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1
Cheng-hua, Shi, Wang Ang, Sun Xiao-he, and Yang Wei-chao. "Research on the movement law and traffic safety zoning of spalled blocks in the linings of high-speed railway tunnels." Tunnelling and Underground Space Technology 128 (October 2022): 104614. http://dx.doi.org/10.1016/j.tust.2022.104614.
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Mejias, Luis, and Elizabeth Deakin. "Redevelopment and Revitalization along Urban Arterials." Transportation Research Record: Journal of the Transportation Research Board 1902, no. 1 (January 2005): 26–34. http://dx.doi.org/10.1177/0361198105190200104.
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Urban arterials are both promising and problematic locations for infill development and urban revitalization. San Pablo Avenue, a multilane urban arterial traversing nine cities and two counties along the eastern shore of San Francisco Bay in California, is considered here. The road developed over a long period: first as a streetcar line, then as an intercity automobile route, and most recently as a subregional traffic and transit route. Land uses from each of these transportation eras are still present along the avenue and range from neighborhood retail to automobile-oriented strip development. Recent transit service improvements and a strong housing market are leading to new developer interest in San Pablo Avenue. Findings are reported from interviews with 11 developers who recently built infill housing and mixed-use projects on or near the arterial. Developers see San Pablo Avenue's accessibility as a major asset but view transit services as a bonus instead of a necessity; transit availability allows developers to argue for reduced transportation impact fees and reduced parking requirements. Other aspects of the arterial's design, including high speeds and unattractive streetscapes, are problematic, as are zoning ordinances that require high parking ratios, large setbacks, and lengthy, discretionary approval processes. Small land parcels, incompatible adjacent uses, and high development costs are also drawbacks but, with creative development, are manageable. Local governments could provide incentives for private development along arterials such as San Pablo Avenue by improving street designs, reducing parking requirements, and updating zoning codes and approval processes.
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Zhao, Xueting, Liwei Hu, Xingzhong Wang, and Jiabao Wu. "Study on Identification and Prevention of Traffic Congestion Zones Considering Resilience-Vulnerability of Urban Transportation Systems." Sustainability 14, no. 24 (December 16, 2022): 16907. http://dx.doi.org/10.3390/su142416907.
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In order to solve the problem of urban short-term traffic congestion and temporal and spatial heterogeneity, it is important to scientifically delineate urban traffic congestion response areas to alleviate regional traffic congestion and improve road network efficiency. Previous urban traffic congestion zoning is mostly divided by urban administrative divisions, which is difficult to reflect the difference of congestion degree within administrative divisions or traffic congestion zoning. In this paper, we introduce the Self-Organizing Feature Mapping (SOFM) model, construct the urban traffic congestion zoning index system based on the resilience and vulnerability of urban traffic systems, and establish the urban traffic congestion zoning model, which is divided into four, five, six, and seven according to the different structures of competition layer topology. The four vulnerability damage capacity indicators of traffic volume, severe congestion mileage, delay time and average operating speed, and two resilience supply capacity indicators of traffic systems, namely, road condition and number of lanes, are used as model input vectors; the data of Guiyang city from January to June 2021 are used as data sets to input four SOFM models for training and testing and the best SOFM model with six competitive topologies is constructed. Finally, the Support Vector Machine (SVM) is used to identify the optimal partition boundary line for traffic congestion. The results show that the four models predict the urban traffic congestion zoning level correctly over 95% on the test set, each traffic congestion zoning evaluation index in the urban area shows different obvious spatial clustering characteristics, the urban traffic congestion area is divided into six categories, and the city is divided into 16 zoning areas considering the urban traffic congestion control types (prevention zone, control zone, closure control zone). The spatial boundary is clear and credible, which helps to improve the spatial accuracy when predicting urban traffic congestion zoning and provides a new methodological approach for urban traffic congestion zoning and zoning boundary delineation.
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Bing, He, Xu Zhifeng, Xu Yangjie, Hu Jinxing, and Ma Zhanwu. "Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data." Complexity 2020 (November 7, 2020): 1–14. http://dx.doi.org/10.1155/2020/6939328.
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Road link speed is one of the important indicators for traffic states. In order to incorporate the spatiotemporal dynamics and correlation characteristics of road links into speed prediction, this paper proposes a method based on LDA and GCN. First, we construct a trajectory dataset from map-matched GPS location data of taxis. Then, we use the LDA algorithm to extract the semantic function vectors of urban zones and quantify the spatial dynamic characteristics of road links based on taxi trajectories. Finally, we add semantic function vectors to the dataset and train a graph convolutional network to learn the spatial and temporal dependencies of road links. The learned model is used to predict the future speed of road links. The proposed method is compared with six baseline models on the same dataset generated by GPS equipped on taxis in Shenzhen, China, and the results show that our method has better prediction performance when semantic zoning information is added. Both composite and single-valued semantic zoning information can improve the performance of graph convolutional networks by 6.46% and 8.35%, respectively, while the baseline machine learning models work only for single-valued semantic zoning information on the experimental dataset.
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Flintsch, Gerardo W., Brian Ferne, Brian Diefenderfer, Samer Katicha, James Bryce, and Simon Nell. "Evaluation of Traffic-Speed Deflectometers." Transportation Research Record: Journal of the Transportation Research Board 2304, no. 1 (January 2012): 37–46. http://dx.doi.org/10.3141/2304-05.
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Levinson, David M., and Yuanlin Huang. "Windowed Transportation Planning Model." Transportation Research Record: Journal of the Transportation Research Board 1607, no. 1 (January 1997): 45–54. http://dx.doi.org/10.3141/1607-07.
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A transportation planning model that integrates regional and local-area forecasting approaches is developed and applied. Although regional models have the scope to model the interaction of demand and congestion, they lack spatial detail. Local-area analysis typically does not consider the feedback between new project loadings and existing levels of traffic. A windowed model, which retains regional trip distribution information and the consistency between travel demand and congestion, permits the use of a complete transportation network and block-level traffic zones while retaining computational feasibility. By combining the two methods a number of important policy issues can be addressed, including the implications of traffic calming, changes in flow due to alternative traffic operation schemes, the influence of microscale zoning changes on nearby intersections, and the impact of travel demand management on traffic congestion.
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Zefreh, Mohammad Maghrour, and Ádám Török. "DISTRIBUTION OF TRAFFIC SPEED IN DIFFERENT TRAFFIC CONDITIONS: AN EMPIRICAL STUDY IN BUDAPEST." Transport 35, no. 1 (March 18, 2020): 68–86. http://dx.doi.org/10.3846/transport.2019.11725.
