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Spatio-temporal correlation and interaction are significant features of traffic accident data. To account for the spatio-temporal correlation and interaction and the ordinal nature simultaneously, spatio-temporal generalized ordered logit models with different temporal treatments are advocated to examine accident injury severities. In the context of Bayesian inference, we estimate the advocated models and compare them with traditional generalized ordered logit model and spatial generalized ordered logit model using six-year (2014-2019) accident data from the Dongguang section of Guangzhou-Shenzhen Coastal Freeway in China. The results indicate that there are significant spatio-temporal effects and interaction in the accident severity data, and that the spatio-temporal generalized ordered logit models yield low values of deviation information criterion and more reasonable parameter estimates than the generalized ordered logit and spatial generalized ordered logit models do. The estimation results reveal that covariates related to vehicle type, accident type, season, time, horizontal curvature, and bridge have significant effects on accident injury severities. The findings above demonstrate the superiority of the proposed spatio-temporal generalized ordered logit models for predicting accident severities. [ABSTRACT FROM AUTHOR]

High fatality rates in frequent rear-end crashes have underscored significant safety concerns in China. This study aims to explore the mechanisms and determinants of rear-end crashes, with a particular focus on the factors influencing crash severity during weekdays and weekends (W-W). Employing the Random Parameter Logit Model (RPLM) to account for variability in data, we analyzed W-W rear-end crashes on the Beijing-Shanghai Expressway in Jiangsu province from 2017 to 2019, considering three severity levels: no injury, minor injury, and severe injury. Our comprehensive analysis covered variables from temporal, roadway, vehicle, crash, and environmental categories, alongside calculating the marginal effects of each significant variable on crash severity. Findings reveal temporal instability over the three-year period and notable differences in W-W crash severity. Out of all variables, four displayed random parameter characteristics, indicating potential interactions that influence crash outcomes. Specifically, our results indicate that rear-end crashes involving three or more vehicles on bridges are more likely to result in casualties. Interchange segments typically saw no injuries in two-vehicle crashes. Speeding during winter or on sunny days significantly increases the risk of injuries and fatalities. Furthermore, rear-end crashes in interchange areas during winter are particularly prone to causing injuries. These findings offer guidance for the development of effective safety countermeasures targed at different pediods. [ABSTRACT FROM AUTHOR]

The hypothesis that drivers are balancing perceptual speed on both sides of the visual field is proposed through the analysis of the driver's decision on lateral positioning. Moreover, the temporal frequency of the occurrence of visual stimuli may have an impact on perceptual speed. This research conducts a series of experiments on right-turn and left-turn freeway curves to investigate the driver's lateral driving behavior. A series of edge lines are installed on the road surface to act as visual stimuli, with temporal frequency ratios of 1∶2 and 1∶4 on both sides. Three observation sections, namely, the spiral-circle point, midpoint, and circle-spiral point of the curves, are selected to gather the driver's lateral driving behaviors. The results revealed that drivers deviate from the side with a higher temporal frequency compared with that with a lower temporal frequency on both right-turn and left-turn curves. The average deviation distance with a temporal frequency ratio of 1∶4 is significantly greater than that with a ratio of 1∶2. Additionally, there exists a positive correlation between the deviation distance and the disparity in temporal frequency between both sides. Furthermore, the disparity in temporal frequency and the rate of expansion of the visual angle produce interactive impacts on the lateral position of vehicles. Moreover, the original lateral positions of vehicles affect this interaction. The findings of this study suggest a novel approach to enhance lateral driving maneuvers and provide a constructive countermeasure to prevent accidents on freeway curves. [ABSTRACT FROM AUTHOR]

Introduction: To avoid the uncertainty present in a cooperation between manual vehicles (MVs) and automated vehicles (AVs), cooperative connected automated mobility (CCAM) is frequently proposed. In this contribution, we consider the situation of a motorway slip road where an AV (SAE level 3 or higher) on the motorway cooperates by opening a suitable gap for an entering AV. To ensure a meeting of the cooperation partners, speed adjustment by the system might often be necessary. Methods: To investigate the acceptance of an automated cooperative motorway access assistant a driving simulator study (N = 41) was conducted. Participants drove six laps on a virtual test track in a level 3 AV. Three levels of speed adjustment and two levels of an HMI (with or without HMI) were tested. They experienced the automated cooperative motorway access assistant in two situations. Firstly, when they entered the motorway themselves and benefited from the system, and secondly, when the system reduced their vehicle's speed to allow for cooperation with another vehicle on a motorway slip road ahead. Results: The results show that the acceptance of the assistant depends significantly on the magnitude of speed reduction: the greater the speed adjustment, the worse the user rating, and the more often the cooperation was terminated. Obviously, the acceptance depends on the perspective: If the users themselves benefit from the speed adjustment of others, they rate the system significantly better than if they themselves must accept the restriction. Only in the case of a small speed reduction, the assistant is rated as well as if the user benefits from the system. The availability of a human-machine interface (HMI) providing information about the system status influenced acceptance only marginally but had a significant influence on the trust in the automation. Discussion: The results suggest that people are willing to accept a personal disadvantage in order to give others an advantage in the context of CCAM. However, acceptance depends on the magnitude of the disadvantage. The overall agreement scores of the system with HMI did not differ from those without HMI, although the HMI was found to slightly reduce the termination of cooperation. Three possible reasons for this result, which differs from previous research, are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we present a groundbreaking approach to enhance the illumination of traffic boards along superhighways, addressing significant challenges associated with conventional lighting systems. Our innovative method revolves around the strategic placement of remote spotlights at the side of the roadway to illuminate traffic signs equipped with retroreflector film (RTRF). The essence of our approach lies in remote illumination, which requires meticulous adjustment of the divergence angle of the spotlights to match the size of the signs and their distance from the projection source. To achieve the desired spotlight configuration, we have developed a hybrid optical system that incorporates a paraboloid reflector and a lens mounted on a bridge holder situated on top of the mirror. Through spot light illumination, we discovered that the initial divergence angle of the RTRF was too narrow. To improve projection angle tolerance, we recommend attaching a light diffuser film onto the surface of the RTRF. The coverage area ratio of the diffuser film can be adjusted to select the desired divergence angle for the reflected light. Our experimental measurements have yielded significant results, showcasing the half-divergent angle of the RTRF ranging from 3° to 7° for different coverage area ratios of the diffuser. In practical terms, with a target luminance of 300 nits at the white word on the traffic board, the power consumption of the spotlight fixture of the roadway was only 40 W, representing over 75% power savings when compared to traditional lighting methods. Consequently, our approach opts to utilize spotlights for illuminating specific traffic boards on superhighways, offering a more efficient and manageable lighting solution that greatly benefits both motorists and road maintenance personnel. [ABSTRACT FROM AUTHOR]

