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.
On-demand public transport is expected to become an increasingly important component of public transport systems, facilitated by vehicle automation. The potential of rail-bound on-demand services has been largely overlooked. Together with Jesper Haverkmap, who did his master thesis in the Dutch Railways, we determine the capacity requirements of an envisaged automated on-demand rail-bound transit system which offers a direct non-stop service.
The full paper published on Transportation Research Part B: Methodological is available here.
How many vehicles would such a system require? What are the costs associated with such a system? What are the track and station capacity required? What level of service will it deliver? What are the network saturation patterns given that vehicles can now perform route choice and choose the shortest path to passengers’ destination? What are the consequences for equity in service provision?
An optimization model is formulated for determining the optimal track and station platform capacities for an on-demand rail transit system so that passenger, infrastructure and operational costs are minimized. The macroscopic model allows for studying the underlying relations between technological, operational and demand parameters, optimal capacity settings and the obtained cost components.A series of sensitivity analyses are performed to test the consequences of a range of network structures, technological capabilities, operational settings, cost functions and demand scenarios for future automated on-demand rail-bound systems.
The model is applied to a series of numerical experiments followed by its application to part of the Dutch railway network. The performance is benchmarked against the existing service, suggesting that in-vehicle times can be reduced by 10% in the case study network while the optimal link and station capacity allocation is comparable to those currently available in the case study network. While network geometry and demand distribution are always the underlying determinants of both service frequencies and in-vehicle times, line configuration is only a determinant in the conventional system, whereas the automated on-demand rail service better caters for the prevailing demand relations, resulting in greater variations in service provision.
Here is a clip made by the Dutch Railways (NS) on the concept of on-demand services, denominated as Swarming transport.
How do disruptions propagate in public transport networks? for how long and how far away from the source are passengers affected?
In a new paper on Physica A: Statistical Mechanics and its Applications, we propose a method for quantifying the extent to which the network exhibits spillover effect.
Model results are evaluated for the Stockholm network using a dynamic non-equilibrium assignment model.
[News headline: Bomb threat against train travel; Travel chaos following two false bomb alarms; photo from Stockholm Central station]
Is satisfaction the sum of its parts? Behavioral scientists such as Daniel Kahneman and Dan Ariely provide ample evidence that human experience is not a simple summation of its parts. Different biases such as recency and salience effects have been observed.
How is it then with travel satisfaction? Is satisfaction with the door-to-door journey simply the sum of its parts? does the last part determine the overall impression? or does the worst experience loom over anything else?
Read the results of our research – together with Roberto Abenoza and Yusak Susilo from KTH – published in Transportation here. (open access)
Service reliability is often considered only at the operational management phase, while services are assumed to be perfectly reliable at the strategic and tactical planning phases. However, service (un)reliability has consequences on the effective frequency and thus on deficiencies in capacity allocation and passenger waiting times and on-board comfort.
Determining the dispatching headways of bus services in a city network is a multi-criteria problem that typically involves balancing between passenger demand coverage and operational costs.
Together with Costas Gkiotsalitis from NEC Labratories Europe, we develop and apply a reliability-based optimization framework for setting the dispatching headways of bus lines that considers historical operational data and is aware of the passenger waiting time variability at each stop and how it is affected when changing the planned dispatching headways.
Check out the full paper published on Transportation Research Part C – Emerging Technologies following this link
We hope that this work will contribute to a new generation of tactical planning methods that account for service uncertainty.