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Fundamental diagram, a graphical representation of the relationship among traffic flow, speed, and density, has been the foundation of traffic flow theory and transportation engineering for many years. Underlying a fundamental diagram is the relation between traffic speed and density, which serves as the basis to understand system dynamics. Empirical observations of the traffic speed versus traffic density show a wide-scattering of traffic speeds over a certain level of density, which would form a speed distribution over a certain level of density. The main aim of the current research is to study on the distribution of traffic speed in different traffic conditions in the urban roads since the distribution of traffic speed is necessary for many traffic engineering applications including generating traffic in micro-simulation systems. To do so, the traffic stream is videotaped at various locations in the city of Budapest (Hungary). The recorded videos were analysed by traffic engineering experts and different traffic conditions were extracted from these recorded videos based on the predefined scenarios. Then their relevant speeds in that time interval were estimated with the so-called “g-estimator method” using the outputs of the available loop detectors among the videotaped locations. Then different parametric candidate distributions have been fitted to the speeds by Maximum Likelihood Estimation (MLE) method. Having fitted different parametric distributions to speed data, they were compared by three goodness-of-fit tests along with two penalized criteria (Akaike Information Criterion – AIC and Bayesian Information Criterion – BIC) in order to overcome the over-fitting problems. The results showed that the speed of traffic flow follows exponential, normal, lognormal, gamma, beta and chisquare distribution in the condition that traffic flow followed over-saturated congestion, under saturated flow, free flow, congestion, accelerated flow and decelerated flow respectively.
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Ke, Ruimin, Wan Li, Zhiyong Cui, and Yinhai Wang. "Two-Stream Multi-Channel Convolutional Neural Network for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 4 (March 7, 2020): 459–70. http://dx.doi.org/10.1177/0361198120911052.
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Traffic speed prediction is a critically important component of intelligent transportation systems. Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed that achieved high accuracy and large-scale prediction. However, existing studies have two major limitations. First, they predict aggregated traffic speed rather than lane-level traffic speed; second, most studies ignore the impact of other traffic flow parameters in speed prediction. To address these issues, the authors propose a two-stream multi-channel convolutional neural network (TM-CNN) model for multi-lane traffic speed prediction considering traffic volume impact. In this model, the authors first introduce a new data conversion method that converts raw traffic speed data and volume data into spatial–temporal multi-channel matrices. Then the authors carefully design a two-stream deep neural network to effectively learn the features and correlations between individual lanes, in the spatial–temporal dimensions, and between speed and volume. Accordingly, a new loss function that considers the volume impact in speed prediction is developed. A case study using 1-year data validates the TM-CNN model and demonstrates its superiority. This paper contributes to two research areas: (1) traffic speed prediction, and (2) multi-lane traffic flow study.
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Zhao, Dan, Fengchun Han, Meng Meng, Jun Ma, and Quantao Yang. "Exploring the influence of traffic enforcement on speeding behavior on low-speed limit roads." Advances in Mechanical Engineering 11, no. 12 (December 2019): 168781401989157. http://dx.doi.org/10.1177/1687814019891572.
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Speeding on low-speed limit roads is a common traffic offense in China, which could be due to the mild traffic safety enforcement. The article aims to explicit the impact of traffic enforcement measures on the speeding behavior on low-speed limit roads. First, field data were collected to demonstrate the severity of speeding by investigating speed distribution; second, a virtual traffic enforcement was designed by considering three factors related to traffic enforcement, and a stated preference survey questionnaire including six scenarios was designed and implemented; finally, a series of generalized regret random minimization models were established to study the relationship of speeding behavior and traffic enforcement as well as drivers’ personal characteristics. From the stated preference survey analysis, the research figures out that other vehicles’ average speed is the most important reference to choose speed rather than traffic penalties, and the model estimation results show that speeding violation grows severe if traffic enforcements are lenient. Therefore, increasing the violation costs is a powerful means of lowering the probability of speeding for individual, thus proceeding the drop of vehicles’ average speed, and the fall of average speed will contribute to decrease speeding subsequently.
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Cathey, F. W., and D. J. Dailey. "Transit Vehicles as Traffic Probe Sensors." Transportation Research Record: Journal of the Transportation Research Board 1804, no. 1 (January 2002): 23–30. http://dx.doi.org/10.3141/1804-04.
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New algorithms are presented that use transit vehicles as probes for determining traffic speeds and travel times along freeways and other primary arterials. A mass transit tracking system based on automatic vehicle location data and a Kalman filter used to estimate vehicle position and speed are described. A system of virtual probe sensors that measure transit vehicle speeds by using the track data is also described. Examples showing the correlation between probe data and inductance loop speed-trap data are presented. Also presented is a method that uses probe sensor data to define vehicle speed along an arbitrary roadway as a function of space and time, a speed function. This speed function is used to estimate travel time given an arbitrary starting time. Finally, a graphical application is introduced for viewing real-time speed measurements from a set of virtual sensors that can be located throughout King County, Washington, on arterials and freeways.
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Zhang, H. M. "Link-Journey-Speed Model for Arterial Traffic." Transportation Research Record: Journal of the Transportation Research Board 1676, no. 1 (January 1999): 109–15. http://dx.doi.org/10.3141/1676-14.
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Dell'acqua, Gianluca. "Reducing Traffic Injuries Resulting from Excess Speed." Transportation Research Record: Journal of the Transportation Research Board 2203, no. 1 (January 2011): 94–99. http://dx.doi.org/10.3141/2203-12.
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Zhang, Shuyu, Chenhan Zhang, Shiyao Zhang, and James J. Q. Yu. "Attention-Driven Recurrent Imputation for Traffic Speed." IEEE Open Journal of Intelligent Transportation Systems 3 (2022): 723–37. http://dx.doi.org/10.1109/ojits.2022.3215621.
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Wang, Liang, Yu Jie Wang, and Ling Yu Wang. "Simulation Research of Flow and Speed on Urban Expressway Interchange Entrance." Advanced Materials Research 779-780 (September 2013): 796–99. http://dx.doi.org/10.4028/www.scientific.net/amr.779-780.796.
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The urban expressway overpass entrance is an important node of the urban road system. Traffic jams often happen at entrance. The characteristics of urban expressway entrance and the advantages of the microscopic traffic simulation were combined to analyze capacity of entrance. VISSIM simulation software was used and the validity of the simulation model was verified. The influence of the main road traffic flow and the desired speed of entrance to flow and speed of the urban expressway entrance were analyzed through simulation experiment. On the whole, traffic capacity of urban expressway entrance reduces with the increase of traffic flow on the main road. The higher the desired speed is, the faster traffic capacity reduces. The increase of speed and control of main road traffic flow is of great significance for improving the capacity and service level of expressway.
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Zhang, Chunbo, Zhaoguo Huang, and Yonggang Wang. "A TRAFFIC FUNDAMENTAL DIAGRAM CALIBRATING METHODOLOGY TO AVOID UNBALANCED SPEED–DENSITY OBSERVATIONS." Transport 36, no. 1 (February 24, 2021): 13–24. http://dx.doi.org/10.3846/transport.2021.14302.
