8,028 research outputs found
Towards a Testbed for Dynamic Vehicle Routing Algorithms
Since modern transport services are becoming more flexible, demand-responsive, and energy/cost efficient, there is a growing demand for large-scale microscopic simulation platforms in order to test sophisticated routing algorithms. Such platforms have to simulate in detail, not only the dynamically changing demand and supply of the relevant service, but also traffic flow and other relevant transport services. This paper presents the DVRP extension to the open-source MATSim simulator. The extension is designed to be highly general and customizable to simulate a wide range of dynamic rich vehicle routing problems. The extension allows plugging in of various algorithms that are responsible for continuous re-optimisation of routes in response to changes in the system. The DVRP extension has been used in many research and commercial projects dealing with simulation of electric and autonomous taxis, demand-responsive transport, personal rapid transport, free-floating car sharing and parking search
Testing demand responsive shared transport services via agent-based simulations
Demand Responsive Shared Transport DRST services take advantage of
Information and Communication Technologies ICT, to provide on demand transport
services booking in real time a ride on a shared vehicle. In this paper, an
agent-based model ABM is presented to test different the feasibility of
different service configurations in a real context. First results show the
impact of route choice strategy on the system performance
Analysis and operational challenges of dynamic ride sharing demand responsive transportation models
There is a wide body of evidence that suggests sustainable mobility is not only a technological question, but that automotive technology will be a part of the solution in becoming a necessary albeit insufficient condition. Sufficiency is emerging as a paradigm shift from car ownership to vehicle usage, which is a consequence of socio-economic changes. Information and Communication Technologies (ICT) now make it possible for a user to access a mobility service to go anywhere at any time. Among the many emerging mobility services, Multiple Passenger Ridesharing and its variants look the most promising. However, challenges arise in implementing these systems while accounting specifically for time dependencies and time windows that reflect users’ needs, specifically in terms of real-time fleet dispatching and dynamic route calculation. On the other hand, we must consider the feasibility and impact analysis of the many factors influencing the behavior of the system – as, for example, service demand, the size of the service fleet, the capacity of the shared vehicles and whether the time window requirements are soft or tight. This paper analyzes - a Decision Support System that computes solutions with ad hoc heuristics applied to variants of Pick Up and Delivery Problems with Time Windows, as well as to Feasibility and Profitability criteria rooted in Dynamic Insertion Heuristics. To evaluate the applications, a Simulation Framework is proposed. It is based on a microscopic simulation model that emulates real-time traffic conditions and a real traffic information system. It also interacts with the Decision Support System by feeding it with the required data for making decisions in the simulation that emulate the behavior of the shared fleet. The proposed simulation framework has been implemented in a model of Barcelona’s Central Business District. The obtained results prove the potential feasibility of the mobility concept.Postprint (published version
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
Proactive empty vehicle rebalancing for Demand Responsive Transport services
Worldwide, ridesharing business is steadily growing and has started to receive attention also by public transport operators. With future fleets of Autonomous Vehicles, new business models connecting schedule-based public transport and feeder fleets might become a feasible transport mode. However, such fleets require a good management to warrant a high level of service. One of the key aspects of this is proactive vehicle rebalancing based on the expected demand for trips. In this paper we model vehicle rebalancing as the Dynamic Transportation Problem. Results suggest that waiting times can be cut by around 30 % without increasing the overall vehicle miles travelled for a feeder fleet in rural Switzerland
An adaptive route choice model for integrated fixed and flexible transit systems
Over the past decade, there has been a surge of interest in the transport
community in the application of agent-based simulation models to evaluate
flexible transit solutions characterized by different degrees of short-term
flexibility in routing and scheduling. A central modeling decision in the
development of an agent-based simulation model for the evaluation of flexible
transit is how one chooses to represent the mode- and route-choices of
travelers. The real-time adaptive behavior of travelers is intuitively
important to model in the presence of a flexible transit service, where the
routing and scheduling of vehicles is highly dependent on supply-demand
dynamics at a closer to real-time temporal resolution. We propose a
utility-based transit route-choice model with representation of within-day
adaptive travel behavior and between-day learning where station-based
fixed-transit, flexible-transit, and active-mode alternatives may be
dynamically combined in a single path. To enable experimentation, this
route-choice model is implemented within an agent-based dynamic public transit
simulation framework. Model properties are first explored in a choice between
fixed- and flexible-transit modes for a toy network. The framework is then
applied to illustrate level-of-service trade-offs and analyze traveler mode
choices within a mixed fixed- and flexible transit system in a case study based
on a real-life branched transit service in Stockholm, Sweden.Comment: 33 pages, 9 figures, preprin
Approaching delivery as a service
This paper explores the new logistics business model of Delivery as a Service, a concept aiming at a more efficient, fast and customer-oriented practice, linking IT solution development, urban logistics operations, supply chain efficiency and new business models. Delivery as a Service (DaaS) is defined as a service-oriented delivery and business processes in line with customer expectations and needs in the on-demand economy. The approach of this paper is an industry report based on evidence collected in multiple exploratory European projects integrating ambitious and strategic findings on Internet of Things, urban planning, consolidation centres, transport optimisation, and clean vehicle use. It contributes to a future scenario of urban logistics business models
Analysis and operational challenges of dynamic ride sharing demand responsive transportation models
© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/There is a wide evidence that sustainable mobility is not only a technological question, automotive technology will be part of the solution as a necessary but not sufficient condition, sufficiency is emerging as a combination of a paradigm shift from car ownership to vehicle usage consequence of socio-economic changes, withthe application of Information and Communication Technologies (ICT) that make possible for a userto have access to a mobility service from anywhere to anywhere at any time. Among the many emergent mobility services Multiple Passenger Ridesharing and its variants look the more promising. However, implementations of these systems accounting specifically for time dependencies, and time windows reflecting users’ needs raise challenges in terms of real-time fleet dispatching and dynamic route calculation. On the other handthe feasibility and impacts analysis in terms of the many factors influencing the behavior of the system, as for example the service demand, the size of the service fleet, the capacity of the shared vehicles, the time windows requirements, soft or tight. This paper analyzes both aspects. The first is approached in terms of a Decision Support System whose solutions are computed in terms of ad hoc heuristics of variants of Pick Up and Delivery Problems with Time Windows and Feasibility and Profitability criteria rooted on Dynamic Insertion Heuristics. For the evaluation of the applications a Simulation Framework is proposed based on a microscopic simulation model thatemulates real-time traffic conditions and a real traffic information system, and interacts with the Decision Support System feeding it with the required data to make the decisions that are implemented in the simulation to emulate the behavior of the shared fleet. The proposed simulation framework has been implemented in a model of Barcelona’s Central Business District. The paper is completed with the discussion of the achieved resultsPeer ReviewedPostprint (published version
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