# PRE2016 3 Groep13

(Difference between revisions)
 Revision as of 14:21, 5 March 2017 (view source)S156757 (Talk | contribs)← Older edit Revision as of 14:58, 5 March 2017 (view source)S156757 (Talk | contribs) (→A First Attempt)Newer edit → Line 114: Line 114: The program optimizes this matrix by minimizing the costs. Therefore, if there are sufficiently many rounds, no cars will be left behind, because if the number of rounds goes to infinity the cost of leaving cars behind will go to infinity as well. The program optimizes this matrix by minimizing the costs. Therefore, if there are sufficiently many rounds, no cars will be left behind, because if the number of rounds goes to infinity the cost of leaving cars behind will go to infinity as well. + + ''The program'' + + The program, written in Python with Tensorflow, has as input the matrix initialized with some values which should not matter too much. It then uses a Gradient Descent Optimizer to minimize the cost. ''Drawbacks of this approach'' ''Drawbacks of this approach'' Line 119: Line 123: *We have to know for each road the travel time function. *We have to know for each road the travel time function. *This generalizes and scales badly, because the matrix will get very large, and you need a matrix for every possible trajectory between two cities. *This generalizes and scales badly, because the matrix will get very large, and you need a matrix for every possible trajectory between two cities. + + ''Problems with the current program'' + *It is very slow. + *When normalizing the distribution matrix values to a fraction of the total amount of cars instead, it should be more stable because it does not have to change values to whatever big amount of cars there may be so the learning rate can be smaller, but instead the results get very unstable. + *Currently when on the first round each cars take for example ten minutes to clear the road, the next round can depart after one minute if that is the minimal travel time. Changing this leads to unstable behaviour. == References == == References ==

## Introduction

This wiki page will display the possibilities of self learning navigational software. Throughout the report several points of attention will be introduced, investigated and processed in the prototype navigational system. The goal of the system is to create a cooperative/inter-vehicular, high-level (for example, no traffic lights) and based on the current situation (hardly any self driving cars, not all people have navigational systems). This research is set up trying to answer the following research question:

How can the travel time be minimized while maximizing overall utility, looking at user and society, by using a Self Learning, Inter-Vehicular Cooperative Navigation System (SLIVCNS)?

## Problem description

Every day in the Netherlands people go to work with their cars, and every day the same thing happens, traffic jams causing a lot of pollution and waiting time. Most of the time people are traveling a lot longer than necessary to reach their goal. This gave us the idea to create a system that will reroute this traffic through secondary roads to minimize the overall waiting time or maybe even prevent traffic jams. By doing a lot of user research we hope to create a user friendly system that will help the user and society with this problem.

### The simple problem

The problem in the simplest form is as follows, there is a city A and city B, in the morning people move from A to B to go to work. There is a highway (which is the fast route) and 2 secondary roads. When the people move from A to B a traffic jam occurs, this causes people to wait and increases their travel time. By rerouting a part of these people to the secondary roads this traffic jam can be minimized in such a way that two things can happen, or a mix of these: 1. The traffic jam is prevented and therefore the overall travel time is a lot lower, this is hard to implement as people who had to take the secondary road have longer travel time. 2. The traffic jam has become smaller and all the people whether they take the secondary road or the highway have the same travel time. This options seems more fair but still causes the problem of people waiting in traffic jams. Which one is to be preferred has to be researched as the user is very important in this decision.

### The full problem

As seen above the problem can be made very simple but also a bit unrealistic, because in the end the system has to run in the real world, where there are thousands of ways to get from A to B. Also in the real world a lot of problems arise. Traffic is very unpredictable and can come from anywhere and go anywhere. It is not simply people moving from A to B. Also a lot of people will not be using our system, we will get no information from them, but they will have impact on our system. The biggest problem is all the unknowns, a lot of things can happen on the road. For example an accident can cause a traffic jam that can not be predicted. Because of all these problems we will be using self learning software to deal with these problems. This program will try to compensate for all the unknowns.

## Objectives

### Minimize travel time

The main goal of the system is to minimize travel time by rerouting users, to prevent longer waiting times caused by traffic jams.

