On Providing Incentives to Collect Road Traffic Information


The prediction accuracy of road traffic prediction systems are based on sufficient and validate input data. Comparing with fixed installation of road side sensors, Using GPS probe vehicles incorporating with participatory sensing to collect traffic data is a more scalable and efficient method. On the other hand, when the device owner are participant, how to providing an incentive mechanism to promote users’ contribution becomes an important issue. In this paper, we propose a new incentive mechanism for participatory sensing based road traffic prediction system. Users could earn virtual credits by uploading their data, and they need to pay credits when they want to know the future traffic condition by accessing our prediction service. To designthe reasonable price of users’ data, we define“improved accuracy” which represent the contribution of the data, and use it as the price to encourage people to collect more useful data. We use a detailed vehicular simulator to evaluate our incentive mechanism. In the first experiment, we evaluate the fairness of our mechanism. We proved that the proposed mechanism could distinguish the quality of data. Data which can’t reflect the real road speed will have lower price. In the second experiment, we analyze the relationship between the number of nodes, variation of speed, and prediction accuracy. Finally, we let the nodes follow the recent price of data on roads to decide their route. The experiment result shows that the proposed incentive mechanism could improve the prediction accuracy and reduce traffic congestions.


Kun-chan Lan, and Han-yi Wang, "On Providing Incentives to Collect Road Traffic Information," International Wireless Communications & Mobile Computing Conference (IWCMC'13), Cagliari, Sardinia, Italy, 1-5 July 2013


@ARTICLE{lan2013: ,
AUTHOR = {Kun-chan Lan, and Han-yi Wang},
TITLE = {On Providing Incentives to Collect Road Traffic Information},
BOOKTITLE = {International Wireless Communications & Mobile Computing Conference (IWCMC'13)},
MONTH = {July},
YEAR = {2013}


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