Transportation Network Scheduling for Energy and Flow Optimization
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Solution Overview
Problem
Existing transportation network scheduling systems face challenges in coordinating vehicle schedules to balance speed and fuel efficiency, as they often compete with each other, leading to inefficiencies and increased fuel consumption due to unforeseen events disrupting travel.
Innovation Solution
A system that includes a scheduling unit and a communication unit to form and modify vehicle schedules, incorporating trip plans from energy management systems to reduce energy consumption and emissions, while avoiding interference with other vehicles, by adapting to anomalies in the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the fuel optimization algorithm slows down vehicles to reduce fuel consumption, then fuel efficiency is improved, but the arrival time at destination locations deteriorates
Solution Approach 1:
The system dynamically adjusts vehicle speeds based on real-time conditions rather than using fixed slow-down patterns. The coordination algorithm continuously optimizes speed profiles to balance fuel efficiency with schedule adherence, allowing vehicles to maintain higher speeds when conditions permit while still achieving fuel savings.
Solution Approach 2:
The system implements feedback loops where vehicle positions, speeds, and fuel consumption data are continuously monitored and fed back to the coordination algorithm. This enables real-time adjustments to speed commands, allowing the system to respond to changing conditions and maintain optimal balance between fuel efficiency and timeliness.
2Productivity
If the network planning algorithm coordinates schedules to avoid congestion, then the flow of vehicles is improved, but the system complexity increases
Solution Approach 1:
The coordination system is divided into distributed components, with each vehicle equipped with an onboard controller that independently calculates its optimal speed profile based on local conditions and received schedule information. This segmentation reduces central system complexity while maintaining overall network coordination.
Solution Approach 2:
The system uses simplified parameter representations of vehicle dynamics and network conditions, focusing on key variables such as speed, position, and time headways. By changing the parameter space to essential variables only, the coordination algorithm achieves effective flow management without excessive computational complexity.
3Reliability
If vehicles abruptly slow down or stop to avoid collisions, then safety is improved, but fuel consumption increases
Solution Approach 1:
The coordination algorithm calculates and communicates speed adjustments to vehicles in advance of potential conflict situations. By providing preliminary speed reduction commands before collisions or abrupt stops would be necessary, the system maintains safety while avoiding the fuel waste associated with emergency braking and repeated acceleration.
Data Source
AI summary
A method includes forming a first schedule for a first vehicle to travel in a transportation network. The first schedule includes a first arrival time of the first vehicle at a scheduled location. The method also includes receiving a first trip plan for the first vehicle from an energy management system. The first trip plan is based on the first schedule and designates at least one of tractive efforts or braking efforts to be provided by the first vehicle to reduce at least one of an amount of energy consumed by the first vehicle or an amount of emissions generated by the first vehicle when the first vehicle travels through the transportation network to the scheduled location. The method further includes determining whether to modify the first schedule to avoid interfering with movement of one or more other vehicles by examining the trip plan for the first vehicle.