Traffic Index Research Articles

Optimisation of Driver’s Traffic Literacy Evaluation Index from the Perspective of Information Contribution Sensitivity

The practice has proven that relying solely on large-scale transportation facilities cannot fundamentally alleviate urban transportation problems. Since the motor vehicle drivers are the main participants of transportation, they should improve their knowledge of transportation. Moreover, the drivers should equally cope with transportation problems. This paper establishes an evaluation index system for traffic literacy of urban drivers. In addition, it proposes a method of information contribution sensitivity to optimise the index system. The main results and achievement of this paper include the following: (1) A traffic literacy evaluation index system including 13 evaluation indexes such as traffic rules and general knowledge of machinery has been constructed. (2) Based on the calculation results of the sensitivity of the information contribution, the first 10 indexes that satisfy the cumulative information contribution rate’s value of greater than 90% are retained and 3 indexes with lower contribution rates are eliminated. This study provides a theoretical framework and basic methods to evaluate the traffic literacy of urban drivers.

Statistical Techniques for Detecting Cyberattacks on Computer Networks Based on an Analysis of Abnormal Traffic Behavior

Represented paper is currently topical, because of year on year increasing quantity and diversity of attacks on computer networks that causes significant losses for companies. This work provides abilities of such problems solving as: existing methods of location of anomalies and current hazards at networks, statistical methods consideration, as effective methods of anomaly detection and experimental discovery of choosed method effectiveness. The method of network traffic capture and analysis during the network segment passive monitoring is considered in this work. Also, the processing way of numerous network traffic indexes for further network information safety level evaluation is proposed. Represented methods and concepts usage allows increasing of network segment reliability at the expense of operative network anomalies capturing, that could testify about possible hazards and such information is very useful for the network administrator. To get a proof of the method effectiveness, several network attacks, whose data is storing in specialised DARPA dataset, were chosen. Relevant parameters for every attack type were calculated. In such a way, start and termination time of the attack could be obtained by this method with insignificant error for some methods.

How Does Travel Demand Follow the Change in Infrastructure? Multiple-Year Eigenvector Centrality Analysis

The road network is one of the most permanent elements of the physical structure of cities, and the long-term impacts should be considered for effective and efficient road network improvement. It is therefore important to catch up on how the road will be used after construction. However, we do not have much knowledge on the pattern and time lag in the change process of travel demand and supply in the real situation. To explore such changes, this study proposes to evaluate a network with eigenvector centrality (EC) measures that can evaluate the importance of nodes in a network. We believe the analysis based on topological properties by the graph theory is suitable to verify the evolution of road networks. This study analysed long-term changes over 20 years in an actual city to understand the impact of road network improvements. The EC analysis with the weights of traffic indices obtained from survey data evaluates the connectivity of road services on the supply side, and traffic concentration on the demand side.

Integrating storm surge modeling with traffic data analysis to evaluate the effectiveness of hurricane evacuation

An integrated storm surge modeling and traffic analysis were conducted in this study to assess the effectiveness of hurricane evacuations through a case study of Hurricane Irma. The Category 5 hurricane in 2017 caused a record evacuation with an estimated 6.8 million people relocating statewide in Florida. The Advanced Circulation (ADCIRC) model was applied to simulate storm tides during the hurricane event. Model validations indicated that simulated pressures, winds, and storm surge compared well with observations. Model simulated storm tides and winds were used to estimate the area affected by Hurricane Irma. Results showed that the storm surge and strong wind mainly affected coastal counties in south-west Florida. Only moderate storm tides (maximum about 2.5 m) and maximum wind speed about 115 mph were shown in both model simulations and Federal Emergency Management Agency (FEMA) post-hurricane assessment near the area of hurricane landfall. Storm surges did not rise to the 100-year flood elevation level. The maximum wind was much below the design wind speed of 150–170 mph (Category 5) as defined in Florida Building Code (FBC) for south Florida coastal areas. Compared with the total population of about 2.25 million in the six coastal counties affected by storm surge and Category 1–3 wind, the statewide evacuation of approximately 6.8 million people was found to be an over-evacuation due mainly to the uncertainty of hurricane path, which shifted from south-east to south-west Florida. The uncertainty of hurricane tracks made it difficult to predict the appropriate storm surge inundation zone for evacuation. Traffic data were used to analyze the evacuation traffic patterns. In south-east Florida, evacuation traffic started 4 days before the hurricane’s arrival. However, the hurricane path shifted and eventually landed in south-west Florida, which caused a high level of evacuation traffic in south-west Florida. Over-evacuation caused Evacuation Traffic Index (ETI) to increase to 200% above normal conditions in some sections of highways, which reduced the effectiveness of evacuation. Results from this study show that evacuation efficiency can be improved in the future by more accurate hurricane forecasting, better public awareness of real-time storm surge and wind as well as integrated storm surge and evacuation modeling for quick response to the uncertainty of hurricane forecasting.