Remote sensing road extraction based on deep learning is an important method for road extraction. However, in complex remote sensing images, different road information often exhibits varying frequency distributions and texture characteristics, and it is usually difficult to express the comprehensive characteristics of roads effectively from a single spatial domain perspective. To address the aforementioned issues, this article proposes a road extraction method that couples global spatial learning with Fourier frequency domain learning. This method first utilizes a transformer to capture global road features and then applies Fourier transform to separate and enhance high-frequency and low-frequency information. Finally, it integrates spatial and frequency domain features to express road characteristics comprehensively and overcome the effects of intra-class differences and occlusions. Experimental results on HF, MS, and DeepGlobe road datasets show that our method can more comprehensively express road features compared with other deep learning models (e.g., Unet, D-Linknet, DeepLab-v3, DCSwin, SGCN) and extract road boundaries more accurately and coherently. The IOU accuracy of the extracted results also achieved 72.54%, 55.35%, and 71.87%. [ABSTRACT FROM AUTHOR]

As an important component of road traffic facilities, bridges play a crucial role in daily traffic operations, and changes in their status can have an impact on traffic operation. The existing research mainly focuses on monitoring the status of bridges themselves or analyzing the operation status of road traffic, and rarely considers the changes in traffic operation status caused by changes in bridge status. Therefore, in order to evaluate the impact relationship between the two, this article designs an algorithm that combines the Analytic Hierarchy Process (AHP), the Delphi method, and Singular Value Decomposition (SVD) based on the traditional evaluation of bridges and road traffic operation status, and establishes a bridge traffic operation status impact assessment model. Then, simulation analysis and actual data verification will be conducted based on the specific situation of Ma'anshi Bridge on the Chongqing Wuhan Expressway. The experimental results show that the evaluation model established in this paper conforms to the characteristics of traffic operation, can reflect the impact of bridge state changes on traffic operation status well, effectively promote the automation level of bridge traffic impact management, and has high reliability and accuracy. [ABSTRACT FROM AUTHOR]

The short-distance continuous diversion area plays a crucial role within mountainous urban expressway systems, significantly enhancing the efficiency of specialized road sections through capacity analysis. This study develops a capacity calculation model tailored to the diversion area's unique characteristics and principal capacity-influencing factors. Initially, the research focuses on a specific short-distance continuous diversion area of a mountainous urban expressway, employing video trajectory tracking technology to gather trajectory data. This data serves as the basis for analyzing road and traffic characteristics. Subsequently, the model computes the capacity influenced by eight variables, including diversion point spacing and deceleration lane length, using VISSIM simulation experiments. A gray correlation analysis identifies key factors, which guide the establishment of the model's fundamental structure through two-factor surface fitting results. Mathematical statistical methods are then applied to resolve the model's parameters, culminating in a robust capacity calculation model. The findings reveal that diversion point spacing, along with primary and secondary diversion ratios, significantly influence capacity. Notably, the capacity exhibits a marked quadratic polynomial relationship with the primary diversion ratio and diversion point spacing, and a linear relationship with the secondary diversion ratio. The model's validity is confirmed through a case study at the diversion area north of Huacun Interchange in Chongqing Municipality, where the discrepancy between calculated and actual capacities is under 5%, underscoring the model's high accuracy. These results offer valuable theoretical and methodological support for the planning, design, and traffic management of diversion areas. [ABSTRACT FROM AUTHOR]

Nanofluidic membranes are currently being explored as potential candidates for osmotic energy harvesting. However, the development of high‐performance nanofluidic membranes remains a challenge. In this study, the ultrathin MXene membrane (H‐MXM) is prepared by ultrathin slicing and realize the ion horizontal transportation. The H‐MXM membrane, with a thickness of only 3 µm and straight subnanometer channels, exhibits ultrafast ion transport capabilities resembling an "ion freeway". By mixing artificial seawater and river water, a power output of 93.6 W m−2 is obtained. Just as cell membranes have an ultrathin thickness that allows for excellent penetration, this straight nanofluidic membrane also possesses an ultrathin structure. This unique feature helps to shorten the ion transport path, leading to an increased ion transport rate and improveS performance in osmotic energy conversion. [ABSTRACT FROM AUTHOR]

In this article, I explore the hypothesis that urban interstices around urban motorways could be intended as spaces of creative, political and performative possibilities not responding to planning and market logic. Urban interstices are context-dependent spaces in a minoritarian position compared to more powerful spaces. Their relationship with planning and investments is ambiguous, because they are by-products of urban processes but temporarily neglected spaces. This leads to a certain degree of freedom in the experimentation of different uses and dynamics. Grounding in Gibson-Graham's politics of possibility, I provide a re-reading of urban motorways as spaces of possibility where undisciplined spatial forms and dynamics are made visible, conceivable and therefore possible. With an ethnographic approach, I explored the daily practices taking place in the interstices of the urban motorway SS 554, which encircles the city of Cagliari, in Sardinia. I provide an understanding of the daily practices of its inhabitants as a form of non-conflictual everyday politics, intended as a practice of producing urban space always-in-the-making. By observing daily life in urban interstices, I highlight the political possibilities that infrastructure entails and suggest an overcoming of the technocratic and normative gaze usually adopted in work on urban motorways. These urban interstices appear to be spaces of possibility, where the discarded pieces of urban processes are appropriated by inhabitants to produce a different urbanity. [ABSTRACT FROM AUTHOR]

