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• Explore the road closure issue caused by traffic accidents in tunnels with the real-world data. • Tunnel traffic accidents are found to cause the longer road closure time than non-tunnel ones. • Important factors affecting the road closure time of tunnel traffic accidents are identified. With the increase of roadway tunnels, tunnel traffic accidents are also increasing. Compared with open roadways, road closures caused by traffic accidents in the tunnel have greater influences on traffic operations, as tunnels are often the key junctions of the roadway network. With the traffic accident data of Pennsylvania from 1997 to 2020, this study is designed to identify the features and trends of road closure time due to tunnel traffic accidents. First, features of tunnel and non-tunnel traffic accidents are compared from the perspectives of accident consequences, environmental factors, risky driver behavior, and vehicle features. The data confirms that tunnel traffic accidents are more likely to lead to road closures than non-tunnel ones. Besides, tunnel traffic accidents are mainly consisted of rear-end and hit-fixed-object collisions with many speeding cases. Then, a Negative Binomial model is built to statistically identify the factors significantly influencing the road closure time, which is recorded as non-negative integer in the data. The estimation results indicate that collision type, accident severity, road type, driving under the influence, the involvement of heavy trucks, and the number of involved vehicles could significantly influence the road closure time, whereas lighting, road condition, speeding, and area are insignificant. Finally, based on the research findings, some countermeasures are proposed to reduce the road closure time caused by tunnel traffic accidents. [ABSTRACT FROM AUTHOR]

Facing the challenges of motorised traffic, many cities around the globe started implementing measures to transform their urban transport systems. One of the major challenges for the success of adequate policies is not only their effectiveness but also whether they are accepted by city residents. With a quantitative case study in four neighbourhoods in Frankfurt am Main (N = 821), this article investigates the acceptability of three measures: (i) parking management, (ii) the conversion of car lanes into cycle lanes and (iii) the closure of an inner city arterial road to car traffic. The results show a surprisingly high acceptability for all measures if the benefits for local residents are tangible. Thus, successful policy packages may combine push measures with either pull measures, as suggested frequently in the literature, or with improvements for other land uses (e.g. re-using former car-parking spaces for non-transport purposes, such as greenery or seating areas). Furthermore, the perceived effectiveness, daily travel practices and intentions to reduce car use, the built environment and, to a lesser degree, socio-demographics explain differences in acceptability by population group., Competing Interests: Competing interestsOn behalf of all authors, the corresponding author declares no conflict of interest., (© The Author(s) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

The effects of global climate change include more extreme weather events that harm lifeline infrastructure such as road access. The questionnaire-based study takes a novel natural experiment approach to subjective personal experiences and perceptions of lifeline vulnerability in two seaside communities in Norway that have been sporadically isolated due to avalanches, heavy snowfall, and/or snowdrifts. The enquiry aims at filling a research gap on sudden winter climate-induced disconnections and road travel hazards in advanced societies. The results show that weather-induced road closures lead to worries about road travel and practical problems, but also that many people are able to adjust to reduce their vulnerability. The authors concluded that community characteristics such as available services and social and human capital are important for understanding people’s vulnerabilities, worries, and hazard preparedness. [ABSTRACT FROM PUBLISHER]

Excessive post-construction settlement is often found for highways built on soft soil, slowing driving speed and increasing the risk of traffic accidents. Increasing attention has been paid to lightweight backfill to reduce ground settlement because of its light density, reasonable cost, and ease of construction. However, the existing technology for lightweight backfilling requires road closure, resulting in serious traffic jams and considerable economic losses. In this paper, a new technology is proposed for lightweight backfilling without road closure. A field trial was conducted to verify the practicality of the newly proposed technology. Ground settlements during and after construction were measured in the field trial with lightweight backfilling and in the adjacent zone without lightweight backfilling. The measured results were compared, discussed, and explained. It was found that the ground settlement was significantly mitigated by lightweight backfilling. The total settlement of ground surface was reduced by 36 %, and the average settlement rate was reduced by 40 % with a decreasing trend. It is suggested that the proposed technology with lightweight backfilling could solve the post-construction settlement problem well without road closure in busy highway. [ABSTRACT FROM AUTHOR]

