduling problem

Vehicle scheduling problem consists in assigning vehicles to a set of time-tabled trips, wishing to minimize the objective cost function. Two aprroaches of the problem were studied: single-depot vehicle scheduling problem (SDVSP) and multiple-depot vehicle scheduling problem (MDVSP). There are known several methos of solving this problem based mainly on liniar programming, branch and bound, graph colouring thechniques. SDVSP is solvable in polynomial time, while MDSVP is NP hard. The main propose of this paper is to present a model based on Petri nets for the vehicle sceduling problem. We defined priced timed workflow nets as a suitable model for SDVSP. We tried to emphasize the advantages of using Petri nets in order to describe this kind of planning problems.

Is naturalness associated with positive learning outcomes during environmental education field trips?

Do more natural settings improve students’ learning? We collected surveys immediately following 283 U.S.-based environmental education (EE) field trip programs for youth and used land cover data to examine the relationship between levels of naturalness, defined as the percentage of natural land cover of the EE field trip site, and student learning outcomes. We also examined whether differences in levels of naturalness between students’ day-to-day environment and the field trip setting were related to student learning outcomes. When controlling for grade and race, both levels of naturalness and novel levels of naturalness accounted for approximately 4% of the variance in student outcomes suggesting that other field trip characteristics are more influential.

A Multi-Stage Optimisation Approach to Design Relocation Strategies in One-Way Car-Sharing Systems With Stackable Cars

One of the main operational challenges faced by the operators of one-way car-sharing systems is to ensure vehicle availability across the regions of the service areas with uneven patterns of rental requests. Fleet balancing strategies are required to maximise the demand served while minimising the relocation costs. However, the design of optimal relocation policies is a complex problem, and global optimisation solutions are often limited to very small network sizes for computational reasons. In this work, we propose a multi-stage decision support system for vehicle relocation that decomposes the general relocation problem into three independent decision stages to allow scalable solutions. Furthermore, we adopt a rolling horizon control strategy to cope with demand uncertainty. Our approach is highly modular and flexible, and we leverage it to design user-based, operator-based and robotic relocation schemes. Besides, we formulate the relocation problem considering both conventional cars and a new class of compact stackable vehicles that can be driven in a road train. We compare the proposed relocation schemes with two recognised benchmarks using a large data set of taxi trips in New York. Our results show that our approach is scalable and outperforms the benchmark schemes in terms of quality of service, vehicle utilisation and relocation efficiency. Furthermore, we find that stackable vehicles can achieve a relocation performance close to that of autonomous cars, even with a small workforce of relocators.

The effect of complex load on the reliable operation of solar photovoltaic and wind power stations integrated into energy systems and into off-grid energy areas

A mass renewable power stations commissioning, first of all solar photovoltaic stations (SPVS) and wind power stations (WPS) shall be associated with the properly implemented complex load modeling while calculating their electric operation modes. This article deals with the basic failures of standard algorithm of SPVS and WPS invertors/converters, Fault Ride Through (FRT), that has to reduce the active power output and to increase that of reactive power, in case of voltage drop. In order to prevent outages of SPVS and WPS equipment during emergency disturbances, the trip set points of SPVS and WPS inventors/converters shall be adjusted to those of the adjacent power network protective relay. In this article, an approach to selecting methods for complex loads modeling, in industrial energy areas, has been developed. A method for calculating the value of critical voltage across the load bars has been considered, and the relevancy to apply the dynamic scale model of electric motor has been substantiated. The specific aspects of the voltage-dependent and frequency-dependent load static characteristics employment, in calculations of electromechanical transient processes, have been analyzed. Recommendations on representing electric motors integrated into complex load circuits, in various software packages designed for operation modes calculations, have been stipulated. Various methods making it possible to take account of electromagnetic transient processes, in rotors of asynchronous motors, have been described. A proper account of complex loads enables to ensure reliable functioning of SPVS and WPS integrated into energy systems and operating in isolated energy areas avoiding their outages including those disturbances in power supply of power consuming equipment of industrial areas.

Anti-Islanding Method Development Based on Reactive Power Variation under Grid Support Environments

As the proportion of distributed generation (DG), including Photovoltaic (PV) generation, in the power grid system increases, there are dropouts of large-scale distributed power generation sources due to some transient conditions that negatively affect the power grid stability and power quality. Accordingly, the inverter for DG generation is in a transition period, requiring more complex control performance. Anti-islanding function requirements in particular are becoming more strict because sophisticated grid-connection requirements are demanded, such as voltage ride-through, frequency ride-through, rate of change of frequency, and other functions. Thus, highly advanced anti-islanding methods are required to detect the islanding condition quickly and accurately to stop the inverter. This paper presents the improved anti-islanding method based on reactive power variation (RPV) under grid-support environments for single-phase DG inverters. In order to verify the validity of the proposed method, PSIM simulation was conducted. The proposed method meets the requirements of IEEE Std. 1547-2018 and KS C 8564:2021 by preventing islanding within 0.007 s under the newly adopted voltage/frequency trip setting, while the conventional RPV method fails.

