PRE2018 4 Group7

Group members:

Name	Student ID	Email	Study
Emiliyan Greshkov	1281666	e.greshkov@student.tue.nl	Computer Science
Thomas Janss	1006697	t.f.w.janss@student.tue.nl	Mechanical Engineering
Perry Kloet	1236356	p.a.j.kloet@student.tue.nl	Computer Science
Bram Schut	1019001	b.b.j.schut@student.tue.nl	Computer Science
Sem de Werdt	1017882	s.j.a.d.werdt@student.tue.nl	Automotive Technology

General information:

Problem statement and objectives

As our urban environment grows, so does the need for means of transport for commuters and leisure travellers. Trains are getting cramped and more overcrowded. This makes it increasingly more important to use the available space and seating optimally to maximize the amount of people in the train and the comfort in which these people can travel.

A lot can be improved in terms of space usage in trains. We want to achieve this goal by providing train passengers with accurate and real-time boarding data. This data can be used to give the passengers information on where best to board the train. We will deliver a system to measure live information about compartments, with a good accuracy/cost ratio. This information can be displayed on all kinds of interfaces,such as websites and apps, but also on interactive train platforms or on screens within the trains.

Our focus during the project will be on trains in the Dutch railway network, since trains, stations, railway networks and stakeholders differ a lot all over the world so it will be hard to make a uniform design that will work everywhere. However, this system design can get tweaked for implementation in different trains.

Many railway companies are already experimenting on this subject. The state of the art in the Netherlands is the NS, who is showing in their app, how crowded each compartment is. They get this information by building weybridges into the tracks of each stationplatform and weigh each compartment. However this method only creates a rough estimate of how crowded each compartment is and this measuring can only be done upon departure of the train. We want to make a system that does active measuring, that can make an estimation in real-time and have a higher accuracy than the system that NS is using now.

In this project we will focus on three parts. The first part is the measuring. As said above we want to measure in real-time and achieve a higher accuracy. Part two is the processing of this measuring data. What information can we get from this data and in what ways it can be used. The final part of the project will be how this information can be communicated to the train passengers. There we will give examples in what ways this data can be used to make passengers better distribute over the train.

Previous done work

1) NS App, The NS app shows how crowded each carriage is on some trajects, using sensors in the rails that measure the weight of the train.
2) Schiphol airport train station, has an Intelligent Platform Bar (IPB) giving the passengers boarding information like where the doors will be.
3) s'Hertogenbosch train station has an Intelligent Platform Bar showing if there is space in the carriage.
4) Trainstation in Stuttgart Germany has a luminous platform

Research from the University of Queensland has shown that passenger distribution within train carriages is skewed. They studied if this skewness would improve by showing waiting passengers prior to the arrival of each train. They made an agent-based simulation to model the behaviour of passengers with occupancy information and without occupancy information. The simulation where passengers were provided with occupancy level information got much better distribution over the train and got a lower overall passenger boarding time. https://www.atrf.info/papers/2016/files/ATRF2016_Full_papers_resubmission_194.pdf

Study from the ETH Zürich shows that the distributions of passengers in strongly uneven. They performed manual observations at two railway station in Zürich and mapped their prefered waiting locations. https://www.researchgate.net/publication/317545500_Distribution_of_passengers_on_railway_platforms

Users

Sort of users <br\> Researching the user’s needs is of large importance for the current crowding in trains. A distinction between different groups of users could be made: Primary, secondary and tertiary users. Train travelers belong to the primary user group. Secondary users consist out of train companies or track operators. Tertiary users typically consist of software/system developers. <br\><br\> Train travelers will benefit from an increased spread of people along the train. Passengers will be able to spread out more at the platform, due to displayed information before the arrival of the train at the platform. Having boarded the train, the chance of a seat will be higher than when randomly boarding the train. This way, uneven distribution of clusters of travelers in different carriages can be prevented. This ensures more comfort for passengers, both inside of the train and while boarding or waiting for the train.<br\> <br\> Secondary users also profit from this new technology as a side effect. Fewer irritated travelers will complain, providing a better image for train operators such as NS (Nederlandse Spoorwegen) or DB (DeutscheBahn). Next to that, with better spread of passengers, the amount of carriages of a train can be optimized/minimized, leading to lower costs for these public transport companies. <br\><br\> The tertiary users consist of software and system developers. More technology in the train means that they have more work, leading to a higher revenue for these companies. Next to this, improvements on the platform also have an influence on Prorail, which manages the platforms. These platforms could be designed in a better way, leading to a higher train density at stations located in mostly populated environments. <br\>

