Relevant bibliographies by topics / Traffic safety Data processing

Academic literature on the topic 'Traffic safety Data processing'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Traffic safety Data processing"

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Wiwik, Budiawan, Singgih Saptadi, and Ary Arvianto. "The Development of Data Warehouse to Support Data Mining Technique for Traffic Accident Prediction." E3S Web of Conferences 73 (2018): 12007. http://dx.doi.org/10.1051/e3sconf/20187312007.

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Traffic accidents are one of the major health problems that cause serious death in the world and ranks 9th in the world. Traffic accidents in Indonesia ranks 5th in the world. One effort to improve traffic safety is to design traffic accident prediction models. Prediction models will utilize accident-related data in traffic through data mining processing. The data warehouse offers benefits as a basis for data mining. Building an effective data warehouse requires knowledge and attention to key issues in database design, data acquisition and processing, as well as data access and security. This study is the first step in the development of data mining accidents based prediction system. The output of this initial stage is the design of data warehouses that can provide periodic and incidental data to the data mining process, especially in the prediction of accidents. The method used to design data warehouse is Entity Relationship Diagram (ERD).
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Sun, Yuan, Hao Xu, Jianqing Wu, Jianying Zheng, and Kurt M. Dietrich. "3-D Data Processing to Extract Vehicle Trajectories from Roadside LiDAR Data." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 45 (June 8, 2018): 14–22. http://dx.doi.org/10.1177/0361198118775839.

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High-resolution vehicle data including location, speed, and direction is significant for new transportation systems, such as connected-vehicle applications, micro-level traffic performance evaluation, and adaptive traffic control. This research developed a data processing procedure for detection and tracking of multi-lane multi-vehicle trajectories with a roadside light detection and ranging (LiDAR) sensor. Different from existing methods for vehicle onboard sensing systems, this procedure was developed specifically to extract high-resolution vehicle trajectories from roadside LiDAR sensors. This procedure includes preprocessing of the raw data, statistical outlier removal, a Least Median of Squares based ground estimation method to accurately remove the ground points, vehicle data clouds clustering, a principle component-based oriented bounding box method to estimate the location of the vehicle, and a geometrically-based tracking algorithm. The developed procedure has been applied to a two-way-stop-sign intersection and an arterial road in Reno, Nevada. The data extraction procedure has been validated by comparing tracking results and speeds logged from a testing vehicle through the on-board diagnostics interface. This data processing procedure could be applied to extract high-resolution trajectories of connected and unconnected vehicles for connected-vehicle applications, and the data will be valuable to practices in traffic safety, traffic mobility, and fuel efficiency estimation.
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Cai, Xiaoyu, Cailin Lei, Bo Peng, Xiaoyong Tang, and Zhigang Gao. "Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic Data." Journal of Advanced Transportation 2020 (February 26, 2020): 1–13. http://dx.doi.org/10.1155/2020/3024101.

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Currently, research on road traffic safety is mostly focused on traffic safety evaluations based on statistical indices for accidents. There is still a need for in-depth investigation on preaccident identification of safety risks. In this study, the correlations between high-incidence locations for aberrant driving behaviors and locations of road traffic accidents are analyzed based on vehicle OBD data. A road traffic safety risk estimation index system with road traffic safety entropy (RTSE) as the primary index and rapid acceleration frequency, rapid deceleration frequency, rapid turning frequency, speeding frequency, and high-speed neutral coasting frequency as secondary indices is established. A calculation method of RTSE is proposed based on an improved entropy weight method. This method involves three aspects, namely, optimization of the base of the logarithm, processing of zero-value secondary indices, and piecewise calculation of the weight of each index. Additionally, a safety risk level determination method based on two-step clustering (density and k-means clustering) is also proposed, which prevents isolated data points from affecting safety risk classification. A risk classification threshold calculation method is formulated based on k-mean clustering. The results show that high-incidence locations for aberrant driving behaviors are consistent with the locations of traffic accidents. The proposed methods are validated through a case study on four roads in Chongqing with a total length of approximately 38 km. The results show that the road traffic safety trends characterized by road safety entropy and traffic accidents are consistent.
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Zhang, Qiuchen, Zhen Yang, and Dazhi Sun. "Automated Data Collection and Safety Analysis at Intersections Based on a Novel Video Processing System." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (April 2019): 136–44. http://dx.doi.org/10.1177/0361198119838979.

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A novel video-based system is presented which collects trajectories and motion parameters of all objects at intersections. First, a modified ViBe method is used to extract the foreground of moving objects. Then, an object-point-contour matching approach is developed for pairing, tracking, and generating trajectories. Finally, raw trajectories are corrected through post-processing and motion parameters are estimated after object classification. This system demonstrates better performance while tracking tardy and shadowed objects compared with previous studies. The accuracies of 86% and 91% are obtained for traffic counts and velocity validation, respectively. This paper also presents a sample safety analysis using traffic conflict technology to demonstrate the possible implementation for traffic management and safety analysis.
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B, Moh Baktiar, St Maryam, and Lambang Basri Said. "Moderasi Variabel Penegakan Hukum Berlalulintas Terhadap Pengaruh Disiplin Dan Keselamatan Berlalu Lintas Di Kabupaten Pinrang." INTEK: Jurnal Penelitian 6, no. 1 (May 25, 2019): 52. http://dx.doi.org/10.31963/intek.v6i1.1125.

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Abstract—This study aims to analyze the influence of trafficdiscipline and enforcement of traffic law on traffic safety inPinrang Regency and analyze the relationship between lawenforcement moderation and the influence of traffic discipline ontraffic safety in Pinrang Regency. Data collection techniques usedin this study were questionnaires, measurement of variables inthis study using a Likert scale. The data processing in this studyuses the SmartPLS3 Program. Valid data to be sampled are asmany as 100 taken by Slovin techniques, the sample of this studyis the people in Pinrang Regency. Based on the results of thisstudy concluded that traffic discipline and traffic lawenforcement have a positive effect on traffic safety in the districtof Pinrang and law enforcement does not mediate thestrengthening of the influence of traffic discipline on the trafficsafety of road users.
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Dyduch, Janusz, and Mieczysław Kornaszewski. "Selection of exploitation data from railway traffic control devices for the purposes of gathering and analyzing data processes from railway transport objects." Transportation Overview - Przeglad Komunikacyjny 2019, no. 5 (May 1, 2019): 43–53. http://dx.doi.org/10.35117/a_eng_19_05_04.

