Traffic Road Research Articles

SRNN-RSA: a new method to solving time-dependent shortest path problems based on structural recurrent neural network and ripple spreading algorithm

AbstractInfluenced by external factors, the speed of vehicles in the traffic network is changing all the time, which makes the traditional static shortest route unable to meet the real logistics distribution needs. Considering that the existing research on time-dependent shortest path problems (TDSPP) do not include the topological information of the traffic network, it is unable to reflect the spatial and temporal dynamic characteristics of the traffic network during the vehicle travelling process and is unable to update to the changes of the vehicle speed in real time, and poor scalability. Therefore, we used the structural RNN (SRNN) model containing topological information of the road network is used to predict time-varying speeds in the traffic road network. We proposed an SRNN-RSA framework for solving the TDSPP problem, which achieves a synergistic evolution between the real-time vehicle speed change process and the RSA solving process, and the scalability of the proposed SRNN-RSA is demonstrated and validated using different real data. Compared with other algorithms, the results show that SRNN-RSA has the lowest error with the actual situation, which can balance the solution accuracy and calculation speed and is more consistent with the real traffic road network, with better stability and expandability.

The influences of human factors on road accidents in Romania in the period 2013 – 2022.

The main objective of the study is to establish a correlation between the main causes of road traffic accidents in Romania and “human factors”. The aim of the study is to show that accidents are attributed exclusively to human mistakes in a higher percentage than. The data used in the research are taken from the database of the Traffic Police-for Road traffic accidents during 2013-2022 and from the database of the Romanian Road Authority - ARR - for road safety inspections during 2018-2021. From the perspective of human factors, the research will give answers on the nature of the causes that lead to the production / favouring of road accidents in Romania and will propose solutions to address road risks to improve road infrastructure safety. As a case study, the research will focus on the human factors that contribute to road accidents on National Road 2. Compared to other national roads, on DN 2 road accidents occur with the highest number of fatalities, approximately 8.28% of all road deaths nationwide. The analysis of findings from road safety inspection reports carried out on DN2 shows that there is a correlation between road infrastructure non-conformities and causes of road traffic accidents.

Large rectangular cross-section tunnel undercrossing urban road by micro pipe jacking and joint assembly structure (MPJ & JAS) method in soft soils

With the continuous construction of urban traffic roads, more and more new roads are cut off by existing roads to form “dead end roads”. There is an urgent need for a trenchless method suitable for urban ultra-shallow overburden to build the undercrossing tunnel. To solve this problem, this paper proposed the micro pipe jacking and joint assembly structure (MPJ & JAS) method, which has the characteristics of shallow burial depth, low cost, short construction time, flexible cross-section setting and high space utilization. The MPJ & JAS method construct a large cross-section tunnel through assembling small cross-section elements, quite different from traditional methods. Therefore, this paper designed a CT-shaped integrated joint, the mechanical performance of which was verified and clarified by tensile test. The bending test and finite element (FE) analysis proved the reliability of MPJ & JAS tunnel structure, and confirmed the structure performances such as the failure models, crack behaviors, load–deflection response and stress–strain distribution. Moreover, the influences of the steel plate thickness, concrete strength and shear connector spacing were determined by the FE analysis. On the basis of test results and reasonable assumptions, a theoretical design method considering the influence of the CT-shaped integrated joint was proposed, which can effectively predict the bending strength of the MPJ & JAS tunnel structure with an error of less than 10%. Finally, in view of the characteristics of the MPJ & JAS method, the suitable micro pipe jacking machine, soil reinforcement measure, hydraulic traction construction technology, high-precision guidance system and concrete construction quality detection method based on the phased array ultrasonic imaging technology were developed, supporting the accurate and efficient construction of the MPJ & JAS tunnel.

Analysis of Road Infrastructure Problems in Urban City, Karachi

Road infrastructure is a significant part of the economic development of countries. Better performance of road communication system is significant part of all over the world. Developed countries make a significant effort for designing a well-managed traffic arrangement for the individuals. Furthermore, an efficient transport system is the key element for improving the country's economic conditions. Sustainable and efficient traffic management system is the backbone for humanizing the economic growth of developing countries. In case of Pakistan, Karachi is the main industrial hub for whole country's economy and accommodates enormous number of migrants. Due to this reason the population of city is increasing day by day. This paper presents the current situation of road infrastructure troubles in Karachi. The urban city is facing several numbers of issues that were already documented by researchers such as traffic congestion and road safety problems. On an urgent basis an efficient mass transit system is required to reduce the traffic jam conditions. The transport system of Karachi requires instant proper management rules and designed transport facilities. The aim of the paper is to identify the main traffic problems of the large urban city, Karachi, which is faced by the people in their daily life and to suggest recommendations for reducing the current situation related to traffic management strategies. The research methodology for this paper will be based on qualitative analysis of the road infrastructure troubles that are identified by the individuals through interviews and detailed group meetings.

