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, but it also would not be very cost- nor 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 is the so-called "smart road". 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 January). 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 June). Traffic flow optimization: A reinforcement learning approach. Retrieved 4 mei 2019, from https://www.sciencedirect.com/science/article/abs/pii/S0952197616000038

8. Nguyen T. (2018, 16-19 Sept.). Ahead of the Curb: Smart Roads. Retrieved 5 May 2019, from https://ieeexplore.ieee.org/document/8656667

9. El-Wakeel A., Li J., Rahman M. (2017, 14-16 Nov). Monitoring road surface anomalies towards dynamic road mapping for future smart cities. Retrieved 5 May 2019

10. Arbi Z., Belkahla O., Sbai M.K. (2017, 17-19 Feb). A multi-agent system for monitoring and regulating road traffic in a smart city. Retrieved 5 May 2019, from https://ieeexplore.ieee.org/document/8071843

11. Wang C., David B., Chalon R. (2014, 1-3 May). Dynamic road lane management: A smart city application. Retrieved 5 May 2019, from https://ieeexplore.ieee.org/document/6864085

12. Hausknecht M.m, Au T., Stone P., Fajardo D., Waller T. (2011, 5-7 Oct.). Dynamic lane reversal in traffic management. Retrieved 5 May 2019, from https://ieeexplore.ieee.org/document/6082932

13. Amditis A., Bimpas M., Thomaidis G., Netto M. (2010, 8 July). A Situation-Adaptive Lane-Keeping Support System: Overview of the SAFELANE Approach. Retrieved 5 May 2019, form https://ieeexplore.ieee.org/document/5504223

14. Chen Y., Krumm J. (n.d.). Probabilistic Modeling of Traffic Lanes from GPS Traces. Retrieved 5 May 2019

15. Yi R. (2016, 23 Jan). A Probability-Based Model of Traffic Flow. Retrieved 5 May 2019

16. Calvert S.C., Taale H., Snelder M., Hoogendoorn S.P., (2012, June). Probability in traffic: a challenge for modelling. Retrieved 5 May 2019

17. Caprani C., (2005, 10 Jan). Probalistic analysis of highway bridge traffic loading. Retrieved 5 May 2019

18. Li J., Gong S., Xiang T. (n.d.). Global Behaviour Inference using Probabilistic Latent Semantic Analysis. Retrieved 5 May 2019

USE

In the case of smart roads, the following stakeholders in the USE frame can be defined. The stakeholders in the case of smart roads are:

Users

• Commuters (people who use the roads to go from their residence to their work)
• Leisure traffic (people who use the road for travels to the vacation or a day out)
• Public transport (as example buses and taxis)
• Residents living next to the roads (requirement: good traffic flow for a minimum noise problem)

Societity

• Government (the Dutch government is responsible for the maintenance and construction of the roads)
• Rijkswaterstaat
• Environment action groups

Enterprise

• Transport (transport of goods with use of lorries or other heavy traffic)
• Rijkswaterstaat (the active maintenance of the roads)

Requirements

The defined stakeholders and users in the list above have certain requirements in the case of smart roads. The requirements for each stakeholder can be found in the list below.

USERS

Commuters -> Commuters need well-maintained roads with enough lanes such they roads won't get jammed at rush-hour. Commuters use the roads to get to work. In the case that the roads get jammed, the commuters will be late on work or have to depart much earlier. But options are temporary, so the requirements for commuters are well accessible well-maintained roads which won't get jammed at rush-hour (between 07:00-09:00 and 17:00-19:00).

Leisure traffic -> Leisure traffic is traffic which uses the roads for pleasure purposes. The requirements for leisure traffic is the same as the requirements for commuters. The only difference is when the roads are needed. For leisure traffic, the roads need to be accessible around the weekends and vacation days.

Public transport Again the requirements are the same as the requirements mentioned above.

To conclude the requirements for the USER stakeholders are in general the same. The requirements for the USER are well accessible and well-maintained roads.

Societity

Goverment -> Because roads are constructed from taxes money, is it key to keep it as cost efficient as possible. Another requirement for the government is that roads are well-useable. (ik heb echt geen idee)

Environment action groups -> These groups are fighting for less emission which is better for the milieu. This can be obtained by fewer cars on the (which isn't likely to happen) or better traffic flow such that there is less emission.

Enterprise

Transport traffic -> The requirements for transport traffic are well-maintained and accessible roads. An important note is that an important requirement for transport traffic is that it won't bother other road users. If transport traffic bothers road users, then the requirements of the above-mentioned users become in danger.

Rijkswaterstaat -> The requirements of for the maintenance of the road (rijkswaterstaat is responsible for the maintenance) is that the smart won't be too expansive to construct and not too expansive to maintain. So it is a requirement to keep the smart roads as simple as possible. If the smart road is simple, then maintance and the construction is in the scope of the construction workers.

Approach

Producing an actual prototype for a smart road in 8 weeks seems rather unlikely. Instead, the problem will be tackled by 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 analyzed 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.

Simulation

The simulation will be made using the Unity3D software package. This software package will allow for relatively easy 3D simulations using ready-made models. It also includes physics simulations and scriptable interfaces.[1]