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Finding ways to boost transportation access for underserved populations can unlock broad social benefits. Micromobility programs, including bikesharing, offer scalable solutions. National, state, and regional housing and urban development agencies promote affordable housing and transit-accessible developments by funding programs such as the Low-Income Housing Tax Credit and Community Development Block Grants. However, these efforts are not always coordinated and the physical distance between affordable housing and transit access continues to grow. The problem is compounded by low car ownership rates in lower income urban communities. These circumstances have led to inequitable mobility access. To correct course, pairing affordable housing developments with reliable transit services is essential. This practice can increase equity and accessibility. A team at the University of California, Davis, conducted a case study in Sacramento, California, to explore bikesharing as an option for connecting affordable housing residents with transit services. This brief summarizes the findings from that research and provides implications for the field. View the NCST Project Webpage

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]

Planners and engineers need to know how to assess the impacts of proposed cycling infrastructure projects, so that projects that have the greatest potential impact on the actual and perceived cycling safety are selected over those that would be less effective. Planners also need to be able to communicate these impacts to decision-makers and the public. This research addresses these problems using the BikewaySim cycling shortest path model. BikewaySim uses link impedance functions to account for link attributes (e.g., presence of a bike lane, steep gradients, the number of lanes) and find the least impedance path for any origin-destination pair. In this project, BikewaySim was used to assess the impacts of using time-only and time with attribute impedances, as well as two proposed cycling infrastructure projects, on 28,392 potential trips for a study area in Atlanta, Georgia. These impacts were visualized through bikesheds, individual routing, and betweenness centrality. Two metrics, percent detour and change in impedance, were also calculated. Results demonstrate that BikewaySim can effectively visualize potential improvements of cycling infrastructure and has additional applications for trip planning. An expanded study area was also used to demonstrate bike + transit mode routing for four study area locations. Visualizations examine the accessibility to TAZs, travel time, and the utilized transit modes for each location. Compared to the walk + transit mode, the bike + transit mode provided greater access to other TAZs and reached them in a shorter amount of time. The locations near the center of the transit network where many routes converge offered the greatest accessibility for both the bike + transit and walk + transit modes. The difference in accessibility was greatest for locations near fewer transit routes. This research demonstrated how BikewaySim can be used to both examine the current cycling network and show changes in accessibility likely to result from new infrastructure. Both BikewaySim and TransitSim are open-source Python based tools that will be made available for practitioners to use in bicycle network planning. Future research will focus on calibrating link impedance functions with revealed preference data (cycling GPS traces) and survey response data (surveys on user preference for cycling infrastructure). View the NCST Project Webpage

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]

Background: Cardiovascular deserts are areas that lack medical facilities, specialists and equipment to effectively diagnose, treat and manage cardiovascular diseases (CVDs). Romania registers the highest incidence and the highest mortality due to CVDs in Europe. Population ageing is a significant concern, as it increases the risk of CVDs and the demand for specialised care. Although almost 50% of Romanians still live in rural areas, most medical resources are concentrated in a few large cities, leaving large parts of the country underserved. Methods: This study used the Application Programming Interface (API) Matrix service from Google Maps and open data sources to identify cardiovascular (CV) deserts. Results: This research indicates that over 64% of the Romanian population resides in areas lacking CV care, having to travel more than 60 km and over 30 min to reach the nearest facility that offers specialised treatment. Moreover, 14% live in areas affected by a high degree of cardiovascular desertification. These areas are primarily located in northeastern, southern and western Romania. They experience higher mortality rates from CVDs and an ageing population, along with a shortage of general physicians and a scarcity of cardiologists. Conclusions: The identified cardiovascular deserts in this study overlap mountainous regions, the Danube Delta and remote rural areas with poor transportation infrastructure. Implementing telemedicine or mobile healthcare services, involving community healthcare workers and policy support could be solutions to expand access to specialised care in cardiovascular deserts. [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]

