Changes In Travel Time Research Articles

Optimizing multi-modal urban traffic flow: Utilizing macroscopic fundamental diagram and Model Predictive Control

Urban transportation systems, characterized by multiple modes and complex dynamics, present significant challenges for the efficient management and optimization of traffic. Addressing these challenges, this study utilizes the Macroscopic Fundamental Diagram to develop and implement Model Predictive Control (MPC) strategies aimed at optimizing traffic flow across multiple urban reservoirs. By designing optimal controllers that regulate the transfer flow of trucks and passenger vehicles, this study aims to maintain vehicle accumulation at a critical level. For this purpose, Centralized Model Predictive Control (C-MPC) and Decentralized Model Predictive Control (DC-MPC) approaches have been formulated to maximize the accumulation of passenger vehicles while reducing the number of trucks in the reservoir system. The findings reveal that the unified approach of C-MPC effectively reduces truck traffic but results in a higher change in passenger travel time. The outcome for segmented C-MPC shows a slower rate of change in vehicle accumulation. While DC-MPC offers a better balance and keeps accumulation for both trucks and passenger vehicles within predefined limits. It contributes to the theoretical understanding of traffic flow optimization and practical insights for city planners and engineers seeking to implement advanced traffic management solutions. Future work can explore the scalability of these controllers and their adaptation to real-time traffic data.

Humbang Hasundutan Regency, North Sumatra, Indonesia, Regional Road Grant Program: Econometric Descriptive Analysis Accessibility and Sustainability

The Regional Road Grant Program (PHJD) is a strategic government initiative aimed at improving regional accessibility through the development and maintenance of road infrastructure. This study examines the impact of PHJD on accessibility, land use changes, and socio-economic dynamics in Humbang Hasundutan Regency. The research employs spatial analysis using overlay maps of land use from 2018 and 2023, primary surveys with 100 respondents, and paired sample t-tests to measure travel time changes before and after the program. The findings reveal a 50% increase in built-up land along PHJD roads, accompanied by an 18% decrease in green and agricultural land, indicating intensified economic activity but with ecological trade-offs. Community accessibility improved significantly, with average travel times reduced by 35%, enhancing mobility to health, education, and economic centers. Additionally, the program led to a 50% increase in built-up land and a corresponding decline in agricultural and green areas, highlighting intensified economic activity. Statistical analysis confirms that PHJD implementation directly affected travel time (p < 0.05) and land use changes, underscoring its transformative role in regional development. However, challenges such as unplanned land use changes and increased congestion require attention to ensure sustainable growth.

Perforation Evaluation Using Multiscale Traveltime Tomography: Insights from Borehole Data

Perforation is essential for the development of unconventional oil and gas, hydrogen, geothermal energy, and deep subsurface exploration, yet an effective evaluation method has remained elusive. The Multiscale Tomography (MST) method is applied to analyze downhole perforation, improving inversion accuracy through multi-scale discretization of velocity model grids. Numerical experiments demonstrate the method's reliability in determining perforation depth intervals, radial penetration depth, and velocity variations outside the well. Simulations before and after perforation reveal significant travel time changes, supported by logging data showing delays and velocity shifts. A comparison of imaging results with various receiver configurations highlights the critical role of receiver arrangement in achieving accurate subsurface imaging. These findings validate MST as an effective tool for perforation assessment and velocity imaging around wells, with an emphasis on optimal receiver configuration.

The impact of long-haul travel and 13h time change on sleep and rest activity circadian rhythm in speed skaters during World Cup competitions.

