Faster hyperpath generating algorithms for vehicle navigation

Ma, Jiangshan; Fukuda, Daisuke; Schmöcker, Jan-Dirk

doi:10.1080/18128602.2012.719165

Cited by 17 publications

(16 citation statements)

References 11 publications

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“…New algorithms can be developed simply by implementing the "IAlgorithm" interface. (*"Fib" refers to Fibonacci heap data structure, "TD" refers to time-dependent, "di" refers to the accelerated search in Ma et al, 2013) Figure 6 illustrates the user interfaces of GeoRouting.Net including the setting panel and the GIS visualization form. The GIS visualization is supported by the open source SharpMap project.…”

Section: An Example: Hyperpath Route Planningmentioning

confidence: 99%

“…We implemented SF di algorithm (Ma et al, 2013), which is a variant of SF algorithm by implementing optimistic node potentials and node-directed search for better performances. At the stage of designing and testing algorithms, we focused on the correctness and stability of algorithms so that we mainly used the data structure of linked list or Fibonacci heap to maintain the updated nodes/links.…”

Section: Figure 6 User Interfaces Of Georoutingnetmentioning

confidence: 99%

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A Note on Route Planning in Transportation with Open Sources

Fukuda²

2014

Cictp 2014

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Route planning is one of the core issues in transportation fields such as logistics, route guidance and traffic assignments. Many useful algorithms dealing with various problems have been proposed in the past and some of them have been implemented in commercial software, while some others are still far from practice. In recent decades, the open source GIS has been developing very fast and many excellent projects have been built. In this paper, we make a note on route planning in transportation by utilizing such open sources. A data storage model as well as the architecture of open sources is introduced. As an example, the process of developing the hyperpath algorithm is illustrated. This paper may be useful for those who would like to customize GIS-based transportation analyzing tools by themselves for various purposes. INTRODUCTIONTransportation is one of the most important and increasingly popular applications of GIS (Wiggins et al. 2000). Geographic information systems for transportation (GIS-T) are interconnected hardware, software, data, people, organizations and institutional arrangements for collecting, storing, analyzing and communicating particular types of information about the Earth (Miller and Shaw, 2001). Many fields in transportation such as travel behavior analysis, urban transportation planning, network analysis, and logistics have taken advantage of GIS tools to achieve objectives more efficiently, conveniently and intuitively.Commercial GIS software such as MapInfo and ArcGIS are costly and not specially designed for transportation studies. Transportation software such as TransCAD and CUBE also provide GIS modules for visualizing planning. Although they are extensible by providing API to developers, many researchers tend to get lost in the large complicated libraries of commercial GIS software. Besides, the implementations of provided API are invisible, which results in limited flexibility.

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Section: An Example: Hyperpath Route Planningmentioning

confidence: 99%

Section: Figure 6 User Interfaces Of Georoutingnetmentioning

confidence: 99%

A Note on Route Planning in Transportation with Open Sources

Fukuda²

2014

Cictp 2014

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“…The performance is the same as the original SF di algorithm. The details are shown in Ma et al (2013). Algorithm 1 illustrates the algorithm structure for the proposed framework leaving V i and V a to be specified according to application contexts and modelers.…”

Section: The Modified Sf DI Algorithmmentioning

confidence: 99%

“…A greedy-type algorithm, which resembles the Spiess-Florian algorithm with directed node search and admissive node potentials (SF di ) proposed by Ma et al (2013), can be used to solve for the hyperpath, together with GEVcorrespondent link-choice probabilities. In order to be in line with the original SF di algorithm, we let V denote the disutilities instead of utilities in Algorithm 1 and the algorithm actually seeks for a minimization of disutility, which is equivalent to solve the original problem that maximizes the utility.…”

Section: The Modified Sf DI Algorithmmentioning

confidence: 99%

A Hyperpath-based Network Generalized Extreme-value Model for Route Choice under Uncertainties

Fukuda

2015

Transportation Research Procedia

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Previous route choice studies treated uncertainties as randomness; however, it is argued that other uncertainties exist beyond random effects. As a general modeling framework for route choice under uncertainties, this paper presents a model of route choice that incorporates hyperpath and network generalized extreme-value-based link choice models. Accounting for the travel time uncertainty, numerical studies of specified models within the proposed framework are conducted. The modeling framework may be helpful in various research contexts dealing with both randomness and other non-probabilistic uncertainties that cannot be exactly perceived.

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“…Based on the idea of "Optimal strategy", widely employed in frequency-based transit assignment (see Spiess and Florian, 1989), Bell (2009) proposed the shortest hyperpath search algorithm called "Hyperstar". Algorithm variations under various conditions have been further developed in Bell et al (2012) and Ma et al (2013) for risk-averse vehicle navigation.…”

Section: Introductionmentioning

confidence: 99%

The Multi-Agent Transport Simulation MATSim

Axhausen¹

2016

500

123

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PrefaceDeveloping complex so ware for over a decade with a heterogeneous group of engineers and scientists, each with widely di erent skill levels and expertise across multiple locations around the world, requires dedication and mechanisms unusual for a university environment. This book is one of these mechanisms. It allows us, collectively, to take stock and present a coherent state-of-the-system: for us and anyone interested in this approach. It highlights basics for the student who wants to undertake a small rst research project as part of his or her degree, provides a description of the main functionalities, in detail, for the engineer setting up MATSim (MultiAgent Transport Simulation) to conduct a policy analysis and, nally, ts the approach into the theoretical background of complex systems in computer science and physics.The choice of the additional e-book format is an advantage, as it allows us to keep the book upto-date with future chapters, revisions and, if necessary, errata. Equally importantly it allows you, the readers, to select those sections relevant to your needs.The book comes at an important time for the system; for most of the rst decade, its use was limited to the original developers and users in Berlin and Zürich. It is now much more widely consulted around the world, as we document in the chapter summarizing contributions on scenarios so far.Scenario: This term will occur again and again. In MATSim context, it is de ned as the combination of speci c agent populations, their initial plans and activity locations (home, work, education), the network and facilities where, and on which, they compete in time-space for their slots and modules, i.e., behavioral dimensions, which they can adjust during their search for equilibrium. Within these scenarios, the user can experiment and explore with behavioral utility function parameters, with the sampling rate of the population between 1 % and 100 %, with algorithm parameters, e.g., the share of the sample engaged in replanning in any iteration, or behavioral dimensions or exact settings necessary to avoid gridlock due to the tra c ow dynamics. The creation of a scenario is a substantial e ort, and the framework makes a number of tools available to accelerate it: population synthesizers, network editors, network converters between popular formats and the MATSim representation, e.g., OSM (OpenStreetMap) or GTFS (General Transit Feed Speci cation), semi-automatic network matching to join information, among others.A large group of colleagues has been involved and many of them are contributors to this book; this is a list of those involved, other than ourselves, in Berlin, Singapore and Zürich. Additional contributors are mentioned as authors of their respective chapters in this book. We hope to acknowledge the contributions of more colleagues from other groups in future versions of this book and in the so ware.Special thanks go to a number of people who greatly helped improving this book beyond their own chapters. Benjamin Kickhöfer's deep knowledge of ...

show abstract

Faster hyperpath generating algorithms for vehicle navigation

Cited by 17 publications

References 11 publications

A Note on Route Planning in Transportation with Open Sources

A Note on Route Planning in Transportation with Open Sources

A Hyperpath-based Network Generalized Extreme-value Model for Route Choice under Uncertainties

The Multi-Agent Transport Simulation MATSim

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