Trip Time Research Articles

Innovative Loss of Field Protection Algorithm for Small-Scale Synchronous Generators

Although conventional loss of field (LOF) protection schemes, especially impedance-based ones, are widely used for large-scale synchronous generators, they may not provide proper performance for small-scale synchronous generators (SSSGs) due to various concerns including, (i) large tripping time for unrecoverable LOF (ULOF) which may lead to severe voltage drop and stalling of nearby induction motors, (ii) LOF function maloperation during islanding events, and (iii) LOF function maloperation during recoverable LOF events. To resolve these issues, a new adaptive LOF protection logic is proposed for SSSGs. After ULOF events, the automatic voltage regulator (AVR) output gradually increases, while the SSSG internal voltage does not considerably increase. Based on this principle, some measurable or easily calculable parameters, including AVR output and SSSG internal voltage, are adopted to discriminate ULOF from other disturbances. Meanwhile, the proposed logic is wisely equipped with an adaptive time delay to prevent stalling of nearby induction motors and enhance operation speed during ULOF. Performance of the proposed algorithm is evaluated under various LOF and islanding events as well as short circuits. Obtained promising results confirm desirable performance of the proposed approach. Besides, compared to the impedance-based method, the proposed adaptive time delay considerably improves the security and reliability.

Congestion in cities: Can road capacity expansions provide a solution?

Road network congestion; a traffic state characterised by slower speeds, longer trip times, and increased vehicular queuing; is a major issue in most urban areas around the globe. Building more roads is a commonly employed policy intervention to reduce congestion. This strategy, however, is controversial because under certain conditions road capacity expansions may induce growth in traffic volumes. A crucial precursor to understanding whether road capacity expansions provide a solution to congestion is to quantify the technology driving congestion in urban road networks. This congestion technology describes the variation in performance of the network, often represented by traffic flow through the road network, over its intensity of use given by the number of vehicles in the network. However, obtaining empirical estimates of congestion technology from data on traffic variables is challenging due to statistical biases that emerge via the complex interactions between traffic flow, traffic controls, and capacity. To adjust for such biases, this paper presents an approach based on causal statistical modelling to quantify the nature and form of congestion technology in road networks in twenty-four cities worldwide. Our results suggest that increasing network capacity is in general not an efficient solution to manage congestion, in the sense that the average travel speed in the network does not increase substantially with an increase in capacity. This result and our congestion technology estimates have important implications for optimal urban transportation strategies.

Assessment of communication channel effects on time-domain protection functions tripping times

This paper investigates the effects of communication channel latency (CCHL) on time-domain line protection functions. The Alternative Transients Program (ATP) was used to simulate the monitored power system and to model both the analyzed relays and communication channels via MODELS language. By doing so, the need for extra long optical fibers for laboratory tests is overcome. To provide a reliable investigation on the CCHL effects, a time-domain relay model is firstly implemented and validated by comparing its response against an actual device. Then, the communication channel is set to properly represent practical CCHL values under different fault scenarios for transmission lines with different lengths. The obtained results show that including the CCHL effects on time-domain protection testing procedures is of utmost importance because, depending on the data propagation time through the communication channel, it may have a relevant influence in what we call here “race of protection functions”.

An efficient optimization algorithm for electric vehicle routing problem

AbstractThere has been a steady increase in the prevalence of electric vehicles (EVs) (electric cars) in the transportation industry. The limited range of EVs and the lack of charging facilities make it harder to gain widespread adoption of the EV service. Several optimisation algorithms for EV routes have not yet resolved the problems of trip time and travel cost. The study provides a Bat optimisation technique to reduce trip time and costs while taking into account customer service requests and EV charging schedules. Taking into account battery capacity, charging duration, and delivery/pickup needs, the suggested method may determine the optimal path to supercharger stations. To demonstrate its effectiveness, it is compared to other state‐of‐the‐art algorithms across a variety of benchmark cases.

