Traffic Data Collection Research Articles

Strategy to reduce the use of private cars and motorbikes to the Universitas Andalas campus, Limau Manih Padang, to support the Green Campus Program

The United Nation states that vehicle fuel gas emissions from the transportation sector are a significant contributor to global warming. One of the preventative steps that universities can take is to limit the use of vehicles to and within the campus environment. Andalas University as one of the largest campuses in West Sumatra is very interested in taking an active role in reducing vehicle fuel gas emissions. The first aim of this research is to observe the ratio of vehicles entering campus to the campus population, and calculate the percentage reduction in private vehicle use, in order to increase the UI green metric level. Second, identifying potential private vehicle users who will switch to using Trans Padang public transportation. Traffic volume data collection is carried out using video recordings. A questionnaire interview survey was also carried out to analyze the campus population's preference for switching to public transportation. The survey results show that the volume of vehicles entering campus is more than twelve thousand vehicles. To achieve an increase in the value of one level of the UI green metric requires a reduction in private vehicles of 63.4%. The results of the chi-square test for the variables of employment, private vehicle ownership, and monthly income influence the variable frequency of private vehicle use. Users of motorbikes, private cars, online taxis and paratransit wish to shift mode using Trans Padang, and conventional motorcycle taxi users really wish to shift mode using Trans Padang.

A Machine Learning Scheme for Speed Prediction in Intelligent Transportation Systems Using a Bi-LSTM Based Model

Congestion has become a big issue in major cities throughout the world. Numerous transportation activities have been affected, and travel times have increased as a result of many travelers spending lengthy hours on the road. To overcome these challenges, the Intelligent Transportation System (ITS), which provides efficient traffic service and management, has sparked widespread attention. Collection and analysis of traffic data has been made possible by the algorithms implemented by the ITS. Huge volumes of data are produced by the vast wide range of sensors used in the Internet of Things (IoT), enabling the collection of a variety of traffic information. Development of short-term traffic speed prediction, has been made possible using deep learning models such as Long Short Term Memory (LSTM) and Bidirectional LSTM. Numerous variables, including the weather, the state of the roads, and traffic congestion, can have long-term dependencies and influence traffic speed. The bidirectional architecture of Bi-LSTMs enables them to handle long-term dependencies in sequences and efficiently capture both past and future context in a sequence, which is crucial for producing accurate forecasts of traffic speed. In this paper, the upstream and downstream flow of traffic speed on various pathways has been investigated using a traffic path planning algorithm based on Bi-LSTM models. The algorithm considers the factors affecting the flow of traffic at different seasons and time of the day and tries to predict the average speed associated to that path several timeslots ahead. The experimental results demonstrated that the Bi-LSTM model has the benefit of predicting speed for various timeslots while retaining a high level of accuracy.

Road safety impact assessment practices and recommendations

Abstract This article reviews the practices for conducting Road Safety Impact Assessment (RIA). An overview of current research and methodologies related to this field is presented, and various aspects of the preparation of RSIA are analysed. The DIRECTIVE OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on road infrastructure safety management (RISM Directive) defines the procedure “road safety impact assessment” as a strategic comparative analysis of the impact of a new road or a substantial modification to the existing network on the safety performance of the road network”; The RISM Directive provides only the basic framework for conducting such analysis by just pointing out its basic elements without providing specific methodology for it. Some countries have developed their own methodologies, others not. Although the RIA is a procedure, that was included in the initial RISM Directive (2008), Bulgaria has not yet had much experience with it, nor has a developed methodology or recommendations for it. The main aim of this article is to review and analyse international experience in conducting road safety impact assessment and to present some recommendations about it for such procedures from national teams. The article draws attention to the methods and tools used in the preparation of the RIA. This includes the collection and analysis of road infrastructure and traffic data, the study of previous safety analyses, modelling and simulations of the proposed measures, and the assessment of social and economic aspects. One of the main aspects discussed in the article is the possibility of using computer models and artificial intelligence (AI) in the preparation of the RIA. AI can be applied to process large volumes of data and perform complex risk and scenario analyses. This allows practitioners to obtain more accurate and predictable results to serve as a basis for making informed road safety decisions. In conclusion, the article emphasizes the importance of preparing Road safety impact assessment and presents different practices and methodologies that can be used in this process. The use of computer models and AI can improve the efficiency and accuracy of RSIA and contribute to achieving higher standards of road safety.

