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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

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

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]

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]

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.)

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]

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]

Copyright of Journal of Intelligent Transportation Systems & Applications is the property of Journal of Intelligent Transportation Systems & Applications 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.)

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]

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.

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.

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.

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.

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

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]

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.

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]

Shifting continental business travel from carbon-intensive modes to rail is crucial for emissions reduction. Behavioral interventions are a way to achieve this, but a gap exists in understanding their efficacy for sustainable business travel behavior. Based on online experiments with frequent business travelers, we scrutinize the impact of descriptive social norm interventions on mode choice intention, considering potentially negative employee reactions. While revealing factors influencing reactance and intention, contrary to expectations derived from the theory of planned behavior, behavioral interventions literature, and psychological reactance theory, our social norm treatments did not significantly impact mode choice intention and resulted in low reactance levels. Despite these unexpected results indicating that our interventions did not yield the desired changes, our study underscores the challenges of influencing sustainable business travel behavior and emphasizes the need for tailored interventions and incentives in practice, suggesting avenues for further research. [ABSTRACT FROM AUTHOR]

Introduction: To characterize the impact of rural patients' travel time to obtain healthcare on their reported utilization of preventive healthcare services and personal health outcomes. Methods: Online survey data from rural adults ages 50+ years living in the Northeastern United States were collected from February to August 2021. Study measures included self-reported travel time to obtain healthcare, use of preventive healthcare, and health outcomes. The associations between travel time with use of preventive care and health outcomes were assessed using linear, Poisson, and logistic regression analyses controlling for demographic variables. Results: Our study population included 1052 rural adults, with a mean travel time of 18.5 min (range: 0-60). Travel time was greater for racial/ethnic minority participants and for higher-income participants (both P <.05), but it was not associated with use of preventive healthcare. Greater travel time was associated with poorer mental health and more comorbidities, including cancer and diabetes (all P <.05). Conclusions: Travel time varied by patient demographic factors, and it was associated with mental health and comorbidities. There was no association between travel time and preventive care use, suggesting that other barriers likely contribute to suboptimal use of these services within rural communities. Further research is needed to elucidate the causal pathways linking travel time to mental health and comorbidities within rural communities, as increased travel may exacerbate intrarural health disparities. [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]

Transportation research has shown that socio‐demographic factors impact people's mobility patterns. During the COVID‐19 pandemic, some of these effects have changed in accordance with changing mobility needs adapting to the pandemic, including restrictions on in‐person gatherings, closure of in‐person businesses, and working from home. We investigate two gaps in current knowledge in this area of transportation research: to what extent the associations between socio‐demographic factors and mobility metrics have changed, and how these associations vary across geographic space. We used aggregate deidentified cell tower location data to measure two mobility metrics—movement time and radius of gyration—and socio‐demographic data from the 2016 Canadian Census to model these associations across Ontario, Canada in 2020 using a linear model and a geographically weighted regression model. We find that certain associations between socio‐demographics and mobility have changed from what we previously observed before the pandemic, and we can see the variation of these associations across space. These findings will improve our understanding of how socio‐demographic factors affect mobility patterns in different communities and demonstrate the importance of measuring these associations at a more fine‐grained level using models that consider spatial variation to best reflect the nature of these associations. Key messages: Geographically weighted regression was used to study associations between mobility and five socio‐demographic factors in Ontario, Canada during the COVID‐19 pandemic.All five variables exhibited positive and negative associations with mobility change for at least one period of analysis, highlighting the complexity of these relationships.Our results provide new information on the geographical relationships between mobility change during COVID‐19 and neighbourhood‐level socio‐demographic variables. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate handling and transport variables as potential causes of disturbed behaviour in response to seeing a person on an overhead observation platform during pre-slaughter in batches of females and castrated males composed of pure or crossed Bos Taurus animals (Aberdeen Angus, Red Angus, Hereford and Charolais) with a maximum of 37.5% Bos indicus, and maturity at slaughter varying from zero to eight teeth, coming from pasture and confined systems. For the batches of castrated males, the independent variables included the distance covered on the trip, and the handling and facilities on the farms. For the female batches, the variables were travel time and waiting time prior to unloading. For both sexes, the presence of horns in the batches and the load density were included in the regression equations. Disturbed behaviour, the result of factors related to animal handling and transportation, is detrimental to satisfactory results in production systems. It is up to the managers in the production chain to seek alternatives in order to minimise inappropriate behaviour, which is one determinant of loss. Handling and transport are determining factors in the behaviour of beef cattle during the pre-slaughter period. [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]

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.

Insufficient multimodal transportation infrastructure in Indian cities increases travel time, waiting time, traffic congestion, traffic accidents, travel costs, fast-growing energy consumption, and walking distance. The multimodal transportation system's journey time performance must be analysed to tackle these transportation concerns. Thus, this research provides a fundamental method for assessing multimodal transportation system trip time performance. This study proposes three steps for assessing travel time performance: identifying factors, generating individual indices, and evaluating the Multimodal Transport System Travel-Time Performance Index. The travel-time performance index of Bhopal city was 0.79 after evaluating the results of the individual indices (Access 0.49, In-vehicle 0.39, and Egress 0.38). Consequently, this study will help identify areas for improvement in Bhopal's multimodal transportation system and provide insights for enhancing its efficiency and user satisfaction. The proposed methodology can be applied to other cities facing similar challenges, aiding in the development of more sustainable and user-friendly transportation systems.