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Recently the value of roadside vegetation as habitat for pollinators has gained increased attention, particularly in areas dominated by agriculture where there is little native vegetation available. However, many factors, including safety, cost, public perception, erosion control, and weedy plants must be considered when managing roadside vegetation. Although their decisions influence thousands of hectares of public rights-of-way, how engineers and roadside managers maintain roadside vegetation has been the subject of little research. In this study, we surveyed county engineers and roadside managers who manage vegetation along secondary roads in Iowa, USA to assess how they maintain roadside vegetation. Some counties employ roadside managers, who often have an environmental sciences background, to implement the on-the-ground management of roadside vegetation, while some counties use other staff. Compared to engineers, roadside managers more strongly agreed that using the ecological principles of integrated roadside vegetation management (IRVM) provided environmental benefits. Engineers in counties with a roadside manager more strongly agreed that IRVM practices reduce the spread of invasive species and provide attractive roadsides. Both engineers and roadside managers mentioned challenges to managing roadside vegetation, including interference with some native plantings by adjacent landowners, and ranked safety and soil erosion concerns as the highest priorities when making decisions. Four in ten roadside managers said their counties had protected native plant community remnants on secondary roadsides. Our findings can inform conservation outreach efforts to those responsible for managing roadside vegetation, and emphasize the importance of addressing safety and soil erosion concerns in roadside research and communications.

Concentrations of Cd, Cu, Fe, Pb, and Zn were measured in the samples of street dust and surface roadside soil before Jordan switched to unleaded fuel usage. The samples were collected from Petra, the most tourist-attractive site in Jordan. The samples were analyzed for heavy metals by atomic absorption spectrophotometry. Our results show that the distribution of metals in the soil samples is affected by wind direction in the investigated area. The highest level of metals was found in the eastern parts of the roads due to the westerly-dominant wind in the studied area. The contamination levels of metals decrease as the distance from the edge of the road increases. In the roadside soil samples, the means for the concentrations of the metals at 1 m from the east side of the main road are 1.0, 19.1, 3791.4, 177.0, and 129.0 mg kg −1 for Cd, Cu, Fe, Pb, and Zn, respectively. In the samples of street dust, the means of the concentrations of the metals in the investigated area are 9.7, 11.8, 4694.4, 31.6, and 24.8 mg kg −1 for Cd, Cu, Fe, Pb, and Zn, respectively. In conclusion, the lithogenic origins (traffic emissions) are responsible for the diffusion of these metals in the studied region.

ContextKnowledge about spatial patterns of human dimensions data within landscape ecology is nascent despite its importance in natural resources management. We explored this topic within the context of utility roadside forest management, a complex situation involving ecological, cultural, and aesthetic aspects of forests and reliable power.ObjectivesWe applied spatial interpolation to investigate patterns of human attitudes toward roadside vegetation management data across an exurban landscape.MethodsMail surveys (n = 1962) were used to collect social science data from residents in four areas of Connecticut, USA. For each area, three attitudes variables were evaluated for spatial autocorrelation using Moran’s I statistic. Based on identified autocorrelation distance or scale, attitudes were interpolated using inverse distance weighting. Model validation of interpolated surfaces was completed using root mean square error.ResultsSignificant spatial autocorrelation was present for five of 12 study area-attitude pairings (one focused on professionalism; two focused on safety; three focused on tradeoffs between reliable power and maintaining trees) at distances ranging from 200 to 2400 m. Accuracy of interpolations varied among study areas, suggesting that the choice of spatial scale of analysis influenced model results.ConclusionsSocial processes influencing attitudes were spatially heterogeneous, existing at disparate scales for the same variables in different locations. Collectively, “enough” roadside forest may exist to ameliorate intermittent vegetation management aesthetically, yet underlying social processes influencing roadside forest outcomes likely are not mutually exclusive. Interpolation assumptions often applied toward ecological studies did not work well for social processes studied in this analysis.

Environmental practitioners must understand those they collaborate with to implement programs that are both socially and ecologically effective. Practitioners who understand decision-makers’ perspectives are better able to collaborate to lower political, financial, and cultural obstacles. In this study, we surveyed decision-makers involved with a voluntary environmental program in Iowa, USA. Iowa counties can choose to manage their roadside vegetation using an ecological approach, called integrated roadside vegetation management. Key decision-makers who decide whether a county has a roadside program are the county board of supervisors and the county conservation board. We used a mixed-mode design to survey the conservation board directors and chairs of the board of supervisors in each county. Our main goals were to understand the decision-makers’ perceived benefits and barriers to having a roadside program in their counties, as well as the key factors influencing their decisions about roadside vegetation management. Safety, maintenance cost savings, and erosion control were the main factors that influenced decision-making, while pollinators and other wildlife received the least consideration. However, decision-makers in counties with a roadside vegetation manager were more influenced by pollinators and other wildlife compared to their counterparts in counties without a roadside vegetation manager. The main barriers to having a program include a lack of resources or other concerns being a higher priority. Emphasizing safety, cost savings, and erosion control benefits of roadside programs, and identifying ways to lower startup costs may increase buy-in with county decision-makers.