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Traffic fundamental diagram is extremely important to analyse traffic flow and traffic capacity, and the central part of traffic fundamental diagram is to calibrate speed–density relationship. However, because of unbalanced speed–density observations, calibrating results using Least Square Method (LSM) with all speed–density points always lead to inaccurate effect, so this paper proposed a selecting data sample method and then LSM was used to calibrate four well-known single-regime models. Comparisons were made among the results using LSM with all speed–density points and the selecting data sample. Results indicated that the selecting data sample method proposed by this paper can calibrate the singleregime models well, and the method overcomes the inaccurate effect caused by unbalanced speed–density observations. Data from different highways validated the results. The contribution of this paper is that the proposed method can help researchers to determine more precise traffic fundamental diagram.
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Yakimov, M. R. "ESTIMATION OF TRANSPORT ACCESSIBILITY LEVEL ON THE BASIS OF FORECAST MODELS." World of Transport and Transportation 15, no. 6 (December 28, 2017): 6–19. http://dx.doi.org/10.30932/1992-3252-2017-15-6-1.
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[For the English abstract and full text of the article please see the attached PDF-File (English version follows Russian version)].ABSTRACT The article presents a methodology for calculating the parameters of transport accessibility level in the territories of large cities. With the help of the proposed model, it is possible to assess the situation with the volume of transport correspondence, as well as the change in the indicators of accessibility after the implementation of individual scenarios for development of transport infrastructure. The methods for calculating modeling, forecasting the road load, introducing zoning principles give specialists in the field of urban planning, transport planning and traffic management in the cities a clear tool for formation of a reliable transport system. Keywords: urban transport system, modeling, organization of traffic, transport accessibility of territories, transport accessibility level, zoning, infrastructure, civil engineering, urban geography.
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Leni, Siregar, Tri Tjahjono, and N. Nahry. "Endogenous relationship of accident occurrence with speed, traffic heterogeneity and driving environment on inter-urban roads in Indonesia." Journal of Applied Engineering Science 18, no. 4 (2020): 475–84. http://dx.doi.org/10.5937/jaes0-25837.
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Speed performances and characteristics of traffic have mostly been considered as homogeneous across vehicles. In countries where the roads are dominated by mixed types of vehicles, the heterogeneity needs to be considered. This study is aimed at modeling how traffic heterogeneity as captured in speed, speed deviation, and traffic volume determines the fatality rates and accident rates. Traffic volume, road geometry (bendiness, hilliness, bend density and hill density) and road surface condition (represented by IRI) become the independent variables in a simultaneous regression using structural equation model (SEM). SEM is adopted to represent the hierarchical causal effects between the independent variables and dependent variables. The data cover inter-urban roads in eight provinces in Indonesia from 2012-2016 and 2019. Speed is not significant in predicting accident rate, and speed deviation is not significant in predicting fatality rate. An increase in speed deviation lowers the accident rates; an increase in speed increases fatality rates. Road geometry and traffic volume negatively impact the speed deviations of all vehicle categories, indicating that when there is more traffic on the road, the speeds of all vehicle categories become more homogenous. Bend density, bendiness, hill density and hilliness negatively affect both the speed and the speed deviations of the vehicles of all categories The findings of the study can contribute to traffic policing and traffic safety improvement schemes for heterogeneous traffic.
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Thomas, Jomy, Karthik K. Srinivasan, and Venkatachalam Thamizh Arasan. "VEHICLE CLASS WISE SPEED-VOLUME MODELS FOR HETEROGENEOUS TRAFFIC." TRANSPORT 27, no. 2 (June 26, 2012): 206–17. http://dx.doi.org/10.3846/16484142.2012.697442.
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Link performance functions commonly used for traffic assignment are often based on Volume Delay Functions (VDF) developed for homogeneous traffic. However, VDFs relating stream speed to the volume of traffic based on homogeneous lane-based traffic are not adequate for traffic assignment in developing countries due to the heterogeneous nature of traffic that is characterized by a mix of a wide range of vehicle classes with significant differences in static and dynamic characteristics and an imperfect lane discipline. Unfortunately, the use of VDFs similar to those for homogeneous traffic flow situations imposes strong restrictions considering two respects: 1) travel times at path and link levels can be obtained for an aggregated stream but not for individual vehicle types; 2) the effect of varying composition and asymmetric interactions is captured only to a limited extent by converting all vehicles into equivalent Passenger Car Unit (PCU). Hence, this paper proposes the development of VDFs specific to different classes of heterogeneous traffic, as it is more realistic in traffic assignment than the use of the same VDF for all classes of vehicles in a link. This study is aimed at developing models to determine the speed of each vehicle class as a function of flow and composition for six lane roads with heterogeneous traffic based on data obtained from Chennai city, India. Heterogeneity in this study mainly refers to differences in vehicle types (two-wheeler, car, bus, etc.) participating in mixed traffic. To develop multiple user class VDFs, the speed and flow of each vehicle class for a wide range of traffic flow conditions need to be recorded. As this is not possible using field measurements, an established micro-simulation model (HETEROSIM) is used for determining speeds for each vehicle type by systematically varying the volume and composition levels over a range of values that represent relevant and practical traffic conditions observed in six lane divided roads in Chennai city. The proposed delay functions are different from standard single user class VDFs in three key respects: first, they enable more realistic behaviour by modelling differences in class wise speeds at a given volume and composition level; second, they allow for capturing asymmetric interactions of different vehicle types on an average speed of a given vehicle class. Finally, speed-flow relationships for each class are also allowed to vary across volume levels which enable the representation of differential interactions at different levels of congestion in mixed traffic. The need for homogenizing the volumes in terms of a single class is obviated. The models significantly outperformed single class VDFs in both calibration and validation datasets. Further, the proposed models are used for analyzing heterogeneous traffic characteristics. Empirical evidence of asymmetric interactions and the impact of composition on classwise performance are also found and quantified. Finally, two applications of the proposed models are demonstrated for the level of service analysis of different classes and impact analysis of excluding some classes. The proposed models may have applications such as determining class wise road user costs and performance measures (e.g. emissions) that depend on class-specific speeds.
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Chen, Wenjin, Gang Qian, Weiwen Qi, Gang Luo, Lin Zhao, and Xiaoling Yuan. "Layout Method of Met Mast Based on Macro Zoning and Micro Quantitative Siting in a Wind Farm." Processes 10, no. 9 (August 27, 2022): 1708. http://dx.doi.org/10.3390/pr10091708.