Vehicles planning to exit at an upcoming freeway off-ramp require adequate exit distance to execute lane change maneuvers, otherwise, traffic disturbances may be experienced. This research assesses the changes in exit distances for a mixed traffic environment comprising automated vehicles (AVs) and human-driven vehicles (HDVs) on freeways with and without a managed lane (ML). A left-side continuous ML was designed and eligible vehicles were AVs. Traffic microsimulation exercises were conducted on a 3.5 km freeway segment, and scenarios varied based on traffic demand, the number of freeway lanes, and AV adoption rates. Traffic demand was set relative to the queue discharge flow rate (qQ). The results indicated an increase in qQ as the AV adoption rate increased. Also, the exit distances were influenced by the traffic environment, the freeway configuration, and the traffic demand. The optimal exit distance increased when an ML was implemented at 25% and 50% AV adoption rates. [ABSTRACT FROM AUTHOR]

Due to the effects of low-pressure and hypoxic environments at high altitudes, drivers in high-altitude areas exhibit increased perceived reaction times, leading to challenges in accurate speed estimation and handling judgment. This study aims to quantitatively analyze the impact of plateau environments on operating speeds at interchange exit ramps. Utilizing a UC/win-road simulator, six scenarios of expressway exit ramps were constructed. The simulation experiments involved 50 participants (35 males, 15 females) from Nanjing, China (altitude of 50 m) and 50 participants (36 males, 14 females) from Lhasa, China (altitude of 3,650 m). This research focused on examining the influence of the plateau environment on drivers' operating speeds, investigating variations in speed between drivers in plain and plateau areas, across genders, and during different acclimation periods. It also aimed to predict operating speeds at the midpoint and exit of the curve on the exit ramp for drivers in both plain and plateau areas. Based on these predictions, the study elucidated the trend of operating speed as influenced by the low-pressure and hypoxic conditions of the plateau, as well as the characteristics of the exit ramp's horizontal curve. Additionally, the research uncovered the internal correlations and potential reasons linking operating speed to drivers' perception and response abilities, physiological and visual load levels, and driving styles. [ABSTRACT FROM AUTHOR]

Copyright of Transportation Science & Technolgy is the property of Transportation Science & Technology 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.)

Connected and Autonomous Vehicles (CAVs) hold great potential to improve traffic efficiency, emissions and safety in freeway on-ramp bottlenecks through coordination between mainstream and on-ramp vehicles. This study proposes a bi-level coordination strategy for freeway on-ramp merging of mixed traffic consisting of CAVs and human-driven vehicles (HDVs) to optimize the overall traffic efficiency and safety in congested traffic scenarios at the traffic flow level instead of platoon levels. The macro level employs an optimization model based on fundamental diagrams and shock wave theories to make optimal coordination decisions, including optimal minimum merging platoon size to trigger merging coordination and optimal coordination speed, based on macroscopic traffic state in mainline and ramp (i.e., traffic volume and penetration rates of CAVs). Furthermore, the micro level determines the real platoon size in each merging cycle as per random arrival patterns and designs the coordinated trajectories of the mainline facilitating vehicle and ramp platoon. A receding horizon scheme is implemented to accommodate human drivers' stochastics as well. The developed bi-level strategy is tested in terms of improving efficiency and safety in a simulation-based case study under various traffic volumes and CAV penetration rates. The results show the proposed coordination addresses the uncertainties in mixed traffic as expected and substantially improves ramp merging operation in terms of merging efficiency and traffic robustness, and reducing collision risk and emissions, especially under high traffic volume conditions. [ABSTRACT FROM AUTHOR]

Macroscopic fundamental diagram (MFD)–based perimeter traffic flow control has been attempted to develop coordinated ramp metering (CRM) strategies for multibottleneck freeway networks operating around capacity. However, the effectiveness of these strategies may be limited by the minimum admissible on-ramp flow (e.g., due to queue saturation or minimum green time), potentially resulting in local congestion. This paper addresses these challenges from two aspects. First, a coordinated ramp control approach was developed to handle situations where the desired flow of an on-ramp falls below the minimum admissible flow. Second, variable speed limits (VSLs) were integrated into the coordinated RM to enlarge the available control space. The proposed approach employs a three-layered control structure. The first layer determines the total on-ramp flow entering the freeway network, using an extended MFD-based feedback perimeter regulator. This regulator utilizes dynamic network accumulation set points to enhance the regulator's effectiveness in the presence of heterogeneity. The second layer distributes the total on-ramp flow among the on-ramps, which includes determining the on-ramp flows without considering flow constraints and coordinating successive on-ramps to address potential constraint violations. The third layer determines VSL rates based on the control information of RM in the second layer and the coordinated control for successive VSLs. The proposed approach was evaluated using SUMO, a microscopic simulator, on a realistic freeway network (stretch), and compared with feedback RM, feedback VSLs, and their integration. Simulation results highlight that the proposed integrated controller (1) more effectively addresses congestion in the freeway, owing to its enhanced robustness and network-wide control benefits, (2) enlarges the control space to alleviate freeway congestion, and (3) performs better in terms of the density heterogeneity of the freeway. [ABSTRACT FROM AUTHOR]

The vehicle dynamic load can greatly affect the service life of pavement. To study the vehicle dynamic load accurately and conveniently under different pavement characteristics and driving conditions, a multibody dynamics method was employed to establish a three-dimensional (3D) vehicle dynamics simulation model of a six-axle heavy vehicle (the most typical heavy vehicle on an expressway). Experimental tests of vehicle component parameters and axle spindle loads were performed to provide simulation parameters and to validate this vehicle dynamics model, respectively. The validation results showed that the accuracy and robustness of the dynamics model were satisfactory. Based on the validated model, the effects of International Roughness Index (IRI) and vehicle speed on the dynamic load coefficient (DLC) were explored. The DLC increased by about 80% when the speed or IRI changed from 30 km/h or 1.184 m/km to 70 km/h or 3.551 m/km , respectively. To better evaluate the effect of dynamic load, the relationship between the DLC of the vehicle and the vehicle speed and IRI is proposed according to simulation results. This relation equation can quickly and accurately study the DLC of this heavy vehicle under different IRI pavements and driving speeds. Because the vehicle dynamics model is convenient for studying the dynamic load under different conditions, this method is recommended in vehicle–pavement coupled analysis. [ABSTRACT FROM AUTHOR]

