Travel Time Research Articles

Sub-attosecond time delays of photoemission from asymmetric cores of molecules

We theoretically study the extreme ultraviolet (XUV) photoemission of the asymmetric molecular ion HeH2+ beyond the dipole approximation. Based on the double-slit interference of the photoelectron emitted from two atomic cores, we propose a scheme to obtain the XUV photoemission time delays originated from different physical mechanisms. In our study, two types of sub-attosecond photoemission time delays are identified and extracted from the observable photoelectron momentum distributions. One is the so-called birth time delay, originated from the travel time of the photon across the molecule, and the other is the escaping time delay of the photoelectron from two different cores. The present study shows the observable evidence demonstrating that in single-photon ionization, it takes more time for the photoelectron escaping from the helium nucleus than that from the proton. Published by the American Physical Society 2024

Parotidectomy Trends Toward Outpatient for Benign Disease.

We evaluate the safety of outpatient parotidectomy. We evaluate factors that lead to planned admission and compare costs. We evaluate trends toward outpatient, and the outcomes of switching admission status, total versus superficial approach, and ambulatory versus hospital site. Retrospective cohort study. Single tertiary academic center. Retrospective review of patients who underwent parotidectomy for benign tumors from 2018 to 2023. Of 370 parotidectomies performed, there were a planned 162 admissions and 208 outpatient procedures. A travel time > 60 minutes (odds ratio [OR] = 0.487, confidence interval [CI]: 0.296-0.803, P = .005) and total parotidectomy (OR = 0.448, CI: 0.226-0.89, P = .022) decreased the odds of a planned outpatient procedure. In a multivariable model, longer operative time increased the odds of switching to inpatient (n = 29, OR = 1.02, CI: 1.007-1.033, P = .002) and drain placement decreased the odds of switching to outpatient (n = 15, OR =0.035, CI: 0.004-0.298, P = .002). There was no significant difference in surgical complications, phone calls, clinic visits, readmission rates, or recurrence between outpatient and inpatient. This remained true when comparing surgical facility and superficial versus total parotidectomy. After COVID was declared an emergency, there was a trend toward outpatient parotidectomy (72.7% vs 48.9%, P < .001), but no change in complication rates. At our institution, outpatient parotidectomy saved $3838 compared to overnight admission. This study supports that outpatient parotidectomy is safe. This remained true for patients switching admission status, undergoing superficial or total parotidectomy, and having their operation at an ambulatory site. We demonstrate that institutions can safely increase outpatient parotidectomy rates and outpatient parotidectomy is cost effective.

Evaluation of groundwater travel time through a multilayered aquifer using multiple tracers and a novel transport approximation

AbstractAquifer long-term replenishment (ALTR) is a managed aquifer recharge (MAR) strategy by which reclaimed water is continuously delivered by injection wells to depleted, confined aquifer systems to increase groundwater storage and increase the potentiometric surface over space and time. One implication of large-scale continuous recharge is a large radial impact and the need to quantify transport in radially extensive strata. The use of an artificial tracer can be cost-prohibitive as the radial front moves further from the injection well. This investigation employs a novel approximation for radial transport to track the radial front of recharge, injectate constituents and simulation of tracer breakthrough concentrations under transient recharge rates, variable depth-dependent flow distributions over time, and variable influent concentrations. Six constituents—sulfate, chloride, total organic carbon (TOC), fluoride, 1,4-dioxane and N-nitrosodimethylamine (NDMA)—were chosen to evaluate conservative transport and semiqualitatively assess attenuation of nonconservative constituents relative to conservative tracers. Results indicate that sulfate acted as the most effective conservative tracer for characterization of transport and travel times at the study site. The analytical model was modified to account for variable operations at the MAR demonstration facility and was effective in simulating breakthrough curves over the period of performance, particularly sulfate concentrations at a monitoring well located 104 m from the injection well. The behavior of the remaining constituents is discussed, and the qualities of an effective intrinsic tracer for future ALTR projects are identified.

