Road Type Research Articles

Response of road erosion to hydrological connectivity under a heavy rainstorm in an agricultural watershed on the Loess Plateau

Road network extension is closely associated with soil erosion in watersheds during frequent extreme rainfall events. Identifying potential risk areas that actively deliver surface runoff to road structures and channel networks is critical for comprehending hydrological processes and controlling road erosion. In this study, the road erosion (landslides, collapses, scour pits, and gullies on monitored roads) on the Loess Plateau during a heavy rainstorm was analyzed using field survey and unmanned aerial vehicle (UAV) data. We defined hydrological connections as the intersections of roads and channel networks, and the activated hydrological connections represent hydrological connectivity. The results of this study highlight the exacerbating effect of hydrological connectivity on road erosion during a rainstorm in an agricultural watershed in the Loess Plateau region. The results showed that the road erosion intensity ranged from 2.16 × 102 to 1.02 × 104 t/ha. Strong hydrological connectivity resulted in average soil erosion amounts of landslides, collapses, scour pits, and gullies in the downslope area that were 2.16, 1.78, 5.46, and 6.98 times greater than those without hydrological connectivity, respectively. The presence of hydrological connectivity generated a 1.88 times higher road surface erosion intensity than that without hydrological connectivity. The Upslope-Road connections created the most significant enhancement effect on road erosion, and up to 65.37 % landslide erosion and 41.97 % collapse erosion amounts were influenced by these connections.The enhancement effect of Upslope–Road connections on road erosion varied with different road types, this impact of Upslope–Road connections on road erosion were only observed on the landslide erosion of petroleum transport roads and the collapse erosion of agricultural roads. Conversely, Road–Downslope connections did not significantly impact road erosion (p > 0.05). To effectively control the negative effect of road erosion, hydrological connections should be identified and regulated to impede hydrological connectivity activation during rainstorms by modifying tillage activities, improving road maintenance, and strengthening drainage systems.

Study of genetic variations of oat varieties under insufficient and insufficient irrigation

A field experiment was carried out in the fields belonging to the Faculty of Agricultural Engineering Sciences /University of Baghdad-Jadriya during the season 2020-2021 in order to study the genetic variations of varieties of oats under lack of irrigation. The experiment was carried out using the design of complete random sectors and the distribution of splintered panels and two factors, the first was the treatment of irrigation at the arrival of humidity ( 50%, 25% and 10%), while the second factor was the varieties (Genzania, Anatolia, plmulao Algoda and healing ) the experiment was carried out three times. The results showed that the third irrigation level exceeded the number of days of planting up to 75% flowering in the second irrigation treatment gave the highest averages for all other qualities studied, and the V4 variety gave the highest average for all qualities except for the weight of 250 grains. . While there was a significant significant difference for the second irrigation level in the values of genetic variation for all accuracy except for the single trait, while we note that accuracy is the number of branches and the total number of branches only and it was highly effective at the first irrigation level. Later, the effect was good at the second irrigation level. The difference is in appearance. It is noted that the values at the second level of road types, except for the obtained characteristic and the nature ratio, gave the second level of irrigation the highest average for most types of resistance. Why is it not different from what was mentioned above, which did not adopt a study of scientific variations in the expected researchers, as you would expect, the second irrigation and the V4 variety, which recorded the highest averages for the characteristics that were studied.

Safety Assessment of Suburban-Type Arterial Roadways: New Findings Using Heterogeneity Models

This study investigates the safety performance of suburban-type roads (STRs), a category of medium-speed arterial roads characterized by high vehicular traffic and limited pedestrian and cyclist mobility. Such roadways present unique challenges in road safety, often overlooked in traditional road design focused predominantly on driver needs. By employing a correlated random parameter ordered probit model to analyze police-reported crash severity data from STRs, this research uncovers critical insights into the factors influencing crash injury severities on these roads. Our findings highlight the nuanced impact of various dimensions, such as road design, traffic volume, and environmental conditions, on STR safety; the results offer a detailed understanding of crash contexts on STRs and point toward targeted interventions for enhancing road safety. These interventions include the potential redesign of roadways to accommodate diverse user needs and the implementation of speed regulation measures tailored to specific road characteristics. This research contributes significantly to the existing body of knowledge on suburban road safety, providing evidence-based recommendations for improving the overall safety of these increasingly prevalent road types. The implications of these findings are far-reaching, offering a foundation for future studies to explore diverse methodologies in road safety analysis and to extend this research to other geographic contexts.

Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning

Numerous solutions are proposed for the Traffic Signal Control (TSC) tasks aiming to provide efficient transportation and alleviate traffic congestion. Recently, promising results have been attained by Reinforcement Learning (RL) methods through trial and error in simulators, bringing confidence in solving cities' congestion problems. However, performance gaps still exist when simulator-trained policies are deployed to the real world. This issue is mainly introduced by the system dynamic difference between the training simulators and the real-world environments. In this work, we leverage the knowledge of Large Language Models (LLMs) to understand and profile the system dynamics by a prompt-based grounded action transformation to bridge the performance gap. Specifically, this paper exploits the pre-trained LLM's inference ability to understand how traffic dynamics change with weather conditions, traffic states, and road types. Being aware of the changes, the policies' action is taken and grounded based on realistic dynamics, thus helping the agent learn a more realistic policy. We conduct experiments on four different scenarios to show the effectiveness of the proposed PromptGAT's ability to mitigate the performance gap of reinforcement learning from simulation to reality (sim-to-real).

A Study on the Dust Retention Effect of the Vegetation Community in Typical Urban Road Green Spaces—In the Case of Ying Tian Street in Nanjing City

This study aimed to investigate the association between the plant community structure, leaf surface microstructure, nutrient element content, and the dust-retention capacity of garden plants in urban road green spaces. The plant community located along Ying Tian Street in Nanjing City was selected as the focal point of the investigation. Random sampling was performed on the urban road green spaces, determining the amount of dust trapped in plant leaves. Subsequently, the microstructure of the leaf surface was observed, and the content of nutrient elements in the plant leaves was determined. The study also entailed an analysis of the interrelationships between the leaf surface microstructure, plant nutrient element content, and the dust-retention ability of the plants. The findings of this study revealed notable variations in the dust-retention capacity of garden plants and the community structure observed along Ying Tian Street. Among the tree species, Cedrus deodara and Ginkgo biloba exhibited a remarkable dust-retention ability per unit leaf area. Among the shrub species, Abelia × grandiflora and Loropetalum chinense displayed a strong dust-retention capacity per unit leaf area. Similarly, Ophiopogon japonicus and Cynodon dactylon exhibited a robust dust-retention ability per unit leaf area among the herbaceous plants. Furthermore, the dust-retention ability of the plants exhibited a strong positive correlation with the dimensions of leaf stomata, specifically the length and width, while displaying a moderate positive correlation with the width of grooves on the upper and lower surfaces of the leaves. Conversely, the thickness of the leaves did not exhibit a significant correlation. Additionally, the nitrogen content of the leaves exerted a significant influence on the dust-retention ability of the plants (p < 0.05), although the phosphorus and potassium content factors did not exhibit a significant influence (p > 0.05). Based on the findings, it is recommended to prioritize the utilization of plants with robust dust-retention abilities, such as C. deodara, A. grandiflora, O. japonicus, and C. dactylon, and implement a mixed planting approach encompassing a combination of trees, shrubs, and herbaceous plants within urban road green spaces.

