The following studies which I have been involved in together with students and colleagues will be presented at TRB this year:
The Potential of Demand Responsive Transport as a Complement to Public Transport: An Assessment Framework and an Empirical Evaluation. (Session 293, Monday10:15 AM-12:00 PM Convention Center, 147A) Alonso-Gonzalez M., Liu T., Cats O., van Oort N. and Hoogendoorn S.
Individual, Travel and Bus Stop Characteristics Influencing Traverlers’ Safety Perceptions. (Session 556, Tuesday10:15 AM-12:00 PM Convention Center, 143B) Abenoza R.F., Ceccato V., Susilo Y. and Cats O.
Constructing Spatiotemporal Load Profiles of Transit Vehicles with Multiple Data Sources. (Session 649, Tuesday1:30 PM- 3:15 PM Convention Center, Hall E) Lou D., Bonnetain L., Cats O. and van Lint H.
Strategic Planning and Prospects of Rail-bound Demand Responsive Transit. (Session 660, Tuesday1:30 PM- 3:15 PM Convention Center, Hall E) Cats O. and Haverkamp J.
Demand-anticipatory Flexible Public Transport Service. (Session 784, Tuesday 8:00 AM- 9:45 PM Convention Center, Hall E) van Engelen M., Cats O., Post H. and Aardal K.
In addition, will be presiding:
Poster session 650 on Transit Service Disruptions: Impacts and Mitigation Measures (Tuesday 1:30 PM- 3:15 PM, Convention Center, Hall E)
Poster session 651 on Economic and Optimization Models for Integrated Service Planning (Tuesday 1:30 PM- 3:15 PM, Convention Center, Hall E)
In conjunction with the TRB conference, Jaime Soza Parra and I meet with Washington Metropolitan Area Transit Authority on Jan 11 to present and discuss the preliminary results of our evaluation of their headway-control experiment.
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.
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.
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.
New working paper entitled “Optimal Infrastructure Capacity for Rail-bound Demand Responsive Transit” is now available. Click here for full paper: Rail DRT
Abstract: Fully-automated services allow for greater flexibility in operations and lower marginal operational costs. The objective of this study is to determine the capacity requirements of an envisaged automated rail demand responsive transit (DRT) system which offers a direct non-stop service. An optimization model for determining the optimal track and station platform capacities for a rail-DRT system so that passenger, infrastructure and operational costs are minimized is formulated. The macroscopic model allows for studying the underlying relations between technological, operational and demand parameters, optimal capacity settings and the obtained cost components. The model is applied to a series of numerical experiments followed by its application to part of the Dutch railway network. The results of the numerical experiments and the case study application indicate that – unlike conventional rail systems in which stations often are capacity bottlenecks – link capacity properties are more critical for the performance of automated rail-DRT systems than station capacity. The performance is benchmarked against the existing service suggesting that in-vehicle times can be reduced by 10% in the case study network with the optimal link and station capacity allocation comparable to those currently available in this heavy rail network. A series of sensitivity analyses was performed to test the consequences of network and demand settings as well as the characteristics of future automated rail-DRT systems.