Road Users Research Articles

Unveiling Anomalies in Surveillance Videos

Abstract: The implementation of Convolutional Neural Network (CNN) algorithm for the detection of anomalies like fire and potholes in images or videos. Leveraging deep learning technique, the model aims to accurately identify fire, potholes, etc. within various contexts. The CNN architecture is trained on a dataset comprising diverse images to enhance its ability to generalize. This proposed project not only addresses safety concerns by promoting helmet usage and proper maintenance of roads but also contributes to the broader field of computer vision and object detection. This implementation involves training the CNN on annotated datasets, fine-tuning the model for optimal performance, and integrating it into a practical application for efficient anomaly identification. With a focus on real-time detection, the system utilizes image processing techniques and a trained CNN model to analyze visual data from cameras or video feeds. The system's versatility allows integration into surveillance systems, industrial sites, or any environment where fire detection is crucial for safety. By automating the detection process, the project contributes to minimizing human error and ensuring consistent monitoring. The proposed solution holds the potential to significantly impact safety protocols, particularly in industries where protective headgear is paramount. Potholes pose a significant threat to both drivers and pedestrians, leading to accidents and infrastructure damage. The proposed solution leverages CNN's ability to effectively process visual data, making it well-suited for image recognition tasks. Our approach involves training the CNN on a diverse dataset of road images to enable it to accurately identify potholes. The model will learn distinctive features and patterns associated with potholes, allowing for robust detection under various environmental conditions. Real-time implementation on embedded systems or cameras along roadways will enable instantaneous identification and alerting.

Road Traffic Enforcement: Using Technology Through Public-Private Partnerships (PPP)

The past two decades have seen a wide - ranging wave of privatizations in formerly government owned or - controlled activities. A government task or service which is funded and operated through a partnership between a public sector authority and private sector company based on guidelines and performance standards set by a public party is typically referred to as a Public Private Partnership (PPP). In most countries traffic enforcement cameras and other equipment are purchased, owned, and operated by government organisations. Back - offices, where violation data are processed, citations issued, and traffic fines collected, have traditionally been government - run activities. In a PPP model, a private party invests and installs enforcement equipment (e. g. speed or red - light cameras) and conducts back office processing, such as sending out violation notices and managing and confirming fine collection. The investment in equipment and related processing and support activities are funded by fine revenues collected from the motoring public over the contract term. Gaining public support, and ultimately acceptance, for the PPP model is crucial, which is why transparency, publicity and continuous communication are key factors for successful and sustainable implementation. Full service PPPs are often organised on a municipal level in close consultation with the local administration and police. Due to the high initial investment and gradual repayment, such PPPs are typically based on longer term contracts. Moreover, the regulatory environment may require some changes to allow involvement of a private party. One process that is difficult to outsource is the formal confirmation of a violation by a review of the photo or video evidence. Generally, this can only be validly performed by an authorised government official, such as a police officer. Depending on laws and regulations applicable to specific jurisdictions, there could be more issues that may limit the extent of the private party’s involvement in these PPPs. When properly implemented, PPP’s in traffic enforcement can considerably contribute to the reduction of casualties, injuries and crashes, as well as improve traffic flow, and increase quality of life with more safety, lower emissions and less noise pollution. Speed reduction also significantly benefits the safety of vulnerable road users such as pedestrians, and two - wheelers Personal injury and death on roadways also have a significant impact on the economy through medical costs, lost resources and wages and disability pay outs. These costs are significant and are estimated by the WHO to amount to an average of around 3% of a country’s GNP. This figure rises to 5% in some low - and middle - income countries

CONTROL OF ENGINEERING GEODETIC WORKS DURING THE RECONSTRUCTION OF THE TERRITORY OF THE HIGHWAY

The article now considers the issues of the necessity to increase the bearing capacity of weak road spoil bases in a number of cases during overhaul or reconstruction of highways. Reconstruction of highways is an important process that allows modernizing and improving the existing infrastructure. This is necessary to ensure the safety and comfort of road traffic. This problem is particularly relevant when it is necessary to increase the capitalization of road surfaces, which is usually associated with increased stiffness and, therefore, sensitivity to deformable earth bed in general. In general, this study considers engineering-geodetic works during the reconstruction of the highway of republican significance "Kalbatau-Maikapshagai". Stages, sections of engineering geodetic works were carried out, deformation analysis works were carried out. The obtained results were analyzed, proposals for other measuring works were excluded, the possibility of forecasting on these proposals was considered. The overall objective is to encompass various activities aimed at improving the safety, passability and comfort of the road. Reconstruction of an area is carried out due to deterioration of the road surface, outdated infrastructure, non-compliance of the road with modern safety and traffic requirements and changing needs and volumes of traffic flow. Reconstruction of highways is an integral part of their maintenance and development. It is necessary to ensure the safety and comfort of road users, as well as to support and improve the transportation infrastructure. In the article reconstruction stages of planning, financing and technical implementation are an important step in infrastructure development.

