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Abstract: The importance of using adaptive traffic signal control for figuring out the unpredictable traffic congestion in today’s metropolitan life cannot be overemphasized. The vehicular ad hoc network (VANET), as an integral component of intelligent transportation systems (ITSs), is a new potent technology that has recently gained the attention of academics to replace traditional instruments for providing information for adaptive traffic signal controlling systems (TSCSs). Meanwhile, the suggestions of VANET-based TSCS approaches have some weaknesses: (1) imperfect compatibility of signal timing algorithms with the obtained VANET-based data types, and (2) inefficient process of gathering and transmitting vehicle density information from the perspective of network quality of service (QoS). This paper proposes an approach that reduces the aforementioned problems and improves the performance of TSCS by decreasing the vehicle waiting time, and subsequently their pollutant emissions at intersections. To achieve these goals, a combination of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications is used. The V2V communication scheme incorporates the procedure of density calculation of vehicles in clusters, and V2I communication is employed to transfer the computed density information and prioritized movements information to the road side traffic controller. The main traffic input for applying traffic assessment in this approach is the queue length of vehicle clusters at the intersections. The proposed approach is compared with one of the popular VANET-based related approaches called MC-DRIVE in addition to the traditional simple adaptive TSCS that uses the Webster method. The evaluation results show the superiority of the proposed approach based on both traffic and network QoS criteria.

The authors describe a traffic signal control strategy for urban areas using VANET or connected vehicle technologies. The authors’ careful consideration of realistic communication systems to ensure that the proposed system is feasible is appreciated. It is good that their model was based on a real-world environment with buildings to influence interference, rather than a theoretical environment. Their approach has some promising innovations, primarily the V2V density estimation strategy. Their preliminary results seem to show promise when compared with alternative signal control strategies, especially in saturated and over-saturated conditions.

采用车载通信的自适应绿色交通信号控制

[1]Akçelik, R., Besley, M., 2003. Operating cost, fuel consumption, and emission models in aaSIDRA and aaMOTION. 25th Conf. of Australian Institutes of Transport Research, p.1-15.

[2]Akçelik, R., Besley, M., Chung, E., 1998. An evaluation of SCATS Master Isolated control. Proc. 19th ARRB Transport Research Conf., p.1-24.

[3]Akçelik, R., Smit, R., Besley, M., 2012. Calibrating fuel consumption and emission models for modern vehicles. IPENZ Transportation Group Conf., p.1-13.

[4]Bester, C., Meyers, W., 2007. Saturation flow rates. SATC.

[5]Bruce, R.L., 1984. Loop Detector for Traffic Signal Control. US Patent 4 430 636 A.

[6]Chang, H.J., Park, G.T., 2013. A study on traffic signal control at signalized intersections in vehicular ad hoc networks. Ad Hoc Netw., 11(7):2115-2124.

[7]Dickinson, K., Wan, C., 1990. An evaluation of microwave vehicle detection at traffic signal controlled intersections. 3rd Int. Conf. on Road Traffic Control, p.153-157.

[8]Eichler, S., 2007. Performance evaluation of the IEEE 802.11p WAVE communication standard. IEEE 66th Vehicular Technology Conf., p.2199-2203.

[9]Gordon, R.L., Tighe, W., Siemens, I., 2005. Traffic Control Systems Handbook. US Department of Transportation, Technical Paper No. FHWA-HOP-06-006, Federal Highway Administration, Office of Operations, Washington, D.C., USA.

[10]Gradinescu, V., Gorgorin, C., Diaconescu, R., et al., 2007. Adaptive traffic lights using car-to-car communication. IEEE Vehicular Technology Conf., p.21-25.

[11]Haklay, M., Weber, P., 2008. Openstreetmap: user-generated street maps. IEEE Perv. Comput., 7:12-18.

[12]Huang, Q., Miller, R., 2004. Reliable wireless traffic signal protocols for smart intersections. ITS America Annual Meeting.

[13]Jabbarpour, M.R., Md Noor, R., Khokhar, R.H., et al., 2014. Cross-layer congestion control model for urban vehicular environments. J. Netw. Comput. Appl., 44:1-16.

[14]Köhler, E., Strehler, M., 2010. Traffic signal optimization using cyclically expanded networks. 10th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, p.114-129.

[15]Koonce, P., Rodergerdts, L., Lee, K., et al., 2008. Traffic Signal Timing Manual. Report No. FHWA-HOP-08-024, Federal Highway Administration, Washington, D.C., USA.

[16]Kraft, W.H., Homburger, W.S., Pline, J.L., 2009. Traffic Engineering Handbook. ITE, Washington, D.C., USA.

[17]Krajzewicz, D., Erdmann, J., Behrisch, M., et al., 2012. Recent development and applications of SUMO—simulation of urban mobility. Int. J. Adv. Syst. Meas., 5(3-4):128-138.

