Public transit route planning through lightweight linked data interfaces

While some public transit data publishers only provide a data dump – which only few reusers can afford to integrate within their applications – others provide a use case limiting origin-destination route planning api. The Linked Connections framework instead introduces a hypermedia api, over which the extendable base route planning algorithm “Connections Scan Algorithm” can be implemented. We compare the cpu usage and query execution time of a traditional server-side route planner with the cpu time and query execution time of a Linked Connections interface by evaluating query mixes with increasing load. We found that, at the expense of a higher bandwidth consumption, more queries can be answered using the same hardware with the Linked Connections server interface than with an origin-destination api, thanks to an average cache hit rate of 78%. The findings from this research show a cost-efficient way of publishing transport data that can bring federated public transit route planning at the fingertips of anyone

Trip-Based Public Transit Routing

We study the problem of computing all Pareto-optimal journeys in a public transit network regarding the two criteria of arrival time and number of transfers taken. We take a novel approach, focusing on trips and transfers between them, allowing fine-grained modeling. Our experiments on the metropolitan network of London show that the algorithm computes full 24-hour profiles in 70 ms after a preprocessing phase of 30 s, allowing fast queries in dynamic scenarios.Comment: Minor corrections, no substantial changes. To be presented at ESA 201

Trip-Based Public Transit Routing Using Condensed Search Trees

We study the problem of planning Pareto-optimal journeys in public transit networks. Most existing algorithms and speed-up techniques work by computing subjourneys to intermediary stops until the destination is reached. In contrast, the trip-based model focuses on trips and transfers between them, constructing journeys as a sequence of trips. In this paper, we develop a speed-up technique for this model inspired by principles behind existing state-of-the-art speed-up techniques, Transfer Pattern and Hub Labelling. The resulting algorithm allows us to compute Pareto-optimal (with respect to arrival time and number of transfers) 24-hour profiles on very large real-world networks in less than half a millisecond. Compared to the current state of the art for bicriteria queries on public transit networks, this is up to two orders of magnitude faster, while increasing preprocessing overhead by at most one order of magnitude

General scores for accessibility and inequality measures in urban areas

In the last decades, the acceleration of urban growth has led to an unprecedented level of urban interactions and interdependence. This situation calls for a significant effort among the scientific community to come up with engaging and meaningful visualizations and accessible scenario simulation engines. The present paper gives a contribution in this direction by providing general methods to evaluate accessibility in cities based on public transportation data. Through the notion of isochrones, the accessibility quantities proposed measure the performance of transport systems at connecting places and people in urban systems. Then we introduce scores rank cities according to their overall accessibility. We highlight significant inequalities in the distribution of these measures across the population, which are found to be strikingly similar across various urban environments. Our results are released through the interactive platform: www.citychrone.org, aimed at providing the community at large with a useful tool for awareness and decision-making

Route Planning in Transportation Networks

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4, previously published by Microsoft Research. This work was mostly done while the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at Microsoft Research Silicon Valle

Transfer Customization with the Trip-Based Public Transit Routing Algorithm

In the context of routing in public transit networks, we consider the issue of the customization of walking transfer times, which is incompatible with the preprocessing required by many state-of-the-art algorithms. We propose to extend one of those, the Trip-Based Public Transit Routing algorithm, to take into account at query time user defined transfer speed and maximum transfer duration. The obtained algorithm is optimal for the bicriteria problem of optimizing minimum arrival time and number of transfers. It is tested on two large data sets and the query times are compatible with real-time queries in a production context