Evaluation of map‐matching algorithms for smartphone‐based active travel data

Global Positioning System (GPS) data on walking and cycling trips can generate useful insights for transportation systems but require substantial processing. One of the key GPS data processing steps is “map-matching”, or inference of the sequence of network links traversed during travel. The objective of this research is to evaluate the accuracy of existing map-matching algorithms for GPS data on active travel. A method to flag erroneous map-matching results without requiring ground-truth data and improvements for active travel data are also proposed. Six map-matching algorithms are applied to a sample of 63 trajectories, stratified on network density and average heading change, extracted from a large set of real-world trips from metropolitan Vancouver, Canada. Results show that the best performing method is PgMapMatch, which can be further improved by adjustments to link costs and allowing wrong-way travel. Two other algorithms have similarly accurate routes (70–90% accuracy, depending on the measure), but fail to generate routes for about a third of trips. The proposed error detection measure can be used (without ground truth data) to flag matched routes requiring visual inspection, with a recommendation to look for: Wrong-way travel, missing links in the network data, and parallel facilities on the same street.

Arc routing with trip-balancing and attractiveness measures — A waste collection case study

This work focuses on a household (or door to door) waste collection problem in the Portuguese municipality of Seixal, which is modelled as a generalisation of a mixed capacitated arc routing problem (MCARP). The MCARP is known to be NP-hard. The proposed methodology uses: i) a GIS (geographic information system), for input/output and to reduce problem dimensions; ii) a matheuristic that iteratively solves a new hybrid model; and iii) two versions of a two-phase matheuristic. The latter pursues the generation of connected and compacted trips. During the first phase, called initial assignment, some links requiring service are assigned to vehicle services. In the second phase, the hybrid model finishes the assignment and generates a feasible set of trips. The quality of the generated solutions is assessed through the total time, as well as by some attractiveness measures. These measures evaluate the adequacy of the solutions for the real case-study, a crucial aspect for trips that need to be accepted by practitioners. With this purpose, a new attractiveness measure that introduces space dependent penalisation of overlaps, named weighted hull overlap, is also proposed. Computational results with 18 Seixal instances, with 265–1223 nodes and 492–2254 links, point to the good performance of the proposed methodology.

Drone logistics for uncertain demand of disaster-impacted populations

The paper introduces a stochastic optimization-based approach to address the logistics for the timely delivery of aid packages to disaster-affected areas utilizing a fleet of drones when the set of demand points is unknown. The major problem addressed is to locate a set of drone take-off platforms so that with a specified probability α, the maximum total disutility (or cost) under all realizations of the demand locations is minimized. A set of discrete scenarios defines the uncertainty set of the demand points. A Chance Constrained Programming (CCP) formulation is developed to select a set of platform locations whose disutility distribution produces minimum α percentile. For each platform location set, and each demand scenario, the total disutility is defined as the total delivery time for serving the demand points plus a penalty for unvisited demand points. For every set of drone platform locations, referred to as a candidate combination of platforms, the resultant disutility distribution is obtained by solving a space-time drone scheduling subproblem for all possible demand scenarios. The drone scheduling subproblem optimally schedules and sequences a set of trips for each drone so that the total disutility is minimized. Owing to the computational complexity of the proposed approach, an approximation method is developed that decomposes the problem into three tractable stages. The first stage identifies a set of most preferable platform combinations. The second stage develops an approximation algorithm based on a greedy approach to mitigate the extensive computational requirements for solving the large number of drone scheduling subproblems. The last stage builds upon the properties of a Sample Average Approximation (SAA) method and of the CCP formulation to select the optimum set of platforms. Finally, the performance of the proposed stochastic approach is evaluated through a series of computational experiments and a case study of Central Florida. The results reveal interesting insights and demonstrate the effectiveness of the proposed logistics system for drone delivery of humanitarian aid packages.

Light-Duty Vehicle Trip Classification Using One-Class Novelty Detection and Exhaustive Feature Extraction

Travel mode classification within travel survey data sets, especially light-duty vehicle (LDV) trips, is foundational, though nontrivial, to emerging mobility systems, travel behavior analysis, and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground-truth travel mode labels. The detection approaches are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the data set requirements. This article proposes an LDV trip detection model as a supplement to current travel mode detection methods for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique—one-class support vector machines—and a novel exhaustive feature extraction technique on continuous time series data [i.e., global positioning system (GPS) speed profiles] for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of pre-existing in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size and feature selection, which will benefit applications limited by highly imbalanced data. This article also discusses performance comparison with regular machine learning approaches, the model's robustness, and the potential to extend the proposed model to multimodal trip prediction.

