Reduce Travel Time Research Articles

NEMECYS: Addressing Challenges to Building Security Into Connected Medical Devices

The European health care system is moving toward personalised, distributed, and home-based services. This is made possible via new and improved connected medical devices (CMDs), and will benefit health care providers in terms of reduced cost and improved service. Patients will see improved quality of life in terms of reduced travel time and reduced stress via treatment at home or where they want it. However, for these benefits to be fully realised, the cybersecurity of CMDs needs to be ensured. This paper presents a brief survey of challenges to building security into CMDs, and introduces NEMECYS, an EU-funded project which will help practitioners to (a) comply with Medical Device (MD) regulations; (b) be able to apply proportionate MD cybersecurity, and (c) build in cybersecurity by design for both MDs and the connected scenarios they operate in.

Radical timetable innovations in long-distance railway passenger transport: How might these affect railway passenger demand?

The positive impact of coordinated timetable innovations throughout national railway networks has been shown exemplarily in the 1970 s and 80 s, when so-called integrated periodic timetables (IPT) were installed in the Netherlands and in Switzerland and then gradually improved. After large-scale changes of the former train offer, rail passenger demand increased significantly. A similar timetable innovation was recently decided for the German railway network. However, the project’s impact on overall demand is uncertain. To approach this question, an elasticity-based forecast of long-distance passenger demand is proposed and adopted to a modelized railway network section that has changed to an IPT. Massive travel time reductions turned out as the most important factor for demand growth followed by demand effects due to the increase of train frequency and changes of a modelized ticket price system. Additional factors influencing nationwide rail passenger demand are conceivable but difficult to generalize.

Multiple Intelligent Control Strategies for Travel-Time Reduction of Connected Emergency Vehicles

Multiple Intelligent Control Strategies for Travel-Time Reduction of Connected Emergency Vehicles

Proximity and Knowledge Spillovers: Evidence from the Introduction of New Airline Routes

This paper examines the causal relationship between proximity and knowledge diffusion by estimating the elasticity of core-based statistical area (CBSA) pair-level citations to variations in travel time induced by the introduction of new flight routes. The findings reveal that decreasing travel time between U.S. cities by 20% increases knowledge flow by 0.5%, which corresponds to an increase of over 15,000 citations at the aggregate level. Rather than boosting within-firm knowledge transfer, travel time reduction leads to a rise in knowledge spillovers primarily across firm boundaries, particularly among those that form joint ventures, have block holdings in each other, or form supply chain relationships. These effects are stronger among city pairs located farther away from each other, with higher absorptive capacity, in complex technology classes, and for newly developed technologies. Additional mechanism tests suggest that the most likely channel through which travel time reduction impacts knowledge spillover is by influencing the transfer of tacit knowledge via facilitating cross-CBSA inventor flow and information acquisition. This paper was accepted by Tomasz Piskorski, finance. Funding: S. Zhou receives support from the National Natural Science Foundation of China [Grant 71804155] and China Fundamental Research Funds for the Central Universities [Grant 20720181047]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.01717 .

An algorithm for flexible transshipments with perfect synchronization

Direct and instant deliveries have gained popularity among customers, but have also increased the burden on cities due to rising transport incidents, especially as delivery vehicles often drive empty when returning to their origin. Flexible transshipments with perfect synchronization would allow loads to be transshiped between these vehicles without the need for storage facilities. This may increase their average proximity to their origin and reduce the proportion of empty rides. We present a problem-specific algorithm with three sub-steps to solve this concept. The first evaluates the relational position between two deliveries and, based on 36 structured cases, decides which pairs to exclude from the solution space. The second formulates the optimization problem to find the best transshipment location while considering time-limit constraints and transfer times. Finally, we apply the request-pair combination problem to graph theory to determine the lowest overall travel time. In our paper, we consider a continuous problem definition which allows flexible transshipments, and assume unit-sized capacity per vehicle and a maximum of one transshipment per request.We show that our algorithm reduces the initial total travel time of direct deliveries by 9.6% in a numerical simulation, and by 11% in a case study using food delivery data from the city of Bordeaux. We also demonstrate that the total time is best reduced by increasing time constraints rather than adding requests to increase the chance of transshipments. Our algorithm not only helps to reduce travel time, but also improves sustainability, mitigates driver shortages and is easy to implement.