Breaking down public transit travel time for more accurate transport equity policies: A trip component approach

In the Northern hemisphere, low-income transit users typically take shorter trips and use buses in a greater proportion, but there is limited knowledge on inequalities in the detailed characteristics of public transit trips.In this paper, we seek to understand and explain differences in segment components of transit trips across socio-economic groups. Trip components include access time, wait time, total travel time, overall trip speed and percent of trip time spent in access and wait (off-vehicle time).Using the Montreal metropolitan region travel survey (2013), we focus on the 46,186 transit trips made by 20,138 individuals for which household income was available. This data is augmented by time estimations of the aforementioned trip components, and combined with trips specific transit service measures calculated using General Transit Feed Specification (GTFS). We first assess the relationship between trip components and income using descriptive statistics. OLS regressions are then conducted to identify which individual and transport-based factors contribute to variations in trip component outcomes.Low-income transit users take more transit trips (especially bus and Metro) than their higher income counterparts and combine multiple transit modes through more frequent transfers. While average access and wait time are individually lower than those for trips of wealthier respondents, low-income total trip time is similar but trip speed is considerably lower. Lower-income populations also spend a greater share of their trips in access and wait time due to service and trip discrepancies including trip timing, proximity to metro stations and requirement for transfers. When controlling for other factors, the conditions under which trips by low-income individuals are taken mainly drive income differences.

Reinforcement learning method for the multi-objective speed trajectory optimization of a freight train

With the rising urge to mitigate the green house effect, reducing the energy consumption of the freight train attracts much attention. Multiple constraints should be taken into account to solve the energy-efficient control problem, which can be reformulated as the multi-objective optimization. This paper proposes a Reinforcement Learning (RL) method for the multi-objective speed trajectory optimization with the goal of the energy-efficiency, punctuality and accurate parking simultaneously. Since the solution space for the optimization problem in this paper is large and discrete, a Gated Recurrent Unit (GRU)-based network is proposed to achieve the fast approximation of the optimal value function instead of the lookup Q-table. Meanwhile, a new architecture, including the embedding matrix, is used to model the control sequence that generates the speed trajectory. Besides, this paper constructs a Deep Q-Network (DQN) framework to train the GRU network without relying on the prior knowledge of the freight train model. Finally, the Intelligent Train Operation (ITO) algorithm is proposed and verified, using the data of Beijing–Guangzhou Railway Line and HXD1B electric locomotive. The case studies indicate that the reward function for the ITO algorithm converges rapidly and the energy consumption monotonically decreases with the trip time, which satisfies the multiple optimization objectives. In terms of saving the energy consumption, the ITO algorithm performs better than Fuzzy Predictive Control (FPC), Genetic Algorithm (GA) and the field test data. The computation time of different speed trajectories demonstrates that the ITO algorithm is applicable to generating the optimal speed trajectory off-line.

A Data-Driven Iterative Learning Approach for Optimizing the Train Control Strategy

The energy-efficient train control (EETC) problem is investigated in this paper. And a soft actor-critic-based method is proposed to optimize the train driving strategy. Firstly, EETC problem is converted to the inverse problem, i.e., minimizing the trip time of the journey with constant energy consumption. Based on the conversion, the EETC problem is reformulated as a finite Markov decision process which can be solved by deep reinforcement learning algorithms. Secondly, an optimization method based on the soft actor-critic (SAC) method is designed to calculate the optimal driving strategy of the train with introducing the reservoir sampling method. Finally, some case studies are conducted to verify the effectiveness and performance of the proposed method. Simulation results demonstrate that a good energy-saving performance can be achieved. In single interval, the SAC-based method can reduce about 1.65% of the energy consumption compared with numerical method. And the energy consumption reduction can be extended to be 6.49% when the proposed approach is applied in multiple intervals.

Performance Evaluation of an IEEE 802.15.4-Based Thread Network for Efficient Internet of Things Communications in Smart Cities

The increasing demand for Internet of Things (IoT) applications has resulted in vast amounts of data, requiring the utilization of big data analytics. The integration of big data analytics in IoT-based smart cities can greatly benefit from the development of wireless communication protocols, among which the Thread protocol has emerged as a promising option. Thread is IEEE 802.15.4 based and has advanced capabilities like mesh networking, IPv6 support, and multiple gateways providing no single point of failure. This paper presents the design and evaluation of a low-cost mesh network using Raspberry Pi, nRF52840 dongle, and OpenThread 1.2 (i.e., an open-source software implementation of the Thread protocol stack). The research elaborates on the hardware and software solutions used, as well as the network topologies adopted. To evaluate the performance of the developed system, extensive real-time tests are performed, considering parameters, such as jitter, packet loss, and round trip time. These tests effectively demonstrate the effectiveness of the Thread network. Furthermore, the impact of varying payload size and bitrate on the network is analyzed to understand its influence. The behavior of the multi-hop network is also examined under link failure scenarios, providing insights into the network’s robustness. Our findings provide valuable insights for researchers interested in designing low-cost and efficient mesh networks for various IoT applications, including home automation, building/campus monitoring systems, distributed industrial IoT applications, and smart city infrastructure.

