Related papers: On the Role of Multi-Objective Optimization to the Transit Network Design Problem

On the Role of Multi-Objective Optimization to the Transit Network Design Problem

URL: http://arxiv.org/abs/2201.11616v1
Date: Thu, 27 Jan 2022 16:22:07 GMT
Title: On the Role of Multi-Objective Optimization to the Transit Network Design Problem
Authors: Vasco D. Silva, Anna Finamore, Rui Henriques
Abstract summary: This work shows that single and multi objective stances can be synergistically combined to better answer the transit network design problem (TNDP) As a guiding case study, the solution is applied to the multimodal public transport network in the city of Lisbon, Portugal. The proposed TNDP optimization proved to improve results, with reductions in objective functions of up to 28.3%.
Score: 0.7734726150561088
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Ongoing traffic changes, including those triggered by the COVID-19 pandemic, reveal the necessity to adapt our public transport systems to the ever-changing users' needs. This work shows that single and multi objective stances can be synergistically combined to better answer the transit network design problem (TNDP). Single objective formulations are dynamically inferred from the rating of networks in the approximated (multi-objective) Pareto Front, where a regression approach is used to infer the optimal weights of transfer needs, times, distances, coverage, and costs. As a guiding case study, the solution is applied to the multimodal public transport network in the city of Lisbon, Portugal. The system takes individual trip data given by smartcard validations at CARRIS buses and METRO subway stations and uses them to estimate the origin-destination demand in the city. Then, Genetic Algorithms are used, considering both single and multi objective approaches, to redesign the bus network that better fits the observed traffic demand. The proposed TNDP optimization proved to improve results, with reductions in objective functions of up to 28.3%. The system managed to extensively reduce the number of routes, and all passenger related objectives, including travel time and transfers per trip, significantly improve. Grounded on automated fare collection data, the system can incrementally redesign the bus network to dynamically handle ongoing changes to the city traffic.

Related papers

Short Run Transit Route Planning Decision Support System Using a Deep Learning-Based Weighted Graph [0.0]
We propose a novel deep learning-based methodology for a decision support system that enables public transport planners to identify short-term route improvements rapidly. By seamlessly adjusting specific sections of routes between two stops during specific times of the day, our method effectively reduces times and enhances PT services. Using self-supervision, we train a deep learning model for predicting lateness values for road segments. These lateness values are then utilized as edge weights in the transportation graph, enabling efficient path searching.
arXiv Detail & Related papers (2023-08-24T14:37:55Z)
APE: An Open and Shared Annotated Dataset for Learning Urban Pedestrian Path Networks [16.675093530600154]
Inferring the full transportation network, including sidewalks and cycleways, is crucial for many automated systems. This work begins to address this problem at scale by introducing a novel dataset of aerial satellite imagery, map imagery, and annotations of sidewalks, crossings, and corner bulbs in urban cities. We present an end-to-end process for inferring a connected pedestrian path network map using street network information and our proposed dataset.
arXiv Detail & Related papers (2023-03-04T05:08:36Z)
Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework [10.75153377806738]
We propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN) We employ GCN to extract features from the graph by learning useful topology information and introduce a deep clustering method to recognize mobility patterns hidden in bus passengers. To the best of our knowledge, this paper is the first work to adopt a multipattern approach to predict the bus passenger flow from graph learning.
arXiv Detail & Related papers (2022-02-17T06:17:23Z)
Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization [79.96177511319713]
We invoke deep learning (DL) to assist routing in aeronautical ad-hoc networks (AANETs) A deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. We extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime.
arXiv Detail & Related papers (2021-10-28T14:18:22Z)
Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
arXiv Detail & Related papers (2021-09-29T07:51:49Z)
An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
arXiv Detail & Related papers (2021-08-02T08:13:57Z)
Optimal transport in multilayer networks [68.8204255655161]
We propose a model where optimal flows on different layers contribute differently to the total cost to be minimized. As an application, we consider transportation networks, where each layer is associated to a different transportation system. We show an example of this result on the real 2-layer network of the city of Bordeaux with bus and tram, where in certain regimes the presence of the tram network significantly unburdens the traffic on the road network.
arXiv Detail & Related papers (2021-06-14T07:33:09Z)
Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning [79.61517670541863]
How to use AI to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for dockless PBS (DL-PBS) We propose a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning (MORL-BD) to provide the optimal bicycle dispatching solution for DL-PBS.
arXiv Detail & Related papers (2021-01-19T03:09:51Z)
Multi-Agent Routing Value Iteration Network [88.38796921838203]
We propose a graph neural network based model that is able to perform multi-agent routing based on learned value in a sparsely connected graph. We show that our model trained with only two agents on graphs with a maximum of 25 nodes can easily generalize to situations with more agents and/or nodes.
arXiv Detail & Related papers (2020-07-09T22:16:45Z)
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control [4.273991039651846]
Scaling adaptive traffic-signal control involves dealing with state and action spaces. We introduce Inductive Graph Reinforcement Learning (IG-RL) based on graph-convolutional networks. Our model can generalize to new road networks, traffic distributions, and traffic regimes.
arXiv Detail & Related papers (2020-03-06T17:17:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.