Travel Time Research Articles

Travel time prediction for an intelligent transportation system based on a data-driven feature selection method considering temporal correlation

Travel-time prediction is a critical component of Intelligent Transportation Systems (ITS), offering vital information for tasks such as accident detection, congestion management, and traffic flow optimisation. Accurate predictions are highly dependent on the selection of relevant features. In this study, a two-stage methodology is proposed which consists of two layers of Optimisation Algorithm and one Data-Driven method (OA2DD) to enhance the accuracy and efficiency of travel-time prediction. The first stage involves an offline process where interconnected optimisation algorithms are employed to identify the optimal set of features and determine the most effective machine learning model architecture. In the second stage, the real-time process utilises the optimised model to predict travel times using new data from previously unseen parts of the dataset. The proposed OA2DD method was applied to a case study on the M50 motorway in Dublin. Results show that OA2DD improves the convergence curve and reduces the number of selected features by up to 50%, leading to a 56% reduction in computational costs. Furthermore, using the selected features from OA2DD, reduced the prediction error by up to 29% compared to the full feature set and other feature selection methods, demonstrating the method's effectiveness and robustness.

Adaptive budget allocation in simheuristics applied to stochastic home healthcare routing and scheduling

The organization of home care is of pivotal importance in response to the trend of delivering more care directly to clients’ residences. With more vulnerable clients residing at home, variability in care durations needs careful consideration. This paper addresses the operational home healthcare routing and scheduling problem (HHCRSP) with uncertainty in travel and service times. The quality of the routes is evaluated based on the expected objective function, incorporating travel time, waiting time, and shift overtime. Due to the complexity of the problem, a simheuristic approach is applied that carefully integrates the optimization of the deterministic HHCRSP with simulation. This study proposes a new framework, based on optimal computing budget allocation and the expected opportunity costs, to dynamically determine when to employ optimization and when to use simulation. A practical case study demonstrates the effectiveness of the approach and reveals the substantial impact of accounting for randomness in the HHCRSP.

Development of a prioritized traffic light control system for emergency vehicles

This research presents a model for an adaptive traffic signal control system aimed at improving urban traffic regulation. It dynamically adjusts signal timing based on vehicle volume at intersections, prioritizing emergency vehicles by allowing them immediate passage. Utilizing Arduino coding, the system controls traffic light intensity according to the traffic flow, enhancing road safety and efficiency. This innovative approach not only facilitates faster clearance for emergency services without human intervention but also reduces congestion and accident rates. This research creates a model for a prioritized traffic signal control system. When the vehicular volume at the intersection varies, the signal time alters autonomously. It identifies the ambulance/emergency vehicles and allows the green light for emergency vehicles like ambulances, and fire engines. This approach may be used to detect traffic accidents and infractions of automobile spiral motions. When erected on the road, the entire system allows for quick traffic clearing for rescue vehicles without requiring a policeman. The system's design eliminates the need for sensors or radio frequency identification (RFID) tags, simplifying traffic management. Simulations validate that emergency vehicle travel time is significantly reduced, proving the system's effectiveness in streamlining urban traffic flows.

Network-based resilience assessment of an urban rail transit infrastructure with a multi-dimensional performance metric

With urban and public transportation development, the urban rail transit networks (URTN) become increasingly complex, evolving into multi-layer and intricate systems. The operating environment is complex, and the rising frequency of emergencies significantly impacts the entire network. Consequently, the resilient ability of URTN to deal with risk attacks has become an important research field. In this paper, a bilayer URTN model is constructed, encompassing urban and suburban rail transit, proposing the adjacency matrix and the coupling mechanism of subnetworks. Then, the performance of a URTN jointly considering network efficiency, network structure entropy, and the number of trips per unit time is addressed. By considering passenger travel alternatives under disruptions, we develop an improved Logit stochastic user equilibrium (SUE) passenger assignment model based on generalized travel time costs, introducing the failure degree of the stations, to predict passenger flow paths. A multi-dimensional resilience assessment model is proposed using the resilience change curve for attack scenarios and recovery strategies, and a comprehensive network performance system through the AHP method to determine the weight coefficients of indicators. Finally, we present a case study based on the Shanghai URT network to explore the resilience of URTN in the morning peak and simulate the network resilience under different failure scenarios. The results indicate that the proposed model can assess the resilience of the Shanghai MRTN under random and malicious failure, and the former has less comprehensive performance losses and greater resilience. Additionally, the sensitivity analysis is conducted to explore the impact of the number of failed stations, the failure degree of stations, and recovery ability on network resilience. This study provides theoretical support for urban managers and builders regarding network structure and emergency management, and offers relevant suggestions.

Evaluating low-emission-zone impacts on urban road transport system in large city

The purpose of the study was to assess the impact that a low emission zone (LEZ) would have on the road transport subsystem. Different scenarios were adopted regarding: the spatial extent of the zone itself; the stages of its implementation, and the vehicles on the network (by age, engine displacement, and fuel type). The study was based on a macroscopic traffic model of Łódź employed to analyse how any LEZ-related restrictions may affect the operation of the urban road transport system in the city. The conducted analyses showed that while the implementation of a low emission zone would positively affect the environment, it would have a negative impact on the performance of the urban transport system itself. The simulations for each spatial scenario of the LEZ revealed that it would increase the travel times and distances necessary within the city limits, and also decrease travel speeds.

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.