Wildlife mortality risk posed by high and low traffic roads.

Wildlife mortality due to collisions with vehicles (roadkill) is one of the predominant negative effects exerted by roads on many wildlife species. Reducing roadkill is therefore a major component of wildlife conservation. Roadkill is affected by various factors, including road attributes and traffic volume. It is theorized that the effect of traffic volume on roadkill probability should be unimodal. However, empirical evidence for this theory is lacking. Using a large-scale roadkill database of 18 wildlife species in Israel, encompassing 2846km of roads over 10years, we explored the effects of traffic volume and road attributes (e.g., road lighting, verge vegetation) on roadkill probability with a multivariate generalized linear mixed model. A unimodal effect of traffic volume was identified for the striped hyena (Hyaena hyaena), whereas 5 species demonstrated a novel quadratic U-shaped effect (e.g., golden jackal [Canis aureus]). Four species showed a negative linear effect (e.g., wild boar [Sus scrofa]). We also identified varying effects of road attributes on roadkill. For instance, road lighting and roadside trees decreased roadkill for several species, whereas bus stops and concrete guardrails led to increased roadkill. The theorized unimodal effect of traffic volume may only apply to large, agile species, and the U-shaped effect could be related to intraspecies variability in traffic avoidance behavior. In general, we found that both high-traffic and low-traffic roads can pose a high mortality risk for wildlife. It is therefore important to monitor roadkill on low-traffic roads and adapt road attributes to mitigate roadkill. Road design for effective roadkill mitigation includes reducing the use of concrete guardrails and median barriers where possible and avoiding dense bushes in verge landscaping. These measures are complemented by employing wildlife detection systems, driver warnings, and seasonal speed reduction measures on low-traffic roads identified as roadkill hotspots.

Law Enforcement of Single Traffic Accidents Causing Death and Injury of Passengers

The high number of accidents in Indonesia is one of the impacts of the lack of awareness of road users and road transportation providers in maintaining traffic order and road transportation security. Services that are safe, secure, orderly, smooth, and integrated with other modes of transportation can boost the national economy, advance public welfare, strengthen national unity, and be able to uphold the dignity of the nation. This study aims to analyze law enforcement by police officers in single traffic accidents that cause passengers to die and the responsibilities of drivers and the Otobus Company (PO) to victims. The method uses normative legal research with a statutory approach and the concept of law enforcement. The research shows that the maximum punishment imposed on the perpetrators of traffic crimes normatively can be explained by the relatively low maximum punishment ranging from one year to five years and until now the legal basis for capturing the perpetrators of traffic crimes still depends on the Criminal Code inherited from the Dutch Colonial Government. Law enforcement by police officers with the investigation and investigation of criminal cases in the case of a single accident experienced by the suspect on the toll road ensnares the Road Traffic and Transportation Law (LLAJ Law). Drivers and POs are responsible for victims by providing compensation to accident victims following the LLAJ Law by not removing or reducing criminal liability.

Determining Priorities for Handling Environmental Road Sections in Palu City (Case Study in West Palu District)

Palu City has numerous roads, including environmental road networks, where the function of many environmental roads has decreased due to heavy traffic and damaged road structures. The improvement of environmental road infrastructure is a complex issue that requires objective policymakers. Sometimes, the work carried out to address environmental issues on roads is not prioritized effectively. To tackle this problem, a research study was conducted to determine the priority order for handling environmental roads in the West Palu sub-district of Palu City. The study used a multi-criteria analysis approach to determine priorities, where the criteria were weighted based on input from respondents from government agencies related to road infrastructure. The criteria used to determine priorities in this research were road conditions, traffic volume, land use, and development planning deliberation proposals. The criteria for road conditions and traffic volume were given the highest priority weighting with weight values of 39.10% and 28.10%, respectively. This was followed by the development planning deliberation proposal criteria at 21.50% and land use criteria at 11.30%. By weighting criteria and scoring criteria based on operational criteria variables, the analysis obtained the top 5 priority orders for handling environmental road sections on Jl. Srikaya. Lr. Sirsak II Kel. Kamonji, Jl. Mas Mansyur Lr. Kel. Baru, Jl. Samudra II Lr. II Kel. Lere, Jl. Lasoso Lr I Kel. Lere and Jl. Durian Lr. Kel. Kamonji.

