Search

The paper presents an analytical model for travel time evaluation in double-deep, triple-deep, quadruple-deep and fivefold-deep tier-captive shuttle-based storage and retrieval system (SBS/RS). Under the assumption of the deepest location rule for the storage process and the nearest neighbour rule for the retrieval process, the expressions for single-command and dual-command cycle times of the shuttle vehicle and elevator are developed, from which the SBS/RS performance can be estimated. The model based on the probability theory enables the evaluation of all individual times that make up the cycle times and assessment of the average number of totes relocated during a retrieval process. Special attention is devoted to the modelling of the relocation of totes which block access to the requested tote during the retrieval process. The validity of the analytical model is examined via the developed simulation model. The given case study shows that the proposed analytical travel time model results match the results of the simulation model. The presented modelling strategy could be applied to bigger system racking depths following the same modelling logic. [ABSTRACT FROM AUTHOR]

Queue length is one of the indicators of the state of traffic and is often used to measure the operational state of signalized intersections. Many studies have proposed estimating queue length from vehicle trajectory data (e.g., floating car GPS data); however, its sparse spatio-temporal distribution and low sampling frequency present substantial challenges in practice. In some jurisdictions, the widespread deployment of automatic vehicle identification (AVI) technologies presents the opportunity to improve queue length estimation at signalized intersections by combining AVI and trajectory data from floating (probe) vehicles. The method proposed in this paper is applicable for both under and oversaturated traffic conditions, is evaluated using field data [Next Generation Simulation (NGSIM) data set] and simulation data, and is compared to ground truth and the method proposed by the author Tan. The results from the field data evaluation indicate that the method provides a good estimation of the queue size (mean average error less than three vehicles for a floating vehicle penetration rate of 5% and a GPS sampling interval of 10 s). The simulation data evaluation indicated that the proposed method performs better than the Tan's method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With growing environmental concerns and the exploitation of ubiquitous big data, smart transportation is transforming logistics business and operations into a more sustainable approach. To answer questions in intelligent transportation planning, such as which data are feasible, which methods are applicable for intelligent prediction of such data, and what are the available operations for prediction, this paper offers a new deep learning approach called bi-directional isometric-gated recurrent unit (BDIGRU). It is merged to the deep learning framework of neural networks for predictive analysis of travel time and business adoption for route planning. The proposed new method directly learns high-level features from big traffic data and reconstructs them by its own attention mechanism drawn by temporal orders to complete the learning process recursively in an end-to-end manner. After deriving the computational algorithm with stochastic gradient descent, we use the proposed method to perform predictive analysis of stochastic travel time under various traffic conditions (especially for congestions) and then determine the optimal vehicle route with the shortest travel time under future uncertainty. Based on empirical results with big traffic data, we show that the proposed BDIGRU method can (1) significantly improve the predictive accuracy of one-step 30 min ahead travel time compared to several conventional (data-driven, model-driven, hybrid, and heuristics) methods measured with several performance criteria, and (2) efficiently determine the optimal vehicle route in relation to the predictive variability under uncertainty. [ABSTRACT FROM AUTHOR]

Performance measures that are commonly used in winter maintenance provide valuable information. However, some metrics are subjective (i.e., visual inspection), rely on historical records, depend on agencies' practices, or only capture specific information. Winter maintenance performance measures are evaluated independently, and relationships among variables are not assessed. Therefore, the metric travel disruption (TD) is proposed in this study to perform a multispectral evaluation with input, output, and outcome-based performance measures to compare salt brine with solid salt applications. The metric TD integrates different sources of information to estimate a continuous performance measure that accounts for storm, vehicle operations, and roadway conditions over time instead of intermittent measurements or one-time observations, such as roadway friction or time to bare/wet. The input performance measures included storm conditions, type of chemical material, frequency of application, and application rate. The output performance measures were the amount of material used and the lane kilometer (miles) covered. The outcome performance measures consisted of time to bare/wet, speed reduction period, speed recovery period, storm impact period, maximum speed reduction, and TD. For salt brine, previous research has shown reduced salt usage, shorter times to bare/wet, and higher friction. However, there are some concerns regarding the operational and safety performance of roadways that are treated with salt brine. Winter treatment field data and vehicle probe data from the National Performance Management Research Data Set (NPMRDS) were collected from the study (salt brine) and control (solid salt) routes in Wisconsin. For the amount of salt used, the study routes used 32.6% less overall salt than the control routes per storm. The TD indicated that, on average, there was no statistically significant difference between the study and control routes. Despite the lower amount of salt that was used, roadways that were treated with salt brine had a similar operational performance as roadways that were conventionally treated with solid salt, which made salt brine application a cost-effective strategy. This evaluation demonstrated a multispectral performance evaluation of winter maintenance to appropriately quantify the effectiveness of treatments. [ABSTRACT FROM AUTHOR]

Recovery of endangered salmon species in the Central Valley of California amidst prolonged drought and climate change necessitates innovative water-management actions that balance species recovery and California's water demands. We describe an individual-based ecological particle-tracking model (ECO-PTM) that can be used to assess the efficacy of proposed actions. Based on a "random walk" theory, the model tracks the travel time, routing, and survival of individual particles in a flow field simulated by the Delta Simulation Model 2 hydrodynamic module (DSM2 HYDRO). The random walk particles are parameterized to have fish-like swimming behaviors, including upstream and downstream swimming, probabilistic holding behaviors, and stochastic swimming velocities. Particle routing at key junctions is based on wellestablished statistical models, and route-specific survival is calculated using the XT mean freepath length model. Behavioral parameters were estimated by fitting several competing models to a multi-year dataset of travel times from acoustictagged juvenile salmon. The model's baseline simulations under historical flow conditions from 1991 to 2016 successfully replicated essential relationships between salmon outmigration survival and hydrodynamic conditions, consistent with previous studies and the STARS (Survival Travel Time and Routing Simulation) statistical simulation model. Simulation results for management scenarios revealed multi-faceted influences on fish survival, including Delta flow, flow at key junctions, route alterations, seasons, and water-availability characteristics. Importantly, these results highlight ECO-PTM's potential to predict improvements in fish survival from proposed actions, serving as a foundation for informed future research, decision-making, and effective management strategies to enhance the survival prospects of outmigrating salmonids within the Sacramento-San Joaquin Delta ecosystem. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Pine Island Academy, present in Nocatee Community, Jacksonville, Florida, uses various modes of transport for picking up and dropping off children at school. The parents using private vehicles to pick up and drop off their kids tend to take a long time due to delays and congestion in their routes. Parents who use golf carts (GC) for this activity tend to take a short time to pick up and drop off their kids. Due to this, it is necessary to analyze the use of GC as an alternative mode of transport for children pick up and drop off while reducing delay and congestion. This paper uses VISSIM microscopic simulation for analysis whereby the traffic flow in the morning for drop-off and the afternoon for pickup was observed at different GC traffic compositions regarding the relative delay, queue delay, and travel times. From the analysis, it was observed that the delays and travel times for private vehicles decrease as the GC composition increases in the network. These results show that a GC may be suitable for picking up and dropping off children at school. This factor is further supported by the ANOVA test of significance. Due to the results from this paper, recommendations to the transportation agencies responsible for school management include promoting GC usage, establishing designated GC lanes to ensure smooth traffic flow and minimal interaction with other vehicles, implementing a multi-modal approach that includes GCs, and emphasizing safety training and awareness for GC users. [ABSTRACT FROM AUTHOR]