In this article we trace the emergence ofroad-closures– i.e. the barricading by local residents of public roads ostensibly in response to crime – in the northern suburbs of Johannesburg in the decade after apartheid. We argue that road-closures manifest an attempt at material “fixing” an urban order of privilege, even as privilege and inequality is increasingly “deterritorialised” in the city of the global South. While conventionally theorised as part of a broader global trend towards the privatisation and securitisation of urban space, we demonstrate that road-closures contain qualitatively different expectations of the urban order to e.g. private gated communities. Whereas gated communities are premised on and driven by a political economy of self-exclusion from urban life, road-closures simultaneously resist and prefigure this “deterritorialised” reordering of privilege in the post-apartheid city. Based on archival research in local community newspapers over a 10-year period between and 2004 (the high-water mark of the so-called road-closure “debates”), we trace shifting discourses about road closures and the city: from anxieties about crime and loss of privilege, to fantasies of abandonment, to the assertion (and rupture) of a mythical suburban utopia. Drawing on a literature onruinsas the material effects of a past order manifest in the urban order of the present, we assert that despite anxieties about the loss of privilege, these enclosed neighbourhoods remain spaces of extreme privilege, now implicated into an emergent geography in which old and new spaces of privilege overlap to reinforce spatial inequalities in post-apartheid Johannesburg. [ABSTRACT FROM PUBLISHER]

Three-dimensional (3D) surface information is becoming the data of choice for pavement inspection and maintenance. Pavement digital reconstruction still faces various challenges, including field-of-view (FOV) limits, traffic influences, data acquisition speed and cost. This paper presents a full-FOV pavement stereo reconstruction framework integrating unmanned aerial vehicle (UAV) photography, object detection and multi-view occlusion optimization. A UAV-YOLO vehicle detector embedding depth-wise separable convolution and resolution adjustment unit is developed for noise localization. To improve reconstruction quality and speed, multi-view occlusion optimization is proposed for determining the optimal image series spatial distribution. The results show that the UAV-YOLO detector achieves an overall AP 75 of 93.69% with an inference speed of 157 FPS. Through multi-criterion evaluation, the pavement digital models reconstructed under dynamic traffic conditions have satisfactory performance in terms of point cloud noise, density, similarity and accuracy. In addition, the proposed stereo reconstruction workflow saves 30.27% processing time over conventional SfM workflow. • UAV photogrammetry and deep learning are integrated for pavement stereo reconstruction with traffic influences. • A UAV-YOLO detector is developed for high-accurate vehicle localization in multi-altitude UAV photography. • Multi-view occlusion optimization is proposed to improve full FOV pavement reconstruction quality and speed. • A 2D/3D digital surface dataset is established for future study of automated defect detection. [ABSTRACT FROM AUTHOR]

• A deep leaning network was designed for farmland segmentation in satellite images. • Two-part framework of pseudo label generation and segmentation model fusion. • New block-wise metrics were introduced for parcel-level evaluation. • Experiments show significant performance improvements at the parcel level. The participation of insured smallholders and involvement of related requirements have been emerging and increasing with the popularization and promotion of agricultural insurance. Compared with traditional field surveys, remote sensing and deep learning technologies, with their rapid development in recent years, have provided an automatic and effective means for underwriting control in the agricultural insurance industry. Moreover, "smallholder-wise underwriting" is intensively expected by the local government and insurance companies to ensure precise claim settlements. In this process, farmland segmentation is a crucial and fundamental step for smallholder farmland claim settlements. However, segmentation of smallholder farmland parcels from satellite images remains a challenge, as the ridges and roads between farmland parcels are usually narrow and display confusing characteristics with their neighboring farmlands. This challenge causes undersegmentation on the farmland boundaries, leading to inaccurate and incomplete farmland parcel recognition. In this study, we aim to solve this problem by proposing a novel Deep Edge Enhancement Semantic Segmentation Network to refine parcels' boundary segmentation and improve the closure level of the farmland segmentation. We designed a framework for farmland edge enhancement through pseudo road label generation and model fusion. The proposed network aggregates feature extraction for ridge and road segmentation to improve its performance on farmland parcel recognition. Furthermore, we found that the commonly used image segmentation evaluation metric, such as pixel-wise–based mean Intersection over Union, cannot objectively reflect the effectiveness of the smallholder farmland segmentation. Therefore, we proposed two block-wise farmland evaluation metrics that are consistent with the practical evaluation rules and requirements of farmland segmentation at the parcel level. We implemented experiments in the study area of Jiaxiang County in northern China using the GaoFen-1 4-band multispectral images (red, green, blue, and near-infrared) at a spatial resolution of 2 m. Experimental results demonstrated the effectiveness of our method, which outperformed the baseline DeepLabv3+ network. [ABSTRACT FROM AUTHOR]