The role of transportation in international trade cannot be overlooked; hence, the need for regular upgrades of roads and marketplaces. The Karakorum Highway (KKH), a vital part of the China–Pakistan economic corridor that connects China with Arabian waters, has not received significant attention from the National Highway Authority (NHA) due to uncertainties in many regions. To address this issue, the study employs Dombi operations using Polytopic fuzzy sets to explore uncertainty in decision-making. The Dombi t-norm and t-co-norm can capture inconsistencies, making it an effective tool in the decision-making process. The study applies the Polytopic fuzzy Dombi-weighted averaging (PF-DWA) operator, the Polytopic fuzzy Dombi-ordered weighted averaging (PF-DOWA) operator, and the Polytopic fuzzy Dombi hybrid-weighted averaging (PF-DHWA) operator to demonstrate how the model can help the NHA open bidding for interested companies to repair damaged areas, bridges, and side barriers affected by floods. The study reveals that infrastructure is essential for the development of any country, and the most suitable choice for reconstruction can be made using the proposed methods. [ABSTRACT FROM AUTHOR]

Why do freeways affect spatial structure? We identify and quantify the local disamenity effects of freeways. Freeways cause slower growth in central neighborhoods (where local disamenities exceed regional accessibility benefits) compared with outlying neighborhoods (where access benefits exceed disamenities). A quantitative model calibrated to Chicago attributes one-third of the effect of freeways on central-city decline to reduced quality of life. Barrier effects are a major factor in the disamenity value of a freeway. Local disamenities from freeways, as opposed to their regional accessibility benefits, had large effects on the spatial structure of cities, suburbanization, and welfare. [ABSTRACT FROM AUTHOR]

Roadwork zones represent a hazardous place within the highway system, with higher crash rates and injury severity, often due to excessive speed and noncompliance with speed limits. On freeways, a common layout is to close a full roadway and to divert traffic onto the opposite roadway, with one lane closed to accommodate the redirected traffic, by driving through the median strip. In this scenario, the second chicane can be a hazardous point if it is not correctly signaled. This paper examines the need to install a speed monitoring display (SMD) before the returning chicane on a bypass. A two-phase study was conducted on a Spanish freeway where a roadway was temporarily closed. Two measurement points were established on the bypass, one in the middle and one at the end, prior to the return chicane through the median strip. During Phase 1, the portable SMD was installed and during Phase 2, it was removed. The average and the 85th percentile of the speed distribution at each point during both phases were compared. Additionally, mean difference tests were conducted and a speed prediction model was developed. With the SMD, drivers reduced their speed from the midpoint to the returning chicane, within the range of 7-10 km/h. Conversely, upon removal of the SMD, motorists increased their speed while driving through the bypass, resulting in excessive speed at the most hazardous point, the chicane leading back to the original roadway. The difference in mean speed between the two phases was 18 km/h at the returning chicane. In addition to the traffic calming measures implemented prior to entering roadwork zones on freeways, which are conveniently established in the standards; it is necessary to evaluate potentially dangerous areas of the layout and implement additional measures where required. Specifically, in the case of final chicanes of bypasses with reduced radii, it is recommended that a speed monitoring display be installed as a mandatory element in order to inform drivers of this challenging segment. Highway administrations around the world should maintain a SMD at the returning chicane of a bypass while roadworks last. [ABSTRACT FROM AUTHOR]

Expressway networks are continuously developing and emergency rescue demand is increasing proportionately. The location of expressway emergency rescue nodes needs refinement to meet changing requirements. In this study, the expressway was modeled as an expressway network. The differences in the origin destination (OD) distribution matrices for working days and major holidays were used as the bases for determining the need for temporary emergency rescue nodes. Overlapping and non-overlapping community detection algorithms were used to extract the distribution characteristics of OD during both day categories. These distributions were used to determine permanent and temporary emergency rescue sites. In this study, we considered the differences in traffic volume, distance, and impact of four vehicle types on traffic accidents to select the location of emergency rescue nodes, and allocate emergency resources. An emergency rescue node selection model for an expressway network was established based on spatio-temporal characteristics. The results based on a regional example determined that 22 permanent and 25 temporary emergency rescue nodes were appropriate. The average rescue time for traffic accidents during working days and major holidays compared to the P-center location model, was reduced by approximately 27.08% and 6.70%, respectively. The coefficient of variation of emergency rescue time was reduced by approximately 28.22% and 21.41%, respectively. The results indicated that the model satisfied the expressway emergency rescue demand requirements, and improved the rationality of the rescue center node layout. [ABSTRACT FROM AUTHOR]

As renewable energies need to be extended massively, new concepts are necessary to prevent land conflicts with other uses. Such concepts should have a high generality to offer a swift expansion of renewables anywhere. Within the project, the Photovoltaic Road Roofing Concept (PV-SÜD), a concept for the roofing of roadways with solar panels, was investigated. Its effects on the road infrastructure were analyzed, and a demonstrator was built. The technical boundary conditions and requirements resulting from the specific application type were determined regarding the photovoltaic technology, the possible energy generation, and the supporting structure. The study was completed for a technical solution of 10 m length, 17 m width, and 6.8 m height, with the option of a pent roof (highways running east–west) or gable roofs (highways running north–south). The main target aim was to investigate the potential for widespread use at any site, in contrast to previous studies which mainly aimed at a singular site or demonstrator project. The final solution can support a 38.5 kWp photovoltaic system with a specific annual yield of between 37.5 MWh and 44.0 MWh. The yield variation in sites in Austria and Germany was 14.7% and 17.9%, respectively. One demonstrator roofing was realized as a steel-frame construction with active glass–glass photovoltaic technology at a highway in Hegau (GE). [ABSTRACT FROM AUTHOR]