ENHANCING ROAD SAFETY FOR VULNERABLE ROAD USERS – THE ROLE OF AUTONOMOUS EMERGENCY BRAKING SYSTEMS

Vulnerable road users are the largest group of road accident victims in the world. At the same time, it should be noted that most road accidents are caused by the motor vehicle driver. An opportunity to increase the level of road safety is emergency braking systems installed in vehicles. Their task is to detect the risk of a collision with another object, issue a warning to the driver and, in the absence of reaction, perform emergency braking. The publication presents the results of research conducted by the Motor Transport Institute as part of the PEDICRASH project. As part of the work, a series of tests of the autonomous emergency braking system were carried out to check unusual but probable cases of events in which it should work. The tests were carried out in a closed area using a few selected models of popular passenger car brands. An additional data acquisition system and dummies imitating vulnerable road users: pedestrians and cyclists were used. The constructions represent the silhouette of an adult person and meet the requirements of the Euro NCAP AEB Protocol. The time of the collision at the time of issuing a warning to the driver was analyzed. Assuming that the vehicle was moving uniformly, the distance from the obstacle was calculated at the time of issuing the warning and after the driver's reaction time (0.7 s and 1 s were assumed) from this signal. The deceleration necessary to brake the vehicle was calculated and it was determined whether the driver would have a chance to brake the vehicle before a pedestrian or a cyclist in a given situation. For cases in which the driver would not be able to brake in time, the speed of the vehicle with which it would hit the vulnerable road users was calculated. Possible injuries and accident costs were then estimated.

Adaptive control of transportation infrastructure in an urban environment using a real-coded genetic algorithm

The management of urban areas requires the development of an effective strategy for the evolution of transportation infrastructure to ensure the smooth flow of traffic and pedestrians. A crucial component of this infrastructure is the traffic light system, which plays a vital role in traffic control and traffic safety. Improving the efficiency of traffic control systems in intelligent transportation systems (ITS) has a significant impact on a city’s economy. As a result, the cost of fuel for road users can be reduced, and their level of social comfort can be improved, among other benefits. This paper proposes a novel approach to optimizing traffic flows in smart cities, based on the combined use of the genetic optimization algorithm and the ITS simulation model we developed. The proposed method aims to enhance the efficiency of existing traffic control systems and achieve optimal traffic flow patterns, thereby contributing to a more sustainable and efficient urban environment. The optimization algorithm shown here aggregates the objective functions using a simulation model of a real region of the Moscow road network. The model includes intersections, pedestrian crossings and other features that are implemented in the AnyLogic system. The research aims to create a decision-support system for managing urban transport infrastructure. This system will be used to optimize the duration of traffic light phases in order to minimize the time vehicles spend passing through key nodes in the urban road network. It will also optimize pedestrian flow, reducing the impact of traffic on the environment and improving fuel efficiency. By applying this approach, the capacity of the street network can be significantly increased. Additionally, the negative effects of traffic flow on the environment can be reduced by optimizing fuel use and reducing waiting times at intersections managed by traffic lights. The research methodology involves the development of a hybrid evolutionary search algorithm, the creation of a simulation model for transportation and pedestrian flows in the AnyLogic and a series of optimization experiments that demonstrate the effectiveness of the proposed approach when applied to the modeling of complex urban transportation systems.