Structural and hydrodynamic controls on fluid travel time distributions across fracture networks

Fracture networks are preferential flow paths playing a critical role in a wide range of environmental and industrial problems. Their complex multiscale structure leads to broad distributions of fluid travel times, affecting many biogeochemical processes. Yet, the relationship between the fracture network structures, their hydrodynamic properties, and the resulting anomalous transport dynamics remains unclear. We use a large database of fracture network models to investigate the factors controlling fluid velocity and travel-time distributions across a wide range of networks, from synthetic to field-calibrated models, with aperture variability at both fracture and network scales. Analysis reveals that transport statistics have generic properties across investigated networks, including notably heavy-tailed travel time distributions. Networks of increasing complexity and heterogeneity lead to broader velocity distributions and more channeled velocity fields, where flow concentrates in a narrow channel web in the three-dimensional (3D) fracture structure. While heterogeneity in point-velocity statistics increases travel-time variability, channeling tends to reduce it. This counterintuitive phenomenon challenges current theories, which assume that long travel time power law exponents are determined solely by point-velocity statistics. By analyzing velocity and travel time statistics for different flow structures, we develop a coupled Continuous Time Random Walk framework capturing the unexpected control of the velocity field's spatial structure on anomalous transport in fracture networks. This leads to a unique class of random walk models capturing the respective roles of velocity heterogeneity and spatial structure on transport in networks. These findings open a prospective for characterizing, modeling, and predicting transport dynamics in complex networks, with potential applications to geological, biological, and engineered networks.

A Hybrid Model Combining Improved Weighted Wolf Optimization and Reinforcement Learning for Estimating Electric Vehicle Travel Time

A Hybrid Model Combining Improved Weighted Wolf Optimization and Reinforcement Learning for Estimating Electric Vehicle Travel Time

Refining Heterogeneities near the Core–Mantle Boundary Beneath East Pacific Regions: Enhanced Differential Travel-Time Analysis Using USArray

Recent advancements in seismic data analysis have enhanced our grasp of the seismic heterogeneities near the core–mantle boundary (CMB). Through seismic tomography, persistent lower-mantle structures like the large low shear velocity provinces (LLSVPs) beneath the Pacific and South Africa have been identified. However, variations in the finer-scale features across different models raise questions about their origins. This study utilizes differential travel-time measurements from the USArray, operational across the contiguous United States from 2007 to 2014, to examine the impact of upper-mantle heterogeneities on tomographic models. By averaging the P-wave travel times and calibrating them with diffracted P-waves at the same stations, we mitigate the effects of shallow heterogeneities. The findings confirm that this method accurately maps the seismic anomalies beneath the USArray, consistent with other North American studies. Calibrated Pdiff travel-time data indicate significant anomalies in the mid-Pacific and Bering Sea and lesser anomalies in the northern Pacific, aligning with the global tomographic images. Moreover, the study highlights sharp travel-time variations over short distances, such as those across the northern boundary of the mid-Pacific anomaly, suggesting a chemically heterogeneous Pacific LLSVP.

Space-time interference: The asymmetry we get out is the asymmetry we put in.

Temporal judgments are more affected by space than vice versa. This asymmetry has often been interpreted as primacy of spatial representations over temporal ones. This interpretation is in line with conceptual metaphor theory that humans conceptualize time by spatial metaphors, but is inconsistent with the assumption of a common neuronal magnitude system. Here we review the accumulating evidence for a genuinely symmetric interference between time and space and discuss potential explanations as to why asymmetric interference can arise, both with respect to the interaction between spatial size and temporal duration, and the interaction between traveled distance and travel time. Contrary to the view of hierarchical representations of time and space, our review suggests that asymmetric interference can be explained on the basis of working memory processes and the aspect of speed inherent in dynamic stimuli. We conclude that the asymmetry we often get out (space affects time more than vice versa) is a consequence of the asymmetry we put in (by using biased paradigms and stimuli facilitating spatial processing).

Cities beyond proximity.

The concept of 'proximity-based cities' has gained attention as a new urban organizational model. Most prominently, the 15-minute city contends that cities can function more effectively, equitably and sustainably if essential, everyday services and key amenities are within a 15-minute walk or cycle. However, focusing solely on travel time risks overlooking disparities in service quality, as the proximity paradigm tends to emphasize the mere presence of an element in a location rather than bringing up more complex questions of identity, diversity, quality, value or relationships. Transitioning to value-based cities by considering more than just proximity can enhance local identity, resilience and urban democracy. Fostering bottom-up initiatives can create a culture of local care and value, while predominantly top-down governing strategies can lead to large inequalities. Balancing these approaches can maximize resilience, health and sustainability. This equilibrium has the potential to accompany sustainable growth, by encouraging the creation of innovative urban solutions and reducing inequalities.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

