Traffic Analysis Method Research Articles

Interdependency analysis of junctions for congestion mitigation in Transportation Infrastructures

The resilience of the Transportation road infrastructure network is of major importance, since failures such as prolonged road congestion in specific parts of the infrastructure often initiate major cascading effects that block transportation and/or disrupt services of other infrastructures over wide areas. Existing traffic flow analysis methods lack the ability to understand cascading effect of congestions and how to improve overall resilience in greater areas. Dependency risk graphs have been proposed as a tool for analyzing such cascading failures using infrastructure dependency chains. In this paper, we propose a risk-based interdependency analysis methodology capable to detect large-scale traffic congestions between interconnected junctions of the road network and provide mitigation solutions to increase traffic flow resilience. Dependency risk chains of junctions provide important information about which junctions are affected when other major junctions are congested in the road transportation network. Targeted mitigation mechanisms for traffic congestion can be proposed and the causes of bottlenecks can be analyzed to introduce road constructions or reparations with the best possible results in relieving traffic. We applied the proposed methodology on data collected by the UK government using cyber-physical traffic sensors over the course of 6 years. Our tool analyzed the UK major/A road transportation network, detected n-order junction dependencies and automatically proposed specific mitigation solutions to increase the overall resilience of the road infrastructure network. Simulation results indicate that detected mitigation options, if applied, can increase overall congestion resilience in wider areas of the network up to 12% by lowering likelihood of congestion.

Traffic Decorrelation Techniques for Countering a Global Eavesdropper in WSNs

We address the problem of preventing the inference of contextual information in event-driven wireless sensor networks (WSNs). The problem is considered under a global eavesdropper who analyzes low-level RF transmission attributes, such as the number of transmitted packets, inter-packet times, and traffic directionality, to infer event location, its occurrence time, and the sink location. We devise a general traffic analysis method for inferring contextual information by correlating transmission times with eavesdropping locations. Our analysis shows that most existing countermeasures either fail to provide adequate protection, or incur high communication and delay overheads. To mitigate the impact of eavesdropping, we propose resource-efficient traffic normalization schemes. In comparison to the state-of-the-art, our methods reduce the communication overhead by more than 50 percent, and the end-to-end delay by more than 30 percent. To do so, we partition the WSN to minimum connected dominating sets that operate in a round-robin fashion. This allows us to reduce the number of traffic sources active at a given time, while providing routing paths to any node in the WSN. We further reduce packet delay by loosely coordinating packet relaying, without revealing the traffic directionality.

Safety Performance Evaluation of a Three-Leg Unsignalized Intersection Using Traffic Conflict Analysis

Traffic conflicts were used to evaluate safety performance of a three-leg unsignalized intersection. With the aid of a video camera, data were collected at the intersection and 15-second time span was used in each observation to overcome the drawbacks of traditional methods of traffic conflict analysis. Time to collision (TTC), a widely accepted indicator, was used to identify whether an interaction between two vehicles was a traffic conflict. By using Poisson regression, a prediction model for traffic conflicts at the intersection was developed. Based upon the model, assuming that other factors remain constant, when time headway or speed of eastbound traffic on major road, which is crossed by left-turning traffic from minor road, increases, the number of traffic conflicts at the intersection decreases. When volume of left-turning traffic on minor road or speed of left-turning vehicles on minor road increases, the number of traffic conflicts at the intersection increases if other factors remain constant. Explanations for the influence of the factors, which were represented by independent variables of the prediction model, were then analyzed in detail.

Research on the Method of Traffic Organization and Optimization Based on Dynamic Traffic Flow Model

The modern transportation system is becoming sluggish by traffic jams, so much so that it can harm the economic and society in our country. One of the reasons is the surging vehicles day by day. Another reason is the shortage of the traffic supply seriously. But the most important reason is that the traffic organization and optimization hardly met the conditions of modern transport development. In this paper, the practical method of the traffic organization and optimization used in regional area is explored by the dynamic traffic network analysis method. Firstly, the operational states of the regional traffic network are obtained by simulation method based on the self-developed traffic simulation software DynaCHINA, in which the improved traffic flow simulation model was proposed in order to be more suitable for actual domestic urban transport situation. Then the appropriated optimization model and algorithm were proposed according to different optimized content and organization goals, and the traffic simulation processes more suitable to regional optimization were designed exactly. Finally, a regional network in Tai’an city was selected as an example. The simulation results show that the proposed method is effective and feasible. It can provide strong scientific and technological support for the traffic management department.

