Equity Scores for Public Transit Lines from Open-Data and Accessibility
Measures
- URL: http://arxiv.org/abs/2210.00128v1
- Date: Fri, 30 Sep 2022 22:58:11 GMT
- Title: Equity Scores for Public Transit Lines from Open-Data and Accessibility
Measures
- Authors: Amirhesam Badeanlou, Andrea Araldo, Marco Diana, Vincent Gauthier
- Abstract summary: Current transit suffers from an evident inequity: the level of service of transit in suburbs is much less satisfying than in city centers.
To achieve sustainability goals and reduce car-dependency, transit should be (re)designed around equity.
- Score: 0.3058685580689604
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Current transit suffers from an evident inequity: the level of service of
transit in suburbs is much less satisfying than in city centers. As a
consequence, private cars are still the dominant transportation mode for
suburban people, which results in congestion and pollution. To achieve
sustainability goals and reduce car-dependency, transit should be (re)designed
around equity. To this aim, it is necessary to (i) quantify the "level of
equity" of the transit system and (ii) provide an indicator that scores the
transit lines that contribute the most to keep transit equitable. This
indicator could suggest on which lines the transit operator must invest to
increase the service level (frequency or coverage) in order to reduce inequity
in the system.
To the best of our knowledge, this paper is the first to tackle (ii). To this
aim, we propose efficient scoring methods that rely solely on open data, which
allows us to perform the analysis on multiple cities (7 in this paper). Our
method can be used to guide large-scale iterative optimization algorithms to
improve accessibility equity.
Related papers
- Fair collaborative vehicle routing: A deep multi-agent reinforcement
learning approach [49.00137468773683]
Collaborative vehicle routing occurs when carriers collaborate through sharing their transportation requests and performing transportation requests on behalf of each other.
Traditional game theoretic solution concepts are expensive to calculate as the characteristic function scales exponentially with the number of agents.
We propose to model this problem as a coalitional bargaining game solved using deep multi-agent reinforcement learning.
arXiv Detail & Related papers (2023-10-26T15:42:29Z) - AccEq-DRT: Planning Demand-Responsive Transit to reduce inequality of
accessibility [7.615022055373833]
We focus on accessibility provided by Public Transit (PT)
There is an evident inequality in the distribution of accessibility between city centers or close to main transportation corridors and suburbs.
We propose a DRT planning strategy, which we call AccEq-DRT, aimed at reducing accessibility inequality.
arXiv Detail & Related papers (2023-10-06T16:13:28Z) - 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) - DenseLight: Efficient Control for Large-scale Traffic Signals with Dense
Feedback [109.84667902348498]
Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network.
Most prior TSC methods leverage deep reinforcement learning to search for a control policy.
We propose DenseLight, a novel RL-based TSC method that employs an unbiased reward function to provide dense feedback on policy effectiveness.
arXiv Detail & Related papers (2023-06-13T05:58:57Z) - Towards Understanding the Benefits and Challenges of Demand Responsive Public Transit- A Case Study in the City of Charlotte, NC [3.678540247562326]
This study investigates the challenges faced by transit-dependent populations in Charlotte, NC.
Our research initially evaluates critical issues such as extended wait times, unreliable schedules, and limited accessibility.
This evaluation included an analysis of the existing Charlotte Area Transit System (CATS) mobile applications and the exploration of user acceptance for a proposed smart, on-demand transit technology.
arXiv Detail & Related papers (2023-04-09T03:10:36Z) - Designing Equitable Transit Networks [2.2720742607784183]
We present a formulation for transit network design that considers different notions of equity and welfare explicitly.
We study the interaction between network design and various concepts of equity and present trade-offs and results based on real-world data from a large metropolitan area in the United States of America.
arXiv Detail & Related papers (2022-12-22T19:30:53Z) - Identifying the Factors that Influence Urban Public Transit Demand [0.0]
The rise in urbanization throughout the United States (US) in recent years has required urban planners and transportation engineers to have greater consideration for the transportation services available to residents of a metropolitan region.
These improvements can be achieved by identifying and understanding the factors that influence urban public transit demand.
Common factors that can influence urban public transit demand can be internal and/or external factors.
arXiv Detail & Related papers (2021-11-16T05:37:51Z) - Frustratingly Easy Transferability Estimation [64.42879325144439]
We propose a simple, efficient, and effective transferability measure named TransRate.
TransRate measures the transferability as the mutual information between the features of target examples extracted by a pre-trained model and labels of them.
Despite its extraordinary simplicity in 10 lines of codes, TransRate performs remarkably well in extensive evaluations on 22 pre-trained models and 16 downstream tasks.
arXiv Detail & Related papers (2021-06-17T10:27:52Z) - Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2
Benchmark [133.46066694893318]
We evaluate the performance of neural network-based solvers for optimal transport.
We find that existing solvers do not recover optimal transport maps even though they perform well in downstream tasks.
arXiv Detail & Related papers (2021-06-03T15:59:28Z) - Urban Sensing based on Mobile Phone Data: Approaches, Applications and
Challenges [67.71975391801257]
Much concern in mobile data analysis is related to human beings and their behaviours.
This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
arXiv Detail & Related papers (2020-08-29T15:14:03Z) - Polestar: An Intelligent, Efficient and National-Wide Public
Transportation Routing Engine [43.09401975244128]
We present Polestar, a data-driven engine for intelligent and efficient public transportation routing.
Specifically, we first propose a novel Public Transportation Graph (PTG) to model public transportation system in terms of various travel costs.
We then introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation.
Experiments on two real-world data sets demonstrate the advantages of Polestar in terms of both efficiency and effectiveness.
arXiv Detail & Related papers (2020-07-11T05:14:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.