Urban systems are vulnerable to disturbances from both natural and human origins, which can disrupt their normal functioning. Evaluating the resilience of these systems, particularly the main transportation networks and their usage levels, is crucial and innovative for understanding the impacts of such disturbances. Thus, this work aims to assess resilience in urban mobility through the probability of a particular journey using a specific mode: "surviving" through critical travel time. To achieve this, a methodology was developed based on the Weibull model with gamma heterogeneity (hazard-based duration models), which was applied to a medium-sized Portuguese municipality. Eighteen groups representing active populations were set and compared. The results indicated that using the bus and cycling are the most resilient modes of transport, whereas walking is the least resilient. Additionally, a specific group was identified as having lower mobility resilience, making them more vulnerable to disruptions in the transport system. Finally, the findings of this study demonstrate the practical application of this methodology, which relies on travel time to assess resilience and, thus, guide political attention and actions to the less resilient mode. Future research should aim to develop a comprehensive framework that incorporates several variables to fully describe the complex nature of transport systems and their resilience. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background. Reducing the delay between the detection of a fire and the arrival of the initial attack (IA) crew can have a significant impact on the likelihood of the IA's success. Aims. The objective of this study was to identify factors influencing same-day getaway time, next-day getaway time and travel time of helitack IA crews in the province of Quebec, Canada. Methods. Using generalised linear modelling and model selection, we analysed the impact of multiple factors on these three distinct times. Key results. Our results show that factors such as the distance between the departure base and the fire, the number of flight legs to reach a fire, dispatch hour, departure base location, the fire's rate of spread, Julian date, the number of active fires, fuel type and the fire's size at detection all influenced getaway time and travel time with varying degrees of influence. Conclusions. The factors with the highest influence were distance for travel time and dispatch hour for both same-day and next-day getaway times. Implications. Addressing these high-impact factors through the modification of deployment policies and the positioning of helitack crews could help reduce response times. [ABSTRACT FROM AUTHOR]

In response to increasing flood risks driven by the climate crisis, urban areas require advanced forecasting and informed decision-making to support sustainable development. This study seeks to improve the reliability of reservoir-based flood forecasting and ensure adequate lead time for effective response measures. The main objectives are to predict hourly downstream flood discharge at a reference point, compare discharge predictions from a single reservoir with a four-hour lead time against those from three reservoirs with a seven-hour lead time, and evaluate the accuracy of data-driven approaches. The study takes place in the Han River Basin, located in Seoul, South Korea. Approaches include two non-deep learning (NDL) (random forest (RF), support vector regression (SVR)) and two deep learning (DL) (long short-term memory (LSTM), gated recurrent unit (GRU)). Scenario 1 incorporates data from three reservoirs, while Scenario 2 focuses solely on Paldang reservoir. Results show that RF performed 4.03% (in R2) better than SVR, while GRU performed 4.69% (in R2) better than LSTM in Scenario 1. In Scenario 2, none of the models showed any outstanding performance. Based on these findings, we propose a two-step reservoir-based approach: Initial predictions should utilize models for three upstream reservoirs with long lead time, while closer to the event, the model should focus on a single reservoir with more accurate prediction. This work stands as a significant contribution, making accurate and well-timed predictions for the local administrations to issue flood warnings and execute evacuations to mitigate flood damage and casualties in urban areas. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Random traffic flows and the influence of traffic signals at intersections often cause significant variations in travel times. This has implications for signal control planning and design. This study aims to compare the variability between signalized and unsignalized corridors in Lahore and identify the challenges faced by road users. The study was conducted through a literature survey that collected data on trip purpose, frequency, travel time, and transportation mode choice from various sources. Results show that signalized corridors have lower accident rates and shorter travel times compared to unsignalized corridors. Unsignaled corridors, on the other hand, offer better access and lower fuel costs. Although signalized corridors are more effective in managing traffic flow and improving safety, further research is needed to explore alternative solutions such as roundabouts that can improve mobility and sustainability. The results of this study can serve as a reference for transportation planners and policy makers in designing a more efficient traffic system. This study also suggests the need for further evaluation of traffic control in various urban contexts to optimize safety and efficiency. [ABSTRACT FROM AUTHOR]

Waves can cause large fluctuations in sea surface height, affecting the energy levels in the high frequency band of travel time measurements. An inverted echo sounder system for significant wave height measurements is proposed based on the relationship between significant wave height (SWH) and the dispersion of sound round-trip travel time measurements (defined as the variance of travel time in the frequency band with a period of less than 6 h). An algorithm was developed to retrieve the SWH from the round-trip travel time measurements. The system uses a push-pull inverter and a power regulating circuit to drive the acoustic transducer, a three-stage frequency amplifier circuit to detect the echo, and a master-slave controlling unit to control the main work and collect data. A five-day experiment was conducted near the Zhongsha Reef in Jiaozhou Bay, Qingdao, and the results proved that the instrument has excellent ability to measure SWHs. The correlation between the dispersion of travel time and forecasted SWHs reached 0.83 and the retrieved SWHs captured 88% of the variance of the forecasted SWHs. [ABSTRACT FROM AUTHOR]

