Recovery time and propagation effects of passenger transport disruptions

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]

How does satisfaction sum up?

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)

Method for the reliable determination of service frequencies

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.

Demand anticipatory operations

On-demand (known also as flexible or demand responsive) services rely on algorithms that determine which vehicle to assign to which passenger travel request. This becomes especially relevant as developments in vehicle automation and the shared economy call for new developments in routing flexible transport services.

Together with colleagues from Applied Mathematics and Transdev (Connexxion) in the Netherlands, we propose a new type of insertion algorithm: an online dynamic insertion algorithm with demand forecasts. Hence, this algorithm beyond responsiveness by incorporating demand anticipatory capabilities. The performance of this algorithm is tested in a simulation model for a case study network located in vicinity to Amsterdam.

See the full paper in Transportation Research Part E, by following this link.

When combining the new insertion algorithm with empty vehicle rerouting, 98% of passenger rejections are eliminated and travel and waiting times are reduced by up to 10 and 46% respectively, compared to traditional insertion algorithms. A sensitivity analysis tested performance robustness to variations in operational and demand conditions including different fleet compositions.

 

 

Individual and Synergetic Effects of Transit Service Improvement Strategies

When introducing several measures  to prioritize public transport services (i.e.dedicated lanes, holding control and boarding from all the rear door) at the same time – is the total effect larger than its parts?  In a new paper with Jens West, we assess the implementation of several bus service improvement measures in a simulation model. We then analyze the effect of isolated and combinations of measures, and validates the model using field experiment data from Stockholm. We found that the three tested measures exercised negative synergy effects, with their combined effect being smaller than the sum of their marginal contributions, except for headway-based holding, which exercised positive synergy effects with the two other measures.

Click here for the full paper

(agency’s campaign signs in Swedish advising passengers that they can also board the bus from the rear door when boarding bus line 4)