### Maximize overall utility

The utility can be split in three groups.

#### User

• Minimize travel time, this is the main objective as mentioned above.
• Estimate arrival time by predicting traffic jams so the user knows the optimal route and best moment of departure to reach the goal location on time.
• Making the system fair for all users. This will require user research in finding the fairest solution.
• Taking the privacy of the user into account by doing user research.

#### Society

• Traffic jam prevention to minimize unnecessary pollution, by rerouting to minimize unnecessary waiting time.
• Minimize disturbance by rerouting through less populated locations.

#### Enterprises

• Creating a system that is better than the present navigation systems, so potential users will be interested in buying this system.

## State of the Art

There are a lot of different navigation devices available currently. The most simple one is only able to tell you the shortest route. A small upgrade also allows the user to exclude certain routes, like toll roads and tunnels. These often also give a choice for the user to either take a fastest (time) or shortest (distance) route. Some devices also allow traffic information incorporation which ask you to reroute if there is a traffic jam. When combining such a navigation system with a self driving car it is also possible to make the decision automatically. These all however respond to a situation instead trying to avoid certain situations.

## Improvement over state of the art

To improve the state of the art it is important to know what the implications are of the current system. These systems will reroute users as soon as there is a faster route available and thus will distribute traffic equally over the roads available, which may seem like the optimal situation. A self learning system however should have more roads available, since it can predict traffic jams that haven't even happened yet. It can reroute the user many kilometers before the actual traffic jam and thus prevent new traffic jams in reroute roads.

## Approach

The goal of the research is to improve the current traffic situation. This can only be considered an improvement when the changes do not diminish the overal utility. This means that within the navigation system the user as well as the society have to play an important role. As the time window is quite a tight one this research will be conducted simultaniously. The group of researchers will be split where four members of the team will work on making the self learning navigation work. The remaining two members will focus on the users and society. By frequent interaction, the team will be able to create a working navigational system in which the user and society will be strongly embedded. This also allows the prototype to be as representable as possible.

The completion of the program will also include a visual simulation which allows all team members as well as external personel to use and understand the way the system operates, and what conclusions it provides.

## User and Society

People behave differently when wearing something to hide their identity[1]. This also happens in a car. People are not directly visible and thus act more selfish. As long as people are driving their car (instead of autonomous), we can investigate further if/how we want to incorporate this.

Furthermore our navigation idea will request people to drive different roads, which at points will probably make them lose time on their journey. When looking at literature we find something called: “risk aversion”[2][3]. Risk aversion is a human trait where someone would avoid taking a favorable bet (overall travel time decrease) when it means they can directly lose out (more travel time)[4].

## User research

To make sure all user preferences are taken into account a user research is conducted, focusing on the key aspects which should provide a strong basis for the product to be released to the public. The aim of the research, and thus leading factors into choosing the approach, is to gain as much information about as many different drivers as possible. Since the aspects of the research are predetermined, the best research method available is a questionnaire. Google Drive is used to host the questionnaire, because it has all features (sort of questions/ set-up) required for this research.

The link below will bring you to the questionnaire:

The questionnaire is devided in several sections. Just like any research the questionnaire begins with a brief introduction of our idea followed by asking the participants permission to use the data they provide through an inform consent form. In all following sections the key issues are presented to possible users.

Section 1: Introduction questions

Section 2: Willingness to follow the system

Section 3: Fairness

The system can decrease travel time and traffic jams in several ways. The participants are presented with this way in a honest way. This means that we do present options where a user of the system is always equal or worse of to a person not using SLIVCNS. This sections is designed to get a better understanding of the target group, and to give a better insight into the goal our system should aim for.

Section 4: Privacy

The current state of the world means that almost all information gets shared. (insert link/reference) This however does not mean that all users all necessarily willing to share all information. Since our system needs to know certain things about users it is important to know that people do not mind sharing this information (anonymously). We ask the participants if they would be willing to share their current location aswell as their destination. If the participant is willing to share this he/she is taking to the next section with follow-up questions. If the participant is not willing to share this information he is taken to the final stage of the questionnaire.