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The evaluation of the technical systems reliability and safety requires collecting and processing of reliable data which characterizes the processes. The data from current exploitation is particularly important for decision-making processes. It can be used for creation of occurring exploitation phenomena models and allows to determine the expected object behaviour in the future. The railway traffic control devices often work in very difficult exploitation and environmental conditions. The information about their technical condition can be gathered and used for a proper prophylaxis as well as a predictive maintenance of railway traffic. It will allow to choose a maintenance strategy which will consist in optimal use of railway traffic control devices.
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Bui, Ngoc Dung, Dinh Tran Ngoc Huy, and Tuan Thanh Nguyen. "Application of traffic conflict technique for traffic safety evaluation at intersection based on image processing." LAPLAGE EM REVISTA 7, no. 3A (September 2, 2021): 134–42. http://dx.doi.org/10.24115/s2446-6220202173a1379p.134-142.

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Traffic accidents occur frequently at intersections area because of conflicts among vehicles. Many researchers have been assessing traffic safety, however most of them are based on accident data that happened and caused injury as well as economic damage. Recently, conflict techniques provide general views and solutions to prevent early collisions. This paper proposes a method of conflict analysis using image processing technique and fuzzy comprehensive evaluation. Based on vehicles detection, the conflict parameters collected from two intersections in Hanoi, Vietnam were processed and evaluated by fuzzy comprehensive evaluation to give out the safety level of conflict points. The experimental results show that the proposed methods have successfully shown the distribution and location of dangerous conflict points according to the actual situation of the two intersections. Based on our results, authorities can consider reorganizing traffic light pattern at these intersections to reduce possible collisions.
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Kim and Lee. "Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with Parallel Context Acquisition and Machine Learning." Sensors 19, no. 23 (November 29, 2019): 5273. http://dx.doi.org/10.3390/s19235273.

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Excessive information significantly increases the mental burden on operators of critical monitoring services such as maritime and air traffic control. In these fields, vessels and aircraft have sensors that transmit data to a control center. Because of the large volume of collected data, it is infeasible for monitoring stations to display all of the information on monitoring screens that have limited sizes. This paper proposes a method for automatically selecting maritime traffic stream data for display from a large number of candidates in a context-aware manner. Safety is the most important concern in maritime traffic control, and special care must be taken to avoid collisions between vessels at sea. It presents an architecture for an adaptive information visualization system for a maritime traffic control service. The proposed system adaptively determines the information to be displayed based on the safety evaluation scores and expertise of vessel traffic service operators. It also introduces a method for safety context acquisition to assess the risk of collisions between vessels, using parallel and distributed processing of maritime stream data transmitted by sensors on the vessels at sea. It provides an information-filtering, knowledge extraction method based on the work logs of traffic service operators, using a machine learning technique to generate a decision tree. We applied the proposed system architecture to a large dataset collected at a port. Our results indicate that the proposed system can adaptively select traffic information according to port conditions and to ensure safety and efficiency.
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II Kim, Kwang, Keon Myung Lee, and Jang Young Ahn. "Methods of ship trajectory data processing for applying artificial neural network in port area." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 145. http://dx.doi.org/10.14419/ijet.v7i2.12.11112.

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Background/Objectives: In Vessel Traffic Service (VTS), prediction of the flow of vessel traffic is essential to serve safety information and control ship traffic. However, it is difficult to predict a ship’s speed due to many external forces and environmental conditions. This study proposes a data processing method to convert ship speed data to categorical data by dividing ship navigating routes into several gate lines.Methods/Statistical analysis: A ship’s trajectory is converted to each route’s gate line speed. To determine the gate line speed, we convertedthe previous and subsequent gate line speeds into category data. The input and output category data were applied to a multilayer perceptron network using as input variablesthe previous speed variance category, ship type, and ship length, and as output variable the subsequent speed variance.Findings: These results are useful because categorical data can be applied to various neural network models. As a result of the conducted experiments, the accuracy of the model improved when many gate lines are included.Improvements/Applications: The study results can be applied topredict ship traffic flow for VTS operators.
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Kim, Yonghoon, Jun-Ho Huh, and Mokdong Chung. "Traffic Inference System Using Correlation Analysis with Various Predicted Big Data." Electronics 10, no. 3 (February 2, 2021): 354. http://dx.doi.org/10.3390/electronics10030354.

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Currently, most of the transportation systems require changes to intelligent transportation systems, but most of them focus on efficient transportation rather than on improvement in human life. Sometimes, traffic systems are designed for economic value, and safety-related issues are neglected. A traffic information system that reflects various kinds of environmental information related to people’s safety must be able to reflect not only the existing economic goals but also a safe traffic environment. The traffic environment can be thought of as safety and direct information such as rainfall, including information on specific days when many people are scheduled to be gathered for certain events nearby. Intelligent transportation systems using this information can provide safety-related information for traveling to a specific area or for business trips. In addition, traffic congestion is a social problem and is directly related to a comfort life for individuals. Therefore, addressing various social and environmental factors could make human life more stable and reduce stress as a result. To do that, we need to estimate the impact on traffic based on environmental Big Data. The data can generally be divided into structured data and unstructured data. In inference, structured data analysis is relatively easy due to the precise meaning of the data. Nonetheless, it can be very difficult to predict environmentally sensitive data, such as traffic volume in intelligent transportation systems. To cope with this problem, there are a few systems for handling unstructured data to find out specific events that affect the traffic volume and improve its reliability. This paper shows that it is possible to estimate the exact volume of traffic using correlation analysis with various predicted data. Thus, we may apply this technique to the existing intelligent transportation system to predict the exact volume of traffic with environmentally sensitive data including various unstructured data.
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Dissertations / Theses on the topic "Traffic safety Data processing"

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Chisalita, Ioan. "Communication and Networking Techniques for Traffic Safety Systems." Doctoral thesis, Linköping : Department of Computer and Information Science, Linköpings universitet, 2006. http://www.bibl.liu.se/liupubl/disp/disp2006/tek1018s.pdf.