The Use of Artificial Intelligence to Optimize the Routing of Vehicles and Reduce Traffic Congestion in Urban Areas

The swift urbanization of cities has given rise to an unparalleled surge in vehicular traffic, leading to substantial congestion, heightened pollution, and a diminished quality of life. This investigation explores the capacity of artificial intelligence (AI) to transform urban mobility by optimizing vehicle routing and alleviating traffic congestion. The objective is to create AI-powered solutions that augment transportation efficiency, diminish travel times, and mitigate environmental repercussions. This paper thoroughly scrutinizes existing AI algorithms, vehicle routing, and traffic management techniques. The study integrates real-time traffic data, road network characteristics, and individual travel patterns to formulate intelligent routing strategies. The proposed AI system adjusts to dynamic traffic conditions through machine learning and optimization algorithms, pinpointing optimal routes and redistributing traffic flows to minimize congestion hotspots. To assess the effectiveness of the AI-driven approach, extensive simulations and case studies are conducted in representative urban areas. Performance metrics, including travel time reduction, fuel consumption, and emissions reduction, are employed to quantify the impact of the proposed system on traffic congestion and environmental sustainability. Furthermore, the study evaluates the scalability, feasibility, and economic viability of implementing AI-based traffic management solutions on a larger scale. The outcomes of this research provide valuable insights into the potential advantages of AI in reshaping urban mobility. By optimizing vehicle routing and diminishing traffic congestion, the proposed AI-driven system has the potential to elevate overall transportation efficiency, reduce energy consumption, and contribute to a healthier urban environment. The findings carry substantial implications for policymakers, urban planners, and transportation authorities seeking innovative solutions to tackle the challenges of contemporary urbanization while promoting sustainable development.

An empirical investigation of the distribution of travellers’ willingness-to-pay: A comparison between a parametric and nonparametric approach

Understanding the distribution of the willingness-to-pay (WTP) to reduce travel time or other unpleasant travel attributes experienced by travellers is crucial to making well-informed choices about infrastructure investments and the pricing of services. Given multimodality, heavy tails, and the impact of averaging errors in the classical parametric distribution-based analysis of WTP, this study compares WTP distributions across six distinct datasets using a range of model specifications, including multinomial logit (MNL), mixed logit (MMNL) and nonparametric logit mixed logit (LML). The models are estimated directly in willingness-to-pay space, highlighting that WTP is not uniformly distributed with a high likelihood of having a multi-modal distribution across the population. The findings confirm the intuition on the presence of negative WTP for some undesirable travel attributes. This study raises awareness among transport researchers, modellers, and authorities about using the correct WTP distributions in practice. It adds some empirical evidence regarding the true underlying WTP distribution of various travel attributes to the transport literature.

Network-wide safety impacts of dedicated lanes for connected and autonomous vehicles

Cooperative, Connected and Automated Mobility (CCAM) enabled by Connected and Autonomous Vehicles (CAVs) has potential to change future transport systems. The findings from previous studies suggest that these technologies will improve traffic flow, reduce travel time and delays. Furthermore, these CAVs will be safer compared to existing vehicles. As these vehicles may have the ability to travel at a higher speed and with shorter headways, it has been argued that infrastructure-based measures are required to optimise traffic flow and road user comfort. One of these measures is the use of a dedicated lane for CAVs on urban highways and arterials and constitutes the focus of this research. As the potential impact on safety is unclear, the present study aims to evaluate the safety impacts of dedicated lanes for CAVs. A calibrated and validated microsimulation model developed in AIMSUN was used to simulate and produce safety results. These results were analysed with the help of the Surrogate Safety Assessment Model (SSAM). The model includes human-driven vehicles (HDVs), 1st generation and 2nd generation autonomous vehicles (AVs) with different sets of parameters leading to different movement behaviour. The model uses a variety of cases in which a dedicated lane is provided at different type of lanes (inner and outer) of highways to understand the safety effects. The model also tries to understand the minimum required market penetration rate (MPR) of CAVs for a better movement of traffic on dedicated lanes. It was observed in the models that although at low penetration rates of CAVs (around 20%) dedicated lanes might not be advantageous, a reduction of 53% to 58% in traffic conflicts is achieved with the introduction of dedicated lanes in high CAV MPRs. In addition, traffic crashes estimated from traffic conflicts are reduced up to 48% with the CAVs. The simulation results revealed that with dedicated lane, the combination of 40-40-20 (i.e., 40% human-driven – 40% 1st generation AVs– 20% 2nd generation AVs) could be the optimum MPR for CAVs to achieve the best safety benefits. The findings in this study provide useful insight into the safety impacts of dedicated lanes for CAVs and could be used to develop a policy support tool for local authorities and practitioners.

