TY - JOUR
T1 - Comparison of SUMO's vehicular demand generators in vehicular communications via graph-theory metrics
AU - Urquiza-Aguiar, Luis
AU - Coloma-Gómez, William
AU - Barbecho Bautista, Pablo
AU - Calderón-Hinojosa, Xavier
N1 - Publisher Copyright:
© 2020
PY - 2020/9/1
Y1 - 2020/9/1
N2 - Simulations are the first approach to test ITS applications due to the difficulties involved in deploying real test scenarios. In addition to the use of detailed network simulators, realistic vehicular movements are key to guarantee reliable results. SUMO is one of the tools most used to generate synthetic traces of vehicles for this objective. In this paper, a qualitative review of the five traffic demand generation tools of SUMO: DUArouter, JTRrouter, DFrouter, OD2trips, and MArouter, is presented. We include the advantages and limitations of each of these tools to simulate vehicles’ mobility in real scenarios. Moreover, we compare the vehicular traces obtained with these tools using real input data and a carefully debugged map of the financial district of Quito by using well-known graph metrics for network analysis. We found that despite that the tools used the same input, the value of some metrics of the ad-hoc communication network can differ up to one magnitude order.
AB - Simulations are the first approach to test ITS applications due to the difficulties involved in deploying real test scenarios. In addition to the use of detailed network simulators, realistic vehicular movements are key to guarantee reliable results. SUMO is one of the tools most used to generate synthetic traces of vehicles for this objective. In this paper, a qualitative review of the five traffic demand generation tools of SUMO: DUArouter, JTRrouter, DFrouter, OD2trips, and MArouter, is presented. We include the advantages and limitations of each of these tools to simulate vehicles’ mobility in real scenarios. Moreover, we compare the vehicular traces obtained with these tools using real input data and a carefully debugged map of the financial district of Quito by using well-known graph metrics for network analysis. We found that despite that the tools used the same input, the value of some metrics of the ad-hoc communication network can differ up to one magnitude order.
KW - Intelligent transportation systems
KW - Network metrics
KW - Realistic vehicular traces
KW - SUMO
KW - VANET
UR - https://www.scopus.com/pages/publications/85087337698
U2 - 10.1016/j.adhoc.2020.102217
DO - 10.1016/j.adhoc.2020.102217
M3 - Artículo
AN - SCOPUS:85087337698
SN - 1570-8705
VL - 106
JO - Ad Hoc Networks
JF - Ad Hoc Networks
M1 - 102217
ER -