Semi-supervised Mode Classification of Inter-city Trips from Cellular Network Data

Good knowledge of travel patterns is essential in transportation planning. Cellular network data as a large-scale passive data source provides billions of daily location updates allowing us to observe human mobility with all travel modes. However, many transport planning applications require an understanding of travel patterns separated by travel mode, requiring the classification of trips by travel mode. Most previous studies have used rule-based or geometric classification, which often fails when the routes for different modes are similar or supervised classification, requiring labelled training trips. Sufficient amounts of labelled training trips are unfortunately often unavailable in practice. We propose semi-supervised classification as a novel approach of classifying large sets of trips extracted from cellular network data in inter-city origin–destination pairs as either using road or rail. Our methods require no labelled trips which is an important advantage as labeled data is often not available in practice. We propose three methods which first label a small share of trips using geometric classification. We then use structures in a large set of unlabelled trips using a supervised classification method (geometric-labelling), iterative semi-supervised training (self-labelling) and by transferring information between origin–destination pairs (continuity-labelling). We apply the semi-supervised classification methods on a dataset of 9545 unlabelled trips in two inter-city origin–destination pairs. We find that the methods can identify structures in the cells used during trips in the unlabelled data corresponding to the available route alternatives. We validate the classification methods using a dataset of 255 manually labelled trips in the two origin–destination pairs. While geometric classification misclassifies 4.2% and 5.6% of the trips in the two origin–destination pairs, all trips can be classified correctly using semi-supervised classification.

Exact algorithms for the multiple depot vehicle scheduling problem with heterogeneous vehicles, split loads and toll-by-weight scheme

We study a general version of the multiple depot vehicle scheduling problem, which is motivated by the practice of line-haul transportation in Chinese express delivery firms. In this problem, there are a set of trips to be served by a fleet of vehicles. Each trip has a certain amount of packages to be transported from an origin to a destination. Each vehicle can serve a sequence of trips, but it has to depart from and return to its base depot. We aim at assigning trips to vehicles such that all trips are fully served and the total transportation cost is minimized. This problem is general because several practical features have been considered, including heterogeneous vehicles, service time windows, split loads, and the toll-by-weight scheme. These features greatly broaden the applicability of this problem, but at the price of complicating it. We first formulate this problem into a nonlinear route based model, and then equivalently transform it into a linear route-cover based model. To solve the model, we propose two exact algorithms, namely, a branch-and-price and a branch-and-benders decomposition. Both algorithms are built upon a branch-and-bound framework. Particularly, we design a column generation procedure equipped with an efficient label setting method to solve subproblems involved in the two algorithms. Computational experiments are conducted on a set of random instances, and the results have substantiated the effectiveness and efficiency of our model and algorithms.

Designing algorithms for commodities transportation on a planar graph modelling a map

Algorithms hold great potential for solving real-world challenges. Dr Tetsuo Asano, Kanazawa University, Japan, is a computer scientist with more than 40 years experience in the field of computational geometry. He is also a former president of the Japan Advanced Institute of Science and Technology (JAIST). Asano is interested in using computational geometry to describe solutions in terms of geometry. His present research looks to propose a new framework for solving a transportation problem. Each node may have supplies of some commodities and/or demands of some other commodities. He is exploring whether there is a specific set of trips that can be made by vehicles available at all nodes to meet all demands in an efficient way. Traditionally, an important consideration is finding the most economical way to visit nodes. Asano's work is novel as it uses many vehicles instead of just one. Additionally, each vehicle only travels between neighbours. Mathematical induction is key to Asano's work, enabling him to confirm that a given algorithm works correctly. To date, the researchers have been able to obtain some reasonably fast algorithms if a network contains one cycle but the team is keen to solve the problem in general terms. Next, Asano and the team plan to extend the idea behind the project to an efficient algorithm for finding a schedule for sending vehicles in two or more steps to meet all demands.

Bike-Sharing Demand Prediction at Community Level under COVID-19 Using Deep Learning.

An important question in planning and designing bike-sharing services is to support the user’s travel demand by allocating bikes at the stations in an efficient and reliable manner which may require accurate short-time demand prediction. This study focuses on the short-term forecasting, 15 min ahead, of the shared bikes demand in Montreal using a deep learning approach. Having a set of bike trips, the study first identifies 6 communities in the bike-sharing network using the Louvain algorithm. Then, four groups of LSTM-based architectures are adopted to predict pickup demand in each community. A univariate ARIMA model is also used to compare results as a benchmark. The historical trip data from 2017 to 2021 are used in addition to the extra inputs of demand related engineered features, weather conditions, and temporal variables. The selected timespan allows predicting bike demand during the COVID-19 pandemic. Results show that the deep learning models significantly outperform the ARIMA one. The hybrid CNN-LSTM achieves the highest prediction accuracy. Furthermore, adding the extra variables improves the model performance regardless of its architecture. Thus, using the hybrid structure enriched with additional input features provides a better insight into the bike demand patterns, in support of bike-sharing operational management.

Support Structures for Seal Switches Located Near Bus Lines

The authors propose three designs for seal switch support structures located near power plant bus lines. The first one has a plate that is attached to bus lines located in the same plane. The second uses a support insulator as the case and can be attached to several bus lines at the same time or each of them individually to provide a wide application range. The third design stipulates seal switch installation on overhead line pylons. The authors provide detailed descriptions of the designs and explain how the trip settings are adjusted on them.