User requirements
Primary users:<br\>

• Passengers should be distributed equally over the carriages to improve the chance of getting a seat to maximize comfort
• Passengers should be be able to know in advance where they can board the train with the assistance of a user-friendly interface
• Passenger privacy should be respected, thus their data should only be used for counting

Secondary users:<br\>

• NS should have a proper way of counting people inside of a train
• Ns should be able to know exactly what kind of material is used

Scenarios and personas

John: John uses the NS app on a daily base already to find a good place to get some work done for his work. John can travel first class because his boss is paying for it. He prefers to sit in the row with signle seats. Since John only knows where the train stops, he frequenctly has to search for the 1st class. When he has arrived, most of the single-seaters are already occupied. John Would like to have an indication where the 1st class is located, such that he has the most chance of having a proper place to work.<br\>

Iris: Iris is a 2nd year Bachelor student that travels daily from her home in Roermond to the TU of Eindhoven. Iris always wants to find the least crowded spot in the train. The only ways of doing this now is walking through the train or using the app, which only shows the occupancy scarcly. Iris would like to have an app that always says where on the platform she has to stay to have the biggest chance of a seat.<br\>

Boris: Boris is a 84 year old grandfather. Once a month he travels to his grandchildren by train since his driving license has expired. Due to his age, Boris walks really slow and therefore he sometimes has to rush if a train stops at a distance at the platform. Boris is very sceptical about having a smartphone and thus cannot use the NS app to see where the trains stops. His opinion is that information should also be available for people without a smartphone.

Approach, milestones and deliverables

Approach
The problem will be divided into two subproblems. A counting system and a user interface. The counting system has to accurately count to amount of people in each compartment and compare it with the available space to get the information about the available space. For this, nowadays a weigh bridge is used. However, we want to provide real-time data, since there is much activity around arrival or departure in a train. The user interface has to pass the information on to the passengers in a clear and understandable way. This user interface will consist of tiles that can have different colors or an LED screen providing information. Next to the tiles at the platform, an integrated app which also will be shown inside the trains will be developed. Combining both should provide the best possible solution for the problem of overcrowded train compartments and ineffective use of space.
Milestones
The first milestone is finding out which counting technique will be used. When this is known, the basic layout for the app can be made. Also the basic layout for the physical system can get designed then. Second milestone is writing the code and checking if it works, by applying different test cases. In the meantime, the counting system can be completely designed and constructed. Merging these two systems is the next milestone. Coupling this data to NS' live data will be the last milestone.

Deliverables
The deliverables at the end of the project will be a real-time system that accurately counts the amount of people in each compartment, and a user interface that passes that information onwards to the passengers as effectively as possible. Both parts will be combined to make a total system that gets accurate information about the occupation of each compartment and passes it onwards to the passengers through a straightforward user interface within the train, at the platform and in the app.

Questionnaire 1 (Customer needs)

Scope of the questionnaire

To get a grip on the customer needs for the concept which helps people to board a train more uniform, a first questionnaire is made which has been distributed among a diverse group of people. The goal of the questionnaire is splitted in two parts. Firstly, the satisfaction of train travelers of their chance of getting a seat is monitored and whether the use the current NS app to improve their chance. The second goal of this questionnaire is determining content of the improved app that the travelers want themselves.

General respondants

The majority (76%) of people travels high frequently with the train. Only about 30% travels less frequent than once a month. Of these people, 65.2% travels outside the rush hour. Only 2 respondents out of 23 have a form of visual impairent.<br\> Only 73.9% of the respondants have the NS app downloaded on their phone. These people mainly use the app to plan a trip (78.3%), find the departure platform and time (73,9%) and to see how long the train is (52.2%). However, only 13% uses the app to see how busy the compartments are. After this questionnaire 26.1% says that they will start to use this function when it is available.

