Network evolution

Network indicators are widely used for characterizing  transport network topology and its performance as well as provide insights on possible developments. Public transport networks change dramatically over time and are have a significant impact on urban and regional development. The structure of transport networks is the outcome of a large number of infrastructure investment decisions taken over a long time span. Little is known however on how rail bound public transport networks and their network indicators have evolved into their current form as it is surprisingly difficult to obtain data on historical network states.

This study reports a longitudinal analysis of the topological evolution of a multimodal rail network by investigating the dynamics of its topology for the case of Stockholm in 1950-2025. The starting year marks the opening of the metro system while the end year is set to mark the completion of the current development plan.

In the link below you can get an impression of the network evolution with 5 years intervals. Note the changes in coverage (scale) and density.

https://www.dropbox.com/s/iklil4shu2wgd6b/Evolution.wmv?dl=0

Based on a compilation of network topology and service properties, a year-on-year analysis of changes in global network efficiency and directness as well as local nodal centrality were conducted.

Changes in network topology exhibit smooth long-term technological and spatial trends as well as the signature of top-down planning interventions. Stockholm rail network evolution is characterized by contraction and stagnation periods followed by network extensions and is currently undergoing a considerable densification, marking a shift from peripheral attachment to preferential attachment. It is remarkable that in 2025 the Stockholm network will offer the same level of directness, connectivity and accessibility that were offered in 1950 for a much smaller area. This is driven by the dramatic shift in the modal composition of Stockholm rail-bound network during the analysis period.

Read the full paper following this link.

How much are you willing to pay for flight safety?

Following the tragic events of MH17 plane crash where 298 passengers and crew members were killed in July 2014 (for details see this article on the BBC), flight safety and security was high on the public agenda in the Netherlands.

This made me wonder:

(1) how important is safety in our choice of flight?

(2) what are the factors that determine how safe do we perceive a flight alternative to be?

Joey Blange decided to take this challenge for his master thesis work, and together with Eric Molin and Caspar Chorus, we designed two stated preference experiments: in a first experiment, combinations of airline and route attributes are evaluated in terms of safety that is captured on a rating scale; in a second experiment, safety perception is treated as an attribute and traded-off against other flight attributes to arrive at a flight choice

The results of both models are then combined to calculate the willingness to pay values for improvements made to a range of airline and route attributes, taking into account socio-demographic variables and psychological traits. The median willingness to pay value to improve safety perception with 1 point on a 1-7 scale varies between 75 and 448 euro, depending greatly on the initial value.

For the full details of the study including the results, see our publication on the Journal of Air Transport Management. 

Prediction for proactive mitigation of bus bunching

 

A new paper proposes a data driven method to predict Bus Bunching in real-time followed by the selection and deployment of a corrective action based on the assessment of bunching likelihoods. The method was validated using one-year data of 18 real-world bus routes. This combined prediction-control approach can contribute to more proactive bus operations and improved service reliability.

 

Link to the full paper

IMGP0091_c

 

 

 

 

 

 

 

Exposing the role of exposure in public transport risk analysis

In the last several years, I have investigated the impact of service disruptions in public transport networks. In a series of network topology and dynamic transit assignment studies, I have looked into indicators of link criticality, measures of impacts on system performance, mitigation value of real-time information provision, identifying strategic links for increased capacity and the robustness value of new links and extension plans.

One common limitation to all of these studies was the lack of information on the probabilities associated with disruptions. This prevented a complete risk analysis and assessing the (e.g. annual) costs and benefits associated with disruptions and mitigation measures.

Together with Menno Yap and Niels van Oort, the frequency and duration of various disruption types on each public transport mode (train, metro, tram and bus) were estimated based on a unique dataset. We also identify which is the primary predictor of each variable to allow researchers and professionals in other contexts to estimate disruption probabilities in the lack of local data.

TramDHdisrupted

We propose a method for embedding link exposure into the identification and evaluation of critical links and perform a risk analysis for the multi-modal public transport network of the Rotterdam The Hague Metropolitan Area. By comparing the results with the conventional measures, we demonstrate that disregarding exposure risks prioritizing heavily utilized links instead of those which are actually the weakest.

Click here for the link to the full paper.