Modeling traffic fatalities to assess the significance of gender in road safety

Road crashes continue to be the leading cause of death across all age groups despite several efforts being taken by the government and non-government organizations. The brunt of traffic fatalities affects males and females differently. Studies on assessing gender-wise exposure to road safety are limited in the Indian context. Therefore, an attempt is made to understand the role of gender in fatality risk assessment. The aim is to evaluate the change in exposure to different motorization growth scenarios for males and females. The fatality-risk model is developed based on the interaction between the victim (pedestrian, bicyclist, two-wheeler, car, and bus) and threat (pedestrian, bicyclist, two-wheeler, car, bus, and environment) road users combined with the at-risk distance traveled by modes for Bangalore Metropolitan Region, India. Male and female traffic fatalities are estimated based on the interaction severity between victim and threat modes. The results show that pedestrians, bicyclists, and two-wheeler users are more vulnerable. Both males and females demonstrate an increased risk of traffic fatalities with the rise in the motorization mode share due to the severity of interaction of vulnerable modes with another motorized mode such as two-wheelers and cars. The most significant reduction in road crashes for both genders is observed for the High Bus scenario, which assumes that 80 % of the total motorized distance is traveled by bus. Maximum traffic deaths are estimated for the High Car scenario; the fatalities remained high even with 100 % motorization indicating higher risk with high motorization rates. The study outcomes would help practitioners and decision-makers to make informed policy decisions. Further, the fatality risk is assessed considering the interaction of at most two road users, and the study can be extended to the simultaneous evaluation of multiple road users.

Investigation on the Impact of Ancillary Roadworks on Road Safety in Uganda: A Case of Kampala –Jinja Road

Increased accidents on Ugandan roads compelled the construction of road ancillaries, with a major aim of improving road safety, to attain sustainable economic development and further achieve SDG target 3.6 and SDG target 9.1. This study investigated the impact of ancillary roadworks on road safety in Uganda, a case of the Kampala – Jinja road. The study used a correlation research design with a quantitative approach. Primary data were collected from 30 field traffic officers, 19 drivers, 03 OC traffic police stations, 36 road engineers, 01 IOV, 01 RTO, and 12 market leaders whereas secondary data were extracted from accident data from Uganda Police. Questionnaire data were analysed with SPSS version 24.0. Results indicated that 39% of installed informatory signs, 47% of installed regulatory signs, and 60% of installed warning signs had been vandalised, exposing road users to the risk of accidents. Guardrails were still existing but some had rusted and others covered with grown grass, making them not seen by road users. The majority of road accidents were serious, accounting for 149 (46.3%) cases, followed by 130 fatal accidents (40.4%) and 43 (13.3%) minor accidents for the period from 2017 to 2022. Correlation results indicated that all ancillary road works were significant except road signs. Yet, regression results indicated that road marking (βeta = 0.307), guardrails (βeta = 0.286), and humps and rumble strips (βeta = 0.360) have a positive significant effect on road safety unlike road lights, road signs, walkways, and service lanes which were insignificant. Police traffic law enforcement and regular improvement on road infrastructure are required. It is concluded that road marking, guardrails, humps, and rumble strips significantly affect road safety. The study recommended that the construction of humps and rumble strips of moderate size, road marking with higher retro reflectivity (>200 mcd/lx/m2), and strong guardrails, can help in ensuring road safety with the support of enforcement of traffic laws of Uganda.

Activities of the militia in Ukraine to ensure road safety in the 1950s

In the 1950s, the daily functions of the militia continued to include combating violations of street traffic rules and controlling the decrease in motor vehicle accidents. The accident rate on the roads of the republic at that time remained high. An analysis of the current state of road safety shows that the level of accidents, injuries and deaths on the roads was influenced by such objective factors as the level of development of the regulatory framework, the technical condition of vehicles, the level of development of the road network, its quality, and weather and climatic conditions. Subjective factors included the level of legal culture of road users (drivers and pedestrians), the degree of their readiness to participate in traffic, the professional qualifications and material and technical equipment of the employees of the State Automobile Inspection. During this period, state bodies carried out significant, but ambiguous in quality, rule-making work, the purpose of which was to develop measures aimed at improving both objective and subjective conditions that affected the state of road safety and were of a mutually agreed nature. The established driver training mechanism was one of the important components of the effective operation of the road safety system. Therefore, the activities of state bodies, in particular the State Automobile Inspection of the Ministry of Internal Affairs of the Ukrainian SSR, were aimed at forming not only stable skills in driving vehicles among the drivers, but also behavioral stereotypes that require compliance with legal norms in the field of road safety. At the same time, it was necessary to develop and legally establish criteria for assessing the quality of their training in the relevant institutions. Militia bodies carried out quite active work to improve the situation with road accidents, which included measures of administrative influence, involvement of the public in monitoring violations of traffic rules, various propaganda activities, but its effectiveness was ultimately insignificant.