Accurately estimating the duration of freeway incidents can enhance emergency management practices and reduce the likelihood of secondary incidents. To investigate the mechanisms through which key factors influence incident duration, this study sorted out the characteristics and variables of the incident duration on a special freeway in Zhejiang Province, that is, the ring road, and developed a latent class accelerated hazard model. Heterogeneity was incorporated into the model. Three distributions (Weibull, Log-normal, and Log-logistic) were compared, and the Log-logistic distribution exhibited superior performance. The analysis revealed two distinct latent classes: Latent Class 1 and Class 2, had class membership probability of 0.53 and 0.47, respectively, with a total of 11 variables being statistically significant at the 0.05 significance level. It is worth noting that, some neglected explanatory variables are discussed in depth in this study. For example, the mechanism of which specific lane is closed has an impact on the incident duration, rather than a general discussion of the number of lane closures. Furthermore, the way in which the driver involved in the incident reports to the police has a significant impact on the duration of incidents. Notably, potential heterogeneity and its influencing mechanism are captured in the model. Additionally, by predicting class membership using posterior probabilities, it was determined that most data points were more likely to belong to Class 1, and the incident duration primarily ranged between 0 and 60 minutes. These findings are helpful to reduce the duration of incidents on ring-roads and freeways in China, and provide theoretical support for the formulation of freeway incident management and treatment policies. [ABSTRACT FROM AUTHOR]

Design guidelines for freeway ramp entrances are based on speed and acceleration data collected before 1950. This study investigated driver behavior over the entire freeway entrance area, including the ramp, the acceleration speed-change lane (SCL), and the freeway right lane (FRL). Video-based trajectory and speed profile data were collected using unmanned aerial vehicles (UAVs) and were used for qualitative and quantitative analysis. General trends of the relationships between driver behavior measures and geometric characteristics of entrance ramp terminals were investigated under different traffic and design conditions. Results showed that vehicles tended to merge onto the freeway at relatively low speeds such that the difference between their mean speed at merging and that of FRL vehicles was statistically significant. Results also confirmed that SCL drivers tended to start acceleration after they passed the middle of the ramp controlling curve. Regression models were developed for predicting driver-vehicle behavior on SCLs and on-ramp curves using traditional regression for each parameter separately and simultaneous modeling using structural equation modeling. An example application is presented to demonstrate the use of the developed models in reliability analysis of entrance ramps, which can be used to establish probabilistic road design guidelines. [ABSTRACT FROM AUTHOR]

Both crash frequency analysis (CFA) and real-time crash prediction models (RTCPMs) divide a highway into small segments with a constant length [typically 0.161 km (0.10 mi)] for data aggregation. Many previous studies refer to this constant length as the segment length for data aggregation, but this paper adopts fragment size to avoid confusion with aggregation based on highway geometric features. Several studies have shown that segmentation length impacts the studies' results and recommend not using a length smaller than 0.161 km (0.10 mi) or greater than 0.402 km (0.25 mi) to segment and aggregate traffic data for urban/suburban highways and freeways. Despite the significant impact of the segmentation length on traffic crash aggregation, no specific recommendation for selecting or determining the segmentation length for crash data aggregation exists. This research investigates the impact of segmentation length on traffic crash data aggregation. It establishes a methodology for determining a recommended fragment size (RFS) using hidden heterogeneity in traffic crash data. The study defines featured traffic crash rates using three major traffic crash characteristics: number of vehicles in crash, manner of collision, and crash severity. The analysis uses the Laplacian score with distance-based entropy measure and K-means to cluster highway segments based on the featured crash rates (FCRs) and total crash rates (TCRs) for fragment sizes ranging from 0.161 to 0.402 km (0.10 to 0.25 mi) with an increment of 0.016 km (0.01 mi). The clustering results are compared using their silhouette coefficients. The sample results shows that FCR-based clustering outperforms TCR-based clustering by providing important traffic crash groups within a highway and the RFS to segment and aggregate traffic crash data. The proposed method provides a data-driven comparison of different fragment sizes, revealing the pattern of traffic crashes and a standardized approach for RFS, which reduces the likelihood of fragment misclassification and benefits traffic studies depending on segmentation length. [ABSTRACT FROM AUTHOR]

Objective: This on-road study employed behavioral and neurophysiological measurement techniques to assess the influence of six weeks of practice driving a Level 2 partially automated vehicle on driver workload and engagement. Background: Level 2 partial automation requires a driver to maintain supervisory control of the vehicle to detect "edge cases" that the automation is not equipped to handle. There is mixed evidence regarding whether drivers can do so effectively. There is also an open question regarding how practice and familiarity with automation influence driver cognitive states over time. Method: Behavioral and neurophysiological measures of driver workload and visual engagement were recorded from 30 participants at two testing sessions—with a six-week familiarization period in-between. At both testing sessions, participants drove a vehicle with partial automation engaged (Level 2) and not engaged (Level 0) on two interstate highways while reaction times to the detection response task (DRT) and neurophysiological (EEG) metrics of frontal theta and parietal alpha were recorded. Results: DRT results demonstrated that partially automated driving placed more cognitive load on drivers than manual driving and six weeks of practice decreased driver workload—though only when the driving environment was relatively simple. EEG metrics of frontal theta and parietal alpha showed null effects of partial automation. Conclusion: Driver workload was influenced by level of automation, specific highway characteristics, and by practice over time, but only on a behavioral level and not on a neural level. Application: These findings expand our understanding of the influence of practice on driver cognitive states under Level 2 partial automation. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, characterized by travel restrictions and reduced traffic volumes, heightened the risk of severe single-vehicle crashes on Florida's freeways. This study utilized random parameter multinomial logit models, accounting for heterogeneity in means and variances, to analyze driver injury severities in 2020 and compare variations in the magnitude of factors contributed to these injuries across different freeway systems. The estimated models identified 31 statistically significant variables across Florida's major freeways (I-4, I-10, I-75, and I-95). Among these variables, only two—normal driving and restraint usage—were statistically significant across all four freeway systems. Moreover, the model results indicated that factors contributing to severe driver injuries were most prominent on I-95 compared to the other freeways in 2020. These findings improve our understanding of freeway safety measures during a pandemic and provide valuable insights to enhance traffic management strategies for state highway agencies, prioritizing operational safety in potential future pandemics. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology 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 aimed to investigate the correlation between carbon emissions, fuel consumption, and speed limit. A theoretical model was derived based on the energy conservation law, which expresses the relationship between vehicle's fuel consumption and speed. Subsequently, a total of 40 sets of fuel consumption data were collected through field tests to verify the accuracy of the theoretical model at different speeds and different road longitudinal slope combinations. The fuel consumption was then converted to carbon emissions according to the carbon emission factors specified by Intergovernmental Panel on Climate Change (IPCC). In the field experiment, two types of cars and trucks, which are most common on the expressways in China, were selected. Finally, the travel speed under different posted speed limits was obtained through the previously established model, and the carbon emission changes of different vehicle types at different limited speeds are calculated. The results show that the speed limit has a significant impact on fuel consumption and carbon emissions. When the speed limit increased from 80 to 120 km/h, average vehicle speeds increased about 21%-27%, and fuel consumption and carbon emissions increased from approximately 33%-38%. Another interesting result was that the vehicle's fuel consumption and carbon emissions are only affected by speed. The results of the study explore the effect of speed limits on carbon emissions and provide evidence for road managers to set reasonable speed limits. [ABSTRACT FROM AUTHOR]

