Trajectory Estimation From Sparse Cellular Network Data Based on the Historical Vehicular Data

Mardešić, Nikola; Erdelić, Tomislav; Tišljarić, Leo; Carić, Tonči

Pregled bibliografske jedinice broj: 1226890

Trajectory Estimation From Sparse Cellular Network Data Based on the Historical Vehicular Data

Mardešić, Nikola; Erdelić, Tomislav; Tišljarić, Leo; Carić, Tonči

Trajectory Estimation From Sparse Cellular Network Data Based on the Historical Vehicular Data // International Scientific Conference "The Science and Development of Transport - Znanost i razvitak prometa - ZIRP 2022" / Petrović, Marjana ; Dovbischuk, Irina ; Cunha, André Luiz (ur.).
Šibenik, Hrvatska: Elsevier, 2022. str. 166-173 doi:10.1016/j.trpro.2022.09.020 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

CROSBI ID: 1226890 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Trajectory Estimation From Sparse Cellular Network Data Based on the Historical Vehicular Data

Autori
Mardešić, Nikola ; Erdelić, Tomislav ; Tišljarić, Leo ; Carić, Tonči

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
International Scientific Conference "The Science and Development of Transport - Znanost i razvitak prometa - ZIRP 2022" / Petrović, Marjana ; Dovbischuk, Irina ; Cunha, André Luiz - : Elsevier, 2022, 166-173

Skup
International Scientific Conference “The Science and Development of Transport - Znanost i razvitak prometa” (ZIRP 2022)

Mjesto i datum
Šibenik, Hrvatska, 28.09.2022. - 30.09.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
FCD ; big data ; traffic data analysis ; trajectory estimation

Sažetak
By coupling data-mining techniques with historical cellular and vehicular data, it is possible to find a certain spatiotemporal logic in the observed data. The primary motivation for combining multiple data sources derives from the fact that origin-destination matrices, extracted from cellular data sets, represent only the route's start and end-point without the information about the complete trajectory. This paper proposes a method to estimate sparse cellular data trajectories by reconstructing possible paths from the historical vehicular data. The possible routes are generated by solving the shortest-path problems for given origin- destination pair. A spatiotemporal similarity value is computed to evaluate the paths relative to the ground-truth origin-destination pair. In the end, the path with the highest similarity value is selected. Three criteria for the computation of the spatiotemporal similarity are used: length-based, modified length-based and time-based. The results show the application of proposed methods on the collected historical data, with average percentage similarity ranging from 39.85% to 56.11% (depending on the transport mode and criteria used) with modified-length criteria producing the best results - in some cases producing a trajectory with 98% similarity to the actual cellular trajectory.

Izvorni jezik
Engleski

Znanstvena područja
Tehnologija prometa i transport

Napomena
Transportation Research Procedia Volume 64, Issue C

POVEZANOST RADA

Projekti:

Ustanove:
Fakultet prometnih znanosti, Zagreb

Profili:

Tomislav Erdelić (autor)