Prediction of Road Network Traffic State Using the NARX Neural Network

To provide reliable traffic information and more convenient visual feedback to traffic managers and travelers, we proposed a prediction model that combines a neural network and a Macroscopic Fundamental Diagram (MFD) for predicting the traffic state of regional road networks over long periods. The method is broadly divided into the following steps. To obtain the current traffic state of the road network, the traffic state efficiency index formula proposed in this paper is used to derive it, and the MFD of the current state is drawn by using the classification of the design speed and free flow speed of the classified road. Then, based on the collected data from the monitoring stations and the weighting formula of the grade roads, the problem of insufficient measured data is solved. Meanwhile, the prediction performance of NARX, LSTM, and GRU is experimentally compared with traffic prediction, and it is found that NARX NN can predict long-term flow and the prediction performance is slightly better than both LSTM and GRU models. Afterward, the predicted data from the four stations were integrated based on the classified road weighting formula. Finally, according to the traffic state classification interval, the traffic state of the road network for the next day is obtained from the current MFD, the predicted traffic flow, and the corresponding speed. The results indicate that the combination of the NARX NN with the MFD is an effective attempt to predict and describe the long-term traffic state at the macroscopic level.

Path Optimization Model for Urban Transportation Networks under the Perspective of Environmental Pollution Protection

In view of the lack of consideration of environmental protection performance in the traditional path optimization model, a path optimization model for urban traffic networks from the perspective of environmental pollution protection is proposed. Firstly, the urban real-time traffic condition is expressed by the road traffic state index, and an integer programming model is established to optimize the route with the goal of low carbon and shortest distribution time. Then, a hybrid particle swarm optimization algorithm combined with adaptive disturbance mechanism based on variable neighborhood descent is designed, which can better carry out adaptive disturbance according to the situation that the population falls into local extreme value, and the 2-opt local search method is introduced to improve the quality of solution. Finally, the improved particle swarm optimization algorithm is used to solve the two-objective model to obtain the Pareto front solution set, that is, the path scheme under real-time traffic conditions. The experimental demonstration of the proposed model based on two application scenarios shows that its distribution cost, distribution time, and carbon emission are 1975 yuan, 27 h, and 213 kg, respectively, which are better than other comparison models and have high application value.

Evaluation of Bangkok Flood Vulnerability Index Using Fuzzy Inference System

Evaluating flood vulnerability is being an essential factor in flood risk assessment and damage evaluation. Since it is influenced by several spatial factors covering exposure, susceptibility, and resilience, thus evaluating methods vary and are complicated. This study proposed Flood Vulnerability Index (FVI) evaluation by applying the fuzzy set theory involving subjective uncertainties and ambiguities of judgments. Five sub-catchments in Bangkok were selected, and 14-districts information was gathered. The flood events on June 21, 2016, and October 13, 2017, were simulated by applying a Storm Water Management Model (SWMM) mathematical model with the spatial physical characteristics such as topographic data, land-use data, drainage system, rainfall distribution, and inundation level. The flood vulnerability factor and fuzzy Membership Function (MF) were defined. It changed the magnitude of flood vulnerability factors to linguistic function. The fuzzy rule-based was then constructed in a fuzzy inference engine where the rainfall, drainage performance, population density, traffic condition, and garbage management were set as the primary variables. Further, the spatial FVIs were evaluated by Fuzzy Inference Systems (FIS). The evaluated FVIs were compared with the prior version of FVI, which developed under the consideration of just the physical characteristics and the annual rainfall. The comparisons with the actual floods reveal that the improved FVIs obtained from the proposed method are more consistent with the actual event conditions. Besides, the improved FVI of 14 districts in 5 sub-catchments that range from 0.81 to 0.91 also indicates a very high vulnerability to flooding due to a very high impermeable ratio and the lowest efficiency of drainage systems. The sensitivity analysis suggests that the FVI are sensitive to changes in garbage collection management, especially in the very high impermeable area, follows by drainage efficiency, traffic index, and population density, respectively.