Research on Prediction Model of Particulate Matter in Dalian Street Canyon

In urban areas where populations commonly reside, particle mass concentrations in street canyons can pose significant risks to human health. This study aimed to investigate the diffusion mechanism of particle mass concentrations in urban street canyons by developing and applying a prediction model based on the mathematical modeling of physical processes. The prediction model considered factors such as the influence of traffic wind, natural wind, traffic flow, and other relevant variables influencing particle mass concentrations in street canyons. Field measurements were conducted in Dalian, China, to verify the feasibility of the model. Particle mass concentrations, traffic flow, temperature, relative humidity, and wind speed were measured on Shichang Street (a two-lane one-way road), Tangshan Street (a four-lane two-way road), and Shengli Road (a six-lane two-way road). The results indicated that the majority of traffic peaks occurred around 19:00 on all road types. The PM1.0 mass concentration was well diluted on the four-lane two-way road, with the least dilution observed on the two-lane one-way road. A strong correlation between the particle mass concentrations and traffic flow was discovered. Furthermore, a prediction model was established, accurately predicting the particle mass concentrations when the prediction step was from 5 to 15 s. The coefficient of determination (R2) between the predicted and measured values on the two-lane one-way road, four-lane two-way road, and six-lane two-way road was 0.9319, 0.6582, and 0.9238, respectively. Additionally, the prediction model allowed for a detailed analysis of traffic flow limitations, corresponding to the recommended World Health Organization (WHO) PM2.5 values. Overall, the findings of this study offer valuable insights for forecasting particle exposure levels in street canyons.

Hierarchical Reward Model of Deep Reinforcement Learning for Enhancing Cooperative Behavior in Automated Driving

In recent years, studies on practical application of automated driving have been conducted extensively. Most of the research assumes the existing road infrastructure and aims to replace human driving. There have also been studies that use reinforcement learning to optimize car control from a zero-based perspective in an environment without lanes, one of the existing types of road. In those studies, search and behavior acquisition using reinforcement learning has resulted in efficient driving control in an unknown environment. However, the throughput has not been high, while the crash rate has. To address this issue, this study proposes a hierarchical reward model that uses both individual and common rewards for reinforcement learning in order to achieve efficient driving control in a road, we assume environments of one-way, lane-less, automobile-only. Automated driving control is trained using a hierarchical reward model and evaluated through physical simulations. The results show that a reduction in crash rate and an improvement in throughput is attained by increasing the number of behaviors in which faster cars actively overtake slower ones.

Analysis of E-Scooter Crashes in the City of Bari

The remarkable impact that e-scooters have had on the transportation system drives research on this phenomenon. The widespread use of e-scooters also poses several new safety issues, which should be necessarily studied. The aim of this paper points in this direction, investigating the main contributing factors, causes, and patterns of recorded e-scooter crashes, considering also different crash types and severity, using the City of Bari (Italy) as a case study. The crash dataset based on police reports and referring to the period July 2020–November 2022 (i.e., the first period of e-scooter implementation in the City of Bari) was investigated. Crashes were clustered according to several variables. No fatal crashes occurred, even though crashes mostly resulted in injuries (70%). Considering road type, divided roads were found to be less safe than undivided ones, due to higher mean speeds than on other roads and to a less constrained e-scooter driving behavior. Calm (off-peak) daytime hours seem to lead to more frequent e-scooter crashes with respect to both peak and nighttime hours, even if the latter hours are associated with an increased severity. Once controlled for exposure, season, lighting conditions, and the private/sharing ratio do not seem influential. E-scooters are more prone to be involved in single-vehicle and pedestrian crashes at segments than other vehicles, but they show similar crash trends than other vehicles (i.e., angle crashes) at intersections. As emerged from traffic surveys, not all e-scooter users were found to use cycle paths. Combining this information with crash data, it seems that not using cycle paths is considerably less safe than using them. Besides engineering measures and policies, awareness campaigns should be promoted to elicit safe users’ behavior and to tackle the several violations and misbehaviors emerging from the crash data.