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Dickinson, Keith William. "Traffic data capture and analysis using video image processing." Thesis, University of Sheffield, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306374.

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賴翰笙 and Hon-seng Lai. "An effective methodology for visual traffic surveillance." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B30456708.

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So, Wai-ki, and 蘇慧琪. "Shadow identification in traffic video sequences." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B32045967.

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Boonsiripant, Saroch. "Speed profile variation as a surrogate measure of road safety based on GPS-equipped vehicle data." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28275.

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Thesis (M. S.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Hunter, Michael; Committee Member: Dixon, Karen; Committee Member: Guensler, Randall; Committee Member: Rodgers, Michael; Committee Member: Tsui, Kwok-Leung.
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Goulet, Dennis A., Joseph McMorrow, G. Edward Roberts, and Robert Lynch. "VESSEL TRAFFIC MANAGEMENT SYSTEM A Test Technology Development and Demonstration Project." International Foundation for Telemetering, 1997. http://hdl.handle.net/10150/607390.

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International Telemetering Conference Proceedings / October 27-30, 1997 / Riviera Hotel and Convention Center, Las Vegas, Nevada
The Vessel Traffic Management System is a cooperative effort of the Naval Undersea Warfare Center and the Naval Air Warfare Center Aircraft Division, funded by the OSD's Test Technology Development and Demonstration Program. The project is establishing the capability to acquire ship tracking information from numerous sources (GPS and radar target extractors), and combine them into a comprehensive, integrated view of the range safety target area. The consolidated tracking information will be transmitted to range safety vessel personnel and presented on portable display systems to aid in clearing the surveillance area of unauthorized vessels. The communications module is media independent in that positional and image data can be routed via RF modem, cellular phone, Intranet or Internet, singly or in any combination. The software systems for data acquisition, display and control are also platform independent, with the system under development operating under WindowsNT and Windows95. Additionally, the use of Java and VRML tools permits a user to display data (including three dimensional presentations of the data) without requiring the applications software. This system has numerous applications including range safety, commercial vessel traffic management, port authority and services monitoring, and oceanographic data gathering.
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Craig, David W. (David William) Carleton University Dissertation Engineering Electrical. "Light traffic loss of random hard real-time tasks in a network." Ottawa, 1988.

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Mollet, C. J. "The analysis of road traffic accident data in the implementation of road safety remedial programmes." Thesis, Stellenbosch : Stellenbosch University, 2001. http://hdl.handle.net/10019.1/52483.

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Thesis (M.Ing.)--Stellenbosch University, 2001.
ENGLISH ABSTRACT: A road safety remedial programme has as an objective the improvement of road transportation safety by applying road safety engineering remedial measures to hazardous road network elements in a manner that will be economically efficient. Since accident data is the primary manifestation of poor safety levels it must be analysed in manner that will support the overall objective of economic efficiency. Three steps in the process of implementing a road safety remedial programme, that rely on the systematic analysis of accident data, are the identification of hazardous locations, the ranking of hazardous locations and the evaluation of remedial measure effectiveness. The efficiency of a road safety remedial programme can be enhanced by using appropriate methodologies to measure safety, identify and rank hazardous locations and to determine the effectiveness of road safety remedial measures. There are a number of methodologies available to perform these tasks, although some perform much better than other. Methodologies based on the Empirical Bayesian approach generally provide better results than the Conventional methods. Bayesian methodologies are not often used in South Africa. To do so would require the additional training of students and engineering professionals as well as more research by tertiary and other research institutions. The efficiency of a road safety remedial programme can be compromised by using poor quality accident data. In South Africa the quality of accident data is generally poor and should more attention be given to the proper management and control of accident data. This thesis will report on, investigate and evaluate Bayesian and Conventional accident data analysis methodologies.
AFRIKAANSE OPSOMMING: Die doel van 'n padveiligheidsverbeteringsprogram is om op die mees koste effektiewe manier die veiligheid van onveilige padnetwerkelemente te verbeter deur die toepassing van ingenieursmaatreëls. Aangesien padveiligheid direk verband hou met verkeersongelukke vereis die koste effektiewe implementering van 'n padveiligheidsverbeteringsprogram die doelgerigte en korrekte ontleding van ongeluksdata. Om 'n padveiligheidsverbeteringsprogram te implementeer word die ontleding van ongeluksdata verlang vir die identifisering en priortisering van gevaarkolle, sowel as om die effektiwiteit van verbeteringsmaatreëls te bepaal. Die koste effektiwiteit van 'n padveiligheidsverbeteringsprogram kan verbeter word deur die regte metodes te kies om padveiligheid te meet, gevaarkolle te identifiseer en te prioritiseer en om die effektiwiteit van verbeteringsmaatreëls te bepaal. Daar is verskeie metodes om hierdie ontledings te doen, alhoewel sommige van die metodes beter is as ander. Die 'Bayesian' metodes lewer oor die algemeen beter resultate as die gewone konvensionele metodes. 'Bayesian' metodes word nie. in Suid Afrika toegepas nie. Om dit te doen sal addisionele opleiding van studente en ingenieurs vereis, sowel as addisionele navorsing deur universiteite en ander navorsing instansies. Die gebruik van swak kwaliteit ongeluksdata kan die integriteit van 'n padveiligheidsverbeteringsprogram benadeel. Die kwaliteit van ongeluksdata in Suid Afrika is oor die algemeen swak en behoort meer aandag gegee te word aan die bestuur en kontrole van ongeluksdata. Die doel van hierdie tesis is om verslag te doen oor 'Bayesian' en konvensionele metodes wat gebruik kan word om ongeluksdata te ontleed, dit te ondersoek en te evalueer.
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Popescu, Vlad M. "Airspace analysis and design by data aggregation and lean model synthesis." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49126.