The analysis and planning of suburban traffic flows is an extremely important task to ensure efficient and fluent traffic distribution in existing infrastructure. One of the main sources of this information from existing situations is origin–destination (OD) matrices, usually obtained from traffic CCTV cameras. In this paper, a novel method is proposed for the filtration of transit traffic from the overall traffic through the area. The main feature that differentiates this method from existing outlier (and thus transit traffic) detection methods is that it is focused solely on prolonged trips (which are believed to be caused by vehicles stopping in the investigated area). Initial calibration of the method on training data (sample size of N = 216,159 trips through paired detectors) and verification on test data prove the accuracy of the algorithm on the level of 98% in urban and suburban areas, respectively, in Czech Republic conditions, which gives high hopes for the feasibility of the method. [ABSTRACT FROM AUTHOR]

Time of Concentration (TC) is a widely utilized time variable in hydrological studies. Various approaches exist to estimate TC, including direct measurements, empirical relations, and graphical method (GM). The GM is commonly regarded as a standard by researchers. Nevertheless, this approach faces obstacles due to the scarcity of records in many watersheds.. This research explores a novel technique for TC estimation, focusing on the examination and recommendation of two-dimensional (2D) hydraulic modeling. The proposed method leverages available satellite data to obtain the necessary input for the 2D simulation. Additionally, several empirical relations were evaluated for TC determination. The GM served as reference point for evaluating the results obtained from empirical relations and hydraulic modeling. To accomplish this, analysis was conducted on four distinct watersheds in Iran, comprising a total of 28 recorded rainfall-runoff data.. In numerical modeling, TC was defined as the duration required for a water parcel to traverse from the headwater to the outlet of a basin. In all studied watersheds, the maximum relative error between the benchmark (GM) and the 2-D modeling was limited to less than 10%. This observation establishes the reliability of hydraulic modeling as a viable method for TC calculation, particularly in ungauged watersheds where the GM cannot be employed. [ABSTRACT FROM AUTHOR]

To quantify the travel time and cost characteristics of mixed traffic involving electric vehicles (EVs) and fuel-powered vehicles on roads, in this paper, we comprehensively consider three factors affecting road impedance: queue length, waiting time, and service rate. Initially, a time characteristic function and a cost characteristic function for mixed traffic impedance are constructed. From the perspective of travel time, we consider the impact of EV penetration on the actual road capacity and introduce a capacity coefficient to modify the BPR (Bureau of Public Roads) road impedance function. Given that different types of vehicles might need to wait at charging stations, we employ queuing theory to calculate the queuing time at these stations and construct an impedance model that considers travel time. From the perspective of travel costs, we account for the energy consumption costs and road usage fees for different types of vehicles. The energy consumption cost for travel mileage is obtained by multiplying the unit mileage energy consumption cost of mixed traffic by the travel mileage. For road usage fees, we adopt the conventional method of multiplying the per-kilometer rate for each vehicle type by the travel mileage, thus constructing an impedance model that incorporates travel costs. Finally, in the numerical analysis section, based on the vehicle travel mileage, we categorize travel into short-, medium-, and long-distance trips for analysis. With the constructed mixed traffic impedance model, we conduct a detailed analysis of the travel time and cost characteristics of mixed traffic over different travel distances. We explore the specific impacts of the electric vehicle penetration rate, traffic flow volume, and travel mileage on road impedance. The results indicate that as the penetration rate of electric vehicles increases, the total energy consumption of the transportation system significantly decreases. Moreover, at high electric vehicle penetration rates, although an increase in traffic flow leads to higher traffic impedance and longer travel times, the overall travel costs are reduced. This demonstrates that increasing the penetration rate of electric vehicles positively contributes to reducing the energy consumption and costs of transportation systems. [ABSTRACT FROM AUTHOR]

Travel time to a patient's medical provider represents a significant component of access to care. We examined travel time to the nearest uveitis specialist for the American population and characterize its impact on access to uveitis care. Observational studies using the American Community Survey and American Census Bureau population estimates. Addresses of fellowship-trained uveitis specialists were collected from the American Uveitis Society (AUS) and the Ocular Immunology and Uveitis Foundation (OIUF) websites and geocoded using ArcGIS Pro 2.9. Service areas were defined as all locations within a 60-min drive time from each specialist's location. Demographic and population data for total population, racial groups, household poverty levels, population in dependent groups (younger than 18, older than 65), and health insurance status were overlaid. Data were aggregated for census tracts within and outside service areas and compared using chi-square analysis. Main Outcome Measures: Differences in population demographics for people within and outside service area coverage. 223 uveitis specialist addresses were geocoded into ArcGIS. Of specialist locations, 94% were found in urban areas. Of the total United States population, 63.3% were found to be within service areas. Of these, 55.9% were White and 14.7% were African American, whereas 70% of the people outside service areas were White and 10.1% were African American (p < 0.0001) (Table 1). Only 8.3% of the people within service area coverage had no health insurance compared with 9.5% outside service areas (p < 0.0001). Within coverage areas, 12.7% of the households had a total income below the federal poverty level versus 15.1% of the households outside service area coverage (p < 0.0001). Within service area coverage, 37.4% of the people were in a dependent age group compared with 40.4.% outside service area coverage (p < 0.0001). Our analysis shows a significant travel burden to the nearest uveitis specialist for a large proportion of Americans. More providers are needed in rural areas, as patients there are more likely to live under the poverty line, be uninsured, or belong to a dependent age group. [ABSTRACT FROM AUTHOR]

Air transport networks play a significant role in the modern tourism industry, and their operational performance and stability can be measured by reliability analysis. This study, from the perspective of a travel agency, attempts to assess the reliability of a stochastic air transport network (SATN) by considering multiple passenger demands generated by both the starting airport and several connection airports. The SATN is modeled as a stochastic flow network comprised of several nodes and arcs where nodes represent airports, and arcs represent flights connecting two airports. Each flight is associated with a predetermined stochastic capacity following a specific probability distribution, departure time, arrival time, and discount fare. Accordingly, network reliability refers to the probability that the network can successfully transport passengers from their respective source airports to the destination airport within the given travel time and travel cost limit. An algorithm in terms of the concept of lower boundary point is developed to compute network reliability. Furthermore, a simple example and a case study are presented to demonstrate the proposed reliability method and the managerial implications of reliability for travel agencies. [ABSTRACT FROM AUTHOR]

