Querying and Extracting Timeline Information from Road Traffic Sensor Data

Imawan, Ardi; Indikawati, Fitri Indra; Kwon, Joonho; Rao, Praveen

doi:10.3390/s16091340

Cited by 9 publications

(9 citation statements)

References 32 publications

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“…Traffic monitoring has been a popular topic as it facilitates transport management and planning. Due to the advantage of passive sensing, increasingly many sensors are being used in the transport sector, such as probe vehicles, loops, and cameras [22][23][24][25]. These sensors produce abundant data that capture the urban traffic status, thereby enabling traffic monitoring.…”

Section: Data-driven Traffic Monitoringmentioning

confidence: 99%

Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories

Wang

Lü

2020

Sensors

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Urban traffic pattern reflects how people move and how goods are transported, which is crucial for traffic management and urban planning. With the development of sensing techniques, accumulated sensor data are captured for monitoring vehicles, which also present the opportunities of big transportation data, especially for real-time interactive traffic pattern analysis. We propose a three-layer framework for the recognition and visualization of multiscale traffic patterns. The first layer computes the middle-tier synopses at fine spatial and temporal scales, which are indexed and stored in a geodatabase. The second layer uses synopses to efficiently extract multiscale traffic patterns. The third layer supports real-time interactive visual analytics for intuitive explorations by end users. An experiment in Shenzhen on taxi GPS trajectories that were collected over one month was conducted. Multiple traffic patterns are recognized and visualized in real-time. The results show the satisfactory performance of proposed framework in traffic analysis, which will facilitate traffic management and operation.

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Section: Data-driven Traffic Monitoringmentioning

confidence: 99%

Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories

Wang

Lü

2020

Sensors

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“…yi ðÞÀŷi ðÞ jj (9) An algorithm (Appendix A, Algorithm 1) is designed to predict the speed of w 3 using the modeled equations (M 1 k and M 2 k ) and historical data, i.e., the data available from w 3 before the current time. The error between the observed (from w 3 ) and predicted (with Algorithm 1) travel speeds is calculated with Eq.…”

Section: Polynomial Regression Model and Algorithmmentioning

confidence: 99%

“…The error between the observed (from w 3 ) and predicted (with Algorithm 1) travel speeds is calculated with Eq. (9). The MAE, SD, and hits (percentage of data categorized correctly) for w 3 , using Algorithm 1, are shown in Table 6.…”

Section: Polynomial Regression Model and Algorithmmentioning

confidence: 99%

“…Characterization of the daily temporal variation of congestion is presented in [8], where a fitted model and live data are combined in a ten-parameter exponential smoothing equation. With the purpose of analyzing historical traffic data, a query processing method with timeline information is proposed in [9], along with an analysis of the congestion dependency along roads. The work presented in [10] estimates the average link speed with vehicles equipped with GPS, and therefore the quantity of equipped vehicles required for estimating the speed was established.…”

Section: Introductionmentioning

confidence: 99%

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Procedure to Prepare and Model Speed Data Considering the Traffic Infrastructure, as Part of a Cyber-Physical System

Carrillo-González¹,

Sandoval-Gutierrez²,

Pérez-Martínez³

2020

Sustainability in Urban Planning and Design

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This chapter investigates the relationship between traffic control infrastructure (traffic lights and speed bumps) and the vehicles' travel speeds, for certain hours and days of the week. The authors propose the following procedures: (1) street segmentation, (2) clustering and categorization of speed data, (3) histograms' comparison analysis, (4) outlier detection, (5) modeling, and (6) delivering info to the users. Comparing speed histograms, segments with matching infrastructure presented similarities, regardless of the day of the week. Two techniques to model data were employed: polynomial regression and multinomial logistic regression. The algorithms to predict the travel speed category were also developed. The first technique yields on average 91.3% of data categorized correctly, and the second gets 90.09%. The traffic lights and speed bumps, located on the street segments under consideration, were identified as variables causing different travel speeds. The procedure allows to incorporate more traffic elements and can also be applied to other geographical locations.

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“…Intelligent transportation systems (ITS) are a symbol of smart cities. ITS centers collect and reserve real-time road traffic data from various sources to reduce traffic congestion problems (Imawan et al, 2016). To make closer a city to the smart city, improving the different areas that are part of the city such as the health sector is necessary (Alami-Kamouri et al, 2017).…”

Section: Smart Transportation and Smart Healthmentioning

confidence: 99%

An optimized multi agent-based modeling of smart rescue operation

Azimi

Delavar

Rajabifard

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. After the incidence of a disaster, a high demand for first-aid and a huge number of injured will emerge at the affected areas. In this paper, the optimum allocation of the medical assistance to the injured according to a multi-criteria decision making is performed by Multiplicatively Weighted Network Voronoi Diagram (MWNVD). For consideration of the allocation of the injured in the affected area to the appropriate hospitals using the MWNVD and decreasing the gap between the estimated and expected population in the MWNVDs, Particle Swarm Optimization (PSO) is applied to the MWNVDs. This paper proposes a multi agent-based modeling for incorporating the allocation of the medical supplies to the injured according to the generated Voronoi Diagrams of the PSO-MWNVD, wayfinding of emergency vehicles based on the minimum travel distance and time as well as using smart city facilities to expedite the rescue operation. In the proposed model, considering the priority of the injured for receiving the medical assistance, information transfer about the condition of the injured to the hospitals prior to ambulance arrival for providing appropriate treatment, updating of emergency vehicles route based on the blocked streets and etc. are optimized. The partial difference between the estimated and expected population for receiving the medical assistance in MWNVDs is computed as 37&thinsp;%, while the PSO-MWNVD decreased the mentioned difference to 6&thinsp;%. The relief operation time in the proposed model compared to another multi-agent rescue operation model, which uses MWNVD and does not have some facilities of the proposed model, is improved.

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Querying and Extracting Timeline Information from Road Traffic Sensor Data

Cited by 9 publications

References 32 publications

Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories

Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories

Procedure to Prepare and Model Speed Data Considering the Traffic Infrastructure, as Part of a Cyber-Physical System

An optimized multi agent-based modeling of smart rescue operation

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