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Air traffic demand is growing. New methods of airspace design are required that can enable new designs, do not depend on current operations, and can also support quantifiable performance goals. The main goal of this thesis is to develop methods to model inherent safety and control cost so that these can be included as principal objectives of airspace design, in support of prior work which examines capacity. The first contribution of the thesis is to demonstrate two applications of airspace analysis and design: assessing the inherent safety and control cost of the airspace. Two results are shown, a model which estimates control cost depending on autonomy allocation and traffic volume, and the characterization of inherent safety conditions which prevent unsafe trajectories. The effects of autonomy ratio and traffic volume on control cost emerge from a Monte Carlo simulation of air traffic in an airspace sector. A maximum likelihood estimation identifies the Poisson process to be the best stochastic model for control cost. Recommendations are made to support control-cost-centered airspace design. A novel method to reliably generate collision avoidance advisories, in piloted simulations, by the widely-used Traffic Alert and Collision Avoidance System (TCAS) is used to construct unsafe trajectory clusters. Results show that the inherent safety of routes can be characterized, determined, and predicted by relatively simple convex polyhedra (albeit multi-dimensional and involving spatial and kinematic information). Results also provide direct trade-off relations between spatial and kinematic constraints on route geometries that preserve safety. Accounting for these clusters thus supports safety-centered airspace design. The second contribution of the thesis is a general methodology that generalizes unifying principles from these two demonstrations. The proposed methodology has three steps: aggregate data, synthesize lean model, and guide design. The use of lean models is a result of a natural flowdown from the airspace view to the requirements. The scope of the lean model is situated at a level of granularity that identifies the macroscopic effects of operational changes on the strategic level. The lean model technique maps low-level changes to high-level properties and provides predictive results. The use of lean models allows the mapping of design variables (route geometry, autonomy allocation) to design evaluation metrics (inherent safety, control cost).
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Agafonov, Evgeny. "Fuzzy and multi-resolution data processing for advanced traffic and travel information." Thesis, Nottingham Trent University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271790.

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Books on the topic "Traffic safety Data processing"

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Ogle, Jennifer Harper. Technologies for improving safety data. Washington, D.C: Transportation Research Board, 2007.

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Vira, Carol. The data-smart manual: Use and analysis of data for local highway and traffic safety programs. [Washington, DC]: The Administration, 1999.

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National Center for Statistics and Analysis (U.S.), Rainbow Technology Inc, and United States. National Highway Traffic Safety Administration., eds. The data-smart manual: Use and analysis of data for local highway and traffic safety programs. [Washington, DC]: The Administration, 1999.

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Vira, Carol. The data-smart manual: Use and analysis of data for local highway and traffic safety programs. [Washington, DC]: The Administration, 1999.

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Vira, Carol. The data-smart manual: Use and analysis of data for local highway and traffic safety programs. [Washington, DC]: The Administration, 1999.

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Vira, Carol. The data-smart manual: Use and analysis of data for local highway and traffic safety programs. [Washington, DC]: The Administration, 1999.

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Vira, Carol. The data-smart manual: Use and analysis of data for local highway and traffic safety programs. [Washington, DC]: The Administration, 1999.

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Rutherford, G. Scott. Traffic and safety procedure: Project report on the safety and accident procedures : final report, Research Project Y3399, Task 27. [Olympia?, Wash.]: Washington State Dept. of Transportation, Planning, Research and Public Transportation Division in cooperation with the U.S. Dept. of Transportation, Federal Highway Administration, 1988.

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Dao lu jiao tong an quan zong he ping gu fang fa, shi xian ji shu yu ying yong. Beijing: Ke xue chu ban she, 2012.

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Finison, Karl S. Standardized reporting using CODES (Crash Outcome Data Evaluation System). [Washington, D.C.]: U.S. Dept. of Transportation, National Highway Traffic Safety Administration, 2000.

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Book chapters on the topic "Traffic safety Data processing"

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Serrano, Maria A., Erez Hadad, Roberto Cavicchioli, Rut Palmero, Luca Chiantore, Danilo Amendola, and Eduardo Quiñones. "Distributed Big Data Analytics in a Smart City." In Technologies and Applications for Big Data Value, 475–96. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78307-5_21.

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AbstractThis chapter describes an actual smart city use-case application for advanced mobility and intelligent traffic management, implemented in the city of Modena, Italy. This use case is developed in the context of the European Union’s Horizon 2020 project CLASS [4]—Edge and Cloud Computation: A highly Distributed Software for Big Data Analytics. This use-case requires both real-time data processing (data in motion) for driving assistance and online city-wide monitoring, as well as large-scale offline processing of big data sets collected from sensors (data at rest). As such, it demonstrates the advanced capabilities of the CLASS software architecture to coordinate edge and cloud for big data analytics. Concretely, the CLASS smart city use case includes a range of mobility-related applications, including extended car awareness for collision avoidance, air pollution monitoring, and digital traffic sign management. These applications serve to improve the quality of road traffic in terms of safety, sustainability, and efficiency. This chapter shows the big data analytics methods and algorithms for implementing these applications efficiently.
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Magnien, Airy, Gabriele Cecchetti, Anna Lina Ruscelli, Paul Hyde, Jin Liu, and Stefan Wegele. "Formalization and Processing of Data Requirements for the Development of Next Generation Railway Traffic Management Systems." In Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification, 35–45. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05814-1_3.

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Kołodziej, Joanna, Cornelio Hopmann, Giovanni Coppa, Daniel Grzonka, and Adrian Widłak. "Intelligent Transportation Systems – Models, Challenges, Security Aspects." In Cybersecurity of Digital Service Chains, 56–82. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04036-8_3.