New Normal Era: Blended Learning Experience from the Lens of the Student Nurses

Purpose of the Study: This research aims to explore student nurses' experience with blended learning since it is new to the nursing school system in the Philippines. Furthermore, its goal is to discover students' voices dealing with blended learning. Research Method: The study utilized a qualitative descriptive phenomenological research design. It aims to understand and interpret emotionally laden topics. The investigation uncovers what a lived experience means to the individual through in-depth interviews. The researcher conducted this study according to ethical principles to avoid potential participant harm. The School's Research Ethics Committee reviewed the manuscript and granted authorization approval. The research team ensured the participants' privacy throughout the study by maintaining confidentiality for all collected data. Each participant was assigned a code to uphold confidentiality. In this study, ten (10) participants were interviewed via zoom and the transcripts were analyzed using the Giorgi method of thematic analysis. Findings: The ten (10) transcripts yielded four hundred forty-seven (447) significant statements, four hundred ninety-five (495) final codes, fourteen (14) subthemes, and finally, two (2) emergent themes, namely: 1) The future of Nursing Education through adaptation, innovation, and revolutionary learning and 2) Blended learning empowers diversity of learners. Implications: Based on the findings, teaching strategies heavily affect students' interaction, engagement, and attentiveness. Safe learning environments, rest or comfort at home, affordability, and reduced travel time are the advantages of using blended learning strategies for online learning, while simulations, schedules, reduced travel time, actual clinical exposures, and improved knowledge retention are the benefits of using blended learning strategies for face-to-face learning while fragile internet access, meeting deadlines for requirements, increased costs, teachers' differing approaches to handling requirements, schedule conflicts, and getting up early in the morning are challenges for student nurses using blended learning methodologies. Hence, creating a more equitable and effective system that benefits everyone is by transforming education and care through innovation and inclusion.

Reduction of Environmental Pollutants and Travel Burden Through an Academic Medical Center-based Electronic Consultation Program.

Background: We evaluated the impact of electronic consultation (eConsult) in reducing the environmental pollutants associated with health care delivery. Methods: A retrospective analysis of the eConsult data between July 2018 and December 2022 was extracted from the electronic health record (Epic). Travel time and mileage from the patient home to the academic medical center (AMC) were calculated along with fuel expenditure and greenhouses gas savings. Projected savings through the end of the decade were forecast using a random walk model. Results: A total of 15,499 eConsults were submitted to AMC specialist providers from community primary care providers. Completed eConsults (n = 11,590) eliminated the need for a face-to-face visit with a specialist provider, eliminating mileage, fuel, time, and pollutants associated with face to face visits. In-state travel distance saved was 310,858 miles, travel time saved was 5,491 h, with an associated fuel reduction of 13,575 gallons and $56,893 savings. This reduced greenhouse gas emissions by 128 metric tons of carbon dioxide, 0.022 tons of nitrogen oxide, 0.005 tons of methane, and 0.001 tons of nitrous oxide. Out of state travel distance saved was 188,346 miles with 2,842 h reduced travel time, and associated fuel reduction of 8,225 gallons and of $34,118. Reduced greenhouse gas emissions were equivalent to 77 metric tons of carbon dioxide, 0.0132 tons of nitrogen oxide, 0.0033 tons of methane, and 0.0007 tons of nitrous oxide. Conclusion: This study indicates that medical care provided through telehealth modalities reduces the environmental impact of pollutants associated with face to face visits.

