Large-scale Ridesharing DARP Instances Based on Real Travel Demand

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Large-scale Ridesharing DARP Instances Based on Real Travel Demand
Original language description
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However, the benchmarking of state-of-the-art DARP solution methods has been limited to small, artificial instances or outdated non-public instances, hindering direct comparisons.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.
Czech name
—
Czech description
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Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 26th IEEE International Conference on Intelligent Transportation Systems
ISBN
979-8-3503-9946-2
ISSN
2153-0009
e-ISSN
—
Number of pages
8
Pages from-to
2750-2757
Publisher name
Institute of Electrical and Electronics Engineers
Place of publication
Brighton
Event location
Bilbao
Event date
Sep 24, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001178996702113

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