Many researchers argue that autonomous vehicles (AV) will create a safer and more efficient transport infrastructure. However, some studies have drawn attention to potential problems in relation to how AVs and human-driven vehicles will co-exist. We investigated the effect of speed compliance behavior of AVs on an urban two-lane road section with alternating speed limits of 50 and 30 km/h. A Vissim microsimulation model was used where we gradually increased the traffic volume and the market penetration rate (MPR) of AVs. We found that under low traffic flow conditions the increase in MPR will lead to a decrease in the average speed and worsening of the average travel time. Under medium and, specifically, under saturated conditions, a high share of AVs can bring stability to the system. They can also significantly improve the speed compliance rate. [ABSTRACT FROM AUTHOR]

This study delves into the effects of autonomous vehicle behaviors on travel times and vehicle arrivals along the Kirkuk-Sulaymaniyah Highway, employing simulations spanning 3600, 5400, and 7200 seconds. Across varied traffic volumes ranging from 350 to 950 vehicles and autonomous vehicle behaviors categorized as cautious, normal, aggressive, aggressive platoons, and a mix alongside human-driven vehicles, the research unveils significant findings. Results highlight substantial reductions in average travel times and heightened vehicle arrivals among autonomous vehicles, particularly those exhibiting aggressive behaviors, compared to their human-driven counterparts. Across all simulation scenarios, aggressive autonomous vehicles consistently demonstrate superior performance, showcasing potential efficiency gains through aggressive driving algorithms. Furthermore, with increasing traffic volume, the advantages of aggressive autonomous behaviors become more pronounced, suggesting their adaptability to congested conditions. However, safety implications and traffic flow dynamics warrant caution, especially in scenarios with high volumes and aggressive behaviors. These insights underscore the importance of further research and policy considerations to leverage the full potential of autonomous vehicles while ensuring safety and efficiency on highways. [ABSTRACT FROM AUTHOR]

Headway variability has a negative impact on the public transport passengers' perception of service quality. However, most of the existing methods aimed at improving the headway regularity operate in real time and require precise vehicle location data, making it difficult to implement them in practice. On the other hand, planning-level methods can be used to increase the resilience of public passenger transport (PPT) to the accumulation of headway disturbances. As this is typically done from the operator's perspective, the passengers' perspective tends to be overlooked, motivating the current work. In this article, an optimisation procedure for evaluating the viability of diametrical line splitting in terms of passenger travel time and headway regularity is proposed. The aim is to increase the robustness/resistance of the PPT system to the propagation of headway disturbances without reducing the service quality. The developed optimisation procedure was validated by applying it to real data pertaining to an urban PPT line. The results show that there is a positive correlation between the transport demand and the effects of the optimisation procedure, whereby an increase in the primary headway disturbance increases the sensitivity of the optimisation procedure to the transport demand. [ABSTRACT FROM AUTHOR]

School dropout is a pressing social problem that stems from systemic inadequacies in the education system and socio-economic background. The aim of this study was to analyze how the travel time and financial difficulties impact school dropout in satellite towns near Bucharest, the capital of Romania. Data on dropout rates in recent years were provided by the Ilfov County General School Inspectorate and were supplemented by 30 semi-structured interviews with the parents, caregivers, or the legal representatives of students who have dropped out of school or are at risk of dropping out. The study's findings reveal significant correlations between the travel time to school, familial financial situation, and attitudes towards education, impacting dropout rates across various satellite towns. Addressing the challenge of school dropout promises societal improvement and empowers policymakers to enact more inclusive policies benefiting all members of society. [ABSTRACT FROM AUTHOR]

This study explores the influence of a high percentage of motorcycles on the traffic flow and congestion in Marrakech by examining the impact of motorcycle positioning in shaping urban traffic dynamics, in particular, the start-up lost time at signalized intersections. Different motorcycle positioning strategies are analyzed to improve intersection efficiency and safety. A twofold approach was followed to achieve this objective. First, empirical data were collected using computer vision techniques. Second, different strategies were simulated in VISSIM based on the collected data. The approach adopted for data collection was based on mobile phone video recording at a representative signalized intersection in Marrakech, capturing traffic behaviors during four distinct time periods. Then the YOLOv8 algorithm was employed for real-time object detection and analysis, allowing precise monitoring of motorcycle positioning and examining its influence on the start-up lost time. Afterwards, VISSIM simulations were implemented, on the basis of the collected data, to explore various scenarios, such as motorcycles sharing lanes with cars or dedicated motorcycle lanes. The results reveal a compelling correlation between motorcycle proximity to cars and traffic congestion, with closer positioning leading to increased congestion, longer travel times, reduced average vehicle speeds, and extended queue lengths at intersections. On the contrary, scenarios with dedicated motorcycle lanes consistently show reduced congestion and smoother traffic flow. [ABSTRACT FROM AUTHOR]

Road traffic which results in significant time lags, increased fuel consumption, and financial losses, remains a noteworthy challenge in developed and developing countries. As a result, the Ethiopian Government and the City Administration of Addis Ababa have built extensive road networks and imposed restrictions on driving, vehicle acquisition and parking. However, despite all these efforts, drivers and passengers waste significant time on long traffic queues, resulting in unpredictable and delayed travel. The current study investigated the cost of travel time delay, vehicle operating costs, time reliability, and the factors influencing these variables. The study used questionnaires, measurements, and traffic counting techniques to collect data from nine road segments. The sample comprised 3240 participants. The cost functions of both drivers and passengers were examined using a multiple linear regression model, with estimation performed using ordinary least squares. According to the findings, the economic costs of congestion depend on the number of lanes, the length of the road segment, the volume of traffic, and the respondents' income level. The study also revealed that travel, vehicle operation, and unreliability costs account for 74%, 6%, and 20%, respectively, of the total congestion costs. [ABSTRACT FROM AUTHOR]

In an emergency like an urban fire, road accident, or critical condition of patients, the shortest possible route to reach the destination is one of the most sought-after issues in transportation studies. However, the physical distance of the shortest route becomes longer distance than the travel time distance due to traffic congestion in metropolitan cities like that of Kolkata. So, the selection of an alternative route is essential to organize the journey logically. A well-structured road network provides multiple options to road users for selection of the best route from the shortest and alternative route according to their demand. So> the present study aims to identify the structural characteristics of road netwoiks by graph theory and the selection of the best route using the GIS technique. It has been found that the road network of Kolkata is characterized by complex structure and grid configuration of the netwoik. Selection of the best route also ranges from the shortest route to alternative route due to spatial variation of traffic speed on different roads in peak hours. The current study identifies the optimal route in terms of travel time to reach the required destinations during peak hour duration in the city of Kolkata. [ABSTRACT FROM AUTHOR]