Massive declines in insect biodiversity and biomass are reported from many regions and habitats. In urban areas, creation of native wildflower meadows is one option to support insects and reduce maintenance costs of urban green spaces. However, benefits for insect conservation may depend on previous land use, and the size and location of new wildflower meadows. We show effects of conversion of roadside plantings-from exotic shrubs into wildflower meadows-on (1) the abundance of 13 arthropod taxa-Opiliones, Araneae, Isopoda, Collembola, Orthoptera, Aphidoidea, Auchenorrhyncha, Heteroptera, Coleoptera, Nematocera, Brachycera, Apocrita, Formicidae-and (2) changes in maintenance costs. We assessed the influence of vegetation type (meadow vs. woody), meadow age, size, location (distance to city boundary), and mowing regime. We found many, but not all, arthropod taxa profiting from meadows in terms of arthropod activity abundance in pitfall traps and arthropod density in standardized suction samples. Arthropod number in meadows was 212% higher in pitfall traps and 260% higher in suction samples compared to woody vegetation. The increased arthropod number in meadows was independent of the size and isolation of green spaces for most taxa. However, mowing regime strongly affected several arthropod taxa, with an increase of 63% of total arthropod density in unmown compared to mown meadow spots. Costs of green space maintenance were fivefold lower for meadows than for woody vegetation. Our study shows that (1) many different arthropod taxa occur in roadside vegetation in urban areas, (2) replacement of exotic woody vegetation by native wildflower meadows can significantly increase arthropod abundance, especially if meadow management permits temporarily unmown areas, and (3) maintenance costs can be considerably reduced by converting woody plantings into wildflower meadows. Considering many groups of arthropods, our study provides new insights into possible measures to support arthropods in urban environments.

The present study was conducted to analyze the effects of traffic volumes on Cd, Cu, Pb, Ni, and Zn contents in roadside soils and in two dominant herbaceous species ( Achillea wilhelmsii and Cardaria draba ) along highways and to evaluate the dynamic characteristics of these elements and their accumulation by the aerial parts and roots of these herbaceous species. The plant samples were collected along 700 m of a 9-km segment of each of the three major highways in Hamedan Province (West Iran) with different traffic volumes: Hamedan-Goltapeh (HG), Hamedan-Razan (HR), and Hamedan-Kermanshah (HK). The results indicated that the mean contents of Cd, Cu, Pb, Ni, and Zn in the soil samples were 0.26, 18.74, 14.98, 18.21, and 62.25 mg kg −1 , respectively. Furthermore, the mean contents of elements (mg kg −1 ) in aerial parts of A. wilhelmsii were 0.16 for Cd, 4.52 for Cu, 1.91 for Pb, 1.70 for Ni, and 44.80 for Zn, while in the aerial part samples of C. draba , the concentrations (mg kg −1 ) and the mean contents were 0.16, 2.29, 2.58, 1.60, and 31.29, respectively. This meant that the traffic volume affected the contents of the metals in the soil and the herbaceous species. The metal content in herbaceous tissues varied significantly between plant species. A. wilhelmsii tended to accumulate the metals in the roots while C. draba retained them mostly in the aerial parts. The significant positive correlations of Cd, Cu, Ni, and Zn content in root and aerial parts of the herbaceous plant with those found in the soil samples showed the potential of the studied species for application in biomonitoring studies. Comprehensive analysis (effect of traffic volumes and relationships between the content of elements in plant tissues and soil samples) indicated that Cu in both herbaceous plants was mainly derived from soil, while A. wilhelmsii absorbed Cd and C. draba absorbed Zn mainly through the stomata from atmospheric depositions. Without considering atmospheric depositions due to intense traffic volumes, in A. wilhelmsii , the translocation factor (TF) values of Cu and Zn were 1.06 and 1.44, respectively and in C. draba , the TF values of Cd, Cu, and Pb were 1.06, 1.09, and 1.13, respectively, thus suggesting that both herbaceous species had high potentials for transferring metals from the roots to aerial parts.

