Real-Time Traffic Assignment Using Fast Queries in Customizable Contraction Hierarchies

Given an urban road network and a set of origin-destination (OD) pairs, the traffic assignment problem asks for the traffic flow on each road segment. A common solution employs a feasible-direction method, where the direction-finding step requires many shortest-path computations. In this paper, we significantly accelerate the computation of flow patterns, enabling interactive transportation and urban planning applications. We achieve this by revisiting and carefully engineering known speedup techniques for shortest paths, and combining them with customizable contraction hierarchies. In particular, our accelerated elimination tree search is more than an order of magnitude faster for local queries than the original algorithm, and our centralized search speeds up batched point-to-point shortest paths by a factor of up to 6. These optimizations are independent of traffic assignment and can be generally used for (batched) point-to-point queries. In contrast to prior work, our evaluation uses real-world data for all parts of the problem. On a metropolitan area encompassing more than 2.7 million inhabitants, we reduce the flow-pattern computation for a typical two-hour morning peak from 76.5 to 10.5 seconds on one core, and 4.3 seconds on four cores. This represents a speedup of 18 over the state of the art, and three orders of magnitude over the Dijkstra-based baseline

Learning-Based Approaches for Graph Problems: A Survey

Over the years, many graph problems specifically those in NP-complete are studied by a wide range of researchers. Some famous examples include graph colouring, travelling salesman problem and subgraph isomorphism. Most of these problems are typically addressed by exact algorithms, approximate algorithms and heuristics. There are however some drawback for each of these methods. Recent studies have employed learning-based frameworks such as machine learning techniques in solving these problems, given that they are useful in discovering new patterns in structured data that can be represented using graphs. This research direction has successfully attracted a considerable amount of attention. In this survey, we provide a systematic review mainly on classic graph problems in which learning-based approaches have been proposed in addressing the problems. We discuss the overview of each framework, and provide analyses based on the design and performance of the framework. Some potential research questions are also suggested. Ultimately, this survey gives a clearer insight and can be used as a stepping stone to the research community in studying problems in this field.Comment: v1: 41 pages; v2: 40 page

Preferenssien mallinnus multimodaalisissa reititysalgoritmeissa

In this thesis, we study the ongoing change in the field of passenger transport. We focus on the required technological solutions and introduce an idea of a technological platform connecting all the transport service providers seamlessly to the available interfaces offering combined transportation services for the travellers. We present a reference architecture for the platform and identify that development is needed to more accurately model the travellers' preferences in the multimodal routing algorithms used in the platform. Label constrained shortest path problem Dijkstra's (LCSPP-D) algorithm is one typically used to model the traveller's preferences in the journey planning. We propose two ways to improve the preference modelling with this algorithm. Firstly, the travellers should be clustered into similar groups so that the parameters describing the preferences could be shared within the group. This way more emphasis could be given to the optimization of the group specific parameters. Secondly, instead of returning journey plans using a single objective function, a set of journey plans should be returned where each would describe the travellers' preferences in different situations. Then, depending on temporary variables such as the weather, a travelling companion or the amount of luggage the traveller could select the plan most suitable for the specific situation. We focus on the second improvement and build a test framework in order to evaluate the LCSPP-D algorithm more closely in our sample network. We define multiple models to describe the travellers' preferences and use these to return journey plans from the sample network. The results show that journey plans modelling the travellers' preferences can be returned and using the designed preference models for a single trip we can return multiple plans each describing different kind of preferences. However, further research is needed to study how well the algorithm can actually model the traveller's preferences and how the preference models used in the algorithm should be defined.Tässä tutkimuksessa tutustumme muutokseen, joka on käynnissä henkilöliikenteen alalla. Erityisesti meitä kiinnostavat tarvittavat teknologiset ratkaisut ja esittelemme ideamme teknologia-alustasta, joka yhdistäisi liikkumispalveluiden tarjoajat saumattomasti kaikkiin eri rajapintoihin, jotka tarjoavat keskitetysti liikkumispalveluita kuluttajille. Esittelemme viitearkkitehtuurin kyseiselle alustalle ja tätä kautta tunnistamme, että kehitystä tarvitaan ainakin parantamaan preferenssien mallinnusta reititysalgoritmeissa, joita alustassa käytetään. Ehdotamme kahta parannusta tukemaan preferenssien mallinnusta olemassa olevia algoritmeja hyödyntäen. Matkustajat tulisi ensinnäkin luokitella ryhmiin preferenssiensä perusteella. Tätä kautta preferenssimallit voitaisiin jakaa ryhmän kesken ja enemmän panostusta voitaisiin käyttää ryhmäkohtaisten mallien kehittämiseen. Toiseksi sen sijaan, että reititysalgoritmit palauttaisivat yhden tavoitefunktion mukaan optimoituja reittejä, niiden tulisi palauttaa joukko erilaisia reittejä, jotka kaikki pyrkivät kuvaamaan matkustajan preferenssejä erilaisissa tilanteissa. Sitten riippuen vallitsevista muuttujista, kuten säästä, matkustusseurasta ja kantamusten määrästä, voisivat matkustajat valita tilanteeseen sopivimman reittisuunnitelman. Tutkimme jälkimmäistä parannusehdotusta tarkemmin ja rakennamme kehikon, jonka avulla voimme testata reititysalgoritmeja testiverkostossamme. Määrittelemme useampia malleja kuvaamaan matkustajien preferenssejä ja haemme näiden avulla reittejä testiverkostostamme. Tulokset osoittavat, että preferensseihin mukautuvia reittiehdotuksia voidaan palauttaa ja muokkaamalla preferenssimalleja oikein on mahdollista palauttaa samalle reitille joukko erilaisia preferenssejä kuvaavia reittejä. Jatkotutkimusta kuitenkin tarvitaan arvioimaan, kuinka hyviä nykyiset reititysalgoritmit ovat oikeastaan kuvaamaan matkustajan preferenssejä ja kuinka ryhmäkohtaiset preferenssimallien parametrit tulisi tarkemmin määrittää

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