Estimation of the Effect of Self-Driving and En Route Preparation in Urban Parcel Delivery: The Case in Seoul

Self-driving technology is on the verge of commercialization and is expected to be utilized in delivery services. At the same time, low efficiency and physical limitations of last mile robots (especially for non-standardized cargo, stairs, etc.) are being pointed out. In addition, as leading overseas companies design robots to complete delivery through interaction with the recipient, it may be difficult to implement the robots in delivery environments like South Korea, where most parcels are not delivered face-to-face. Therefore, this study considers a parcel delivery model in which a person, not a robot, is on-board an autonomous truck for door-to-door deliveries. This study takes three expected effects of a self-driving vehicle into consideration: (i) reduced travel time, owing to alleviation of traffic congestion; (ii) reduced service time, owing to relaxed parking restrictions; and (iii) benefits from en route preparation (planning and collecting in advance of arrival) that can be realized when a person is not required in the driver’s seat. The findings from a case study conducted in Seoul indicate that advanced self-driving capabilities and en route preparation can potentially lead to a doubling of the delivery capacity at a terminal, along with a reduction of over 40% in operating costs per box. As suggested in this study, the en route preparation activities are verified to bring benefits.

Smart Traffic Management System

With the rapid urbanization and increasing vehicular density in modern cities, the demand for efficient and intelligent traffic management systems has become paramount. This paper presents a comprehensive overview of a Smart Traffic Management System (STMS) designed to optimize traffic flow, enhance safety, and minimize congestion in urban areas. The STMS leverages advanced technologies such as artificial intelligence, sensor networks, and data analytics to create a dynamic and adaptive traffic control infrastructure. The key components of the proposed system include real-time traffic monitoring, predictive analytics, and adaptive signal control. Utilizing a network of sensors, cameras, and other data sources, the STMS continuously gathers information on traffic conditions, identifying patterns and potential issues. This data is then processed through machine learning algorithms to predict traffic trends, enabling proactive management strategies. One of the innovative features of the STMS is its adaptive signal control mechanism. Traditional traffic signal timings are often static and fail to respond promptly to changing conditions. The STMS, however, dynamically adjusts signal timings based on real-time traffic data, optimizing traffic flow and minimizing delays. This adaptability is further enhanced by the integration of vehicle-to-infrastructure (V2I) communication, allowing direct communication between vehicles and the traffic management system. Additionally, the STMS incorporates intelligent decision-making algorithms to prioritize emergency vehicles, public transportation, and other critical services. This ensures a swift and efficient response to emergencies while maintaining overall traffic efficiency. Furthermore, the system includes a user-friendly interface accessible through mobile applications and web platforms. This interface provides real-time traffic updates, alternative route suggestions, and other relevant information to empower commuters with the knowledge needed to make informed travel decisions. The implementation of the Smart Traffic Management System holds the potential to revolutionize urban mobility by significantly reducing travel times, fuel consumption, and environmental impact. This paper discusses the underlying technologies, design considerations, and potential benefits of the STMS, highlighting its role in creating smarter and more sustainable urban transportation networks

EE114 Reductions in Travel Time and CO2 Emissions Arising From Patient Transportation to Hospitals in Ireland for Treatment With Intravenous Iron: An Analysis Comparing Ferric Derisomaltose With Ferric Carboxymaltose

EE114 Reductions in Travel Time and CO2 Emissions Arising From Patient Transportation to Hospitals in Ireland for Treatment With Intravenous Iron: An Analysis Comparing Ferric Derisomaltose With Ferric Carboxymaltose

Optimized Design of a Backbone Network for Public Transportation in Montevideo, Uruguay

This article presents the application of exact and metaheuristic approaches to the problem of designing the backbone network of a hierarchically public transportation system for Montevideo, Uruguay. This is a very relevant problem in nowadays smart cities, as it accounts for many social and environmental impacts and also affects the dynamics of the cities. The design of the proposed backbone network is conceived in combination with the bus network, with the main objective of improving the overall quality of service and reducing travel times. Three different variants of the problem are solved, considering different design premises. Exact solvers are proposed for simpler variants of the problem, which account for maximum resilience and bounded travel times. An evolutionary algorithm is proposed for a multiobjectie version of the problem that optimizes cost and quality of service. The main results indicate that the computed optimized designs provide reduced end-to-end travel times, which improve up to five times over the current system, and are economically viable to be implemented.