Self-Explaining Roads: Effects of road design on speed choice

One of the leading principles for road design is the notion of Self-Explaining Roads(SER). According to this notion, the design and layout of the road environment should automatically elicit behavior that is appropriate for that type of road. The current study investigated the effects of the road environment (i.e., the presence of particular road elements such as the presence/absence of edge lines, and a physical separation between driving lanes) on driving speed choice in the Netherlands for roads in- and outside the city limits (ICL and OCL). A total of 462 participants (all car drivers) were exposed to either 152 ICL or 152 OCL pictures of road environments for either a short (300 ms in Experiment 1, 200 ms in Experiment 2) or long duration (1500 ms) and indicated as fast as possible which speed they would drive given the road environment they viewed. All images were labeled with respect to the presence or absence of particular road elements. A linear mixed model was used to determine the effects of road elements on speed choice and response time. The results showed that the presence of certain road elements impacted the speed chosen. For example, inside the city limits, relative to no bicycle lane, a bicycle lane painted on the road reduced driving speed while a separate bike lane increased the speed chosen. Also, central line markings (relative to no line marking) and a road made of asphalt (relative to pavers) were associated with higher speeds. Outside city limits, having multiple lanes (versus one lane) was related to higher speeds and having two driving directions without separation (versus a one-way road) was related to lower speed choices. Importantly, exposure duration (200 /300 ms versus 1500 ms) only had a marginal effect, indicating that road users generally only need a brief glimpse of the road to be able to decide what speed to drive. Consistent with the principles of SER, we conclude that categorization and the associated speed decision is fast, operating within a single glance and is impacted by the road elements present in the environment. Finally, we believe our method constitutes a valuable tool in road design, as it allows one to efficiently and effectively gauge the impact of various road elements in large population samples.

A Study on Reducing Traffic Congestion in the Roadside Unit for Autonomous Vehicles Using BSM and PVD

With the rapid advancement of autonomous vehicles reshaping urban transportation, the importance of innovative traffic management solutions has escalated. This research addresses these challenges through the deployment of roadside units (RSUs), aimed at enhancing traffic flow and safety within the autonomous driving era. Our research, conducted in diverse road settings such as straight and traffic circle roads, delves into the RSUs’ capacity to diminish traffic density and alleviate congestion. Employing vehicle-to-infrastructure communication, we can scrutinize its essential role in navigating autonomous vehicles, incorporating basic safety messages (BSMs) and probe vehicle data (PVD) to accurately monitor vehicle presence and status. This paper presupposes the connectivity of all vehicles, contemplating the integration of on-board units or on-board diagnostics in legacy vehicles to extend connectivity, albeit this aspect falls beyond the work’s current ambit. Our detailed experiments on two types of roads demonstrate that vehicle behavior is significantly impacted when density reaches critical thresholds of 3.57% on straight roads and 34.41% on traffic circle roads. However, it is important to note that the identified threshold values are not absolute. In our experiments, these thresholds represent points at which the behavior of one vehicle begins to significantly impact the flow of two or more vehicles. At these levels, we propose that RSUs intervene to mitigate traffic issues by implementing measures such as prohibiting lane changes or restricting entry to traffic circles. We propose a new message set in PVD for RSUs: road balance. Using this message, RSUs can negotiate between vehicles. This approach underscores the RSUs’ capability to actively manage traffic flow and prevent congestion, highlighting their critical role in maintaining optimal traffic conditions and enhancing road safety.

Quantifying the Impact of Battery Degradation in Electric Vehicle Driving through Key Performance Indicators

As the Electric Vehicle market grows, understanding the implications of battery degradation on the driving experience is key to fostering trust among users and improving End of Life estimations. This study analyses various road types, charging behaviours and Electric Vehicle models to evaluate the impact of degradation on the performance. Key indicators related to the speed, acceleration, driving times and regenerative capabilities are obtained for different degradation levels to quantify the performance decay. Results show that the impact is highly dependent on the road type and nominal battery capacity. Vehicles with long and medium ranges show a robust performance for common driving conditions. Short-range vehicles perform adequately in urban and rural road conditions, but on highways, speed and acceleration reductions of up to 6.7 km/h and 3.96 (km/h)/s have been observed. The results of this study suggest that degradation should not be a concern for standard driving conditions and mid- and long-range vehicles currently dominate the market. In addition, the results are used to define a functional End of Life criterion based on performance loss, beyond the oversimplified 70–80% State-of-Health threshold, which does not consider individual requirements.