Pooled trips in private vehicles, or pooling, can lead to smaller environmental impacts and more efficient use of the limited roadway capacity, especially during peak hours. However, pooling has not been well adopted in part because of difficulties in coordinating schedules among various travelers and the lack of flexibility to changes in schedules and locations. In the meantime, ridehailing (RH) provides pooled services at a discounted fare (compared to the single-travel-party option) via advanced information and communication technology. This study examines individuals’ preferences for/against pooled RH services using information collected among travelers answering a set of questions related to their last RH trip. In doing so, both trip attributes and rider characteristics are considered. Taste heterogeneity is modeled in a way that assumes the presence of unobserved groups (i.e., latent classes), each with unique preferences, in a given sample of RH riders (N=1,190) recruited in four metropolitan regions in Southern U.S. cities from June 2019 to March 2020. The researchers find two latent classes with qualitatively different preferences, choosy poolers and non-selective poolers, regarding their choice in favor of/against pooling based on wait time, travel costs, purpose, and travel party size of the last RH trip. Personal characteristics are also identified, specifically age and three attitudes (travel satisfaction, environmentalism, and travel multitasking), which account for individuals’ class membership. This research contributes to the literature by explicitly modeling taste heterogeneity towards pooled ridehailing. In addition, unlike existing studies either at the person level or employing stated-preference data, a trip-level analysis is performed in connection with revealed preferences, which generates more realistic and relevant implications to policy and practice.View the NCST Project Webpage

Agricultural managed aquifer recharge (Ag-MAR) is a promising approach to replenish groundwater resources using flood water and cropland as spreading grounds. However, site selection, particularly the layering of sediment deposits in the subsurface, can greatly influence Ag-MAR efficacy as it controls water flow and solute transport in the vadose zone. In this study, we use the HYDRUS-1D software to simulate water flow and solute transport from the land surface to the groundwater table in three vadose zone profiles (LS, MS, HS) characterized by differing fractions of sand (44 %, 47 %, and 64 %). For each profile, the single- and dual-porosity models (i.e., considering or not nonequilibrium water flow and solute transport) were calibrated using observed surface ponding, soil water content, and KBr breakthrough data. Water flow and bromide transport in the profile with the lowest sand fraction (LS) were best captured using the model that considered both preferential flow and nonequilibrium bromide transport. Water flow and bromide transport in the profile with the highest sand fraction (HS) was best simulated with the model that considered preferential flow and equilibrium bromide transport. Uniform water flow and nonequilibrium bromide transport provided the best fit for the third profile (MS). The degree of preferential flow was highest in the profile with the largest sand fraction (HS), which also showed the largest flow velocities compared to the profiles with lower sand amounts (LS and MS). Preferential flow did not significantly impact the overall water balance (within 3 %), but caused a significant decrease in vadose zone travel times (bromide) by up to 38 %, relative to a single-porosity model fit. Recharge efficiency varied between 88 % and 90 %, while the average travel times from the soil surface to groundwater varied up to 119 % (from 3.6 to 7.9 days) between the three sites. This study demonstrates that similar recharge efficiency can be achieved at sites with differing soil texture profiles, but subsurface heterogeneity can substantially affect contaminant transport processes and their travel times.

BACKGROUND AND OBJECTIVES: One of the most challenging aspects of city living is traffic congestion. The multitude of vehicular modes and their sheer volume coupled with inadequate road infrastructure unable to keep up with the escalated travel demand of Kolkata is a major cause of concern. Recognizing the significant toll on commuters' time and health, the research aims to identify the root causes of congestion across fifteen selected Traffic Intersection Points, explore its multifaceted impacts on the environment, economy, and society, and propose solutions to alleviate this pressing issue effectively.METHODS: This study employed a mixed-method approach. An on-site survey with 375 regular commuters in Kolkata was conducted, utilizing questionnaires and focus group discussions. The survey gathered data on travel patterns, socio-demographic information, and perceptions of traffic congestion. The Garrett Ranking method and Relative Importance Index (RII) were employed to evaluate the significance of various contributing factors, their impact on commuters, and potential solutions. Statistical analysis using Microsoft Excel and SPSS 26 complemented the data analysis, with cartographic visualizations providing spatial insights. Additionally, the Wilcoxon Signed-Rank Test validated the differences in travel times during congested and free-flowing traffic conditions.FINDINGS: The results revealed that the commuters’ average daily travel delay owing to congestion is approximately 17 minutes. Utilizing Garrett score ratings, the most significant obstacles to smooth traffic flow were identified as intersectional conflicts (66.19) and curbside parking (64.75).Following the same methodology, increased reliance on personalized vehicles (69.87) and encroached road space (64.3) were attributed to rush hour saturation, whereas political rallies (71.36) and demonstrations (59.74) contributed to unprecedented incidents. Work schedule disruptions and hearing anomalies were the most common offshoots of this hazard. Relative Importance Index (RII) scores highlighted the consensus among commuters emphasizing the economic, environmental, and social impact of congestion, with particular emphasis on enhanced fuel consumption (RII=1), decreased economic opportunities (RII=0.96), worsened pollution levels (RII=0.91), and reduced family time (RII=0.93). Congestion pricing (RII=0.88) and ride-sharing (RII=0.87) emerged as themost viable strategies to mitigate congestion. Furthermore, Garrett ratings indicated training of drivers (63.74) and road users (61.03) along with parking management (63.51) to be the most desired areas of improvisation suggested.CONCLUSION: Implementing the ‘Avoid, Shift, Improve’ framework in conjunction with a 'people-first' mentality would encourage sustainable urban living by placing a premium on public transport, land use planning, and technological improvements to reduce traffic congestion and enhance commuter well-being in Kolkata.