Investigating Pedestrian Crossing Patterns at Crossing Locations Based on Trajectory Data Collected by UAV

Previous studies on pedestrian crossing have mostly focused on pedestrian crossing decisions; while as an important behavioral aspect, the pedestrian crossing process, i.e., their motions during the entire crossing process, has been narrowly studied. Understanding how pedestrian moves across the street during their entire crossing process helps identify risky movements and reasons for such movements, which can further help in the implementation of effective countermeasures. Therefore, this paper proposed a new and easily applied approach for investigating and understanding the pattern of the pedestrian crossing process at crosswalks based on vision-based trajectory tracking technology and UAV (unmanned aerial vehicle) data. This study uses UAV for collecting video data which is timesaving and has a sufficient coverage area, compared to other methods. For trajectory extraction, the vision-based Deep-SORT-Yolov5 architecture is applied. An improved DBSCAN (density-based spatial clustering of applications with noise) algorithm is introduced for clustering and identification of patterns of pedestrian crossing processes based on their trajectories. This approach is tested via a case study involving six marked crosswalks in Shanghai, China. By using the proposed method, different crossing patterns are extracted and compared. The results show reasonable outputs of trajectory patterns, which reasonably explain the potential instincts of the pedestrians and affecting factors on the behavior of the pedestrian crossing process. Suggestions are made based on the results. This paper contributes to a more comprehensive safety analysis of pedestrian crossings by considering the pedestrian crossing process. The model, along with the UAV-based trajectory observation method, provides an easily-applied and low-cost way of traffic data collection for the purpose of pedestrian safety evaluation.

FTDA: Performance Enhancement of WSN using Fuzzy based Traffic Data Analysis

In the present time in Wireless Sensor Network plays an essential role in the monitoring of different physical phenomena. Monitoring of city traffic data analysis is very important in different metro cities due to rapid increase in population. This research work proposes a model for traffic data analysis using wireless sensor network incorporated with fuzzy technique. The proposed model is tested for performance parameters such as node dead rate , data packed received. The proposed model improved the efficiency compared to existing techniques of the WSN network for traffic data collection and analysis.

INFLUENCE OF SIGHT DISTANCE CHARACTERISTICS ON SAFETY OF INTERSECTIONS USING SURROGATE SAFETY MEASURES

Recent road accident statistics indicate that more than 30% of accidents occur at intersections. Whereas, total percentage of intersections present in the entire road network is less than 2%. From design point of view, one way to ensure reduction of accidents at intersections is to provide sufficient sight distance and clear sight triangles. However, it was observed that even when minimum sight distance is provided, it does not ensure non-occurrence of accidents because available sight distance at intersection changes with time. Hence there is a need to understand the effect of sight distance characteristics on safety at uncontrolled intersections. From safety studies, it was observed that crash data is highly unreliable as it is generally underreported. Hence in the present study, surrogate safety measures were used instead of crash data. To accomplish the study objectives, six uncontrolled three-legged intersections were selected. Geometric data such as sight distance was measured as per IRC SP 41 (1994) guidelines. Traffic data collection at each intersection was carried out using videography method. From the recorded videos, volume data, speed data, and Post Encroachment Time (PET) data were extracted manually. The effect of various geometric and traffic factors on the critical conflict rate was studied using correlation and regression analysis. From the models developed, it is observed that for every 1 unit increase in logarithm of major and minor road sight distance, critical conflict rate decreased by 18.14% and 33.56%, respectively. Also, it was found that, the available sight distance is far shorter than the design/minimum sight distance specified by IRC SP 41 (1994).