Determination of concentration of selected heavy metals in the organs of Guinea pigs reared at the University of Port Harcourt, Rivers State, Nigeria

Vegetation along major traffic roads are predisposed to heavy metals contamination due to wear and tear of vehicular components and emissions from vehicles. The concentration of ten heavy metals (Fe, Mn, Zn, Pb, Cu, Ni, Cr, Co Ag and Cd) in the leaves of four test plants (Panicum maximum Jacq.,Eleusine indica L., Xanthosoma mafafa Schott. and Amaranthus spinosus L.) growing along four busy roads (Aba road, Ikwerre road, East/West road and NTA road) in parts of Obio/Akpor Local Government Area and a control ( in Ozuguru in Ikwerre Ngwo) in Etche Local Government Area, both in Rivers State, Nigeria were investigated in the study area. Along each road, four sampling points were set up at 1 km apart. The test plants were collected randomly and were pooled together to form composite sample. The collected test plants at each sample location were put in different sterile cellophane bags, labelled and served as soilage to the experimental animals. Eighteen guinea pigs were used for this investigation. Three guinea pigs were picked out of the eighteen guinea pigs using randomized block design for a preliminary test. The remaining fifteen guinea pigs were fed with the test plants from the experimental and control sites for six weeks. The concentration of the ten heavy metals were determined using Atomic Absorption Spectrophotometry (ASS). The results revealed that Aba road had the highest mean concentration value of heavy metals in Cu, Zn, Co, Cr and Cd with values (28.75±1.34), (32.46±0.9), (5.14±0.11), (4.79±0.21) and (3.17±0.12) respectively. The highest mean concentration value for Mn and Pb were observed in East/West road with values (7.57±1.48) and (5.39±1.53) respectively, while the highest mean concentration value for Fe was observed in Ikwerre road (192.84±2.64). NTA road had the least values for all heavy metals tested while Ni and Ag were not detected in all livers and kidneys of the experimental animals. The results from this findings show the health implication of roadside grazing of ruminants.

A Multi-Scale Residual Graph Convolution Network with hierarchical attention for predicting traffic flow in urban mobility

Accurate prediction of traffic flow is essential for optimizing transportation resource allocation and enhancing urban mobility efficiency. However, traffic data generated daily are vast and complex, involving dynamic and intricate changes in the traffic road network and traffic flow. Therefore, real-time and accurate prediction of traffic flow is a challenging task that requires modeling the intricate spatial–temporal dynamics of traffic data. In this paper, we propose a novel approach for traffic flow prediction, based on a Multi-Scale Residual Graph Convolution Network with hierarchical attention. First, we design a novel encoder–decoder with multi-independent channels to capture traffic flow information from different time scales and diverse temporal dependencies. Second, we employ a coupled graph convolution network with residual graph attention to dynamically learn the varying spatial features among and within traffic stations. Third, we utilize channel attention to fuse the multi-scale spatial–temporal dependencies and accurately predict traffic flow. We evaluate the proposed approach on multiple benchmark datasets, and the experimental results demonstrate its superior performance compared to state-of-the-art approaches in terms of various metrics.

An optimized secure dual authentication for VANETs

“Vehicular Ad-hoc Network (VANET)” has emerged as a hotbed of research and development. This technology has been utilized to improve vehicle and road safety by transmitting information about traffic congestion and road conditions during emergencies in order to soothe drivers and passengers. However, such environments are more vulnerable to security risks. Thus, the primary challenge in such an environment is to provide authentication services while maintaining user confidentiality. To that end, a trustworthy authority (TA) is established, which offers services to users as well as authentication of communications transmitted between trusted authorities and VANET nodes. A trusted authority (TA) is established using VANETs to supply clients with a range of online premium services. As a result, preserving the confidentiality and authenticity of messages conducted between the TA and VANET nodes is crucial. Our proposed method minimizes recognition latency by up to 85% when compared to On-site Driver ID, and decreases authentication latency by up to 94%, respectively.

