City-scale traffic estimation from a roving sensor network

Aslam, Javed A.; Lim, Sejoon; Pan, Xinghao; Rus, Daniela

doi:10.1145/2426656.2426671

Cited by 135 publications

(77 citation statements)

References 11 publications

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“…For example, some systems are proposed to assist taxicab operators better oversee taxicabs and to provide timely services to passengers, e.g., predicting passenger demand [3] [4] [5], detecting anomalous taxicab trips to discover driver fraud [9], and discovering temporal and spatial causal interactions to provide timely and efficient services in certain areas with disequilibrium [10]. In addition to taxicab operators, several systems are proposed for the benefit of passengers or drivers, e.g., allowing taxicab passengers to query the expected duration and fare of a planed trip based on previous trips [11] and estimating city traffic volumes for drivers [12]. However, our model is different from the existing research by its novel inference method based on real-time and historical data from roving sensor networks.…”

Section: Sensitivity Of Dmodelmentioning

confidence: 99%

Dmodel: Online Taxicab Demand Model from Big Sensor Data in a Roving Sensor Network

Stankovic

Lin

et al. 2014

2014 IEEE International Congress on Big Data

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Abstract-Investigating passenger demand is essential for the taxicab business. Existing solutions are typically based on offline data collected by manual investigations, which are often dated and inaccurate for real-time analysis. To address this issue, we propose Dmodel, employing roving taxicabs as real-time mobile sensors to (i) infer passenger arriving moments by interactions of vacant taxicabs, and then (ii) infer passenger demand by a customized online training with both historical and real-time data. Such huge taxicab data (almost 1TB per year) pose a big data challenge to us. To address this challenge, Dmodel employs a novel parameter called pickup pattern (accounts for various real world logical information, e.g., bad weather) to reduce the size of data to be processed. We evaluate Dmodel with a real world 450 GB dataset of 14, 000 taxicabs for a half year, and results show that compared to ground truth, Dmodel achieves a 76% accuracy on the demand inference and outperforms a statistical model by 39%. We further present an application where Dmodel is used to dispatch vacant taxicabs to achieve an equilibrium between passenger demand and taxicab supply across urban regions.

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Section: Sensitivity Of Dmodelmentioning

confidence: 99%

Dmodel: Online Taxicab Demand Model from Big Sensor Data in a Roving Sensor Network

Stankovic

Lin

et al. 2014

2014 IEEE International Congress on Big Data

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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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“…Traditional public transportation planning methods have relied on human surveys to understand people's mobility patterns and their choice among different transportation modes [4,21]. Despite the substantial time and cost spent on the survey process, the macroscopic analysis based on surveys is too static to reflect the fast development of urban areas.…”

Section: Introductionmentioning

confidence: 99%

Intelligent bus routing with heterogeneous human mobility patterns

Liu

Yuan

et al. 2016

Knowl Inf Syst

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Optimal planning for public transportation is one of the keys helping to bring a sustainable development and a better quality of life in urban areas. Compared to private transportation, public transportation uses road space more efficiently and produces fewer accidents and emissions. However, in many cities people prefer to take private transportation other than public transportation due to the inconvenience of public transportation services. In this paper, we focus on the identification and optimization of flawed region pairs with problematic bus routing to improve utilization efficiency of public transportation services, according to people's real demand for public transportation. To this end, we first provide an integrated mobility pattern analysis between the location traces of taxicabs and the mobility records in bus transactions. Based on the mobility patterns, we propose a localized transportation mode choice model, with which we can dynamically predict the bus travel demand for different bus routing by taking into account both bus and taxi travel demands. This model is then used for bus routing optimization which aims to convert as many people from private transportation to public transportation as possible given budget constraints on the bus route modification. We also leverage the model to identify region pairs with flawed bus routes, which are effectively optimized using our approach. To validate the effectiveness of the proposed methods, extensive studies are performed on real-world data collected in Beijing which contains 19 million taxi trips and 10 million bus trips.

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City-scale traffic estimation from a roving sensor network

Cited by 135 publications

References 11 publications

Dmodel: Online Taxicab Demand Model from Big Sensor Data in a Roving Sensor Network

Dmodel: Online Taxicab Demand Model from Big Sensor Data in a Roving Sensor Network

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

Intelligent bus routing with heterogeneous human mobility patterns

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