Securing eWallet and eTicket Apps

Last year, I was requested to do a research on Blockchain and looking for ways to implement it here. At that time, due to limited resources, I just spend a few days to do the research and thought of a few applications based on the Blockchain.

Two of them are eWallet and eTicket. Out of many, I found Aventus is one of the key company doing it. Today, they have released another feature of their product. Check it out here.

Of course, the implementation of it won't be easy. Unless you do understand how blockchain works.
Check it out at CNET to get a brief on how it works.

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Improving JP with Improvised Prediction Model

Yesterday I wrote on low ridership (read here), of which out of many factors, information availability for journey planning contributes 12% from overall factors. However, the value is based on a survey on one location; that is Penang. Nonetheless, I believed, information availability is the key importance for a smooth journey planning, no matter of the services used or the impact to ridership. The reason for this is based on comments in Google Play for various Journey Planner such as Moovit, Transit Apps, SWIVL, SITS, etc, whereby many user stated their frustration on the accuracy of information displayed by the apps.

Beside information availability being a vital role for riders to plan their journey and services to use, based on the articles referred to in the post, the information must be also reliable, accurate and at real-time (or at least near real-time). If you received an information that was accurate a few minutes ago, there is a probability of the information to be inaccurate at the time of view or receive resulting in the inaccurate plan and action.

How to improve information accuracy, no matter how and when the information arrives at the user?
I did a quick review too on the following articles:

1. https://www.papercast.com/insights/predict-accurate-bus-arrival-journey-times/
2. https://core.ac.uk/download/pdf/82293981.pdf
3. https://escholarship.org/uc/item/51t364vz
4. http://gamma.cs.unc.edu/TROUTE/
5. https://www.researchgate.net/publication/274028208_Multimodal_Public_Transit_Trip_Planner_with_Real-Time_Transit_Data
6. https://repositorio-aberto.up.pt/bitstream/10216/6817/2/26915.pdf
7. https://jungleworks.com/predicting-accurate-arrival-time/
8. https://www.researchgate.net/publication/332342499_Survey_of_ETA_prediction_methods_in_public_transport_networks
9. https://dl.acm.org/citation.cfm?id=3219819.3219874
10. https://pdfs.semanticscholar.org/8c95/f20cd049e5f0d35466544958631e3e10c258.pdf
11. https://www.papercast.com/wp-content/uploads/2017/06/Papercast_A4_Better-ETA_2017.pdf
12. https://datascience.stackexchange.com/questions/10301/how-to-predict-eta-using-regression
13. https://ieeexplore.ieee.org/abstract/document/1212964
14. https://www.tandfonline.com/doi/abs/10.1080/15472450600981009
15. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8667.2004.00363.x
16. https://journals.sagepub.com/doi/abs/10.3141/1666-12
17. https://link.springer.com/chapter/10.1007/978-981-13-3393-4_29
18. https://patents.google.com/patent/US10254119B2/en
19. https://arxiv.org/abs/1904.05037
20. https://patents.google.com/patent/US20190130260A1/en
21. https://www.tandfonline.com/doi/abs/10.1080/19427867.2017.1366120
22. https://link.springer.com/article/10.1007/s12652-019-01198-1
23. https://arxiv.org/abs/1904.03444
24. https://patents.google.com/patent/US20190051154A1/en
25. https://ieeexplore.ieee.org/abstract/document/8691701

Based on the articles above, below is the gist of the findings:

1. Reliable and accurate information at real-time (or near real-time) is critical for smooth journey planning
2. Recent technology (BDA, AI, ML & IoT) has resulted in many new algorithms to predict accurate ETA to be used in JP
3. The most recent is KNN which requires lots of historical data and real-time tracking for accurate prediction

Recommendation:

1. To research and try-n-error all the algorithms to find the best algorithm to predict accurate ETA to be used in the Malaysian environment
2. To define the best algorithm based on time of request (peak or non-peak)
3. To come out with a new algorithm and flow to ensure the ETA for JP is 95% accuracy during non-peak and 90% accuracy during peak.