Combining jumping knowledge into traffic forecasting: An attention-based spatial-temporal adaptive integration gated network

Traffic forecasting has become a core component of Intelligent Transportation Systems. However, accurate traffic forecasting is very challenging, caused by the complex traffic road networks. Most existing forecasting methods do not fully consider the topological structure information of road networks, making it difficult to extract accurate spatial features. In addition, spatial and temporal features have different impacts on traffic conditions, but the existing studies ignore the distribution of spatial-temporal features in traffic regions. To address these limitations, we propose a novel graph neural network architecture named Attention-based Spatial-Temporal Adaptive Integration Gated Network (AST-AIGN). The originality of AST-AIGN is to obtain a spatial feature that more accurately reflects the topological structure of the road networks by embedding Graph Attention Network (GAT) into Jumping Knowledge Net (JK-Net). We propose a data-dependent function called spatial-temporal adaptive integration gate to process the diversity of feature distribution and highlight features in road networks that significantly affects traffic conditions. We evaluate our model on two real-world traffic datasets from the Caltrans Performance Measurement System (PEMS04 and PEMS08), and the extensive experimental results demonstrate the proposed AST-AIGN architecture outperforms other baselines.

Effective Space Utilization System In Smart Cities Using IOT And Image Processing

In the world of Smart Cities, we're exploring new ways to improve our urban spaces using the Internet of Things (IoT). By using devices like Arduino, we're creating models for Smart City solutions, focusing on things like Smart Parking, Traffic Road Dividers, and Patrolling Vehicles.Our main goal is to make cities safer and better by using technology like IoT and image processing. For example, we're working on special road dividers that help manage traffic by separating cars going in different directions. These "Smart Road Dividers" are designed for cities and use sensors and technologies to adapt to how busy the roads are, helping traffic flow better and easing congestion.Cameras are being used on patrolling vehicles to spot cars parked in the wrong places. If they see something wrong, they quickly tell the right people so they can sort it out. This helps make sure that people park their cars properly and follow the rule.

Can we trust our eyes? Interpreting the misperception of road safety from street view images and deep learning

Road safety is a critical concern that impacts both human lives and urban development, drawing significant attention from city managers and researchers. The perception of road safety has gained increasing research interest due to its close connection with the behavior of road users. However, safety isn't always as it appears, and there is a scarcity of studies examining the association and mismatch between road traffic safety and road safety perceptions at the city scale, primarily due to the time-consuming nature of data acquisition. In this study, we applied an advanced deep learning model and street view images to predict and map human perception scores of road safety in Manhattan. We then explored the association and mismatch between these perception scores and traffic crash rates, while also interpreting the influence of the built environment on this disparity. The results showed that there was heterogeneity in the distribution of road safety perception scores. Furthermore, the study found a positive correlation between perception scores and crash rates, indicating that higher perception scores were associated with higher crash rates. In this study, we also concluded four perception patterns: “Safer than it looks”, “Safe as it looks”, “More dangerous than it looks”, and “Dangerous as it looks”. Wall view index, tree view index, building view index, distance to the nearest traffic signals, and street width were found to significantly influence these perception patterns. Notably, our findings underscored the crucial role of traffic lights in the “More dangerous than it looks” pattern. While traffic lights may enhance people's perception of safety, areas in close proximity to traffic lights were identified as potentially accident-prone regions.

Survey of Common Infectious Diseases in Urban Foxes (Vulpes spp.) in Southeastern Iran.

The red fox (Vulpes vulpes) is one of the most common species of wild Canidae and is relatively abundant in Iran. Foxes (Vulpes spp.) transmit many zoonotic diseases, the most important of which are visceral leishmaniasis, rabies, hydatidosis, toxocariasis, and trichinellosis. In this study, visceral leishmaniasis, rabies, ectoparasites, canine gastrointestinal helminths, dermatophytosis, distemper, parvovirus infection, and heartworm infections were evaluated among live-trapped and rescued foxes injured by traffic road accidents referred to the teaching hospital of Kerman, Iran, veterinary faculty. Skin scraping and direct microscopic examination were used to detect ectoparasites and dermatophytosis. Immunochromatography rapid kits were used to detect dirofilariasis, parvovirus infection, and distemper. Necropsy was used to check for gastrointestinal parasites. Rabies and visceral leishmaniosis were screened for with direct fluorescent antibody test and ELISA methods, respectively. Gastrointestinal helminth infections, including Toxocara canis, Taenia taeniaeformis, Dipylidium caninum, Joyeuxiella echinorhyncoids, Toxascaris leonina, Taenia hydatigena, Echinococcus granulosus, Rictolaria spp., Oxynema spp., Macracanthorhynchus hirudinaceus, and Physaloptera spp., were detected. Skin scrapings showed dermatophytosis and various ectoparasites, including Rhipicephalus sanguineus, Ctenocephalides canis and Ctenocephalides felis, and Sarcoptes scabiei, in foxes with dermal lesions. Distemper and parvovirus infection (26.66%) were the common viral diseases, and rabies infection rate was quite high (16.66%). Dirofilariasis and leishmaniasis were detected in 10% of the population. This study showed that urban foxes which often cohabit with humans and domestic animals are carriers of many different pathogens. This interaction may facilitate indirect cross-species transmission of zoonotic disease. Periodic health monitoring and multidisciplinary cooperation for the diagnosis, control, and prevention of these zoonoses is highly recommended.