A Latent-Factor-Model-Based Approach for Traffic Data Imputation with Road Network Information

With the rapid development of the economy, car ownership has grown rapidly, which causes many traffic problems. In recent years, intelligent transportation systems have been used to solve various traffic problems. To achieve effective and efficient traffic management, intelligent transportation systems need a large amount of complete traffic data. However, the current traffic data collection methods result in different forms of missing data. In the last twenty years, although many approaches have been proposed to impute missing data based on different mechanisms, these all have their limitations, which leads to low imputation accuracy, especially when the collected traffic data have a large amount of missing values. To this end, this paper proposes a latent-factor-model-based approach to impute the missing traffic data. In the proposed approach, the spatial information of the road network is first combined with the spatiotemporal matrix of the original traffic data. Then, the latent-factor-model-based algorithm is employed to impute the missing data in the combined matrix of the traffic data. Based on the real traffic data from METR-LA, we found that the imputation accuracy of the proposed approach was better than that of most of the current traffic-data-imputation approaches, especially when the original traffic data are limited.

Analyzing the Impact of C-ITS Services on Driving Behavior: A Case Study of the Daejeon–Sejong C-ITS Pilot Project in South Korea

This paper analyzes the impact of C-ITS service on driving behavior, focusing on a pilot project in Daejeon–Sejong, South Korea. C-ITS, an advanced technology, enables bidirectional wireless communication between vehicles or infrastructure, allowing for real-time traffic data collection and dissemination. The study uses a unique analytical method, employing parallel processing techniques for variable extraction and a paired t-test to examine the short-term effects of C-ITS on driving behavior. Findings indicate a significant change in drivers’ behavior, particularly in average speed, hard braking rate, severe deceleration rate, speeding rate, and excessive speeding rate, towards safer trends after receiving C-ITS warning services. Reductions in hard braking and severe deceleration were immediate after C-ITS service initiation, while a decrease in excessive speeding was observed after four months. Further research is needed to identify other potential influencing variables and provide an unbiased evaluation of C-ITS effectiveness. The study’s implications highlight its role in promoting public acceptance of C-ITS-service-based cooperative autonomous driving strategies.

Enhancing computational energy transportation in IoT systems with an efficient wireless tree-based routing protocol

In this era of technological advancements, our globe has witnessed the tremendous potential of wireless technology, which has revolutionized data transport. One area with a lot of promise for increasing the usage of this technology is traffic control. Real-time path planning algorithms have emerged as an important tool for reducing traffic congestion in urban areas, addressing current state-of-the-art. Real-time traffic data collection, which leverages the capabilities of Vehicle Ad-Hoc Networks (VANETs), offers opportunities to reduce travel costs and complexity. Our study offers an optimized cluster tree-based routing protocol (OCTRPL) within the framework of an advanced IoT-based computational energy transportation system to accomplish this. Vehicles serve as network nodes in this architecture, while traffic stations serve as hubs and routers. We assure the efficient implementation of a shortest-path strategy by using the OCTRPL, thereby lowering data traffic between network nodes and increasing the network's overall lifespan. The Euclidean distance facilitates the development of an ideal cluster, allowing for the selection of Cluster Heads (CHs) using a game theoretic method. Following that, criteria such as expected transmission count, queue utilization ratio, and residual energy ratio (RER) are considered in the route selection process. This set of criteria, known as ETC, assists in selecting the best path for efficient energy transit throughout the network. We illustrate the outstanding performance of our suggested approach in meeting its intended aims by a comparative study of data. The sophisticated IoT-based computational energy transportation system, in conjunction with the efficient wireless tree-based routing protocol, demonstrates promising results and opens up new options for further research and development in this sector.

