678 research outputs found
The Influence of Multi-agent Cooperation on the Efficiency of Taxi Dispatching
The paper deals with the problem of the optimal collaboration scheme in taxi dispatching between customers, taxi drivers and the dispatcher. The authors propose three strategies that differ by the amount of information exchanged between agents and the intensity of cooperation between taxi drivers and the dispatcher. The strategies are evaluated by means of a microscopic multi-agent transport simulator (MATSim) coupled with a dynamic vehicle routing optimizer (DVRP Optimizer), which allows to realistically simulate dynamic taxi services as one of several different transport means, all embedded into a realistic environment. The evaluation is carried out on a scenario of the Polish city of Mielec. The results obtained prove that the cooperation between the dispatcher and taxi drivers is of the utmost importance, while the customer–dispatcher communication may be reduced to minimum and compensated by the use of more sophisticated dispatching strategies, thereby not affecting the quality of service
A Microscopic Simulation Approach for Optimization of Taxi Services
This paper presents a simulation platform along with several on-line dispatching algorithms developed in order to optimize taxi services. First, the issue of simulation-based optimization of modern transport services, especially taxi services, is presented. Next, the proposed approach to microscopically simulate taxi services is explained, followed by a description of the on-line taxi dispatching algorithm framework and three selected dispatching strategies implemented within this framework. The next section presents the simulation scenario of Mielec that the strategies were tested on. Then, the simulation results obtained are analysed and the strategies compared. The paper ends with conclusions on the main properties and other possible applications of the proposed simulation approach, as well as on future plans concerning further improvements of the taxi dispatching algorithms
A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers
We propose a ridesharing strategy with integrated transit in which a private
on-demand mobility service operator may drop off a passenger directly
door-to-door, commit to dropping them at a transit station or picking up from a
transit station, or to both pickup and drop off at two different stations with
different vehicles. We study the effectiveness of online solution algorithms
for this proposed strategy. Queueing-theoretic vehicle dispatch and idle
vehicle relocation algorithms are customized for the problem. Several
experiments are conducted first with a synthetic instance to design and test
the effectiveness of this integrated solution method, the influence of
different model parameters, and measure the benefit of such cooperation.
Results suggest that rideshare vehicle travel time can drop by 40-60%
consistently while passenger journey times can be reduced by 50-60% when demand
is high. A case study of Long Island commuters to New York City (NYC) suggests
having the proposed operating strategy can substantially cut user journey times
and operating costs by up to 54% and 60% each for a range of 10-30 taxis
initiated per zone. This result shows that there are settings where such
service is highly warranted
Benchmarking minimum passenger waiting time in online taxi dispatching with exact offline optimization methods
This paper analyses the use of exact offline optimization methods for benchmarking online taxi dispatching strategies where the objective is to minimize the total passenger waiting time. First, a general framework for simulating dynamic transport services in MATSim (Multi-Agent Transport Simulation) is described. Next, the model of online taxi dispatching is defined, followed by a formulation of the offline problem as a mixed integer programming problem. Three benchmarks based on the offline problem are presented and compared to two simple heuristic strategies and a hypothetical simulation with teleportation of idle taxis. The benchmarks are evaluated and compared using the simulation scenario of taxi services in the city of Mielec. The obtained (approximate) lower and upper bounds for the minimum total passenger waiting time indicate directions for further research
Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach
The integrated development of city clusters has given rise to an increasing
demand for intercity travel. Intercity ride-pooling service exhibits
considerable potential in upgrading traditional intercity bus services by
implementing demand-responsive enhancements. Nevertheless, its online
operations suffer the inherent complexities due to the coupling of vehicle
resource allocation among cities and pooled-ride vehicle routing. To tackle
these challenges, this study proposes a two-level framework designed to
facilitate online fleet management. Specifically, a novel multi-agent feudal
reinforcement learning model is proposed at the upper level of the framework to
cooperatively assign idle vehicles to different intercity lines, while the
lower level updates the routes of vehicles using an adaptive large neighborhood
search heuristic. Numerical studies based on the realistic dataset of Xiamen
and its surrounding cities in China show that the proposed framework
effectively mitigates the supply and demand imbalances, and achieves
significant improvement in both the average daily system profit and order
fulfillment ratio
Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning
A fundamental question in any peer-to-peer ridesharing system is how to, both
effectively and efficiently, dispatch user's ride requests to the right driver
in real time. Traditional rule-based solutions usually work on a simplified
problem setting, which requires a sophisticated hand-crafted weight design for
either centralized authority control or decentralized multi-agent scheduling
systems. Although recent approaches have used reinforcement learning to provide
centralized combinatorial optimization algorithms with informative weight
values, their single-agent setting can hardly model the complex interactions
between drivers and orders. In this paper, we address the order dispatching
problem using multi-agent reinforcement learning (MARL), which follows the
distributed nature of the peer-to-peer ridesharing problem and possesses the
ability to capture the stochastic demand-supply dynamics in large-scale
ridesharing scenarios. Being more reliable than centralized approaches, our
proposed MARL solutions could also support fully distributed execution through
recent advances in the Internet of Vehicles (IoV) and the Vehicle-to-Network
(V2N). Furthermore, we adopt the mean field approximation to simplify the local
interactions by taking an average action among neighborhoods. The mean field
approximation is capable of globally capturing dynamic demand-supply variations
by propagating many local interactions between agents and the environment. Our
extensive experiments have shown the significant improvements of MARL order
dispatching algorithms over several strong baselines on the gross merchandise
volume (GMV), and order response rate measures. Besides, the simulated
experiments with real data have also justified that our solution can alleviate
the supply-demand gap during the rush hours, thus possessing the capability of
reducing traffic congestion.Comment: 11 pages, 9 figure
Optimising police dispatch for incident response in real-time
It is crucial that police forces operate in a cost efficient manner and, in the case of incident response, that the most efficient resources are allocated. The current procedure is that police response units are allocated manually by a dispatcher using a resource list and mapping software. The efficiency of this process can be improved by the use of integrated mathematical approaches embedded within an automatic framework, yielding the optimal selection framework developed in this paper. The framework combines mapping and routing algorithms, and a decision process to facilitate optimal officer selection for incident response. The decision process considers information such as quickest response time, predicted traffic conditions, driving qualifications, response unit availability and demand coverage. The selection framework has been tested and validated through simulation and has shown to increase the efficiency of response units through reduced response times, increased response unit availability, and greater demand coverage
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