| Title |
Semi-synthetic data and testbed for long-distance E-vehicle routing / |
| Authors |
Barauskas, Andrius ; Brilingaitė, Agnė ; Bukauskas, Linas ; Čeikutė, Vaida ; Čivilis, Alminas ; Šaltenis, Simonas |
| DOI |
10.1007/978-3-030-85082-1_6 |
| ISBN |
9783030850814 |
| eISBN |
9783030850821 |
| Full Text |
|
| Is Part of |
New trends in database and information systems - {ADBIS} 2021 short papers, doctoral consortium and workshops: DOING, SIMPDA, MADEISD, MegaData, CAoNS, Tartu, Estonia, August 24-26, 2021: proceedings / Bellatreche L. et al. (eds).. Cham : Springer International Publishing, 2021. p. 61-71.. ISBN 9783030850814. eISBN 9783030850821 |
| Keywords [eng] |
semi-synthetic data ; data generation ; testbed ; electric vehicle long-distance EV routing ; time-dependent road network |
| Abstract [eng] |
Electric and autonomous mobility will increasingly rely on advanced route planning algorithms. Robust testing of these algorithms is dependent on the availability of large realistic data sets. Such data sets should capture realistic time-varying traffic patterns and corresponding travel-time and energy-use predictions. Ideally, time-varying availability of charging infrastructure and vehicle-specific charging-power curves should be included in the data to support advanced planning. We contribute with a modular testbed architecture including a semi-synthetic data generator that uses a state-of-the-art traffic simulator, real traffic distribution patterns, EV-specific data, and elevation data to generate time-dependent travel-time and energy-use weights in a road-network graph. The experimental study demonstrates that the testbed can reproduce travel-time and energy-use patterns for long-distance trips similar to commercially available services. |
| Published |
Cham : Springer International Publishing, 2021 |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2021 |
| CC license |
|