Traffic safety assessment for roundabout intersections using drone photography and conflict technique

Road design deficiencies and improper driver behavior at roundabout intersections may result in traffic bottlenecks, irregular traffic patterns, and potential crashes. Thus, road safety inspection is conducted to identify potential safety hazards and propose safety measures. The traditional safety inspection depends on unreliable traffic collision data visual data collection and superficial analysis. In this regard, surrogate safety assessment approaches are utilized to overcome the limitations found in traditional approaches. This paper employs an innovative surrogate approach for such a process by analyzing videos captured by a drone. A video processing technique is applied to determine the vehicle trajectories and extract conflict points. Accordingly, the conflict data are analyzed in terms of location, direction, and post-encroachment time (PET) as a safety measure to identify potential safety problems related to intersection geometry and driver behavior. This methodology is applied to an intersection case study in New Cairo City, Egypt. The findings of this study confirm the interaction between intersection geometry, drivers’ behavior, and road safety on the examined safety measures.

Efficacy of Bluetooth-Based Data Collection for Road Traffic Analysis and Visualization Using Big Data Analytics

Effective management of daily road traffic is a huge challenge for traffic personnel. Urban traffic management has come a long way from manual control to artificial intelligence techniques. Still real-time adaptive traffic control is an unfulfilled dream due to lack of low cost and easy to install traffic sensor with real-time communication capability. With increasing number of on-board Bluetooth devices in new generation automobiles, these devices can act as sensors to convey the traffic information indirectly. This paper presents the efficacy of road-side Bluetooth scanners for traffic data collection and big-data analytics to process the collected data to extract traffic parameters. Extracted information and analysis are presented through visualizations and tables. All data analytics and visualizations are carried out off-line in R Studio environment. Reliability aspects of the collected and processed data are also investigated. Higher speed of traffic in one direction owing to the geometry of the road is also established through data analysis. Increased penetration of smart phones and fitness bands in day to day use is also established through the device type of the data collected. The results of this work can be used for regular data collection compared to the traditional road surveys carried out annually or bi-annually. It is also found that compared to previous studies published in the literature, the device penetration rate and sample size found in this study are quite high and very encouraging. This is a novel work in literature, which would be quite useful for effective road traffic management in future.

Detection of HTTP DDoS Attacks Using NFStream and TensorFlow

This paper focuses on the implementation of nfstream, an open source network data analysis tool and machine learning model using the TensorFlow library for HTTP attack detection. HTTP attacks are common and pose a significant security threat to networked systems. In this paper, we propose a machine learning-based approach to detect the aforementioned attacks, by exploiting the machine learning capabilities of TensorFlow. We also focused on the collection and analysis of network traffic data using nfstream, which provides a detailed analysis of network traffic flows. We pre-processed and transformed the collected data into vectors, which were used to train the machine learning model using the TensorFlow library. The proposed model using nfstream and TensorFlow is effective in detecting HTTP attacks. The machine learning model achieved high accuracy on the tested dataset, demonstrating its ability to correctly identify HTTP attacks while minimizing false positives.

Evaluating Traffic Operation Conditions during Wildfire Evacuation Using Connected Vehicles Data

With climate change and the resulting rise in temperatures, wildfire risk is increasing all over the world, particularly in the Western United States. Communities in wildland–urban interface (WUI) areas are at the greatest risk of fire. Such fires cause mass evacuations and can result in traffic congestion, endangering the lives of both citizens and first responders. While existing wildfire evacuation research focuses on social science surveys and fire spread modeling, they lack data on traffic operations during such incidents. Additionally, traditional traffic data collection methods are unable to gather large sets of data on historical wildfire events. However, the recent availability of connected vehicle (CV) data containing lane-level precision historical vehicle movement data has enabled researchers to assess traffic operational performance at the region and timeframe of interest. To address this gap, this study utilized a CV dataset to analyze traffic operations during a short-notice evacuation event caused by a wildfire, demonstrating that the CV dataset is an effective tool for accurately assessing traffic delays and overall traffic operation conditions during the selected fire incident. The findings also showed that the selected CV dataset provides high temporal coverage and similar travel time estimates as compared to an alternate method of travel time estimation. The study thus emphasized the importance of utilizing advanced technologies, such as CV data, to develop effective evacuation strategies and improve emergency management.