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In order to promote the wind monitoring accuracy and provide a quantitative planning method for met mast layout in practical projects, this paper proposes a two-stage layout method for met mast based on discrete particle swarm optimization (DPSO) zoning and micro quantitative siting. Firstly, according to the wind turbines layout, rotational empirical orthogonal function and hierarchical clustering methods are used to preliminarily determine zoning number. Considering the geographical proximity of wind turbines and the correlation of wind speed, an optimal macro zoning model of wind farm based on improved DPSO is established. Then, combined with the grid screening method and optimal layout evaluation index, a micro quantitative siting method of met mast is proposed. Finally, the rationality and efficiency of macro zoning method based on improved DPSO, as well as the objectivity and standardization of micro quantitative siting, are verified by an actual wind farm.
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Aysha, Ms. "Vehicle Detection and Traffic Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 1791–94. http://dx.doi.org/10.22214/ijraset.2021.38269.
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Abstract: On the road, vehicle detection processes are utilized for vehicle tracking, vehicle counting, vehicle speed, and traffic analysis. For vehicle detection, the Tensorflow object detection API method is employed. The Object Detection API in Tensorflow is a powerful tool that allows anyone to easily design and deploy effective picture recognition applications. Another way to control traffic is to use a traffic control system. Multiple linear regression is utilized to regulate the traffic system, while the OpenCV approach is used to identify vehicle speed. A system for fine payment is also offered. It makes police officers' jobs easier. The exact results of vehicle speed and traffic control are provided. Keywords: Vehicle detection, Tensorflow object detection API, Multiple linear regression, OpenCV, Fine payment
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Shabarek, Abdullah, Steven Chien, and Soubhi Hadri. "Deep Learning Framework for Freeway Speed Prediction in Adverse Weather." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 10 (August 27, 2020): 28–41. http://dx.doi.org/10.1177/0361198120947421.
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The introduction of deep learning (DL) models and data analysis may significantly elevate the performance of traffic speed prediction. Adverse weather causes mobility and safety concerns because of varying traffic speeds with poor visibility and road conditions. Most previous modeling approaches have not considered the heterogeneity of temporal and spatial data, such as traffic and weather conditions. This paper presents a framework, consisting of two DL models, to predict traffic speed under normal conditions and during adverse weather, considering prevailing traffic speed, wind speed, traffic volume, road capacity, wind bearing, precipitation intensity, and visibility. To ensure the accuracy of speed prediction, different DL models were assessed. The results indicated that the proposed one-dimensional convolutional neural network model outperformed others in relation to the least root mean square error and the least mean absolute error. Considering real-time weather data feeds on a 15-min basis, a tool was also developed for displaying predicted traffic speeds on New Jersey freeways. Application of the proposed framework models for predicting spatio-temporal hot-spot congestion caused by adverse weather is discussed.
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Zhang, Zhao, Yun Yuan, and Xianfeng (Terry) Yang. "A Hybrid Machine Learning Approach for Freeway Traffic Speed Estimation." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 10 (July 22, 2020): 68–78. http://dx.doi.org/10.1177/0361198120935875.
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Accurate and timely estimation of freeway traffic speeds by short segments plays an important role in traffic monitoring systems. In the literature, the ability of machine learning techniques to capture the stochastic characteristics of traffic has been proved. Also, the deployment of intelligent transportation systems (ITSs) has provided enriched traffic data, which enables the adoption of a variety of machine learning methods to estimate freeway traffic speeds. However, the limitation of data quality and coverage remain a big challenge in current traffic monitoring systems. To overcome this problem, this study aims to develop a hybrid machine learning approach, by creating a new training variable based on the second-order traffic flow model, to improve the accuracy of traffic speed estimation. Grounded on a novel integrated framework, the estimation is performed using three machine learning techniques, that is, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN). All three models are trained with the integrated dataset including the traffic flow model estimates and the iPeMS and PeMS data from the Utah Department of Transportation (DOT). Further using the PeMS data as the ground truth for model evaluation, the comparisons between the hybrid approach and pure machine learning models show that the hybrid approach can effectively capture the time-varying pattern of the traffic and help improve the estimation accuracy.
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Tettamanti, Tamás, Alfréd Csikós, Krisztián Balázs Kis, Zsolt János Viharos, and István Varga. "PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK." Transport 33, no. 4 (December 5, 2018): 959–70. http://dx.doi.org/10.3846/16484142.2017.1352027.
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A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.
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Al-Kaisy, Ahmed, Amirhossein Jafari, and Scott Washburn. "Measuring Performance on Two-Lane Highways: Empirical Investigation." Transportation Research Record: Journal of the Transportation Research Board 2615, no. 1 (January 2017): 62–72. http://dx.doi.org/10.3141/2615-08.
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This paper presents an empirical investigation into several performance measures for operational analysis on two-lane highways. The performance measures investigated are average travel speed, average travel speed to free-flow speed, percent followers, followers flow, followers density, percent impeded, impeded flow, and impeded density. Field data from 16 study sites in the states of Montana, Idaho, Oregon, and North Carolina representing Class 1, Class 2, and Class 3 highways were used in this study. The level of association between performance measures and some of the most important traffic variables was examined with graphical and statistical techniques. The traffic variables investigated in this study included combined flow in both directions of travel; proportion of traffic in the direction of travel, called in this study “traffic split”; percentage of heavy vehicles; and speed variance. Study results suggest that speed-related measures have weak associations with traffic variables compared with headway-related measures. Further, compound measures involving headway and traffic flow or density exhibited the highest associations with traffic variables. With regard to two-lane highway type, higher associations are exhibited at Class 1 sites compared with Class 2 and Class 3 sites. Performance measures showed the highest associations with combined flow and traffic split.
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Gao, Chao, Jinliang Xu, Xingli Jia, Yaping Dong, and Han Ru. "Influence of Large Vehicles on the Speed of Expressway Traffic Flow." Advances in Civil Engineering 2020 (January 8, 2020): 1–9. http://dx.doi.org/10.1155/2020/2454106.
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Large vehicles impact the quality of traffic flow. To predict the impact of large-scale vehicles on the average speed of traffic flow, vehicle speeds under different vehicle mixing rates were collected through field observations. A laser roadside traffic survey instrument with automatic vehicle type identification functionality was used to collect cross section traffic flow data. The v/C ratio, large vehicle mixing rate, and average speed of traffic were calculated for each data set. A total of 158 traffic flow data sets were captured and divided into three groups according to the v/C ratio of the expressway. The v/C ratio ranges of the three groups are v/C≤0.35, 0.35<v/C≤0.55, and 0.55<v/C≤0.90. SPSS software was used to analyze the correlation between the vehicle mixing rate and the average speed under different traffic flow conditions, and a model was determined between the average speed of the vehicle flow and the large vehicle mixing rate. Analysis of the results with SPSS revealed a negative logarithmic linear relationship between the average traffic speed and the mixing rate of large vehicles. The results could also be applied to passenger cars. The models are considered as corrections of the average speed of the traffic flow after the mixing of large vehicles. When the mixing rate of large vehicles is close to zero, the forecast value of the model is the average speed of passenger cars. Furthermore, as the traffic volume of the road section increased, the influence of the mixing rate on traffic flow speed became more obvious. The adaptability of the proposed prediction model of the expressway mixing rate was verified by evaluating model predictions against actual measurements.