Traffic Planning for Small Cities: A Basic Approach

Background/Objectives: 21st century is the era of urban growth and transportation development which is the major element of the development of nation. As per the growth of population, the need of transportation system has been increased rapidly. For the better transportation system, better traffic facility is considered as a prime element of it. Objective comprises of the improvements in traffic system by congestion and accident reduction, parking and traffic signal planning and to provide a reliable method for traffic analysis of small cities. Methods/Statistical Analysis: Traffic congestion, parking problem, accidents etc. nowadays are the major problems related to transportation facilities. So, there are certain parameters to cope with the transportation problems such as field study, designing & planning, accident analysis, economic evaluation, regulation, planning and administrative functions. In order to perform the traffic planning, there is need to observe the traffic conditions, traffic demand, vehicular characteristics, origin & destination and accident studies. Findings: It covers vehicle growth statics in the city with analysis and forecasting of the vehicle demand for the future. Reduction in accident is the success of efficient traffic system, so its study helps to identify the black spot which helps in improvement of traffic system and planning to minimize the accidents with proper traffic planning. Applications/Improvements: The method discussed is applicable for the aspects of the traffic and transportation planning for small or mid – sized cities. It helps in analyzing the major elements of the traffic systems. Traffic planning for a mid-sized city is an approach towards formulating the methodologies to cope with the traffic related issues for the development of better transportation facilities.

Waterfall Traffic Classification: A Quick Approach to Optimizing Cascade Classifiers

Heterogeneous wireless communication networks, like 4G LTE, transport diverse kinds of IP traffic: voice, video, Internet data, and more. In order to effectively manage such networks, administrators need adequate tools, of which traffic classification is the basis for visualizing, shaping, and filtering the broad streams of IP packets observed nowadays. In this paper, we describe a modular, cascading traffic classification system—the Waterfall architecture—and we extensively describe a novel technique for its optimization—in terms of CPU time, number of errors, and percentage of unrecognized flows. We show how to significantly accelerate the process of exhaustive search for the best performing cascade. We employ five datasets of real Internet transmissions and seven traffic analysis methods to demonstrate that our proposal yields valid results and outperforms a greedy optimizer.

Carbon emission flow from self-driving tours and its spatial relationship with scenic spots – A traffic-related big data method

Tour activities are closely related to carbon emissions. Low-carbon tourism is an important means of saving energy and reducing emissions. With the advent of big data mining technology, it is urgent foracademia to discuss the measurement of self-driving tour carbon emission flows and its spatial relationship with scenic spots based on big data on traffic. This paper measures self-driving tour carbon emission flow data from 2014 and analyzes its spatial relationship with the scenic spots using data mining technology and the tour traffic carbon emission flow analysis method. Results show that (1) the regions that have high expressway traffic and self-driving tour traffic are mostly concentrated along the Yangtze River, while the regions that have low expressway traffic and self-driving tour traffic are concentrated in north Jiangsu. For regions that have high self-driving tour traffic, they are spatially concentrated in Nanjing, Suzhou, Wuxi and Changzhou. (2) The self-driving tour carbon emission flow totals 0.52 Mt in Jiangsu, the top five carbon emitters are the routes from downtown Nanjing to downtown Nanjing, downtown Suzhou to downtown Suzhou, downtown Suzhou to downtown Wuxi, downtown Changzhou to downtown Nanjing, and downtown Suzhou to downtown Nanjing. The total number of carbon emissions from these routes account for 17.25%, making them a major source of carbon emissions in Jiangsu. (3) Carbon emissions from self-driving tours do not have a significant positive correlation with the grades of the scenic spots. That is, high carbon emission flows from self-driving tours may happen to both high-grade and low-grade scenic spots.

Traffic Analysis and OD Travel Time Matrix Based on Two-Fluid Model

The traditional four-step traffic demand forecast model is limited by the long processing time and high cost of origin destination (OD) matrix investigation. A dynamic traffic assignment model is difficult to establish for a large-scale network. To obtain the traffic character and quickly search the shortest path of different zones for dynamic route guidance, the authors analyze travel distance and travel time using taxi global positioning system (GPS) data, fit the travel time and stop time relation curve, and propose a method of traffic analysis using the two-fluid curve. The authors find that the bandwidth of the two-fluid curves is valuable for traffic operation and guidance. Subsequently, the authors analyze the relationship between unit distance travel time and unit distance stop time of different zones. The results verify that the sensitivity of different traffic zones varies. Finally, matrix iteration is used to calculate the shortest travel time path under different unit distance stop times, and the OD travel time matrix is analyzed. The findings indicate that the two-fluid method can be used for dynamic route guidance.