Athletes frequently compete only a few days after long-haul travel. Longitudinal real-world data on athletes' sleep and sleep-wake cycle in competitive settings remain scarce. This study assessed the impact of a long-haul travel across ∼13 time zones on sleep patterns, rest-activity circadian rhythms (RAR), and their subsequent effects on neuromuscular function and race performance in the Canadian Short-Track Speed Skating Team. Nineteen athletes (24±4years, 11 women) travelled from Montréal (UTC-5) to Asia (UTC+8, UTC+9) for World Cup races between 2017 and 2019. Actigraphy data were collected before (Baseline) and during travel, during the stay in Asia (SIA), and during competition days. RAR were computed using cosinor analyses on accelerometry data with 24h phase periods. Countermovement jump height (CMJ) was measured in a subsample (n=10). Compared to baseline (7:08±0:53), athletes obtained less sleep during travel (6:16±1:27) and competition days (6:35±1:10), and more during SIA (7:32±0:46; time effect P<0.0001). Sleep efficiency and CMJ were greater in SIA than baseline (P=0.007 and P=0.0004, respectively). During SIA, sleep time increased by 9 min per night until the fifth day (P<0.0001), with a slight decrease in sleep efficiency (P=0.005) and an increase in CMJ (P<0.0001). For RAR, mean activity peaked on day 2, shifting from late evening to ∼15:00. Race performance was not different from other races of the same season (P>0.254). Our results demonstrated that, despite the possible sleep debt from the long-haul travel, athletes recovered within 5 days, highlighting their adaptability to manage sleep debt and jetlag without impacting competitive outcomes.

Flood risk awareness and transportation mode choice in communities with varying flooding frequencies: The cases of Bozkurt, Kastamonu, Turkey and Malanday, Marikina, Philippines

This study investigates the post-flood transportation mode choices (TMC) of communities exposed to floods of varying frequencies, focusing on Bozkurt, Turkey, recently afflicted by a major flood, and Malanday, Philippines, which endures an average of 10 floods annually. The study considers 17 factors affecting TMC, including age, occupation, income, housing type, flood experience, education level, changes in travel time, primary transportation mode, duration of residence, gender, marital status, relocation status, personal losses, disruption of daily activities, time to return to routine, satisfaction with road maintenance, and satisfaction with flood measures. Data were collected through surveys and analysed using regression analysis.The findings indicate that personal and cultural characteristics, and flood frequency, have limited effects on TMC. The most impactful factors on post-flood TMC are changes in travel time (42.11 %), personal losses (23.75 %), and satisfaction with road maintenance (11.30 %). The study demonstrates that flood risk awareness is directly related to education level and flood experience. In Malanday, where 84 % of the population are university or high school graduates, floods are regarded as routine events. In contrast, in Bozkurt, where only 37 % have similar education levels, the community experienced panic under flood conditions, adversely affecting decision-making processes.The research provides insights into how communities exposed to floods adjust their TMC, offering essential information for mitigating flood risks and increasing flood awareness. Moreover, the findings deliver suggestive information for local governments and planners in developing more effective flood management and transportation planning strategies.

Research on Optimizing Human Resource Expenditure in the Allocation of Materials in Universities

This paper establishes a multivariate function model for natural human load-carrying walking in some typical scenarios such as college equipment and material relocation by students and a large amount of identical freight relocation in commercial activities. For classified material relocation needs and constraints, we obtain the relationship between walking speed and load weight for a single person, as well as the time cost for different round trips. By establishing an integer programming model with the minimum total transportation time cost and shelf life as the objective function and the requirements of negative weight and speed as the constraint conditions, we reach the optimal item allocation methods considering time cost and shelf life. We discover that there is an approximate linear relationship between the change in natural walking speed and travel time when the load is small, thus obtaining the time cost of student transportation under different round-trip situations. The Monte Carlo simulation algorithm, which is more efficient compared with other methods such as the integer programming method, is used to obtain the optimal allocation scheme that meets the efficiency and quality requirements. The analysis methods and results can be used as guidance for task scheduling optimization for material relocation in educational organizations as well as commercial agencies.

Mode Choice and Value of Time on Long-Distance Vacation Trips

Long-distance travel for leisure purposes, especially international travel, has grown dramatically over the past decades. Although its share of the total number of trips is small, it accounts for a significantly larger share of total distance and greenhouse gas emissions, with the accompanying adverse environmental consequences. One remedy might be to urge tourists to travel on vacation by rail rather than car, which is less harmful to the environment. However, it is not clear how to persuade them to do so, since knowledge of the determinants of mode choice for international vacation travel is quite limited. To fill this gap, we collected survey data among tourists, mostly international, visiting the Austrian Alps and estimated a choice model between personal vehicle and rail. The results revealed a cost dumping effect, and the significant influence of factors related to overall travel convenience, luggage transport, travel party size, and the accessibility of the destination by rail. The tourists reacted with little elasticity to changes in cost and travel time, and exhibited a higher value of time than in other studies on long-distance travel, which was also higher than the hourly wage in their home countries. This article delivers valuable evidence that could contribute to improved long-distance rail services, more accurate project appraisals, and better marketing campaigns and transportation policies to make rail more competitive against the personal vehicle, and facilitate more sustainable vacation travel.