Dynamic load prediction of charging piles for energy storage electric vehicles based on Space-time constraints in the internet of things environment

Abstract This paper puts forward the dynamic load prediction of charging piles of energy storage electric vehicles based on time and space constraints in the Internet of Things environment, which can improve the load prediction effect of charging piles of electric vehicles and solve the problems of difficult power grid control and low power quality caused by the randomness of charging loads in time and space. After constructing a traffic road network model based on the Internet of Things, a travel chain model with different complexity and an electric vehicle charging model, the travel chain is randomly extracted. With the shortest travel time as a constraint, combined with the traffic road network model based on the Internet of Things, the travel route and travel time are determined. According to the State of Charge (SOC) and the travel destination, the location and charging time of the energy storage electric vehicle charging pile are determined. After obtaining the time-space distribution information of the energy storage electric vehicle charging pile at different times and in different regions, it is used as the input of the deep multi-step time-space dynamic neural network, and the network output is the dynamic electric vehicle charging pile. The experimental results show that this method can realize the dynamic load prediction of electric vehicle charging piles. When the number of stacking units is 11, the indexes of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are the lowest and the index of R 2 is the largest. The load of charging piles in residential areas and work areas exists in the morning and evening peak hours, while the load fluctuation of charging piles in other areas presents a decentralized change law; The higher the complexity of regional traffic network, the greater the load of electric vehicle charging piles in the morning rush hour.

Assessment of Physical and Biochemical Qualities of Solanum tuberosum L. Under Roadside Traffic Polluted Area

Solanum tuberosum L. is an important crop that yields high-quality of vegetables. anthropogenic activities are increasing traffic and road air pollution day by day. The impact of traffic road air pollution on agricultural vegetation was examined in this study work because traffic air pollution has become a life-threatening problem. We selected sites with and without traffic roads (control) in order to compare crop samples. The values of the photosynthetic pigments decreased (chlorophyll 'a', 5.43 ˃ 4.00; chlorophyll 'b', 8.91 ˃ 8.26; and carotenoid, 5.62 ˃ 4.26), and growth also decreased (thickness: 8.4 ˃ 5.6; length: 23.4 ˃ 13.1; and weight: 7.16 ˃ 5.19) that we observed in our record are also statistically significant. Air quality was determined to be low, photosynthetic pigment quality was lowered, and crop vegetation growing near busy roads had stunted development. These were the outcomes of excessive quantities of dangerous gaseous components found at a traffic road site. Physical and biochemical quality results also showed variation in traffic road and control sites grown crop vegetation.

Anomaly diagnosis of connected autonomous vehicles: A survey

Connected autonomous vehicles (CAVs) are revolutionizing the development of transportation due to their potential to improve transportation performance in many ways, such as enhanced traffic mobility, road compacity, operation safety, and environmental sustainability. Nevertheless, the issue of road vehicle safety in CAVs remains to be fully solved. Data collected from multiple sources provide information about the internal status of the system and the situation of its surroundings, and the occurrence of data anomalies indicates the existence of potential safety risks. Thus, anomaly diagnosis is of major importance to analyze the nature or cause of underlying safety risks and provide insightful information for the subsequent decision-making that ensures safety. Anomaly diagnosis consists of two basic tasks: anomaly detection and anomaly interpretation. In this paper, both of these two tasks are comprehensively surveyed. For anomaly detection, the following four aspects are covered: 1) formalized definition of an anomaly, 2) classification of anomalies, 3) taxonomies of anomaly detection techniques, and 4) review of recent advances for anomaly detection in CAV applications. For anomaly interpretation, related works are investigated in the context of 1) the anomaly detection process, and 2) the tested/monitored system/process, respectively. The novelty particularly lies in the latter, where the interpretation of anomalies combining the analysis of road vehicle safety risks is presented, and related works for anomaly interpretation in CAV applications are reviewed by analyzing 1) functional safety risks, 2) safety of the intended functionality (SOTIF) risks, and 3) cyber security risks, respectively. Finally, open issues, challenges, future directions, and emerging technologies for anomaly diagnosis in CAVs are discussed.

