PROJECT: Y-MAS

PROCESS OPTIMIZATION THROUGH BLENDED LEARNING

in short

Project name: Internationalization of Blended‐Learning Continuing Education in Industrial Process Design and Optimization for the Production Sector in Spanish Speaking Latin America

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‍Project duration: 01.10.2021 – 31.03.2024

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‍Funding ID: 01BE17029C

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‍Location: Execution in Latin America

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‍Project sponsor: DLR-Projektträger

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‍Project leaders: FIR e. V. an der RWTH AachenGrantor: Bundesministerium für Bildung und Forschung (BMBF)

RESEARCH OBJEcTIVES

Objective 1

Reduce transportation time on long hauls by 30% and increase vehicle utilization by 50%

Objective 2

Reduce the need for truck parking spaces and parking search traffic by organizing carpooling in trucks

Objective 3

Create a better working environment for truck drivers, democratize the transport industry, and reduce load robberies and wildly parked trucks by implementing the “relay traffic” system and reducing wasted driving time

Description

The Project in detail

The Challenge

In road haulage, transports are interrupted to comply with driving and rest times. These interruptions not only prolong the transport time, but also occupy truck parking spaces, of which, according to BASt or BGL, there are about 35,000 missing along German highways. The search for parking spaces leads to CO2-intensive parking search traffic, driving time violations, and frustration among truck drivers. Illegally parked trucks cause accidents, disturb residents and according to VEDA, promote cargo theft with damages in the billions.

The interruption of transports can be avoided by securing trailers with an IoT lock at the end of the driving time and then handing them over to rested drivers. In the project, a secure, cross-carrier “relay traffic” is to be researched: on an Internet platform, long distances are to be broken down into partial routes with the help of AI algorithms, which are then mediated between carriers and freight forwarders via a driving time marketplace using real-time data (e.g., traffic, infrastructure, IoT, telematics).

PEM Motion’s Approach and What Has Been Achieved So Far?

In this project, PEM Motion is responsible for the development, prototypical implementation and testing of an intelligent (keyless, remote-controlled, equipped with sensors) kingpin lock.

Progress made so far: a patent application has been submitted, the first revision of hardware/software is ongoing, and the CAD design of the safety mechanism for the kingpin has been completed.

The interruption of transports can be avoided by securing trailers with an IoT lock at the end of the driving time and then handing them over to rested drivers. In the project, a secure, cross-carrier “relay traffic” is to be researched: on an Internet platform, long distances are to be broken down into partial routes with the help of AI algorithms, which are then mediated between carriers and freight forwarders via a driving time marketplace using real-time data (e.g., traffic, infrastructure, IoT, telematics).

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