Visualizing the Influence of New Public Transport Infrastructure on Travel Times

Due to the global climate change and increasing traffic volumes in cities, a shift from individual to public and multimodal transport is aspired. Travel time is one of the most important aspects for many people when choosing their mode of transportation. This leads to the requirement that changes in travel times have to be considered when planning new public transport infrastructure. This research paper presents and compares different techniques for visualizing the impact of new lines in existing public transport networks on travel times. The general approach of simulating timetable data and calculating intermodal travel times considering public transport and walking is being applied to two current infrastructure projects in the city of Bonn and the surrounding region. The created maps generally aim to visualize the spread in travel times between existing and extended transportation networks discretized by different spatial units such as rectangles or postal code areas. In comparison to other common methods which typically require two maps for two different scenarios (e.g. in case of isochrones), our approach gives the opportunity to combine all relevant information within one map. It is also shown how to apply bivariate choropleth maps for displaying travel times and how to visualize improvements in the accessibility of multiple target points of interest at once.

Modelling the impact of sublingual immunotherapy versus subcutaneous immunotherapy on patient travel time and CO2 emissions in Sweden

In Sweden, allergy immunotherapy (AIT) is available as either subcutaneous immunotherapy (SCIT) injections or sublingual immunotherapy (SLIT) tablets and is used to treat moderate-severe allergic rhinitis (AR). This study sought to determine treatment-related CO2 emissions and travel times in Swedish patients receiving either SCIT or SLIT-tablets. A list of specialized Swedish AR clinics that administer AIT was determined, and respective co-ordinates retrieved. Swedish municipality population data were obtained from a national database. The mean distance from each Swedish municipality to the nearest AR clinic was calculated, adjusted using a detour index, and weighted by estimated patient population size. Transport modality data were obtained from a Swedish urban transport study and CO2 emissions were obtained from Government sources. The mean number of annual SLIT-tablets and SCIT doses required were calculated based on product labels and clinical expert input. The annual number of healthcare professional interactions were layered into the model to estimate changes in mean patient travel time, distance, and travel-related CO2 emissions associated with using SCIT versus SLIT-tablets. Mean annual travel-related CO2 emissions were 410 tonnes (to two significant figures [s.f.]; standard deviation [SD] 90) with SLIT-tablets, versus 1700 tonnes (SD 380) for SCIT, resulting in mean annual savings of approximately 1300 tonnes (SD 290) of CO2 if all AIT patients were to receive SLIT-tablets instead of SCIT, over 380 times greater than 2021 average Swedish CO2 emissions per capita. Approximate mean annual travel times for patients taking SLIT-tablets were 66,500 h (three s.f.; SD 14,400), and 278,000 h (SD 60,200) for SCIT, resulting in mean annual savings of 211,000 h (SD 45,800) if all AIT patients were to receive SLIT-tablets instead of SCIT. Compared with SCIT injections, SLIT-tablets led to substantial reductions in treatment-related CO2 emissions and travel times for Swedish patients.

ЭКОНОМИЧЕСКОЕ ОБОСНОВАНИЕ ВЫДЕЛЕННЫХ ПОЛОС ДЛЯ ОБЩЕСТВЕННОГО ТРАНСПОРТА НА ПРИМЕРЕ ГОРОДА ЧЕЛЯБИНСКА

This paper investigates the effectiveness of public transport lanes. The study compares the current cost of the transport work and the cost of transport work when increasing the average speed of travel with dedicated public transport lanes. The recommendations of the Russian Ministry of Transport were used as the basis for the feasibility study. Based on changes in speed and travel times, the effect on the budget is assessed in monetary terms. Depending on the increase in speed, the budget effect is associated with a reduction in the required number of vehicles to deliver the same levels of transport services for the population. The methodology allows us to evaluate not only the change in traffic flow parameters when implementing dedicated lanes for public transport, but also the economic effect of increasing the speed of travel. The work analyzes the economic effect of organizing a dedicated lane using specific municipal routes in the city of Chelyabinsk.