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Brilon, Werner, and Martin Ponzlet. "Variability of Speed-Flow Relationships on German Autobahns." Transportation Research Record: Journal of the Transportation Research Board 1555, no. 1 (January 1996): 91–98. http://dx.doi.org/10.1177/0361198196155500112.
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On German autobahns the average speed is strongly dependent on traffic volume. In addition to traffic volume, there is a comprehensive set of parameters that also cause a time dependency of speeds on autobahns. Speed-flow relationships play a key role in the determination of the type of highway for a given design flow and a given desired traffic flow quality. The question, however, is whether the speed-flow relationships from the guidelines are reliable over longer periods and to what extent the short-term speed variations are larger than the flow dependencies. Therefore, the objective of this research project was to determine typical fluctuations of average speeds on autobahns that are not the result of different volumes. Moreover, the consequences of differing speed-flow relationships on the maximum flow (i.e., capacity) should be pointed out. Studies of influences on the speed-flow relationships for German autobahns are presented. In addition to the fundamental influencing factors such as traffic volume and traffic mix, time dependency is also considered. As a data base, the results from 15 sites equipped with automatic traffic counting machines were used, each over a period of 3 years. This study was based both on conventional analysis, that is, separating different sets of data from the whole sample and on analysis of variance methods. The investigations show that two types of time-dependent influences exist. At first, there are changing environmental factors such as daylight or darkness or weather conditions. Darkness causes an average reduction of velocities by about 5 km/hr and wet roadway conditions reduce velocities by about 10 km/hr. Consequently, a reduction in capacity must be expected. The second type of time dependency is induced by varying driver behavior and traffic mix. Here seasonal variations can be found over the year and also over the days of the week. During predominantly leisure traffic, such as on Sundays or during the summer vacation season, lower average speeds can be detected.
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Dhaliwal, Sawanpreet Singh, Xinkai Wu, John Thai, and Xudong Jia. "Effects of Rain on Freeway Traffic in Southern California." Transportation Research Record: Journal of the Transportation Research Board 2616, no. 1 (January 2017): 69–80. http://dx.doi.org/10.3141/2616-08.
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A number of studies in the past quantified the effect of rain on traffic parameters but were limited to wet areas. This research expands the literature by studying the effect of rain in a dry area such as Southern California and considering regional differences in the impact. Traffic data (loop detectors) and precipitation data (rain gauges) from the Los Angeles, California, metropolitan area were analyzed to access the effect of rain on traffic stream parameters such as free-flow speed, speed at capacity, and capacity. Rainfall events were categorized as light, medium, and heavy as discussed in the 2010 Highway Capacity Manual. Density plots and fundamental diagrams for rain types proved that free-flow speed, speed at capacity, and capacity were reduced by 5.7%, 6.91%, and 8.65%, respectively, for light rain; 11.71%, 12.34%, and 17.4%, respectively, for medium rain; and 10.22%, 11.85%, and 15.34%, respectively, for heavy rain. The reductions for free-flow speed were lower, whereas for speed at capacity and for capacity, they were higher than those reported in the 2010 manual. Moreover, headway increased during rain; this finding shows cautious driving behavior. Multiplicative weather adjustment factors were computed to compensate for the loss of speed and capacity. Also demonstrated was the spatial and temporal effect of rain on traffic. Downstream traffic was not much affected by a rainfall event, whereas the upstream traffic was negatively affected. This study is expected to support weather-responsive traffic management strategies for dry areas.
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Jain, Manish, and Benjamin Coifman. "Improved Speed Estimates from Freeway Traffic Detectors." Journal of Transportation Engineering 131, no. 7 (July 2005): 483–95. http://dx.doi.org/10.1061/(asce)0733-947x(2005)131:7(483).
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Dey, Partha Pratim, Satish Chandra, and S. Gangopadhaya. "Speed Distribution Curves under Mixed Traffic Conditions." Journal of Transportation Engineering 132, no. 6 (June 2006): 475–81. http://dx.doi.org/10.1061/(asce)0733-947x(2006)132:6(475).
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Ali, S. Y., O. Al-Saleh, and P. A. Koushki. "Effectiveness of Automated Speed-Monitoring Cameras in Kuwait." Transportation Research Record: Journal of the Transportation Research Board 1595, no. 1 (January 1997): 20–26. http://dx.doi.org/10.3141/1595-04.
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In 1994 the General Traffic Department installed automatic radar cameras to monitor traffic speed at a number of strategic roadway locations in Kuwait. The aim was to lower the number of high-speed violations and thus reduce road accidents. Recent traffic safety records point to an increase in both the number of violations and the occurrence of road accidents. It is argued in this paper that without live enforcement support and active follow-up of camera-recorded violations, the effectiveness of these cameras in improving road safety is insignificant at best, particularly in the undisciplined driving environment of the oil-rich nations in the Middle East. The speed of traffic was simultaneously measured via radar instruments both at the automatic camera site and at sections approximately 1 km before or after or before and after the cameras at eight camera locations. Measurements were recorded for six 1/2-hr periods at each site for a total of 72 hr over a period of 3 months, so that morning, afternoon, and after-dark hours, as well as different days of the week and roadway types, were covered. Analysis of the speed data showed that for the three daily periods and various roadway types, traffic speeds were consistently higher in sections before or after or before and after the automatic camera at the camera site. Statistical tests indicated that the difference in speed measured at and away from the cameras was at the 99 percent level. The findings demonstrate that in a traffic environment characterized by poor driving behavior, inconsistent and piecemeal driver education programs, and insufficient presence of law enforcement officials, reliance on automatic cameras alone to reduce traffic violations is doomed to fail.
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Koburger, C. W. "Speed Control: A Useful Tool of VTS." Journal of Navigation 38, no. 02 (May 1985): 295–99. http://dx.doi.org/10.1017/s0373463300031428.
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This paper discusses some of the implications of speed control in the vessel traffic picture, emphasizing how speed relates to other safety factors so that control can be utilized with understanding and sensitivity. Speed control can seldom if ever be examined in isolation. The structuring of vessel traffic schemes implies a need for some form of speed control as an integral part of almost all those schemes. One technique reinforces the other. Speed limits become extremely critical at the lower end of the speed scale, the higher the traffic density, the more critical the limits. Again, the requirement for enforcement becomes clear. No over-emphasis of these operational limitations should be allowed to cloud the indisputable fact that — for the individual ship as well as for traffic management — speed control can be a most useful tool. At least three papers have been published in recent years pointing out that speed reduction by ships for safety purposes, either by individual ships themselves or through general imposition by a shore-based vessel traffic system (VTS), needs in all cases to be used with great care, reluctance even. Their warnings can easily be turned into a negative approach to such control. A number of points would therefore seem to demand looking at again.