Analysis and Modelling of Road Traffic Using SUMO to Optimize the Arrival Time of Emergency Vehicles

Traffic simulation tools are used by city planners and traffic professionals over the years for modelling and analysis of existing and future infrastructural or policy implementations. There are numerous studies on emergency vehicle (EV) prioritization in cities all over the world, but every area is unique and requires the data collection and simulation to be done separately. In this case, the focus area is the Mörfelder Landstraße in Frankfurt am Main, Germany, one of the busiest streets in this city. Thestudy illustrates demand modelling, simulation and evaluation of a traffic improvement strategy for EVs. Vehicular traffic such as passenger cars and trams are simulated microscopically. To perform accurate traffic simulation, input data quality assurance and cleansing of Master Data is required. Therefore, the data is adapted to reproduce the real-world scenario and transformed into the readable format for the simulation model. Vehicular demand is calibrated by traffic count data provided by the Frankfurt Traffic Department. To model road traffic and road network, origin destination matrices using the Gravity Mathematical Model and Open Street Maps are generated, respectively. This process is time-consuming and requires effort. However, this process is critical to get realistic results. In the next step, the road traffic is simulated using SUMO (Simulation of Urban mobility). Finally, EV relevant key performance indicators (KPIs): total trip time and total delay time are derived from simulations. The real-world scenario is compared with five alternative scenarios. The comparison of the KPIs revealed that the real-world scenario results in longer travel times compared to the EV-prioritization scenario. In the least case, the overall travel times for EV has decreased significantly and, as we know, in the case of EVs, even a few seconds saved could prove crucial for a person in need.

Optimal driving strategies for emergency operation of high‐speed trains using on‐board energy storage systems

AbstractA power outage occurs when there is an interruption to traction power system. In such emergency situations, trains are expected to achieve autonomy operation powered by on‐board energy storage systems (OESS). This paper presents optimization models and methods to find optimal driving strategies for train emergency operation. Specifically, a nonlinear and non‐convex program is first formulated in space‐domain to minimize trip time under the limits of power and energy capacity of OESS. To improve computational efficiency, the time dynamics is removed from constraints and adjoined to the cost function. Furthermore, convex modelling steps are proposed to reformulate the problem as a convex program that can be solved efficiently. A bi‐level algorithm is designed to obtain optimal driving strategies that can arrive at destinations as soon as possible. Compared to energy‐optimal driving strategies, the proposed methods can effectively reduce the trip time.

Predicting ICD therapy and post-ablation ventricular tachycardia recurrence using cardiac MRI-based advanced computational reentrant pathway analysis

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): EACVI, Netherlands Heart Institute. Background MRI late gadolinium enhancement (LGE) images can provide novel insights about critical pathways through scar but does not assess the vulnerability of these pathways for sustaining scar-mediated ventricular tachycardia (VT). Computational modelling can augment the insights from imaging derived metrics by providing the functional implications of structural anatomy of the substrate. However, current (monodomain) approaches are computationally expensive and may not, by design, extract all critical pathways. Aim This study evaluated the performance of a novel, reaction-Eikonal based, automated reentrant pathway finding algorithm (VITA) to assess the functional viability of critical circuits identified on LGE and non-invasively predict arrhythmic risk in both an ICD and post-ablation cohort recurrence. Methods ADAS LV and custom-made software was used to generate 3D patient-specific ventricular models in a prospective cohort of post-infarct ICD patients (cohort 1, n=40) and a retrospective cohort of 20 post-infarct VT-ablation patients (cohort 2). Our Virtual Induction and Treatment of Arrhythmias (VITA) framework was then applied to comprehensively probe the viability of the scar substrate to sustaining reentrant circuits. VITA metrics, related to the numbers of induced VTs and their corresponding round trip times (RTTs), were compared with appropriate ICD therapy (cohort 1) and VT-recurrence (cohort 2) during follow-up. Results Patients in both cohorts with an event had higher VITA metrics. In cohort 1 (ICD), VITA demonstrated significantly more inducible VTs (6.6±4.2 vs. 4.1±3.4, p = .044), longer mean RTT (116.2±50.9 ms vs. 76.9±42.6 ms, p = .012) and max RTT (194.4±105.1 ms vs. 109.6±78.7, p =.009) in the event group. In addition, Cox-regression demonstrated a significant independent association with an event: induced VTs (HR 1.67; CI 1.04–2.68, p = .03), mean RTT (HR 2.14; CI 1.11–4.12, p = .02), maximum RTT (HR 2.13; CI 1.19–3.81, p = .01). In cohort 2 (VT-ablation), total induced VTs (85±43 vs. 42±27, p = .01) and unique VTs (9±4 vs. 5±4, p = .04) were significantly higher in patients with- compared to patients without recurrence, and were predictive of recurrence with AUC of .820 and .770, respectively. Max RTT demonstrated a trend towards significance 293 ± 90 ms vs. 200 ± 114 ms (p = .06) for recurrence and non-recurrence, respectively. No differences were observed in mean RTT between the two groups. Conclusion VITA enabled quantitative assessment of pro-arrhythmic vulnerability of the substrate which related directly to patient outcomes. The number of induced VTs were the most robust measure of appropriate ICD therapy and post-ablation arrhythmia recurrence. The RTT metrics, related to viable circuit lengths through scar, demonstrated a significant independent association with appropriate ICD therapy. Future studies should investigate the clinical utility of a VITA-guided selection and treatment approach.