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AbstractAs cars and other transportation devices become increasingly interconnected, mobility takes on a new meaning, offering new opportunities. The integration of new communications technologies in modern vehicles has generated an enormous variety of data from various communications sources. Hence, there is a demand for intelligent transportation systems that can provide safe and reliable transportation while maintaining environmental conditions such as pollution, CO2 emission, and energy consumption. This chapter provides an overview of the Intelligent Transportation Systems (ITS) models. Briefly, it discusses the most important features of the systems and challenges, mostly related to the security in data and information processing. Fast anomalies detection and prevention of external attacks may help solve the problems of traffic congestion and road safety to prevent accidents. The chapter contains the description of the realistic Smart Transportation System developed by the Wobcom company and implemented in Wolfsburg (Germany). That system is also used for practical validation of the security service components of the platform created in the GUARD project.
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Aron, Maurice, Régine Seidowsky, and Simon Cohen. "Ex-ante Assessment of a Speed Limit Reducing Operation - A Data-driven Approach." In Traffic Safety, 177–98. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119307853.ch12.

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Tarefder, Rafiqul, and James Brogan. "A Review of Statewide Traffic Data Collection, Processing, Projection and Quality Control." In Traffic Management, 1–18. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119307822.ch1.

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Lai, Yi, Yuan Zheng, and Jiannong Cao. "Protocols for Traffic Safety Using Wireless Sensor Network." In Algorithms and Architectures for Parallel Processing, 37–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72905-1_4.

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Molnár, P., and T. R. Collins. "Traffic Data Collection Using Image Processing Technology." In Traffic and Granular Flow ’99, 357–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59751-0_35.

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García-Dorado, José Luis, Felipe Mata, Javier Ramos, Pedro M. Santiago del Río, Victor Moreno, and Javier Aracil. "High-Performance Network Traffic Processing Systems Using Commodity Hardware." In Data Traffic Monitoring and Analysis, 3–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36784-7_1.

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Furutani, Nobuaki, Jun Kitazono, Seiichi Ozawa, Tao Ban, Junji Nakazato, and Jumpei Shimamura. "Adaptive DDoS-Event Detection from Big Darknet Traffic Data." In Neural Information Processing, 376–83. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26561-2_45.

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Trinh, Thong Duc, Dinh Khanh Nguyen Diep, and Vinh Dinh Nguyen. "Robust Median-Ternary Pattern for Traffic Light Detection." In Artificial Intelligence in Data and Big Data Processing, 485–94. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97610-1_38.

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Conference papers on the topic "Traffic safety Data processing"

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S, Sushma, Swathi S, Tulasi G, Vinutha M, and Nandakumar A N. "Road Safety Surveillance and Intelligent Traffic Management System." In 3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics. AIJR Publisher, 2018. http://dx.doi.org/10.21467/proceedings.1.3.

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Jiang, Ruoxi, Shunying Zhu, Qiucheng Chen, Ji Qiu, and Pan Wang. "A New Method of Traffic Conflict Data Collection and Processing for Traffic Safety Evaluation." In 2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). IEEE, 2020. http://dx.doi.org/10.1109/icmtma50254.2020.00180.

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Figueiras, Paulo, Ricardo Silva, André Ramos, Guilherme Guerreiro, Ruben Costa, and Ricardo Jardim-Goncalves. "Big Data Processing and Storage Framework for ITS: A Case Study on Dynamic Tolling." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-68069.

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Urban and national road networks in many countries are severely congested, resulting in increased travel times, unexpected delays, greater travel costs, worsening air pollution and noise levels, and a greater number of traffic accidents. Expanding traffic network capacities by building more roads is both extremely costly and harmful to the environment. By far the best way to accommodate growing travel demand is to make more efficient use of existing networks. Portugal has a good but underused toll highway network that runs near to an urban/national road network that is free to use but congested. In choosing not to pay a toll, many Portuguese drivers are apparently accepting greater risk to their safety and longer travel times. As a result, the urban/national road network is used far more intensively than projections anticipated, which raises maintenance costs while increasing levels of risk and inconvenience. The main idea behind the work presented here, is to motivate a shift of traffic from the overused network to the underused network. To this end, a model for calculating variable toll fees needs to be developed. In order to support the model, there is the need to accurately predict the status of road networks for real-time, short and medium term horizons, by using machine learning algorithms. Such algorithms will be used to feed the dynamic toll pricing model, reflecting the present and future traffic situations on the network. Since traffic data quantity and quality are crucial to the prediction accuracy of road networks’ statuses, the real-time and predictive analytics methods will use a panoply of data sources. The approach presented here, is being developed under the scope of the H2020 OPTIMUM, a European R&D project on ITS.
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Figueiras, Paulo, Hugo Antunes, Guilherme Guerreiro, Ruben Costa, and Ricardo Jardim-Gonçalves. "Visualisation and Detection of Road Traffic Events Using Complex Event Processing." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-87909.

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In the recent decades, we have witnessed an increase in the number of vehicles using the road infrastructure, resulting in an increased overload of the road network. To mitigate such problems, caused by the increasing number of vehicles and increasing the efficiency and safety of transport systems has been integrated applications of advanced technology, denominated Intelligent Transport Systems (ITS). However, one problem still unsolved in current road networks is the automatic identification of road events such as accidents or traffic jams, being inhibitor to efficient road management. In order to mitigate this problematic, this paper proposes the development of a technological platform able to detect anomalies (abnormal traffic events) to typical road network status and categorize such anomalies. The proposed work, adopts a complex event processing (CEP) engine able to monitor streams of events and detect specified patterns of events in real time. Data is collectively collected and analysed in real-time from loop sensors deployed in Slovenian highways and national roads, providing traffic flows. This prototype will work with a large number of data, being used to process all data, complex event processing tools. All the data used to validate the present study is based on the Slovenian road network. This work has been carried out in the context of the OPTIMUM Project, funded by the H2020 European Research Framework Program.
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Senel, Numan, Shrivatsa Udupa, and Gordon Elger. "Sensor Data Preprocessing in Road-Side Sensor Units." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2021-acm-120.