This study aimed to predict dental freeway space by examining the clinical history, habits, occlusal parameters, mandibular hard tissue movement, soft tissue motion, muscle activity, and temporomandibular joint function of 66 participants. Data collection involved video-based facial landmark tracking, mandibular electrognathography, surface electromyography of mandibular range of motion, freeway space, chewing tasks, phonetic expressions, joint vibration analysis, and 3D jaw scans of occlusion. This resulted in a dataset of 121 predictor features, with freeway space as the target variable. Six models were trained on synthetic data ranging from 500 to 25,000 observations, with 65 original observations reserved for testing: Linear Regression, Random Forest, CatBoost Regressor, XGBoost Regressor, Multilayer Perceptron Neural Network (MPNN), and TabNet. Explainable AI indicated that key predictors of freeway space included phonetics, resting temporalis muscle activity, mandibular muscle activity during clenching, body weight, mandibular hard tissue lateral displacements, and dental arch parameters. CatBoost excelled with a test error of 0.65 mm using 5000 synthetic data points, while a refined MPNN achieved the best performance with 25,000 synthetic data points and 121 unique predictors, yielding an absolute error of 0.43 mm on the 65 original observations. [ABSTRACT FROM AUTHOR]

Given the rapid construction of freeways in developing countries such as China, land use is constantly under strict constraints, leading to challenges in adopting conventional layouts for interchanges. Implementing right-turn ramps on the left (RTRL) at interchanges can minimize land occupancy; however, the traffic safety level in this type of diversion area design requires extra attention. This study examines the decision sight distance for right-turn exit ramps on the left side. Utilizing unmanned aerial vehicle (UAV) video and the YOLOv3 target detection algorithm, the original trajectory data of vehicles in the diversion area is extracted. Employing Kalman filtering and Frenet coordinate system conversion reveals microscopic vehicle lane-change patterns, velocities, and time headways. Furthermore, the driving simulation experiment assesses driver behaviors in RTRL, with subjective, task performance, and physiological measure indicators. Ultimately, the range of the decision sight distance is defined, and establishing a calculation model involves determining relevant parameters based on measured data and simulation outcomes. The results indicate potential insufficiencies in the decision sight distance when standardized values are applied to RTRL. [ABSTRACT FROM AUTHOR]

This paper describes a Pennsylvanian lycophyte cone collected from a highway cut along the southbound lanes of U.S. Interstate Highway 24 (I-24) north of Vienna, Illinois (USA) and stratigraphically belonging to the basal Tradewater Formation, under the Reynoldsburg Coal Bed (upper Bashkirian, lowermost Pennsylvanian). The cone is incomplete, more than 135 mm long and 26 mm wide, mainly representing the distal part with a missing base. The in situ spores from the cone represent trilete cingulizonate microspores belonging to the Lycospora brevijuga Group. Based on these spores together with morphological characters, the cone was compared with already existing species and differs sufficiently to warrant establishing a new species, here named Lepidostrobus willardii sp. nov. • Key words: Lepidostrobus, Lycospora, Tradewater Formation, Pennsylvanian, Illinois Basin. [ABSTRACT FROM AUTHOR]

Vehicular flow rate is an essential measure commonly collected by inductive-loop detectors for transportation agencies to evaluate freeways and highways. Loop detectors are typically located in urban areas due to installation and maintenance costs, and do not provide large spatial coverage. Crowdsourced data provide large spatial coverage, but typically do not capture vehicular flow rates. Therefore, a dynamically weighted ensemble (DWE) comprised of XGBoost and neural network models is proposed to expand the spatial coverage of vehicular flow rates by estimating flow rates for the Phoenix, AZ, metropolitan area using crowdsourced data. The model is evaluated using K-fold cross-validation methods, achieving a cross-validated mean absolute percent error of 21.74%, outperforming all other comparison models. The trained model is then used to estimate vehicular flow rates along highways and freeways throughout the state of Arizona. The proposed method provides transportation professionals with a transferable, cost-effective solution for large-scale flow rate estimation. [ABSTRACT FROM AUTHOR]

The global COVID-19 pandemic has had a great impact on transportation across the United States. However, there is a lack of studies investigating the pandemic's impact on vehicular traffic at the later stage of the pandemic. Therefore, this paper studies the change of freeway traffic patterns in two metropolitan counties in the State of Utah at the latter stage of the pandemic. We found that with the relaxation of travel restriction and the COVID vaccine, vehicular traffic has recovered to equaling, if not exceeding, pre-pandemic levels. Truck traffic is higher than the pre-pandemic level due to the growth of online shopping and on-demand delivery. To help responsive agencies to prepare for the near-future traffic pattern, a traffic prediction model based on an innovative approach integrating machine learning with graph theory is proposed. The evaluation shows that the proposed prediction model has a desirable performance. The mean absolute percentage prediction error is between 0.38% and 1.74% for different jurisdictions. On average, the modal outperforms the traditional long short-term memory model by 31.20% in terms of root mean squared prediction error. [ABSTRACT FROM AUTHOR]

