The more links the better network robustness is?

Investments in public transport projects are increasingly motivated not merely based on travel time savings but also in relation to improvements in service reliability, comfort and robustness. However, there are no standard practices for incorporating the impacts of alternative investments on network robustness.

Together with Erik Jenelius from KTH,  a methodology for assessing the value of new links for public transport network robustness, considering disruptions of other lines and links as well as the new links themselves, was developed. We applied this methodology to a light rail line in Stockholm.

A distinction is made between the value of robustness, defined as the change in welfare during disruption compared to the baseline network, and the value of redundancy, defined as the change in welfare losses due to disruption. Topological studies concluded that redundancy is an important feature for network robustness. Interestingly, we found that In some cases, the new line results with seemingly paradoxical effects as it leads to greater disruption costs.

Check the full paper here


Planning for the unplanned – where shall we allocate reserve capacity?

In the field of network vulnerability and resilience, there is growing evident that network redundancy is a key determinant of network capability to withstand shocks. However, introducing redundancy to rapid public transport networks is very expensive and it is thus important to identify the network elements that will benefit most network performance in case of disruption.

It is crucial to invest in network redundancy without making redundant investments!

Den Haag CS 2 - smaller

Together with my colleague from KTH, Erik Jenelius, we propose and demonstrate in a new paper a methodology for evaluating the effectiveness of a strategic increase in capacity on alternative public transport network links to mitigate the impact of unexpected network disruptions. For a given disruption scenario, a set of links is first identified as candidates for capacity enhancement based on (i) their initial saturation levels in terms of volume to capacity ratios, and (ii) the overloading in terms of increased saturation that occurs due to the disruption. Second, the effect of capacity increase is evaluated for each candidate link by comparing the disruption impacts with and without increased capacity. Based on the evaluation the most effective of the mitigation actions can be identified.

We applied the methodology to  a case study of the high frequency public transport network of Stockholm. To evaluate the public transport system performance under varying conditions, BusMezzo, a dynamic public transport operations and assignment model was used.

The method presented in this paper could support policy makers and operators in prioritizing measures to increase network robustness by improving system capacity to absorb unplanned disruptions.


Click here for the full paper.


MIT transit group seminar

On Thursday, November 20, I had the privilage to present highlights from my research to MIT transit group led by Prof. Nigel Wilson and thereafter had the opportunity to disucss ongoing research activities with group members.

The seminar was entitled: “Unraveling and modeling the dynamics of public transport systems: Theory and applications”, where I briefly presented the transit operations and assignment model, BusMezzo, and its applications to service reliability and control, congestion and evaluation of increased capacity as well as service disruptions and the value of real-time information provision.

For a reduced version of the presentation, click here: MIT seminar 20112014 v1



What can history tell us about the future? About predicting public transport travel times

It is common wisdom that while man should learn from history. At the same time, another prominent wisdom is that history never repeats itself. Both assertions are also true for public transport operations. When predicting future states of the system, it is advisable to account for historical records whilst bearing in mind that it will not evolve in the exact same fashion. Instead, the underlying dynamics of the current system as well as inherent stochastic will result with different performance. This makes the task of predicting vehicle trajectories and their arrival times at downstream stops a non-trivial task. Such predictions are useful both for operators for better managing the fleet in real-time as well as for passengers that wish to reduce their travel uncertainty and enable them to take more informed decisions.

Today and yesterday I presented on IEEE Intelligent Transport System Conference (ITSC) in Qingdao, China, two studies on prediction schemes for public transport and the generation of real-time travel information.One study together with Masoud Fadaei Oshyani, who is doing his PhD under my supervision, proposes a hybrid model to integrate several sources of information on downstream travel conditions for buses. The contribution of each information source (within-day instantaneous data, day-to-day historical data and static schedule data) and related parameters are estimated using a genetic algorithm optimization. Predictions improve considerably compared with the currently-used scheme and there are good indications on the transferability of the scheme. For the presentation slides click here: Real-time Bus Departure Time Predictions

The second study investigated real-time predictions for light rail trains. Light rail trains are often characterized by mixed-operations: running partially in mixed traffic (in some cases even in pedestrian street) and in segregated right-of-way, alternately as well as with different levels of priority in crossings. A prediction scheme that is based on constructing link-specific speed profile and then computing for a given current position and speed the remaining travel time was applied for a case study in Bergen, Norway (thanks to Edouard Naye). The scheme elevates the prediction accuracy to the same level  that was reported by previous studies for metro systems. For the presentation slides click here: Real-time Predictions for Light Rail Train Systems



How does zero-fare public transport fare?

What are the implications of providing public transport without charging fees from users? Together with Yusak Susilo and Triin Reimal form KTH Royal Institute of Technology in Sweden, we evaluated the impacts of free-fare public transport policy by investigating the case of Tallinn, the capital of Estonia. The city introduced a free-fare public transport policy in January 2013 and became the largest city in the world so far to offer free-fare services to all of its inhabitants. While previous implementations of similar measures shed some light on the anticipated impacts of such a policy, there is lack of analysis which limits its validity. The case of Tallinn is a full-scale experiment that provides a unique opportunity to empirically evaluate economic, social, mobility and level-of-service aspects.

In the first phase of the policy evaluation, we conducted a macro-level empirical analysis of service performance, passenger demand and accessibility for various travelers’ groups. The results indicate that the the free-fare policy accounts for an increase of 1.2% in passenger demand with the remaining increase (1.8%) attributed to  extended network of public transport priority lanes and increased service frequency. The relatively small effect could be explained by the previous price level (36% free + 24% concessions) and public transport share (40%) as well as the consideration of the short-term impact. The evidence-based policy evaluation is instrumental in supporting policy making and facilitating the design of public transport pricing strategies. I discussed our findings in an interview to Citiscope, an urban magazine, which is available here.

The full article where we report our findings is available here. The paper includes a discussion on the transport economy theory and practical arguments for and against the scheme, lessons from previous experiences from which Tallinn clearly differs, a model that accounts for supply changes, an estimation of the elasticity of service frequency and reflecting on the economic viability of this scheme.

In the ongoing second phase of this study, we analyze detailed travel diary that a large sample of Tallinn residents reported directly before and a year after public transport became zero-fare in Tallinn. This will enable to analyze how the policy influences individual travel patterns, modal choice and accessibility. Moreover, we assess changes in mobility patterns for different user groups to find how fair the zero-fare policy is. If you are interested in taking part in this project drop me an email (see about page)!