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To improve safety, mitigate traffic congestion and to reduce pollution caused by vehicles, infrastructure-side sensors can be used, especially at critical locations in cities. In the future, infrastructural safeguarding has large potential, due to availability of advanced sensors (camera, radar, lidar) and vehicle-to-infrastructure(V2I) communications. Currently, camera-based systems are widely used to monitor traffic violations. A smart combination of multiple sensors like camera-RADAR or camera-LIDAR is used to determine the precise velocity and position of the traffic participants. In such scenarios RADAR/LIDAR will be responsible for detection of velocity or position and cameras will be used to identify the traffic participants, i.e. for object classification. However, processing of large amount of data is necessary at the sensor nodes. With the evolution of technology and availability of higher computational power, such systems will become affordable and smarter. Additional hardware can enable such systems to communicate with other traffic participants in order to increase safety and efficiency. Additional hardware and computational power will be limited due to cost overhead, size, weather conditions and power consumption limitations in the open-air roads. To mitigate such limitations, we have could-based solutions where data are acquired at the road side units but processed remotely in the cloud. Although it is a valid solution, it brings limitation regarding the required high bandwidth and is also a potential threat for data leaks, e.g. privacy and data security. To have a large detection range a camera imager needs to have a large chip area and high number of pixels. Therefore, the image size gets large even if the large number of pixels is not required for objects in short distance. In this paper an image pre-processing method is developed to reduce the sensor data size, which in turn reduces the computational power to process or the bandwidth to transmit the data. An increase of detection range is possible keeping the data size at an acceptable level. Reducing the sensor data size is a benefit and reduces the dependency of cloud-based solutions. Even in case of using a cloud-based solution, reduced data size will result in a lower network load, that increase overall performance of could base systems. In the paper, YOLO-V3 is used for object detection and classification of traffic participants. In Addition, the fixed installation of the camera in the infrastructure allows to apply methods for depth estimation when using only mono cameras. The improvement and accuracy of the depth estimation is benchmarked using data from RADAR and LiDAR sensors as ground truth, which are installed at the same sensor node as the camera, i.e. the data of Radar and LiDAR are fused to the camera data.
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Slavík, Martin, and Ondřej Vaculín. "Concept of Mission Control System for IN2Lab testing field for Automated Driving." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2021-acm-119.

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"Automated driving brings very high demands on all vehicle systems. In order to meet these requirements, automated vehicles are equipped with various vehicle sensors to collect information about the actual vehicle environment. Current systems are based on data acquired by in-vehicle sensors, such as radar, lidar and camera, which generate a comprehensive environment model where an automated vehicle locates. The sensors differ in their technical performance parameters such as range, resolution, reliability, sensitivity and robustness. The use of heterogeneous sensors allows the technologies to complement each other in terms of their technical properties. The overall safety level is increased by information from several sensors by means of sensor fusion. Assessment errors of the on-board sensors may occur despite the continuous improvement and optimisation of measurements and fusion. Systems under development are especially prone to these errors. Such issues reduce the reliability and trustworthiness of the whole system. These errors, either from sensors or evaluation algorithms and sensor fusion, should be identified during the development of automated driving functions. Virtual driving tests, proving ground tests and, in later development phases, driving tests in real traffic (field tests) serve this purpose. The filed tests in real traffic are crucial for the validation of automated driving systems. Only the real environmental conditions offer a variety of driving situations to prove the safety of automated vehicles. However, the test vehicles in a traffic flow must under no circumstances worsen road safety and put other road users at risk. The project IN2Lab aims to increase the overall test field safety by an infrastructure based safety system installed along a test field. It consists of sensors, C2X communication and mission control centre. This paper presents a concept of Mission Control System. The system provides additional information about traffic flow, obstacles or weather conditions based on data from infrastructure to connected vehicles. Cameras, radars, lidars and C2X roadside units installed along the public test filed to collect data about the traffic flow. The fundamental functionality of the system is the monitoring of traffic flow and object classification. An additional safety value is a quasi-real-time data processing provides relevant information feedback about dynamic and static objects along the test filed to connected vehicles, especially to automated vehicles. These vehicles can use the information to improve their environmental perception confidence or to plan driving manoeuvers."
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Mueller, Simone, and Dieter Kranzlmueller. "Self-Organising Maps for Efficient Data Reduction and Visual Optimisation of Stereoscopic based Disparity Maps." In WSCG'2022 - 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2022. Západočeská univerzita, 2022. http://dx.doi.org/10.24132/csrn.3201.28.

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Many modern autonomous systems use disparity maps for recognition and interpretation of their environment. The depth information of these disparity maps can be utilised for point cloud generation. Real-time and high-quality processing of point clouds is necessary for reliable detection of safety-relevant issues such as barriers or obstacles in road traffic. However, quality characteristics of point clouds are influenced by properties of depth sensors and environmental conditions such as illumination, surface and texture. Quality optimisation and real-time implemen- tation can be resource intensive. Limiting the amount of data allows optimisation of real-time processing. We use Kohonen network existing self-organising maps to identify and segment salient objects in disparity maps. Kohonen networks use unsupervised learning to generate disparity maps abstracted by a small number of vectors instead of all pixels. The combination of object-specific segmentation and reduced pixel number decreases the memory and processing time towards real-time compatibility. Our results show that trained self-organising maps can be applied to disparity maps for improved runtime, reduced data volume and further processing of 3D reconstruction of salient objects.
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Živković, Slavko, Ivan Barac, Patricia Živković, Ivan Pećina, and Bruno Brckan. "The impact of wildlife on railway track." In 7th International Conference on Road and Rail Infrastructure. University of Zagreb Faculty of Civil Engineering, 2022. http://dx.doi.org/10.5592/co/cetra.2022.1420.

