arXiv subjects

David Fiedler

Publications and source records attributed to David Fiedler.

6 recordsLinked to original sources

arXiv2024

Online Dynamic Pricing for Electric Vehicle Charging Stations with Reservations

This paper introduces a novel model for online dynamic pricing of electric vehicle charging services that integrates reservation, parking, and charging into a comprehensive bundle priced as a whole. Our approach focuses on the individual high-demand, fast-charging location, employing a Poisson process as a model of charging reservation arrivals, and develops an online dynamic pricing strategy optimized through a Markov Decision Process (MDP). A key contribution is the novel analysis of discretization error introduced when incorporating the continuous-time Poisson process into the discrete MDP framework. The MDP model's feasibility is demonstrated with a heuristic dynamic pricing method based on Monte-Carlo tree search, offering a viable path for real-world applications.

cs.MA↗

arXiv2024

Optimal Chaining of Vehicle Plans with Time Windows

For solving problems from the domain of Mobility-on-Demand (MoD), we often need to connect vehicle plans into plans spanning longer time, a process we call plan chaining. As we show in this work, chaining of the plans can be used to reduce the size of MoD providers' fleet (fleet-sizing problem) but also to reduce the total driven distance by providing high-quality vehicle dispatching solutions in MoD systems. Recently, a solution that uses this principle has been proposed to solve the fleet-sizing problem. The method does not consider the time flexibility of the plans. Instead, plans are fixed in time and cannot be delayed. However, time flexibility is an essential property of all vehicle problems with time windows. This work presents a new plan chaining formulation that considers delays as allowed by the time windows and a solution method for solving it. Moreover, we prove that the proposed plan chaining method is optimal, and we analyze its complexity. Finally, we list some practical applications and perform a demonstration for one of them: a new heuristic vehicle dispatching method for solving the static dial-a-ride problem. The demonstration results show that our proposed method provides a better solution than the two heuristic baselines for the majority of instances that cannot be solved optimally. At the same time, our method does not have the largest computational time requirements compared to the baselines. Therefore, we conclude that the proposed optimal chaining method provides not only theoretically sound results but is also practically applicable.

math.OC↗

arXiv2023

Large-scale Ridesharing DARP Instances Based on Real Travel Demand

Accurately predicting the real-life performance of algorithms solving the Dial-a-Ride Problem (DARP) in the context of Mobility on Demand (MoD) systems with ridesharing requires evaluating them on representative instances. However, the benchmarking of state-of-the-art DARP solution methods has been limited to small, artificial instances or outdated non-public instances, hindering direct comparisons. With the rise of large MoD systems and the availability of open travel demand datasets for many US cities, there is now an opportunity to evaluate these algorithms on standardized, realistic, and representative instances. Despite the significant challenges involved in processing obfuscated and diverse datasets, we have developed a methodology using which we have created a comprehensive set of large-scale demand instances based on real-world data. These instances cover diverse use cases, one of which is demonstrated in an evaluation of two established DARP methods: the insertion heuristic and optimal vehicle-group assignment method. We publish the full results of both methods in a standardized format. The results show significant differences between areas in all measured quantities, emphasizing the importance of evaluating methods across different cities.

cs.AI↗

arXiv2023

Large-scale Online Ridesharing: The Effect of Assignment Optimality on System Performance

Mobility-on-demand (MoD) systems consist of a fleet of shared vehicles that can be hailed for one-way point-to-point trips. The total distance driven by the vehicles and the fleet size can be reduced by employing ridesharing, i.e., by assigning multiple passengers to one vehicle. However, finding the optimal passenger-vehicle assignment in an MoD system is a hard combinatorial problem. In this work, we demonstrate how the VGA method, a recently proposed systematic method for ridesharing, can be used to compute the optimal passenger-vehicle assignments and corresponding vehicle routes in a massive-scale MoD system. In contrast to existing works, we solve all passenger-vehicle assignment problems to optimality, regularly dealing with instances containing thousands of vehicles and passengers. Moreover, to examine the impact of using optimal ridesharing assignments, we compare the performance of an MoD system that uses optimal assignments against an MoD system that uses assignments computed using insertion heuristic and against an MoD system that uses no ridesharing. We found that the system that uses optimal ridesharing assignments subject to the maximum travel delay of 4 minutes reduces the vehicle distance driven by 57 % compared to an MoD system without ridesharing. Furthermore, we found that the optimal assignments result in a 20 % reduction in vehicle distance driven and 5 % lower average passenger travel delay compared to a system that uses insertion heuristic.

math.OC↗

arXiv2019

Map Matching Algorithm for Large-scale Datasets

GPS receivers embedded in cell phones and connected vehicles generate a series of location measurements that can be used for various analytical purposes. A common pre-processing step of this data is the so-called map matching. The goal of map matching is to infer the trajectory that the device followed in a road network from a potentially sparse series of noisy location measurements. Although accurate and robust map matching algorithms based on probabilistic models exist, they are computationally heavy and thus impractical for processing of large datasets. In this paper, we present a scalable map-matching algorithm based on Dijkstra shortest path method, that is both accurate and applicable to large datasets. Our experiments on a publicly-available dataset showed that the proposed method achieves accuracy that is comparable to that of the existing map matching methods using only a fraction of computational resources. In result, our algorithm can be used to efficiently process large datasets of noisy and potentially sparse location data that would be unexploitable using existing techniques due to their high computational requirements.

cs.NI↗

arXiv2017

Impact of Mobility-on-Demand on Traffic Congestion: Simulation-based Study

The increasing use of private vehicles for transportation in cities results in a growing demand for parking space and road network capacity. In many densely populated urban areas, however, the capacity of existing infrastructure is insufficient and extremely difficult to expand. Mobility-on-demand systems have been proposed as a remedy to the problem of limited parking space because they are able to satisfy the existing transportation demand with fewer shared vehicles and consequently require less parking space. Yet, the impact of large-scale vehicle sharing on traffic patterns is not well understood. In this work, we perform a simulation-based analysis of consequences of a hypothetical deployment of a large-scale station-based mobility-on-demand system in Prague and measure the traffic intensity generated by the system and its effects on the formation of congestion. We find that such a mobility-on-demand system would lead to significantly increased total driven distance and it would also increase levels of congestion due to extra trips without passengers. In fact, 38% kilometers traveled in such an MoD system would be driven empty.

cs.MA↗