Illegal short-term vehicle stalls are frequent in developing countries, which disturb smooth traffic flow and results in congestion, road blockage, and even lead to accidents. Short-term stalls span a few seconds—e.g., a vehicle stopping to make way for people crossing the road. However, traditional stall detection techniques detect stalls that span 10 s or more. Moreover, existing techniques, such as background subtraction algorithms, are vulnerable to illumination changes and indicate too many false stalls with smaller stopping criteria. Furthermore, for violation indication, post-stall analysis is required to filter out the legitimate stalls where a vehicle stops for some valid reason. We present a two-fold methodology to detect illegally parked vehicles. An object identification-based technique is employed over stationary images to detect stall. Furthermore, the legitimacy of the stall is determined by performing pedestrian detection and its position analysis. We tested our proposed approach using publicly available advanced video and signal based surveillance 2007 PV dataset and ViSOR dataset. The technique successfully detects stalls with a recall of 0.96, after weaving the stopping criterion from the ground truth. [ABSTRACT FROM AUTHOR]

• In this paper, a novel method of online multi-object tracking (MOT) is proposed. • The proposed method is based on the delta Generalized Labeled Multi-Bernoulli (δ -GLMB) filter. • The main contributions of the paper are handling ID switch and addressing occlusion and miss-detection issues. • Experimental results show the superiority of the proposed method in handling MOT issues. In this paper, we propose an online multi-object tracking (MOT) method based on the delta Generalized Labeled Multi-Bernoulli (δ -GLMB) filter framework to address occlusion and miss-detection issues and recover identity switch (ID switch). Along with the principal δ -GLMB filter that performs multi-object tracking, we propose a one-step δ -GLMB filter to handle occlusion and miss-detection. The one-step δ -GLMB filter is non-iterative and only requires current measurements. The filter is based on a proposed measurement-to-reappeared track association method and addresses MOT issues by incorporating all occluded and miss-detected objects. We introduce a novel similarity metric to apply in the measurement-to-reappeared track association process to define the weight of hypothesized reappeared tracks. To ensure the track consistency, we also extend the principal δ -GLMB filter to efficiently recover switched IDs using the cardinality density, size, and visual features of the hypothesized tracks. In addition, we perform an ablation study to demonstrate the contribution of the main parts of the proposed method. We evaluate the proposed method on well-known and publicly available test datasets focused on pedestrian tracking. Note that our proposed method is online and not based on the learning paradigm. So it does not use any additional source of information such as private detections and pre-trained networks. Despite that, we achieved a reliable performance in multiple persons tracking at complex scenes by applying occlusion/miss-detection and ID switch handlers. Experimental results show that the proposed tracker performs better or at least at the same level of the state-of-the-art online and offline MOT methods. [ABSTRACT FROM AUTHOR]

• More than one-fifth of China experienced sustainability change over two decades. • The dominant types of change divide China into four zones. • Roads and ecological engineering are the key drivers for the changes. The sustainable development level, coordinated among three sub-systems in the economy, society, and environment, has changed globally or regionally. The key factors and mechanisms driving these changes remain uncharacterized due to the lack of a systematic method. Here, within the production-living-ecological framework, this study quantified China's sustainable development change at a fine scale and identify the key factors as well as explored its driving mechanism by combining a machine learning approach with multi-dimensional datasets. The results indicate that more than one-fifth of China has experienced sustainability change over two decades, and the dominant types of change divide this nation into four zones. The socioeconomic and the man-made environment rather than nature itself have greatly influenced this change. Road density is the key driver of sustainability change except in east China where arresting environmental deterioration via artificial plantation and mountain closure is the dominant factor for the change to take place. The findings provide a novel insight into understanding sustainable development change and its driving mechanism. [ABSTRACT FROM AUTHOR]