The growth and development of communities are affected by various social and cultural problems that can hinder their proper functioning. Among these problems, insecurity stands out as one of the most significant, which is why government authorities require a considerable commitment to mitigate its impact on the population. In this context, a coverage analysis using geographic accessibility is proposed, with the objective of evaluating the current situation of the security equipment system in the municipality of Pitalito, Huila. The purpose of this analysis is to identify areas with deficiencies and propose appropriate solutions. The evaluation methodology proposes the use of geostatistical models implemented through statistical and GIS type software (Geographic Information Systems); these allow the generation of graphs and images that facilitate the interpretation of the results obtained. [ABSTRACT FROM AUTHOR]

Roads in mesoscopic traffic simulation models are often parametrized by a free speed attribute, which describes the speed at which a vehicle can traverse a road in the absence of congestion. Unlike microscopic models, mesoscopic models do not typically simulate traffic lights, individual driver behavior or acceleration and deceleration of vehicles. In consequence, the free speed parameter of each road must account for all the aforementioned effects. Modeling free speed accurately is important, as it determines the travel time, which in turn affects the decision-making of agents in the simulation. This paper introduces an approach combining microscopic simulation with machine learning models to estimate road segment free speeds. Using the microscopic simulator SUMO, we generate training data for a model search employing Bayesian optimization. Subsequently, these models undergo fine-tuning via gradient-based optimization using real-world point-to-point travel times. A significant advantage is the adaptability of this approach to diverse road networks, including those from OpenStreetMap, and the ability to incorporate routing data from various online providers independently. Our evaluation illustrates a notable decrease in prediction error between 30% and 60% compared to baseline models that assume a uniform free speed reduction for all urban roads. The fine-tuned models are able to generalize well to unseen regions and are therefore applicable to case studies where data for new or altered roads is unavailable. [ABSTRACT FROM AUTHOR]

In maritime logistics, accurately predicting the Estimated Time of Arrival (ETA) of vessels is pivotal for optimizing port operations and the global supply chain. This study proposes a machine learning method for predicting ETA, drawing on historical Automatic Identification System (AIS) data spanning 2018 to 2020. The proposed framework includes a preprocessing module for extracting, transforming, and applying feature engineering to raw AIS data, alongside a modeling module that employs an XGBoost model to accurately estimate vessel travel times. The framework's efficacy was validated using AIS data from the Port of Houston, and the results indicate that the model can estimate travel times with a Mean Absolute Percentage Error (MAPE) of just 5%. Moreover, the model retains consistent accuracy in a simplified form, pointing towards the potential for reduced complexity and increased generalizability in maritime ETA predictions. [ABSTRACT FROM AUTHOR]

This article focuses on a very narrowly specific segment of this issue, considering the impacts of HSR in the long term, not as transport arteries connecting the most important metropolitan areas but, on the contrary, as an internal factor of development within a metropolitan region. The paper aims to transfer the present operational European HSR commuting connection frequency experience to a generally applicable frequency estimation formula. Through a quantitative approach, the research analyses the total sample of 1,446 train connections from 10 European metropolitan regions. It answers the secondary research question of what the range of accessibility change between the metropolitan core and the intermediate city is after introducing HSR connection inside metropolitan regions in the time of 1 hour of journey. The estimation model formula calculates the optimal number of HSR connections between the metropolitan core and intermediate city in the HSR developing countries. It is derived and verified from the analysed dataset values. The potential operational change scenarios are presented and described based on the example of 34 metropolitan city pairs. [ABSTRACT FROM AUTHOR]

Automated guided vehicles (AGVs) are vital for optimizing the transport of material in modern industry. AGVs have been widely used in production, logistics, transportation, and commerce, enhancing productivity, lowering labor costs, improving energy efficiency, and ensuring safety. However, path planning for AGVs in complex and dynamic environments remains challenging due to the computation of obstacle avoidance and efficient transport. This study proposes a novel approach that combines multi-objective particle swarm optimization (MOPSO) and the dynamic-window approach (DWA) to enhance AGV path planning. Optimal AGV trajectories considering energy consumption, travel time, and collision avoidance were used to model the multi-objective functions for dealing with the outcome-feasible optimal solution. Empirical findings and results demonstrate the approach's effectiveness and efficiency, highlighting its potential for improving AGV navigation in real-world scenarios. [ABSTRACT FROM AUTHOR]

Background: Geographic, demographic, and socioeconomic differences in health outcomes persist despite the global focus on these issues by health organizations. Barriers to accessing care contribute significantly to these health disparities. Among these barriers, those related to travel time—the time required for patients to travel from their residences to health facilities—remain understudied compared with others. Objective: This study aimed to explore the impact of telehealth in addressing health disparities associated with travel time to hospitals for patients with recurrent hospital admissions. It specifically examined the role of telehealth in reducing in-hospital length of stay (LOS) for patients living farther from the hospital. Methods: We sourced the data from 4 datasets, and our final effective sample consisted of 1,600,699 admissions from 536,182 patients from 63 hospitals in New York and Florida in the United States from 2012 to 2015. We applied fixed-effect models to examine the direct effects and the interaction between telehealth and patients' travel time to hospitals on LOS. We further conducted a series of robustness checks to validate our main models and performed post hoc analyses to explore the different effects of telehealth across various patient groups. Results: Our summary statistics show that, on average, 22.08% (353,396/1,600,699) of patients were admitted to a hospital with telehealth adopted, with an average LOS of 5.57 (SD 5.06) days and an average travel time of about 16.89 (SD 13.32) minutes. We found that telehealth adoption is associated with a reduced LOS (P <.001) and this effect is especially pronounced as the patients' drive time to the hospital increases. Specifically, the coefficient for drive time is –0.0079 (P <.001), indicating that for every additional minute of driving time, there is a decrease of 0.0079 days (approximately 11 minutes) in the expected LOS. We also found that telehealth adoption has a larger impact on patients frequently needing health services, patients living in high internet coverage areas, and patients who have high virtualization potential diseases. Conclusions: Our findings suggest that telehealth adoption can mitigate certain health disparities for patients living farther from hospitals. This study provides key insights for health care practitioners and policy makers on telehealth's role in addressing distance-related disparities and planning health care resources. It also has practical implications for hospitals in resource-limited countries that are in the early stages of implementing telehealth. [ABSTRACT FROM AUTHOR]