Opinion on chance of getting a seat

Respondents evaluate their chance as good. 78.3% typically finds a seat and are satisfied with their chance. This number is slightly lower than the number NS distributes in their yearly report about the chance of getting a seat, being 95,1% in 2018. There is a slight difference between people traveling during rush hour and people traveling outside of these peak hours. The former evaluate there chance of having a seat as 3.22 out of 5, while the latter evaluate this chance at 4.33 out of 5. The satisfaction that belongs to this chance of seat is 3.44 out of 5 for rush hour and 4.33 outside rush hour (on a scale of 5 again). Crowdedness in the trains is judged neither a problem, nor positive. A general preference can be found for the specific compartment where a commuter boards the train. 84,7% mostly walk to a place with low people to area density at the platform to avoid a crowd gathered around the predicted stopping place. Of this majority, an absolute 51% always walk to increase their chance of a seat. Of these people, 85% would walk towards the other side of the platform, if their chance of getting a seat is (almost) guaranteed, while only about 15% would maybe do it or not at all (4.3%). This behaviour is the same for passengers during and outside rush hour. A minor difference of 0.08 on a scale of 10 is found.

What information do people want to know

A majority of 52.2% only want to know which carriage they have to board to maximize their chance of getting a seat. 39.1% wants to know the approximate number of free seats. Next to free seats, travelers really 95.7% find it convenient to know where 1st and 2nd class carriages are located. Only 17.4% wants to know where they can store their bicycle and 4.3% where they can find silent compartments. Place of the compartments where a service is provided is mostly preferred (65.2%), while 30% also wants to know the wxact location within the carriage to find the correct door to board.

Layout and colors

If people could use two ways of obtaining the critical information, they would either prefer an app for it (56.5%) and displayed information on the train itself (52.2%). LED strips at the platform are chosen in 43.5% of the cases. TV screens are found almost useless. Only 13% would choose this method.<br\> A slight majority (56.5%) would chose colors that also suit for color blind people. However, for distinguishing 1st and 2nd class, people respecitively tend to choose red (65.2%) and blue (69.9%). Both numbers greatly outnumber the other options with a difference of 50% with the second choices

Conclusion and discussion

Designing a system that helps people find a better spot in the train would be perceived quite good. Especially when the train is quite busy, like at the edges of rush hour, people value their chance or satisfacting of getting a seat 0.5 to 1 number lower on a scale of 5 than what they would value those criteria while traveling outside rush hour. Thus the most improvements can be made at situations where the trains are quite crowded, but not completely full.<br\> Current systems like "Zitplaatszoeker" unfortunately are quite unknown to the public. Main reasons for introducting this boarding assistance are displaying the place where different kind of compartmens (1st and 2nd class) will stop at the platform. Although colors like red, orange and green are psychological better to use to display the crowdedness in the train, people tend to choose for the colorblind alternative (working with blue- and orangeish colors.<br\> <br\> One interesting fact has came out of the questionnaire. It is quite interesting to see that people prefer information, like where 1st and 2nd class is located, displayed on the train itself. One of the reasons to skip this answer in the analysis is the we think the people perceived te question wrong. Fact is that people cannot see where the have to manoever themselves and therefore can only line up to the platform door if the train already has arrived.

Technical Content

Counting

Counting people could be done in various ways. Currently, NS already uses weightbridges (19), Amtrak and some busses in America are using treadle mats (8) and infra red beams are used in South-Africa to monitor the corrupt behaviour of some bus drivers (7). However, all these options require quite some adaptions to the public transportation or the railnetwork itself and therefore can be quite expensive.

Mobile phones

A more prommesing option would be using the location of a mobile phone of travelers. This can be done in several ways:
Bluetooth: (raw) Bluetooth tracking systems could also be used. Of all people who have a phone, about 40% has Bluetooth turned on. Therefore we could use Bluetooth-beacons to count the number of Bluetooth devices and extrapolate the data. Bluetooth beacons cost about $10 per piece and have a range of about 50meters. According to nu.nl , the ns wants to have 4077 compartments in 2022, so installing 2 beacons per compartment would cost $81.540,-. This is definitely the cheapest option, however the question is how accurate this is. Research did show that in a crowd of ~16,000 people it has an accuracy of about 17.9%, but in trains you don’t work with crowds of this size. Since Bluetooth counting is less accurate than the current system using weight-bridges, we decided to shelf this idea also.

Wifi: Determining the cost of a wifi-tracking system would be a very difficult task, since we don’t know what kind of hardware the ns already has for their wifi-network. We do know however that wifi-beacons are 4-5x more expensive than Bluetooth beacons. Since wifi-tracking is also less accurate than bluetooth tracking we discarded this method.

Bliptrack: Bliptrack is a system combining the 2 methods above. It detects WiFi/BLE/Bluetooth-enabled devices (18). By placing these sensors at inside critical places, such as inside the coupé and at the balcony, a proper measurement can be made of how many mobile phones are present inside the train.

