AccEq-DRT: Planning Demand-Responsive Transit to reduce inequality of
accessibility
- URL: http://arxiv.org/abs/2310.04348v1
- Date: Fri, 6 Oct 2023 16:13:28 GMT
- Title: AccEq-DRT: Planning Demand-Responsive Transit to reduce inequality of
accessibility
- Authors: Duo Wang and Andrea Araldo and Mounim A. El Yacoubi
- Abstract summary: 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.
- Score: 7.615022055373833
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Accessibility measures how well a location is connected to surrounding
opportunities. 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. In the latter,
poor PT service leads to a chronic car-dependency. Demand-Responsive Transit
(DRT) is better suited for low-density areas than conventional fixed-route PT.
However, its potential to tackle accessibility inequality has not yet been
exploited. On the contrary, planning DRT without care to inequality (as in the
methods proposed so far) can further improve the accessibility gap in urban
areas.
To the best of our knowledge this paper is the first to propose a DRT
planning strategy, which we call AccEq-DRT, aimed at reducing accessibility
inequality, while ensuring overall efficiency. To this aim, we combine a graph
representation of conventional PT and a Continuous Approximation (CA) model of
DRT. The two are combined in the same multi-layer graph, on which we compute
accessibility. We then devise a scoring function to estimate the need of each
area for an improvement, appropriately weighting population density and
accessibility. Finally, we provide a bilevel optimization method, where the
upper level is a heuristic to allocate DRT buses, guided by the scoring
function, and the lower level performs traffic assignment. Numerical results in
a simplified model of Montreal show that inequality, measured with the Atkinson
index, is reduced by up to 34\%.
Keywords: DRT Public, Transportation, Accessibility, Continuous
Approximation, Network Design
Related papers
- Public Transport Network Design for Equality of Accessibility via Message Passing Neural Networks and Reinforcement Learning [4.6289929100615]
We focus on Public Transport (PT) accessibility, i.e., the ease of reaching surrounding points of interest via PT.
We combine state-of-the-art Message Passing Neural Networks (MPNN) and Reinforcement Learning.
We show the efficacy of our method against metaheuristics in a use case representing in simplified terms the city of Montreal.
arXiv Detail & Related papers (2024-10-11T14:16:58Z) - 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) - LoLep: Single-View View Synthesis with Locally-Learned Planes and
Self-Attention Occlusion Inference [66.45326873274908]
We propose a novel method, LoLep, which regresses Locally-Learned planes from a single RGB image to represent scenes accurately.
Compared to MINE, our approach has an LPIPS reduction of 4.8%-9.0% and an RV reduction of 73.9%-83.5%.
arXiv Detail & Related papers (2023-07-23T03:38:55Z) - Equity Promotion in Public Transportation [18.057286025603055]
We propose an optimization model to study how to integrate the two approaches together for equity-promotion purposes.
We have designed a linear-programming (LP) based rounding algorithm, which proves to achieve an optimal approximation ratio of 1-1/e.
Experimental results confirm our theoretical predictions and demonstrate the effectiveness of our LP-based strategy in promoting social equity.
arXiv Detail & Related papers (2022-11-26T10:06:00Z) - Equity Scores for Public Transit Lines from Open-Data and Accessibility
Measures [0.3058685580689604]
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.
arXiv Detail & Related papers (2022-09-30T22:58:11Z) - SRRT: Exploring Search Region Regulation for Visual Object Tracking [58.68120400180216]
We propose a novel tracking paradigm, called Search Region Regulation Tracking (SRRT)
SRRT applies a proposed search region regulator to estimate an optimal search region dynamically for each frame.
On the large-scale LaSOT benchmark, SRRT improves SiamRPN++ and TransT with absolute gains of 4.6% and 3.1% in terms of AUC.
arXiv Detail & Related papers (2022-07-10T11:18:26Z) - SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory
Prediction [64.16212996247943]
We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction.
Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians.
visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
arXiv Detail & Related papers (2021-04-04T03:17:42Z) - Higher Performance Visual Tracking with Dual-Modal Localization [106.91097443275035]
Visual Object Tracking (VOT) has synchronous needs for both robustness and accuracy.
We propose a dual-modal framework for target localization, consisting of robust localization suppressingors via ONR and the accurate localization attending to the target center precisely via OFC.
arXiv Detail & Related papers (2021-03-18T08:47:56Z) - Comparing Probability Distributions with Conditional Transport [63.11403041984197]
We propose conditional transport (CT) as a new divergence and approximate it with the amortized CT (ACT) cost.
ACT amortizes the computation of its conditional transport plans and comes with unbiased sample gradients that are straightforward to compute.
On a wide variety of benchmark datasets generative modeling, substituting the default statistical distance of an existing generative adversarial network with ACT is shown to consistently improve the performance.
arXiv Detail & Related papers (2020-12-28T05:14:22Z) - Crowding Prediction of In-Situ Metro Passengers Using Smart Card Data [11.781685156308475]
We propose a statistical model to predict in-situ passenger density inside a closed metro system.
Based on the prediction results, we are able to provide accurate prediction of in-situ passenger density for a future time point.
arXiv Detail & Related papers (2020-09-07T04:07:37Z) - Physical-Virtual Collaboration Modeling for Intra-and Inter-Station
Metro Ridership Prediction [116.66657468425645]
We propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs.
Specifically, a physical graph is directly built based on the realistic topology of the studied metro system.
A similarity graph and a correlation graph are built with virtual topologies under the guidance of the inter-station passenger flow similarity and correlation.
arXiv Detail & Related papers (2020-01-14T16:47:54Z)
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.