Mobile Emergency Power Source Configuration Scheme considering Dynamic Characteristics of a Transportation Network

Recently, with the large scale of power grids and the increase in frequency of extreme weather, the safe and stable operation of power systems is facing great challenges. Therefore, mobile emergency power source (MEPS) are a promising and feasible way to deal with extreme weather and reduce economic losses. However, the current urban power grid and transportation network are closely coupled, and the congested traffic hinders the rapid configuration of MEPSs. Therefore, this paper proposes an MEPS configuration scheme considering real-time traffic conditions. Firstly, the dynamic road traffic index (DRTI) is defined, which can fully describe the dynamic characteristics of traffic. The wavelet neural network (WNN) is used to predict the traffic flow. Then, combined with the knowledge of graph theory, an A-Star algorithm (AS) is used to determine the optimal path. Secondly, the optimal installation location of MEPSs is determined by forward–backward sweep method in distribution network. Finally, the feasibility, accuracy, and time cost of the proposed method are verified by numerical simulations, which can meet the requirements of online application.

Research on intelligent calculation method of intelligent traffic flow index based on big data mining

To understand the operating status of the road network and measure the traffic congestion problem, an intelligent calculation method for the intelligent traffic flow index based on big data mining is proposed. According to the error data discriminating rules, the error data in the traffic flow data is discriminated, all lanes are detected according to the data discriminating result, the traffic data of each lane are recorded in chronological order, and the traffic data is converted. Fuzzy data mining technology is used to predict the converted traffic flow, combined with traffic flow sequence segmentation and BP neural network model to realize the intelligent calculation of the smart traffic flow index. Experimental results show that the method can achieve accurate calculation of daily and weekly smart traffic index, and the calculation time is short, indicating that it can provide a reliable data basis for traffic operation state estimation and traffic early warning mechanism formulation.

TRAFFIC CONGESTION LEVEL IN 10 SELECTED CITIES OF POLAND

Transport has a great impact on human activities but contributes to many negative phenomena occurring in road traffic, for example, road traffic accidents, emission of toxic exhaust fumes into the atmosphere and a high share of cars in road traffic. For the above reasons, many initiatives have been taken in the field of road traffic management and urban logistics. Based on a literature review, it was found that the problem of the phenomenon of traffic congestion in urban areas remains an ongoing issue. In the first part of this article, the theoretical issues of traffic flow and congestion formation in the city road networks were presented. While the second part outlines the situation of transport congestion in 10 Polish cities based on the worldwide TomTom Traffic Index in the years 2008-2018. This study is a brief analysis of the trends relating to transport congestion based on the TomTom Traffic Index in these cities.

Evaluation and Analysis of CFI Schemes with Different Length of Displaced Left-Turn Lanes with Entropy Method

As an unconventional design to alleviate the conflict between left-turn and through vehicles, Continuous Flow Intersection (CFI) has obvious advantages in improving the sustainability of roadway. So far, the design manuals and guidelines for CFI are not enough sufficient, especially for the displaced left-turn lane length of CFI. And the results of existing research studies are not operational, making it difficult to put CFI into application. To address this issue, this paper presents a methodological procedure for determination and evaluation of displaced left-turn lane length based on the entropy method considering multiple performance measures for sustainable transportation, including traffic efficiency index, environment effect index and fuel consumption. VISSIM and the surrogate safety assessment model (SSAM) were used to simulate the operational and safety performance of CFI. The multi-attribute decision-making method (MADM) based on an entropy method was adopted to determine the suitability of the CFI schemes under different traffic demand patterns. Finally, the procedure was applied to a typical congested intersection of the arterial road with heavy traffic volume and high left-turn ratio in Xi’an, China, the results showed the methodological procedure is reasonable and practical. According to the results, for the studied intersection, when the Volume-to-Capacity ratio (V/C) in the westbound and eastbound lanes is less than 0.5, the length of the displaced left-turn lanes can be selected in the range of 80 to 170 m. Otherwise, other solutions should be considered to improve the traffic efficiency. The simulation results of the case showed CFI can significantly improve the traffic efficiency. In the best case, compared with the conventional intersection, the number of vehicles increases by 13%, delay, travel time, number of stops, CO emission, and fuel consumption decrease by 41%, 29%, 25%, 17%, and 17%, respectively.

National Trends in Cycling in Light of the Norwegian Bike Traffic Index.

