Ontario Ministry of Transportation

About: Ontario Ministry of Transportation is a government organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Poison control & Asphalt. The organization has 4652 authors who have published 3882 publications receiving 59011 citations.

Evaluating the logistics performance of intermodal transportation in Thailand

Abstract: Purpose – The purpose of this paper is to evaluate the logistics performance of intermodal freight transportation, a reasonably innovative methodology, to Thailand.Design/methodology/approach – Fuzzy set techniques are applied to assess the meaning of logistics performance within the decision process of freight operators. The fuzzy‐analytical hierarchy process is applied to identify the relative significance among the attributes in the hierarchy framework levels. Consequently, the fuzzy‐multi‐criteria analysis is used to assess decision makers' perception of the logistics performance via proper assignment of numerical scores. The subjective judgments for hierarchical attributes are transformed into fuzzy degrees of score.Findings – Results show a summary of performance indices for different levels in the hierarchy framework. The results demonstrate that the lack of coordination among modes limit the attractiveness of intermodal system. In addition, the methodology is proposed to examine changing variables...

Cooperative Autonomous Driving Oriented MEC-Aided 5G-V2X: Prototype System Design, Field Tests and AI-Based Optimization Tools

Abstract: Vehicle-to-Everything (V2X) requirements from cooperative autonomous driving can be characterized as ultra-reliable, low latency, high traffic, and high mobility. These requirements introduce great challenges in the air interface and protocol stack design, resource allocation, network deployment, and all the way up to mobile (or multi-access) edge computing (MEC), cloud and application layer. In this paper, we present a cooperative autonomous driving oriented MEC-aided 5G-V2X prototype system design and the rationale behind the design choices. The prototype system is developed based on a next-generation radio access network (NG-RAN) experimental platform, a cooperative driving vehicle platoon, and an MEC server providing high definition (HD) 3D dynamic map service. Field tests are conducted and the results demonstrate that the combination of 5G-V2X, MEC and cooperative autonomous driving can be pretty powerful. Considering the remaining challenges in the commercial deployment of 5G-V2X networks and future researches, we propose two artificial intelligence (AI) based optimization tools. The first is a deep-learning-based tool called deep spatio-temporal residual networks with a permutation operator (PST-ResNet). By providing city-wide user and network traffic prediction, PST-ResNet can help to reduce the capital expense (CAPEX) and operating expense (OPEX) costs of commercial 5G-V2X networks. The second is a swarm intelligence based optimization tool called subpopulation collaboration based dynamic self-adaption cuckoo Search (SC-DSCS), which can be widely used to solve complex optimization problems in future researches. The effectiveness of proposed optimization tools is verified by real-world data and benchmark functions.

Policy challenges of increasing automation in driving

Abstract: The convergence of information and communication technologies (ICT) with automotive technologies has already resulted in automation features in road vehicles and this trend is expected to continue in the future owing to consumer demand, dropping costs of components, and improved reliability. While the automation features that have taken place so far are mainly in the form of information and driver warning technologies (classified as level I pre-2010), future developments in the medium term (level II 2010–2025) are expected to exhibit connected cognitive vehicle features and encompass increasing degree of automation in the form of advanced driver assistance systems. Although autonomous vehicles have been developed for research purposes and are being tested in controlled driving missions, the autonomous driving case is only a long term (level III 2025 +) scenario. This paper contributes knowledge on technological forecasts regarding automation, policy challenges for each level of technology development and application context, and the essential instrument of cost-effectiveness for policy analysis which enables policy decisions on the automation systems to be assessed in a consistent and balanced manner. The cost of a system per vehicle is viewed against its effectiveness in meeting policy objectives of improving safety, efficiency, mobility, convenience and reducing environmental effects. Example applications are provided that illustrate the contribution of the methodology in providing information for supporting policy decisions. Given the uncertainties in system costs as well as effectiveness, the tool for assessing policies for future generation features probabilistic and utility-theoretic analysis capability. The policy issues defined and the assessment framework enable the resolution of policy challenges while allowing worthy innovative automation in driving to enhance future road transportation.