Multi-agent infrastructure for distributed planning of demand-responsive passenger transportation service

Demand-responsive transport services are systems that assign users ’ specific transport requests to different vehicles enabled to fulfill the required service. In order to contain costs, maximize profits or get a higher service level, different planning algorithms have been described in literature. In our work we propose a multi-agent architecture, which adopts a mixed planning model. This model is an integration of centralized and negotiation based decision-making approaches. The advantage of using this model respect to a centralized approach is that the estimation of the uti1ir) function is avoided (the client is involved in the final decision by means of a negotiation process). The model advantages a complete decentralized approach in giving results that are not so far from the optimum for the whole system

Train schedule coordination at an interchange station through agent negotiation

In open railway markets, coordinating train schedules at an interchange station requires negotiation between two independent train operating companies to resolve their operational conflicts. This paper models the stakeholders as software agents and proposes an agent negotiation model to study their interaction. Three negotiation strategies have been devised to represent the possible objectives of the stakeholders, and they determine the behavior in proposing offers to the proponent. Empirical simulation results confirm that the use of the proposed negotiation strategies lead to outcomes that are consistent with the objectives of the stakeholders

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 agent solution to flexible planning and scheduling of passenger trips

In a highly competitive market, BT1 faces tough challenges as a service provider for telecommunication solutions. A proactive approach to the management of its resources is absolutely mandatory for its success. In this paper, an AI-based planning system for the management of parts of BT’s field force is presented. FieldPlan provides resource managers with full visibility of supply and demand, offers extensive what-if analysis capabilities and thus supports an effective decision making process.IFIP International Conference on Artificial Intelligence in Theory and Practice - Industrial Applications of AIRed de Universidades con Carreras en Informática (RedUNCI

A double dynamic fast algorithm to solve multi-vehicle Dial a Ride Problem

Abstract In this work a two level heuristic algorithm is described for a nearly real-time multi-vehicle many-to-many Dial-A-Ride Problem (DARP). This algorithm is ready to support a Demand Responsive Transportation System in which we face the problem of quickly evaluate a good-quality schedule for the vehicles and provide fast response to the users. The insertion heuristic is double dynamic nearly real-time and the objective function is to minimize the variance between the requested and scheduled time of pickup and delivery. In the first level, after a customer web-request, the heuristic returns an answer about the possibility to insert the request into the accepted reservations, and therefore in a vehicle schedule, or reject the request. In the second level, during the time elapsed between a request and the following, and after a reshuffling of the order of the incoming accepted requests, the same heuristic works for the whole set of accepted requests, trying to optimize the solution. We intensively tested the algorithm with a requests-generating software that has allowed us to show the competitive advantage of this web-based architecture

Optimal Parking Planning for Shared Autonomous Vehicles

Parking is a crucial element of the driving experience in urban transportation systems. Especially in the coming era of Shared Autonomous Vehicles (SAVs), parking operations in urban transportation networks will inevitably change. Parking stations will serve as storage places for unused vehicles and depots that control the level-of-service of SAVs. This study presents an Analytical Parking Planning Model (APPM) for the SAV environment to provide broader insights into parking planning decisions. Two specific planning scenarios are considered for the APPM: (i) Single-zone APPM (S-APPM), which considers the target area as a single homogeneous zone, and (ii) Two-zone APPM (T-APPM), which considers the target area as two different zones, such as city center and suburban area. S-APPM offers a closed-form solution to find the optimal density of parking stations and parking spaces and the optimal number of SAV fleets, which is beneficial for understanding the explicit relationship between planning decisions and the given environments, including demand density and cost factors. In addition, to incorporate different macroscopic characteristics across two zones, T-APPM accounts for inter- and intra-zonal passenger trips and the relocation of vehicles. We conduct a case study to demonstrate the proposed method with the actual data collected in Seoul Metropolitan Area, South Korea. Sensitivity analyses with respect to cost factors are performed to provide decision-makers with further insights. Also, we find that the optimal densities of parking stations and spaces in the target area are much lower than the current situations.Comment: 27 pages, 9 figures, 9 table

The impact of pricing and service area design on the modal shift towards demand responsive transit

In this study, an agent-based transport simulation is used to look into different design concepts for demand responsive transit (DRT). In different simulation experiments for a real-world case study of the Greater Berlin area, the DRT service area is either set to the inner-city center area or the entire city area, and the DRT pricing scheme is varied. The existing simulation framework is extended by an iterative approximation approach to improve the computational performance. The simulation results show that a small service area and too low prices may result in an unwanted mode shift effect from walk and bicycle to DRT. For higher fares, the unwanted mode shift effect is reduced and fewer users switch from bicycle and walk to DRT. The simulation experiments also show that a larger DRT service area contributes towards an increase of the desired mode shift effect from car to DRT.BMVI, 16AVF2160, AVÖV - Räumlich und zeitlich hochauflösende Evaluation und Optimierung automatisierter und vernetzter Bedienkonzepte im öffentlichen Verkeh

Demand-Responsive Shared Transportation: A Self-Interested Proposal

[EN] With the world population highly increasing, efficient methods of transportation are more necessary than ever. On the other hand, the sharing economy must be explored and applied where possible, aiming to palliate the effects of human development on the environment. In this paper we explore demand-responsive shared transportation as a system with the potential to serve its users' displacement needs while being less polluting. In contrast with previous works, we focus on a distributed proposal that allows each vehicle to retain its private information. Our work describes a partially dynamic system in which the vehicles are self-interested: they decide which users to serve according to the benefit it reports them. With our modelling, the system can be adapted to mobility platforms of autonomous drivers and even simulate the competition among different companies.This work is partially supported by grant RTI2018-095390-B-C31 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe". Pasqual Marti is supported by grant ACIF/2021/259 funded by the "Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana".Martí, P.; Jordán, J.; De La Prieta, F.; Billhardt, H.; Julian, V. (2022). Demand-Responsive Shared Transportation: A Self-Interested Proposal. Electronics. 11(1):1-14. https://doi.org/10.3390/electronics1101007811411