Large-scale Ridesharing DARP Instances Based on Real Travel Demand

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00382177" target="_blank" >RIV/68407700:21230/23:00382177 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ITSC57777.2023.10422146" target="_blank" >https://doi.org/10.1109/ITSC57777.2023.10422146</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ITSC57777.2023.10422146" target="_blank" >10.1109/ITSC57777.2023.10422146</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Large-scale Ridesharing DARP Instances Based on Real Travel Demand

Popis výsledku v původním jazyce

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However, the benchmarking of state-of-the-art DARP solution methods has been limited to small, artificial instances or outdated non-public instances, hindering direct comparisons.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 repre-sentative 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 data3. These instances cover diverse use cases, one of which is demon-strated 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.

Název v anglickém jazyce

Large-scale Ridesharing DARP Instances Based on Real Travel Demand

Popis výsledku anglicky

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However, the benchmarking of state-of-the-art DARP solution methods has been limited to small, artificial instances or outdated non-public instances, hindering direct comparisons.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 repre-sentative 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 data3. These instances cover diverse use cases, one of which is demon-strated 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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 26th IEEE International Conference on Intelligent Transportation Systems

ISBN

979-8-3503-9946-2

ISSN

2153-0009

e-ISSN

—

Počet stran výsledku

8

Strana od-do

2750-2757

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Brighton

Místo konání akce

Bilbao

Datum konání akce

24. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001178996702113