Traffic Data Sources Research Articles

A Hybrid Approach for Intrusion Detection by Dragonfly Optimization and Bayesian Neural Network Methodologies

The technique of keeping an eye out for malicious activity or policy violations on a computer network or system is known as intrusion detection. It entails examining system logs, network traffic, and other data sources to spot potential security breaches, misuse, and illegal access. Network-based and host-based intrusion detection systems (IDS) are the two types available. The former analyzes network traffic and packets, while the latter concentrates on specific hosts or devices. This paper investigates how to improve intrusion detection systems in cyber security by combining dragonfly optimization (DFO) and Bayesian neural network (BNN) methodologies. The NSL-KDD dataset, which is tested and trained in the system, is the source of the dataset used in the study. By utilizing the optimization skills of DFO and the probabilistic reasoning powers of BNN, the hybrid technique improves the accuracy and flexibility of detecting and addressing network intrusions. This research illustrates the effectiveness of the suggested methodology in navigating dynamic network environments and identifying emerging dangers through a thorough examination. In addition to improving detection performance, the integration of BNN and DFO gives enterprises insightful information about infiltration patterns and the ability to take proactive measures to thwart hostile activity. The created model's enhanced intrusion detection performance is demonstrated by the experimental findings. These results are validated in terms of accuracy, precision, recall, execution time, and F1 score by comparing them with models that are currently in use.

Leveraging Crowdsourcing for Mapping Mobility Restrictions in Data-Limited Regions

This paper introduces a novel methodology for the real-time mapping of mobility restrictions, utilizing spatial crowdsourcing and Telegram as a traffic event data source. This approach is efficient in regions suffering from limitations in traditional data-capturing devices. The methodology employs ArcGIS Online (AGOL) for data collection, storage, and analysis, and develops a 3W (what, where, when) model for analyzing mined Arabic text from Telegram. Data quality validation methods, including spatial clustering, cross-referencing, and ground-truth methods, support the reliability of this approach. Applied to the Palestinian territory, the proposed methodology ensures the accurate, timely, and comprehensive mapping of traffic events, including checkpoints, road gates, settler violence, and traffic congestion. The validation results indicate that using spatial crowdsourcing to report restrictions yields promising validation rates ranging from 67% to 100%. Additionally, the developed methodology utilizing Telegram achieves a precision value of 73%. These results demonstrate that this methodology constitutes a promising solution, enhancing traffic management and informed decision-making, and providing a scalable model for regions with limited traditional data collection infrastructure.

Road traffic crash data in Southwestern Nigeria: a qualitative analysis of sources, contents and management methods

Objective This study qualitatively examined road traffic crash (RTC) data collection and management in southwest Nigeria, with the goal of addressing the tenets of the UN Decade of Action Plan on Road Safety’s call for an effective data system. Methods Data on RTCs data collection in the study area was obtained through key informant interviews with five hospital accident emergency unit directors, three Federal Road Safety Commission (FRSC) Sector commanders, and three Nigeria Police Force (NPF) commissioners. Datasets were transcribed, categorized, and interpreted using content analysis, descriptive statistics, and WHO recommended minimal crash data element requirements. Results The study found disparate systems, a lack of synergy, and discrepancy in the various data sources in the country when compared with WHO recommendations on road traffic data collection systems, with 55% of agencies using non-standardized paper forms. The study also reveals that the minimum crash data elements recommended by WHO are not consistently captured in the NPF, FRSC, and hospital traffic crash data sources. Conclusions The study suggested the use of an upgraded National Road Traffic Crash Data Management System (NRTCDMS) Data Template to unify data gathering and linkage issues, but recommends a digital version of the template.