As robots acquire increasingly sophisticated skills and see increasingly complex and varied environments, the threat of an edge case or anomalous failure is ever present. For example, Tesla cars have seen interesting failure modes ranging from autopilot disengagements due to inactive traffic lights carried by trucks to phantom braking caused by images of stop signs on roadside billboards. These system-level failures are not due to failures of any individual component of the autonomy stack but rather system-level deficiencies in semantic reasoning. Such edge cases, which we call semantic anomalies, are simple for a human to disentangle yet require insightful reasoning. To this end, we study the application of large language models (LLMs), endowed with broad contextual understanding and reasoning capabilities, to recognize such edge cases and introduce a monitoring framework for semantic anomaly detection in vision-based policies. Our experiments apply this framework to a finite state machine policy for autonomous driving and a learned policy for object manipulation. These experiments demonstrate that the LLM-based monitor can effectively identify semantic anomalies in a manner that shows agreement with human reasoning. Finally, we provide an extended discussion on the strengths and weaknesses of this approach and motivate a research outlook on how we can further use foundation models for semantic anomaly detection. Our project webpage can be found at https://sites.google.com/view/llm-anomaly-detection.

Roadside LiDAR-based perception has the potential to provide comprehensive and high-precision sensing information, which is of critical importance for safety and efficiency in intelligent transportation systems. However, when deploying 3D roadside perception methods in new traffic scenarios, data acquisition and annotation are labor-intensive and costly. Unsupervised Domain Adaptation (UDA) can extract knowledge from existing data and transfer it to unknown domains for self-supervised model training, making it a promising approach to reduce the need for labeled roadside perception data. However, the effectiveness of UDA is constrained by the generalization capability of the pre-trained model from source domain. Foundational pre-trained large models such as SAM hold the potential for achieving cross-domain generalization across diverse scenarios. Inspired by this sight, this paper proposes a Segment Anything Model Transfer (SAMTrans) approach to enhance model performance. Firstly, we design a Bird’s-Eye View (BEV) pseudo-image generation method to enable Segment Anything Model (SAM) to interpret point cloud data. Additionally, we introduce a pseudo label fusion strategy based on confidence scores and Gaussian kernel density estimation. Evaluated on popular 3D roadside benchmarks, the proposed method demonstrates strong competitiveness in improving upon state-of-the-art performance. For instance, when integrated with the ST3D framework on the nuScenes to V2X-Seq task, our method boosts the 3D Average Precision (AP3D) from 30.1% to 40.6% under the Easy difficulty setting.

An emergency can occur at any time. To overcome that emergency efficiently, we require seamless movement on the road to approach the destination within a limited time by using an Emergency Vehicle (EV). This paper proposes an emergency vehicle management solution (EVMS) to determine an efficient vehicle-passing sequence that allows the EV to cross a junction without any delay. The proposed system passes the EV and minimally affects the travel times of other vehicles on the junction. In the presence of an EV in the communication range, the proposed system prioritizes the EV by creating space for it in the lane adjacent to the shoulder lane. The shoulder lane is a lane that cyclists and motorcyclists will use in normal situations. However, when an EV enters the communication range, traffic from the adjacent lane will move to the shoulder lane. As the number of vehicles on the road increases rapidly, crossing the EV in the shortest possible time is crucial. The EVMS and algorithms are presented in this study to find the optimal vehicle sequence that gives EVs the highest priority. The proposed solution uses cutting-edge technologies (IoT Sensors, GPS, 5G, and Cloud computing) to collect and pass EVs’ information to the Roadside Units (RSU). The proposed solution was evaluated through mathematical modeling. The results show that the EVMS can reduce the travel times of EVs significantly without causing any performance degradation of normal vehicles.

Aiming at the safe overtaking in the autonomous driving of the intelligent vehicle, the overtaking method of intelligent vehicle under vehicle road coordination is advanced. First, according to the current vehicle movement state and the surrounding environment, the overtaking behaviour decision is proposed. Second, with the support of vehicle-road collaboration technology, the principle of the overtaking method is analysed. The position and speed of the associated vehicles within the real-time range are collected with the Road Side Unit (RSU). The RSU communicates with the autonomous vehicle in two-way, and vehicle selects overtaking behaviour. Finally, the validity of this method is verified by the cases, the overtaking method includes overtaking in the borrowing lane, the extended overtaking lane and the emergency lane. The research results of this study not only provide a basis for the method of the safe overtaking in the process of the intelligent vehicle autonomous driving, but also lay a foundation for the intelligent transportation system (ITS).