A novel approach to traffic modelling based on road parameters, weather conditions and GPS data using feedforward neural networks

This article presents the development of a estimation model using an artificial neural network to estimate the traffic factor parameter, which reflects changes in travel time for a single transport connection. The modeling of traffic is highly challenging due to the complex and non-linear nature of road systems, the interactions between diverse road users, and the ripple effects of congestion. The research problem addressed is the need to consider a wide range of factors that contribute to congestion, as identified in the literature. To address this challenge, a universal Feedforward Neural Network (FNN) was designed, specifically the Multilayer Perceptron (MLP) 38–10-1 model. The input layer neurons of the MLP receive a vector of input values representing various factors such as road types, technical properties, time of day, days of the week, incidents, weather conditions, and population density. The model was trained using a dataset consisting of 41,945 records of fully completed input variables, extracted from a larger dataset of 300,000 travel time measurements collected from a real transport network mapped on a macro scale (Mazovia, Poland). During the research, the model achieved a satisfactory level of Mean Absolute Percentage Error (MAPE) for the test set (9.12%) and a Normalized Root Mean Squared Error (NRMSE) below 0.02, indicating good estimation performance. Furthermore, this work proposes specialized models with the structure of MLP 34–10-1 for individual types of roads, which also demonstrated satisfactory estimation abilities. These models have practical applications in traffic management and planning. Overall, this research addresses the research problem of modeling factors for predicting travel time changes using an artificial neural network approach. The developed models provide accurate estimations and offer potential applications in transportation systems.

Comparing the stretching technique and the wavelet cross-spectrum technique for measuring stress-induced wave-velocity changes in concrete

Coda wave interferometry (CWI) holds promise as a technique for concrete stress monitoring. This is because the coda, which consists of multiply scattered arrivals, is the result of propagation through the medium over large distances. As such, it is sensitive to both minute structural changes and small velocity changes in that medium. Previous studies focusing on concrete have predominantly utilized the time-domain-based stretching technique to measure travel-time changes. There is, however, a lack of consensus on how to quantify these changes effectively. In this study, we conduct a systematic comparison between two techniques, namely the stretching technique and the wavelet cross-spectrum (WCS) technique, for measuring stress-induced velocity changes in a cylindrical concrete sample. Our comparison focuses on two key aspects: (i) stability against cycle skipping and (ii) consistency in retrieving velocity changes. Experimental results reveal that both the WCS technique and the stretching technique yield consistent velocity changes. In terms of stability, it is challenging to determine which technique performs better, due to differences in the mechanisms triggering cycle skipping. However, when considering waves with frequencies ranging from 50 kHz to 80 kHz, both techniques exhibit comparable performance. Based on our findings, we offer the following recommendations for utilizing these CWI techniques in concrete stress monitoring:For the stretching technique, selecting the time window length based on the wave frequency and the expected magnitude of velocity change.For the WCS technique, operating it in the frequency band where spectral decomposition shows sufficiently high energy in the signal and can accommodate the expected magnitude of velocity change.

Willingness to pay for changes in travel time and work time: A stated choice experiment of US commuters

Studying the tradeoffs between commuting, working, and money—including the willingness to pay (WTP) for changes to travel time and work time—is of interest in both travel behavior research and transportation practice. Our study's objectives were to: (1) quantify WTP for travel time and work time changes; (2) measure preference heterogeneity in these time values; and (3) examine sensitivities to changes in income and travel cost. First, we conducted a stated choice experiment that added work time and income to traditional travel time and cost attributes, collecting data from 675 US adult commuters in late 2020. Second, we estimated pseudo panel mixed multinomial logit models with random and systematic preference heterogeneity. Average WTP was larger for travel time changes than for work time changes, although values varied across personal, household, and commute/work characteristics. Overall and on the margin, US commuters appear to dislike commuting more than they dislike working, a finding which differs from previous studies in Chile and Austria. Although future work could improve the realism of the choice experiment, our research offers a simple stated preference method to help work towards advancing an understanding of the values and tradeoffs involving both working and commuting.