Identification and quantification of trace metal(loid)s in water-extractable road dust nanoparticles using SP-ICP-MS

Resuspension of road dust is a major source of airborne particulate matter (PM) in urban environments. Inhalation of ultrafine particles (UFP; < 0.1 μm) represents a health concern due to their ability to reach the alveoli and be translocated into the blood stream. It is therefore important to characterize chemical properties of UFPs associated with vehicle emissions. We investigated the capability of Single-Particle ICP-MS (SP-ICP-MS) to quantify key metal(loid)s in nanoparticles (NPs; < 0.1 μm) isolated from road dust collected in Toronto, Canada. Water extraction was performed to separate the <1-μm fraction from two different road dust samples (local road vs. arterial road) and a multi-element SP-ICP-MS analysis was then conducted on the samples' supernatants. Based on the particle number concentrations obtained for both supernatants, the metal(loid)-containing NPs could be grouped in the following categories: high (Cu and Zn, > 1.3 × 1011 particles/L), medium (V, Cr, Ba, Pb, Sb, Ce, La), low (As, Co, Ni, < 4.6 × 109 particles/L). The limit of detection for particle number concentration was below 5.5 × 106 particles/L for most elements, except for Cu, Co, Ni, Cr, and V (between 0.9 and 7.7 × 107 particles/L). The results demonstrate that road dust contains a wide range of readily mobilizable metal(loid)-bearing NPs and that NP numbers may vary as a function of road type. These findings have important implications for human health risk assessments in urban areas. Further research is needed, however, to comprehensively assess the NP content of road dust as influenced by various factors, including traffic volume and speed, fleet composition, and street sweeping frequency. The described method can quickly characterize multiple isotopes per sample in complex matrices, and offers the advantage of rapid sample scanning for the identification of NPs containing potentially toxic transition metal(loid)s at a low detection limit.

PERFORMANCE ANALYSIS OF JALAN PIET A. TALLO IN KUPANG CITY

Jalan Piet A. Tallo is a major arterial road connecting several areas with the city center of Kupang. The large traffic flow that passes through Jalan Piet A. Tallo, resulting in traffic congestion during rush hour, is the background of this research. This research aims to determine the right alternative solution to reduce traffic congestion on Jalan Piet A. Tallo. This research uses a descriptive method by obtaining data directly from surveys and analyzing the data according to the guidelines of the 1997 Indonesian Road Capacity Manual (MKJI). Then, the analysis produces values for the degree of saturation (DS) and level of service (LOS) of the road. Overall, from the results of this research, traffic jams are caused by several factors; namely, there is a narrowing of the road from road type 4/2 D to type 2/2 UD, many vehicles enter and exit from Jalan M. Sabaat to Jalan Piet A. Tallo, and many drivers still violate traffic signs. Therefore, an alternative that can be done is to contact the relevant authorities about the prohibition of right turns onto Jalan M. Sabaat and the need to install road dividers from STA 0+300 to STA 0+550. Apart from this, the road can be widened by 1.7 m in the inner city lanes so that the degree of saturation (DS) during the afternoon rush hour can be reduced by 12.67% to 0.875. Another alternative is to change the road geometry from type 2/2 UD to 4/2 D and build a new connecting bridge. Thus, the degree of saturation (DS) during the afternoon rush hour decreases by 52.89% to 0.472. This value shows that the traffic flow is stable and the speed is controlled. Keywords: Road Performance, Congestion, Piet A. Tallo Road, Kupang City

Roll stability of all-terrain forklift trucks considering different road types

All-terrain forklift trucks often work on complex terrain such as soft and muddy terrain in the wild, making them prone to rolling and rollover. In this work, a seven-degree-of-freedom nonlinear dynamic model and a wheel-ground interaction model between resilient wheels and soft/hard road surfaces based on the structural characteristics of the articulated steering + swinging bridge of an all-terrain forklift truck are established. The mechanism of all-terrain forklift trucks’ roll instability and a simulation comparison of four types of road models (rigid, sandy, loam, and clay) are analyzed. All-terrain forklift trucks are least prone to rolling when being driven on rigid roads. On soft roads, sand brings the greatest risk of rollover, followed by loam and clay. The roll stability indexes HR-LTR and SR-LTR considering different road surfaces are proposed. The safety limit of roll stability based on the indexes is divided, and an anti-rollover control strategy based on road surface recognition is proposed. On the basis of the road surface recognition results, corresponding stability index calculation methods and actuator control algorithms are selected. Simulation and experimental results show that with the use of no control and TTR-based hierarchical control, the forklift truck yaw velocity is reduced by up to 73.31% and 63.52%, respectively, and the roll angle is reduced by up to 72.7% and 48.27%, respectively. The proposed roll stability index and anti-rollover control strategy for different road surfaces can effectively improve the roll stability of all-terrain forklift trucks when being driven on different complex road surfaces, reduce the risk of rollover, and improve the rollover stability of all-terrain forklift trucks on different complex roads, thereby improving the safety of all-terrain forklift trucks.

