Congrats Jorik Grolle for winning the second prize for his graduation thesis at the CVS transport colloquium! Jorik studied the costs and benefits of network designs for the European high-speed rail network.
What is the crowding level that passengers find acceptable under various infection rate levels of the corona epidemic?
Fresh results from our survey show that train users are divided on this topic. Do you count yourself among the ‘crowd avoiders’ or ‘willing travelers’?
Together with Sanmay Shelat and Sander van Cranenburgh.
You can also find a short piece on the impacts of the pandemic on public transport here:
You may also check out TU Delft’s page which compiles studies on analyses and technologies of interest in the transition phase.
At the Smart Public Transport Lab we are currently undertaking a series of studies related to the corona crisis. Given the urgency and the gravity of what is at stake, we share results as soon as we are confident that we can contribute to the policy and scientific debate with sound models and empirical findings.
By now we are ready to share findings from two studies focusing on the role of mass transit and ride-sharing in spreading the virus. Welcome to check out our short articles following these links:
– Virus spreading in public transport networks: the alarming consequences of the business as usual scenario [A slightly modified version is available on ResearchGate]
And stay tuned for upcoming results from on-going studies.
Presenting at the Transit Data 2019 – the 5th International Workshop and Symposium on research and applications on the use of massive passive data for public transport on 8th -10th July in Paris, France. Contributions include the following studies:
– Generating network-wide travel diaries using smartcard data
– Enhanced complex network representation of public transport for accessibility assessment based on General Transit Feed Specification data
– Impact analysis of a new metro line in Amsterdam using automated data sources
– Predicting and clustering station vulnerability in urban networks
– Investigating the effects of real-time crowding information (RTCI) systems in urban public transport under different demand conditions
You may find the sessions by browsing the program.
Crowding in public transport can be of major influence on passengers’ travel experience and therefore affect route and mode choice. The impact of crowding on passenger choices has been estimated in many studies by means of stated-preferences choice experiments. Respondents are then asked to make hypothetical choices under a range of scenarios based on which choice models can be estimated, including quantifying the impact of on-board crowding on route choice. This results in in-vehicle multiplier values ranging between 1-2.7 (!). Results from meta-analysis of these studies have been for example reported and used in evaluating capacity increase investments (see the case of our study of a metro line in Stockholm).
These estimates seem strangely high. They imply that passengers will rather travel twice as long if they can have a seat instead of to travelling in a densely crowded vehicle. These has severe ramifications for project appraisal – do you invest in increasing vehicle size, higher frequency or higher speed? In a choice experiment it is easy to indicate that you rather wait for the next vehicle or travel longer than to ride a busy vehicle. However, there was very scarce evidence that people actually do these trade-offs in reality. We therefore wanted to find out to what extent crowding impacts passenger route choices based on observed behavior. This is now possible thanks to large-scale smart card deployment.
See full paper here: “Crowding valuation in urban tram and bus transportation based on smart card data”
In this study, crowding valuation for urban tram and bus travelling is determined fully based on revealed preference data. Urban tram and bus crowding valuation is estimated in a European context based on a Dutch case study network. Based on the estimated discrete choice model, we conclude that crowding plays a significant role in passengers’ route choice in public transport. The average crowding multiplier of in-vehicle time equals 1.16 when all seats are occupied. For frequent travellers, this value is equal to 1.31. Our study results suggest that infrequent travellers do not incorporate expected crowding in their route choice. These values are significantly lower than those reported in past studies based on choice experiments.
The insights gained from our study can support the decision-making process of policy-makers, by quantifying the benefits of measures aiming to reduce crowding levels for example in a cost–benefit analysis framework.