Use of molybdenum as a pressure calibrant in a cubic press and sound velocity measurement of tungsten to 5 GPa

ABSTRACT The pressure limit of the conventional cubic press is normally below 6 GPa, and the suitable pressure calibrant of the fixed-point method is scarce. In this study, we developed a new method for pressure determination using an acoustic travel time approach. Based on the reported ultrasonic travel times of polycrystalline molybdenum under high pressure in literature, an acoustic gauge of polycrystalline molybdenum was derived. The shear wave travel times of molybdenum were measured at high pressures and utilized for the pressure calibration in a cubic press. The pressures derived from this new travel time method are in excellent agreement with those from the fixed-point method. Subsequent compressional and shear wave velocities of polycrystalline tungsten were measured up to about 5 GPa at room temperature to further verify this method. This travel time method for pressure calibration is valuable for the sound velocity measurement of other materials in an offline laboratory.

Simplifying traffic simulation - from Euclidean distances to agent-based models

AbstractUrban settings require a thorough understanding of traffic patterns to best manage traffic, be prepared for emergency scenarios and to guide future infrastructure investments. In addition to analyzing collected traffic data, traffic modeling is an important tool that often requires detailed simulations that can be computationally intensive and time-consuming. A well-known comprehensive simulation framework is MATSim. On the other hand, simpler shortest-path routing systems that compute trips on an individual basis promise faster computations. The primary focus of this study is to assess the viability of a fast shortest path routing system as a method of traffic simulation. This study compares the MATSim with the Graphhopper routing system. Key metrics include travel time accuracy, congestion levels, route similarity, vehicle miles traveled, and average travel time. By analyzing these metrics, this study shows that a shortest-path routing system can serve as an effective and expedient approximation of more resource intensive simulation frameworks. This has significant implications for authorities and planners, as it offers a quick and efficient tool for traffic management and decision-making during critical events, enhancing their ability to respond quickly and effectively to dynamic traffic conditions.

Integration of cost-effective datasets to improve predictability of strategic noise mapping in transport corridors in Delhi city, India.

Assessing exposure to environmental noise levels at transport corridors remains complex in conditions where no standardized noise prediction model is available. In planning and policy implementation for noise control, noise mapping is an important step. In the present study, land use regression model has been developed to predict the environmental noise levels in Delhi city, India, using previously developed approaches along with machine learning techniques, however improved using new datasets. Lday, Lnight, LAeq,24h, and Ldn were modeled at daily resolution by utilizing an annual noise levels dataset from 31 locations in Delhi city. The noise-monitored data was integrated with travel time data, nighttime light data along with common parameters including land use, road networks, and meteorological parameters. The developed LUR models showed good fit with R2 of 0.72 for Lday, 0.55 for Lnight, 0.71 for LAeq,24h, and 0.61 for Ldn, which was further improved up to 0.88 for Lday, 0.79 for Lnight, 0.86 for LAeq,24h, and 0.81 for Ldn by integrating machine learning approaches. The developed models were validated through tenfold cross validation and by comparison to a separate noise level dataset. The average travel time variable was observed to be the most influential predictor of LUR models for Lday and LAeq,24h, which signifies the crucial impact of road traffic congestion on environmental noise levels. The study also analyzed the parametric sensitivity of various infrastructural factors reported in the study, which shall be helpful for planning for smart cities.

Virtual Healthcare for Older Adults With Preventable Chronic Conditions: A Meta-synthesis of Quality Aspects.

Our study employed a six-step meta-synthesis approach to understand quality aspects of virtual healthcare trajectories and identify effective strategies for older adults with preventable chronic conditions across the world. Review of twenty-one articles revealed four key themes: functionality of virtual healthcare, equity in service delivery, satisfaction with care, and cost-effectiveness. Despite the challenges posed by technological and clinical complexities, virtual healthcare initiatives have significantly improved accessibility and availability of health services, thereby reducing disparities. Notably, telehealth saved an average of 166-minutes of travel time, demonstrating a clear benefit of virtual healthcare. While all parties expressed satisfaction with virtual healthcare, several challenges were reported, including a digital divide, limited access to digital devices, absence of a multidisciplinary approach, and lack of rural-specific programs.