Travel Time and Costs for Abortion for Military Service Members After the Dobbs Decision

This study quantifies the change in travel times for military service personnel to abortion facilities following the US Supreme Court Dobbs decision and estimates the cost of an abortion-related travel reimbursement policy.

Deep Q-Network-Enabled Platoon Merging Approach for Autonomous Vehicles

Platoon merging control of autonomous vehicles driving in a platoon formation can improve traffic efficiency. However, current platoon merging control approaches primarily rely on rules, making it challenging to achieve optimal control. In this study, we propose a platoon merging control approach based on a deep Q-network (DQN). First, we specify the state and action of the vehicle and establish a set of reward functions to ensure safe driving. Then, the DQN algorithm is used to train a neural network suitable for merging the controls of connected and automated vehicles (CAVs) to continuously approach the state-value function. Finally, we compare the proposed approach with a rule-based (RB) vehicle merging approach using a MATLAB simulation. In particular, CAVs are driven simultaneously using the two approaches in a random environment. The simulation results show that the proposed DQN-based vehicle merging approach requires less merging travel time and fewer vehicle lane change times than the RB approach. Additionally, merging can result in improved capacity in medium and high traffic densities compared with no-merging: the higher the CAV penetration rate, the larger the improvement. We verify the effectiveness of the proposed approach for different initial conditions. We suggest that the proposed method is a safe and robust method for CAV platoon merging, and that it can be applied to increase the capacity of freeways and roads.

PET-measured human dopamine synthesis capacity and receptor availability predict trading rewards and time-costs during foraging

Foraging behavior requires weighing costs of time to decide when to leave one reward patch to search for another. Computational and animal studies suggest that striatal dopamine is key to this process; however, the specific role of dopamine in foraging behavior in humans is not well characterized. We use positron emission tomography (PET) imaging to directly measure dopamine synthesis capacity and D1 and D2/3 receptor availability in 57 healthy adults who complete a computerized foraging task. Using voxelwise data and principal component analysis to identify patterns of variation across PET measures, we show that striatal D1 and D2/3 receptor availability and a pattern of mesolimbic and anterior cingulate cortex dopamine function are important for adjusting the threshold for leaving a patch to explore, with specific sensitivity to changes in travel time. These findings suggest a key role for dopamine in trading reward benefits against temporal costs to modulate behavioral adaptions to changes in the reward environment critical for foraging.

Seismic Ocean Thermometry Using CTBTO Hydrophones

AbstractDue to limited observational coverage, monitoring the warming of the global ocean, especially the deep ocean, remains a challenging sampling problem. Seismic ocean thermometry (SOT) complements existing point measurements by inferring large‐scale averaged ocean temperature changes using the sound waves generated by submarine earthquakes, called T waves. We demonstrate here that Comprehensive Nuclear‐Test‐Ban Treaty Organization (CTBTO) hydrophones can record T waves with a higher signal‐to‐noise ratio compared to a previously used land‐based T‐wave station. This allows us to use small earthquakes (magnitude &lt;4.0), which occur much more frequently than large events, dramatically improving the resulting temporal resolution of SOT. We also find that the travel time changes of T waves at the land‐based T‐wave station and the CTBTO hydrophone show small but systematic differences, although the two stations are only about 20 km apart. We attribute this feature to their different acoustic mode components sampling different parts of the ocean. Applying SOT to two CTBTO hydrophones in the East Indian Ocean reveals signals from decadal warming, seasonal variations, and mesoscale eddies, some of which are missing or underestimated in previously available temperature reconstructions. This application demonstrates the great advantage of hydrophone stations for global SOT, especially in regions with a low seismicity level.