PENGARUH PROPORSI SEPEDA MOTOR TERHADAP KINERJA JALAN PERKOTAAN

The Gunung Agung Denpasar road section is an urban road that experiences high traffic loads. This is due to the fact that land use along this road is very dense with various activity centers, such as shopping centers and general trade, education, offices, and other public facilities. In addition, Gunung Agung Road is a connecting road and traffic crossing between the city center and suburban areas. The traffic load is dominated by motorbikes with a higher proportion than other modes of transportation. The purpose of this study was to determine the performance of the Gunung Agung road. Traffic data collection is done by a manual survey to record traffic volume, speed, and side resistance. The results of the analysis show road performance, such as the maximum traffic volume obtained is 2,177.30 pcu/hour, with the proportion of motorbikes at 87.16%, the average speed is 23.47 km/hour, the average degree of saturation (DS) is 0,85. The level of road service includes level D in Segment II and E which occurs in Segment I, the flow is approaching unstable, and drivers are starting to feel congestion. High traffic density, and side barriers of 362.5 (300-4999) are included in the medium category, namely industrial areas with shopping centers (shops) on the side of the road.

Quality of Automatic Traffic Volume Counting by Cameras and Impact on the Qualitative Indicators of Traffic

This paper presents research on the collection, analysis, and evaluation of the fundamental data needed for road traffic systems. The basis for the research, analysis, planning and projections for traffic systems are traffic counts and data collection related to traffic volume and type. The quality and accuracy of this data are very important for traffic planning or optimization. Therefore, the purpose of this research is to apply advanced methods of automatic counting of motorized traffic and to evaluate the impact of this data on the measurement of important traffic indicators. The accuracy of measurements arising from the traditional method of data collection through manual counting will be compared with the most advanced methods of automatic counting through cameras. For this purpose, an analytical algorithm for the recognition and processing of data related to road users as a function of the time of day was applied. The program was written in the programming language Python, and the accuracy of the data and its effect on the results of qualitative traffic indicators were analyzed using the Synchro software model. The developed program is capable of recognizing and classifying different types of vehicles in traffic, such as motorbikes, motorcycles, cars, pick-ups, trucks, vans and buses, as well as counting the traffic volume over time. The results obtained from these two models show the advantages of applying advanced methods of data collection and processing related to dynamic traffic processes, as well as the quality in terms of the impact on the measurement of qualitative traffic indicators. A comparison of the quality of results for the different time intervals and varying levels of visibility in traffic is presented using tables and graphs. At nighttime, when visibility was poor, the discrepancy between the manual and automatic counting methods was around 9.5%. However, when visibility was good, the difference between manual counting and the automated program was 4.87% for the period 19:00–19:15 and 3.64% for the period 05:00–05:15. This discrepancy was especially noticeable when distinguishing between vehicle categories, due to the limitations in the accuracy in recognizing and measuring the dimensions of these vehicles. The difference between the two calculation models has a minor effect on qualitative traffic indicators such as: approach LOS, progression factor, v/s, v/c, clearance time, lane group flow, adj. flow, satd, and flow approach delay.

Research on Intelligent Sensing Technology of Traffic Volume in Yancheng Inland Trunk Waterway

In order to solve the problems that the existing channel traffic monitoring methods for ships in Yancheng City cannot meet the actual monitoring needs and the existing channel traffic monitoring methods cannot meet the requirements of development planning. This paper realizes automatic collection and accurate acquisition of channel traffic data by using intelligent sensing technologies such as AIS, video surveillance and lidar, and builds an integrated channel traffic monitoring system platform which integrates multi-dimensional perception, fusion processing and statistical analysis.

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street.