O'Hare Airport roadway traffic prediction via data fusion and Gaussian process regression

This study proposes an approach of leveraging information gathered from multiple traffic data sources at different resolutions to obtain approximate inference on the traffic distribution of Chicago's O'Hare Airport area. Specifically, it proposes the ingestion of traffic datasets at different resolutions to build spatiotemporal models for predicting the distribution of traffic volume on the road network. Due to its good adaptability and flexibility for spatiotemporal data, the Gaussian process (GP) regression was employed to provide short-term forecasts using data collected by loop detectors (sensors) and supplemented by telematics data. The GP regression is used to make predictions of the distribution of the proportion of sensor data traffic volume represented by the telematics data for each location of the sensors. Consequently, the fitted GP model can be used to determine the approximate traffic distribution for a testing location outside of the training points. Policymakers in the transportation sector can find the results of this work helpful for making informed decisions relating to current and future transportation conditions in the area.

Identifying Urban functional regions: A multi-dimensional framework approach integrating metro smart card data and car-hailing data

Urban functions often diverge from initial planning due to changes driven by residents’ behaviors. Effective urban planning and renewal require accurately identifying urban functional regions based on residents’ behavior data (including activity and travel data). However, previous methods have primarily relied on either point of interest (POI) data or a single source of traffic data, and often ignore the combined influence of residents’ activities and travel behaviors. In this study, we introduce a novel framework that integrates multiple sources of traffic data (such as metro smart card data and car-hailing data) with POI data to identify urban functional regions. This approach is unique because it simultaneously considers two critical dimensions of residents’ behavior: travel and activity behaviors. By combining these dimensions, we extract a comprehensive set of characteristics, including travel time, travel flow, origin-destination patterns, activity types, and activity time, which are then aggregated at the regional level (i.e., traffic analysis zone). To process these characteristics, we use latent Dirichlet allocation (LDA) to extract high-level semantic features from each data type. Additionally, to handle the sparse data from metro smart cards, we employ a specialized clustering technique. The integration of diverse and complementary information from multiple data sources enables more accurate and nuanced identification of urban functional regions than single data source and k-means clustering algorithm, providing valuable insights for urban planners.

Commercial floating car data application in Sub-Saharan African transport planning contexts: A critical review and research agenda

Floating car data (FCD) are a source of traffic information reported by GPS-enabled probes from within the traffic stream. FCD define speed, travel time and movement patterns, all key inputs for transport planning. FCD are readily available over the entire road network without roadside sensors and communications infrastructure, making this a particularly relevant traffic data source in low- and middle-income countries (LMICs) where resource limitations restrict the expansive distribution of traffic sensors. This review article aims to investigate the applicable use-cases of FCD in LMICs, while simultaneously sensitising transport practitioners to the potential data biases that may be present in FCD. Particularly, this paper considers whether the context of Sub-Saharan African countries allows for the application of FCD similarly to high-income country FCD use-cases, critically examining insights from literature. Sub-Saharan African countries typically differ from high-income nations with respect to urban form, population distribution (and segregation), modal choice, driver population, and technology penetration. To date, there has been no literature that concatenates the current practice of FCD use-cases in Sub-Saharan Africa, nor defines the fundamental differences between transport and land use planning in high- and low-income countries with the view to determine if these dissimilarities impact FCD collection and use. Literature revealed novel research involving the application of FCD to investigate informal public transport systems that are specific to Sub-Saharan Africa, while also determining that accuracy and potential biases of FCD are not widely understood in Sub-Saharan Africa. Findings show that biases are likely to occur in FCD in Sub-Saharan Africa because of the representation and behaviours of the sample reporting FCD, as well as the urban form of Sub-Saharan African cities. The paper concludes by developing a research agenda that will assist to further the knowledge of FCD sample bias and the applicable use-cases of FCD in Sub-Saharan Africa.

Vehicle Detection for Accident Prevention

Abstract: One of the key components of the smart traffic concept is vehicle detection and tracking. Modern city planning and development is impossible to achieve without a thorough understanding of the city's existing traffic flows. Surveillance video is an underutilized source of traffic data that can be discovered using a wide range of information technology tools and solutions, including machine learning techniques. A critical step in these systems is robust and reliable vehicle detection. It employs an object detection model to locate vehicles in images captured by an outdoor surveillance camera. A review of recent vision-based on-road vehicle detection systems is presented in this paper. YOLO generates a region proposal network and classifies these region proposals at the same time. The purpose of this paper is to address the issues raised above. This model has good performance in object detection.

