Road Safety Resource Allocation Using Interactive Multiobjective Optimization

2021 ◽  
pp. 1-16
Author(s):  
Maria Grazia Augeri
Keyword(s):  
Resource Allocation ◽  
Rough Set ◽  
Road Safety ◽  
Critical Issue ◽  
Allocation Of Resources ◽  
Multi Objective Optimization ◽  
Preference Model ◽  
Integer Variables ◽  
Safety Improvement ◽  
Improvement Programs

A model is proposed for allocating safety resources to various hazard sites. Due to budget constraints, allocation of resources for necessary countermeasures is a critical issue in safety improvement programs. Therefore, the Decision Maker needs a tool that can prioritize the identified countermeasures looking at several objectives, the most important of which are: reducing the number of accidents and minimizing the costs. A number of countermeasures could be implemented simultaneously in the same location and this was considered, so that the solution that best optimizes the objectives was selected. Since the considered objectives are not commensurable, a new methodology with interactive multi-objective optimization in the case of 0-1 integer variables was proposed, based on the application of a logical preference model built using dominance-based Rough Set Approach (IMO-DRSA). Finally, an application of the methodology is presented considering a sample of Italian urban intersections and a set of mutually exclusive alternatives at each location.

Road Safety Improvement Program on Indian Reservations in North Dakota and South Dakota

2015 ◽  
Vol 2531 (1) ◽  
pp. 146-152
Author(s):  
Debbie S. Shinstine ◽  
Khaled Ksaibati
Keyword(s):  
South Dakota ◽  
North Dakota ◽  
Road Safety ◽  
Technical Assistance ◽  
Assistance Program ◽  
Indian Reservations ◽  
Safety Improvement ◽  
Roadway Safety ◽  
Technical Assistance Program ◽  
Improvement Programs

Tribal communities recognize the need to improve roadway safety. A five-step methodology was developed by the Wyoming Technology Transfer Center, Local Technical Assistance Program (WYT2/LTAP), to improve roadway safety on Indian reservations. This methodology was implemented initially on the Wind River Indian Reservation (WRIR); the success of this implementation was the impetus for the Wyoming Department of Transportation, Cheyenne, to fund three systemwide, low-cost safety improvement projects. Given the success of the program on the WRIR, tribes across the country became interested in the program. WYT2/LTAP and the Northern Plains Tribal Technical Assistance Program (NPTTAP) assist tribes to implement this program on their reservations in the Great Plains region and developed criteria to identify tribes to participate. Reservations in North Dakota and South Dakota applied to NPTTAP, and three tribes were accepted to participate: the Standing Rock Sioux Tribe (SRST), the Sisseton Wahpeton Oyate Tribe, and the Yankton Sioux Tribe. Although work had begun on all three reservations, this study focused on the implementation on the roadway safety program by the SRST. Members of the SRST were located in North Dakota and South Dakota, and crash data were collected from each state separately. Because the reporting and years of data differed, several analyses were performed to identify trends in crashes on the SRST. The South Dakota portion of the reservation was compared with statewide rural roads and with the WRIR because the two reservations were of similar size and character. Many challenges and differences were identified through the analysis, which demonstrated that a single procedure would not work for all reservations. Through extensive coordination and collaboration with the tribes and government agencies, WYT2/LTAP and the technical assistance program centers could provide the technical assistance that the tribes would need to develop their own road safety improvement programs.

Using claims prediction model for road safety evaluation

2001 ◽  
Vol 28 (5) ◽  
pp. 804-812 ◽  
Author(s):  
Paul de Leur ◽  
Tarek Sayed
Keyword(s):  
Prediction Model ◽  
Road Safety ◽  
Claims Data ◽  
Safety Evaluation ◽  
Safety Analysis ◽  
Insurance Claims ◽  
Insurance Claim ◽  
Auto Insurance ◽  
Improvement Programs ◽  
Traffic Collision

Road safety analysis is typically undertaken using traffic collision data. However, the collision data often suffer from quality and reliability problems. These problems can inhibit the ability of road safety engineers to evaluate and analyze road safety performance. An alternate source of data that characterize the events of a traffic collision is the records that become available from an auto insurance claim. In settling an auto insurance claim, a claim adjuster must make an assessment and determination of the circumstances of the event, recording important contributing factors that led to the crash occurrence. As such, there is an opportunity to access and use the claims data in road safety engineering analysis. This paper presents the results of an initial attempt to use auto insurance claims records in road safety evaluation by developing and applying a claim prediction model. The prediction model will provide an estimate of the number of auto insurance claims that can be expected at signalized intersections in the Vancouver area of British Columbia, Canada. A discussion of the usefulness and application of the claim prediction model will be provided together with a recommendation on how the claims data could be utilized in the future.Key words: road safety improvement programs, auto insurance claims, road safety analysis, prediction models.

Multi-Objective Optimization Based on TTR Interference Analysis

2013 ◽  
Vol 753-755 ◽  
pp. 1217-1220
Author(s):  
Da Wei Ji ◽  
Yi Huang ◽  
Qi Zhang
Keyword(s):  
Deep Water ◽  
Present Method ◽  
Critical Issue ◽  
Multi Objective Optimization ◽  
Interference Analysis ◽  
Multi Objective ◽  
Riser Design ◽  
Offshore Industry

Riser interference has become a critical issue in riser design with the progression of offshore industry into deep water. It indicates that the potential for interference between Top Tension Risers (TTR) depends not only on the Top Tension Factor (TTF), but also on the riser spacing size. For riser system, each impassive factor of interference could make a different effect (cost and safety), which is often incompatible. A Multi-Objective Optimization (MOO) method is proposed to harmonize the two incompatible objectives: cost and safety. Therefore, it greatly facilitates to adapt the present method to riser interference optimization. Example is given to demonstrate the effectiveness and robustness of the proposed method.

scholarly journals Recovery and Resource Allocation Strategies to Maximize Mobile Network Survivability by Using Game Theories and Optimization Techniques

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Pei-Yu Chen ◽  
Frank Yeong-Sung Lin
Keyword(s):  
Resource Allocation ◽  
Mobile Network ◽  
Critical Issue ◽  
Optimization Techniques ◽  
Network Survivability ◽  
Support Network ◽  
Network Attack ◽  
Damage Degree ◽  
Optimal Resource ◽  
Allocation Strategies

With more and more mobile device users, an increasingly important and critical issue is how to efficiently evaluate mobile network survivability. In this paper, a novel metric called Average Degree of Disconnectivity (Average DOD) is proposed, in which the concept of probability is calculated by the contest success function. The DOD metric is used to evaluate the damage degree of the network, where the larger the value of the Average DOD, the more the damage degree of the network. A multiround network attack-defense scenario as a mathematical model is used to support network operators to predict all the strategies both cyber attacker and network defender would likely take. In addition, the Average DOD would be used to evaluate the damage degree of the network. In each round, the attacker could use the attack resources to launch attacks on the nodes of the target network. Meanwhile, the network defender could reallocate its existing resources to recover compromised nodes and allocate defense resources to protect the survival nodes of the network. In the approach to solving this problem, the “gradient method” and “game theory” are adopted to find the optimal resource allocation strategies for both the cyber attacker and mobile network defender.

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