With all these methods, there is a small group of people that is not accounted for in the trains: People that are not using their phone because they just don't, or people that do not even have a phone. Figure 1 shows the amount of people living in the Netherlands without a phone. A questionaire under 3000 people has showed that 87 percent of Dutch citizens are in possesion of a phone. The most promising growing group consists of seniors. That group was growing by 19 percent per year in 2016, while 18-34 has grown with 3 percent and 35-54 with 7 percent. Other instances like statica find of mobile phone usage of 85 percent. Of those approximate 14.5 million people, 52 percent use their phone while using public transportation. However, there is 1 problem with this method. Usage of smartphones and therefore also bluetooth can differ per coupé. If more younger or older people are seated in a carriage, the number of people in the carriage can get misjudged, since these people all deviate from the average users.

Different methods of counting are given below:

Other methods of counting

Cameras: This is easy to implement and relatively cheap. However this raises privacy concern so this will not be a good solution.

Tracking ov-chipcards. Till 2008, all ov-chipcards had an RFID-chip. Nowadays they have an NFC-chip. We cannot track people using an NFC-chip, but we can using an RFID-chip. RFID-chips are relatively cheap (€0.06 per piece), so we could put them back in the ov-chipcards, separate from the NFC-chip, making it such that almost all paying passengers can be tracked. The main drawback is that it would take 5 years to phase the RFID-chips in (Since that’s the time an ov-chipcard is valid). The range of such a cheap chip is also only a couple of meters, requiring a lot of beacons.

Ínfra red sensors This would be a good solution since the sensors are relatively cheap, they go from $80 a peace. With out system each compartment would need a minimal of 2 sensors. This has to be implemented in 4077 compartments, so the materials would cost $652.320 plus installation costs. Study that implemented infra red counting system in busses has showen that people did not notice the presence of the sensors and that such system gave accuracy levels around 99%. [bron 7]. Disadvantage of such system is that each counting error will not be corrected till the end of the traject. So the longer train is active the more these small errors will add up.

Carbon Dioxide This would also be cheap to implement, carbondioxide sensors cost around $100 and you would only need one per compartment. Carbondioxide sensors can reach a high accuracy in a closed environment for low amount of people. Accuracy for up to 4 people can reach 94%. Accuracy for more people in a small closed room will be 60%-80%. However a train is not a closed environment with doors open and closing at each stop. So this accuracy is likely to be on the low end of the 60%-80% range.

https://arxiv.org/ftp/arxiv/papers/1706/1706.05286.pdf

Needed Data

In order to be able to effectively determine the rate of occupancy for each compartment, the following few pieces of data are needed:

• The lengths and positions of all passenger cars upon arival.
• The (average) weight of passenger cars when empty.
• The average human's weight. According to the London School of Hygiene & Tropical Medicine the average human worldwide weights around 62kg, and in the netherlands - around 69kg.
  People also carry clothes and carry luggage - which should also be taken into account.
• The number of seats in each passenger car, so that it can be calculated whenever a carriage has no free seats, or even additional, standing passengers as well.

https://www.telegraph.co.uk/news/earth/earthnews/9345086/The-worlds-fattest-countries-how-do-you-compare.html

Data Processing

Knowing the needed data, whenever a carriage gets weighed with the passengers inside, by subtracting its empty weight and dividing by the average person's weight we can get how many people would be in that compartment. Calculating the number of people on the comparment can also be done using alternative methods such as pressure mats or infra red beams.
Then, the ratio of people in each compartment to the seat capacity can be mapped to colours from green to red, which would depict how full each compartment is, and this would be sent to the display and shown to the users.

Our solution

We chose to do the counting using infra red sensors. Since their accuracy is very good and they are pretty easy to install and very cheap compared to weybridges.

More valuable data

The data gathered from the infra red sensors is more valuable than the data from the weybridges, because the infrared gates can distinguish between the occupance levels of the upper and lower compartment in double dekker trains. They could also distinguish between first and second class by adding one extra gate. Another advantage is that the infra red sensors can keep count of the passengers during the trip and the weybridges can only measure when the train is leaving the platform.

Accuracy

The accuracy of infrared counting is higher than weybridges. On a full train weybridges counts can vary up to 26 people. Where infrared has an accuracy of 99% at the start of the traject and 94% near the end, because of error additions.

Costs

Much cheaper to implement infrared gates in each train, than weybridges on each platform in the Netherlands.