Alternative method of highway traffic safety analysis for developing countries using delphi technique and Bayesian network

Highway traffic accidents all over the world result in more than 1.3 million fatalities annually. An alarming number of these fatalities occurs in developing countries. There are many risk factors that are associated with frequent accidents, heavy loss of lives, and property damage in developing countries. Unfortunately, poor record keeping practices are very difficult obstacle to overcome in striving to obtain a near accurate casualty and safety data. In light of the fact that there are numerous accident causes, any attempts to curb the escalating death and injury rates in developing countries must include the identification of the primary accident causes.This paper, therefore, seeks to show that the Delphi Technique is a suitable alternative method that can be exploited in generating highway traffic accident data through which the major accident causes can be identified. In order to authenticate the technique used, Korea, a country that underwent similar problems when it was in its early stages of development in addition to the availability of excellent highway safety records in its database, is chosen and utilized for this purpose. Validation of the methodology confirms the technique is suitable for application in developing countries. Furthermore, the Delphi Technique, in combination with the Bayesian Network Model, is utilized in modeling highway traffic accidents and forecasting accident rates in the countries of research.

A Traffic Accident Causation Analysis Method Based on AHP-Apriori

The multivariate, layered nature of traffic accident analysis (TAA) determines that any TAA method must be categorized according to causational factors and encompass all four basic traffic elements, i.e. driver, vehicle, road and environment. The Analytic Hierarchy Process (AHP) is used to sequence accident causation factors according to their relative importance, with the purpose of singling out the most influential ones. The Apriori algorithm is then applied to analyze the degree of an accident or the level of influence. Through applications, the method proves itself capable of determining the type and severity of accidents caused by multiple factors. The method also demonstrates improvement in the accuracy and efficiency of calculation in dealing with large data samples.

Evaluation of a Traffic Noise Predictive Model for an Active Noise Cancellation (ANC) System

PURPOSES : The purpose of this thesis is to evaluate the effectiveness of an active noise cancellation (ANC) system in reducing the traffic noise level against frequencies from the predictive model developed by previous research. The predictive model is based on ISO 9613-2 standards using the Noble close proximity (NCPX) method and the pass-by method. This means that the use of these standards is a powerful tool for analyzing the traffic noise level because of the strengths of these methods. Traffic noise analysis was performed based on digital signal processing (DSP) for detecting traffic noise with the pass-by method at the test site. METHODS : There are several analysis methods, which are generally divided into three different types, available to evaluate traffic noise predictive models. The first method uses the classification standard of 12 vehicle types. The second method is based on a standard of four vehicle types. The third method is founded on 5 types of vehicles, which are different from the types used by the second method. This means that the second method not only consolidates 12 vehicle types into only four types, but also that the results of the noise analysis of the total traffic volume are reflected in a comparison analysis of the three types of methods. The constant percent bandwidth (CPB) analysis was used to identify the properties of different frequencies in the frequency analysis. A-weighting was applied to the DSP and to the transformation process from analog to digital signal. The root mean squared error (RMSE) was applied to compare and evaluate the predictive model results of the three analysis methods. RESULTS : The result derived from the third method, based on the classification standard of 5 vehicle types, shows the smallest values of RMSE and max and min error. However, it does not have the reduction properties of a predictive model. To evaluate the predictive model of an ANC system, a reduction analysis of the total sound pressure level (TSPL), dB(A), was conducted. As a result, the analysis based on the third method has the smallest value of RMSE and max error. The effect of traffic noise reduction was the greatest value of the types of analysis in this research. CONCLUSIONS : From the results of the error analysis, the application method for categorizing vehicle types related to the 12-vehicle classification based on previous research is appropriate to the ANC system. However, the performance of a predictive model on an ANC system is up to a value of traffic noise reduction. By the same token, the most appropriate method that influences the maximum reduction effect is found in the third method of traffic analysis. This method has a value of traffic noise reduction of 31.28 dB(A). In conclusion, research for detecting the friction noise between a tire and the road surface for the 12 vehicle types needs to be conducted to authentically demonstrate an ANC system in the Republic of Korea.

