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

A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers

We propose a ridesharing strategy with integrated transit in which a private on-demand mobility service operator may drop off a passenger directly door-to-door, commit to dropping them at a transit station or picking up from a transit station, or to both pickup and drop off at two different stations with different vehicles. We study the effectiveness of online solution algorithms for this proposed strategy. Queueing-theoretic vehicle dispatch and idle vehicle relocation algorithms are customized for the problem. Several experiments are conducted first with a synthetic instance to design and test the effectiveness of this integrated solution method, the influence of different model parameters, and measure the benefit of such cooperation. Results suggest that rideshare vehicle travel time can drop by 40-60% consistently while passenger journey times can be reduced by 50-60% when demand is high. A case study of Long Island commuters to New York City (NYC) suggests having the proposed operating strategy can substantially cut user journey times and operating costs by up to 54% and 60% each for a range of 10-30 taxis initiated per zone. This result shows that there are settings where such service is highly warranted

A Lagrangian discretization multiagent approach for large-scale multimodal dynamic assignment

This paper develops a Lagrangian discretization multiagent model for large-scale multimodal simulation and assignment. For road traffic flow modeling, we describe the dynamics of vehicle packets based on a macroscopic model on the basis of a Lagrangian discretization. The metro/tram/train systems are modeled on constant speed on scheduled timetable/frequency over lines of operations. Congestion is modeled as waiting time at stations plus induced discomfort when the capacity of vehicle is achieved. For the bus system, it is modeled similar to cars with different speed settings, either competing for road capacity resources with other vehicles or moving on separated bus lines on the road network. For solving the large-scale multimodal dynamic traffic assignment problem, an effective-path-based cross entropy is proposed to approximate the dynamic user equilibrium. Some numerical simulations have been conducted to demonstrate its ability to describe traffic dynamics on road network.multimodal transportation systems; Lagrangian discretization; traffic assignment; multiagent systems

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

Rough-Cut Capacity Planning in Multimodal Freight Transportation Networks

A main challenge in transporting cargo for United States Transportation Command (USTRANSCOM) is in mode selection or integration. Demand for cargo is time sensitive and must be fulfilled by an established due date. Since these due dates are often inflexible, commercial carriers are used at an enormous expense, in order to fill the gap in organic transportation asset capacity. This dissertation develops a new methodology for transportation capacity assignment to routes based on the Resource Constrained Shortest Path Problem (RCSP). Routes can be single or multimodal depending on the characteristics of the network, delivery timeline, modal capacities, and costs. The difficulty of the RCSP requires use of metaheuristics to produce solutions. An Ant Colony System to solve the RCSP is developed in this dissertation. Finally, a method for generating near Pareto optimal solutions with respect to the objectives of cost and time is developed