scholarly journals Real-Time Dispatching of Large-Scale Ride-Sharing Systems: Integrating Optimization, Machine Learning, and Model Predictive Control

2020 ◽  
Author(s):  
Connor Riley ◽  
Pascal van Hentenryck ◽  
Enpeng Yuan
Keyword(s):  
Machine Learning ◽  
New York ◽  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Large Scale ◽  
Waiting Times ◽  
Service Guarantees ◽  
Ride Sharing ◽  
Dispatching Algorithm

This paper considers the dispatching of large-scale real-time ride-sharing systems to address congestion issues faced by many cities. The goal is to serve all customers (service guarantees) with a small number of vehicles while minimizing waiting times under constraints on ride duration. This paper proposes an end-to-end approach that tightly integrates a state-of-the-art dispatching algorithm, a machine-learning model to predict zone-to-zone demand over time, and a model predictive control optimization to relocate idle vehicles. Experiments using historic taxi trips in New York City indicate that this integration decreases average waiting times by about 30% over all test cases and reaches close to 55% on the largest instances for high-demand zones.

scholarly journals Fast Model Predictive Control Based on Adaptive Alternating Direction Method of Multipliers

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Yu Li ◽  
Qiming Zou ◽  
Xiaoru Ji ◽  
Chanyuan Zhang ◽  
Ke Lu
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Large Scale ◽  
Alternating Direction Method ◽  
Control Input ◽  
Method Of Multipliers ◽  
Time Step ◽  
Prediction Horizon ◽  
Alternating Direction

Model Predictive Control (MPC) can effectively handle control problem with disturbances, multicontrol variables, and complex constraints and is widely used in various control systems. In MPC, the control input at each time step is obtained by solving an online optimization problem, which will cause a time delay in real time on embedded computers with limited computational resources. In this paper, we utilize adaptive Alternating Direction Method of Multipliers (a-ADMM) to accelerate the solution of MPC. This method adaptively adjusts penalty parameter to balance the value of primal residual and dual residual. The performance of this approach is profiled via the control of a quadcopter with 12 states and 4 controls and prediction horizon ranging from 10 to 40. The simulation results demonstrate that the MPC based on a-ADMM has a significant improvement in real-time and convergence performance and thus is more suitable for solving large-scale optimal control problems.

scholarly journals A Model Predictive Control Framework for Real-Time Optimisation of Water System Operations

10.29007/fg7g ◽  
2018 ◽  
Author(s):  
Henrik Madsen ◽  
Anne Katrine Falk ◽  
Rasmus Halvgaard
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Case Studies ◽  
Predictive Control ◽  
Large Scale ◽  
Water System ◽  
River System ◽  
Quadratic Program ◽  
Real Time Control ◽  
Time Control

We have developed a versatile Model Predictive Control (MPC) framework, which can handle real-time control of a large variety of water systems. The framework combines a fast-solvable optimisation model (a quadratic program) with evaluation and realignment by a detailed hydrological-hydrodynamic model. The flexibility of the MPC framework is highlighted by two case studies: (1) a large-scale river system with several weeks of travel time, and (2) an urban storm and wastewater system with a concentration time of about half an hour to one hour. Both case studies demonstrate a large potential for improving operations by system-wide real-time optimisation.

scholarly journals On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment

2017 ◽  
Vol 114 (3) ◽  
pp. 462-467 ◽  
Author(s):  
Javier Alonso-Mora ◽  
Samitha Samaranayake ◽  
Alex Wallar ◽  
Emilio Frazzoli ◽  
Daniela Rus
Keyword(s):  
New York ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
High Capacity ◽  
Fleet Size ◽  
Ride Sharing ◽  
Large Numbers ◽  
General Mathematical Model ◽  
Travel Delay

Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.

scholarly journals An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 105
Author(s):  
Zhenzhong Chu ◽  
Da Wang ◽  
Fei Meng
Keyword(s):  
Model Predictive Control ◽  
Prediction Model ◽  
Real Time ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Working Environment ◽  
Gray Wolf ◽  
Trajectory Tracking Control ◽  
System Output

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.

Sign in / Sign up

Export Citation Format

Share Document