A Generic Approach to Estimate Freeway Traffic Time Using Vehicle ID‐Matching Technologies

Li, Pengfei; Souleyrette, Reginald R.

doi:10.1111/mice.12159

Cited by 14 publications

(8 citation statements)

References 27 publications

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“…[42] survey the evolution and application of these systems. For example, they have been used to estimate travel time [43,44]. [45] use GPS data from buses and Bluetooth detectors data for other vehicles and studies the travel patterns, and detects communities based on these patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing passenger and freight vehicle movements from automatic-Number plate recognition camera data

Hadavi

Buldeo

Verlinde

et al. 2020

Eur. Transp. Res. Rev.

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Modern urban-transport planning requires evidence-based insights into current transport flows to better understand the needs and impacts of policymaking. Urban transport includes passenger and freight vehicles, which have different behavior, and the need for such a separation is often ignored in research and practice [1]. New digital data sources provide an opportunity to better understand urban transport and identify where policy interventions are required. We review the literature on digital counting techniques to monitor transport flows, including loops, Automatic-Number Plate Recognition (ANPR) cameras and floating car data. We further investigate the potential of ANPR cameras, which are widely deployed, and which can be augmented with vehicle category information. This article presents the methodology that we follow for transforming raw augmented ANPR camera data into practical knowledge for city planners. Our is aim is to provide a better understanding of passenger and freight vehicle movements and stops, identifying similarities and differences between vehicle categories. We demonstrate our methodology on a case study for the Mechelen-Willebroek district in Belgium, encompassing augmented data from 122 ANPR cameras for a period of two weeks. Additionally, we also look at the car-reduced zone and how time restrictions affect the different vehicle categories’ actions. The findings are validated with GPS data from heavy-good vehicles in the same period. The potential of augmented ANPR camera data and promising themes and applications of this data source are illustrated through the case study.

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Section: Literature Reviewmentioning

confidence: 99%

Analyzing passenger and freight vehicle movements from automatic-Number plate recognition camera data

Hadavi

Buldeo

Verlinde

et al. 2020

Eur. Transp. Res. Rev.

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show abstract

“…Besides making use of the probe data to understand human mobility (e.g., González et al, 2008;Jiang et al, 2009) and to estimate travel time (e.g., Jenelius and Koutsopoulos, 2015;Li and Souleyrette, 2016), how to visualize and extract traffic conditions and dynamics from the probe data is an interesting and meaningful topic that helps people learn more about the traffic performance and further manage the traffic. To visualize the traffic conditions by using the probe data, the simplest way is to directly plot the positions of probe vehicles in spots on a map and color them based on their instantaneous speeds (Ferreira et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Mapping to Cells: A Simple Method to Extract Traffic Dynamics from Probe Vehicle Data

Zheng

Chen

et al. 2017

Computer aided Civil Eng

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In the era of big data, mining data instead of collecting data are a new challenge for researchers and engineers. In the field of transportation, extracting traffic dynamics from widely existing probe vehicle data is meaningful both in theory and practice. Therefore, this article proposes a simple mapping‐to‐cells method to construct a spatiotemporal traffic diagram for a freeway network. The method partitions a network region into small square cells and represents a real network inside the region by using the cells. After determining the traffic flow direction pertaining to each cell, the spatiotemporal traffic diagram colored according to traffic speed can be well constructed. By taking the urban freeway in Beijing, China, as a case study, the mapping‐to‐cells method is validated, and the advantages of the method are demonstrated. The method is simple because it is completely based on the data themselves and without the aid of any additional tool such as Geographic Information System software or a digital map. The method is efficient because it is based on discrete space‐space and time‐space homogeneous cells that allow us to match the probe data through basic operations of arithmetic. The method helps us understand more about traffic congestion from the probe data, and then aids in carrying out various transportation researches and applications.

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“…The existing methods for travel time estimation are generally categorised into two types: the direct method and indirect method. The former one is a sampling-based method using data obtained from probe vehicles such as automatic vehicle identification (AVI) [1][2][3][4], cellular phone [5][6][7], vehicle re-identification (e.g. GPS) [8] and Bluetooth techniques [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Real‐time estimation of freeway travel time with recurrent congestion based on sparse detector data

Wang

et al. 2017

IET Intelligent Transport Systems

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A Generic Approach to Estimate Freeway Traffic Time Using Vehicle ID‐Matching Technologies

Cited by 14 publications

References 27 publications

Analyzing passenger and freight vehicle movements from automatic-Number plate recognition camera data

Analyzing passenger and freight vehicle movements from automatic-Number plate recognition camera data

Mapping to Cells: A Simple Method to Extract Traffic Dynamics from Probe Vehicle Data

Real‐time estimation of freeway travel time with recurrent congestion based on sparse detector data

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