Data-Driven Decision-Making in Transportation Management Using AI

The rapid growth of data generation and the advancements in artificial intelligence (AI) have opened up new opportunities for data-driven decision-making in transportation management. This article presents a comprehensive review of the applications of AI in transportation, focusing on machine learning techniques, optimization algorithms, and predictive analytics. The article proposes a novel AI-based decision-making framework that integrates data preprocessing, AI modeling, and performance evaluation to address complex transportation challenges. A case study on traffic congestion management is conducted to demonstrate the effectiveness of the proposed framework in reducing travel times and improving system efficiency compared to traditional methods. The results highlight the potential of AI in optimizing transportation operations and supporting informed decision-making. However, the article also discusses the limitations and challenges of implementing AI-based decision-making in transportation, such as data quality, privacy concerns, and computational requirements. Future research directions, including transfer learning, integration with emerging technologies, and explainable AI, are identified to facilitate the widespread adoption of AI-based decision-making in transportation management. The findings of this article contribute to the growing body of knowledge on data-driven intelligent transportation systems and provide valuable insights for researchers, practitioners, and policymakers in the field.

Improving Access and Timeliness of Employee Annual Exams.

Access to preventive healthcare improves health outcomes and reduces risk of chronic disease. Annual examinations were integrated into an underutilized clinic at the University of Utah to improve access to preventive care. Nurse practitioners conducted these examinations, which included history, physical examination, age-based screenings, and immunizations. The service was part of a wellness program offering health premium discounts for participants. With employee permission, visit documentation was shared with primary care providers (PCPs) or used to help establish a PCP. Human resources received monthly reports of participating employees identification numbers, validating participation in order to receive premium discount. In 2023, 373 examinations were performed, including 719 preventive services and screenings, with 143 referrals for specialty and primary care. Patient satisfaction remained high throughout implementation. This initiative effectively increased access to preventive care for employees, demonstrating the feasibility of offering annual examinations in worksite clinics. The program addresses common barriers to preventive care such as long wait times, PCP shortages, and travel time to appointments.

Monitoring System Design Using GPS Tracker and Operational Vehicle Maintenance Case Study of PT. Pertamina (Persero)

The Utilization of GPS and GSM technology in operational vehicle monitoring offers convenience and efficiency in management. This research designs a GPS-based vehicle monitoring system that is integrated with vehicle maintenance management to assist companies in making decisions related to fuel efficiency, predictive maintenance scheduling, and safety aspects. By monitoring vehicle position and condition in real-time, companies can optimize travel routes, reduce travel time and reduce operational costs. The system also includes tire pressure and fuel percentage monitoring, which serve as important indicators in an accurate and sustainable vehicle maintenance strategy. This research uses quantitative methods to analyze various aspects in designing a GPS-based monitoring system that is integrated with operational vehicle maintenance management. With the resulting data, managers are able to identify trends that support predictive maintenance as well as preventive steps to reduce the risk of sudden damage. The research results show that the integration of GPS and GSM provides significant benefits in improving performance and reliability for vehicles, especially in the distribution and logistics sectors. The implementation of this system is expected to support the company in maintaining optimal vehicle performance and contribute to operational sustainability.

Optimization of bus stop layout considering multiple factors including passenger flow direction.

Bus stop layout typically requires consideration of urban population distribution, traffic conditions, and passenger flow demand to establish an efficient foundation for the bus system's operation. Based on the above key factors, this paper introduces a strategic method to optimize the bus stop layout from a macro perspective in order to save passengers' travel time and improve the attractiveness of the bus system. This approach accounts for the matching degree between the Origin-Destination (OD) direction of passengers and their walking direction heading to bus stops. Initially, we take into account factors such as the population and area of traffic districts, and urban road conditions. Utilizing the hypernetwork multidimensional data clustering method along with GIS technology, we construct an alternative set of bus stops based on the hypernetwork framework. This set serves as a reference for the positioning of newly built and moved bus stops. Subsequently, we develop a two-stage model for bus stop layout decision-making. The first stage focuses on determining the bus stop layout at the traffic district level, taking into account multi-factors including the passenger flow matching degree. The second stage is designed to mitigate the negative impact of bus stop optimization on the overall service level of the urban bus system. A case study conducted in XT city demonstrates the effectiveness of our approach. Post-optimization, there is a 15.83% increase in the alignment between passenger flow direction and bus stop layout. Additionally, the average travel time for passengers is reduced by 7.55 minutes.