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Is JP contributors to low ridership?

I was thrown a question on this and was also told the reason for low ridership. I am curious about it when it does not make sense to me. The two reasons shared to me are:
1. Ridership goes down because there is no Journey Planner
2. The customer doesn't need real-time data. They just want to have information about the services. It can be 3 minutes ago of information as long as we stated that the information is true 3 minutes ago.


It actually raised a few questions in my mind.
1. Was it really because of JP? or there are different factors.
2. If the information true 3 minutes ago, and I was standing in from of station or stops getting information that was updated saying that the bus or train is at 1 km away 3 minutes ago, will there be bus now (after 3 minutes) or has the bus pass through me 1 minutes ago because the information I get is delay 3 minutes. If I'm doing the planning right now, how can I plan with previous 3 minutes data? Should I add 3 minutes and assume the location of the bus upon receiving the info?

So, to answer my question, I do a quick review of many studies done earlier as listed below.

1. https://www.edmonton.ca/city_government/documents/RoadsTraffic/transit_factors_ridership.pdf
2. https://www.researchgate.net/publication/282656951_Factors_Affecting_to_Public_Bus_Transport_Ridership_A_Case_of_Capital_Colombo
3. https://pdfs.semanticscholar.org/d3f0/63f546d97e676029dc1267ae1c09d9d298c3.pdf
4. https://www.researchgate.net/publication/298371532_Factors_Affecting_Bus_Ridership_With_Respect_to_Passenger_Demography_A_Case_Study_of_Seberang_Perai_Pulau_Pinang_Malaysia
5. http://www.reconnectingamerica.org/assets/Uploads/ridersipfactors.pdf
6. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.683.858&rep=rep1&type=pdf
7. https://fas.org/sgp/crs/misc/R45144.pdf
8. http://www.ncarpo.org/uploads/1/3/8/1/13819061/factorsaffectingridership_20181004.pdf
9. https://la.streetsblog.org/2016/01/29/what-factors-are-causing-metros-declining-ridership-what-next/

Findings:
1. From all papers, only 2 mentioned about information availability as one of the factors impacting ridership.
2. From the two papers, 1 mentioned real-time info is required but it is not one of the key factors. It is 1 out of many factors.
3. On the other papers (studies on bus ridership in Penang) - the author stated that the availability of information only accounts for 12% of the total factors contributing to low ridership.
4. The biggest factors contributed to low ridership are:
4.1 Service availability & reliability
4.2 Drivers behavior
4.3 Fare
4.4 Access to the services
4.5 Disruptor - e-hailing services

Further review on trip planning in new york based on the following two papers revive a few interesting facts.

1. https://dl.acm.org/citation.cfm?id=2811271.2811272
2. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.888.1033&rep=rep1&type=pdf

1. Despite was told that in new york, the information display shown is updated 3 minutes ago, I found out that it is actually a real-time update. Just that it stated updated 3 minutes ago because there is no new information at that time of the query.
2. The above two papers clearly state the importance of real-time information and it must be reliable info.

Conclusions:
1. JP is not the main key factors contributing to low ridership but JP availability can affect the decision of the public to use available services.
2. Service availability & reliability is the biggest contributor. If the OTR is reduced, definitely there will be a reduction of riders compared before OTR reduced.
3. e-Hailing has played a significant role in low ridership too. I believed, if Gojek successfully landed here in Malaysia, it will play a vital role in lowering the ridership too.
4. Real-time information and real-time update of information is the key to provide reliable info.

Recommendation:
1. JP is something needed but the information displayed in it shall be reliable and trustworthy. Thus improving the information relay process shall be the main focus.
2. Service availability and reliability shall be improved.
3. Implementation of BOS to complement each other for the benefit of the public.


* it is updated with a recommendation as many have shared their opinion on the post.

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