# Travel Time, Distance and Costs Optimization for Paratransit Operations using Graph Convolutional Neural Network

arXiv: https://arxiv.org/abs/2205.10507 · HTML: https://mlsanity.com/paper/2205.10507/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 21 May 2022.

## Builds on (most cited first, 10 of 10)

- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [ETA Prediction with Graph Neural Networks in Google Maps](https://mlsanity.com/paper/2108.11482/) (2021)
- [A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees](https://mlsanity.com/paper/2104.06856/) (2021)
- [Mobile Sensing for Multipurpose Applications in Transportation](https://mlsanity.com/paper/2106.10733/) (2021)
- [Comparative Analysis of Machine Learning Models for Predicting Travel Time](https://mlsanity.com/paper/2111.08226/) (2021)
- [Developing a Machine Learning Algorithm-Based Classification Models for the Detection of High-Energy Gamma Particles](https://mlsanity.com/paper/2111.09496/) (2021)
- [Machine Learning-Based Classification Algorithms for the Prediction of Coronary Heart Diseases](https://mlsanity.com/paper/2112.01503/) (2021)
- [A Robust Completed Local Binary Pattern (RCLBP) for Surface Defect Detection](https://mlsanity.com/paper/2112.04021/) (2021)

## Built on by (most cited first, 1 of 1)

- [Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification](https://mlsanity.com/paper/2211.06463/) (2022)

## Abstract (arXiv)

The provision of paratransit services is one option to meet the transportation needs of Vulnerable Road Users (VRUs). Like any other means of transportation, paratransit has obstacles such as high operational costs and longer trip times. As a result, customers are dissatisfied, and paratransit operators have a low approval rating. Researchers have undertaken various studies over the years to better understand the travel behaviors of paratransit customers and how they are operated. According to the findings of these researches, paratransit operators confront the challenge of determining the optimal route for their trips in order to save travel time. Depending on the nature of the challenge, most research used different optimization techniques to solve these routing problems. As a result, the goal of this study is to use Graph Convolutional Neural Networks (GCNs) to assist paratransit operators in researching various operational scenarios in a strategic setting in order to optimize routing, minimize operating costs and minimize their users' travel time. The study was carried out by using a randomized simulated dataset to help determine the decision to make in terms of fleet composition and capacity under different situations. For the various scenarios investigated, the GCN assisted in determining the minimum optimal gap.