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Ma, Yongfeng, Xin Gu, Jaeyoung Lee, and Qiaojun Xiang. "Investigating the Affecting Factors of Speed Dispersion for Suburban Arterial Highways in Nanjing, China." Journal of Advanced Transportation 2019 (December 31, 2019): 1–11. http://dx.doi.org/10.1155/2019/7965479.
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Suburban arterial highways are usually characterized by mixed traffic environments, which is a major contributor to traffic crashes. It has been known that speed dispersion as a surrogate safety measure has a strong correlation with safety. The objective of this study is to identify the influencing factors of speed dispersion for suburban arterial highways. Two definitions of speed dispersion are proposed for comparison: (1) an individual vehicle speed variation along a highway segment and (2) a vehicle speed variation at a cross-section. Vehicle speeds, traffic composition, and driving interference data were obtained from high-resolution videos from 20 segments of the G205 highway in Nanjing, China. An exploratory factor analysis was used to detect initial relationships between latent influencing factors and 13 candidate variables selected based on traffic condition, road condition, and driving behavior. A multivariable regression model was applied to identify the impacts of latent influencing factors on speed dispersion. The results from the two models showed substantial differences. The road condition factor was not significant in the cross-sectional speed dispersion model, but was interpretive in the segmented speed dispersion model. Driving interferences and illegal driving behaviors had a greater effect on the segmented speed dispersion. Consequently, segmented speed dispersion showed a better performance for the analysis of suburban arterial highways. On the other hand, traffic disturbance caused by driving interferences and illegal driving behaviors is the greatest contributor to high speed dispersion on suburban arterial highways, which may be mitigated by effective traffic management measures. It is expected that this work will help traffic managers better understand speed dispersion in mixed traffic environments and to develop effective safety improvement strategies.
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Abdel-Aty, Mohamed, Nizam Uddin, and Anurag Pande. "Split Models for Predicting Multivehicle Crashes during High-Speed and Low-Speed Operating Conditions on Freeways." Transportation Research Record: Journal of the Transportation Research Board 1908, no. 1 (January 2005): 51–58. http://dx.doi.org/10.1177/0361198105190800107.
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The future of traffic management and highway safety lies in proactive traffic management systems. Crash prediction models that use real-time traffic flow variables measured through a series of loop detectors are the most important component of such systems. A previous crash prediction model was developed with the matched case–control logistic regression technique. Although the model achieved reasonable classification accuracy, it remained open to improvement because of the limited study area, sample size, and transferability issues. Therefore, the previous work had been extended. Multivehicle freeway crashes under high- and low-speed traffic conditions were found to differ in severity and in their mechanism. The distribution of 5-min average speeds obtained immediately before the crash from the loop detector station closest to the crash shows two approximate mound-shaped distributions. This distribution is used as the basis to separate the models for crashes occurring under the two speed conditions. The results show that, as expected, variables that entered in the final models (for crashes under high and low speeds) were not the same. However, they were found to be consistent with the probable mechanisms of crashes under the respective speed conditions. A possible implementation of the separate models with the use of the odds ratios and with the balancing of the threshold between achieving high classification of crash potential and the false alarm situation is presented.
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Junevičius, Raimundas, Marijonas Bogdevičius, and Ádám Török. "MODELLING OF INTERNAL COMBUSTION ENGINES’ EMISSION THROUGH THE USE OF TRAFFIC FLOW MATHEMATICAL MODELS." TRANSPORT 26, no. 3 (October 5, 2011): 271–78. http://dx.doi.org/10.3846/16484142.2011.621978.
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Road traffic flows on a straight road segment are modelled in this article. The mathematical model of traffic flows has been constructed by using the method of lumped parameters. CO2, CO, CH, NOx, PM regression equations of internal combustion engines’ (ICE) emission has been developed. The accuracy of regression equations is 0.98÷0.99. The article presents assumptions for constructing the mathematical model, description of the mathematical model and gives simulation results. Traffic flow parameters, such as traffic flow concentration and traffic flow speed are presented as modelling results. ICE emission depending on the concentration and traffic flow speed are presented as well.
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Wang, Xu, Yuwei Bie, Tony Z. Qiu, and Lei Niu. "Effect of speed limits at speed transition zones." Canadian Journal of Civil Engineering 44, no. 1 (January 2017): 10–17. http://dx.doi.org/10.1139/cjce-2016-0125.
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Speed limits are a common traffic regulation for balancing traffic mobility and safety on roadways. Speed transition zones bear complicated driver behaviours. However, driver behaviours are even more complex when speed transition zones are dynamically created and shifted by variable speed limits (VSLs). Much existing research has estimated long-term driver compliance, evaluated effectiveness of speed enforcement, and attempted to involve compliance into VSL control algorithms. Whereas, limited research provides convincing solutions for representing speed limit effect and estimating real-time driver compliance at speed transition zones. To fill this research gap, this paper analyses field data from two speed transition zones along southbound of 97th Street, Edmonton, Canada. Temporal and spatial variations of speed and driver compliance are investigated in detail using statistical tests. A linear regression is then established to rank the contributions of the selected factors. Finally, some suggestions and guidelines for VSL algorithm design and implementation are proposed.
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Vasudevan, Vinod, Aniruddha Rajurkar, Rahul Soni, and Akhil Tiwari. "Design and Evaluation of K-Pass: A Bicycle-Friendly Modification of Speed Bumps." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 36 (July 4, 2018): 157–66. http://dx.doi.org/10.1177/0361198118783171.
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Traffic safety is a major concern across the world. Since speed is a major cause of traffic crashes, various traffic calming measures are used to enhance the safety of the road users in urban areas. Speed humps and bumps are the most widely used traffic calming devices. As a considerable number of bicyclists use the road along with motorized users, it is important to consider them while deploying any traffic calming devices. Studies have shown that bicyclists experience significantly larger discomfort as compared to motorized vehicles while passing over speed humps. In this study, a modification has been proposed to enhance riding comfort of bicyclists without compromising the traffic calming attribute of a speed bump. This modification is named “K-pass.” Analysis of data collected before and after the modification justifies the effectiveness in reducing the discomfort of bicyclists. Over a period of eight months, 70% of the bicyclists chose to use K-pass. Data also revealed that a high proportion of motorized two-wheeler users also used the K-pass at reduced speeds. It is expected that the K-pass will be a useful modification to the existing speed humps and speed bumps in areas where bicycle usage is expected.
