01456605

Component

http://www.trb.org/Publications/Blurbs/168134.aspx

As globalism continues to blend cultures throughout the world, perhaps those in industrialized nations will begin to accept that security risks can never be fully mitigated regardless of the forethought devoted or the amount of funding available. However, it is imperative that security risks to the transportation system are minimized in an optimal fashion. Artificial intelligence (AI) and other advanced computing applications are potential tools for minimizing security risks in an efficient manner without the need for major investments in human resources. Questions remain as to whether we will optimize to objective standards or human perceptions of risk. Transportation infrastructure includes a vast network of physical interconnects as well as a less-visual network of sensors, communication systems, and software. For each of these systems, different factors influence the continued evolution of security efforts. These factors include the continued advancement of computer processing capabilities, increasing data availability and quality, continued technology deployment, advancing globalism, and progressing sustainability initiatives. Despite all of this change surrounding the transportation security spectrum, there are multiple promising AI tools that can help transportation agencies and practitioners thrive instead of just survive in maximizing security apparatus in the multidimensional and multimodal transportation system. This article presents a synopsis of these AI applications by different areas related to transportation.

01456594

English

pp 106-112

2012-11

Transportation Research Circular

Web

References

Artificial intelligence; Infrastructure; Multimodal transportation; Security; Transportation

Data and Information Technology; Security and Emergencies; Transportation (General); I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Dec 11 2012 9:29AM

• Advantages and Limitations of Artificial Intelligence
• Agent-Based Modeling and Simulation
• Analysis of Public Transportation Planning and Operations
• Artificial Intelligence and Microscopic Traffic Simulation Models: Applications to Parameter Calibration and Origin–Destination Estimation
• Design and Construction of Transportation Infrastructure
• Difference Between Artificial Intelligence and Traditional Methods
• Neural Networks for Travel Time Prediction on Interrupted Flow Facilities
• Ramp Metering
• Short-Term Traffic and Travel Time Prediction Models
• Traffic Signal Timing and Optimization
• Transportation Safety Analysis
• Travel Behavior Research
• Urban Travel Demand Forecasting