This paper studies the effects of aerial cable cars on mode of transport, time use, and employment in the metropolitan area of La Paz, Bolivia. Using an instrumental variables approach, we estimate local average treatment effects of cable car use for residents who use the system due to proximity to a cable car station. Results suggest that cable car users substitute private transport in favor of public transit and experience large savings in commute time, which is reallocated toward educational activities. Users also increase self-employment activities, potentially reflecting improved access to local labor markets. The positive effects of the cable car are driven by residents of El Alto, a city with a high concentration of poor and indigenous households on the high plateau bordering La Paz. The economic benefits of the cable car outweigh the costs by a ratio of 1.05 to 2.16. [ABSTRACT FROM AUTHOR]

Papua New Guinea (PNG) has among the worst maternal and child health (MCH) outcomes in the Asia–Pacific region. Improving population access to and use of health services is a key government priority. However, it is unclear from the international evidence whether the observed inverse relationship between distance to health facilities and health outcomes is due to health service use. This article first seeks to estimate the relationship between distance to health facilities, the use of MCH services and health outcomes in PNG. The second goal is to assess whether the observed relationship between distance to health services and health outcomes occurs due to health service use. Cross‐sectional data from the Demographic and Health Survey 2016–18 for PNG were used to estimate the association between distance to health facilities, indicators of health service use and indicators of child health outcomes, using regression methods. Causal mediation methods were used to assess whether the observed associations between health outcomes and distance to health facilities were explained by health service use, or not. We find an inverse association between distance to health facilities and utilisation of MCH services; and between distance to health facilities and child health. However, our analyses do not support causal mediation via impact of distance on health service use. At odds with a large literature that implicitly assumes that health service use improves health outcomes, our findings reflect broader concerns about quality of care in PNG. [ABSTRACT FROM AUTHOR]

Land transportation mode is the main means of transportation to reach campus II Unkhair Gambesi which can be accessed using motorbikes and public transportation. Ternate City is one of the cities in North Maluku Province which has many important activities in the fields of education, politics, and economy. In addition, the center of government of North Maluku province is also still in the Ternate City area, so it also has an impact on the development of the lives of the people of Ternate City. This study aims to determine the effect of cost and travel time on the selection of transportation modes between private vehicles (motorcycles) and public transportation using the state preference method. This study uses the Stated preference method used for an approach by using respondents' opinions on various alternative options on the attributes of travel costs and travel time to transportation modes Private Vehicles (Motorcycles) and Public Transportation Case Study: Central Ternate – Campus II Unkhair Gambesi. The results of this study used linear regression analysis and the binary logit method. And it can be concluded that the chance of choosing a Private Vehicle (Motorcycle) is 74% while the chance of choosing public transportation is 26%. [ABSTRACT FROM AUTHOR]

In this article, we introduce the command cntraveltime, which can calculate both the travel distance and the travel time between two locations in China with respect to different modes of transportation (driving, public transport, and cycling). Existing commands such as georoute, traveltime, and mqtime have difficulties in parsing Chinese locations. cntraveltime solves this outstanding challenge via a feature that enables it to call route-planning services from the Baidu Maps Open Platform. The results of rigorous testing on the features of the command show that, relative to similar existing commands, cntraveltime has the highest capacity in terms of functionality and precision. This suggests that it can be regarded as a useful complement to other existing commands, especially when calculating travel distance and time for locations within China. [ABSTRACT FROM AUTHOR]

An urban arterial can be considered a series of intersections, intermediate sections (defined as those that link intersections and mid-block areas), and mid-block sections. The intermediate section is characterized by varying speed characteristics at its entry and exit. A vehicle exiting the intersection and entering the intermediate section would accelerate and gradually attain uniform speed when it leaves the intermediate section (entry to the mid-block). Most of the reported travel time studies considered the delays at intersections and the travel times in mid-blocks to get the network level travel time without paying attention to the variability of speeds and travel time at intermediate sections. The present study concentrates on the travel time estimation of the intermediate section, which is not well-discussed in the literature. A speed estimation scheme was developed for the entry and exit of the intermediate section (characterized by varying speed ranges) using a dynamic model-based estimation scheme that captured the speed variations effectively. Intermediate link travel time was then estimated using a weighted average speed-based method. The effects of heterogeneity and limited lane discipline in the traffic stream of mixed traffic were considered using two model formulations, one that represented the traffic stream in Passenger Car Units (PCU) and the other in different vehicle classes for travel time estimation. The developed estimation schemes can be used as a possible application in Intelligent Transportation Systems (ITS) for real-time estimation of travel time at urban arterials under mixed traffic conditions. [ABSTRACT FROM AUTHOR]

Today's roadways are subject to traffic congestion, the deterioration of surface-assets (often due to the overreliance on private vehicle traffic), increasing vehicle-operation and fuel costs, and pollutant emissions. In Abu Dhabi, private car traffic forms the major share on urban highways, as the infrastructure was built to a high quality and the public transport network needs expansion, resulting in traffic congestion on major highways. These issues are arguably addressable by appropriate decisions at the planning stage. Microsimulation modeling of driving behavior in Abu Dhabi is presented for empirical assessment of traffic management scenarios. This paper presents a technique for developing, calibrating, validating, and the scenario analysis of a detailed VISSIM-based microsimulation model of a 3.5 km section of a 5-lane divided highway in Abu Dhabi. Traffic-count data collected from two sources, i.e., the local transport department (year 2007) and municipality (2007 and 2015–2016) were used. Gaps in traffic-counts between ramps and the highway mainline were noted, which is a common occurrence in real-world data situations. A composite dataset for a representative week in 2015 was constructed, and the model was calibrated and validated with a 15% (<100 vehicles per hour) margin of error. Scenario analysis of a potential public bus transport service operating at 15 min headway and 40% capacity was assessed against the base case, for a 2015–2020 projected period. The results showed a significant capacity enhancement and improvement in the traffic flow. A reduction in the variation between vehicle travel times was observed for the bus-based scenario, as less bottlenecking and congestion were noted for automobiles in the mainline segments. The developed model could be used for further scenario analyses, to find optimized traffic management strategies over the highway's lifecycle, whereas it could also be used for similar evaluations of other major roads in Abu Dhabi post-calibration. [ABSTRACT FROM AUTHOR]

