EditorialEmerging Information and Communication Technologies forTraffic Estimation and Control

Anastasios Kouvelas ,1 Andy Chow,2 Eric Gonzales,3

Mehmet Yildirimoglu,4 and Rodrigo Castelan Carlson5

1 Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland2City University of Hong Kong, Kowloon Tong, Hong Kong3University of Massachusetts Amherst, Amherst, MA, USA4University of Queensland, Brisbane, QLD, Australia5Federal University of Santa Catarina, Florianopolis, SC, Brazil

Correspondence should be addressed to Anastasios Kouvelas; tasos.kouvelas@epfl.ch

Received 25 March 2018; Accepted 26 March 2018; Published 1 August 2018

Copyright © 2018 Anastasios Kouvelas et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Information and communication technologies (ICT) arefundamental components of any development in the fieldof traffic estimation and control. The design and deploy-ment ofmultimodal advanced transportation systems involveinterdisciplinary expertise and effective utilization of newtechnologies. Nowadays, there are an increasing numberof instrumented networks and transportation deploymentsaround the world, and, as a consequence, the researchcommunity in this domain has entered the era of big data.The wealthy amount of traffic data that is available in realtime can support the development of modern systematicapproaches and methodological tools for estimation andcontrol.

Recent advances in transportation (e.g., emerging vehiclecommunication and automation technologies) have gener-ated new types of measurements, vehicles and infrastructurecommunications, and control actuation capabilities. All theseenable the transportation community to adapt its researchtopics accordingly and exploit the opportunities resultingfrom these new developments to improve traffic estimationand control. There is an essential and timely need for newtheoretical techniques and algorithms that can accompanythe technological achievements and consequently enhancethe performance of transportation systems.

The purpose of this special issue is to publish high qualityresearch papers addressing innovative approaches to improve

traffic estimation and control by utilizing recent advances anddevelopments in the area of ICT. Among all the submittedmanuscripts, six papers were selected for publication on thisspecial issue after a peer review process, as follows.

V. Lucken et al. propose a density-based statistical clus-tering approach. They present an integrated approach thatcombines smart street lamps with traffic sensing technology.Infrastructure-based ultrasonic sensors, which are deployedtogether with a street light system platform, are utilized formultilane traffic participant detection and classification.Theydevelop an algorithmic approach that combines statisticalstandardization with clustering techniques from the field ofunsupervised learning.

H. Chang and B. Yoon proposed a data-driven 𝑘-nearestneighbour nonparametric regression (KNN-NPR) frame-work that generates short-term traffic volume predictions.The basic characteristic of their approach is high-speedcomputational performance when dealing with enormousamounts of historical data. To prove the efficacy of theirapproach, they have conducted an experimental test withlarge-size traffic volume data.

Y. Liu and Y. Wang presented and compared the verticaland horizontal queueing modelling paradigms for design-ing and analysing traffic signal timings in oversaturatedconditions with limited mobile sensing data. The analyticalresults were validated by using AIMSUN model. Their paper

HindawiJournal of Advanced TransportationVolume 2018, Article ID 8498054, 2 pageshttps://doi.org/10.1155/2018/8498054

2 Journal of Advanced Transportation

addressed the temporal consistency between vertical andhorizontal queueing models.

R. A. Paielli proposes a standard Trajectory SpecificationLanguage (TSL) based on the Extensible Markup Language(XML) for serializing and communicating aircraft trajecto-ries.The language can be used to downlink trajectory requestsfrom air to ground and to uplink trajectory assignments fromground to air.The language is human-readable andmay serveas starting point for the development of a communicationstandard for the Trajectory Specification concept.

X. Hu et al. utilized GPS trajectories from Shenzhen,China, to explore taxi drivers’ operation behavior and pas-sengers’ demand. Their work focuses on exploring the taxidrivers’ operation behavior by utilizing the measurements ofactivity space and the connection between different activityspaces for different time durations. This research is based onreal data and can be helpful for taxi drivers to search for a newpassenger and passengers to help them find a taxi’s location.

G. A. Klunder et al. presented a study which gives insightinto the size of improvement that is possible with individualin-car routing advice based on the actual traffic situationderived from Floating Car Data (FCD). The study uses realloop detector data from the region of Amsterdam, a routegenerating algorithm for in-car routing advice, and emulatedFCD to generate the routing advice. The case with in-carrouting advice is compared to the base case, where driversbase their routing decisions on average knowledge of traveltimes in the network.

Anastasios KouvelasAndy Chow

Eric GonzalesMehmet Yildirimoglu

Rodrigo Castelan Carlson

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