Advanced Decision Support for Winter Road Maintenance: FHWA Documentation of Requirements for Intelligent Transportation Systems

2001 ◽  
Vol 1741 (1) ◽  
pp. 129-136
Author(s):  
Paul A. Pisano ◽  
Gary G. Nelson
Keyword(s):  
Decision Support ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Road Maintenance ◽  
Winter Road ◽  
Winter Road Maintenance

Predicting Weather and Road Conditions: Integrated Decision-Support Tool for Winter Road-Maintenance Operations

2003 ◽  
Vol 1824 (1) ◽  
pp. 98-105 ◽  
Author(s):  
William P. Mahoney ◽  
William L. Myers
Keyword(s):  
Decision Support ◽  
Private Sector ◽  
Weather Prediction ◽  
User Needs ◽  
Road Maintenance ◽  
Surface Transportation ◽  
Road Condition ◽  
Winter Road ◽  
Maintenance Decision ◽  
Winter Road Maintenance

Winter road-maintenance practitioners have expressed a strong interest in obtaining weather and road-condition forecasts and treatment recommendations specific to winter road-maintenance routes. These user needs led the FHWA Office of Transportation Operations Road Weather Management Program to support the development of a prototype winter road-maintenance decision-support system (MDSS). The MDSS is a unique data-fusion system designed to provide real-time treatment guidance (e.g., treatment times, types, rates, and locations) specifically regarding winter road-maintenance routes to winter maintenance decision makers. The system integrates weather and road data, weather and road-condition model output, chemical concentration algorithms, and anti-icing and deicing rules of practice. FHWA began the multiyear project in 2001 by engaging several national laboratories that had expertise in weather prediction and winter road engineering. A user-needs assessment for surface transportation weather information, performed by FHWA in 2000, formed the basis for the development effort. FHWA required that the system be developed in an open environment with significant input from the stakeholders (state transportation personnel and private-sector meteorological services). The resulting technologies have been released (in an initial version) on a nonexclusive basis to the surface transportation community. It is anticipated that the prototype MDSS will provide a springboard for the development and rapid deployment of operational systems by the private sector.

scholarly journals Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning Data

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5030 ◽  
Author(s):  
Yang ◽  
Liu ◽  
Jiang ◽  
Xu ◽  
Sheng ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Laser Scanning ◽  
Large Scale ◽  
Auxiliary Information ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Road Maintenance ◽  
The Road ◽  
Large Scale Data ◽  
Road Width

Accurate road information is important for applications involving road maintenance, intelligent transportation, and road network updates. Mobile laser scanning (MLS) can effectively extract road information. However, accurately extracting road edges based on large-scale data for complex road conditions, including both structural and non-structural road types, remains difficult. In this study, a robust method to automatically extract structural and non-structural road edges based on a topological network of laser points between adjacent scan lines and auxiliary surfaces is proposed. The extraction of road and curb points was achieved mainly from the roughness of the extracted surface, without considering traditional thresholds (e.g., height jump, slope, and density). Five large-scale road datasets, containing different types of road curbs and complex road scenes, were used to evaluate the practicality, stability, and validity of the proposed method via qualitative and quantitative analyses. Measured values of the correctness, completeness, and quality of extracted road edges were over 95.5%, 91.7%, and 90.9%, respectively. These results confirm that the proposed method can extract road edges from large-scale MLS datasets without the need for auxiliary information on intensity, image, or geographic data. The proposed method is effective regardless of whether the road width is fixed, the road is regular, and the existence of pedestrians and vehicles. Most importantly, the proposed method provides a valuable solution for road edge extraction that is useful for road authorities when developing intelligent transportation systems, such as those required by self-driving vehicles.