Analysis for flow termination in the decentralized and centralized pre-congestion notification architecture

This paper discusses decentralized and centralized pre-congestion notification (PCN)-based measured-rate flow termination (FT) in the case of a single bottleneck. The paper finds out that asynchronous measurement periods between ingress–egress aggregates (IEAs) cause overtermination in the decentralized PCN although RFC6661 states that the operation of PCN is not affected if the starting and ending times of the measurement periods for IEAs are different. Also, the paper finds that the asynchronous measurement periods can prevent a FT for one IEA from terminating enough flows in one shot. Furthermore, the paper proves that overtermination occurs in the decentralized PCN due to asynchronous measurement periods regardless of the same or different round trip time (RTT) and synchronous measurement period with different RTTs. In contrast, the centralized PCN can avoid overtermination due to effects of the asynchronous measurement periods and different RTTs.

Optimal Thermal Management, Charging, and Eco-Driving of Battery Electric Vehicles

This paper addresses optimal battery thermal management, charging, and eco-driving of a battery electric vehicle (BEV) with the goal of improving its grid-to-meter energy efficiency. Thus, an optimization problem is formulated, aiming at finding the optimal trade-off between trip time and charging cost. The formulated problem is then transformed into a hybrid dynamical system, where the dynamics in driving and charging modes are modeled with different functions and with different state and control vectors. Moreover, to improve computational efficiency, we propose modeling the driving dynamics in a spatial domain, where decisions are made along the traveled distance. Charging dynamics are modeled in a temporal domain, where decisions are made along a normalized charging time. The actual charging time is modeled as a scalar variable that is optimized simultaneously with the optimal state and control trajectories, for both charging and driving modes. The performance of the proposed algorithm is assessed over a road with a hilly terrain, where two charging possibilities are considered along the driving route and the battery is soaked to the ambient before departure. According to the results, trip time including driving and charging times, is reduced by 44%, compared to a case without active heating/cooling of the battery.

Predictors of distractive activities to walking in Accra, Ghana

ABSTRACT Walking is a fundamental mode of transport for many people globally, with immense health and environmental benefits. However, increased walking is associated with an increased risk of road traffic crashes and injuries, especially where traffic enforcement is poor, and pedestrians could easily be distracted. Despite this, a plethora of evidence exists on driver distraction; however, there is a dread of evidence of pedestrian distraction (i.e. distracted walking), particularly in Africa. This paper uses a quantitative methodology to examine the predictors of distractive activities to walking in Accra, Ghana. The study surveyed 400 pedestrians within Accra’s Central Business District (CBD). A questionnaire was deployed via Kobo Toolkit and Jamovi was used in analysing the data. The study observed that three of the top four distractive activities to walking were digital devices-related (e.g. the use of mobile phones). We found that listening to music, making, receiving phone calls, and conversing with other people while walking are the main distractive activities. The binary logistic regression model found sex, age, level of education, occupation, reasons for walking, weekly time for walking and time for common trips as significant predictors of distractive activities to walking.