• This study introduced an open-source approach by using the CARLA virtual simulator to evaluate the effectiveness of the AEB system under the occlusion conditions. • The evaluation was conducted by exploring the collision between a pedestrian crossing at the red light and a through vehicle, which is one of the most dangerous conditions at intersections. • The modeling results suggested that the pedestrian's and ego vehicle's speeds could have significant effects on the effectiveness of the AEB system. • The effectiveness of the AEB system has been evaluated extensively in this study if the pedestrian is occluded by a stopping vehicle. Pedestrians' red-light crossing can present a threat to themselves and the safety at intersections, especially for the through vehicles since their speeds are higher compared to the turning vehicles. The automated emergency braking (AEB) system could actively detect pedestrians and react to avoid potential conflicts. This study contributes to evaluate the effectiveness of the AEB system under occlusion conditions. The braking algorithm was developed in the virtual simulator CARLA to control the ego vehicle. Three occlusion scenarios in which the sensor of the AEB system could not detect the pedestrian if the pedestrian is occluded by a stopping vehicle. The evaluation experiments were conducted at a typical 4-leg intersection considering different motion statuses of the ego vehicle and pedestrian. The effects of field of view (FOV) of the sensor and activation threshold of the AEB system were also explored. The study indicated that the effectiveness of the AEB system could be reduced by the occlusion time. A longer activation threshold is recommended if the pedestrian is potentially occluded for a long time. The effects of other factors such as the speed of the ego vehicle and pedestrian and scenarios were also identified. [ABSTRACT FROM AUTHOR]

The effectiveness of any crowd management plan is constrained by the behavior of individuals in the crowd. In mass religious gatherings, the strict time schedules to perform specific activities and the arrival and departure constraints force pilgrims to incorporate multiple stops in the same tour based on the proximity of these sub-destinations. This paper explores the role of accessibility of prime activity locations, possible trip chaining patterns, and the resultant shortest paths in the context of crowd management. The extracted mean speed of pedestrians from the video data of Kumbh Mela, Ujjain (2016) is used as an input to generate the spatial time-based accessibility map. Two methods – space syntax and accessibility considering travel time are used to understand the pilgrim's travel choices. The findings from the study confirm that both distance and directions are the major determinants of pedestrian route choice in mass religious gatherings. The study also evaluates the effect of temporary road closures on the route choices of pilgrims The methodology adopted, and insights derived from the analysis could be used to manage the crowd more effectively by ensuring that critical attention is given to the routes that are more likely to carry the diverted pedestrians during road closures. • We proposed a framework incorporating two measures of accessibility to assess crowd management strategies. • Effect of temporary road closures on roads connecting three prime activity locations in Kumbh Mela, Ujjain was analyzed. • Mahakleshwar Temple-Harisiddhi Chauraha link possesses the highest accessibility with a low mean number of turns. • Pedestrians minimize both distance and angular distance in their route choices while accommodating multiple stops in tours. • Need to consider trip chaining patterns and proximity of sub-destinations prior to impromptu decisions to manage crowd. [ABSTRACT FROM AUTHOR]

The transportation system is often considered a critical infrastructure as the failure of its components can have a substantial impact on the economy and social well-being of a country. Extreme weather events such as urban flooding disrupt transportation networks with delays, diversions, and partial or full closure of roads and, thereby, influence human movement patterns. Most studies in the field of transportation network failure due to urban flooding are focused only on micro-scale, in-situ approaches rather than referring to the holistic impact on the entire transportation system. Moreover, such approaches are failed to address the changes in travel behaviour concerning their trip purpose, mode choice, and income which can be changed due to inundation. Hence, this study presents a novel approach to assess the entire transportation system failure due to urban flooding. Consequently, the impact of urban flooding on human movement patterns was assessed. The proposed framework is developed based on the network centrality measures, namely, betweenness centrality and closeness centrality. The study is predominantly based on Open-Source GIS tools and the case study is carried out in Colombo, Sri Lanka which has the highest concentration of population and transportation network. The hotspots of the failure of the network were statistically and spatially illustrated. The study revealed three key findings; (a) the fragmented network due to inundated road segments triggers the spatial changes in the accessibility, particularly decreasing the accessibility in most of the core areas whereas increasing the accessibility in some peripheral areas which are not significant under the baseline condition (2) the functional changes of the entire transport network occurs due to flooding events (3) the considerable changes in the trip length occurs due to the disruption of transport network links and the impact is mostly notable in long-distance trips compared to short distance trips. The proposed framework can be utilized as a planning tool for both disaster practitioners and infrastructure operators in identifying the flood risk; and prioritizing flood mitigation investments, thereby, making the transportation system more robust to extreme flooding. • The study proposed a novel approach to measure the impact of urban flooding on the entire transportation system. • The proposed model incorporates the network centrality measures to investigate the impact of failures of critical road infrastructure due to flooding on the overall transport network and its performance. • The proposed method does not demand extensive data; and computed by utilizing open-source software applications and open data. [ABSTRACT FROM AUTHOR]

The article focuses on the temperature and amount of rainfall in New Zealand in 2015 and presents an appendix of datasets and sources for general phenomenon or variable. Topics discussed include the observed above-average temperature anomalies in January and March, the low annual rainfall total in 2015 which was typically observed during El Niño, and the flooding, loss of electricity, and road closures caused by heavy and prolonged rainfall in Dunedin.