PEAK HOUR FACTOR AT URBAN ROAD NETWORK SYSTEM WITH FIXED HOURLY INTERVAL AND MOVING HOURLY INTERVAL (A CASE STUDY AT URBAN ROAD IN CIMAHI CITY)

The Peak Hour Factor (PHF) is an important variable for determining road capacity. The value of PHF will vary greatly depending on location characteristics and classification of road functions. This study aims to obtain the estimated value of PHF in the urban road network system with variations in the classification of functions and types of roads. In addition this study also aims to obtain a method of approaching the PHF value near to fluctuations in traffic flow which has limited resources for surveys based on the duration specified in the traffic survey guidelines in Indonesia. The method used is descriptive statistical analysis and parametric test using Independent T sample test. The PHF is calculated based on Fixed Hourly Interval and Moving Hourly Interval and their inverse. The results of the study show the value of PHF in the road network system in urban areas for variations function and type of road that is in the range of 0.79 to 0.98 with an average of 0.91. Other findings show that the inverse method of Moving Hourly Interval can be used as an approach to obtain the PHF value under conditions of resource constraints

Extremity Injuries in Pillion Riders of Fatal 2-Wheeler Road Traffic Accidents: An Autopsy-Based Study.

The extremities are the important anatomical regions that are disproportionately injured during road traffic accidents in poor and medium-income countries. The data regarding extremity injuries, particularly in pillion passengers, are minimal globally. We analyzed the pattern of extremity injuries and their association with various parameters such as collision type, seating position, road type, and type of vehicle. This was a hospital-based cross-sectional study. The pillion riders of fatal motorized 2-wheeler road accident cases that were subjected to autopsy were studied over a period of 2 years. We analyzed 73 cases of pillion riders. Females (60%) were the most commonly affected group. The upper extremities were injured in 55 cases (75.3%) and the lower extremities in 49 cases (67.1%). The shoulder (41%), hand (31.3%), and elbow (23.2%) were the predominantly injured anatomical regions of the upper limb. In the lower extremity, knee (41%) and foot (21.9%) were mostly affected. Forty-nine (67.1%) and 48 (65.8%) cases had abrasions in the upper limbs and lower limbs, respectively, followed by lacerations and contusions. Upper extremity injuries are more common compared with lower extremity. The majority of the pillion riders were seated in a side-saddle position, and self-fall from the bike without any collision with other vehicles was the most common mode of injury. Thus, appropriate seating posture with safety gear can be used to reduce extremity injuries.

Solar roads – a new potential renewable energy for Great Britain

ABSTRACT Global warming has become a much more realised issue and an immediate threat, accelerating due to the anthropogenic carbon release associated with escalating energy demand. Consequently, pressures are building up to reduce anthropogenic carbon footprint by employing renewable energy resources, among which solar energy is the main. Solar roads are a new and innovative concept as they do not require land to be specifically allocated. This is because the road infrastructure already exists. Therefore, this emerging technique of embedding solar panels into pavements and roadways is becoming a more attractive proposition. However, a hurdle to its successful application is the lack of knowledge regarding its feasibility and viability. This study aims to evaluate the potential of solar roads to inform future feasibility and viability studies in varying contexts and implications. Within the scope of this study, the case of Great Britain is considered to evaluate the potential of how much energy can be generated via the solar roads technology that can be embedded in the country’s road infrastructure. A mathematical exercise is performed in which calculations are executed to develop a basic numerical model of the potential. Therefore, the study is quantitative, and the factors considered include seasonal changes, average daily traffic covering roads, tree and building shades, road types and sizes, solar irradiance, solar panel types, and alike. It is estimated that solar roads may supply up to 96.42% of the UK's total electricity, which is a substantially promising potential.