Analysis in the field of volume-delay function research

One of the key parts of the travel demand model is the correct determination of the performance of the road system. Performance is partially determined by the correct choice of volume-delay functions (VDFs) and their input parameters. Worldwide, transport modellers have a wide range of VDFs to choose from, but research is needed to select the one that provides the most accurate estimates for specific conditions. The problem is that road conditions and traffic conditions are specific to each country, so assuming that we can use VDFs and their parameters based on traffic data from another country can lead to errors. For this reason, there is a need for research on VDFs in the context of a particular country's traffic conditions.The main objective of this paper is to provide knowledge and practices that can be used as a basis for future research. This paper discusses the most commonly used VDFs for links, their parameterisation and the types of data required for calibration, and approaches to their collection. This paper also aims to introduce crowdsourced real-time data as a possible new source of traffic data, with a focus on traffic data from the Google Maps Directions API. The results of this paper will be used in future research to select and parameterise the best-fitting VDFs for specific types of road facilities and traffic conditions within the national transport model of the Slovak Republic.

Few-Shot Learning-Based Network Intrusion Detection through an Enhanced Parallelized Triplet Network

Network intrusion detection is one of the critical techniques to enhance cybersecurity. Several few-shot learning-based methods have recently been proposed to alleviate the dependence on large training samples in many supervised learning methods. However, it is still a challenge to achieve real-time higher-accuracy intrusion detection which is an essential requirement for high-speed network security. In this study, we propose a novel few-shot learning-based network intrusion detection method to address this challenge. Specifically, we improve the detection accuracy and real-time processing speed simultaneously in the metric procedure via two mechanisms: (i) we utilize a hard sample selection scheme as a refining stage of our triplet network model training to increase the detection accuracy; and (ii) we design a lightweight embedding network and parallelize the metric feature extraction process to achieve real-time analysis speed. To evaluate the proposed method, we construct few-shot learning-based datasets by using two real and heterogeneous network traffic intrusion detection data sources. Extensive results demonstrate that our method outperforms the state-of-the-art methods in terms of real-time performance and high detection accuracy of malicious samples.

A METHOD TO ESTIMATE TRAFFIC PENETRATION RATES OF COMMERCIAL FLOATING CAR DATA USING SPEED INFORMATION

Floating Car Data (FCD) are being increasingly used as an alternative traffic data source due to its lower cost and high coverage area. FCD can be obtained by tracking vehicle trajectories individually or by processing multiple tracks anonymously to produce average speed information commercially. For commercial FCD, the spatio-temporal distribution of these vehicles in actual traffic, traffic Penetration Rate (PR) is the most important factor affecting the accuracy of speed estimations, despite the high number of registered vehicles feeding to an FCD provider, denoting the market PR. This study proposes a method for assessing the traffic PR of commercial FCD by evaluating its speed estimation quality compared to Ground Truth (GT) data. GT speed data were employed to generate different levels of traffic PR using Monte Carlo (MC) simulations, which resulted in the development of Quality-PR (Q-PR) relations for Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) as selected Measures of Effectiveness (MoE). Simulation-based FCD results at an urban road segment in Ankara (Turkey) showed that a quality of FCD with traffic PR of 15% or more would improve significantly. Use of the developed Q-PR relations suggested an approximately 5% traffic PR for the commercial FCD speeds at the location.