Downsides

This technology however has two important downsides. The first problem is that counting errors will add up during the traject, because when a person is accidently counted twice, the compartment will keep counting one person to much until the whole counter is reset. However we do not think that this error is a lot since the accuracy of the counting is very high and we can reset the count after each traject. The second problem is that the infrared sensors have to be cleaned regulary for them to work optimally. This can be done during the cleaning of the train when the bins get emptied, which is done regulary enough.

Platform indication

Figure 2: DB Stuttgart-Bad Cannstatt

In 2018, the German railway operator has tested a form of "assisted" boarding in Stuttgart-Bad Cannstatt at a S-Bahn station. Information about the train is given via tiles with LEDs. Occupancy rates are just like the Netherlands estimated via load of the train and some new methods, such as CCTV, door sensors and ticket information. The concrete slaps at the platform can provide information where the doors are located and how busy the coupés approxmiately are. However, there are a lot of things that cannot be done yet with this system. Therefore, this project will focus on improving the customer experience of these tiles. A few requirements are set for these tiles. The tile should be able to:

• Show where the doors will be
• Show where 1st and 2nd class is located (also top or bottom)
• Show how full the carriages are
• Show where special carriages are located (e.g. wheelchair or bicycle)
• Show that traveler has not reached the boarding area yet
• Show that a train cannot be boarded
• Show that a train is about to leave

<br\>

Figure 3: Concepts tile

The tiles should at least consist of three seperate colors of LEDs. Red, which can be associated with 1st class, blue, which can be associated with 2nd class and white or any other color, which indicates where the doors are located. The most convenient pattern for these platform tiles is a cross with LED diods and two horizontal strips of LED diods in the middle of the tile. There are two possible ways for the doors, either only LED diods could light up where doors will locate, or information could be displayed at small LED screens. The two horizontal lines can cope with indication where first class and second class is located by adjusting the color. By adjusting the DPI (Dots Per Inch) of those lines, the occupancy of a carriage can be displayed, where a low DPI-concentration would stand for many places available and a continuous line means a full carriage. At the small LED screens, also additional information like wheel carriage et cetera could be displayed. For trains that cannot be boarded due to arrival on their final destination, all crosses could emit red light, reminding the passenger to board another train. While decoupling trains due to under-occupancy, which happens a lot at stations like Eindhoven and Amsterdam, arrows could indicate the train travelers to board the front of the train.<br\> <br\> <br\>

App indication

NS has already experimented with showing train carriage capacity in their apps. However with our live and more precise counting we can make improvements on that App system. With this new information we can distinguish between the upper and lower compartment of the train and we can even distinguish between first and second class. This is especually useful when a train is very full. In this case the second class is, but often there are seats enough in the first class. The app can show first class travelers where seats will be available.

Figure 4: App design

Figure 5: App shows where wheelchair and bike entrances are

The app displays every compartment of the train with an indication color which shows how busy it is. The coloring goes from green (not busy) via yellow to red (very busy). With this information train travellers can decide to board a carriage which is less full. (see figure 4)

In the questionair we found that train travellers would want to know where bike and wheelchair entrances are in the app. So we made a design to keep the app simple but also give this extra information. The design uses a toggle button for wheelchair and bike, when the user presses this button the coloring of the carriages changes to gray for not available or to blue for available. (see figure 5)

Lastly the app gives a traveller advice to the user. For example see figure 4; "62% more chance for a seat in wagon 3". These messages will be live generated and can be personalised. Which would say that if 40 people check the app at the same time, they will not all be sent to wagon 3, but the app will sent next users to a different wagon.

The app could be extended, by calculating estimates to predict how full a train will be at what time, so travellers can decide to take the next train or another route. These estimates can be based on previous data of the same train on the same traject and time. This way the app shows live data when available and estimated data when not available.

NS trains

Some information on the trains that the NS uses has been disclosed to us.

VIRM train	Compartment 1	Compartment 2	Compartment 3	Compartment 4	Compartment 5	Compartment 6	Weight while empty(tons)	Number of seats
VIRM III	mBvk1	ABv3	mBvk2				183.4	275
VIRM IVa	mBvk1	ABv5	ABv4	mBvk2			234	372
VIRM IVb	mBvk1	ABv6	ABv3	mBvk2			236.8	371
VIRM VI	mBvk1	ABv5	mBv7	ABv6	ABv4	mBvk2	352.3	567

Seat calculation inaccuracy

From the data above, and using average male and female human weights, we have computed the following possible deviations:

Gender	Weight	Rounded in tons	Difference
Men	7812	~8	96
Women	6510	~6.5	84
Mixed	7161	~7	91

When taking into account only the seated places in the train, depending on the gender composition of the passengers, the number of people on the train can vary by 12.