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Schoepflin, Todd N., and Daniel J. Dailey. "Correlation Technique for Estimating Traffic Speed from Cameras." Transportation Research Record: Journal of the Transportation Research Board 1855, no. 1 (January 2003): 66–73. http://dx.doi.org/10.3141/1855-08.
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A new algorithm is presented for estimating speed from roadside cameras in uncongested traffic, congested traffic, favorable weather conditions, and adverse weather conditions. Individual vehicle lanes are identified and horizontal vehicle features are emphasized by using a gradient operator. The features are projected into a one-dimensional subspace and transformed into a linear coordinate system by using a simple camera model. A correlation technique is used to summarize the movement of features through a group of images and estimate mean speed for each lane of vehicles.
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Wang, Yinhai, and Nancy L. Nihan. "Freeway Traffic Speed Estimation with Single-Loop Outputs." Transportation Research Record: Journal of the Transportation Research Board 1727, no. 1 (January 2000): 120–26. http://dx.doi.org/10.3141/1727-15.
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Traffic speed is one of the most important indicators for traffic control and management. Unfortunately, speed cannot be measured directly from single inductance loops, the most commonly used detectors. To calculate space-mean speed, a constant, g, is often adopted to convert lane occupancy to traffic density. However, as illustrated by data from the present study, such a formula consistently underestimates speed whenever a significant number of trucks or other longer vehicles are present. This is because g is actually not a constant but, rather, a function of vehicle length. To calculate the value of g suitably, one needs to know the percentage of long vehicles or the mean vehicle length in real time. However, such information is not directly available from single-loop outputs. It is shown how the occupancy variance obtained from single-loop data can be used to estimate the percentage of long vehicles and how a log-linear regression model for mean vehicle length estimation based only on single-loop outputs can be developed. The estimated mean vehicle length is used to calculate the corresponding g-value in real-time to estimate speed more accurately. The speed estimations with corrected g-values are very close to the speeds observed by the speed trap in the present study.
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Jadhav, Rohit. "Design and Simulation of a Vehicle Speed Detection and Control System Using Speed Restriction Signboards." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 1568–76. http://dx.doi.org/10.22214/ijraset.2022.40053.
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Abstract: Over speeding vehicles is the most serious problem in any country. For control, the government imposed speed limits for cars traveling on various roadways. We can identify vehicle speed in real-time using speed cameras, but we can't slow down the driver. So, in this project, we connect automobiles to traffic speed cameras, which can inform drivers about their speed on a digital display and slow them down. Keywords: Speed Detection of Vehicle, Automatic Cruise Control, Speed Signboard Adaptation, Traffic Analysis, Vehicle Speed Control System, Over speed Control System.
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Wu, Ke Man, and Lian De Zhong. "A Method for Determining Length of Freeway Work Zone Based on Classification of Service Level." Advanced Materials Research 779-780 (September 2013): 491–97. http://dx.doi.org/10.4028/www.scientific.net/amr.779-780.491.
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based on the relationship between speed and density according to the classification criteria of freeway service level when the design speed is 80km/h in Highway capacity manual, the method of microscopic traffic flow simulation supported by VISSIM software is adopted, such that, in the situation the capacity and desired speed are guaranteed to be consistent with the reality, the proposed values of the length of work zone under different traffic composition and traffic volume are given in the condition that a 2-way-8-lane freeway is half closed for construction with 2-way-4-lane being left open for traffic.
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Altintasi, Oruc, Hediye Tuydes-Yaman, and Kagan Tuncay. "A METHOD TO ESTIMATE TRAFFIC PENETRATION RATES OF COMMERCIAL FLOATING CAR DATA USING SPEED INFORMATION." Transport 37, no. 3 (August 5, 2022): 161–76. http://dx.doi.org/10.3846/transport.2022.17069.
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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.
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Liu, Guo Xin, and Al Popoff. "Provincial-Wide Travel Speed and Traffic Safety Study in Saskatchewan." Transportation Research Record: Journal of the Transportation Research Board 1595, no. 1 (January 1997): 8–13. http://dx.doi.org/10.3141/1595-02.
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The latest speed limit increases in the United States have brought about debates about the relationship between travel speed and traffic safety. The relationship between travel speed and collision severity is clear: Increase in travel speed leads to a dramatic increase in collision severity. However, the relationship between speed and collision involvement is more complicated. Examined here is the relationship between travel speed and collision involvement on Saskatchewan provincial highways. Saskatchewan data indicate that the most prevalent source of human error contributing to collisions may be speed-related. The study of the relationship between vehicle speed and collisions therefore is fundamental for developing countermeasures to achieve compliance with speed regulations and to reduce the number of collisions. According to nine provincial-wide speed surveys and corresponding accident data from the last 26 years, traffic casualties and casualty rates on provincial highways are closely correlated to the surveyed average travel speed. This finding is not surprising, considering the fact that about 60 to 80 percent of all collisions on provincial highways are single-vehicle collisions. The relationship indicates that casualties will be reduced by about 7 percent for every 1-km/hr (0.62-mph) reduction in average travel speed on provincial highways. Casualty rates on provincial highways are closely correlated to speed differentials—higher speed differentials lead to higher casualty rates. Some difficult issues relating to speed and safety are also discussed.
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Singh, Sandeep. "Assessing the impacts of heavy vehicles on traffic characteristics of highways under mixed traffic platooning conditions." European Transport/Trasporti Europei, no. 86 (March 2022): 1–15. http://dx.doi.org/10.48295/et.2022.86.3.
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The study aims to investigate the impact of heavy vehicles (HVs) on traffic characteristics under platoon conditions on Indian highways. Traffic volume, speed, and time headway data were gathered from different highway sections using infra-red sensors. The mean relative speed criteria were used as an indicator of variability to estimate the critical time headway. The threshold value of a critical time headway of 4 sec was determined to represent vehicles into non-platoon followers and platoon followers. The speed-flow-density model curves were developed for two different traffic regimes, one without platoons and the other with platoons created by the HVs. The results show that under platoon conditions, the speed at capacity, density at capacity, and traffic capacity reduced by 11.2%, 12.5%, and 22.3%, respectively, compared to non-platoon conditions. Additionally, the average travel time and travel delay increased by 18.1 s/km and 12.7 s/km, respectively. The study’s findings emphasize the importance of considering platoon dynamics under the influence of HVs to better understand their impact on traffic characteristics.
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Xie, Han, Qinghua Ren, and Zheng Lei. "Influence of Lane-Changing Behavior on Traffic Flow Velocity in Mixed Traffic Environment." Journal of Advanced Transportation 2022 (January 18, 2022): 1–26. http://dx.doi.org/10.1155/2022/8150617.
