Oulu Traffic Pilot is the enabler for collecting rich data sets and developing and testing advanced ITS services in the wild.

We aim for better situation awareness. For this, diverse data need to be collected and fulsed together. Therefore, many companies and reserch organizations joined their forces to develop methods, solutions and services to tackle the challenges for situation awareness.

Digile Ltd,Data to Intelligence,Traffic ecosystem

EC-Tools

OULU

M. Hippi, J. Miettinen, J. Jämsä and J. Pahkala: Braking distance application developed on Finnish D2I project. 17th International Road Weather Conference SIRWEC 2014. 30 January - 1 February 2014, la Massana, Andorra.

Selected use cases:Braking Distance system informs drivers if the safe following distance is too short. This dynamic system takes into account the velocity of the car, the distance between cars and the slipperiness of the road surface. The service relies on fast data transmission and processing and intelligent support for decision making.

Driving coach is an adaptive recommendation system for more skilled driving, speci�cally for avoiding aggressive driving, improving trip planning, and driving ina fuel-e�cient manner. The service is based on:

1. Fusion of on-board information and real-time information from thirdparty services.

2. Identi�cation of personal driving factors a�ecting the fuel use incertain situations.

3. Adaptation of the system’s decision-making with respect to a driver’sprogress and responses to recommendations.

E. Gilman, A. Keskinarkaus, S. Tamminen, S. Pirttikangas, J. Röning, J. Riekki (2015): Personalised Assistance for Fuel-Efficient Driving. Transportation Reserach Part C: Emerging Technologies, Volime 58, Part D, pp. 681-705, http://dx-.doi.org/10.1016/j.trc.2015.02.007, Open Access.

Driv ing Coach

8:08

DRIVINGROUTE

IoT : Edges of the Network. Solutions are needed to gather, store, and operate with high amount of diverse data availale in ta�c domain in e�cient way.

For instance, distributed reasoning could facilitate quicker detection of events and situations from real time data.

Another approach to optimize data collection and processing is to utilize mobile agents, which autonomously migrate to the data source, and perform the job required, hence distributes the communication cost into the participating devices.

Contact details:Dr Susanna Pirttikangas, susanna.pirttikangas@ee.oulu.fiProf. Jukka Riekki, jukka.riekki@ee.oulu.fi

A. Maarala, X. Su, and J. Riekki, “Semantic Data Provisioning and Reasoning for the Internet of Things,” in International Conference on the Internet of Things, 2014, pp. 13–18.Leppänen, T., Álvarez Lacasia, J., Tobe, Y., Sezaki, K. and Riekki, J. “Mobile Crowdsensing with Mobile Agents,” Autono-mous Agents and Multi-agent Systems, 2015, DOI: 10.1007/s10458-015-9311-7. [In Press]Leppänen, T., Liu, M., Harjula, E., Ramalingam, A., Ylioja, J., Närhi, P., Riekki, J. and Ojala, T. “Mobile Agents for Integration of Internet of Things and Wireless Sensor Networks,” In: IEEE SMC 2013, pp. 14-21.