National and international strategies and recommendations are intended to increase physical activity in the general population. Active transportation is included in interdisciplinary strategies to meet these recommendations. Cycling seems to be more health enhancing than walking for transportation since cycling seems to reduce the risk of cardiovascular disease and associated risk factors. Furthermore, the health benefits of cycling are proven to outrun the risk of injuries and mortality. Politicians seem to approve costly infrastructure strategies to increase the amount of cycling in the population to improve public health and shift to more sustainable travel habits. A linear relationship between cycle-friendly infrastructure and the amount of commuter cycling has been demonstrated. However, in Norway and on a global level, there is a lack of robust evaluations of actions and sensitive monitoring systems to observe possible change. Therefore, we aimed to develop the Norwegian bike traffic index and describe the national, regional, and local trends in counted cycle trips. We used a transparent methodology so that the index can be used, developed, and adapted in other countries. We included 89 stationary counters from the whole country. Counters monitored cycling from 2018 onward. The index is organized at local, regional, and national levels. Furthermore, the index is adjusted for population density at the counter level and presented as ratio of counted cycle trips, comparing 2018 to subsequent years. The index is presented as a percentage change with 95% confidence intervals. In Norway, counted cycle trips increased by 11% from 2018 (100, 100–100) to 2020 (111.0, 106.2–115.1), with large geographical differences. In Southern Norway, there was a significant increase of 23%, and in Northern Norway, there was a nonsignificant decrease by 8% from 2018 to 2020. The indices may indicate possible related effects of local to national cycling strategies and how the COVID-19 pandemic has affected Norwegian travel habits in urban areas.

A Hybrid Deep Convolutional Neural Network Approach for Predicting the Traffic Congestion Index

Traffic congestion is one of the most important issues in large cities, and the overall travel speed is an important factor that reflects the traffic status on road networks. This study proposes a hybrid deep convolutional neural network (CNN) method that uses gradient descent optimization algorithms and pooling operations for predicting the short-term traffic congestion index in urban networks based on probe vehicles. First, the input data are collected by the probe vehicles to calculate the traffic congestion index (output label). Then, a CNN that uses gradient descent optimization algorithms and pooling operations is applied to enhance its performance. Finally, the proposed model is chosen on the basis of the R-squared (R2) and root mean square error (RMSE) values. In the best-case scenario, the proposed model achieved an R2 value of 98.7%. In addition, the experiments showed that the proposed model significantly outperforms other algorithms, namely the ordinary least squares (OLS), k-nearest neighbors (KNN), random forest (RF), recurrent neural network (RNN), artificial neural network (ANN), and convolutional long short-term memory (ConvLSTM), in predicting traffic congestion index. Furthermore, using the proposed method, the time-series changes in the traffic congestion status can be reliably visualized for the entire urban network.

Spatial analysis and evaluation of road traffic safety performance indexes across the provinces of Turkey from 2015 to 2019

Limited road safety based spatial analysis studies have been conducted in developing countries. Also, little is known about the relationships between province-level road safety performance indexes (RSPIs). Hence, spatial, regression and correlation analysis were used to identify road safety-deficient provinces and determine the relationship between RSPIs. The gathered data comprise 14 RSPIs and nine socioeconomic indicators. Moran’s I and Local Moran indexes were used for conducting the spatial analysis. The natural breaks method was used to cluster similar provinces according to RSPIs. Regarding studied RSPIs, huge local clusters of provinces detected. Eastern provinces had higher road traffic crash (RTC) severity indexes. RTCs were more severe in regions with lower income level. Regions with higher socioeconomic indexes such as population had higher RTC rates. Using RSPIs calculated with distinct exposure measures creates completely different local cluster maps. significant relationships between studied RSPIs were detected. A standard system is needed to organize and categorize the RSPIs. Road safety policies should be region-specific to reduce RSPIs efficiently. Regarding the observed various locations of hot spots in terms of studied RSPIs, further consideration should be given in the process of selecting an RSPI for comparing administrative divisions of a country.

Региональные аэропорты ЦФО: перспективы развития в условиях макроэкономической нестабильности

Based on the analysis of passenger traffic at the airports of the Moscow Aviation Hub and regional airports of the Central Federal District, the impact of the COVID-19 pandemic on the dynamics of passenger traffic was assessed. As a result of the system analysis, it was concluded that the growth of passenger traffic at the airports of Yaroslavl and Kursk is due to the expansion of the internal route network. As a result of the introduction of temporary prohibitive and restrictive measures for international air transportation, there was an increase in demand for domestic destinations. The airports of Yaroslavl and Kursk, in the summer of 2020, opened regular flights to the resort cities of the Southern Federal District. Based on the analysis of passenger traffic dynamics, it is concluded that an important factor in the development of regional airports in the Central Federal District is their structural modernization. The current passenger traffic index of regional airports can be significantly increased if the runways and terminal complex are expanded. The results of the study can be applied in the development of a strategy for the development of regional airports, of which there are more than 200 as of 2021 on the territory of the Russian Federation and many of which need structural modernization. In the context of the development of the airport complex in the Russian market of passenger air transportation, there will be a dynamic increase in passenger traffic.