To estimate the impact of Hurricane Ike of 2008 on local communities, service areas of hospitals and corresponding service populations were calculated at both the county and facility levels within the Houston metropolitan statistical area. A hospital access indicator was defined to measure the level of access to medical services when compared to the prestorm level. The result indicated that the hospital access indicator reached its lowest value on the next day after the hurricane landfall due to lane closures and hospital shutdowns before showing a steady comeback. However, the recovery was briefly interrupted around September 18, 2008, as a result of road closures to remove debris on State Highway 146. Access to hospitals nearly returned to the prestorm level by the end of October 2008. Simulations were then performed to identify priorities for repairing damaged roads in order to minimize storm's adverse impact. This paper offered a simplified method to measure, monitor, and analyze the effect of transportation network on access to healthcare facilities in the aftermath of a hurricane. Since only a small number of publicly available variables were required, the method could be applied to other catastrophic events in support of disaster response and recovery decisions. [ABSTRACT FROM AUTHOR]

CicLAvia in Los Angeles, CA is the open streets program that closes streets to motorized vehicles and invites people to walk, run, play or ride their bicycles on these streets, allowing them to experience the city in a new way and get exercise at the same time. Since the events reduce the motorized traffic flow, which is a significant source of air pollution, on the streets, it is reasonable to hypothesize that the CicLAvia events can reduce the concentrations of traffic-emitted air pollutants during the road closure. This study is the first experiment to test this hypothesis. The on-road and community-wide ultrafine particle (UFP) and PM 2.5 were measured on the Event-Sunday (October 5th, 2014) and the Pre- and Post- Sundays (September 28 th and October 12 th , 2014). Data analysis results showed the on-road UFP and PM 2.5 reduction was 21% and 49%, respectively, and the community-wide PM 2.5 reduction was 12%. [ABSTRACT FROM AUTHOR]

In 2009, a low-volume gravel road in Sweden was stabilised using fly ash from a local paper mill. The objective was to examine whether fly ash could be used to enhance the stability of the road and how the nearby environment would be affected. The technical and environmental properties of the road have been monitored for two, six, and eleven years. Because the construction costs are higher for ash upgrading than for conventional upgrading, knowing for how long the improved properties will remain is relevant. Strength development was studied using a falling weight deflectometer and compressive strength tests. Environmental properties were studied by chemical analysis of road samples, soil, soil pore water, and vegetation. The results showed that the ash sections had higher stiffness than the reference sections. Leaching tests of road samples showed that the mobility of potassium, sodium, chloride, and sulfate decreased with time. Trace element concentrations in soil samples, except barium, were below the Swedish guideline values for sensitive land use. Chromium, lead, and copper were as high or even higher along the reference section than along the ash section. After 11 years, the pH closest to the ash section was slightly enhanced. Concentrations of zinc and cadmium in the soil pore water were the lowest closest to the ash road, although the total concentrations in the soil were at their highest at the same distance. No toxic levels of trace elements were found in the vegetation close to the road, although a clear difference was observed between plants from the ash section and the reference section, using multivariate data analysis. The positive effects on the geotechnical road properties from ash stabilisation remained after 11 years. The environmental impacts on nearby soil and vegetation can be considered low and acceptable. This study demonstrates that the use of biofuel fly ash in infrastructure projects can contribute to the circular economy and effective use of resources because the demand for pristine materials will be reduced. • Technical and environmental properties of gravel road upgraded with fly ash. • Higher stiffness in ash section than in reference sections after eleven years. • No toxic levels of trace elements in the vegetation close to the ash road. • Environmental impact on nearby soil and vegetation can be considered low. • Biofuel fly ash in infrastructure projects can contribute to circular economy. [ABSTRACT FROM AUTHOR]

