dFDA-VeD: A Dynamic Future Demand Aware Vehicle Dispatching System
- URL: http://arxiv.org/abs/2106.05737v1
- Date: Thu, 10 Jun 2021 13:40:17 GMT
- Title: dFDA-VeD: A Dynamic Future Demand Aware Vehicle Dispatching System
- Authors: Yang Guo and Tarique Anwar and Jian Yang and Jia Wu
- Abstract summary: We propose a dynamic future demand aware vehicle dispatching system.
It can search the relocation centers considering both travel demand and traffic conditions.
We demonstrate that the proposed system significantly improves the serving ratio and with a very small increase in operation cost.
- Score: 21.17974307683502
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the rising demand of smart mobility, ride-hailing service is getting
popular in the urban regions. These services maintain a system for serving the
incoming trip requests by dispatching available vehicles to the pickup points.
As the process should be socially and economically profitable, the task of
vehicle dispatching is highly challenging, specially due to the time-varying
travel demands and traffic conditions. Due to the uneven distribution of travel
demands, many idle vehicles could be generated during the operation in
different subareas. Most of the existing works on vehicle dispatching system,
designed static relocation centers to relocate idle vehicles. However, as
traffic conditions and demand distribution dynamically change over time, the
static solution can not fit the evolving situations. In this paper, we propose
a dynamic future demand aware vehicle dispatching system. It can dynamically
search the relocation centers considering both travel demand and traffic
conditions. We evaluate the system on real-world dataset, and compare with the
existing state-of-the-art methods in our experiments in terms of several
standard evaluation metrics and operation time. Through our experiments, we
demonstrate that the proposed system significantly improves the serving ratio
and with a very small increase in operation cost.
Related papers
- GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching [82.19172267487998]
GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.
This paper introduces GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.
arXiv Detail & Related papers (2024-08-19T08:23:38Z) - Uncertainty Quantification for Image-based Traffic Prediction across
Cities [63.136794104678025]
Uncertainty quantification (UQ) methods provide an approach to induce probabilistic reasoning.
We investigate their application to a large-scale image-based traffic dataset spanning multiple cities.
We find that our approach can capture both temporal and spatial effects on traffic behaviour in a representative case study for the city of Moscow.
arXiv Detail & Related papers (2023-08-11T13:35:52Z) - Spatial, Social and Data Gaps in On-Demand Mobility Services: Towards a
Supply-Oriented MaaS [3.299672391663527]
After a decade of on-demand mobility services, the Shared Autonomous Vehicle (SAV) service is expected to increase traffic congestion and unequal access to transport services.
A paradigm of scheduled supply that is aware of demand but not on-demand is proposed.
arXiv Detail & Related papers (2023-02-20T10:04:41Z) - A Bibliometric Analysis and Review on Reinforcement Learning for
Transportation Applications [43.356096302298056]
Transportation is the backbone of the economy and urban development.
Reinforcement Learning (RL) that enables autonomous decision-makers to interact with the complex environment.
This paper conducts a bibliometric analysis to identify the development of RL-based methods for transportation applications.
arXiv Detail & Related papers (2022-10-26T07:34:51Z) - An Online Approach to Solve the Dynamic Vehicle Routing Problem with
Stochastic Trip Requests for Paratransit Services [5.649212162857776]
We propose a fully online approach to solve the dynamic vehicle routing problem (DVRP)
It is difficult to batch paratransit requests together as they are temporally sparse.
We use Monte Carlo tree search to evaluate actions for any given state.
arXiv Detail & Related papers (2022-03-28T22:15:52Z) - Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion [87.77727495366702]
We introduce the new task of pedestrian stop and go forecasting.
Considering the lack of suitable existing datasets for it, we release TRANS, a benchmark for explicitly studying the stop and go behaviors of pedestrians in urban traffic.
We build it from several existing datasets annotated with pedestrians' walking motions, in order to have various scenarios and behaviors.
arXiv Detail & Related papers (2022-03-04T18:39:31Z) - 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) - A Queueing-Theoretic Framework for Vehicle Dispatching in Dynamic
Car-Hailing [technical report] [36.31694973019143]
We consider an important dynamic car-hailing problem, namely textitmaximum revenue vehicle dispatching (MRVD)
We use existing machine learning algorithms to predict the future vehicle demand of each region, then estimates the idle time periods of drivers through a queueing model for each region.
With the information of the predicted vehicle demands and estimated idle time periods of drivers, we propose two batch-based vehicle dispatching algorithms to efficiently assign suitable drivers to riders.
arXiv Detail & Related papers (2021-07-19T07:51:31Z) - Value Function is All You Need: A Unified Learning Framework for Ride
Hailing Platforms [57.21078336887961]
Large ride-hailing platforms, such as DiDi, Uber and Lyft, connect tens of thousands of vehicles in a city to millions of ride demands throughout the day.
We propose a unified value-based dynamic learning framework (V1D3) for tackling both tasks.
arXiv Detail & Related papers (2021-05-18T19:22:24Z) - Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle
Network [1.599072005190786]
We formulate the problem of passenger-vehicle matching in a sparsely connected graph.
We propose an algorithm to derive an equilibrium policy in a multi-agent environment.
arXiv Detail & Related papers (2021-02-13T03:18:44Z) - A Distributed Model-Free Ride-Sharing Approach for Joint Matching,
Pricing, and Dispatching using Deep Reinforcement Learning [32.0512015286512]
We present a dynamic, demand aware, and pricing-based vehicle-passenger matching and route planning framework.
Our framework is validated using the New York City Taxi dataset.
Experimental results show the effectiveness of our approach in real-time and large scale settings.
arXiv Detail & Related papers (2020-10-05T03:13:47Z)
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.