Dynamic Time-Dependent Route Planning in Road Networks with User Preferences

There has been tremendous progress in algorithmic methods for computing driving directions on road networks. Most of that work focuses on time-independent route planning, where it is assumed that the cost on each arc is constant per query. In practice, the current traffic situation significantly influences the travel time on large parts of the road network, and it changes over the day. One can distinguish between traffic congestion that can be predicted using historical traffic data, and congestion due to unpredictable events, e.g., accidents. In this work, we study the \emph{dynamic and time-dependent} route planning problem, which takes both prediction (based on historical data) and live traffic into account. To this end, we propose a practical algorithm that, while robust to user preferences, is able to integrate global changes of the time-dependent metric~(e.g., due to traffic updates or user restrictions) faster than previous approaches, while allowing subsequent queries that enable interactive applications

Energy-Optimal Routes for Electric Vehicles

Abstract. We study the problem of electric vehicle route planning, where an important aspect is computing paths that minimize energy consumption. Thereby, any method must cope with specific properties, such as recuperation, battery constraints (over- and under-charging), and frequently changing cost functions (e. g., due to weather conditions). This work presents a practical algorithm that quickly computes energy-optimal routes for networks of continental scale. Exploiting multi-level overlay graphs [26, 31], we extend the Customizable Route Planning approach [8] to our scenario in a sound manner. This includes the efficient computation of profile queries and the adaption of bidirectional search to battery constraints. Our experimental study uses detailed consumption data measured from a production vehicle (Peugeot iOn). It reveals for the network of Europe that a new cost function can be incorporated in about five seconds, after which we answer random queries within 0.3ms on average. Additional evaluation on an artificial but realistic [22, 36] vehicle model with unlimited range demonstrates the excellent scalability of our algorithm: Even for long-range queries across Europe it achieves query times below 5ms on average—fast enough for interactive applications. Altogether, our algorithm exhibits faster query times than previous approaches, while improving (metric-dependent) preprocessing time by three orders of magnitude.

Towards Realistic Pedestrian Route Planning

Pedestrian routing has its specific set of challenges, which are often neglected by state-of-the-art route planners. For instance, the lack of detailed sidewalk data and the inability to traverse plazas and parks in a natural way often leads to unappealing and suboptimal routes. In this work, we first propose to augment the network by generating sidewalks based on the street geometry and adding edges for routing over plazas and squares. Using this and further information, our query algorithm seamlessly handles node-to-node queries and queries whose origin or destination is an arbitrary location on a plaza or inside a park. Our experiments show that we are able to compute appealing pedestrian routes at negligible overhead over standard routing algorithms

Algorithmic Analysis of Intermodal Transport Network

Tato práce je zaměřena na analýzu intermodální dopravní sítě pomocí multikriteriálního algoritmu s ohledem na priority města. Nejprve popisujeme reprezentaci intermodální dopravní sítě. Poté definujeme úlohu analýzy nad danou reprezentací. Jedná se o algoritmickou analýzu, tedy na základě zadané poptávky cestujících vyhodnocujeme klíčové indikátory. Mezi zahrnuté indikátory patří počet přeplněných úseků spojů, doba jízdy všech cestujících a celkové náklady všech cestujících. Cílem analýzy je optimalizovat počet přeplněných úseků dopravní sítě tím, že nabídneme cestujícím alternativní jízdy. Tyto cesty se snaží vyhnout úsekům dopravní sítě, kde jsou spoje přeplněné. Vyhnout se lze vybráním jiného spoje veřejné dopravy, jízdou na kole, nebo využitím taxi služby. Popisujeme multikriteriální algoritmus, který pro každého cestujícího vyhledá vhodnou cestu, přičemž optimalizuje čtyři kritéria: obsazenost vozu, dobu jízdy, cestovní náklady a počet přestupů. Také implementujeme nástroj pro analýzu, který obsahuje tento multikriteriální algoritmus a z nalezených cest vypočítá chtěné klíčové indikátory. Pomocí našeho nástroje provádíme analýzu intermodální dopravní sítě hlavního města Prahy. Při evaluaci námi vygenerované poptávky cestujících dosahujeme snížení počtu přeplněných úseků spojů v intermodální dopravní síti o 79,4 %.This work focuses on the analysis of the intermodal transport network using a multi-criteria algorithm that considers preferences of the city. To perform the analysis, we first describe the representation of the intermodal transport network. Given the representation, we define the intermodal transport network analysis problem with preferences of the city. We aim at algorithmic analysis, which computes key performance indicators using given travel demand. Thus, we provide various key performance indicators, e.g., the number of overcrowded trip segments, the total duration of all passenger journeys, and the total costs of passenger journeys. The goal of the analysis is to optimize the number of overcrowded parts of the public transport network. To achieve the goal, we offer passengers alternative journeys. These journeys try to avoid public transport vehicles with occupancy beyond a certain level of comfort. In other words, a passenger may choose another public transport connection, ride a bike, or use a taxi service. We propose a multi-criteria algorithm that finds a suitable journey for each passenger while optimizing four criteria, i.e., vehicle occupancy, duration, costs, and the number of interchanges. We also implement an analysis tool that includes the multi-criteria algorithm and calculates the required key performance indicators. By using the analysis tool, we perform an analysis using the intermodal transport network of the capital city of Prague. In the evaluation, we achieve the reduction in the number of overcrowded trip segments in the intermodal transport network by 79.4 % on randomly generated travel demand

Algorithm Engineering for Realistic Journey Planning in Transportation Networks

Diese Dissertation beschäftigt sich mit der Routenplanung in Transportnetzen. Es werden neue, effiziente algorithmische Ansätze zur Berechnung optimaler Verbindungen in öffentlichen Verkehrsnetzen, Straßennetzen und multimodalen Netzen, die verschiedene Transportmodi miteinander verknüpfen, eingeführt. Im Fokus der Arbeit steht dabei die Praktikabilität der Ansätze, was durch eine ausführliche experimentelle Evaluation belegt wird

Engineering Algorithms for Route Planning in Multimodal Transportation Networks

Practical algorithms for route planning in transportation networks are a showpiece of successful Algorithm Engineering. This has produced many speedup techniques, varying in preprocessing time, space, query performance, simplicity, and ease of implementation. This thesis explores solutions to more realistic scenarios, taking into account, e.g., traffic, user preferences, public transit schedules, and the options offered by the many modalities of modern transportation networks

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