• Considering road-related variables (i.e., design speed, circular curve radius), LiDAR technical parameters (e.g., angle resolution, mounting height), and point threshold for detection, the available sight distance at as-built horizontal curved roads for LiDAR-based automated vehicles were investigated through virtual simulations. • Maximum speeds against inadequate sight distances for LiDAR-based automated vehicles were derived and speed limits for specific curved geometry conditions were proposed. • A larger radius or design speed, fewer LiDAR channels, or a higher point threshold for detection cause a shorter available sight distance. • Level 3 LiDAR-based automated vehicles struggle to adapt to as-built horizontal curved roads, and level 4 or 5 automated vehicles cannot assure adequate available sight distances on high-type curved roads. Emerging automated vehicle (AV) technology is being deployed on as-built roadways due to its promising safety improvements. However, realistic problems concerning whether and how perception sensor-based AVs can safely adapt to the existing roadway infrastructures remain to be well addressed due to a lack of consideration of the sensor's angular resolution and detection threshold. In this study, we aim to assess whether LiDAR-based AVs (LAVs) could safely adapt to as-built horizontal curved roads from the perspective of available sight distances (ASDs) through virtual simulations. In specific, i) numerous driving scenarios featuring the design speed (V d : 40 ∼ 100 km/h), circular curve radius (R : limited minimum radius ∼ common minimum radius), LAV (with LiDAR technical parameters, e.g., number of channels, N c : 32, 64, 128), and the front target vehicle were simulated in PreScan/MATLAB/Simulink co-simulation platform; ii) an ASD extraction algorithm was proposed considering the point threshold for detection (N T); iii) effects of V d , R , N c , and N T on the ASD were analyzed and polynomial models were adopted to capture relationships between the ASD, V d , R at different N c and N T ; iv) the minimum speed against as-built sight obstructions along the roadside and the maximum speed against inadequate sight distance were proposed by comparing the ASD with the required stopping sight distance of human-driven vehicles and LAVs (level 3 ∼ 5), respectively; and v) speed limits (V L) against inadequate sight distances for level 3 ∼ 5 LAVs were proposed. The results indicate that: i) a larger R or V d , fewer N c , or a higher N T would cause a shorter ASD in general; ii) attention should be paid to the occlusion imposed by as-built roadside infrastructures even with more N c or/and a lower N T , particularly to curved roads with more rigorous geometric design controls (e.g., small V d); and iii) level 3 LAVs struggle to adapt to as-built horizontal curved roads, and level 4 or 5 LAVs cannot assure adequate ASDs on high-type curved roads (e.g., large V d). These findings shall help road administrators make decisions on speed limits for LAVs on as-built curved roads. [ABSTRACT FROM AUTHOR]

• Two types of in-vehicle warnings for work zone areas are designed including Leading Vehicle Brake Warning (LVBW), and Lane-Closed Warning & Leading Vehicle Brake Warning (LCW & LVBW). • The Lane-Closed Warning & Leading Vehicle Brake Warning has significant safety benefits and can improve driver's car-following behavior. • Foggy weather seriously hinders driver's identification of the enclosure area of work zone. • Female drivers are more conservative when approaching the work zone area, which can better reduce the risk of accidents. Work zone area on roads is a critical component of road networks which concerns the safety of workers and passing by drivers. However, the passive speed reduction and lane changes caused by lane closure have led to frequent rear-end collisions in work zone areas. To help drivers better anticipate work zone situation and reduce collision risks, this paper proposes two types of in-vehicle warnings for work zone areas: Leading Vehicle Brake Warning (LVBW), and Lane-Closed Warning & Leading Vehicle Brake Warning (LCW & LVBW). The LVBW delivers a danger warning message to drivers upon the brake of the leading vehicle, while the LCW & LVBW provides an additional work-zone position message to remind drivers to decelerate in advance. A driving simulator experiment was conducted with 44 participants (24 males and 20 females) to test drivers' performance in work zone area under different conditions, comprising two warning types (LVBW vs. LCW & LVBW), four warning times (3 s, 5 s, 7 s and 9 s) and two visibility conditions (clear and foggy weather). The results showed significant safety benefits of the lane-closed warning message under the LCW & LVBW condition. In contrast, the warning of leading vehicle's brake in both LVBW and LCW & LVBW conditions had limited efficacy, which indicates that earlier warning about lane-closure is important to assist drivers in anticipating the complex situations in work zones. Drivers' speed control and collision avoidance performances were impaired in fog, but the impairment was compensated by the warning messages. Compared with male drivers, female drivers tend to be more cautious when approaching the work zone areas. Overall, this study plays a pioneering role in developing effective safety countermeasures for work zone areas and providing strong support for implementing in-vehicle warning technologies. [ABSTRACT FROM AUTHOR]