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In mixed traffic with autonomous vehicles, the relationship between speed and lane-changing behavior is an important basis for mixed traffic control. In this study, we use empirical, simulation, and data-driven methods to study the relationship between speed and lane change rates in mixed traffic under different autonomous vehicle penetration rates. We use the empirical data to establish the corresponding road simulation models. Based on the simulation model, the traffic flow simulation experiments under the conditions of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90% penetration rate of autonomous vehicles were carried out. The analysis of the simulation results found that: (1) the penetration of autonomous vehicles into the road has a positive impact on the lanes far away from the entrance and exit, while the impact on the lanes closer to the entrance and exit is not obvious. (2) Lane-changing behavior has effectively decreased with the penetration of autonomous vehicles, but it is not obvious when the penetration rate exceeds 10%, and there is no significant drop in the lane connecting the entrance and exit. (3) There is a linear relationship between speed and lane-changing rate. Under different penetration rates, the data-driven analysis is used to perform multiple linear regressions, and the regression formula fits are all above 0.7. Based on the above findings, the linear formula of the fitting is proposed, and the value interval of the parameters in different states is given as well. Due to the small changes in the parameter values under different permeability conditions, the model has a certain degree of stability. The speed-lane change rate model proposed in this study can better describe the relationship between the speed of the ring-shaped urban expressway and the lane-changing behavior in the mixed traffic environment with the larger traffic flow.
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Riaz, Adnan, Hameedur Rahman, Muhammad Ali Arshad, Muhammad Nabeel, Affan Yasin, Mosleh Hmoud Al-Adhaileh, Elsayed Tag Eldin, and Nivin A. Ghamry. "Augmentation of Deep Learning Models for Multistep Traffic Speed Prediction." Applied Sciences 12, no. 19 (September 27, 2022): 9723. http://dx.doi.org/10.3390/app12199723.
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Traffic speed prediction is a vital part of the intelligent transportation system (ITS). Predicting accurate traffic speed is becoming an important and challenging task with the rapid development of deep learning and increasing traffic data size. In this study, we present a deep-learning-based architecture for network-wide traffic speed prediction. We propose a deep-learning-based model consisting of a fully convolutional neural network, bidirectional long short-term memory, and attention mechanism. Our design aims to consider both backward and forward dependencies of traffic data to predict multistep network-wide traffic speed. Thus, we propose a model named AttBDLTSM-FCN for multistep traffic speed prediction. We augmented the attention-based bidirectional long short-term memory recurrent neural network with the fully convolutional network to predict the network-wide traffic speed. In traffic speed prediction, this is the first time that augmentation of AttBDLSTM and FCN have been exploited to measure the backward dependency of traffic data, as a building block for a deep architecture model. We conducted comprehensive experiments, and the experimental evaluations illustrated that the proposed architecture achieved better performance compared to state-of-the-art models when considering the short and long traffic speed prediction, e.g., 15, 30, and 60 min, in multistep traffic speed prediction.
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Sun, Lu, and Jie Zhou. "Development of Multiregime Speed–Density Relationships by Cluster Analysis." Transportation Research Record: Journal of the Transportation Research Board 1934, no. 1 (January 2005): 64–71. http://dx.doi.org/10.1177/0361198105193400107.
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Empirical speed–density relationships are important not only because of the central role that they play in macroscopic traffic flow theory but also because of their connection to car-following models, which are essential components of microscopic traffic simulation. Multiregime traffic speed– density relationships are more plausible than single-regime models for representing traffic flow over the entire range of density. However, a major difficulty associated with multiregime models is that the breakpoints of regimes are determined in an ad hoc and subjective manner. This paper proposes the use of cluster analysis as a natural tool for the segmentation of speed–density data. After data segmentation, regression analysis can be used to fit each data subset individually. Numerical examples with three real traffic data sets are presented to illustrate such an approach. Using cluster analysis, modelers have the flexibility to specify the number of regimes. It is shown that the K-means algorithm (where K represents the number of clusters) with original (nonstandardized) data works well for this purpose and can be conveniently used in practice.
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Liu, Chun Yuan, Zhi Hui Yue, and Yu Liu. "The Suitability Evaluation of Underground Space Development and Exploit about Shijiazhuang Zhengding Newly-Developed Area." Advanced Materials Research 671-674 (March 2013): 2398–401. http://dx.doi.org/10.4028/www.scientific.net/amr.671-674.2398.
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Development and exploit of underground space is an important direction for future urban development, in a certain extent, exploit underground space could alleviate the modern urban’s contradictions such as land, traffic jam and so on. The exploit of underground space involves the rational distribution of the natural resources, construction technology, costs, and many other factors[1]. This topic use the Analytic Hierarchy Process defines the weight of the topography, stratigraphic structure, formation stability, groundwater depth, and the other 15 factors which impact the exploit of underground space, and then use the gray mathematical theory to calculate the Correlation Degree between evaluation index and suitable levels. Finally, based on the GIS map, numerical each evaluation index, and then use the integrate superimposed way obtained the Suitability zoning map of Shijiazhuang Zhengding newly-developed area,this zoning map has important guiding significance for construction projects, especially for the plan, design,exploit and construction about underground space.
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Zu, Yue, Chenhui Liu, and Ran Dai. "Distributed Traffic Speed Control for Improving Vehicle Throughput." IEEE Intelligent Transportation Systems Magazine 11, no. 3 (2019): 56–68. http://dx.doi.org/10.1109/mits.2019.2919621.
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Asif, Muhammad Tayyab, Justin Dauwels, Chong Yang Goh, Ali Oran, Esmail Fathi, Muye Xu, Menoth Mohan Dhanya, Nikola Mitrovic, and Patrick Jaillet. "Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction." IEEE Transactions on Intelligent Transportation Systems 15, no. 2 (April 2014): 794–804. http://dx.doi.org/10.1109/tits.2013.2290285.
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Gao, Yuan, Jiandong Zhao, Ziyan Qin, Yingzi Feng, Zhenzhen Yang, and Bin Jia. "Traffic Speed Forecast in Adjacent Region between Highway and Urban Expressway: Based on MFD and GRU Model." Journal of Advanced Transportation 2020 (December 2, 2020): 1–18. http://dx.doi.org/10.1155/2020/8897325.
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Traffic congestion in the adjacent region between the highway and urban expressway is becoming more and more serious. This paper proposes a traffic speed forecast method based on the Macroscopic Fundamental Diagram (MFD) and Gated Recurrent Unit (GRU) model to provide the necessary traffic guidance information for travelers in this region. Firstly, considering that the road traffic speed is affected by the macroscopic traffic state, the adjacent region between the highway and expressway is divided into subareas based on the MFD. Secondly, the spatial-temporal correlation coefficient is proposed to measure the correlation between subareas. Then, the matrix of regional traffic speed data is constructed. Thirdly, the matrix is input into the GRU prediction model to get the predicted traffic speed. The proposed algorithm’s prediction performance is verified based on the GPS data collected from the adjacent region between Beijing Highways and Expressway.