Modeling urban traffic dynamics in coexistence with urban data streams

Moosavi, Vahid; Hovestadt, Ludger

doi:10.1145/2505821.2505822

Cited by 17 publications

(32 citation statements)

References 19 publications

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“…The work of [Crisostomi et al (2011a)] was well received by the transport community giving rise to further research along the same lines, see [Crisostomi et al (2011b)], [Schlote et al (2012)], [Jensen et al (2014)], [Bekkerman et al (2013)] and [Morimura et al (2013)]. Also, the findings of [Crisostomi et al (2011a)] were further validated using real data of Beijing [Moosavi and Hovestadt (2013)].…”

Section: State Of the Art And Related Workmentioning

confidence: 83%

A big-data model for multi-modal public transportation with application to macroscopic control and optimisation

Faizrahnemoon

Schlote

Maggi

et al. 2015

International Journal of Control

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This paper describes a Markov-chain-based approach to modelling multi-modal transportation networks. An advantage of the model is the ability to accommodate complex dynamics and handle huge amounts of data. The transition matrix of the Markov chain is built and the model is validated using the data extracted from a traffic simulator. A realistic test-case using multi-modal data from the city of London is given to further support the ability of the proposed methodology to handle big quantities of data. Then, we use the Markov chain as a control tool to improve the overall efficiency of a transportation network, and some practical examples are described to illustrate the potentials of the approach

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Section: State Of the Art And Related Workmentioning

confidence: 83%

A big-data model for multi-modal public transportation with application to macroscopic control and optimisation

Faizrahnemoon

Schlote

Maggi

et al. 2015

International Journal of Control

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“…Nevertheless, current intelligent transportation systems (ITS) continue to rely on an infrastructure composed of static sensors and cameras installed on roads, making it difficult to collect, aggregate, and analyze data, especially in real-time [7][8][9]. Moreover, due to the high cost of installation and maintenance, ITS are often restricted to particular roads or neighborhoods [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Smartphone-based outlier detection: a complex event processing approach for driving behavior detection

Vasconcelos

Olivieri

et al. 2017

J Internet Serv Appl

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The majority of fatal car crashes are caused by reckless driving. With the sophistication of vehicle instrumentation, reckless maneuvers, such as abrupt turns, acceleration, and deceleration, can now be accurately detected by analyzing data related to the driver-vehicle interactions. Such analysis usually requires very specific in-vehicle hardware and infrastructure sensors (e.g. loop detectors and radars), which can be costly. Hence, in this paper, we investigated if off-the-shelf smartphones can be used to online detect and classify the driver's behavior in near real-time. To do so, we first modeled and performed an intrinsic evaluation to assess the performance of three outlier detection algorithms formulated as a data stream processing network which receives as input and processes data streams of smartphone and vehicle sensors. Next, we implemented a novel scoring mechanism based on online outlier detection to quantitatively evaluate drivers' maneuvers as either cautious or reckless. Thus, we adapted a data mining mechanism which takes into account a sensor's data rates and power to determine driver behavior in the scoring process. Finally, as the intrinsic evaluation does not necessarily reveal how well an algorithm will perform in a real-world scenario, we evaluated the algorithm that achieved the best result in a real-world case study to assess drivers' driving behavior. Our results indicate that the algorithm performs quickly and accurately; the algorithm classifies driver behavior with 95.45% accuracy. Moreover, such results are obtained within 100 milliseconds of processing time on average.

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“…That paper employed graph theory and Markov chain ideas to reveal some non-trivial patterns of urban mobility and to support engineers with practical tools to solve a number of mobility applications, such as routing, traffic light regulation and road works planning. Such a work has been recently extended along a number of lines (see among other [18] and [19]) and further validated over the small district of De Uithof, in Utrecht (in [20]) and for real data of the city of Beijing ( [21]).…”

Section: Introductionmentioning

confidence: 99%

A Markov-chain based model for a bike-sharing system

Crisostomi

Faizrahnemoon

Schlote

et al. 2015

2015 International Conference on Connected Vehicles and Expo (ICCVE)

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Bike-sharing systems are recently becoming ubiquitous in most cities, as an environmentally friendly alternative to other means of transportation. An optimal management of the bike-sharing service would in principle benefit from the availability of a mathematical model underlying the system. Accordingly, in this paper we propose a Markov-chain based approach to model the bike-sharing system, which we believe has a potential to develop alternative methods to implement classic control actions in a bike-sharing system (e.g., in terms of implementing alternative relocation strategies or planning advertising campaigns). The proposed methodology is validated on real data from the bike-sharing system in Boston, USA, and a first application of the proposed model is preliminarily illustrated in the paper

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Modeling urban traffic dynamics in coexistence with urban data streams

Cited by 17 publications

References 19 publications

A big-data model for multi-modal public transportation with application to macroscopic control and optimisation

A big-data model for multi-modal public transportation with application to macroscopic control and optimisation

Smartphone-based outlier detection: a complex event processing approach for driving behavior detection

A Markov-chain based model for a bike-sharing system

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