Design of Alert Criteria for an Intersection Decision Support System

2005 ◽  
Vol 1910 (1) ◽  
pp. 1-9
Author(s):  
Steven E. Shladover ◽  
Joel VanderWerf ◽  
David R. Ragland ◽  
Ching-Yao Chan
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Intelligent Transportation Systems ◽  
Significant Proportion ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Preliminary Evaluation ◽  
Left Turn ◽  
System Designs

This paper describes the design and preliminary evaluation of the criteria for alerting drivers to a specific set of intersection hazards. The research is being conducted as part of the development of an intersection decision support (IDS) system that uses the sensing and computational technologies of infrastructure-based intelligent transportation systems. The IDS system under consideration is intended to help drivers avoid conflicts with oncoming traffic when they are making left turns under a permissive (i.e., unprotected) green signal. These conflicts account for a significant proportion of intersection crashes and are difficult to mitigate without imposing serious costs and burdens on intersection capacity associated with providing a protected left-turn signal cycle. The human factors and sensing issues that need to be considered in designing the system are discussed and are followed by a description of the logic used to define the gaps in opposing traffic that should be considered adequate for left-turn maneuvers. The simulation model used to evaluate alternative system designs is described, and sample results are shown for evaluation of the effectiveness of a warning under a relatively stressful scenario. The influence of alternative sensor configurations on the effectiveness of the warning is illustrated and indicates the importance of providing information about both the presence and speed of approaching vehicles sufficiently far from the intersection.

Quality indicator of the smart city transportation and logistics system

2020 ◽  
Vol 19 (11) ◽  
pp. 2116-2135
Author(s):  
G.V. Savin
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart City ◽  
Urban Areas ◽  
Human Life ◽  
Life Quality ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Logistics System ◽  
Organization And Management

Subject. The article considers functioning and development of process flows of transportation and logistics system of a smart city. Objectives. The study identifies factors and dependencies of the quality of human life on the organization and management of stream processes. Methods. I perform a comparative analysis of previous studies, taking into account the uniquely designed results, and the econometric analysis. Results. The study builds multiple regression models that are associated with stream processes, highlights interdependent indicators of temporary traffic and pollution that affect the indicator of life quality. However, the identified congestion indicator enables to predict the time spent in traffic jams per year for all participants of stream processes. Conclusions. The introduction of modern intelligent transportation systems as a component of the transportation and logistics system of a smart city does not fully solve the problems of congestion in cities at the current rate of urbanization and motorization. A viable solution is to develop cooperative and autonomous intelligent transportation systems based on the logistics approach. This will ensure control over congestion, the reduction of which will contribute to improving the life quality of people in urban areas.

Modeling the Snow Deposit Process on the Highways in the Flowvision Software

2020 ◽  
pp. 72-83
Author(s):  
Т. В. Самодурова ◽  
О. В. Гладышева ◽  
Н. Ю. Алимова ◽  
Е. А. Бончева
Keyword(s):  
Hydrodynamic Model ◽  
Geometric Model ◽  
Information Resources ◽  
Road Maintenance ◽  
Snow Removal ◽  
Experimental Section ◽  
Deposit Modeling ◽  
Winter Road ◽  
Highway Embankment ◽  
Winter Road Maintenance

Постановка задачи. Рассмотрена задача моделирования отложения снега во время метелей на автомагистралях с барьерными ограждениями в программе FlowVision . Результаты. В качестве опытного участка рассмотрен участок автомагистрали, проходящий в насыпи. Создана геометрическая модель участка автомагистрали. Обоснованы информационные ресурсы для создания гидродинамической модели обтекания насыпи автомагистрали с барьерными ограждениями снеговетровым потоком во время метелей. Проведено моделирование процесса снегонакопления на опытном участке с использованием программного комплекса FlowVision во время метелей с различными параметрами. Выводы. Сделан вывод о возможности применения программного комплекса FlowVision для совершенствования методики назначения снегозащитных устройств и определения параметров снегоочистки при зимнем содержании автомобильных дорог. Statement of the problem. The problems of snow deposit modeling on the highways with crash barriers during blizzards in the FlowVision was discussed. Results. The highway section passing in the embankment as an experimental section has been considered. The geometric model of the highway section was created. The information resources for designing a hydrodynamic model of a snowflow stream of highway embankment with barriers during blizzard were identified. The modeling of the snow deposit process in the experimental section using the FlowVision software during blizzards with different parameters was carried out. Conclusions. It was concluded that it is possible to use the FlowVision software to improve the methodology for snow protection designing and determining snow removal parameters for winter road maintenance.

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