NDLI: Better transit routing by exploiting vehicle GPS data

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Better transit routing by exploiting vehicle GPS data

Content Provider	ACM Digital Library
Author	Delling, Daniel Santaroni, Federico Italiano, Giuseppe F. Pajor, Thomas
Abstract	Current public transport journey planners are mostly based on timetables, i.e., their planning assumes implicitly that all transit vehicles run on schedule. Unfortunately, this is not always the case, as unpredictable delays may occur frequently and for many unplanned reasons. In this scenario, deviations from the original schedule may have quite a negative impact on the quality of the journeys provided by timetable-based planners, as they increase the probability of missed connections and thus result in longer and unpredicted waiting times for the passengers. In this paper, we try to find effective solutions for mitigating this problem and evaluate them empirically on the metropolitan public transportation network of Rome (Italy), where delays are frequent. We first try to assess whether the availability of dynamic information on the geo-location of transit vehicles (via GPS data) may help to improve the quality of the journeys offered by a planner. The main findings of our experiments are that GSP data provides substantial benefits for short journeys, while it does not seem to provide enough insight to improve long journeys. In particular, even with the use of GPS data, a journey planner can still be rather unreliable for long journeys, as there can be large fluctuations between the times predicted by the planner and the actual travel times incurred by passengers. As a second contribution of our work, we provide and evaluate experimentally new routing algorithms to improve the reliability of public transport journey planners.
Starting Page	31
Ending Page	40
Page Count	10
File Format	PDF
ISBN	9781450331388
DOI	10.1145/2674918.2674923
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-11-04
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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