Comparative Evaluation of Road Pricing Schemes: A Simulation Approach (Australian Perspective)

Road network pricing and congestion charging continue to be debated as efficient instruments to address traffic congestion and emissions. For cities where the schemes have not been implemented yet, the impacts of these schemes are typically evaluated using transport simulation models to understand the impacts and design effective solutions before the schemes are deployed. This paper considers a simulation approach for the city of Melbourne in Australia to investigate the potential impacts of road network pricing on reducing private vehicle travel, road congestion, and vehicle emissions. The study uses a dynamic traffic simulation model developed for Melbourne using the AIMSUN modeling tool, which was extended for modeling road user pricing and congestion charging, including considerations and formulations of distance-based, delay-based, joint-distance-and-delay-based, and cordon-based schemes under low-cost, medium-cost, and high-cost regimes. The study’s contributions also include an extension of the modeling framework to include public transport options to allow for providing travelers with the option of choosing an alternative mode of transport if they do not wish to pay. A mesoscopic stochastic route choice modeling approach was adopted to examine the impact of road pricing inside a nominated charging zone within the network. The results showed it would be possible to achieve a reduction of 11% in vehicle count, a 20% reduction in travel time, a 13% reduction in emissions, and a 3% increase in travel speed within the proposed pricing zone under a high-cost pricing scenario. The results also showed a significant reduction in emissions resulting from shifting drivers who are not willing to pay the congestion charge to public transport. When 20% of car drivers shifted to public transport, carbon emissions were reduced by up to 30% and network performance improved by 45%, compared to the baseline scenario without pricing. The findings of this research provide important directions for policymakers in deciding on the type and scope of charging schemes to use and how these could reshape transportation taxation systems by moving away from taxes on vehicles through registration fees and towards user-pay taxations where travelers pay for the amount of travel they do or the pollution and emissions they are responsible for.

Exploring Information communication Technology-enabled travel Pattern in a city region of India

ABSTRACTInformation and Communication Technology (ICT) is advocated to contribute to sustainable transportation by prompting a change in travel patterns. Therefore, using an ICT-enabled city region in India as the study context, the objectives of the study were to examine the influence of ICT on travel patterns in city regions and explore the strategies to enhance ICT usage for positively changed travel patterns. Findings suggest that definite linkages between ICT usage and travel patterns exist. An increase in ICT usage in daily activities of people will enable a significant reduction in trip generation, travel distance and travel time. Strategic interventions including enhancing networking and ICT connectivity, provisioning free Wi-Fi hot spots at important nodal points, and enabling the availability of real-time road transportation-related information can enhance ICT usage leading to a positively changed travel pattern. This is likely to improve traffic movement and reduce congestion contributing to sustainable urban road transportation.

Mode choice analysis of first-mile shared autonomous vehicles service in Bangkok, Thailand

Shared autonomous vehicles (SAVs) are a promising first-mile mode to access public transportation. This study focuses on the mode choice analysis assuming that SAVs are introduced in Bangkok, Thailand. Stated preference experiments based on efficient design were conducted. The rescaled logit model was obtained from the nested logit trick on the combined revealed-preference and stated-preference data. The results showed that with the introduction of SAVs, the shares of existing taxi, motorcycle taxi (MC), and light public transit (LPT) modes will decrease by approximately 28.24%, 27.50% and 21.93%, respectively. These indicate that people who are accustomed to using taxi and MC as their first-mile services are respectively most likely and second-most likely to change their choices to SAVs. The values of times for income groups were estimated. Service providers should consider reducing travel cost to attract more lower-income users, and reducing travel time to attract more higher-income users. The average elasticities and simulation results imply that the improvement of the travel cost of SAVs should be a priority over travel time in order to effectively attract more individuals to the SAVs mode, and the improvement of travel time of SAVs leads to the greatest shift from taxi and MC.

A situation-aware emergency evacuation (SAEE) model using multi-agent-based simulation for crisis management after earthquake warning