Abstract Improving travel time by public transport is a major task for city planners and policy makers to increase its competitiveness relative to cars which can be facilitated by implementing public transport preferential infrastructure treatments such as signal priority. We study the impact of one such preferential treatment, signal priority, on accessibility to jobs between 2015 and 2022 in Warsaw, Poland, which implemented signal priority on 187 intersections during this time period. We develop a method to extract inter-stop travel times from the General Transit Feed Service. We find that signal priority implementation lead to (1) a travel time decrease over the network by 6.7%, (2) an increase in accessibility by 5–8.5%, (3) a full signal priority setting drives the accessibility change over time, and (4) the location of SP and travel time segments at the entry point to high-density jobs drives accessibility change. Our analysis provides a method to help decision makers evaluate the impact of signal priority on accessibility to jobs. Our results indicate significant effectiveness of signal priority implementation.

Abstract 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 R 2) better than SVR, while GRU performed 4.69% (in R 2) 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.

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]

A medium is isotropic if its elastic properties do not change with direction. The usual meaning of seismic anisotropy is variation of seismic velocity, which itself depends on the elastic properties of the medium, with the direction in which it is measured. In sedimentary rock sequences, the anisotropy may be caused by preferred orientation of anisotropic mineral grains, preferred orientation of cracks or thin bedding of layers. Anisotropy parameters that defined by Thomsen are ε and δ. Thomsen’s anisotropic parameters are estimated by well and seismic data. However, one difficulty in addressing anisotropy lies not in the algorithms, but in the reliable estimation of anisotropic parameters. Most of seismic data analysis assume isotropic behavior for subsurface while the earth is often anisotropic and seismic velocity isn’t constant in different directions. So, this analysis must consider anisotropic assumption. One of the most common anisotropy-related phenomena is seismic imaging. Output of migration is the section that is similar to the geological model. Depth migration leads to correct image when velocity changes laterally and vertically in the subsurface. Generally, isotropic Pre-Stack Depth Migration (PSDM) corrects only for lateral velocity heterogeneity; however, anisotropic PSDM algorithms also correct for velocity changing with direction. Anisotropic PSDM corrects for vertical shifts, correctly positions events in depth, and properly focuses diffraction energy. Thomsen’s anisotropic parameters, ε and δ, are two main parameters required in a velocity model for PSDM imaging. Accurate estimation of travel time is very essential in seismic imaging and velocity analysis. Inaccurate approximation of travel time leads to migration errors. Several equations have been developed for nonhyperbolic travel time approximation in transversely isotropic media with vertical symmetry axis (vertical transverse isotropy, VTI). In this study, 2D seismic line and well data are used for doing isotropic and anisotropic PSDM. Conventional processing sequences are performed on seismic data. Initial velocity model was created and then 3 horizons were picked on seismic section. Anisotropy parameters are calculated using true depth of horizons in seismic and well data and then PSDM is performed with and without considering anisotropic parameters. Results of applying anisotropic PSDM in seismic data show that this method moves the events to correct positions and can significantly reduce seismic-to-well mis ties, hence, providing more accurate structural images in depth domain. In addition, anisotropic PSDM attenuates the hockey stick events on CDP gathers and so improves reflector continuity in the subsurface image especially in shallow parts. [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]