Graph-Based Neural Networks’ Framework Using Microcontrollers for Energy-Efficient Traffic Forecasting

This paper illustrates a general framework in which a neural network application can be easily integrated and proposes a traffic forecasting approach that uses neural networks based on graphs. Neural networks based on graphs have the advantage of capturing spatial–temporal characteristics that cannot be captured by other types of neural networks. This is due to entries that are graphs that, by their nature, include, besides a certain topology (the spatial characteristic), connections between nodes that model the costs (traffic load, speed, and road length) of the roads between nodes that can vary over time (the temporal characteristic). As a result, a prediction in a node influences the prediction from adjacent nodes, and, globally, the prediction has more precision. On the other hand, an adequate neural network leads to a good prediction, but its complexity can be higher. A recurrent neural network like LSTM is suitable for making predictions. A reduction in complexity can be achieved by choosing a relatively small number (usually determined by experiments) of hidden levels. The use of graphs as inputs to the neural network and the choice of a recurrent neural network combined lead to good accuracy in traffic prediction with a low enough implementation effort that it can be accomplished on microcontrollers with relatively limited resources. The proposed method minimizes the communication network (between vehicles and database servers) load and represents a reasonable trade-off between the communication network load and forecasting accuracy. Traffic prediction leads to less-congested routes and, therefore, to a reduction in energy consumption. The traffic is forecasted using an LSTM neural network with a regression layer. The inputs of the neural network are sequences—obtained from a graph that represents the road network—at specific moments in time that are read from traffic sensors or the outputs of the neural network (forecasting sequences). The input sequences can be filtered to improve the forecasting accuracy. This general framework is based on the Contiki IoT operating system, which ensures support for wireless communication and the efficient implementation of processes in a resource-constrained system, and it is particularized to implement a graph neural network. Two cases are studied: one case in which the traffic sensors are periodically read and another case in which the traffic sensors are read when their values’ changes are detected. A comparison between the cases is made, and the influence of filtering is evaluated. The obtained accuracy is very good and is very close to the accuracy obtained in an infinite precision simulation, the computation time is low enough, and the system can work in real time.

Vehicle Trajectory-Based Traffic Volume Prediction on Urban Roads With Fast-Communication License Plate Recognition Data

Real-time traffic volume prediction is critical for proactive traffic control and road guidance via fast-communication networks. However, existing research relies on historical traffic volume and fails to consider vehicle travel route choices, leading to lower prediction accuracy and insufficient capture of real-time travel dynamics. To address this limitation, we propose a novel approach for real-time traffic volume prediction on urban links that incorporates vehicle travel trajectories extracted from recorded license plate recognition data using Radio Frequency Identification. A Recurrent Neural Network is used to identify route patterns and predict future routes, and link traffic volume is estimated by aggregating all predicted vehicle trajectories that pass through each base station. Our approach achieves a high prediction accuracy with a mean value of 85.19% and a standard deviation of 5.29%. Comparing our approach with a traditional Recurrent Neural Network based on historical traffic volume data, the vehicle trajectory-based approach yields an average accuracy improvement of 39.70%. These results highlight the superiority of our approach for predicting traffic volume in real-time and demonstrate its potential as a valuable tool for traffic control and road guidance.

Simuleau: a tool for hybrid and batches Petri nets

Simuleau is a modeling, analysis and simulation tool for discrete event models expressed by batches Petri nets, a formalism that enriches hybrid Petri nets of David and Alla. Batches PNs incorporate timed-transition discrete PNs, constant continuous PNs, and new types of nodes called batch nodes. This formalism and Simuleau have been exploited among diverse application areas such as manufacturing systems, communication networks, and traffic road. After presenting batches PN formalisms and their application domains, this paper focuses on the main characteristics of Simuleau, including the tool structure, its menu interface, the input model description and the outputs obtained after simulation.