This study identifies the significant risk factors associated with crash frequency on expressways in India. For this crash data was collected from over 391 km of three expressways comprising a total of 13345 crashes. The expressways were divided into 981 segments based on uniform traffic and geometric characteristics, and traffic, geometry, and lighting conditions-related data from these segments was also gathered. The final dataset comprised 6577 data points, and it was analysed using M5P and random effect negative binomial (RENB) models. The results obtained from the modeling approaches were compared based on the correlation coefficient (CC), root mean square error (RMSE), and mean absolute error (MAE). Although both models performed well in identifying the risk factors, the crash prediction of M5P model was better than the RENB model. It provided simple linear equations for sets of conditions defined by the tree. The findings of M5P model also suggest that it better explains the effect of various independent variables on crash probability under different circumstances. Furthermore, sensitivity analysis utilising the M5P model indicates that it can generalise and predict crashes' physical events well. The findings of this study suggest that modification in the design of horizontal and vertical profiles, median openings, interchanges, minor accesses, and bus bays/truck laybys are needed to increase traffic safety on expressways in India. [ABSTRACT FROM AUTHOR]

The paper builds on earlier research in which a full Bayesian approach was used to derive crash modification factors (CMFs) from regression models that capture the variability in these CMFs. The approach estimates a non-linear function that reflects the logical reality that the CMF for a given change in a feature's value depends not only on the amount of the change but also on the original value. However, in developing the approach, it was necessary to amalgamate panel data over several years by averaging annual traffic volumes and modeling the summation of annual crash counts. Such aggregation could be problematic if there is significant temporal variability in traffic volumes and crash frequencies, as would be the assumption that there is no site-to-site variation in the effects of independent variables on the crash frequency. The research for this paper examined whether these limitations can materially affect the CMF estimates by using simulated data to introduce substantial temporal and spatial variability in expected crash frequencies. A non-linear model with random effects that captures this variability was developed and compared to the non-linear model with fixed effects. The results show that the parameter estimates and CMFs for freeway median width, the feature used for the case study, were almost identical for the two non-linear approaches. This suggests that the aggregation approach in the earlier research may be adequate for capturing the non-linear safety effects of roadway attributes. The finding has practical importance in the sense that the amalgamation is often necessary when traffic data are not available for each year and also in the sense that when such amalgamation is done, the extent of the temporal variability in traffic volumes and expected crash frequencies is usually unknown. Future studies should expand the investigation to datasets that exhibit temporal variations in some features, such as pavement friction. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles (CAVs) are being developed and designed to operate on existing roads. Their safe and efficient operation during roadworks, where traffic management measures are often introduced, is crucial. Two alternative measures are commonly applied during roadworks on motorways: (i) closing one or multiple lanes (ii) narrowing one or all lanes. The former can cause delays and increased emissions, while the latter can pose safety risks. This study uses a VISSIM‐based traffic microsimulation to compare the effectiveness of these two strategies on traffic efficiency and safety, considering various market penetration rates (MPR) of CAVs. The model was calibrated and validated with the data collected from M1 motorway in the United Kingdom. Results show that average delays per vehicle‐kilometre‐travelled decreased from 102.7 to 2.5 s (with lane closure) and 23.6 to 0.6 s (with narrow lanes) with 0% and 100% CAV MPR, respectively. Moreover, safety in narrow lanes improved by 4.8 times compared to 1.5 times improvement in lane closure with a 100% CAV MPR; indicating that narrow lanes would result in better safety performance. These findings could assist transport authorities in designing temporary traffic management measure that results in better CAV performance when navigating through roadworks. [ABSTRACT FROM AUTHOR]

Introduction: While driving, drivers frequently adapt their driving behaviors according to their perception of the road's alignment features. However, traditional two-dimensional alignment methods lack the ability to capture these features from the driver's perspective. Method: This study introduces a novel method for road alignment recognition, employing image recognition technology to extract alignment perspective features, namely alignment perspective skewness (APS) and alignment perspective kurtosis (APK), from in-real driving images. Subsequently, the K-means clustering algorithm is utilized for road segment classification based on APS and APK indicators. Various sliding step length for clustering are employed, with step length ranging from 100m to 400m. Furthermore, the accident rates for different segment clusters are analyzed to explore the relationship between alignment perspective features and traffic safety. A 150 km mountain road section of the Erlianhaote-Guangzhou freewway from Huaiji to Sihui is selected as a case study. Results: The results demonstrate that using alignment perspective features as classification criteria produces favorable clustering outcomes, with superior clustering performance achieved using shorter segment lengths and fewer cluster centers. The road segment classification based on alignment perspective features reveals notable differences in accident rates across categories; while traditional two-dimensional parameters-based classification methods fail to capture these differences. The most significant differences in accident rates across categories are observed with segment length of 100m, with the significance gradually diminishing as segment length increases and disappearing entirely when the length exceeds 300m. Implication: These findings validate the reliability of using alignment perspective features (APS and APK) for road alignment classification and road safety analysis, providing valuable insights for road safety management. [ABSTRACT FROM AUTHOR]

The area where vehicles conduct interchanges is designated as the on-ramp merging area. The traffic efficiency in the ramp merging area drastically decreases if the mainline and ramp traffic flow density reaches saturation. As a current research hotspot in transportation, intelligent network technology, relying on the high- precision motion control and high-efficiency communication of connected-automated vehicle (CAV), can significantly improve the traffic efficiency in the merging area. The fusion strategies used by CAV are assessed in this research utilizing three different control paradigms: feedback control, optimal control, and reinforcement learning. The shortcomings of the three methods in this scenario are summarized, and specific improvement measures are given by reviewing existing research. Also, it offers a thorough summary of the most recent developments and trends in this particular scientific field. [ABSTRACT FROM AUTHOR]