On the Detection of Vulnerable Road Users—Simulating Limits of an FMCW Radar

The complexity of urban traffic environments poses unique challenges for automotive radar sensors. Limited range and Doppler resolution hinder the ability to distinguish closely spaced objects and detect vulnerable road users, particularly pedestrians. Therefore, it is important to know the limits of FMCW radars. To assess these limitations, the WinProp channel simulator is used to model and simulate traffic scenarios regarding the detection of the range, velocity, and SNR of targets. To validate WinProp as a simulation tool for traffic scenarios, a real traffic scenario is configured and the simulation results compared to the measurement results in order to demonstrate the suitability of WinProp for simulating traffic scenarios and evaluating radar detection performance. The close agreement between the simulation and measurement results provides validation for WinProp’s accuracy in modeling detections and signal processing within traffic scenarios. To showcase the limitations of FMCW radars, two scenarios with challenging detection conditions are simulated: an occluded pedestrian between parked vehicles and a densely populated road. A monostatic setup with a single patch antenna and a 16 × 16 patch antenna array is evaluated, using chirp bandwidths of 1 GHz and 4 GHz. The simulations have shown that although it is feasible to detect an occluded pedestrian by multipath propagation, there is still a need in improving the detection performance when there is no multipath from an occluded pedestrian. Furthermore, the results indicate that higher bandwidth improves target separation but is insufficient for occluded pedestrian detection without multipath. However, with some targets, even the range resolution at 4 GHz was not sufficient, which required a separation in the Doppler dimension, emphasizing the need for overall resolution improvement of FMCW radars. Future work should focus on developing more complex road user models and simulating a broader range of scenarios to comprehensively evaluate radar performance in road traffic environments.

Perception of road users on impact of delayed road construction projects on livelihoods in Ashanti Region

Perception of road users on impact of delayed road construction projects on livelihoods in Ashanti Region

Activity-based and agent-based transport model of Melbourne: an open multi-modal transport simulation model for Greater Melbourne

Activity- and agent-based models for simulating transport systems have attracted significant attention in recent years. However, building these types of models at a city-wide level and including motorized (i.e. cars and public transport) and non-motorized (i.e. walk and bicycle) modes of transport is a complicated and involved task. This paper presents an open workflow for creating large-scale multi-modal agent-based transport simulation models. The workflow brings together a number of external tools, for example, an activity-based demand generation tool and a road network generation tool, and a set of tools developed for the agent-based model parameter estimation, calibration, and simulation post-processing. We used this workflow to create an activity- and agent-based model for Melbourne and compared the output of the simulation model with observations from the real world in terms of mode share, road volume, travel time, and travel distance. Through these comparisons, we showed that our model is suitable for studying mode choice and road usage behavior of travelers. The calibrated model could be used to test road network change interventions. In addition, a similar workflow can be applied for building simulation models for other cities or could be expanded to include more complicated travel behaviors.

Actual safety of the youngest road users at pedestrian crossings

Actual safety of the youngest road users at pedestrian crossings

TSANet: Forecasting traffic congestion patterns from aerial videos using graphs and transformers

Forecasting traffic congestion patterns in lane-less traffic scenarios is a complex task because of the combination of high & irregular vehicle densities, fluctuating speeds, and the presence of environmental obstacles. Existing techniques like vehicle counting and density prediction, which successfully estimate congestion in lane-based traffic, are unsuitable for lane-less traffic scenarios due to the irregular and unpredictable nature of traffic density patterns. To overcome these challenges, we propose traffic states to measure congestion patterns in lane-less traffic scenarios. Each traffic state is characterized by the spatio-temporal distribution of neighbouring road users, including vehicles and motorcyclists. We employ traffic graphs to capture the spatial distribution of neighbouring road users. Also, we propose a novel method for the automated construction of traffic graphs by leveraging the detection and tracking of individual road users in aerial videos. Further, in order to incorporate the temporal distribution, we utilize a transformer model to capture the evolution of spatial traffic graphs over time. This enables us to forecast future spatio-temporal distributions and their associated traffic states. Our proposed model, named Traffic State Anticipation Network (TSANet), can effectively forecast future traffic states by analysing sequences of current traffic graphs, thereby enhancing our understanding of evolving traffic patterns in lane-less scenarios. Also, to address the lack of publicly available lane-less traffic datasets, we introduce EyeonTraffic (EoT), a large-scale lane-less traffic dataset containing three hours of aerial videos captured at three busy intersections in Ahmedabad city, India. Experimental results on the EoT dataset demonstrate the efficacy of our proposed TSANet in effectively anticipating traffic states across diverse spatial regions within an intersection. In addition, we also show that TSANet generalizes well for previously unseen intersections, making it suitable for analysing various traffic scenarios without the need for explicit training, thereby enhancing its practical applicability.

