Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2016 |
| Pages | 19-24 |
| Number of pages | 6 |
| ISBN (electronic) | 9781450345774 |
| Publication status | Published - 31 Oct 2016 |
| Event | 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2016 - Burlingame, United States Duration: 31 Oct 2016 → … |
Abstract
Finding a parking space is a crucial mobility problem, which could be mitigated by dynamic maps of parking availability. The creation of these maps requires current information on the state of the parking stalls, which could be obtained by (I) instrumenting the road infrastructure with sensors, (II) using probe vehicles, or (III) using mobile apps. In this paper, we investigate the potential predictive performances of a random forest binary classifier, comparing these three data collection strategies. As for the dataset, we used real infrastructure measurements in San Francisco for solution I. We simulated the crowdsourcing solutions II and III by downsampling that dataset, based on different assumptions. Evaluations show that the instrumented solution is clearly superior over the two crowdsourcing strategies, but with remarkably small differences to the probe vehicle scenario. On the other hand, a mobile app would require a very high penetration rate in order to be used for meaningful predictions.
Keywords
- Spatial information and society, Spatio-temporal data analysis, Traffic telematics, Transportation
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Social Sciences(all)
- Transportation
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Computational Theory and Mathematics
- Computer Science(all)
- Computer Networks and Communications
- Social Sciences(all)
- Geography, Planning and Development
- Computer Science(all)
- Computer Science Applications
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Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2016. 2016. p. 19-24.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - What are the potentialities of crowdsourcing for dynamic maps of on-street parking spaces?
AU - Bock, Fabian
AU - Di Martino, Sergio
AU - Sester, Monika
PY - 2016/10/31
Y1 - 2016/10/31
N2 - Finding a parking space is a crucial mobility problem, which could be mitigated by dynamic maps of parking availability. The creation of these maps requires current information on the state of the parking stalls, which could be obtained by (I) instrumenting the road infrastructure with sensors, (II) using probe vehicles, or (III) using mobile apps. In this paper, we investigate the potential predictive performances of a random forest binary classifier, comparing these three data collection strategies. As for the dataset, we used real infrastructure measurements in San Francisco for solution I. We simulated the crowdsourcing solutions II and III by downsampling that dataset, based on different assumptions. Evaluations show that the instrumented solution is clearly superior over the two crowdsourcing strategies, but with remarkably small differences to the probe vehicle scenario. On the other hand, a mobile app would require a very high penetration rate in order to be used for meaningful predictions.
AB - Finding a parking space is a crucial mobility problem, which could be mitigated by dynamic maps of parking availability. The creation of these maps requires current information on the state of the parking stalls, which could be obtained by (I) instrumenting the road infrastructure with sensors, (II) using probe vehicles, or (III) using mobile apps. In this paper, we investigate the potential predictive performances of a random forest binary classifier, comparing these three data collection strategies. As for the dataset, we used real infrastructure measurements in San Francisco for solution I. We simulated the crowdsourcing solutions II and III by downsampling that dataset, based on different assumptions. Evaluations show that the instrumented solution is clearly superior over the two crowdsourcing strategies, but with remarkably small differences to the probe vehicle scenario. On the other hand, a mobile app would require a very high penetration rate in order to be used for meaningful predictions.
KW - Spatial information and society
KW - Spatio-temporal data analysis
KW - Traffic telematics
KW - Transportation
UR - http://www.scopus.com/inward/record.url?scp=85002152442&partnerID=8YFLogxK
U2 - 10.1145/3003965.3003973
DO - 10.1145/3003965.3003973
M3 - Conference contribution
AN - SCOPUS:85002152442
SP - 19
EP - 24
BT - Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2016
T2 - 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2016
Y2 - 31 October 2016
ER -