Roadside parking is a common traffic phenomenon in China. Narrow urban streets, high parking demands, and a shortage of parking lots force the public to engage in random parking along the roadside. A protocol is proposed to determine the impact of a roadside-parked vehicle on passing vehicles. In this investigation, a dual-direction and two-lane urban street in which one vehicle is parked on the roadside is selected for the collection of traffic data. Based on these data, the impact of the roadside-parked vehicles on the trajectory and speed of passing vehicles is determined. In addition, a microsimulation model is applied to determine the impact of roadside parking on the maximum queue length, delay, emissions, and other indicators under different traffic volumes according to the sensitivity analysis. The results show that roadside-parked vehicles affect the trajectory of passing vehicles for approximately 80 m and have a negative effect on speed, with the lowest speed being observed at the location of the roadside-parked vehicle. The sensitivity analysis results suggest that traffic volume increases synchronously with indicator values. The protocol provides a method for determining the effect of roadside parking on travel trajectory and speed. The research contributes to the refined management of future roadside parking.

Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and Challenges

Missing data in Intelligent Transportation Systems (ITS) could lead to possible errors in the analyses of traffic data. Applying Artificial Intelligence (AI) in these circumstances can mitigate such problems. Past works focused only on specific data imputation methods, such as tensor factorization or a specific neural network model. While there are review papers covering singular topics regarding missing data, there are none in the field of traffic, to the best of our knowledge, that introduces the process of missing data collection and the viability of the traffic data collected while also broadly covering the popularly used models of recent years. This has led to non-uniformity of the terms used in missing data imputation, limited research in areas where datasets are not available, and a narrowed view of the methods used for data imputation. Hence, this paper aims to standardize the terms used in missing data classifications, look into the limitations of using available public or private datasets for urban traffic research, and discuss popular statistical and data-driven methods used by recent AI and ITS papers. It was found that tensor decomposition-based methods are the most popular for missing data imputation, followed by Generative Adversarial Networks and Graph Neural Networks, all of which rely on a large training dataset. Meanwhile, Probability Principle Component Analysis (PPCA) methods provide valuable insights via traffic analysis and are used for real-time traffic imputation. This paper also highlights the need for more efficient and reliable methods for traffic data collection, such as online APIs.

Quasi-Optimization of Resource Allocation and Positioning for Solar-Powered UAVs

Unmanned Aerial Vehicles (UAVs) will be an integral part of future smart cities to provide applications such as traffic management, environment monitoring and data collection. UAVs offer flexible deployment, dynamic mobility, and Ultra-Reliable and Low Latency Communications (URLLC). However, UAVs are power-hungry devices, and their limited battery capacity cannot support their flight and communication operations for a long period. Additionally, multi-carrier (MC) techniques will be vital for supporting futuristic multi-user communication systems. To overcome these issues, we propose a solar-powered UAV MC system to support URLLC services for multi-users. In this regard, we aim to maximize the system sum throughput and we jointly optimize UAV positioning and sub-carrier allocation. To solve the optimization problem, we propose the low-complexity coordinate descent approximation algorithm (CDAA). Lastly, we show the proposed algorithm converges quickly and simultaneously yields superior performance than fixed benchmark schemes for two simulated environments.

Prioritization of unmanned aerial vehicles in transportation systems using the integrated stratified fuzzy rough decision-making approach with the hamacher operator

The Integration of Unmanned Aerial Vehicles (UAVs) into transportation systems has numerous benefits, ranging from the ability to record real-time data to having high mobility and broad vision. Because of the increasing levels of congestion in the cities, many of the transportation system tasks, such as traffic management and data collection, have become complicated to handle. The implementation of UAVs in transportation systems is a feasible solution to overcoming these problems. Therefore, UAVs have become a point of interest for many transportation system authorities. This study aims to introduce an integrated Fuzzy Level Based Weight Assessment (LBWA) in a stratification environment (SF-LBWA) and Fuzzy Rough Hamacher Combined Compromise Solution (CoCoSo) model to resolve the prioritization of Unmanned Aerial Vehicles in transportation system problems. The decision framework for such a problem is defined by establishing the relevant alternatives and criteria according to the specialized literature. Hence, a case study illustrates the formulation and solution of the problem. This study will indicate the most suitable alternative for the problem and provide guidance as authorities look for ways to integrate UAVs and transportation systems.