A new time-dependent shortest path algorithm for multimodal transportation network

Idri, Abdelfattah; Oukarfi, Mariyem; Boulmakoul, Azedine; Zeitouni, Karine; Masri, Ali El

doi:10.1016/j.procs.2017.05.379

Cited by 52 publications

(35 citation statements)

References 8 publications

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“…The connecting links in our network serve the role of intermodal edges, and in many cases can be considered as actually walking from one form of transportation to another because the distances/times are based on the actual longitudes and latitudes of the respective nodes. That said, the connecting links are intended to be an abstraction rather than an approximation -although we obviously need some approximation of the intermodal change times (Ayed et al 2011;Idri et al 2017). All of the analyses performed here combine at least two modes (hex + .…”

Section: Connecting Linksmentioning

confidence: 99%

Scoring and classifying regions via multimodal transportation networks

Bramson

Hori²,

Zha³

et al. 2019

Appl Netw Sci

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In order to better understand the role of transportation convenience in location preferences, as well as to uncover transportation system patterns that span multiple modes of transportation, we analyze 500 locations in the Tokyo area using properties of their multimodal transportation networks. Multiple sets of measures are used to cluster regions by their transportation features and to classify them by their synergistic properties and dominant mode of transportation. We use twelve measures collected at five different radii for five distinct combinations of transportation networks to rank locations by their transportation characteristics. We introduce an additional 114 scores derived from the 300 measures to assess, among other things, access to public transportation, the effectiveness of each mode of transportation, and synergies among the modes of transportation. Additionally, we leverage those scores to classify our locations as being train-centric, bus-centric, or car-centric and to uncover geographic patterns in these characteristics. We find that business hubs, despite having low populations, are so conveniently reachable via train and road systems that they consistently achieve the highest sociability and convenience scores. Suburban regions have more serviceable bus systems, but lower connectivity overall resulting in lower reachable populations despite greater local populations. Even though Tokyo has the largest and densest public transportation system in the world we find that the road network consistently dominates the train and bus networks for all accessibility measures.

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Section: Connecting Linksmentioning

confidence: 99%

Scoring and classifying regions via multimodal transportation networks

Bramson

Hori²,

Zha³

et al. 2019

Appl Netw Sci

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“…Passengers or goods are moved from one point to another using two or more modes of transportation. In addition to the use of different modes of transportation, walking and cycling can also be combined in multimodal transportation [15].…”

Section: A Transportationmentioning

confidence: 99%

“…Several studies of multimodal transportation in Computer Science have been conducted including the shortest path problem [15] and multimodal transportation data models [16] [17] [18]. Bieli et.al [16] models multimodal transportation networks using objects.…”

Section: A Transportationmentioning

confidence: 99%

Graph Database Schema for Multimodal Transportation in Semarang

Wirawan

Riyanto

Nugraheni

et al. 2019

JISEBI

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Background: Semarang has broad area that cannot be covered entirely by single transportation mode. To reach a specific location, people often use more than one public transportation mode. Apart from Bus Rapid Transit, another exist namely angkot or city transportation. Multimodal traveler information is then required to help passenger searching for a route. Several studies of multimodal traveler information system has been conducted, however the data model for multimodal transportation did not conceived in detail.Objective: Proposes a database of multimodal transportation design using graph data model by taking Semarang as a case study.Method: We create our model in oriented entity-relationship diagram (O-ERD) and map this O-ERD to the graph database schema.Result: We develop our data model in graph database schema and we implement the model using Neo4J graph database for validation purpose. Our model consist of three graph node label namely Shelter, Angkot Stopper, and Closer Place. To validate our model, we execute a search query using the Cypher query to look for location with closer place to it.Conclusion: Our data model was successfully developed and implemented. Searching transportation route in the implementation of our model has been conducted using cypher query. It can successfully display all possible paths and routes. Our query can distinguish between one mode of transportation with another.Keywords: Graph database, Multimodal transportation, Neo4j, Cypher

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“…Idri et al in [25] proposed the shortest path algorithm for the time-dependent and multimodal networks. Sarbazi-Azad et al in [45] proposed for the all-pairs shortest path problem and the single-pair shortest path problem the method which exploits the GPU computational power.…”

Section: Transit-based Routingmentioning

confidence: 99%

Intelligent route planning system based on interval computing

Chmiel

Skalna

Jędrusik

2018

Multimed Tools Appl

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We investigate the problem of vehicle route planning in a dynamic environment. In order to better reflect real-life situations, we assume that travel times are not known exactly, but bounded from below and from above, i.e., they are given as interval quantities. Accordingly, we develop algorithms for effective route replanning in a highly dynamic road network environment that combines traffic image processing with interval data for dynamic path optimisation. The developed algorithms are integrated into a larger system for traffic management. The efficiency of the proposed algorithms and their ability to support the dynamics of road traffic is verified using real data. The experimental research was also conducted using the microscopic, time-discrete, space-continuous traffic simulator.

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A new time-dependent shortest path algorithm for multimodal transportation network

Cited by 52 publications

References 8 publications

Scoring and classifying regions via multimodal transportation networks

Scoring and classifying regions via multimodal transportation networks

Graph Database Schema for Multimodal Transportation in Semarang

Intelligent route planning system based on interval computing

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