Current Transformer Saturation Detection Method Based on Artificial Neural Network

When current transformer is saturated, mainly due to the presence of an exponentially decaying DC component in the fault current, its secondary current has a distinctive distorted waveform which significantly differs from its primary (true) waveform. It leads to an underestimation of the secondary current value calculated by the relay protection compared to its true value. Thus, in its turn, results in trip time delay or even in a relay protection devices operation failure, since its settings and algorithms are calculated and designed on the assumption that the secondary current waveform is sinusoidal and proportional to the primary. And since, when using classical electromagnetic current transformer, it is impossible to exclude the possibility of its saturation, the detection of such abnormal condition is an urgent technical problem. The article proposes to use an artificial neural network for this purpose, which, together with the traditional method of saturation detection based on adjacent secondary current samples comparison, allows implementing a fast and reliable current transformer saturation detector. The article details the stages of the practical implementation of such an artificial neural network. The MATLAB-Simulink environment was used for assess the proposed saturation detector operation. The experiments that had been performed confirmed that proposed method provides fast and accurate saturation detection within the wide range of the power system and current transformer parameters change.

New York City Taxi Trip Duration Prediction Using Machine Learning

Abstract: Given the complexity of urban transportation networks and the multiple variables that might affect journey times, estimating the length of cab rides in New York City (NYC) is a difficult process. In this study, we provide a unique method for resolving this issue that makes use of machine learning techniques and a wide range of attributes gleaned from taxi trip data. We start by gathering a sizable collection of historical records of NYC taxi trips,providing specifics like pick-up and drop-off points, timestamps, and lengths of trips. To deal with outliers, missing values, and geographical and temporal irregularities, we preprocess the data. Furthermore, we design a broad range of characteristics, such as geographic coordinates, time of day, day of the week, and weather conditions, to capture the spatial, temporal, and contextual elements of each journey. Then, using gradient boosting methods, we create a prediction model that efficiently uncovers the intricate patterns seen in the data. We carefully adjust the model's hyperparameter to enhance performance and use cross-validation techniques to guarantee resilience. In addition, we apply ensemble techniques to enhance prediction precision and minimise model bias. We conduct lengthy tests on a held-out test set and compare the performance of our model to a number of baseline techniques frequently employed in triptime prediction in order to assess the efficacy of our suggested strategy. The outcomes show that our strategy works better than the competition, with lower prediction errors and higher accuracy. We also do interpretability assessments to learn more about the variables that have the most impact on estimates of trip time. Our results demonstrate the potential of feature engineering and M L approaches for precise and trustworthy taxi trip length prediction in NYC. The suggested method not only helps taxi service companies by allowing them to more accurately predict journey lengths, but it also improves customer experience by giving more precise travel time estimates. Additionally, our approach may be used as a starting point for future studies in the field of urban transportation prediction, enabling better efficiency and planning in urban mobility networks.

Detection of Internet‐wide traffic redirection attacks using machine learning techniques

AbstractInternet‐wide traffic redirection attacks have been reported for long, and are mainly caused by Border Gateway Protocol route hijacking. Such attacks can be quite harmful, impairing access to popular Internet sites for long periods. This work addresses the use of machine learning techniques (both unsupervised and supervised) leveraging from a distributed monitoring infrastructure of probes that measure the round trip time to Internet sites under surveillance. The detection process is separated into two stages: per‐probe classification and a combination of individual probe decisions. Our results show that the best strategy is to classify using an unsupervised technique based on Tukey's method and to combine using Hidden Markov Models, due to its performance and adaptability to different attack types.

Predicting ICD therapy and post-ablation ventricular tachycardia recurrence using cardiac MRI-based advanced computational reentrant pathway analysis