Fusion of engineering insights and emerging trends: Intelligent urban traffic management system

Traffic congestion is a great concern, especially in urban areas where the vehicles’ number on roads continues to intensify significantly against the slow development of road infrastructure. To resolve this intractable problem, researchers proposed various Traffic Management Systems that rely on the fusion of traffic data sources and emerging technologies to maximize the traffic flow, and thus fluidify the intersections since they are the origin of traffic congestions, and also reroute the vehicles to avoid traffic congestions areas, and thus reduce fuel consumption and air pollution. Although there are many studies on traffic management, there is still a lack of research covering the entire urban road traffic management system. Therefore, this study intends to fill the gap, generating new insight into the state of research related to the entire Traffic Management System. The principal aim of this survey is to provide a complete view of current approaches proposed in the literature to handle the traffic congestion problem. We first supply a review of the body of knowledge in recent years of Traffic Management Systems. Second, we summarize the benefits and drawbacks of the recent literature. Third, we analyze and discuss the three main subsystems of a Traffic Management System and then classify the various contributions according to the subsystem to which they belong. Finally, we present and discuss future research orientations and open issues that need further investigation for a realistic and efficient Traffic Management System.

Bluetooth Verisi Kullanarak Kentsel Arterlerde Seyahat Süresinin Değerlendirilmesi

Reliable estimation of speed or travel time (TT) of an urban arterial is the fundamental task for better management of the traffic. Traditionally, such data are collected via loop detectors, inductive loops, video cameras, but their installation cost were not always allowed to locate every specific point. Recently, Bluetooth (BT) technology has been widely used as a traffic data source due to the i) low installation cost, and ii) providing continuous data even 24-h period. The principle of BT-based traffic data is simply capturing the timestamped of the unique Media Access Control (MAC) of Bluetooth devices. Detecting the same MAC addresses from multiple different locations enabled to estimate the Origin and Destination (O-D) of the trips and travel time information. This study evaluates the distribution of TT information from different perspectives in which BT-based traffic data were obtained from five consecutive signalized intersections located in Mersin, Turkey during morning peak hours of 07:30-09:30 for the two weekdays. The results indicated that the data had considerable success in estimating travel times and urban traffic monitoring with adequate sampling rates. However, the filtering process must be carefully handled to distinguish the motorized movements from non-motorized ones.

Mapping Bus and Stream Travel Time Using Machine Learning Approaches

Collection of travel time data has always been a strenuous task, especially on Indian roads, due to the highly mixed traffic conditions and the absence of rigid driving characteristics. Travel time data collection methods such as on-board GPS devices and Wi-Fi scanners have their own feasibility issues. The GPS devices cannot be installed on all private vehicles, and Wi-Fi scanners cannot be set up at each and every road corridor. However, many transit agencies of Indian cities are installing GPS units in their public transport buses, making them a rich source of data. These buses travel all over the network and record the travel time and location information of vehicles at a certain interval, which can become the best and reliable source of traffic data under such conditions. The only limitation with this data is that the buses differ greatly from the rest of the vehicles in terms of vehicle characteristics, freedom of driving, effect of bus-stops, etc. So, a viable solution is to model and link the stream and bus travel times. Based on this, the present study proposes a method to map bus travel times with stream travel times using machine learning techniques, namely, Gradient Boosting Method and Support Vector Machines. Preliminary analysis showed that a nonlinear relationship exists between the travel times, and other factors like peak/off-peak hours, day of the week, etc. Results show that the proposed methods can efficiently map bus travel times with stream travel times with an average MAPE of 20% and perform better than existing approaches such as linear regression and artificial neural networks.

Transition State Matrices Approach for Trajectory Segmentation Based on Transport Mode Change Criteria

Identifying distribution of users’ mobility is an essential part of transport planning and traffic demand estimation. With the increase in the usage of mobile devices, they have become a valuable source of traffic mobility data. Raw data contain only specific traffic information, such as position. To extract additional information such as transport mode, collected data need to be further processed. Trajectory needs to be divided into several meaningful consecutive segments according to some criteria to determine transport mode change point. Existing algorithms for trajectory segmentation based on the transport mode change most often use predefined knowledge-based rules to create trajectory segments, i.e., rules based on defined maximum pedestrian speed or the detection of pedestrian segment between two consecutive transport modes. This paper aims to develop a method that segments trajectory based on the transport mode change in real time without preassumed rules. Instead of rules, transition patterns are detected during the transition from one transport mode to another. Transition State Matrices (TSM) were used to automatically detect the transport mode change point in the trajectory. The developed method is based on the sensor data collected from mobile devices. After testing and validating the method, an overall accuracy of 98% and 96%, respectively, was achieved. As higher accuracy of trajectory segmentation means better and more homogeneous data, applying this method during the data collection adds additional value to the data.