MODEL OF MODE SELECTION ANALYSIS AT PONTIANAK – KETAPANG ROUTE WITH STATED PREFERENCE METHOD

There are various reasons and considerations underlying travellers in choosing a mode of transportation. The diversity of transportation mode is an essential factor for transportation business actors in competing to provide a choice of transportation modes that are very popular or no longer in demand for use by travellers traveling from one city to another. Currently, the most frequently used mode of transportation on the Pontianak - Ketapang city route or vice versa is airplane transportation or travel, and this is because both modes of vehicle arrive in the city of Ketapang without transiting in other towns or regions. This study aims to determine the factors that influence the choice of transportation mode and obtain the probability of airplane and travel passengers.This research is conducted with a stated preference method by distributing questionnaires to people traveling from the Pontianak - Ketapang city route using airplanes and travel modes. For the survey, data is collected using a questionnaire distributed to 420 respondents through Google form using four attributes, namely cost, time, headway, and service. Based on the analysis results for calculating transportation mode choice with the stated preference method, a utility model is obtained as follows: U(PT-TR) = 0.291 + -2.883E-6.∆x1 + -0.226.∆x2 + 0.218.∆x3 + 0.023.∆x4 with an airplane probability of 55.92%, while the travel probability is 44.08%. The most sensitive attribute affecting the choice of transportation mode is the time attribute. It is because changes in travel time will result in a relatively greater probability of mode choice when compared to changes in cost, headway, and service attributes.

Enhancing Risk Analysis toward a Landscape Digital Twin Framework: A Multi-Hazard Approach in the Context of a Socio-Economic Perspective

In the last decades, climate and environmental changes have highlighted the fragility and vulnerability of the landscape, especially in mountain areas where the effects are most severe. This study promotes the methodological setup of a landscape digital twin to establish a multi-disciplinary and multi-scalar hazard overview according to a matrix framework implementable over time and space. The original contribution to the research addresses a holistic vision that combines meaningfully qualitative with quantitative approaches within a multi-hazard framework from the socio-economic perspective. This contribution presents road network risk analysis by exploiting flooding and landslide scenarios. The critical road segments or nodes most vulnerable or impacted by network performance and accessibility can be identified with minimal preprocessing from credible open-source sources. Service maps are used to show the spatial distribution of risk scores for different typologies of points of interest and hazards. Origin-destination matrix graphs display changes in travel time between facilities under various scenarios. Using a risk scores formula to generate risk maps has made it possible to effectively represent the interconnectedness among natural hazards, infrastructure, and socio-economic factors, fostering more resilient decision-making processes. The method’s applicability is tested through a case study in northern Italy’s Piedmont Region.

ANALISIS PEMODELAN PEMILIHAN MODA TRANSPORTASI ANTARA SEPEDA MOTOR DENGAN ANGKUTAN UMUM

Movement activities can cause the mode selection process. Tridinanti University, as one of the education centers in Palembang city is one of the centers of movement for students. The basis of this research aims to observe the behavior of student motorcycle users with public transportation and analyze whether mode selection is influenced by changes in cost and travel time. This research was conducted with data from the observation approach, questionnaire results, literature review and data processing with the SPSS program in describing the travel situation for the two modes. An overview of the composition of the characteristics of travelers from survey data, it is known that the general characteristics of users in the selection of modes are 81.14% of motorcycle user respondents while 18.86% of public transportation user respondents. Based on gender, male respondents were 57.43% while female respondents were 42.57%. The reasons for choosing modes for motorcycles are time considerations of 47.18% and cost considerations of 15.85% while the reasons for choosing modes for public transportation are cost considerations of 36.36% and time considerations of 13.64%. The mode selection model between motorcycles and public transportation obtained (Usp-Umk) = -0.767-0.011C-0.024T. When the condition of attribute difference is equal to zero, the probability value of motorcycles is greater than the probability of public transportation for the attributes of travel time and cost. The model obtained from regression analysis of all data has the highest R2 price of 0.88% or 88% of the influence of the two attribute factors considered and the remaining 12% is influenced by attributes that have not been considered. In addition to the 2 travel attributes included in this study, several other attributes could also be considered for testing such as safety, convenience, and other influencing factors.