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The problem of track maintenance as a condition for the quality of railway transport in terms of comfort and speed of traffic has encouraged the modernization of railways. With modernization an important part of the process of managing the railway network is the timely identification of priority areas for maintenance and reconstruction – the so-called hot spot area. Damage to the embankment of railway structure is caused by the settlement of the railway track. Suddenly climate change leads to an increased frequency of deformations and is the most common cause of track instability. In addition to causing an increased risk to traffic safety, they can also cause serious disruptions to traffic networks. The papers present the influence of animals on deformations and damage of railway structures, primarily embankments, and the possible influence on the behaviour of track construction. The phenomenon was noticed at the end of 2020 on the railway line M104 Novska – Tovarnik – State border. As a part of the observed impacts of wild animals, continuous measurements of track subsidence at selected measuring points were carried out, and geotechnical investigation works were performed with the aim of processing and analysing the measured data and preparing a remediation project.
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Praticò, Filippo Giammaria, Rosario Fedele, Sara Pizzi, and Giuseppe Araniti. "INTELLIGENT TRANSPORT SYSTEMS EXPLOITING SMART WIRELESS SENSING NODES." In 11th International Conference “Environmental Engineering”. VGTU Technika, 2020. http://dx.doi.org/10.3846/enviro.2020.624.

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Future smart cities that will exploit the forthcoming fifth-generation (5G) network will strongly contribute to the development of intelligent transport systems, which will be able to effectively manage changing infrastructural conditions, and to timely exchange crucial information with different stakeholders to improve sustainability and safety. To this end, smart wireless sensing nodes can be effectively exploited. Consequently, the objectives of this study are: 1) to describe the setup and the main potentialities of a wireless sensing system designed for monitoring the environmental and structural conditions on road pavements; 2) to provide an overview about the capability of the 5G network to enable the data exchange required by the designed system. Each sensing node includes different sensors, and is able to send the data gathered from the resource-constrained sensors to a web server used for data processing. Vibrational-, acoustical-, and environmental-related data are used to control traffic pollution, road availability and structural status. The paper describes the in-lab tests carried out on asphalt concrete samples to: i) calibrate the sensors; ii) define structural and environmental thresholds. Results show that the tested node is able to provide reliable data that can be used for the above-described purposes.
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Juan, Shen, Zhou Lili, Yao Yong, Zou Guorong, and Chen Yue. "Traffic Safety Analysis of Overloaded Transportation." In 2010 International Conference on Optoelectronics and Image Processing (ICOIP). IEEE, 2010. http://dx.doi.org/10.1109/icoip.2010.268.

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Reports on the topic "Traffic safety Data processing"

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Tarko, Andrew P., Qiming Guo, and Raul Pineda-Mendez. Using Emerging and Extraordinary Data Sources to Improve Traffic Safety. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317283.

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The current safety management program in Indiana uses a method based on aggregate crash data for conditions averaged over several-year periods with consideration of only major roadway features. This approach does not analyze the risk of crashes potentially affected by time-dependent conditions such as traffic control, operations, weather and their interaction with road geometry. With the rapid development of data collection techniques, time-dependent data have emerged, some of which have become available for safety management. This project investigated the feasibility of using emerging and existing data sources to supplement the current safety management practices in Indiana and performed a comprehensive evaluation of the quality of the new data sources and their relevance to traffic safety analysis. In two case studies, time-dependent data were acquired and integrated to estimate their effects on the hourly probability of crash and its severity on two selected types of roads: (1) rural freeways and (2) signalized intersections. The results indicate a considerable connection between hourly traffic volume, average speeds, and weather conditions on the hourly probability of crash and its severity. Although some roadway geometric features were found to affect safety, the lack of turning volume data at intersections led to some counterintuitive results. Improvements have been identified to be implemented in the next phase of the project to eliminate these undesirable results.
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Monsere, Christopher, Myenwoo Lim, Chengxin Dai, and Xiaowei Wu. Development of the Oregon Traffic Safety Data Archive: Phases 1 and 2. Portland State University Library, March 2012. http://dx.doi.org/10.15760/trec.113.

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Dai, Chengyu. Exploration of Weather Impacts on Freeway Traffic Operations and Safety Using High-Resolution Weather Data. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.255.

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Takubo, Nobuaki, and Shinichi Yoshida. An Estimation of Effects of Active Safety Devices by Hayashi's Quantification Theory Analysis of Traffic Accident Data. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0062.

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Zhang, Yongping, Wen Cheng, and Xudong Jia. Enhancement of Multimodal Traffic Safety in High-Quality Transit Areas. Mineta Transportation Institute, February 2021. http://dx.doi.org/10.31979/mti.2021.1920.

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Numerous extant studies are dedicated to enhancing the safety of active transportation modes, but very few studies are devoted to safety analysis surrounding transit stations, which serve as an important modal interface for pedestrians and bicyclists. This study bridges the gap by developing joint models based on the multivariate conditionally autoregressive (MCAR) priors with a distance-oriented neighboring weight matrix. For this purpose, transit-station-centered data in Los Angeles County were used for model development. Feature selection relying on both random forest and correlation analyses was employed, which leads to different covariate inputs to each of the two jointed models, resulting in increased model flexibility. Utilizing an Integrated Nested Laplace Approximation (INLA) algorithm and various evaluation criteria, the results demonstrate that models with a correlation effect between pedestrians and bicyclists perform much better than the models without such an effect. The joint models also aid in identifying significant covariates contributing to the safety of each of the two active transportation modes. The research results can furnish transportation professionals with additional insights to create safer access to transit and thus promote active transportation.
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Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Cristhian Lizarazo. TScan–Stationary LiDAR for Traffic and Safety Applications: Vehicle Interpretation and Tracking. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317402.

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To improve traffic performance and safety, the ability to measure traffic accurately and effectively, including motorists and other vulnerable road users, at road intersections is needed. A past study conducted by the Center for Road Safety has demonstrated that it is feasible to detect and track various types of road users using a LiDAR-based system called TScan. This project aimed to progress towards a real-world implementation of TScan by building two trailer-based prototypes with full end-user documentation. The previously developed detection and tracking algorithms have been modified and converted from the research code to its implementational version written in the C++ programming language. Two trailer-based TScan units have been built. The design of the prototype was iterated multiple times to account for component placement, ease of maintenance, etc. The expansion of the TScan system from a one single-sensor unit to multiple units with multiple LiDAR sensors necessitated transforming all the measurements into a common spatial and temporal reference frame. Engineering applications for performing traffic counts, analyzing speeds at intersections, and visualizing pedestrian presence data were developed. The limitations of the existing SSAM for traffic conflicts analysis with computer simulation prompted the research team to develop and implement their own traffic conflicts detection and analysis technique that is applicable to real-world data. Efficient use of the development system requires proper training of its end users. An INDOT-CRS collaborative process was developed and its execution planned to gradually transfer the two TScan prototypes to INDOT’s full control. This period will be also an opportunity for collecting feedback from the end user and making limited modifications to the system and documentation as needed.
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Chiavassa, Nathalie, and Raphael Dewez. Technical Note on Road Safety in Haiti. Inter-American Development Bank, January 2021. http://dx.doi.org/10.18235/0003250.

