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EditorialSensors in Connected Vehicle Technology: How Sensors Play aCritical Role

Chuan Ding,1 Jia Hu,2 and Xiaolei Ma1

1School of Transportation Science and Engineering, Beihang University, Beijing 100191, China2Federal Highway Administration, Washington, DC, USA

Correspondence should be addressed to Chuan Ding; [email protected]

Received 13 November 2017; Accepted 13 November 2017; Published 28 November 2017

Copyright © 2017 Chuan Ding et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

A rapid development of connected vehicle (CV) technologyhas been observed around the globe in the past decade. Theproblems that the conventional traffic management systemsbear are believed to be solved by introducing the technology.This CV technology puts diagnostic sensors onto vehicles/infrastructures and has the data collected transmitted wire-lessly between vehicles and nearby infrastructures. It wouldno longer rely on conventional data collection equipment,like a loop detector or video detections, and it collects muchmore information than the conventional ways. Measure-ments that are previously unknown are available, whichinclude but are not limited to vehicle speeds, positions,arrival rates, rates of acceleration and deceleration, queuelengths, and stopped time.With this extra information, manyapplications are made possible. However, on the other hand,the accuracy and the stability of sensors cause challenges forthe development of CV application, for instance, the accu-racy of GPS, communication delay, and LIDAR accuracy.These factors require special design embedded in those CVapplications to accommodate the limitation of sensors. Thisspecial issue aims to serve as a major platform to facilitatethe discussion and exchange of research ideas and technologydevelopment, encourage multidimensional knowledge shar-ing, and enhance research activities in investigating sensorsin connected vehicle technology. In total, seven papers areincluded in this special issue and are summarized as follows.

There are several articles focusing on traffic control andanalysis using the sensor-based data to improve traffic sys-tem. The autonomous vehicle is able to facilitate road safetyand traffic efficiency and has become a promising trend of

future development. W. Wu et al. proposed traffic controlmodels based on cellular automata for intersections in theautonomous vehicle environment. Multiagent technology isused to simulate the proposed model. The simulation resultsshow that the control strategy of the proposed model signif-icantly reduces average delays and a number of stops as wellas increasing traffic capacity. A classical control problem foran isolated oversaturated intersection is revisited with a focuson the optimal control policy to minimize total delay. J. Tanet al. used the gradient descent algorithm to convert theoptimal control plan of the continuous-time model to theplan of the discrete-time model in many cases. Analytic proofand numerical tests for the algorithm are also presented.

From the perspective of sensor-based traffic operationand management, several articles analyzed the traffic condi-tion and driver behavior. With the development of connectedvehicle (CV) and vehicle to X (V2X) communication, moretraffic data are being collected from the road network. Inorder to predict future traffic condition from connectedvehicles’ data in real time, P. Wang et al. proposed an onlinetraffic condition evaluation model utilizing V2X communi-cation. The contemporary vehicle data from the on-boarddiagnostic (OBD) is fused with the static road data in theroadside unit (RSU). Compared with traditional evaluationsystems, the proposed model can handle more types of databut demands less data transfer. W. Zhao et al. did a sensor-based visual effect evaluation of chevron alignment signs’colors on drivers through the curves in snow and ice environ-ment. The conclusions provide evident references for freewaywarning products and the design of intelligent vehicles.

HindawiJournal of SensorsVolume 2017, Article ID 8241932, 2 pageshttps://doi.org/10.1155/2017/8241932

https://doi.org/10.1155/2017/8241932

There are several articles focusing on driving and travelbehavior using the GPS data. J. Wang and Y. Cao developeda method to identify whether there is deliberate speed-up orslow-down movement of a bus based on the recovery of GPSdata. The effectiveness of the developed method was demon-strated using the data collected in Harbin, China. The resultsshow that it can help bus enterprises to design reasonabletime of day intervals and significantly improve their level ofservice. S. An et al. proposed a public transit riders’ travelpattern measuring method based on divided cells and publictransit vehicle’s GPS data. A case study is carried out toevaluate the methods, which use the GPS data collectedfrom taxis and buses in Harbin, China. The study is expectedto provide a better understanding of public transit riders’travel patterns.

The remaining one article investigated spatial crowd-sourcing problem caused by the increasingly widespread sen-sors. With the development of sensors, particularly vehiclesensors and mobile sensors, spatial crowdsourcing is gainingeven more attention. B. Du et al. proposed a novel spatialcrowdsourcing problem called software development teamformation (SDTF). They proved that SDTF was NP-hardand designed three greedy algorithms and an index-basedalgorithm to solve the SDTF problem. Extensive experimentsare conducted on synthetic and real datasets, and the resultsverify the effectiveness and efficiency of the algorithms.

Acknowledgments

The guest editors hope the information provided in this spe-cial issue is useful. Finally, we would like to thank theauthors for an excellent contribution of their research worksand very warmly acknowledged the reviewers for their valu-able review comments.

Chuan DingJia Hu

Xiaolei Ma

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