Gender	Weight	Rounded in tons	Difference
Men	12264	~12	156
Women	10220	~10	130
Mixed	11242	~11	143

From this table, it is apparent that the calculated number of people on the train, using the average human weight, can vary 26 people when including standing people.

Costs

Counting

Bluetooth beacons can vary in cost greatly, but a rough estimate of the cost of a beacon is about $10, and we'd need 2 per compartiment. Therefore the bluetooth beacon would definetly be the cheapest way of counting. It is very hard to estimate the cost of wifi-tracking, since we don't know what hardware the ns already has. We do know that tracking phones by wifi is more inaccurate than bluetooth-tracking, and the hardware is more expensive.

LED display tiles

LED display tiles vary in price between 50 and 200 euros, however some of the cheaper models are not sure to withstand being stepped onto. LED display tiles are usually 30x30 or 60x60 tiles. A good estimate price for a 30x30 tile would be 50 euros. Since a train compartment is 25-30 meters long, and since most train configurations include 3-6 compartments, averaging at 4-5, we could estimate 100-120 meters per train. This would mean about 300-400 tiles per train pier, and if we assume 4 piers average per station, this means about 1000 tiles per station, amounting to about 50 thousand euros per station, or 20 million euros total across all stations. https://www.djgear.nl/showtec-dancefloor-sparkle-rgb.html https://www.tme.eu/nl/details/mikroe-2370/accessoires-voor-ontwikkelkits/mikroelektronika/32x32-rgb-led-matrix-panel-5mm-pitch/?brutto=1&gclid=CjwKCAjw8qjnBRA-EiwAaNvhwO4lozUZKg4D8ydjKoQ3gLdrQ5SkViXDx8fD-96iE1AZadhmUTFdDBoCaSkQAvD_BwE https://www.ledpaneelgroothandel.nl/led-paneel-30-30-rgbww?gclid=CjwKCAjw8qjnBRA-EiwAaNvhwKpr5Lu4D-1zS0ht7N8aCtQOFY56zfpcKyO5uS8NKmcwmmDLUJPXBBoCR8QQAvD_BwE

Weighing scales for compartments

A high-quality Weighbridge can cost up to 20 thousand euros to install. According to IndiaMart, an indian in-motion railway weighbridge manufacturer, namely Technoweigh India, offers top-grade weighbridges at the cost of 850 thousand indian rupees, which amounts to roughly 11 thousand euros.
Depending on the station, multiple would have to be installed in each station. This means costs can exceed 100 thousand euros per station. There are a total of 408 stations in the netherlands, with 61 of them being intercity stations, according to wikipedia.
If we take an average of 4 weighbridges per station, the cost for weighbridges would amount to roughly 2.7 million euros for the installation alone, and some additional amount for maintenance and control.
https://www.indiamart.com/proddetail/rail-weigh-in-motion-1853290030.html https://nl.wikipedia.org/wiki/Lijst_van_Nederlandse_intercitystations https://nl.wikipedia.org/wiki/Lijst_van_spoorwegstations_in_Nederland

Other tools

Sadly, we could not find a good estimate for the costs of infrared or bluetooth/wifi counting devices, neither for pressure mats. The reason is that our use for these products are very specific, and therefore existing producs do not satisfy our needs. There are products that fit our needs for counting, however they are still in development and there is no available disclosed information about possible product costs per piece.

Summary

Should we only use LED display tiles and Weightbridges, the cost would amount to 60-75 million euros. However, this is only if it is to be implemented in all stations, and it could potentially be done only for major stations or really busy lines to cut costs.

Planning

Week	What to do
3	In-depth research
4	Concepting
5	Detailing
6	Building and Testing
7	Finish prototype
8	Prepare presentation
9	Finish wiki

Who's doing what?
The problem will be split into several parts. Each group member will work on a part that best fits their skills to optimize the end results.

Raw results questionnaire

• Questionaire results (click to expand figures)
• 

  How often do you travel by train?

• 

  When do you travel the most by train?

• 

  Visual Impairment

• 

  How likely are you to find a seat?

• Q5.png
• 

  How satisfied are you with your chance of a seat?

• 

  How do you assess crowdedness in trains?

• 

  How likely will a crowd in front of a carriage dissuade you from entering that carriage?