Data fusion for estimating Macroscopic Fundamental Diagram in large-scale urban networks

Since the concept of the Macroscopic Fundamental Diagram (MFD) has been introduced, many studies have investigated the existence and characteristics of the MFD using empirical and simulation data. MFD is a powerful and efficient model for monitoring and managing large-scale urban networks. However, estimating the MFD for large-scale networks faces important challenges; monitoring resources are often limited in such networks. Furthermore, common sensors that are used to collect traffic data (i.e., loop detectors and probe vehicles), have limitations of their own. For instance, loop detectors are fixed sensors and cannot provide accurate density measurements. On the other hand, to estimate the MFD using probe vehicle data, the probe penetration rate must be known a priori. Given that the individual sensors cannot provide complete and accurate traffic measurements, combining the traffic data from multiple sources may improve the estimation of the MFD. The aim of this study is to combine two traffic data sources to estimate the MFD for a large-scale urban network, where the distribution of probe vehicles across the network is not necessarily homogeneous. The two traffic data sets used in this study are probe vehicle data with an unknown penetration rate, and full-scale approximate traffic data which is produced based on loop detector data. We compare the results of the fusion method with the results of a baseline method, which only uses loop detector measurements. The average flow and density estimations resulting from the Bayesian fusion method outperform the baseline method. We observe a particularly significant improvement in average density estimations, which reaffirms that loop detectors cannot accurately measure the average density.

Measuring Prediction Accuracy in a Maritime Accident Warning System

Advances in machine learning methods and the availability of new data sources show promise for improving prediction of operational risk. Maritime transportation is the backbone of global supply chains and maritime accidents can lead to costly disruptions. We describe a case study performed for the United States Coast Guard (USCG) to develop a prototype risk prediction system to provide early alerts of elevated risk levels to vessel traffic managers and operators in the Lower Mississippi River, the second largest port of entry in the United States. Integrating incident and accident data from the USCG with environmental and traffic data sources, we tested existing machine learning algorithms in their predictive ability. We found poor accident prediction accuracy in cross‐validation using the traditional measures of precision and sensitivity. In this specific operational context, however, such single‐class accuracy metrics can be misleading. We define action precision and action sensitivity metrics that measure the accuracy of predictions in engendering the correct behavioral response (actions) among vessel operators, rather than getting the specific event classification correct. We use these operationally appropriate measures for maritime risk prediction to choose an algorithm for our prototype system. While the traditional metrics indicated that none of the algorithms would perform sufficiently well to use in the early warning system, the modified metrics show that the top performing algorithm will perform well in this operational context.

Managing in real-time a vehicle routing plan with time-dependent travel times on a road network

Geographic information systems, global positioning systems, traffic flow sensors and cellular phones are sources of real-time traffic data in road networks. However, many vehicle routing algorithms do not account for this real-time information. In this paper, we consider the problem of adjusting in real-time a time-dependent delivery plan to respond to dynamic changes in travel times. We also consider a variant of the problem in which some customer requests can be canceled. The goal is to minimize disruption by maintaining as much as possible the current planned routes, although without compromising too much solution quality. Computational results obtained by solving instances with up to 500 customers are reported and compared with a strategy that maintains the planned routes, whatever the cost.