Network-Matched Trajectory-Based Moving-Object Database: Models and Applications

Tracking and managing the locations of moving objects are essential in modern intelligent transportation systems (ITSs). However, a number of limitations in existing methods make them unsuitable for real-world ITS applications. In particular, Euclidean-based methods are not accurate enough in representing locations and in analyzing traffic, unless the locations are frequently updated. Network-based methods require either digital maps to be installed in moving objects or transmission of prediction policies, which inevitably increase the cost. To solve these problems, we propose a network-matched trajectory-based moving-object database (NMTMOD) mechanism and a traffic flow analysis method using the NMTMOD. In the NMTMOD, the locations of moving objects are tracked through a dense sampling and batch uploading strategy, and a novel edge-centric network-matching method, which is running at the server side, is adopted to efficiently match the densely sampled GPS points to the network. In addition, a deviation-based trajectory optimization method is provided to minimize the trajectory size. Empirical studies with large real trajectory data set offer insight into the design properties of the proposed NMTMOD and suggest that the NMTMOD significantly outperforms other mobile-map free-moving-object database models in terms of precision of both location tracking and network-based traffic flow analysis.

Portable Camera Based Assistive Pattern Recognition for Visually Challenged Persons

Choosing clothes, food recognition and traffic signal analysis are major challenges for visually impaired persons. The existing automatic clothing pattern recognition is also a challenging research problem due to rotation, scaling, illumination, and especially large intra class pattern variations. This project, a camera based assistive framework is proposed to help blind persons for identification of food pattern, clothe pattern and colors in their daily lives. The existing traffic signal using sensors method is difficult to analysis and many components used. A camera based traffic signal analysis method easy to handle, to provide clear traffic signal analysis and reduce the time delay. The system contains the following major components 1) a camera for capturing clothe, food and traffic signal images, a microphone for speech command input; 2) data capture and analysis to perform command control, recognize clothe patterns, food patterns and traffic signal identification by using a wearable computer and 3) a speaker to provide the name of audio outputs of clothe patterns and colors, food patterns and traffic signal analysis, as well as system status. To handle the large intra class variations, a novel descriptor, Radon Signature is proposed to capture the global directionality of clothe patterns, food patterns and traffic signal analysis. To evaluate the effectiveness of the proposed approach CCNY clothes Pattern dataset is used. Our approach achieves 92.55% recognition to improve the life quality, do not depend others. DOI: 10.17762/ijritcc2321-8169.150329

Decision fusion for signalized intersection control

Purpose – Traffic congestion is becoming a serious problem that has adverse consequences on the socio-economy, environment, and public health of various cities worldwide. The purpose of this paper is to contribute to the continuous search for new alternative solutions to prevent or alleviate these concerns. It particularly deals with the development of decision support system based on a data fusion for the management and control of traffic at signalized intersections. The role of such systems is to manage the existing infrastructure to ease congestion and respond to crises. The proposed system is based on multi-detector data fusion, a data processing function that combines imperfect information collected from systems involving several detectors. The developed system is then tested on a virtual junction, and the results obtained are reported and discussed. Design/methodology/approach – This paper presents a new traffic light control based on multi-detectors data fusion theory. The system uses a new multi-detectors data fusion method for traffic data analysis. Moreover, the system integrates a method for the estimation of the reliability degree of different detectors taking into account their imperfection and the conflict between them. These estimated reliability degrees are combined using Dempster’s rule of combination. Findings – The paper provides a decision support system for traffic regulation at intersection based on multi-sensors. It suggests the fusion of captured data by many sensors for measuring information. The system use the Belief Functions Theory for information fusion and decision making using combination and decision rules. Originality/value – The paper proposes a new Adaptive Traffic Control System based on a new data fusion approach that include a method for the estimation of the reliability degree of different detectors taking into account their imperfection and the conflict between them. These estimated reliability degrees are combined using Dempster’s rule of combination.