• Q9 7.png

  Figure 2:

• 

  Would you consider moving 75m for a guarenteed seat?

• Q11 7.png
• Q12 7.png
• Q13 7.png

Relevant scientific papers:

Research

1) https://www.alstom.com/our-solutions/digital-mobility/optimet-real-time-train-occupancy-smoother-passenger-flow-platforms
2) https://www.researchgate.net/publication/280735165_A_robust_system_for_counting_people_using_an_infrared_sensor_and_a_camera
3) https://www.dilax.com/en/public-mobility/portfolio/seat-management/
4) https://www.researchgate.net/publication/323027620_Smart_Bus_An_Automated_Passenger_Counting_System
5) https://www.google.nl/url?sa=t&rct=j&q=&esrc=s&source=web&cd=21&cad=rja&uact=8&ved=2ahUKEwiCudCpgPbhAhUJr6QKHRJQBlM4ChAWMAp6BAgIEAI&url=https%3A%2F%2Fpdfs.semanticscholar.org%2F55a0%2F9a9adb1e7905f99607846f7a286e3f39bf17.pdf&usg=AOvVaw0ZK1-RYUZ15nYZshrA0cHs
6) https://www.usenix.org/legacy/events/hotos03/tech/full_papers/gruteser/gruteser_html/
7) https://www.researchgate.net/publication/267387412_APPROPRIATE_TECHNOLOGY_FOR_AUTOMATIC_PASSENGER_COUNTING_ON_PUBLIC_TRANSPORT_VEHICLES_IN_SOUTH_AFRICA <br\> 8) https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=9&cad=rja&uact=8&ved=2ahUKEwiR9p2Uw4HiAhWNZ1AKHUeuBI4QFjAIegQIAhAC&url=http%3A%2F%2Fwww.movetec.fi%2Fimages%2Fpdf%2FTapeswitch-tuntoreunat.pdf&usg=AOvVaw0I2j-QILXPBH48Jq9N8T_d <br\> 9) http://www.instantcounting.com/technology.html
10) https://reader.elsevier.com/reader/sd/pii/S2352146517307159?token=960AEE718A60D47CF7E7F08701AD27EDC69A9913B5CBAA27DE0D3C81DA363B16C3FF122C62F1C5A3EB601DE44AE97706
11) https://www.tandfonline.com/doi/abs/10.1080/23249935.2013.795199?casa_token=U8PLb3o3OP8AAAAA:0a6jqeVO-6AR9W3YHihtHAag3XO5rRUyfBMkblxZQqgccoFb-AFONkHsasHFF4kCI8CDsYv7PV1B
12) https://ieeexplore.ieee.org/abstract/document/5715528
13) https://www.ns.nl/reisinformatie/service-verbeteren/zitplaatszoeker.html
14) https://www-sciencedirect-com.dianus.libr.tue.nl/science/article/pii/0262885694900531

16) https://patents.google.com/patent/US7788063B2/en
17) https://trid.trb.org/view/481481
18) https://blipsystems.com/hardware-overview/
19) https://nos.nl/artikel/2271485-ns-vrije-zitplaatsen-op-meer-trajecten-in-app-te-zien.html
20) https://repository.tudelft.nl/islandora/object/uuid:a67e550b-5c38-456e-9e21-90802ad36f6a?collection=education
21) https://www.researchgate.net/publication/276408492_Semisupervised_Pedestrian_Counting_With_Temporal_and_Spatial_Consistencies
22) https://www.its.ucla.edu/wp-content/uploads/sites/6/2015/11/passenger-flows-in-underground-railways-stations-platform.pdf
23) http://www.strc.ch/2017/Bosina_EtAl.pdf
24) http://ijtte.com/uploads/2018-12-08/b2ddb9bc-d1c1-4333ijtte.2018.8(4).04.pdf
25) https://www.deutschebahn.com/en/Digitalization/DB_Digital/productworld/Luminous_Plazfrom_en-1214708
26) https://onlinelibrary.wiley.com/doi/abs/10.1002/atr.5670180102
27) https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8117189
28) https://www.mdpi.com/1424-8220/12/10/14196

Categorize

Counting people:
1,2,3,4,7,8,9,11,12,14,17,18,21
Application itself:
4, 19, 25
Walking behaviour at platform:<br\> 10,20,22, 23, 24

