Traffic wastes time and money almost everywhere on the planet, so congestion is the bogeyman many transportation planners hope to defeat. Attendees at the most-recent Transportation Techies Meetup – held at Mobility Lab in Arlington, Va., and focused on traffic solutions – got a taste of several early-stage tech/planning options. "Data and technology are becoming more and more crucial in planning for safer streets. This becomes even more important as autonomous vehicles begin to come online," said Paul Mackie, Mobility Lab's communications director. How are DOTs handling data for projects like AVs and Vision Zero?

MAOS-FSP [1] is a multiagent optimization system (MAOS) for solving the Flowshop Scheduling Problem (FSP). MAOS-FSP shares the MAOS kernel with other MAOS applications (e.g. MAOS-GCP and MAOS-TSP), and contains some modules that are specifically for tacking FSP. Related Information: Please find other related code and software in our Source Code Library. License information: MAOS-FSP is free software; you can redistribute it and/or modify it under the Creative Commons Non-Commercial License 3.0.

MAOS-QKP is a multiagent optimization system (MAOS) for solving the Quadratic Knapsack Problem (QKP). Related Information: MAOS-QKP shares the MAOS kernel with other MAOS applications (e.g. Please find other related code and software in our Source Code Library. License information: MAOS-QKP is free software; you can redistribute it and/or modify it under the Creative Commons Non-Commercial License 3.0. System Requirements: MAOS-QKP is a platform-independent software developed by JAVA version 1.5 or above.

DEPSO [1], or called DEPS, is an algorithm for (constrained) numerical optimization problem (NOP). DEPSO combines the advantages of Particle Swarm Optimization (PSO) and Differential Evolution (DE). It is incorporated into cooperative group optimization (CGO) system [2]. The DEPSO paper has been cited over 400 times with various applications. DEPSO was also implemented (by Sun Microsystems Inc.) into NLPSolver (Solver for Nonlinear Programming), an extension of Calc in Apache OpenOffice.

Highway traffic safety is a serious issue raising increased public concerns. According to the 2015 report by World Health Organization (WHO), globally there were 1.25 million road traffic deaths in 2013, and up to 50 million more per year suffering injuries as a result of road traffic crashes. In USA, 35,092 people died and 1.7 million people injured in 6.3 million police-reported traffic crashes in 2015, according to the statistics by National Highway Traffic Safety Administration (NHTSA). Improving road safety has been the focus of multiple initiatives, such as the Road to Zero Coalition, Toward Zero Deaths (TZD), and Vision Zero. WIOMAX researchers and engineers have done the following work on understanding and improving road traffic safety using smart DOAI (Data Analysis Optimization Artificial Intelligence (AI) Internet of Things (IoT)) technologies.

MAOS-GCP [1] is a multiagent optimization system (MAOS) for solving the Graph Coloring Problem (GCP). Related Information: MAOS-GCP shares the MAOS kernel with other MAOS applications (e.g. MAOS-TSP, MAOS-FSP, MAOS-QAP, MAOS-QKP), and contains some modules that are specifically for tacking GCP. Please find other related code and software in our Source Code Library. License information: MAOS-GCP is free software; you can redistribute it and/or modify it under the Creative Commons Non-Commercial License 3.0.

This system is a real-time adaptive traffic control system, which combines artificial intelligence (AI) and traffic theory to optimize highly dynamic traffic flow in complex real-world urban road networks. As the lead inventor of the system, Dr. Xie has created its core control engine, which combines schedule-driven intersection control (SchIC) with decentralized coordination mechanisms (in the sense of Internet of Smart Intersections, an instance of smart IoT). He has also designed and realized the strengthening strategies to enable the real-world operations of the system in the field. His relevant research work also includes: multimodal traffic control (assisted with machine learning and computer vision techniques), integration with decentralized route choice models and dynamic congestion pricing protocols, vehicle-to-infrastructure (V2I) communication with connected vehicles, energy efficiency optimization, and data-driven self-learning and active congestion management based on performance measurement. The system has been running since June 2012.

This system is a real-time adaptive traffic control system, which combines artificial intelligence (AI) and traffic theory to optimize highly dynamic traffic flow in complex real-world urban road networks. As the lead inventor of the system, Dr. Xie has created its core control engine, which combines schedule-driven intersection control (SchIC) with decentralized coordination mechanisms (in the sense of Internet of Smart Intersections, an instance of smart IoT). He has also designed and realized the strengthening strategies to enable the real-world operations of the system in the field. His relevant research work also includes: multimodal traffic control (assisted with machine learning and computer vision techniques), integration with decentralized route choice models and dynamic congestion pricing protocols, vehicle-to-infrastructure (V2I) communication with connected vehicles, energy efficiency optimization, and data-driven self-learning and active congestion management based on performance measurement. The system has been running since June 2012.

Social Cognitive Optimization (SCO) is an optimization algorithm for solving the (constrained) numerical optimization problem. SCO is a simple agent-based model based on the observational learning mechanism in human social cognition. Related Information: Please find other related code and software in our Source Code Library. License information: SCO is free software; you can redistribute and/or modify it under the terms of Creative Commons Non-Commercial License 3.0. Problem to be solved: (constrained) numerical optimization problem (NOP), or called the nonlinear programming problem.

This is a method using AI techniques to solve a case of pure mathematics applications for finding narrow admissible tuples. The original problem is formulated into a combinatorial optimization problem. In particular, we show how to exploit the local search structure to formulate the problem landscape for dramatic reductions in search space and for non-trivial elimination in search barriers, and then to realize intelligent search strategies for effectively escaping from local minima. Experimental results demonstrate that the proposed method is able to efficiently find best known solutions.