Spatio-Temporal Visual Analysis for Urban Traffic Characters Based on Video Surveillance Camera Data

Urban road traffic spatio-temporal characters reflect how citizens move and how goods are transported, which is crucial for trip planning, traffic management, and urban design. Video surveillance camera plays an important role in intelligent transport systems (ITS) for recognizing license plate numbers. This paper proposes a spatio-temporal visualization method to discover urban road vehicle density, city-wide regional vehicle density, and hot routes using license plate number data recorded by video surveillance cameras. To improve the accuracy of the visualization effect, during data analysis and processing, this paper utilized Internet crawler technology and adopted an outlier detection algorithm based on the Dixon detection method. In the design of the visualization map, this paper established an urban road vehicle traffic index to intuitively and quantitatively reveal the traffic operation situation of the area. To verify the feasibility of the method, an experiment in Guiyang on data from road video surveillance camera system was conducted. Multiple urban traffic spatial and temporal characters are recognized concisely and efficiently from three visualization maps. The results show the satisfactory performance of the proposed framework in terms of visual analysis, which will facilitate traffic management and operation.

Quantitative Index Construction of Website Influence: Taking China Private Universities as Example

Website influence is of great value to the brand promotion of private colleges and universities. This study collects the link index and traffic index data of private university websites in China, conducts relevant analysis, sorts out the relationship between link indicators and traffic indicators, and interprets the strategies of these indicators for brand promotion of private colleges and universities combined with case analysis.

Proposing new traffic operation index to improve interchange type selection procedure

An appropriate interchange type selection would help tackle traffic problems and avoid a decreasing level of traffic service and safety. Design alternatives are usually generated according to an engineer's experience. However, some useful types may be neglected. The criteria for interchange type selection vary from one study to another, but the criterion that should be considered first is the traffic performance. In this paper, an algorithm is suggested to auto-generate design alternatives. A fuzzy-based method is also proposed to investigate traffic performance, as well as to compare and prioritise the interchange design alternatives. The method is applied to two real-world interchanges. The findings show that the traffic performance could be improved by choosing an appropriate placement for loops and directional ramps. Finally, the results imply that more money would be saved by using the proposed method.

МЕТОДИЧНІ ПІДХОДИ ЩОДО ОЦІНКИ ЯКОСТІ ЖИТТЯ НАСЕЛЕННЯ КРАЇН СВІТУ: РЕКОМЕНДАЦІЇ ДЛЯ УКРАЇН

In the article the principles of improving the quality of life of the population of Ukraine were analyzed, a method of calculating the quality of life of the population of the world was developed. The solution of the tasks was carried out with the use of general scientific research methods: abstraction, analysis and synthesis, generalization, formalization, as well as special methods of analysis: structural, statistical, logical. The authors have analyzed the state of factors influencing the quality of life of the population of Ukraine: purchasing power index, safety index, health care index, cost of living index, property price to income ratio, traffic commute time index, pollution index, climate index, GDP by PPP per capita in international dollars, rating of political and civil liberties, life expectancy at birth, unemployment rate, gender equality index. General recommendations for improving the condition of these factors and priority areas have been identified. The general approaches to development of methods of calculation of quality of life of the population of the world countries are defined. A method for calculating the quality of life of the population has been developed: 5 indicators that have a positive impact on the quality of life and 5 indicators that have a negative impact on the quality of life of the population have been identified; mentioned weights of each of these indicators. The Index of Quality of Life of the Population of Ukraine, neighboring countries and developed world countries was calculated according to the developed calculation method, the obtained results have been compared with the Numbeo Quality of Life Index.

Study on Evaluation Index System of Urban Traffic Improvement Based on Analytic Hierarchy Process

The double development of urbanization and motorization has made the traffic demand of our country grow rapidly and diversified. the improvement of urban traffic has become an important condition for the sustainable development of the city. Based on the analytic hierarchy process (AHP), this paper establishes the evaluation index system of urban traffic improvement by comparing the importance of indexing by experts. It is found that improvement of road network, optimization of public traffic and improvement of intersection are the most important in urban traffic improvement. the intersection of the most important level of service. By using qualitative and quantitative methods, the Likert scale was used to classify the results and the four-quadrant method was used to analyze the urban traffic improvement evaluation index system.