Potential Application of Artificial Neural Network (ANN) Analysis Method on Malaysian Road Crash Data

By allowing the movement of commodities and people, road transportation benefits both nations and people. This provides improved access to work opportunities, educational attainment, recreation, and healthcare, all of which have a direct and indirect influence on people. The influence on road transportation, on the other hand, has a detrimental impact on people's health. When addressing road traffic accidents, it is common known that it has merely become a global pandemic, with over a million people dying on the road each year. Malaysia, as a growing country, has identified road safety as a major issue that must be addressed. Reliable road safety statistics are critical for comprehending, assessing, and monitoring the nature and scope of the road safety problem and its solutions, for setting ambitious but realistic safety targets, for designing and implementing effective road safety policies, and for monitoring their success. Several approaches are presently utilized by road safety researchers to produce road safety indicators. In Malaysia, nearly all decisions made by the country's higher authorities to enhance road safety are based on data supplied by relevant stakeholders. As a result, having the proper application of analysis as well as the trustworthiness of the data itself is critical. This article will give a review of the possible use of the Artificial Neural Network (ANN) Analysis technique on traffic road collision data and what it may provide to assist monitor or forecast road safety issues, specifically in Malaysia. A new era in the field of road accident investigation is being ushered in by the development and application of analytical methodologies, which are creating previously unimaginable situations. Due to the convergence of recent advancements in accident research models and the availability of potentially new sources of traffic data, this paradigm shift has been made possible. The study of road crashes has benefited significantly from the development of more advanced data processing methodologies and frameworks, thus the researchers will able to extract significant conclusions from the study of traffic data thanks to the application of these approaches.

Public Transportation Multimodality in the City of Lisbon

Mobility in major European capitals is not yet sustainable. The need to respond to the ongoing changes in public transportation demand, operationalize safety norms of social distancing, and reach carbon neutrality are prompting cities to reassess public transport systems. Cross-mode synergies in multimodal transport systems can be explored (including convenience, reliability, cost, speed and predictability) to foment public and active modes of mobility. In this context, multimodal traffic pattern analysis can unravel cross-mode vulnerabilities, a possibility that is finally rising with the sensorization of cities, integration of ticketing systems, and consolidation of traffic data sources and their situational context.This work introduces a methodology for the analysis of spatiotemporal indices of multimodality against available situational context, aiding specialists to find vulnerabilities on the public transportation network. Traffic generation poles, large-scale public events, and weather records are the considered sources of situational context. We discuss the role of context-aware multimodality indices to understand demand and its emerging changes, assess cross-modal transfers and preferences, and support cross-mode route and schedule planning. This work further discusses the relevance of multimodal pattern discovery to offer data-centric views ensuring: fully transparent decisions to the citizens; and an objective coordination between carriers, municipalities and authorities. Lisbon is further introduced in this work as a reference case study for context-aware multimodal mobility.

6G-Enabled Short-Term Forecasting for Large-Scale Traffic Flow in Massive IoT Based on Time-Aware Locality-Sensitive Hashing

With the advent of the Internet of Things (IoT) and the increasing popularity of the intelligent transportation system, a large number of sensing devices are installed on the road for monitoring traffic dynamics in real time. These sensors can collect streaming traffic data distributed across different traffic sites, which constitute the main source of big traffic data. Analyzing and mining such big traffic data in massive IoT can help traffic administrations to make scientific and reasonable traffic scheduling decisions, so as to avoid prospective traffic congestions in the future. However, the above traffic decision making often requires frequent and massive data transmissions between distributed sensors and centralized cloud computing centers, which calls for lightweight data integrations and accurate data analyses based on large-scale traffic data. In view of this challenge, a big data-driven and nonparametric model aided by 6G is proposed in this article to extract similar traffic patterns over time for accurate and efficient short-term traffic flow prediction in massive IoT, which is mainly based on time-aware locality-sensitive hashing (LSH). We design a wide range of experiments based on a real-world big traffic data set to validate the feasibility of our proposal. Experimental reports demonstrate that the prediction accuracy and efficiency of our proposal are increased by 32.6% and 97.3%, respectively, compared with the other two competitive approaches.