Copyright of Riwayat: Educational Journal of History & Humanities is the property of Riwayat: Educational Journal of History & Humanities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper studies the effectiveness of the bus priority lane (BPL) for public transport buses in the city of Bengaluru in India. We use the travel times on the BPL corridor as a measure of the effectiveness of the BPL. We find that there is a significant improvement in the travel times after the introduction of the BPL; for the worst 10% of the travel times, we find an improvement between 4% and 28%. Our methodology involves extracting trips on the BPL and computing the travel times for these trips from a time series of GPS information. Our methodology is scalable and can be used to compute the travel times between any two given points in other similar studies. We supplement our results with a novel test (called the D-test) for comparing the levels of stressful driving in the following scenarios: (a) morning peak hours (IST 07:00 h to 11:00 h) versus evening peak hours (IST 17:00 h to 21:00 h), and (b) northward trips versus southward trips on the BPL. We are able to infer that the drivers are generally more stressed during the morning peak hours and during the southward trips on the BPL. Partitioning the BPL into segments, we show that a majority of the segments exhibit similar effectiveness and driver stress trends as the full BPL stretch. We anticipate that corrective measures for the betterment of travel times and driver stress levels (e.g., introducing additional buses subject to vehicle re-balancing constraints, carefully planning the bus schedules to regulate bus traffic throughout the day, etc.) in some segments can lead to further improvements in travel times and reduction in driver stress levels. • Implementation of the Bus Priority Lane can reduce travel times. • Segmented analysis helps identify traffic bottlenecks for targeted intervention. • Driver-stress hot-spots can be identified for targeted intervention. • A novel statistical test compares driving stress levels across a pair of routes. [ABSTRACT FROM AUTHOR]

Recent studies have shown that Connected Vehicles (CVs) can improve traffic mobility and safety while saving energy and reducing emissions. Due to the potential abilities of CVs to transform travel patterns through the creation of a safe, interoperable wireless communications network, this research follows three objectives. Initially, the gradual deployment of CVs and its effects on mobility were evaluated under 17 various CV penetration rates in the network during an incident with a 15-minute link blockage. Then, the system-wide effects of CV-equipped vehicles with route guidance features on travel time were analyzed by travel mode. Finally, the use of dynamic bus lanes (DBL) was tested through two scenarios of DBL activation intervals in the incident link to evaluate the effects of dynamic rerouting of CV cars on CV buses. A microscopic hybrid model was developed to simulate the driving behavior of CV guidance with various penetration rates of CVs. The study used travel time as a measure of service quality and the difference in travel time between CV and Non-CV cars and buses. The results showed that as the number of rerouting CV cars increases, the average travel time in rerouting paths decreases. In the network-wide analysis, 85% was found to be the optimum breakpoint of CV Market Penetration Rate (MPR) where the minimum average user's travel time could be obtained. However, under higher network travel demand level, the breakpoint occurred at 50% CV MPR. [ABSTRACT FROM AUTHOR]

Increasing congestion levels and their elusive impact warrants the development of congestion mitigation strategies. Quantifying congestion and analyzing the spatiotemporal variations is imperative to achieve this target. Travel time is being explored as a measure of congestion with the advent of Intelligent Transportation Systems (ITS) and the deployment of related technologies. Researchers identified congested conditions when the average travel time exceeds 1.33 or 1.66 times free-flow travel time (FFTT). It is well known that travel time under congested conditions (T c) is more sensitive to land use, road geometry, and traffic control characteristics than the FFTT. Therefore, the extension of FFTT to derive T c may not be appropriate. This study focuses on developing a hazard-based model to derive T c. Travel time is modeled using a parametric accelerated failure time (AFT) model. The applicability of the proposed methodology is justified using empirical and simulated datasets. The T c derived from the AFT model is close to the travel time for the level of service (LOS F). Based on the T c , a new measure of congestion, termed congestion index (CI), is proposed. The proposed index can quantify the frequency and intensity of congestion on a link or network. The traffic states identified based on CI were mapped on the fundamental diagram (FD) and the macroscopic fundamental diagram (MFD). It was concluded that if travel times are uncertain and unstable under low-density conditions, then the ascending leg of the FD or MFD can be marked congested. Uncertain and unstable travel times indicate that traffic flow is unstable, and therefore, it can be concluded that traffic instabilities significantly affect congestion. • A hazard-based duration model is applied to model travel times to derive travel time under congested conditions. • Travel time corresponding to the inflection point of hazard function was considered as travel time under congested conditions. • Instabilities in travel times under low-density conditions mark the ascending leg of the fundamental diagram as congested. • The application of the study is demonstrated by evaluating the effect of traffic signal control. [ABSTRACT FROM AUTHOR]

Effective performance of arterial corridors is essential to community safety and vitality. Considering the dynamic nature of traffic demand, efficient management of these corridors require frequent updating of the traffic signal timings through various strategies. Agency resources for these activities are commonly scarce and are primarily by public complaints. This study provides a workflow using probe-based data to measure and compare different segments on arterial corridors in terms of the traffic signal performance measures that can capture travel time dynamics across signalized intersections. The proposed methodology identifies a group of dynamic days followed by evaluation of travel rate based upon remaining non-dynamic days. Dynamic days represent the variability of traffic on a segment. Consequently, a corridor having high number of dynamic segments along with poor performance during normal days would be a candidate for adaptive control. Further, to handle the large-scale data source collected from city-wide or statewide traffic signals, the study adopts parallel computation-based strategy using MapReduce technique. A case study was conducted on 11 corridors within Des Moines, Iowa, to demonstrate the efficacy of the proposed approach, which identified two arterial corridors, Merle Hay Road and University Avenue, to be suitable for adaptive traffic signal control. [ABSTRACT FROM AUTHOR]

