Abstract—The main current method for obtaining traffic volume data in surveys involves survey personnel using counters. However, at least two persons are required for each lane to obtain a high level of accuracy, because the counting is done in real time. As a result, work cost is high and reducing cost while maintaining the same level of accuracy is a problem. As a cost reduction measure that addresses that problem, the use of crowdsourcing for measuring traffic is proposed and the usefulness of that measure is examined. Specifically, an evaluation of accuracy when data is acquired by an unspecified large number of workers shows that the proposed method can reduce survey cost while maintaining the same accuracy as the existing method by eliminating unproductive time. The results demonstrate the feasibility of applying crowdsourcing, which uses human processing capability as opposed to big data processing.
Index Terms—Crowd sourcing, Amazon mechanical turk, traffic survey, worker processing.
Takahiro Koita is with Department of Information Systems Design, Doshisha University, Kyoto, Japan (e-mail: tkoita@mail.doshisha.ac.jp). Shota Suzuki is with Hitachi Systems, Tokyo, Japan.
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Cite:Takahiro Koita and Shota Suzuki, "Crowdsourcing for Traffic Survey Work on Amazon Mechanical Turk," International Journal of Computer Theory and Engineering vol. 11, no. 4, pp. 61-65, 2019.