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BACKGROUND AND OBJECTIVES: One of the most challenging aspects of city living is traffic congestion. The multitude of vehicular modes and their sheer volume coupled with inadequate road infrastructure unable to keep up with the escalated travel demand of Kolkata is a major cause of concern. Recognizing the significant toll on commuters' time and health, the research aims to identify the root causes of congestion across fifteen selected Traffic Intersection Points, explore its multifaceted impacts on the environment, economy, and society, and propose solutions to alleviate this pressing issue effectively. METHODS: This study employed a mixed-method approach. An on-site survey with 375 regular commuters in Kolkata was conducted, utilizing questionnaires and focus group discussions. The survey gathered data on travel patterns, socio-demographic information, and perceptions of traffic congestion. The Garrett Ranking method and Relative Importance Index (RII) were employed to evaluate the significance of various contributing factors, their impact on commuters, and potential solutions. Statistical analysis using Microsoft Excel and SPSS 26 complemented the data analysis, with cartographic visualizations providing spatial insights. Additionally, the Wilcoxon Signed-Rank Test validated the differences in travel times during congested and free-flowing traffic conditions. FINDINGS: The results revealed that the commuters' average daily travel delay owing to congestion is approximately 17 minutes. Utilizing Garrett score ratings, the most significant obstacles to smooth traffic flow were identified as intersectional conflicts (66.19) and curbside parking (64.75). Following the same methodology, increased reliance on personalized vehicles (69.87) and encroached road space (64.3) were attributed to rush hour saturation, whereas political rallies (71.36) and demonstrations (59.74) contributed to unprecedented incidents. Work schedule disruptions and hearing anomalies were the most common offshoots of this hazard. Relative Importance Index (RII) scores highlighted the consensus among commuters emphasizing the economic, environmental, and social impact of congestion, with particular emphasis on enhanced fuel consumption (RII=1), decreased economic opportunities (RII=0.96), worsened pollution levels (RII=0.91), and reduced family time (RII=0.93). Congestion pricing (RII=0.88) and ride-sharing (RII=0.87) emerged as themost viable strategies to mitigate congestion. Furthermore, Garrett ratings indicated training of drivers (63.74) and road users (61.03) along with parking management (63.51) to be the most desired areas of improvisation suggested. CONCLUSION: Implementing the 'Avoid, Shift, Improve' framework in conjunction with a 'people-first' mentality would encourage sustainable urban living by placing a premium on public transport, land use planning, and technological improvements to reduce traffic congestion and enhance commuter well-being in Kolkata. [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]

Analysing bus travel time is essential for providing valuable information to users for effective journey planning. This study aimed to analyse bus travel time in Klang Valley, Malaysia, which includes the urban centre of Kuala Lumpur as well as the surrounding suburban areas. A two-part study is presented here. The findings on superiority of proposed mixture model, namely the Burr XII mixture model as a new statistical distribution to fit travel time distribution based on day-to-day variability under spatial (number of signalized intersections, link length, route type) and temporal aggregation (15 minutes aggregated, peak/non-peak period) are validated in the previous publication. Based on the results, the study was deepened to analyse the effects of some factors on travel time variability. As an extension, factors and indicators influencing multimodality travel time patterns on all the bus sections are examined in this paper. This research explores the impact of several factors outlining the usage of Burr XII mixture regression model, particularly the quantile regression analysis on three distinct routes, each exhibiting unique characteristics, originating from both suburban and urban areas. The results have demonstrated that the factors vary depending on the time of day and coverage area. By gaining an understanding of and effectively addressing the crucial elements that influence travel time, the overall effectiveness of bus systems can be enhanced. [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]

Traffic congestion is a major problem in large cities worldwide. This project uses high-frequency data from the Los Angeles metropolitan area combined with an instrument that varies spatially and temporally to estimate the causal impact of an additional vehicle mile traveled on travel times. Specifically, the research team exploits the network structure of the Los Angeles highway system and uses crashes on close alternative routes as exogenous shocks to traffic demand. To do so, the team relies on Google Maps to determine the ideal route and alternatives for over 19,000 real-world commutes. The researchers estimate that at peak times an additional trip reduces speed by, on average, 0.22%. They find the optimal toll at peak times is 33 cents per mile, with the toll being lower, even zero, off-peak. The researchers show how this toll varies over space and time, as well as report on its distributional effects. This toll would more than double highway speeds during peak times and only requires reducing vehicle miles traveled (VMT) at the peak by 10%. The resulting social welfare gains are over two billion dollars per year.View the NCST Project Webpage

