Object modeling and path computation for multimodal travel systems

Bielli, Maurizio; Boulmakoul, Azedine; Mouncif, Hicham

doi:10.1016/j.ejor.2005.02.036

Cited by 80 publications

(51 citation statements)

References 16 publications

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“…Nevertheless, we incorporate the waiting time as part of the total travel time. The multimodal system presented by Bielli et al [22] resembles most to the system presented in this article. It incorporates multiple modes of transportation and deals with the different travel time information of each of these modes.…”

Section: Introductionmentioning

confidence: 99%

Dynamic and stochastic routing for multimodal transportation systems

Demeyer¹,

Audenaert²,

Pickavet³

et al. 2014

IET Intelligent Transport Systems

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-We present a case study of a multimodal routing system that takes into account both dynamic and stochastic travel time information. A multimodal network model is presented that makes it possible to model the travel time information of each transportation mode differently.This travel time information can either be static or dynamic, or either deterministic or stochastic. Next to this, a Dijkstra-based routing algorithm is presented that deals with this variety of travel time information in a uniform way. This research focuses on a practical implementation of the system, which means that a number of assumptions were made, like, for example, the modeling of the stochastic distributions, comparing these distributions, etc. A tradeoff had to be made between the performance of the system and the accuracy of the results.Experiments have shown that our system produces realistic routes in a short amount of time. It is demonstrated that routing dynamically indeed results in a travel time gain in comparison to routing statically. By making use of the additional stochastic travel time information even better (i.e. faster) and more reliable routes can be calculated. Moreover, it is shown that routing in the 2 multimodal network may have its advantages over routing in a unimodal network, especially during rush hours.

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Section: Introductionmentioning

confidence: 99%

Dynamic and stochastic routing for multimodal transportation systems

Demeyer¹,

Audenaert²,

Pickavet³

et al. 2014

IET Intelligent Transport Systems

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“…Bielli et al [4] considered a simplified version of the NFA model but include timedependent arcs and time penalties for turning movements. Their objective is to compute the K− shortest paths under an upper bound of the maximum allowed number of transfers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A simple way of modeling the network, used by many authors [18,15,4], lies in assuming that the set of nodes is partitioned according to the modes. An arc linking two nodes of different subsets is called a transfer arc.…”

Section: Literature Reviewmentioning

confidence: 99%

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State-based accelerations and bidirectional search for bi-objective multi-modal shortest paths

Artigues

Huguet

Guèye

et al. 2013

Transportation Research Part C: Emerging Technologies

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Taking into account the multi-modality of urban transportation networks for computing the itinerary of an individual passenger introduces a number of additional constraints such as mode restrictions and various objective functions. In this paper, constraints on modes are gathered under the concept of viable path, modeled by a non deterministic finite state automaton (NFA). The goal is to find the non-dominated viable shortest paths considering the minimization of the travel time and of the number of modal transfers. We show that the problem, initially considered by Lozano and Storchi [15], is a polynomially-solvable bi-objective variant of the mono-objective regular language-constrained shortest path problem [2,8].We propose several label setting algorithms for solving the problem: a topological label-setting algorithm improving on algorithms proposed by Pallottino and Scutellà [23] and Lozano and Storchi [15], a multi-label algorithm using buckets and its bidirectional variant, as well as dedicated goal oriented techniques. Furthermore, we propose a new NFA state-based dominance rule. The computational experiments, carried-out on a realistic urban network, show that the state-based dominance rule associated with bidirectional search yields significant average speed-ups. On an expanded graph comprising 1 859 350 nodes, we obtain on average 3.5 non-dominated shortest paths in less than 180 ms.keywords: bi-objective regular language-constrained shortest path problem, multimodal transportation, finite state automaton, label-setting algorithms, state-based dominance rule, bidirectional search, state-based estimated travel times.

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“…A wide range of algorithms [1,3,15] supporting these route queries have been developed to account for the problems with modal transfers, schedules, and cost computation. The algorithms utilize a graph model extend well known shortest path algorithms (e.g., Dijkstra's), and have polynomial running times.…”

Section: Transportation Information Systemsmentioning

confidence: 99%

A data model for trip planning in multimodal transportation systems

Booth

Sistla

Wolfson

et al. 2009

Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology

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This paper introduces the problem of modeling urban transportation systems in a database where certain aspects of the data are probabilistic in nature. The transportation network is composed of multiple modes (e.g., automobile, bus, train, pedestrian) that the user can alternate between. A tripa path between an origin and destination subject to some constraints -is the central concept. How these trips and the network can be represented as both a graph and relational model, as well as the requirements for querying are the main contributions of this paper. A set of operators are defined to work over these transportation concepts and they are integrated within a SQL-like syntax to express queries over the uncertain transportation network. Additionally, the paper shows how this model can be integrated within other moving objects and spatio-temporal data models, and how these graph-based queries can be processed.

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Object modeling and path computation for multimodal travel systems

Cited by 80 publications

References 16 publications

Dynamic and stochastic routing for multimodal transportation systems

Dynamic and stochastic routing for multimodal transportation systems

State-based accelerations and bidirectional search for bi-objective multi-modal shortest paths

A data model for trip planning in multimodal transportation systems

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