PRE2018 4 Group6

Group members

Name	Student ID	Department
Tom Vredenbregt	1221775	Applied Physics
Jur Kappé	1252895	Applied Physics
Jannes van Poppelen	1238120	Applied Physics
Yannick de Jong	1250663	Applied Physics
Thom Smits	1227659	Applied Physics

Organizational Matters

Minutes

Throughout the course the group will have official meetings. A summary (minute) of what has been said/achieved in every meeting will be made. These summaries can be found here.

Agendas

Like the minutes, the agendas made by the chair will be published. The agendas for the meetings can be found here.

Problem statement

The implementation of smart traffic lights in big cities reduces the travel time substantially. Whilst this makes the traffic flow more efficiently in the cities, a different solution has to be found to improve the traffic flow on highways. The ever increasing amount of traffic jams during the rush hours in the Netherlands(https://www.anwb.nl/verkeer/nieuws/nederland/2019/april/lichte-filegroei-in-eerste-kwartaal) is a call to arms to find solutions to this time consuming phenomenon known as traffic congestion. One of these solutions is the routing of navigation systems that changes based on the activity on the highways. Traffic jams would be avoided by rerouting the navigation to go around the traffic jams, should it be the faster alternative. Of course this solution is one of many, and it will contribute minimally on its own to the general problem. A different potential solution could be to simply add more lanes to each highway. Not only would this be very excessive outside of the rush hours, it would not be very cost, or time efficient. For this reason we propose to look for a solution in which we would optimize and change the current highways to a state in which it can in fact improve traffic flow in general. This solution we are proposing are the so called "smart roads". These roads will adapt dynamically to the activity of both lanes of the highway, as will be clarified visually later on. During morning rush hours, lanes highways towards big cities are usually very busy, whereas the lanes on the opposite side aren't that busy at all. Being able to distribute the lanes such that both sides would have a sufficient amount of lanes would benefit the traffic flow. The opposite directions would apply for evening peak hours. Solving this issue would not only improve the flow of traffic on highways during rush hours, but also outside of them. Coincidentally, this would also substantially reduce the emission that cars produce in traffic jams by continuously stopping and driving off. Central to this problem would be to research the question: Is the introduction of "smart roads" on the Dutch highways a viable solution to traffic congestion on Dutch highways?

State-of-the-Art (Literature Study)

Evaluation of a movable barrier concrete system

• This report reviews the cost, safety, and effectiveness of a movable barrier system used on highways. This system is not used for our specific use case (creating a flexible and reconfigurable road) but is used for road maintenance. The report analyses specific traffic accidents involving this system, as well as the advantages and disadvantages of the system overall. Eventually, the report states that the system performs adequately in the use case as described in the report.

Moveable Barrier Solves Work-Zone Dilemma

• This article describes a movable barrier system used temporarily during the renovation of a bridge. In this instance three lanes are used, where the middle lane is used based on traffic needs. It also highlights the advantages and disadvantages of this and other types of systems.

State-of-the-Art (Literature Study)

1. Bielli, M., Ambrosino, G., & Boero, M. (1994). Artificial Intelligence Application in Traffic. Retrieved 4 mei 2019, van https://books.google.nl/books?hl=en&lr=&id=3cEEdaHrykAC&oi=fnd&pg=PA3&dq=artificial+intelligence+in+traffic&ots=0qYOXTFD1B&sig=akDTYf3nqHL0U26K8-rPSvZnP6k&redir_esc=y#v=onepage&q=artificial%20intelligence%20in%20traffic&f=false

2. Li, L., Lv, Y., & Wang, F. (2016a, 10 juli). Traffic signal timing via deep reinforcement learning - IEEE Journals & Magazine. Retrieved 4 mei 2019, from https://ieeexplore.ieee.org/abstract/document/7508798

3. Contreras, S., Kachroo, P., & Agarwal, S. (2016, 1 maart). Observability and Sensor Placement Problem on Highway Segments: A Traffic Dynamics-Based Approach - IEEE Journals & Magazine. Retrieved 4 mei 2019, from https://ieeexplore.ieee.org/abstract/document/7317783

4. Satyanarayana, M. (1970, 1 januari). Intelligent Traffic System to Reduce Waiting Time at Traffic Signals f. Retrieved 4 mei 2019, from https://link.springer.com/chapter/10.1007/978-981-10-7868-2_28

5. NDW (z.d.). Documenten - Nationale Databank Wegverkeersgegevens. Retrieved 4 mei 2019, from https://www.ndw.nu/documenten/nl/

6. NDW, C. B. S. (2018, 1 maart). CBS Statline. Retrieved 4 mei 2019, from https://opendata.cbs.nl/statline/

7. Walraven, E. (2016, 1 juni). Traffic flow optimization: A reinforcement learning approach. Retrieved 4 mei 2019, from https://www.sciencedirect.com/science/article/abs/pii/S0952197616000038

Approach

Producing an actual prototype for a smart road in 8 weeks seems rather unlikely. Instead the problem will be tackled by a literature analysis, as well as a simulation of a smart road using a mathematically developed model. The final product for the project would therefore be a combination of a report about the literature analysis, together with the analysed simulation of the smart road.

The literature analysis will include the USE aspects of the selected problem and an analysis of the present state of smart roads. In-depth analyses for user, society and enterprise stakeholders will be made. Since smart roads are designed to accommodate the users needs, the focus will be on the user, its needs, and how to satisfy them.

The simulation of the smart road will be constructed using a mathematical model. Central in this mathematical model is a constructed norm which determines the orientation of the smart road. This norm is based on lane occupation on each side of the highway, as well as the time of the day to account for the rush hours. Whenever this norm is exceeded, the smart road will change in such a way that this norm is no longer exceeded. There is a couple of things that need to be accounted for in the simulation. One of which is the possibility of accidentally ending up on the wrong side of the highway as a result of the smart road adapting to its surroundings.