Scalable, Distributed, Real-Time Map Generation

Davics, J.J.; Beresford, Alastair R.; Hopper, Andy

doi:10.1109/mprv.2006.83

Cited by 199 publications

(171 citation statements)

References 6 publications

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“…For the qualitative evaluation, the results were overlaid onto an OpenStreetMap tile map. For the quantitative evaluation, the F-score [30] was used and compared with those of Cao and Krumm's method [23] and Davies et al's method [20], both of which are classical methods. The experimental data and results are described in the following.…”

Section: Results Of the Case Study Of Beijing Chinamentioning

confidence: 99%

“…Then, F − measure = 2×precision×recall precision+recall . We used their method to quantitatively evaluate our method and compared it to Cao and Krumm's [23] and Davies et al's [20] methods. Figure 9 shows the F-scores of our method compared to those of the two existing methods.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…Biagioni and Eriksson [30] evaluated three representative road network construction algorithms with the F-score using 118 h of GPS traces from the campus shuttles of the University of Illinois at Chicago. The algorithm developed by Davies et al [20] was found to significantly outperform other algorithms under a variety of conditions. In recent years, directed Hausdorff, path-based, shortest-path-based, and graph-sampling-based distance measures have been adopted to evaluate the quality of the topological data and other features of constructed road networks [18,21].…”

Section: Literature Reviewmentioning

confidence: 99%

“…2017, 6, 400 3 of 15 for generating road networks have been proposed in recent years [16,17]. In general, these methods can be organized into three categories [18]: (1) point clustering [19][20][21], which assumes that the input raw data consist of a set of points that are then clustered in various ways (such as by the k-means algorithm) to obtain street segments that are finally connected to form a road network; (2) incremental track insertion [10,[22][23][24][25][26], which constructs a road network by incrementally inserting trajectory data into an initially empty graph; and (3) intersection linking [27][28][29], in which the intersection vertices of the road network are first detected and then linked together by recognizing suitable road segments. Some of the representative algorithms of each category are listed in Table 1.…”

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

Zhang

Liu

Qian

et al. 2017

IJGI

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Automatically acquiring comprehensive, accurate, and real-time mapping information and translating this information into digital maps are challenging problems. Traditional methods are time consuming and costly because they require expensive field surveying and labor-intensive post-processing. Recently, the ubiquitous use of positioning technology in vehicles and other devices has produced massive amounts of trajectory data, which provide new opportunities for digital map production and updating. This paper presents an automatic method for producing road networks from raw vehicle global positioning system (GPS) trajectory data. First, raw GPS positioning data are processed to remove noise using a newly proposed algorithm employing flexible spatial, temporal, and logical constraint rules. Then, a new road network construction algorithm is used to incrementally merge trajectories into a directed graph representing a digital map. Furthermore, the average road traffic volume and speed are calculated and assigned to corresponding road segments. To evaluate the performance of the method, an experiment was conducted using 5.76 million trajectory data points from 200 taxis. The result was qualitatively compared with OpenStreetMap and quantitatively compared with two existing methods based on the F-score. The findings show that our method can automatically generate a road network representing a digital map.

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Section: Results Of the Case Study Of Beijing Chinamentioning

confidence: 99%

Section: Experiments Results and Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

Zhang

Liu

Qian

et al. 2017

IJGI

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show abstract

“…More recently, improvements in technology have meant that the use of mobile sensors has become feasible. The Sentient Van project, for example, is investigating the potential for vehicle-based sensing (Davies et al 2006a). Sensing of traffic and travel conditions by vehicles themselves benefits from the observation that areas of high usage for which detailed sensor data are required are naturally areas with a high density of vehicles and hence sensors.…”

Section: The World Modelmentioning

confidence: 99%

Computing for the future of the planet

Hopper

Rice

2008

Phil. Trans. R. Soc. A.

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Digital technology is becoming an indispensable and crucial component of our lives, society and the environment. We present a framework for computing in the context of problems facing the planet. The framework has a number of goals: an optimal digital infrastructure, sensing and optimizing with a global world model, reliably predicting and reacting to our environment and providing digital alternatives to physical activities. This paper describes our vision in which data centres can scale power consumption in line with performance, run closer to the wire with reduced redundancy and behave as a 'virtual battery' dynamically using spare, or otherwise unusable, generation capacity from renewable sources. On a broader scale, we consider how global sensing might allow us to optimize our daily activities and lives. We highlight the issues and dilemmas inherent in the deployment of global sensing infrastructure and work towards our challenge of a personal energy meter as a tool for informing decisions and providing impetus for reducing the ecological footprint of our society.

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Smartphones for public transport planning and recommendation in developing countries—A review

Verma

Chakraborty

2020

WIREs Data Min & Knowl

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In this era of connected systems that have penetrated everywhere, transport units have become a significant source of data, collected from commuters, vehicles, drivers, or any section being touched by the transport system. These data, which have both spatial as well as temporal aspects, is utilized for a plethora of services like travel assistant systems, multi‐modal transport solutions, real‐time travel information, smart parking, autonomous vehicles, to name a few. With the current buzz of sustainable transport, the use of public transport systems have been popularized owing to the economic and environmental savings. In this review article, we provide a highlight of works, which have tried to utilize techniques to improve multiple sections of the public transport system, primarily focusing on developing economies, thus improving the overall commute experience at various countries. This article is categorized under: Application Areas > Society and Culture Technologies > Internet of Things

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Scalable, Distributed, Real-Time Map Generation

Cited by 199 publications

References 6 publications

An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

Computing for the future of the planet

Smartphones for public transport planning and recommendation in developing countries—A review

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