InterTwin: Deep Learning Approaches for Computing Measures of Effectiveness for Traffic Intersections
Microscopic simulation-based approaches are extensively used for determining good signal timing plans on traffic intersections. Measures of Effectiveness (MOEs) such as wait time, throughput, fuel consumption, emission, and delays can be derived for variable signal timing parameters, traffic flow pa...
Main Authors: | Yashaswi Karnati, Rahul Sengupta, Sanjay Ranka |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-12-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/11/24/11637 |
Similar Items
-
Distributed Multi-Intersection Traffic Flow Prediction using Deep Learning
by: Moumen Idriss, et al.
Published: (2024-01-01) -
A Study of Artificial Neural Network-Based Real-Time Traffic Signal Timing Design Model Utilizing Smart Intersection Data
by: Sang-Tae Oh, et al.
Published: (2023-07-01) -
Review of Intelligent Traffic Signal Control Strategies Driven by Deep Reinforcement Learning
by: YU Ze, NING Nianwen, ZHENG Yanliu, LYU Yining, LIU Fuqiang, ZHOU Yi
Published: (2023-04-01) -
Optimization Control of Adaptive Traffic Signal with Deep Reinforcement Learning
by: Kerang Cao, et al.
Published: (2024-01-01) -
A Single Intersection Cooperative-Competitive Paradigm in Real Time Traffic Signal Settings Based on Floating Car Data
by: Vittorio Astarita, et al.
Published: (2019-01-01)