1:
Alstom is a company that provided sensors tecnology (passive IR) to count people boarding and deboarding. At the platform, LED's show which compartments are full and which are not. With colors they influence people where they board. THe system is modular and can therefore be placed at any station of any length.
2:
pyro-electric infrared (PIR) sensor and a camera (2). Two possible ways: detection-based and map-based methods. Latter option more precise in counting (10,11). Differential PIR sensors used, since this is only possibility for differentiate between entry and exit of an environment and ordinary activities in that area by analyzing body movement. A couple of modules are used that consist of multiple PIR sensors. This data is collected and are input for a list of classifiers. Wavelet transform is used to create output signal. Using only camera yields ~ 80% efficiency, while integrating PIR has ~ 100% efficiency (Tutorial: https://www.youtube.com/watch?v=6Fdrr_1guok )<br\> 3:<br\> DILAX' active infrared sensors are capable of detecting people entering a door. The system is only active while doors are open. Each DILAX LAN can operate 382 sensors. The system is capable of counting in dense popluated areas with a high accuracy.
4:
Also, a pressure sensor could be used. Pressure sensors behaves like a open-closed circuit which delivers voltage while closed that can be placed underneath the padding of the seat. The Voltage is converted from a real output to a binary output which is created by Arduino. The application can read the amount of 0's and 1's and can convert that into images. The used pressure pad detects weight from 20 kg and higher.<br\> 7: <br\> Next to PIR, infrared beams could be used that detect when interrupted. Can be either active or passive: Passive sensors sense IRradiation and with that can find moveing direction, active sensors actually can locate the person. Ultrasonic sensor works approxiametly the same way. Test in South Africa: 28000 Rand (1866 euros) per bus with 1 sensor per door. This gives an accuracy level of 95%, while 3 sensors per door give 99%. Other methods are also given in this article, giving treadle mats, IR again, load cell and normal camera.<br\> 8: <br\> Tapeswitch is a company that produces copyright public transport equipment. Among those also belongs the treadle mat which already are in use at Amtrak trains, Copenhagen and melbourne. These mats commenly are produced with multiple zones that have different functionalities: Opening and closing a door or actions such as counting passengers. <br\> 9: <br\> Instant counting is a company that provided treadle mats with the provided software. This software is capable of detecting the movement of a person when boarding/deboarding. It is possible to link 90 of these mats to eachother in one system. An interface shows the amount of people entering and the amount of people exiting. The system can detect direction of walking and can track different people simmultaniously.
10:
Maybe not directly relevant, but we could take into account how people walk accros the station to the destinated train to improve user experience (USE-aspects)
11:
A system that estimated the number of passengers using the weight of the train. This has the huge advantage that errors in measurement do not propagate (ex: if you use an infraredsensor and you miss one passenger leaving, the system will always keep counting one too many. Weightsensors don't have this issue)
See 13 (<- not an article), appearantly, the ns is already doing this, so we need to expand on this idea.
Appearantly, this idea has been pattented (see 16)
12:
Using face recognition. Might be a bit overkill to install an entire camera for this one purpose, and might give some privacy issues, but seems straigforward
14:<br\> Counting with IR can sometimes give difficulties in dense areas. Image sequence processing does not. Divided into two parts: Target detection and target validation and direction-estimation. This way, the algorithm is rather fast. The system is more accurate and still fast in dense areas.
17:
A study on how to interpret data from multiple pressure sensors (a "pressure mat")
18:
This company counts the ammount of people going in or out a door using sensors that track WiFi/BLE/Bluetooth-enabled devices. When someone walks past this sensor with his mobile phone he wil get counted and tracked with a user ID. When he later leaves the door, the count will go down again.
19:
NS launches an app, that helps find passangers train seating, by putting weight sensors in the tracks, which measure the weight distribution of each carriage.
20:
Research from TU Delft about spreading passangers over the platform. Giving crowdness information did not lead to much more spreading. Giving passengers personal boarding advice has better results.
21:<br\> Available techniques of APC suffer from using sequences of seperate frames, where much energy is being lost. To address this issue, this paper proposes a semisupervised methodology to extract temporal consistency in a continuous sequence of unlabeled frames. The experimental results show that this is more robust and does not require much training data 22:
This study examines the planning and analysis of station passenger queuing and flows to offer rail transit station designers and transit system operators guidance on how to best accommodate and manage their rail passengers.
23:
Study of passanger flows, with hypothesis about passanger behaviour. They study the important factors for stations getting more crowded.
24:
Analysis on how passengers distribute along the platform.
25:
Application of a luminous platform in Stuttgart-Bad Cannstatt station
27:
Bluetooth counting in a city