Road authorities are committed to improving community infrastructure through Bus Rapid Transit (BRT) projects. Yet, these initiatives often disrupt traffic, as seen in the Amman BRT project. A study assessed traffic conditions during various stages to address these disruptions, including simulated BRT operations. The research collected data on traffic volume, travel time, delays, and transit ridership before, during, and after construction and after BRT operations began. As a case study, the study focused on the impact of Amman's first mass transit system at the Yajouz intersection. Traffic volumes decreased by 28% during construction, reducing delays significantly from 118.03 to 91.04 seconds. Although average speeds increased slightly (one km/h, on average), travel time doubled, negatively impacting 84% of businesses due to considering alternative routes for traveller destinations. However, delays significantly reduced after construction to 58 seconds per vehicle despite the higher traffic volume. Traffic simulations during construction underestimated observed delays (53.5 seconds per vehicle vs. 73.7 seconds per vehicle), still indicating a Level of Service (LOS) E. In BRT operation, modelled delays (55.8 seconds per vehicle) were lower than observed (63.8 seconds per vehicle), maintaining same LOS level. BRT minimally impacted ridership, an expected outcome given its trial phase. Environmental assessments revealed emissions peaking during construction, with a 25% modal shift to BRT yielding the lowest emissions. The study stresses sustainable urban mobility, advocating for meticulous planning, adaptable policies, and public awareness, emphasizing the need for detailed pedestrian research in this context. [ABSTRACT FROM AUTHOR]

The efficiency of the planning process in public transport is represented through different measures, which are practically impossible to optimize simultaneously. This study defines a bi-objective optimization problem for the transit network design to analyze the trade-off between minimization of travel times and reducing monetary costs for passengers (which was not addressed in the literature) while considering a hard constraint for operational costs. Indeed, the minimization of monetary costs for passengers is relevant in transport systems without a complete integrated fare system, where passengers may pay for each trip-leg; thus, modeling monetary costs for users is essential when referring to the system's accessibility and route choice. To achieve our goal, we implement an epsilon-constraint algorithm capable of obtaining high-quality approximations of the Pareto front for benchmark instances in hours of computational time, which is reasonable for strategic planning problems. Numerical results show that the conflict between both objectives is evident, and it is possible to identify the more useful lines to optimize each objective, leading to relevant information for the decision-making process. Finally, we perform a sensitivity analysis on the budget parameter of our optimization problem, showing the classic trade-off between the operational costs and the level of service in terms of travel time and monetary cost. • Our study defines a bi-objective problem for the transit network design. • This study analyzes the trade-off in reducing travel times and monetary costs. • We identify the lines that have a more significant impact on each objective. • Our model is a flexible tool to obtain information about transport systems. [ABSTRACT FROM AUTHOR]

We propose an approach for the simultaneous reconstruction of the electromagnetic and acoustic material parameters, in the given medium Ω where we want to image, using the photoacoustic pressure, measured on a single point of the boundary of Ω, generated by plasmonic nanoparticles injected inside Ω. As measurements, we use the generated pressure, that we denote by p* (x, z, s, ω ), depending on only one fixed point x ∈ ∂Ω, where we measure, the time variable s, in a large enough interval, and the incidence frequency ω, in a large enough band. These measurements are repeated while the location z of the nanoparticle is moved inside Ω to scan it. We show that these measurements (which are five-dimensional in terms of dimensionality) are enough to reconstruct both the sound speed, the mass density, and the permittivity inside Ω (i.e., 3 coefficients of three dimensions each in terms of dimensionality). Indeed, from the behavior of the measured pressure in terms of time, we can estimate the travel time of the pressure, for arriving points inside Ω, then using the eikonal equation we reconstruct the acoustic speed of propagation, inside Ω. In addition, we reconstruct the internal values of the acoustic Green's function. From the singularity analysis of this Green's function, we extract the integrals along the geodesics, for internal arriving points, of the logarithmic gradient of the mass density. Solving this (internal) integral geometric problem provides us with the values of the mass density function inside Ω. Finally, from the behavior of p* (x, z, s, ω) with respect to the frequency ω, we detect the generated plasmonic resonances from which we reconstruct the permittivity inside Ω. [ABSTRACT FROM AUTHOR]

Transportation has an important role in people's lives, where the rate of population growth is getting higher, which results in an increase in people's need for transportation. This can increase the use of public transport transportation; this problem is closely related to the traffic flow network, especially in the city of Makassar in the Tamalanrea sub-district. The application of the feeder transport line in the city of Makassar is one form of solution to the problems that occur in the city of Makassar, namely the traffic flow network. Based on the results of the planning of the Feeder Transport Route Model, the route that has been researched can be a solution to reduce congestion on the main road, where public transportation types Metromini can be diverted to local routes with planned routes and road types, which can meet the type of public transport Metromini. For public perception, it is more dominant to agree if the feeder system is implemented in Makassar. The purpose of this study was to determine the performance of feeder transport if applied in the city of Makassar. It can be seen from the number of conflicts that have occurred due to the increasing number of public transport vehicles on the main roads of the city of Makassar. [ABSTRACT FROM AUTHOR]

Although the 'one ticket transfer' mode brings convenience to passengers, it also poses challenges to the passenger flow assignment. The current widely used multi‐path probability assignment model based on traffic cost realizes the passenger flow distribution from the macro‐perspective but lacks the consideration of passenger attributes and the time‐varying characteristics of passenger travel time. Here, a novel spatiotemporal path inference model is proposed for Urban Rail Transit (URT) passengers based on the travel time data and train operation information. In this study, the impact of passengers detained at the platform on passenger travel itinerary is considered by characterizing the passenger detention rate. The proposed method realizes the reverse inference of passenger path from the micro‐perspective, and can accurately describe the specific travel process of each passenger. Moreover, the real‐world data of Shenzhen Metro in China is taken to verify the rationality of the proposed model. The results show that the model is in good agreement with the existing clearance model and can accurately infer the passenger travel itinerary from the micro‐perspective. The proposed method provides a more refined solution for the spatial‐temporal assignment of URT passenger flow. [ABSTRACT FROM AUTHOR]

Convenience store chains are many people's top choice for dining and leisure and have logistics procedures that involve each store receiving multiple deliveries because of the varying delivery periods and suitable temperatures for different goods. The estimated arrival time for each delivery has a huge impact on the route arrangement and convenience store preparation for dispatchers to schedule future deliveries. This study collected global positioning system travel data from a fleet of one of the top convenience store chains in Taiwan between April 2021 and March 2022 and proposed machine learning to establish a model to predict travel times. For unavailable data, we proposed the nonlinear regression equation to fill in the missing GPS data. Moreover, the study used the data between April 2022 and September 2022 with mean absolute percentage error to validate the prediction effects exceeding 97%. Therefore, the proposed model based on historical data and the machine learning algorithm in this study can help logistics fleets estimate accurate travel times for their scheduling of future delivery tasks and arranging routes. [ABSTRACT FROM AUTHOR]