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Netherlands Heart Institute Fellowship, CVON PREDICT2 Young Talent Program Background MRI late gadolinium enhancement (LGE) images can provide novel insights about critical pathways through scar but does not assess the vulnerability of these pathways for sustaining scar-mediated ventricular tachycardia (VT). Computational modelling can augment the insights from imaging derived metrics by providing the functional implications of structural anatomy of the substrate. However, current (monodomain) approaches are computationally expensive and may not, by design, extract all critical pathways. Aim This study evaluated the performance of a novel, reaction-Eikonal based, automated reentrant pathway finding algorithm (VITA) to assess the functional viability of critical circuits identified on LGE and non-invasively predict arrhythmic risk in both an ICD and post-ablation cohort recurrence. Methods ADAS LV and custom-made software was used to generate 3D patient-specific ventricular models in a prospective cohort of post-infarct ICD patients (cohort 1, n=40) and a retrospective cohort of 20 post-infarct VT-ablation patients (cohort 2). Our Virtual Induction and Treatment of Arrhythmias (VITA) framework was then applied to comprehensively probe the viability of the scar substrate to sustaining reentrant circuits. VITA metrics, related to the numbers of induced VTs and their corresponding round trip times (RTTs), were compared with appropriate ICD therapy (cohort 1) and VT-recurrence (cohort 2) during follow-up. Results Patients in both cohorts with an event had higher VITA metrics. In cohort 1 (ICD), VITA demonstrated significantly more inducible VTs (6.6±4.2 vs. 4.1±3.4, p = .044), longer mean RTT (116.2±50.9 ms vs. 76.9±42.6 ms, p=.012) and max RTT (194.4±105.1 ms vs. 109.6±78.7, p =.009) in the event group. In addition, Cox-regression demonstrated a significant independent association with an event: induced VTs (HR 1.67; CI 1.04–2.68, p = .03), mean RTT (HR 2.14; CI 1.11–4.12, p = .02), maximum RTT (HR 2.13; CI 1.19–3.81, p = .01). In cohort 2 (VT-ablation), total induced VTs (85±43 vs. 42±27, p = .01) and unique VTs (9±4 vs. 5±4, p = .04) were significantly higher in patients with- compared to patients without recurrence, and were predictive of recurrence with AUC of .820 and .770, respectively. Max RTT demonstrated a trend towards significance 293 ± 90 ms vs. 200 ± 114 ms (p = .06) for recurrence and non-recurrence, respectively. No differences were observed in mean RTT between the two groups. Conclusion VITA enabled quantitative assessment of pro-arrhythmic vulnerability of the substrate which related directly to patient outcomes. The number of induced VTs were the most robust measure of appropriate ICD therapy and post-ablation arrhythmia recurrence. The RTT metrics, related to viable circuit lengths through scar, demonstrated a significant independent association with appropriate ICD therapy. Future studies should investigate the clinical utility of a VITA-guided selection and treatment approach.

How Time Inefficient and Uncertain are Paratransit Trips Compared to Car Trips

The Americans with Disabilities Act (ADA) stipulates that paratransit mobility be comparable to public transit. However, with just under 5% of the population relying on public transportation, transit is not a representative benchmark. To highlight this inequality, we compare trip times by paratransit to counterfactual car-based travel. Using 2.5 years of demand data from the Denver Regional Transportation District’s Access-a-Ride service, we compare paratransit trip times to counterfactual car-based trips for the same origin–destination pairs, controlling for various trip characteristics. Compared to car travel, the findings indicate high variability and uncertainty associated with paratransit trip times. For the same origin–destination pairs, the mean paratransit trip time is almost twice that of the mean car travel time, with a standard deviation for paratransit trip time fourfold that of the standard deviation for car trip time. For perspective, traveling an average of 10 mi during the 7–8 a.m. morning peak can take about 16 min by car with almost no variability, while that same trip can take an average of 25 min by paratransit, with 5% of trips being outside the 95% confidence interval and thus unpredictable. Paratransit trip time inefficiency tends to be particularly worse for females; older adults; those making trips between 9 and 11 a.m.; cash-paying customers; those making shorter trips; and those traveling during inclement weather, including cold temperatures. These findings suggest a need to re-assess using public transit as a benchmark for paratransit supply as regulated by the ADA.

Application of the HeliosX ICF Advanced Propulsion Mission Analysis Code to Perturbed Interstellar Design Models

HeliosX is a system integrated programming design tool which has the purpose of calculating spacecraft mission profile and propulsion performance for inertial confinement fusion (ICF) driven designs. This code uses the vehicle configuration input and capsule assumptions and then calculates the likely mission profile for a given destination target. The key technology is the inclusion of the fusion propulsion system and an adequate modelling of its likely energy outputs. This paper discusses calculations for perturbed design concepts from a baseline model in both series and parallel thrust mode. These new concepts are collectively known as the advanced baseline models which are presented in preliminary form under the names Resolution, Endeavour and Pegasus. These are for missions to 4.3 ly in trip times of less than 100 years for flyby and rendezvous configurations carrying a 150 tons payload. The designs utilise an ICF capsule mass of 0.288 g filled with D-3He fuel detonated at a pulse frequency in the range 100-150 Hz. The calculations show that the propulsion systems are characterised by thrusts 0.3-2 MNkg-1, jet powers 1.2-9.2 TW and specific powers 2.9-5.1 MWkg-1 for interstellar missions at 0.045-0.049c. In addition to the preliminary mission performance calculations we also discuss the philosophy and methodology used in the design evolution. Keywords: Interstellar Studies, Fusion Propulsion, HeliosX, Project Icarus