ABSTRACT Earthquake is a disastrous natural hazard that threatens numerous cities worldwide. The interval between the foreshock and the main event can sometimes last several minutes. Meanwhile, crowd emergency evacuation and finding shelter are vital for search and rescue managers. At the same time, many unpredicted challenges, such as the sudden increase in travel demand, shifts in public behavior, and the change in the regular transport supply, may arise due to evacuation conditions, which lead to different situations. This paper aims to introduce an approach for quick decision-making and timely evacuation response required by establishing a situation-aware system to minimize these risks and ensure the success of the evacuation plans, to support and predict current and future actions within the dynamic space of the crisis. The main contribution is innovating a Situation-Aware Emergency Evacuation (SAEE) model to enable crisis managers and evacuees to make the right decisions by providing timely and reliable information about the situation. This method is utilized in two situations: designing the emergency evacuation plan and finding the shortest/safest routes to reduce travel time for evacuees. Therefore, a hybrid approach is introduced, which involves a Fuzzy Inference System (FIS) and Deep Long Short-Term Memory (DLSTM) algorithm to identify, infer, and extract the existing situation at different levels (e.g. people, vehicles, and surroundings) after a foreshock using multi-agent-based simulation. The method proposed was simulated in the traffic network of District 6 of Tehran, the capital of Iran. The model results show that the evacuees’ spatial knowledge and perception, as well as awareness of the situation of other agents and their surroundings, led to a significant (40%) reduction in the complete evacuation time. This time is considered the most pivotal factor in saving human lives and their arrival in safer areas. The role of situation awareness systems and increasing human cognition and perception can significantly help in this matter.

The political economy of assisted immigration: Australia 1860–1913

From 1860 to 1913 the six colonies that became states of Australia strove to attract migrants from the UK with a variety of assisted passages. The colonies/states shared a common culture and sought migrants from a common source, the UK, but set policy independently of each other. This experience provides a unique opportunity to examine the formation of assisted immigration policies. Using a panel of colonies/states over the years 1862 to 1913 I investigate the association between measures of policy activism and a range of economic and political variables. Assisted migration policies were positively linked with government budget surpluses and local economic prosperity. They were also associated with political participation including the widening of the franchise and remuneration of members of parliament. While the reduction in travel time to Australia reduced the need for assisted migration, slumps in the UK increased the take-up of assisted passages.

Digital health in the EP lab: Initial experience using remote access and technical support for complex ablation procedures

Abstract Aims/Objectives Three-D electroanatomical mapping (EAM) systems are a prerequisite for treatment of complex arrhythmias. Until date, the EAM system is operated by qualified staff or a field technical engineer (FTE) from the control room. Novel technology allows for remote access (RA) of the EAM from anywhere in the world. RA increases the flexibility of the EP lab, reduces travel time for the FTE and overcomes hospital access limitations e. g. during the COVID-19 pandemic. We report on the safety and feasibility of remote operation of the EAM for complex ablation procedures. Methods RA mapping was achieved by combining the EAM software with an integrated audiovisual solution for remote support. Communication between the operator and the remote support was achieved using internet-based headphones in the RA group. The 3D EAM system was controlled exclusively from home office. In the conventional group (CG), EAM was exclusively controlled from the control room in the EP lab. Ablation procedures were performed as per institutional standard. Results We investigated 50 RA assisted consecutive electrophysiological procedures from 09/2022 to 02/2023 in our University Heart Center. We compared a group of patients undergoing ablation procedures with RA assistance (28/50 (56%) patients were male, median age: 66.5 yrs [IQR 56, 76], BMI: 25.2 kg/m2 [IQR 22, 30]) with a control group (31/50 (62%) male, median age: 66.5 yrs [IQR 54, 78], BMI 28.2 kg/m2 [IQR 24, 30], p > 0.05 for all). Ablation procedures were matched in both groups: 20 pulmonary vein isolations (3 paroxysmal, 17 persistent atrial fibrillation ), 4 cavotricuspidal isthmus ablations, 13 ablations of atrial tachycardia, 1 premature ventricular complex ablation, 3 of ventricular tachycardia, 2 AV reentry tachycardia and 7 AV-node reentry tachycardia cases. In the RA group, the 3D EAM system was controlled exclusively from home office in all patients. In one patient, the internet based headphone connection was instable. Therefore, the communication was established via a conventional smartphone. No other technical issue was noted. No switch to in house support was required. There were no major complications observed. Procedural data (total procedure time median 100 min [IQR 76, 120] vs. control group 86 min [IQR 60, 110], total fluoroscopy time (median 9.1 min [IQR 6, 13] vs. control group 8.7 min [IQR 5, 12], p > 0.05) were comparable to previous procedures without RA. Due to RA travel time could be reduced by 139 hr in total in comparison to the control group. Conclusions RA for EAM of any arrhythmia is feasible and safe in this single center study. Procedural data were comparable to previous procedures. In the future, this new solution might have a great impact on facilitating EP procedures.Table procedural data