Transport Economist
I am very proud to finish my Data Scientist in Python training with Dataquest. Here is my shiny new certificate…
It took 8 months of intensive learning of several hours almost every day. I am now able to perform analysis on large volumes of data that don’t fit in Excel and implement some machine learning algorithms.
Now I know what I don’t know! There is so much more…
Most transportation research techniques have been developed in the 1960s and 1970s, but our computational capabilities have vastly grown since then. A lot has happened in statistics and new techniques have been developed since then. Application of those new approaches in transport economics seems to be slow.
I have lately been learning different prediction techniques, which have advantages over more traditional approaches. Decision trees and neural networks are capable of capturing non-linear relationships in data, with much lower levels of error than traditional k-nearest neighbours, linear and logistic regression approaches.
Application of these techniques in transport economics has not been wide, but certainly has potential. Especially with more transportation data becoming available to researchers.
To me training of a decision tree or a neural network still seems to be a challenging art to me. Challenges come from selection of hyperparameters. Some approaches exist for optimisation of hyperparameters in machine learning, but it seems that the traditional approach of grid search, which is simply searching through manually selected subset of hyperparameter space for the parameters that return a model with the lowest error, seems to work ok for the models that I have been calculating lately.
I am looking forward to new projects in the future to apply the machine learning approaches that I have been learning.
SQL is one of the most popular technologies that is used today for working with data. Of course, one can use visual tools, but SQL becomes important when one has to deal with large ammoutns of structured data to answer increasingly more complex questions.
I have started feeling that MS Access with its query designer that I have been using for decades is not sufficient for the work I am doing anymore. Therefore, using the extra time that the current uncontrolled Corona virus pandemic is providing, I have been learning some SQL. I have just obtained a certificate on SQL for data analysis course that I finished at Dataquest.
I must say I feel rather proud being able to write the query below for one of the training excercises.
Continue reading “Transitioning from MS Access query designer to SQL”
Since spring of 2020 I have been working on the Maritime Connectivity Study for the World Bank. The final report of this study has just been finished.
In my view the most important contribution of this work was the development of Port and Shipping data framework with the purpose of providing a tool for country- and port-level benchmarking. We considered data strategic value and took into account the key performance indicators that are relevant in the context of collecting port and maritime data.
The data framework is developed at two levels. At the country level of the framework, the high level indicators are included. They describe the country-level data in the five areas: connectivity and accessibility, shipping cost, efficiency, environmental impacts, and regulatory environment. At the port level, the framework contains indicators in the same five areas as the country-level framework, but at a more detailed port level.
We validated the framework by applying for benchmarking of three of the Southern Cone countries: Argentina, Paraguay and Uruguay.
The results of this study will help the Infrastructure Chief Economist’s Office (INFCE) with their InfraSAPs country diagnoses to understand the needs for infrastructure interventions.
Volvo has published a blog piece on longer and heavier vehicles called “Longer and heavier vehicles: The ultimate guide”. It gives a nice summary of the benefits that the introduction of higher capacity transport could bring to the road transport. The facts outlined in this blog post are in line with the conclusions that came from a research that we worked on at the ITF/OECD.
Some of the most prominent benefits include reduction of carbon emissions by 15-40%, reduction of requirements of drivers, safety benefits and limited impacts on the road infrastructure.
To add to my PHP and C programming skills, I have lately been following Python training. The language has extensive applications for data processing, analysis and visualisation.
Something that I am particularly interested in is the ability to efficiently process larger data volumes, e.g. 40-50 gigabytes, which I lately have started needing in my research work.
Excited to get my first certificate on Python fundamentals from Dataquest: https://app.dataquest.io/view_cert/0ZU31PY7EZUHU8473EMK/
Transport for London has recently published a study on the economic benefits of walking and cycling. Although the results are not surprising, it is an important data point and a good argument for those of us who are cycling enthusiasts, care about the livability of cities, and hope to improve the situation.
The TfL research shows that retail businesses are benefiting from increased walking and cycling traffic. Improving street design to accommodate better those road users can increase retail spend by up to 30%. As a result, businesses on those streets are able to generate higher revenues.
“People walking and cycling visit high streets more frequently and spend more money there compared to people in cars”. And if you think about it, it is logical – cars are expensive to buy and maintain, and almost impossible to park in a city. It means that car drivers (doing the same jobs) have less disposable income to spend on shopping, and additionally, it is a big effort to find a spot to park the car near a shopping street.
Data show that people who walk to the high street spending up to 40% more than people who drive. And bike parking delivers 5 times more retail spend per square meter than car parking.
Businesses tend to overestimate their customers car use. I have seen this in Brussels, in local council discussions over the renovations of place Jourdan. And this research confirms the same: while businesses in Lea Bridge road think that 63% of their customers come by car, the actual percentage is only 20%. There is aso higher walking and public transport use than businesses think.
Politicians of other cities may be inspired by these conclusions.
https://www.sciencedirect.com/science/article/abs/pii/S0305900617300636
http://content.tfl.gov.uk/walking-cycling-economic-benefits-summary-pack.pdf
http://content.tfl.gov.uk/street-appeal.pdf
Colleagues from the Faculty of Business and Economics at the University of Antwerp have made three very nice videos that explain the current macroeconomic situation (in Dutch). They are giving a very structured and correct explanation of what is happening and why. Even if you know all these things, worth to watch to remind you the basic principles.
1) Current monetary policy of the ECB
2) Fiscal and monetary policy in the corona crisis
3) Inflation or deflation in the corona times
In May 2019 Siemens started a field test of the eHighway system on a strech of A5 highway in Germany. It is now exactly a year since it started its operation. In my view it is one of the most promising technologies for decarbonising road freight transport on large scale in countries with developed road infrastructure.
Here is a nice video showing the eHighway system in operation:
We usually hear that it is dangerous to use mobile phone while driving a car. Most countries even have laws forbidding their use while at the steering wheel.
It turns out that this can be a dangerous activity while at sea too. While doing research on maritime statistics I stumbled on a report of this accident.
In October 2018, a Tunisian ferry Ulysse collided in good weather with the container ship CSL Virginia in the Mediterranean Sea. The ship officer on watch had apparently been distracted by a mobile phone.