A5085352777- Traffic Prediction and Management Techniques
- Traffic control and management
- Transportation Planning and Optimization
- Vehicular Ad Hoc Networks (VANETs)
- Context-Aware Activity Recognition Systems
- Traffic and Road Safety
- Cognitive Computing and Networks
- Service-Oriented Architecture and Web Services
University of Southampton
2017-2021
University College London
2020
The increase in traffic volumes urban areas makes network delay and capacity optimisation challenging. However, the introduction of connected vehicles intelligent transport systems presents unique opportunities for improving flow reducing delays areas. This paper proposes a novel signal control algorithm called Multi-mode Adaptive Traffic Signals (MATS) which combines position information from with data obtained existing inductive loops timing plans to perform decentralised at intersections....
This paper reports on the performance of signalised intersection control using vehicle GPS information compared to fixed-time and inductive loop based control. Traffic congestion forecasts estimate an increase about 60% in 2030. At present, poor choice signal timings by isolated controllers cause traffic delays that have enormous negative impacts economy environment. Signal can be improved vehicles' overcome action deficit at intersections. new algorithm is beneficial for engineers...
This paper develops an innovative person-based control PB-ACAWB with complete flexible signal plans to minimize passenger delay of cars and buses in generalized 8-phases options junction. The algorithm is a scalable framework that can be implemented both vehicular environments all mixture situations buses. core part uses three-layered dynamic programming (DP) efficiently figure out optimal timing within planning horizon. Additionally, phase transition exploration mechanism developed explore...