Prediction model of factors causing traffic accidents on rural arterial roads: A binary logistic regression approach

In rural areas, land use activities around primary arterial roads influence the road section’s traffic characteristics. Regulations dictate the design of primary arterial roads to accommodate high speeds. Hence, there is a mix of traffic between high-speed vehicles and vulnerable road users (pedestrians, bicycles, and motorcycles) around the land. As a result, researchers have identified several arterial roads in Indonesia as accident-prone areas. Therefore, to improve the road user’s safety on primary arterial roads, it is necessary to develop models of the influence of various factors on road traffic accidents. This research uses binary logistic regression analysis. The independent variables are carelessness, disorderliness, high speed, horizontal alignment, road width, clear zone, road shoulder width, signs, markings, and land use. Meanwhile, the dependent variable is the frequency of accidents, where the frequency of accidents consists of multi-accident vehicles (MAV) and single-accident vehicles (SAV). This study collects data for a traffic accident prediction model based on collision frequency in accident-prone areas. The results, road shoulder width, and road sign factor all have an impact on the frequency of traffic accidents. According to a realistic risk analysis, MAV and SAV have no risk difference. After validation, this model shows a confidence level of 92%. This demonstrates that the model generates estimations that accurately reflect reality and are applicable to a wider population. This research has the potential to assist engineers in improving road safety on primary arterial roads. In addition, the model can help the government measure the impact of implemented policies and engage the public in traffic accident prevention efforts.

The racial composition of road users, traffic citations, and police stops

The racial composition of road users, traffic citations, and police stops

S-LDM: Server local dynamic map for 5G-based centralized enhanced collective perception

The automotive field is undergoing significant technological advances, which includes making the next generation of autonomous vehicles smarter, greener and safer through vehicular networks, which are often referred to as Vehicle-to-Everything (V2X) communications. Together with V2X, centralized maneuver management services for autonomous vehicles are increasingly gaining importance, as, thanks to their complete view over the road, they can optimally manage even the most complex maneuvers targeting L4 driving and beyond. These services face the challenge of strictly requiring a high reliability and low latency, which are tackled with the deployment at orchestrated Multi-Access Edge Computing (MEC) platforms. In order to properly manage safety-critical maneuvers, these services need to receive a large amount of data from vehicles, even though the useful subset of data is often related to a specific context on the road (e.g., to specific road users or geographical areas). Decoding and post-processing a large amount of raw messages, which are then for the most part filtered, increases the load on safety-critical services, which should instead focus on meeting the deadlines for the actual control and management operations. On this basis, we present an innovative open-source, 5G & MEC enabled service, called Server Local Dynamic Map (S-LDM). The S-LDM is a service that collects information about vehicles and other non-connected road objects using standard-compliant messages. Its primary purpose is to create a centralized dynamic map of the road that can be shared efficiently with other services managing L4 automation, when needed. By doing so, the S-LDM enables these services to widely and precisely understand the current situation of sections of the road, offloading them from the need of quickly processing a large number of messages. After a detailed description of the service architecture, we validate it through extensive laboratory and pilot trials, involving the MEC platforms and production 5G networks of three major European network operations and two Stellantis vehicles equipped with V2X On-Board Units (OBUs). We show how it can efficiently handle high update rates and process each messages in less than few tenths of microseconds. We also provide a complete scalability analysis with details on deployment options, providing insights on where new instances should be created in practical 5G-based V2X scenarios.