The extraction of urban road features provides indispensable support to numerous high-accurate applications such as autonomous driving and urban high-definition mapping. However, approaches mainly focus on road connectivity, while often overlooking finer details of urban road constituent structures. Data that captures road details, such as LiDAR, may not be always readily available. This article proposes an operational framework for mapping finegrained urban road features by integrating open-source data (OSD). The geometric measurement method is successively presented using projective geometry and prior knowledge for urban road sections. And a feature generation strategy is introduced to achieve and express the fine-grained road features. Compared with the corresponding large-scale topographic map (LTM) and available optical remote sensing image (AORSI), the proposed method regenerated fine-grained features of urban roads with m-level. It provides a cost-effective innovative and alternative method to acquiring fine-grained road datasets in other datascarce regions. [ABSTRACT FROM AUTHOR]

São Paulo's Minhocão (Big Worm) is a 3.5 km elevated expressway that cuts across a dense part of the central city. Opened in 1971, it was controversial from the start, and widely held responsible for the decline of the city's historic centre in the 1970s and 1980s. However, it has been gradually tamed over the years, first closed to traffic at night, and then at weekends and on holidays, becoming an impromptu park, the Parque Minhocão, which has had official status since 2014. Those informal closures have been accompanied by numerous architectural schemes over the years to make the Minhocão a permanent park on the lines of New York's High Line. The Parque Minhocão in its present condition represents a stand-off between various interest groups, all of whom have claims on it as public space. The paper explores the history of the Parque Minhocão since 1969 through different forms of visualisation, arguing that its present condition, however imperfect, keeps multiple and contradictory interests in balance. [ABSTRACT FROM AUTHOR]

In this study, a stable and high-speed vision-based self-position estimation method was proposed that improves upon the existing method of lane detection by recognizing the lighting facilities that are installed in tunnels on Japanese expressways where GNSS cannot be used. In addition, we proposed a method for inspecting multiple cracks at once by estimating the self-position with the successful rate 75% in the traveling direction by counting the lighting with the successful rate 99.85%. The effectiveness of the method was verified by capturing images of cracks in an actual tunnel. The proposed method will enable more frequent inspections for tunnel cracks that lead to flaking while maintaining infrastructure safety, reducing costs, and improving tunnel visual inspection flow efficiency. [ABSTRACT FROM AUTHOR]

The introduction of connected automated vehicles (CAVs) on freeways raises significant challenges, particularly in interactions with human-driven vehicles, impacting traffic flow and safety. This study employs traffic microsimulation and surrogate safety assessment measures software to delve into CAV–human driver interactions, estimating potential conflicts. While previous research acknowledges that human drivers adjust their behavior when sharing the road with CAVs, the underlying reasons and the extent of associated risks are not fully understood yet. The study focuses on how CAV presence can diminish conflicts, employing surrogate safety measures and real-world mixed traffic data, and assesses the safety and performance of freeway interchange configurations in Italy and the US across diverse urban contexts. This research proposes tools for optimizing urban layouts to minimize conflicts in mixed traffic environments. Results reveal that adding auxiliary lanes enhances safety, particularly for CAVs and rear-end collisions. Along interchange ramps, an exclusive CAV stream performs similarly to human-driven ones in terms of longitudinal conflicts, but mixed traffic flows, consisting of both CAVs and human-driven vehicles, may result in more conflicts. Notably, when CAVs follow human-driven vehicles in near-identical conditions, more conflicts arise, emphasizing the complexity of CAV integration and the need for careful safety measures and roadway design considerations. [ABSTRACT FROM AUTHOR]

In this study, the vehicular air pollution from the Nairobi Expressway corridor was estimated by simulation using AERMOD dispersion model. The Vehicle Kilometer Travelled (VKT), emission factors, and traffic data were used to estimate the pollutants from vehicular traffic.. The highest modelled 24-hour and annual concentrations of PM2.5 were 0.274 µg/m3 and 0.152 µg/m3, whereas the highest 24-hour and annual concentrations of PM10 were 0.405 µg/m3 and 0.225 µg/m3, respectively. The maximum 24-hour and annual concentrations of Total Volatile Organic Compounds (TVOCs) were 20.8 µg/m3 and 11.5 µg/m3, respectively, whereas the maximum 8-hour and annual concentrations of carbon monoxide (CO) were 319 µg/m3 and 177 µg/m3, respectively. The highest modeled 24-hour and annual concentrations of NO2 were 28.2 µg/m3 and 15.7 µg/m3, respectively. The 24-hour average measured TVOCs values showed that Nairobi expressway traffic contributed 3.5 percent to the measured TVOCs concentration value, but CO accounted for 55 percent of the measured value. According to the simulated PM2.5 and PM10 concentration values, Nairobi expressway traffic contributed 1.1 and 1.6 percent, respectively, to the 24-hour average measured concentration levels. [ABSTRACT FROM AUTHOR]

For temperate desert ecosystems, understanding the maintenance mechanisms of biodiversity has become a central concern of policymakers, practitioners, and academicians, especially in the context of human disturbances. However, most of the past desert research is based on a single survey and single functional traits or phylogeny, and may lead to incomplete estimates of community assembly. To assess climate factors, soil factors, and human interference in the processes of dispersal assembly, filtering, and limiting similarity in community assembly, we selected 50 sites along the Beijing‐Xinjiang Expressway and conducted plant surveys at sites 0.5, 1.0, and 1.5 km away from the expressway twice, one in spring and one in autumn, and collected 31 functional traits and phylogenetic information of 74 species. We found that incomplete detection can lead to bias in biodiversity estimation due to seasonal surveys. Road disturbance at different distances had no significant effect on species composition and community assembly processes. The trade‐off between dispersal limitation and environmental filtering is the process of maintaining desert plant diversity. Precipitation directly determined the dispersal limitation and environmental filtering. Soil pH mediated the effect of temperature on biotic interaction in the dimension of phylogeny, while soil pH and organic carbon acted as intermediate factors affecting biotic interaction in functional traits. Our findings suggest that climate change, notably precipitation, has a more pronounced impact on desert plant communities than human disturbances. A multifaceted approach considering functional traits and phylogeny across seasons is essential for understanding species coexistence in desert ecosystems. [ABSTRACT FROM AUTHOR]