Recently, single object tracking has demonstrated great success. However, due to various problems caused by fast motion, occlusion, and deformation, it is still intractable for traditional trackers to adapt to changes in object appearance. In this work, a dynamic memory network is proposed for visual tracking to handle the template matching process globally. More specifically, we first build a memory model, which consists of a memory feature fusion module and a memory bank. By the memory model, the network not only accepts the first frame as initial information but also memorizes the selective frames in the video sequence to provide rich time-domain information. Second, an innovative sampling strategy is adopted in the tracking process. By updating the template and guiding the selection of memory frames, our model can output higher-quality features. In addition, a spatial-channel fused attention module that effectively improves the representational capability and discriminability of the model is introduced. Our proposed method obtains compelling results on six challenging tracking benchmarks, including the OTB100, VOT2019, UAV123, NFS, GOT-10k, and LaSOT datasets. Extensive experiments demonstrate that our approach shows satisfactory robustness and leading application potential in real-time speed. [ABSTRACT FROM AUTHOR]

As 5G is being widely commercially deployed around the world, it is of great interest to continue investigating how it can be further improved in the near term or evolved aiming at a longer term. This issue of Mobile Communications and Networks explores the above directions covering several aspects. These include the exploration of joint slicing and functional split in the 5G radio access network (RAN), addressing coverage issues in 5G focusing on the uplink data channel, tackling the issue of temporal blockages and outdated channel feedback in an Internet of Vehicles setting, and investigation of the inter-play between rate-splitting multiple access (RSMA) and intelligent reflecting surface (IRS). [ABSTRACT FROM AUTHOR]

• A deep learning method for oriented and small wheat spike detection was proposed to accurately detect wheat spikes in UAV images. • The method introduces the orientation feature of wheat spikes into the network and reduces the negative effect of background on detection based on circular smooth label. • The method can better solve tiny wheat spikes' occlusion and overlap problems. Detecting and characterizing spikes from wheat field images is essential in wheat growth monitoring for precision farming. Along with various technological developments, deep-learning-based methods have remarkably improved wheat spike detection performance. However, detecting small and overlapping wheat spikes in UAV images is still challenging because high spike occlusion and complex background can cause error detection and miss detection problems. This paper proposes a deep learning method for oriented and small wheat spike detection (OSWSDet). Unlike classical wheat spike detection methods, OSWSDet introduces the orientation of wheat spikes into the YOLO framework by integrating a circle smooth label (CSL) and a micro-scale detection layer. These improvements enhance the ability to detect small-sized wheat spikes and prevent wheat spike detection errors. The experiment results show that OSWSDet outperforms classical wheat spike detection methods, and the average accuracy (AP) is 90.5%. OSWSDet can accurately detect spikes in UAV images with complex field backgrounds and provides technical references for future field wheat phenotype monitoring. [ABSTRACT FROM AUTHOR]

Detection of Bacillus anthracis in the field, whether as a natural infection or as a biothreat remains challenging. Here we have developed a new lateral-flow immunochromatographic assay (LFIA) for B. anthracis spore detection based on the fact that conjugates of B. anthracis spores and super-paramagnetic particles labeled with antibodies will block the pores of chromatographic strips and form retention lines on the strips, instead of the conventionally reported test lines and control lines in classic LFIA. As a result, this new LFIA can simultaneously realize optical, magnetic and naked-eye detection by analyzing signals from the retention lines. As few as 500–700 pure B. anthracis spores can be recognized with CV values less than 8.31% within 5 min of chromatography and a total time of 20 min. For powdery sample tests, this LFIA can endure interference from 25% (w/v) milk, 10% (w/v) baking soda and 10% (w/v) starch without any sample pre-treatment, and has a corresponding detection limit of 6×10 4 spores/g milk powder, 2×10 5 spores/g starch and 5×10 5 spores/g baking soda. Compared with existing methods, this new approach is very competitive in terms of sensitivity, specificity, cost and ease of operation. This proof-of-concept study can also be extended for detection of many other large-sized analytes. [ABSTRACT FROM AUTHOR]