Data driven enhancement of public transport planning and operations

Data driven enhancement of public transport planning and operations: Service reliability improvements and ridership predictions

van Oort, N. (TU Delft Transport and Planning)

2016-04

Automatic Vehicle Location (AVL) and smartcard data are of great value in planning, design and operations of public transport. We developed a transport demand model, which utilizes smartcard data for overall and what-if analyses, by converting these data into passengers per line and OD-matrixes and allowing network changes on top of a base scenario. This new generation model serves in addition to the existing range of transport demand models and approaches. It proved itself in practice during a case study in The Hague, where it helped the operator gain valuable insights into the effect of small network changes, such as a higher frequency.
Data also supports measures to improve service reliability. We introduced a new network design dilemma, namely the length of a transit line vs. its reliability. Long lines offer many direct connections, thereby saving transfers. However, the variability in operation is often negatively related to the length of a line, leading to poorer schedule adherence and additional waiting time for passengers. A data driven case study shows that in the case of long lines with large variability, enhanced reliability resulting from splitting the line could result in less additional travel time. This advantage compensates for the additional time of transferring if the transfer point is well chosen.

public transport
data
ridership prediction
service reliability

http://resolver.tudelft.nl/uuid:ebbab37f-58ef-4980-8a0e-2768d6a65c91

Proceedings of 6th Transport Research Arena: Warsaw, Poland

6th European Transport Research Conference, 2016-04-18 → 2016-04-21, PGE Narodowy, Warsaw, Poland

Institutional Repository

conference paper

© 2016 N. van Oort