Traffic Generated by Mixed-Use Developments

Current methods of traffic impact analysis, which rely on rates and adjustments from ITE, are believed to understate the traffic benefits of mixed-use developments (MXDs) and therefore to lead to higher exactions and development fees than necessary and to discourage otherwise desirable developments. The purpose of this study was to improve methodology for predicting the traffic impacts of MXDs. Standard protocols were used to identify and generate data sets for MXDs in 13 large and diverse metropolitan regions. Data from household travel surveys and geographic information system databases were pooled for these MXDs, and travel and built-environment variables were consistently defined across regions. Hierarchical modeling was used to estimate models for internal capture of trips within MXDs and for walking, biking, and transit use on external trips. MXDs with diverse activities on site were shown to capture a large share of trips internally, so that the traffic impacts of the MXDs were reduced relative to conventional suburban developments. Smaller MXDs in walkable areas with good transit access generated significant shares of walk, bike, and transit trips and thus also mitigated traffic impacts.

Study on Individual Traffic Police On-Duty Behavior Analysis Method with Time Series Scheduling

In the traditional traffic police scheduling and dispatching, the applications of the position information are restricted. This paper presented a model of the traffic police on-duty behavior analysis based on time series, in order to improve the efficiency of traffic police scheduling and dispatching system. Firstly, it proposes the steps for the behavior analysis of individual traffic police on-duty. Secondly, it elaborates division method of individual traffic police on-duty behavior from background element definition and semantic concept description. Thirdly the paper builds a model concerning individual traffic police on duty behavior by applying state automaton. Finally it describes the implementation methods of key technologies on individual traffic police on duty behavior.

A Review on Injury Severity in Traffic System using Various Data Mining Techniques

Traffic Accidents (RTAs) are a major public health concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury. The objective of this study is to evaluate a set of variables that contribute to the degree of injury severity in traffic crashes. The issue of traffic safety has raised great concerns across the globe and it has become one of the key issues challenging the sustainable development of modern traffic and transportation. The study on road traffic accident causes can identify the key factors rapidly, efficiently and provide instructional methods to the traffic accidents prevention and road traffic accidents reduction, which could greatly reduce personal casualty and property loss caused by road traffic accidents. Using the method of traffic data analysis, can improve the road traffic safety management level effectively.

Analysis of Large-Scale Traffic Dynamics in an Urban Transportation Network Using Non-Negative Tensor Factorization

In this paper, we present our work on clustering and prediction of temporal evolution of global congestion configurations in a large-scale urban transportation network. Instead of looking into temporal variations of traffic flow states of individual links, we focus on temporal evolution of the complete spatial configuration of congestions over the network. In our work, we pursue to describe the typical temporal patterns of the global traffic states and achieve long-term prediction of the large-scale traffic evolution in a unified data-mining framework. To this end, we formulate this joint task using regularized Non-negative Tensor Factorization, which has been shown to be a useful analysis tool for spatio-temporal data sequences. Clustering and prediction are performed based on the compact tensor factorization results. The validity of the proposed spatio-temporal traffic data analysis method is shown on experiments using simulated realistic traffic data.

Data Analysis and its Application of Network Security

The purpose of data analysis is to compress the data extraction, given its simplicity general description. In this paper, the dynamic attack traffic data, trying to establish in the metropolitan traffic monitoring data, according to the abnormal test, hypothesis testing and other methods of attack traffic identification and reasonable analysis, we found the type of attack traffic and network data from a wide range of macro-and related features, in order to provide a basis for analytical methods and techniques of network security data.

Analysis Method of External Traffic Demand in City Area

To overcome the disadvantages of the existing method, a “dual four-stage” method” is put forward to analyze external traffic demand in city area considering the characteristics of external traffic. The method is carried out by two steps, and in the first step the city area is regarded as an overall traffic zone and the “four-stage” method of regional traffic demand analysis is used to obtain the total generation volumes of internal-external (IE) & external-internal (EI) trips of city area as well as the traffic volumes of both directions of all the arterial highways through the city in planning years. In the second step, the planning area of city is divided into several parts as internal traffic zones, and the external traffic zones are set near the junctions of highways and cordon line, then the “four-stage” method is applied for the second time to obtain the trip generation and distribution of IE & EI trips and (external-external) EE trips for internal traffic zones and external traffic zones in planning years. The application of the analysis on external traffic demand for comprehensive transportation planning of Zhenjiang City shows that the proposed method combines the advantages of influence area method and Cordon Line method it overcomes disadvantages of the two methods, and the results are more consistent with the real situation.