Travel Time estimation is largely caused by the stochastic process of arrivals and departures of vehicles and its reliability measurements considering important issues for improving operational efficiency and safety for traffic road networks. The exploration of travel time variability and spatio-temporal analysis of urban streets using the Global Positioning System (GPS) concluded that the mixed land uses and travel congestions caused higher travel times and delays. The accessibility indices were increased by increasing access points and decreasing traffic volumes. The Geographic Information System (GIS) networks can produce a model that overcomes some restrictions of accessibility indices. Different prediction models were developed to capture the main parameters related to travel time. It concluded that delay at signalized intersections in terms of stopping delay was the major parameter affecting the total travel time and total delay time of major urban streets. Travel time estimation algorithms based on speed data loop detectors induced insignificant differences when the study route was a relatively short and slow transition from free state to congestion state. Travel time results are affected by the location of sensors and their sparseness, hence estimation errors increase as detector spacing increases.

Connected Vehicles (CV) technology offers significant potential for managing traffic congestion and improving mobility along transportation corridors. This report presents a novel approach using integrated corridor management (ICM) technology to divert CVs to underutilized park-and-ride facilities where drivers can park their vehicle and access public transportation. Using vehicle-to-infrastructure (V2I) communication protocols, the system collects data on downstream traffic and sends messages regarding available park-and-ride options to upstream traffic. A deep reinforcement learning (DRL) program controls the messaging, with the objective of maximizing traffic throughput and minimizing CO2 emissions and travel time. The ICM strategy is simulated on a realistic model of Interstate 5 using Veins simulation software. The results show marginal improvement in throughput, freeway travel time, and CO2 emissions, but increased travel delay for drivers choosing to divert to a park-and-ride facility to take public transportation for a portion of their travel.

Fluid flow throttling is common in industrial and building services engineering. Similar tunnel throttling of vehicular flow is caused by the abrupt number reduction of roadway lane, as the tunnel has a lower lane number than in the roadway normal segment. To predict the effects of tunnel throttling of annular freeway vehicular flow, a three-lane continuum model is developed. Lane III of the tunnel is completely blocked due to the need of tunnel rehabilitation, etc. There exists mandatory net lane-changing rate from lane III to lane II just upstream of the tunnel entrance, which is described by a model of random number generated through a golden section analysis. The net-changing rate between adjacent lanes is modeled using a lane-changing time expressed explicitly in algebraic form. This paper assumes that the annular freeway has a total length of 100 km, a two-lane tunnel of length 2 km with a speed limit of 80 km/h. The free flow speeds on lanes I, II and III are assumed to be 110, 100 and 90 km/h respectively. Based on the three-lane continuum model, numerical simulations of vehicular flows on the annular freeway with such a tunnel are conducted with a reliable numerical method of 3rd-order accuracy. Numerical results reveal that the vehicular flow has a smaller threshold of traffic jam formation in comparison with the case without tunnel throttling. Vehicle fuel consumption can be estimated by interpolation with time averaged grid traffic speed and an assumed curve of vehicle performance. The vehicle fuel consumption is lane number dependent, distributes with initial density concavely, ranging from 5.56 to 8.00 L. Tunnel throttling leads to an earlier traffic jam formation in comparison with the case without tunnel throttling.

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.

This research studies the potential of bikeshare services to bridge the gap between Affordable Housing Communities (AHC) and transit services to improve transport accessibility of the residents. In doing so, the study develops an agent-based simulation optimization modeling (ABM) framework for the optimal design of the bikesharing station network considering improving accessibility as the objective. The study discusses measures of accessibility and uses travel times in a multi-modal network. Focusing on the city of Sacramento, CA, the study gathered information related to affordable housing communities, detailed transit services, demographic information, and other relevant data. This ABM framework is used to run three stages of travel demand modelling: trip generation, trip distribution and mode split to find the travel time differences under the availability of new bikesharing stations. The model is solved with a genetic algorithm approach. The results of the optimization and ABM-based simulation indicate the share of bike and bike & transit trips in the network under different scenarios. Key results indicate that about 60% of the AHCs are within 25-minute active travel time when the number of stations range from 25 to 75, and when the number of stations is increased to 100, most AHCs are within 40 mins of active mode distance and all of them are less than an hour away. In terms of accessibility, for example, having a larger network of stations (e.g., 100) increases by 70% the number of Points of Interest (for work, health, recreation, and other) within a 30-minute travel time. This report then provides some general recommendations for the planning of the bikesharing network considering information about destination choices as well as highlighting the past and current challenges in housing and transit planning.View the NCST Project Webpage