How can we keep you satisfied?

The public transport industry has along tradition of measuring passenger satisfaction for a number of purposes including monitoring and market analysis. Changes in passenger satisfaction over time are typically conceived in terms of absolute of perceived changes concerning service quality. However, we are all admittedly inclined to shift the importance that we attach to service attributes as time evolves.

The Swedish Public Transport Association (Svensk Kollektivtrafik) kindly granted me and Yusak Susilo from KTH access to a large rolling survey that they conduct since 2001. The results of 13 years of evolution in passenger satisfaction were presented last week on TRB in Washington DC. The survey data sums up to more than half a million records collected in 2001-2013. This made one of the committee members that took part in the session to comment that “we in the US can only drool from the possibilities made by it'”.

The work was performed together with Roberto Abenoza and Chengxi Liu. We constructed dynamic priority maps to visualize the trajectory of various service attributes in terms of their relative importance to overall satisfaction and their relative performance. This will support stakeholders such as agencies and operators to identify priority areas and benchmark it against past performance and other service providers.

The presentation is available here: TRB2015 OdedCats SKT


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



hEART 2014

The hEART2014 conference – the 3rd Symposium of the European Association for Research in Transportation was concluded today after 3 days of presentations and discussions in Leeds, UK. I have attended all three hEART conferences that have been organized so far and they have all proven a good opportunity to discuss ongoing works within an interesting research community.

I had two contributions on this conference. The first one is concerned with the evaluation of capacity increase in public transport projects. Many public transport investments are motivated by the need to relief congestion. However, conventional static assignment models that are used for cost-benefit analysis are not suitable for assessingi congestion related benefits as most of the negative conogestion impacts arise from system dynamics (e.g. reliability, crowding and denied boarding) and are therefore not reflected in average volume over capacity levels. Together with Jens West and Jonas Eliasson, we applied a new appraisal scheme for a metro line in Stockholm which takes into consideration the dynamic congestion effects and their variations. The presentation slides are available here: Appraisal of increased pt capacity hEART2014.

The second contribution is an attmept to infer the urban structure based on transport flow data. The availability of pervasive data collection facilitates the analysis of spatial and temporal distribution of activities based on people movements rather than based on land-use density proxies (as is conventionally done in urban planning and trip generation models). Together with Qian Wang and Yu Zhao, we formulated a spatial analysis technique to identify urban clusters and then classify them based on their temporal profiles with respect to incoming and outgoing flows. We applied this technique to Stockholm metropolitan area using public transport flow data. The analysis provide insights on the discrepency between the planning policy and the observed urban structure. This technique might be most valuable for anlayzing urban forms in mega-cities in emerging economies which undergo rapid changes. The presentation slides are avialable here: Urban clusters