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The IDB has been a predominant partner supporting Haiti development efforts for many years. Nowadays, the IDB is the main source of investment for the country. Considering the vital weight of road transport sector in the socio-economy of the country, the IDB has concentrated a large part of investment efforts in rehabilitating and improving national road infrastructures. In the same time, a rapid increase of motorization and relatively higher speeds have contributed to increasing the number of traffic fatalities and injuries. In 2017, road injuries were the fifth cause of mortality in Haiti. The Road Safety situation of the country is preoccupying with many Vulnerable Road Users involved, in particular pedestrians and motorcyclists. The country is facing multi-sector challenges to address this Road Safety situation. Despite recent efforts, high political will has not been continuous in promoting a multi-sector coordination and the success of technical efforts remained mitigated over the last years. Road user awareness is still weak in the country. Risk factors include dangerous driving, bad safety conditions of vehicles, together with limited law enforcement and poor maintenance of safety devices on the roads. In this context, the Road Safety situation of the country may be getting worse in the coming years if no action is taken. However, the new Decade provides with a unique opportunity to achieve Sustainable Development Goals (SDGs) including significant progress in reducing the burden of traffic crashes. The IDB has already initiated vital investments in modernizing crash data collection, promoting institutional dialogue and supporting capacity building in the area of Road Safety. Future actions to address Road Safety challenges in Haiti in the framework of the five UN five pillars would require a range of investments in the area of political commitment, institutional coordination and technical efforts. A change of political paradigm from making roads for travelling faster to making roads safer for all users is highly needed at national level. This technical note on Road Safety in Haiti present the current situation of the country and provides with recommendations for future actions on Road Safety.
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Nafakh, Abdullah Jalal, Franklin Vargas Davila, Yunchang Zhang, Jon D. Fricker, and Dulcy M. Abraham. Safety and Mobility Analysis of Rolling Slowdown for Work Zones: Comparison with Full Closure. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317380.

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There are times when the traffic lanes through a work zone must be kept clear for activities such as placing overhead beams, erecting overhead signs, and installing power lines, and other temporary roadway work activities. As an alternative to a full road closure, a rolling slowdown can typically provide up to 30 minutes to complete such activities without bringing approaching traffic to a complete halt. Using data from recent rolling slowdowns and full closures carried on the Indiana interstate network and a simulation approach, this study compares rolling slowdowns to full closures from safety and mobility standpoints. The study suggests that while rolling slowdowns are more impactful in terms of travel times, they are a safer option than full closures as they often form a forward moving shockwave causing, lower hard braking rates, and consequently a lower probability of causing a crash.
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Williams, Michael, Marcial Lamera, Aleksander Bauranov, Carole Voulgaris, and Anurag Pande. Safety Considerations for All Road Users on Edge Lane Roads. Mineta Transportation Institute, March 2021. http://dx.doi.org/10.31979/mti.2021.1925.

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Edge lane roads (ELRs), also known as advisory bike lanes or advisory shoulders, are a type of shared street where two-way motor vehicle (MV) traffic shares a single center lane, and edge lanes on either side are preferentially reserved for vulnerable road users (VRUs). This work comprises a literature review, an investigation of ELRs’ operational characteristics and potential road user interactions via simulation, and a study of crash data from existing American and Australian ELRs. The simulation evaluated the impact of various factors (e.g., speed, volume, directional split, etc.) on ELR operation. Results lay the foundation for a siting criterion. Current American siting guidance relies only upon daily traffic volume and speed—an approach that inaccurately models an ELR’s safety. To evaluate the safety of existing ELRs, crash data were collected from ELR installations in the US and Australia. For US installations, Empirical Bayes (EB) analysis resulted in an aggregate CMF of .56 for 11 installations observed over 8 years while serving more than 60 million vehicle trips. The data from the Australian State of Queensland involved rural one-lane, low-volume, higher-speed roads, functionally equivalent to ELRs. As motor vehicle volume grows, these roads are widened to two-lane facilities. While the authors observed low mean crash rates on the one-lane roads, analysis of recently converted (from one-lane to two-lane) facilities showed that several experienced fewer crashes than expected after conversion to two-lane roads.
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Bäumler, Maximilian, Madlen Ringhand, Christian Siebke, Marcus Mai, Felix Elrod, and Günther Prokop. Report on validation of the stochastic traffic simulation (Part B). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.243.

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This document is intended to give an overview of the validation of the human subject study, conducted in the driving simulator of the Chair of Traffic and Transportation Psychology (Verkehrspsychologie – VPSY) of the Technische Universität Dresden (TUD), as well of the validation of the stochastic traffic simulation developed in the AutoDrive project by the Chair of Automotive Engineering (Lehrstuhl Kraftfahrzeugtechnik – LKT) of TUD. Furthermore, the evaluation process of a C-AEB (Cooperative-Automatic Emergency Brake) system is demonstrated. The main purpose was to compare the driving behaviour of the study participants and the driving behaviour of the agents in the traffic simulation with real world data. Based on relevant literature, a validation concept was designed and real world data was collected using drones and stationary cameras. By means of qualitative and quantitative analysis it could be shown, that the driving simulator study shows realistic driving behaviour in terms of mean speed. Moreover, the stochastic traffic simulation already reflects reality in terms of mean and maximum speed of the agents. Finally, the performed evaluation proofed the suitability of the developed stochastic simulation for the assessment process. Furthermore, it could be shown, that a C-AEB system improves the traffic safety for the chosen test-scenarios.
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