Route choice sets for very high-resolution data

Rieser-Schüssler, Nadine; Balmer, Michael; Axhausen, Kay W.

doi:10.1080/18128602.2012.671383

Cited by 38 publications

(38 citation statements)

References 17 publications

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“…It is well known that model estimates are strongly influenced by choice set size and composition and that biased parameter estimates and choice probabilities are possible consequences of an incorrectly specified choice set (see, e.g., Bekhor et al, 2006;Prato and Bekhor, 2007;Bliemer and Bovy, 2008;Rasmussen et al, 2017). As enumerating all the paths in a highly-detailed network is unrealistic, Halldórsdóttir et al (2014) tested three choice set generation methods suitable to the task of generating relevant alternatives: (i) breadth first search on link elimination (BSF-LE) (Rieser- Schüssler et al, 2012); (ii) a doubly stochastic generation function (DSGF) (Nielsen, 2000;Bovy and Fiorenzo-Catalano, 2007); (iii) a branch & bound algorithm (B&B) (Hoogendoorn-Lanser et al, 2006;Prato and Bekhor, 2006). As detailed by Halldórsdóttir et al (2014), the tests focused on different multiattribute cost functions that considered not only route length or time, but also bicyclespecific factors such as road types, bicycle infrastructure types, and land-use designations.…”

Section: Choice Set Generationmentioning

confidence: 99%

Evaluation of land-use and transport network effects on cyclists' route choices in the Copenhagen Region in value-of-distance space

Prato

Halldórsdóttir

Nielsen

2018

International Journal of Sustainable Transportation

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Section: Choice Set Generationmentioning

confidence: 99%

Evaluation of land-use and transport network effects on cyclists' route choices in the Copenhagen Region in value-of-distance space

Prato

Halldórsdóttir

Nielsen

2018

International Journal of Sustainable Transportation

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“…Using detailed spatiotemporal trajectories of probe vehicles, much recently, research effort has been devoted to a better understanding of people's behaviour, their interactions with each other and with the environment. For example, travellers' route choice set formulation (Rieser-Schüssler et al 2012, Ramaekers et al 2013, urban mobility patterns (González et al 2008, Liu et al 2012b) and accessibility patterns , Chen et al 2013d, commercial centre attractiveness (Yue et al 2012) and traffic emission estimation (Chang et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Map-matching algorithm for large-scale low-frequency floating car data

Chen

Yuan

et al. 2013

International Journal of Geographical Information Science

154

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Large-scale global positioning system (GPS) positioning information of floating cars has been recognised as a major data source for many transportation applications. Mapping large-scale low-frequency floating car data (FCD) onto the road network is very challenging for traditional map-matching (MM) algorithms developed for in-vehicle navigation. In this paper, a multi-criteria dynamic programming map-matching (MDP-MM) algorithm is proposed for online matching FCD. In the proposed MDP-MM algorithm, the MDP technique is used to minimise the number of candidate routes maintained at each GPS point, while guaranteeing to determine the best matching route. In addition, several useful techniques are developed to improve running time of the shortest path calculation in the MM process. Case studies based on real FCD demonstrate the accuracy and computational performance of the MDP-MM algorithm. Results indicated that the MDP-MM algorithm is competitive with existing algorithms in both accuracy and computational performance.

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“…Basing the search for new paths to introduce to the choice set on the actual costs allows us to attempt to fulfil condition (11) on unused paths. (Frejinger et al, 2009), and breadth first search with network reduction (Rieser-Schüssler et al, 2013). Some of these alternative approaches may also be attractive to apply for the RSUE(min) and RSUE(max).…”

Section: Column Generation Phasementioning

confidence: 99%

Stochastic user equilibrium with equilibrated choice sets: Part II – Solving the restricted SUE for the logit family

Rasmussen

Watling

Prato

et al. 2015

Transportation Research Part B: Methodological

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We propose a new class of path-based solution algorithms to solve the Restricted Stochastic User Equilibrium (RSUE), as introduced in Watling et al (2014). The class allows a flexible specification of how the choice sets are 'systematically' grown by considering congestion effects and how the flows are allocated among routes. The specification allows adapting traditional pathbased stochastic user equilibrium flow allocation methods (designed for pre-specified choice sets) to the generic solution algorithm. We also propose a cost transformation function and show that by using this we can, for certain Logit-type choice models, modify existing path-based Deterministic User Equilibrium solution methods to fit the RSUE solution algorithm. The transformation function also leads to a two-part relative gap measure for consistently monitoring convergence to a RSUE solution. Numerical tests are reported on two real-life cases, in which we explore convergence patterns and choice set composition and size, for alternative specifications of the RSUE solution algorithm.

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Route choice sets for very high-resolution data

Cited by 38 publications

References 17 publications

Evaluation of land-use and transport network effects on cyclists' route choices in the Copenhagen Region in value-of-distance space

Evaluation of land-use and transport network effects on cyclists' route choices in the Copenhagen Region in value-of-distance space

Map-matching algorithm for large-scale low-frequency floating car data

Stochastic user equilibrium with equilibrated choice sets: Part II – Solving the restricted SUE for the logit family

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