Section 5: System interaction

In this section the participants are asked to think about the idea of SLIVCNS, and how they would see such a system fit in their daily lives. It contains three subsections. The first subsection asks the participants what inputs they would like to have on the system. There are six predetermined options, like what type of roads, should the system take pollution into account etc. It is also possible to write your own input, if this is not present in the existing questions. The second question makes the participant think about how they would like to communicate with the system. As explained in Chapter %%%%%%%% it is important that the system knows when a user wants to travel from a to b. This means that a user would need to interact with the system quite frequently, which means that this interaction should be easy. Possible communications are for example a mobile app, an online website or a custom made interface. Once again the questionnaire provides room for a written answer. The final subsection is regarding the route planning of possible users. For the system to work optimally it is beneficial to know way in advance when users are going to drive. This section asks of different occasions (different destinations) the time a user would be able/willing to provide the system with its travel plans before departing.

Section 6: Finalizing questions

This section is focused on the the aim of the navigational system. In this section the participants are asked about possible approaches the system could take regarding traffic/travel time distribution.

A participant is free to not answer a question if he/she does not want to answer, without having to provide an reason. All information helps towards optimizing SLIVCS for the user, and thus there are no mandatory questions (excluding accepting the inform consent form).

## A First Attempt

Please remember that the problem solved here is mainly to get a feel for both the programming language and the type of problem. We know this problem is so simple you could easily solve it by other means, with much less effort, but that is a good way of checking our own results.

We assume there are two cities, A and B, and two roads from A to B. We assume cars leave in driving rounds, so in each round there is a certain amount of cars on the road. For example, a typical distribution of cars over one road would be [12, 3] which means that the first round 12 cars leave city A, and the second round 3 cars do so.

Of each road we know exactly how much time a car needs to travel the road given how much cars are on the road in total. This is a function which we will call the travel time function. We assume here it is of the form max(a * cars - b, min_travel_time), which means that up to a certain amount of cars on the road the travel time equals the minimum travel time needed to traverse the road, and above that the travel time increases linearly. The latter case is of course meant to signify a traffic jam.

One road is a highway, and the other road a secondary road which means that on the secondary road the minimal travel time is larger, a traffic jam occurs with less cars, and increases more per extra car. This is easily extendable with more roads, but for every road you would have to know this travel time function.

So for two roads together we have again a distribution matrix, for example [[8, 3], [3, 1]]. The idea is that we predetermine the amount of driving rounds, because changing the size of this matrix while optimizing is a bit of a problem still. (As a sidenote, in real life you would probably want to limit this because of user preferences anyway)

Optimizing the distribution matrix

We calculate the cost (travel time) of a certain distribution matrix by adding up the travel times of all the cars. This is the value that is going to be minimized. For the cars leaving the first round, the travel time for each car is given by the travel time function. For the cars in the second round, the same holds but we add the time that they had to wait while the first round was driving. We continue this for how many rounds there are. Then, if there are still cars left after all the rounds, we assume all these cars depart in the last round.

The program optimizes this matrix by minimizing the costs. Therefore, if there are sufficiently many rounds, no cars will be left behind, because if the number of rounds goes to infinity the cost of leaving cars behind will go to infinity as well.

The program

The program, written in Python with Tensorflow, has as input the matrix initialized with some values which should not matter too much. It then uses a Gradient Descent Optimizer to minimize the cost.

Drawbacks of this approach

• We have to predetermine the amount of driving rounds.
• We have to know for each road the travel time function.
• This generalizes and scales badly, because the matrix will get very large, and you need a matrix for every possible trajectory between two cities.

Problems with the current program

• It is very slow.
• When normalizing the distribution matrix values to a fraction of the total amount of cars instead, it should be more stable because it does not have to change values to whatever big amount of cars there may be so the learning rate can be smaller, but instead the results get very unstable.
• Currently when on the first round each cars take for example ten minutes to clear the road, the next round can depart after one minute if that is the minimal travel time. Changing this leads to unstable behaviour.