Real-Time Tracking System for City Bus Location And Passenger Count

This survey paper reviews the advancements in real-time tracking and passenger counting systems for city buses, emphasizing the growing need for efficient, data-driven public transit solutions. As urban populations rise, city transport systems face challenges in managing demand, reducing congestion, and improving service reliability. Real-time tracking systems using GPS, GIS, and IoT-enabled sensors enable passengers and transit authorities to monitor bus locations and optimize travel times. Additionally, passenger counting systems—utilizing infrared, AI-based vision models, and pressure sensors—provide data on bus occupancy levels, allowing commuters to make informed travel decisions and helping transit agencies manage resources efficiently. This paper examines existing technologies, compares various tracking and counting methods based on accuracy, cost, and scalability, and explores recent trends in artificial intelligence, big data analytics, and smart city integration. Furthermore, it identifies critical challenges, including data privacy, accuracy in urban environments, and energy consumption, proposing future research directions for enhancing the reliability, sustainability, and user-friendliness of these systems. Our findings underscore the transformative potential of real-time bus tracking and passenger counting in advancing public transportation infrastructure and enhancing urban mobility

Travel Time Reliability Analysis on Paratransit, case study: Substação-Muahivire route, Nampula - Mozambique

In numerous medium-sized cities in Sub-Saharan Africa, such as Nampula, the reliability of mini-bus travel time is frequently a subject of inquiry. One contributing factor to this uncertainty is the considerable number of stops made throughout the route for passenger-related purpose, including boarding and disembarking. This study utilizes data collected during off-peak periods to scrutinize the day-to-day travel time patterns of mini-buses along the route. The route has been segmented into intervals of 500 meters each, and the analysis focuses on the duration of trips at each segment. The evaluation is based on the Coefficient of Variation (CoV) of travel time, indicating the extent of dispersion in mini-bus travel time at the segment level. The results reveal that the CoVs at nearly all segments along the route are minimal, measuring less than 15%. This suggests that day-to-day variability of travel time at the segment level is negligible. However, on road segments characterized by commercial activities, the CoVs range from 15% to 22%.

User Perception-Based Optimal Route Selection for Vehicles of Disabled Persons in Urban Centers of Saudi Arabia

People with disabilities (PWD), in their routine commutes, confront hindrances associated with road infrastructure in busy urban centers. The present study developed a user perception-based methodology to evaluate optimal routes for PWD in urban settlements in the Kingdom of Saudi Arabia (KSA). A survey captured the preferences for 105 PWD, consisting of 37 powered wheelchair users, 62 manual wheelchair users, and 6 artificial limb users. The multi-criteria decision analysis evaluated the accessibility index for PWD based on four criteria: length, number of junctions, absence of footpath, and slope. This study revealed that manual wheelchair users prefer the length criterion, powered wheelchair users emphasized the absence of footpaths, and artificial limb users were concerned about slope. The result showed that only two routes out of ten showed medium, while those remaining exhibited low accessibility. Most routes were relatively long for people with disabilities, focusing on the need for public transportation with special arrangements in most small and medium-sized cities, like the study area of Hail and Qassim province of the KSA, to reduce the distance and travel time. The proposed framework provides valuable insights to route evaluation for persons with special needs in the KSA and elsewhere.

Equity of access to pathological diagnosis and bronchoscopy for lung cancer in Aotearoa New Zealand.

Māori are less likely to survive their lung cancer once diagnosed, but it remains unclear whether this is partially driven by poorer access to best-practice diagnostic services. We examined all lung cancer registrations in Aotearoa New Zealand between 2007-2019 (n=27,869) linked to national administrative health datasets and further stratified by ethnicity, tumour type and stage of disease. Using descriptive and regression analyses, we compared ethnic groups in terms of the basis of diagnosis (e.g., histology, cytology), receipt of bronchoscopy and travel distance and time to access bronchoscopy. We found no differences in access to a pathological diagnosis between ethnic groups regardless of cancer type or stage. We found that Māori within the cohort were marginally more likely to access bronchoscopy than the majority European group; however, we also found that Māori had lower odds of living close to the location of their bronchoscopy, and correspondingly higher odds of living 100-200km (adjusted odds ratio [adj. OR] 1.46, 95% confidence interval [CI] 1.26-1.69) or more than 200km away (1.36, 95% CI 1.15-1.61) than Europeans. Interventions that aim to further support Māori to overcome the systematic and cumulative disadvantages in access to cancer care should be broadly supported and resourced.