[18]Kwatirayo, S., Almhana, J., Liu, Z., 2013. Adaptive traffic light control using VANET: a case study. 9th Int. IEEE Wireless Communications and Mobile Computing Conf., p.752-757. http://dx.doi.org /10.1109/IWCMC.2013.6583651

[19]Lahart, J., Conroy, E., Curley, R., et al., 2013. Design Manual for Urban Roads and Streets. Technical Paper, Department of Transport, Tourism and Sport of Ireland, Dublin, Ireland.

[20]Li, L., Wen, D., Yao, D., 2014. A survey of traffic control with vehicular communications. IEEE Trans. Intell. Transp. Syst., 15(1):425-432.

[21]Litman, T., 2013. Factors to Consider When Estimating Congestion Costs and Evaluating Potential Congestion Reduction Strategies. https://trid.trb.org/view.aspxid=1267787

[22]Liu, H., van Zuylen, H.J., van Lint, H., et al., 2005. Prediction of urban travel times with intersection delays. Proc. IEEE Intelligent Transportation Systems, p.402-407.

[23]Maslekar, N., Boussedjra, M., Mouzna, J., et al., 2011a. C-DRIVE: clustering based on direction in vehicular environment. 4th IFIP Int. Conf. on New Technologies, Mobility and Security, p.1-5.

[24]Maslekar, N., Boussedjra, M., Mouzna, J., et al., 2011b. VANET based adaptive traffic signal control. IEEE 73rd Vehicular Technology Conf., p.1-5.

[25]Maslekar, N., Mouzna, J., Boussedjra, M., et al., 2013. CATS: an adaptive traffic signal system based on car-to-car communication. J. Netw. Comput. Appl., 36(5):1308-1315.

[26]Nafi, N.S., Khan, J.Y., 2012. A VANET based intelligent road traffic signalling system. IEEE Telecommunication Networks and Applications Conf., p.1-6.

[27]National Research Council (NRC), 2000. Highway Capacity Manual. Transportation Research Board, Washington, D.C., USA.

[28]Pandit, K., Ghosal, D., Zhang, H.M., et al., 2013. Adaptive traffic signal control with vehicular ad hoc networks. IEEE Tran. Veh. Technol., 62(4):1459-1471.

[29]Priemer, C., Friedrich, B., 2009. A decentralized adaptive traffic signal control using V2I communication data. 12th Int. IEEE Conf. on Intelligent Transportation Systems, p.1-6.

[30]Puri, A., Valavanis, K., Kontitsis, M., 2007. Statistical profile generation for traffic monitoring using real-time UAV based video data. Mediterranean Conf. on Control & Automation, p.1-6.

[31]Robertson, D.I., Bretherton, R.D., 1991. Optimizing networks of traffic signals in real time: the SCOOT method. IEEE Trans. Veh. Technol., 40(1):11-15.

[32]Scholz, I., 2011. Java OpenStreetMap Community. OpenStreetMap Editor. https://josm.openstreetmap.de/

[33]Schrank, D., Eisele, B., Lomax, T., 2012. TTI’s 2012 Urban Mobility Report. Texas A&M Transportation Institute, Texas, USA.

[34]Shelby, S.G., 2001. Design and Evaluation of Real-Time Adaptive Traffic Signal Control Algorithms. PhD Thesis, University of Arizona, Tucson, USA.

[35]Sommer, C., Eckhoff, D., German, R., et al., 2011a. A computationally inexpensive empirical model of IEEE 802.11p radio shadowing in urban environments. IEEE 8th Int. Conf. on Wireless On-Demand Network Systems and Services, p.84-90.

[36]Sommer, C., German, R., Dressler, F., 2011b. Bidirectionally coupled network and road traffic simulation for improved IVC analysis. Mobile Computing. IEEE Trans., 10(1):3-15.

[37]Stevanovic, A., Stevanovic, J., Zhang, K., et al., 2009. Optimizing traffic control to reduce fuel consumption and vehicular emissions. Transp. Res. Rec., 2128:105-113.

[38]Tomescu, O., Moise, I.M., Stanciu, A.E., et al., 2012. Adaptive traffic light control system using ad hoc vehicular communications network. U.P.B. Sci. Bull. D, 74:68-78.

[39]U.S. Energy Information Administration (EIA), 2009. Emissions of Greenhouse Gases in the United States 2008. Technical Report No. DOE/EIA-0573(2008), Office of Integrated Analysis and Forecasting, U.S. Department of Energy, Washington, D.C., USA.

[40]Varga, A., Hornig, R., 2008. An overview of the OMNeT++ simulation environment. Proc. 1st Int. Conf. on Simulation Tools and Techniques for Communications, Networks and Systems & Workshops, p.60.

[41]Webster, F.V., 1958. Traffic Signal Settings. Technical Paper, Her Majesty’s Stationery Office, London, UK.

[42]Webster, F.V., Cobbe, B.M., 1966. Traffic Signals. Technical Paper, Her Majesty’s Stationary Office, London, UK.

[43]Yousefi, S., Mousavi, M.S., Fathy, M., 2006. Vehicular ad hoc networks (VANETs): challenges and perspectives. Proc. IEEE 6th Int. Conf. on ITS Telecommunications, p.761-766.