activity
20172022
most citedAsymptotically simple spacetimes and mass loss due to gravitational waves

7 citations · 15 across the 6 of their papers we have counts for

collaborators

10 papers

cond-mat.stat-mech2021

Bunching Dynamics of Buses in a Loop

Luca Vismara, Vee-Liem Saw, Lock Yue Chew

Bus bunching is a curse of transportation systems such as buses in a loop. Here we present an analytical method to find the number of revolutions before two buses bunch in an ideal…

physics.soc-ph2021

Optimal assignment of buses to bus stops in a loop by reinforcement learning

Luca Vismara, Lock Yue Chew, Vee-Liem Saw

Bus systems involve complex bus-bus and bus-passengers interactions. We study the problem of assigning buses to bus stops to minimise the average waiting time of passengers, W. An…

physics.soc-ph20207 cited

Chaotic semi-express buses in a loop

Vee-Liem Saw, Luca Vismara, Lock Yue Chew

Urban mobility involves many interacting components: buses, cars, commuters, pedestrians, trains etc., making it a very complex system to study. Even a bus system responsible for d…

physics.soc-ph2020

Analysis and simulation of intervention strategies against bus bunching by means of an empirical agent-based model

Wei Liang Quek, Ning Ning Chung, Vee-Liem Saw +1

In this paper, we propose an Empirically-based Monte Carlo Bus-network (EMB) model as a test bed to simulate intervention strategies to overcome the inefficiencies of bus bunching.…

nlin.AO2019

Stability of anti-bunched buses and local unidirectional Kuramoto oscillators

Lock Yue Chew, Vee-Liem Saw, Yi En Ian Pang

Inspired by our recent work that relates bus bunching as a phenomenon of synchronisation of phase oscillators, we construct a model of Kuramoto oscillators that follows an analogou…

physics.soc-ph20191 cited

Intelligent buses in a loop service: Emergence of no-boarding and holding strategies

Vee-Liem Saw, Luca Vismara, Lock Yue Chew

We study how intelligent buses serving a loop of bus stops learn a \emph{no-boarding strategy} and a \emph{holding strategy} by reinforcement learning. The high level no-bo…