Influence of Information, Education and Communication on Road Safety amongst Boda-boda Motorcyclists in Kenyan Cities

This study sought to investigate the influence of information, education and communication on road safety amongst Boda-boda motorcyclists in Kenyan cities. Road safety has become a major concern with over 1.35 million people losing their lives worldwide annually and over 4,000 people losing their lives in Kenya. Human behavior on the road causes 90 per cent of death and injury amongst road users with Boda-boda motorcyclists contributing 58 per cent of all road accidents in Kenya. This study was anchored on the social cognitive theory, the safety culture theory and the uses and gratification theory. The study used a pragmatic philosophical paradigm with a convergent parallel design of the mixed method research. Stratified sampling and simple random sampling were used to draw the target population of 399 Boda-boda motorcyclists from the four cities in Kenya. Interviews were conducted with twelve key informants. Quantitative data was collected from the motorcyclists by use of semi-structured questionnaires while key informant interviews were used to collect the qualitative data. Descriptive and inferential statistics were used to analyze quantitative data while qualitative data was analyzed using thematic data analysis. The findings show that influence of information, education and communication has a significant impact on predicting road safety hence showing a potential synergistic effect between the two factors. In conclusion, behavior change communication interventions aimed at Boda-boda motorcyclists significantly contribute to road safety. Tailored educational materials and training programs were recommended as a unique way to address the needs and literacy levels of Boda-boda motorcyclists.

Ефективність заходів щодо запобігання дорожньо-транспортним пригодам: огляд сучасних стратегій та підходів

This article investigates the problem of road traffic accidents (RTA) and reviews current strategies and approaches to prevent and reduce them. The significance of this problem is that the health of road users and the safety of vehicle traffic on roads is one of the most important global societal issues, especially with the continuous growth in the number of vehicles and the pace of urban activity. The article examines various aspects of road traffic accident prevention, including legal, technical, psychological and educational measures. It analyzes current trends in the development and implementation of road safety programs, as well as successful practices to reduce the number of accidents and injuries on the roads. Based on a study of the effectiveness of various strategies and programs aimed at reducing the number of accidents and casualties in traffic accidents, the article provides information on which measures are most effective and how they can be implemented in different contexts. In addition, it emphasizes the importance of cooperation between the authorities, public organizations and citizens in implementing road safety. The article also discusses in detail the importance of preventive measures, which may include educational campaigns and awareness-raising activities on vehicle safety on the roads. It also analyzes the legal norms and penalties for violations of traffic rules and the possibility of studying and using the experience of other countries, in particular Poland, in ensuring the safety of vehicles on the roads. The concluding part of the article discusses the prospects for the future development of road safety systems and the challenges that may arise in the future. The findings of this study can be useful for policy makers, researchers, and road safety professionals to improve road safety and reduce the number of road traffic accidents.

Exploring the Concept of Unsafe Antisocial Behavior among Motor Vehicle Drivers: A Qualitative Study

Background: Previous analyses have revealed that the human factor is significant in causing traffic accidents. Antisocial driving behaviors have led to a phenomenon called driving violence, which poses a significant risk to other road users. Accordingly, this study aimed to explore the concept of antisocial driving behavior and its various types. Methods: This qualitative study was carried out using a content analysis approach and involved 12 participants selected through the purposive sampling method. In-depth semi-structured interviews were conducted from June to September 2019 in Tehran, Iran. Data were analyzed using Graneheim’s approach. Results: The study identified a total of 419 primary codes, 10 subcategories, and 5 main categories including culture, violation of laws, reduced traffic safety, reduced social welfare, and risk-taking behaviors. Conclusion: Antisocial behaviors in driving are context-based and pose a potential risk to other road users. They often violate citizenship rights and driving laws while being related to the prevailing culture of the community. Therefore, appropriate interventions and policies should be adopted to reduce these behaviors.

CN+: Vehicular Dataset at Traffic Light Regulated Intersection in Bremen, Germany

Vehicular Ad-Hoc Networks (VANETs) were introduced to avoid vehicular-related accidents and to improve the safety of both vehicular passengers and other road users. In VANETs, the vehicles are expected to communicate with neighbouring vehicles to increase awareness about the surrounding by using V2V (vehicle-to-vehicle) communication links. Since the introduction of VANETs, much research has focused on developing state-of-the-art algorithms to increase safety. However, real-world testing of these developed algorithms has become challenging due to the required high cost and multiple practical reasons. Therefore, simulation-based testing is commonly used for VANETs related applications. Using real datasets inside a simulation can significantly increase the results’ accuracy and help to achieve realistic results. In this study, we present a dataset called ’CN+’, which consists of more than 25,000 vehicles collected over 32 hours at a signalized intersection in Bremen, Germany. paper