Blockage Effects of Road Bridge on mmWave Channels for Intelligent Autonomous Vehicles

Vehicular communication and sensing technologies are key to enabling 6G Intelligent Autonomous Transportation Systems (IATS). With the introduction of massive sensors and artificial intelligence (AI) fusion applications, IATS is needed to support data transmission rates up to 10 Gb/s. Millimeter-wave (mmWave) technology has attracted extensive attention owing to abundant spectrum resources, which can support the timely transmission of massive data. However, performance degradation of mmWave due to signal blockage has become one of the critical technical challenges. Road bridges as one of the common obstacles in urban scenarios, which has severe blockage effects on communication links. Therefore, this paper comprehensively studies the impact of road bridge blockage effects on mmWave vehicle-to-infrastructure (V2I) links and proposes an empirical model that can accurately characterize the bridge blockage effect. First, we use a self-developed mmWave channel sounder to carry out channel measurements on typical urban roads. Measurement results indicate that a maximum extra propagation loss of up to 23 dB is caused by road bridges. In addition, to address the deficiencies of existing propagation prediction models, the Single Road Bridge (SRB) model is proposed in this work. This model reveals for the first time the extra propagation loss caused by the road bridge to the channel. Compared with existing models, the SRB model can make the mean absolute error (MAE) and root mean square error (RMSE) within 5 dB. The proposed SRB model is of great value for accurately simulating real-world road bridge blockage events when designing future IATS.

Tackling the Menace of Speed Violation among Motorcyclists in Nigeria: A Policy Brief

Motorcycle accidents in Nigeria are of particular concern because they rank among the top two vehicle causes of road traffic accidents in the country. Also, the motorcycle plies every road type in the country, and over-speeding tops its causes of accidents, a cause that also ranks top in the human causes of all accidents in all vehicle types. The partial or complete ban on the use of motorcycles has proven to be ineffective in various regions of the country due to its role in sustaining the livelihood of many Nigerians. However, on a policy level, motorcycle accidents have been neglected in Nigeria despite changing policies and intervention programmes since the pre-1939 era of Road Traffic Management. Currently, it is paramount to investigate Motorcycle accidents, emphasizing over-speeding as an emergency. Therefore, the aim of this review is to investigate any existing policy that tackles motorcycle accidents, whether at the national or regional level and develop it to make recommendations for an independent policy that should urgently regulate the motorcycle means of transportation and subsequently tackle motorcycle accidents in Nigeria.

RDPGNet: A road extraction network with dual-view information perception based on GCN

Automatic road extraction from satellite images is a popular research hotspot topic in the field of remote sensing. However, the complexity of road features and structures hinders their precise extraction. Road features have different saliency due to the occlusion of other objects and diverse road types. Road structures are scattered across various locations and affected by geographical conditions, population distribution, and transportation demands. These issues render current methods based on convolutional neural networks prone to fragmented and incomplete road extraction results. To address these issues, a Road Extraction Network with Dual-View Information Perception Based on GCN (RDPGNet) is proposed, where the GCN-based dual-view perceptron (GDVP) uses the superior information interaction capability of the GCN to explore road information under a dual view. In the GDVP, we first design the road feature saliency graph reasoning module (RFSG) and road structure homogeneous space graph reasoning module (RSHS) based on the features and structures. The RFSG obtains the similarity of road feature information in different regions as a similarity matrix to participate in the graph reasoning process, ensuring that equal treatment is given to regions with varying saliency levels. The RSHS projects the road structure onto a corresponding homogeneous space while aggregating and interacting with information through graph convolution, thereby enhancing the network’s perception of roads in diverse locations. Second, because multi-view information usually shares a potential common representation, a multi-view information fusion and alignment strategy (MVFA) is designed to comprehensively model road information. Experimental results obtained from two public datasets indicate that RDPGNet outperforms other state-of-the-art networks.