Predicting Requests in Large-Scale Online P2P Ridesharing
- URL: http://arxiv.org/abs/2009.02997v1
- Date: Mon, 7 Sep 2020 10:27:24 GMT
- Title: Predicting Requests in Large-Scale Online P2P Ridesharing
- Authors: Filippo Bistaffa, Juan A. Rodr\'iguez-Aguilar, Jes\'us Cerquides
- Abstract summary: Peer-to-peer ridesharing (P2P-RS) enables people to arrange one-time rides with their own private cars, without the involvement of professional drivers.
In this paper we tackle the question of assessing the benefit of predicting ridesharing requests in the context of P2P-RS optimisation.
Results on a public real-world show that, by employing a perfect predictor, the total reward can be improved by 5.27% with a forecast horizon of 1 minute.
- Score: 1.8434430658837255
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Peer-to-peer ridesharing (P2P-RS) enables people to arrange one-time rides
with their own private cars, without the involvement of professional drivers.
It is a prominent collective intelligence application producing significant
benefits both for individuals (reduced costs) and for the entire community
(reduced pollution and traffic), as we showed in a recent publication where we
proposed an online approximate solution algorithm for large-scale P2P-RS. In
this paper we tackle the fundamental question of assessing the benefit of
predicting ridesharing requests in the context of P2P-RS optimisation. Results
on a public real-world show that, by employing a perfect predictor, the total
reward can be improved by 5.27% with a forecast horizon of 1 minute. On the
other hand, a vanilla long short-term memory neural network cannot improve upon
a baseline predictor that simply replicates the previous day's requests, whilst
achieving an almost-double accuracy.
Related papers
- Best Practices for 2-Body Pose Forecasting [58.661899246497896]
We review the progress in human pose forecasting and provide an in-depth assessment of the single-person practices that perform best.
Other single-person practices do not transfer to 2-body, so the proposed best ones do not include hierarchical body modeling or attention-based interaction encoding.
Our proposed 2-body pose forecasting best practices yield a performance improvement of 21.9% over the state-of-the-art on the most recent ExPI dataset.
arXiv Detail & Related papers (2023-04-12T10:46:23Z) - Spatially-Aware Car-Sharing Demand Prediction [3.085449079520639]
We analyze the average monthly demand in a station-based car-sharing service with spatially-aware learning algorithms.
We show that the global Random Forest model with geo-coordinates as an input feature achieves the highest predictive performance with an R-squared score of 0.87.
Our study offers effective as well as highly interpretable methods for diagnosing and planning the placement of car-sharing stations.
arXiv Detail & Related papers (2023-03-25T10:10:11Z) - Fairness-enhancing deep learning for ride-hailing demand prediction [3.911105164672852]
Short-term demand forecasting for on-demand ride-hailing services is one of the fundamental issues in intelligent transportation systems.
Previous travel demand forecasting research predominantly focused on improving prediction accuracy, ignoring fairness issues.
This study investigates how to measure, evaluate, and enhance prediction fairness between disadvantaged and privileged communities.
arXiv Detail & Related papers (2023-03-10T04:37:14Z) - Short term prediction of demand for ride hailing services: A deep
learning approach [8.61268901380738]
This paper proposes UberNet, a deep learning Convolutional Neural Network for short-term prediction of demand for ride-hailing services.
By comparing the performance of UberNet with several other approaches, we show that the prediction quality of the model is highly competitive.
This suggests that Ubernet is more naturally suited to including complex motivators in making real-time passenger demand predictions for ride-hailing services.
arXiv Detail & Related papers (2022-12-07T21:08:03Z) - A Baselined Gated Attention Recurrent Network for Request Prediction in
Ridesharing [1.0312968200748118]
Ridesharing has received global popularity due to its convenience and cost efficiency for both drivers and passengers.
The goal of the RSODP (Origin-Destination Prediction for Ridesharing) problem is to predict the future ridesharing requests and provide schedules for vehicles ahead of time.
Most of existing prediction models utilise Deep Learning, however they fail to effectively consider both spatial and temporal dynamics.
arXiv Detail & Related papers (2022-07-11T08:41:24Z) - Efficiency, Fairness, and Stability in Non-Commercial Peer-to-Peer
Ridesharing [84.47891614815325]
This paper focuses on the core problem in P2P ridesharing: the matching of riders and drivers.
We introduce novel notions of fairness and stability in P2P ridesharing.
Results suggest that fair and stable solutions can be obtained in reasonable computational times.
arXiv Detail & Related papers (2021-10-04T02:14:49Z) - PnPNet: End-to-End Perception and Prediction with Tracking in the Loop [82.97006521937101]
We tackle the problem of joint perception and motion forecasting in the context of self-driving vehicles.
We propose Net, an end-to-end model that takes as input sensor data, and outputs at each time step object tracks and their future level.
arXiv Detail & Related papers (2020-05-29T17:57:25Z) - TPNet: Trajectory Proposal Network for Motion Prediction [81.28716372763128]
Trajectory Proposal Network (TPNet) is a novel two-stage motion prediction framework.
TPNet first generates a candidate set of future trajectories as hypothesis proposals, then makes the final predictions by classifying and refining the proposals.
Experiments on four large-scale trajectory prediction datasets, show that TPNet achieves the state-of-the-art results both quantitatively and qualitatively.
arXiv Detail & Related papers (2020-04-26T00:01:49Z) - Local Differential Privacy based Federated Learning for Internet of
Things [72.83684013377433]
Internet of Vehicles (IoV) simulates a large variety of crowdsourcing applications such as Waze, Uber, and Amazon Mechanical Turk, etc.
Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management.
In this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model.
arXiv Detail & Related papers (2020-04-19T14:03:10Z) - PiP: Planning-informed Trajectory Prediction for Autonomous Driving [69.41885900996589]
We propose planning-informed trajectory prediction (PiP) to tackle the prediction problem in the multi-agent setting.
By informing the prediction process with the planning of ego vehicle, our method achieves the state-of-the-art performance of multi-agent forecasting on highway datasets.
arXiv Detail & Related papers (2020-03-25T16:09:54Z)
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.