Variable message signs (VMSs) provide important traffic information to help drivers travel better on a transportation network. The effectiveness of VMSs largely depends on the numbers and locations of VMSs in a transportation network. Although a few optimization models have been proposed to find candidate roads for locating VMSs, few have been devoted to developing algorithms that can be used in a real transportation network. A large amount of traffic data, such as traffic flow data, is widely available, collected by various means. Based on those traffic data, a sorting algorithm for a VMS location problem is presented in this paper. The algorithm gave a VMS location order rather than a location set. The proposed method divided roads into categories according to multilevel attributes and preferentially selected roads of a higher class with larger flows and more information and the minimal effect of existing VMS in a certain order to locate VMS. The proposed algorithm was analyzed and verified through a practical case on the Beijing, China, urban road network.
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References
1.
WardmanM., BonsallP. W., and ShiresJ. D.. Driver Response to Variable Message Signs: A Stated Preference Investigation. Transportation Research Part C: Emerging Technologies, Vol. 5, No. 6, 1997, pp. 389–405. https://doi.org/10.1016/S0968-090X(98)00004-7.
2.
DurkopB. R., and DudekC. L.. Texas Driver Understanding of Abbreviations for Changeable Message Signs. Transportation Research Record: Journal of the Transportation Research Board, No. 1748, 2001, pp. 87–95. https://doi.org/10.3141/1748-11.
3.
MammarS., MessmerA., JensenP., PapageorgiouM., Haj-SalemH., and JensenL.. Automatic Control of Variable Message Signs in Aalborg. Transportation Research Part C: Emerging Technologies, Vol. 4, No. 3, 1996, pp. 131–150. https://doi.org/10.1016/S0968-090X(96)00005-8.
4.
MessmerA., PapageorgiouM., and MackenzieN.. Automatic Control of Variable Message Signs in the Interurban Scottish Highway Network. Transportation Research Part C: Emerging Technologies, Vol. 6, No. 3, 1998, pp. 173–187. https://doi.org/10.1016/S0968-090X(98)00014-X.
5.
AdlerJ. L., ReckerW. W., and McNallyM. G.. A Conflict Model and Interactive Simulator (FASTCARS) for Predicting In-Route Driver Behavior in Response to Real-Time Traffic Condition Information. Transportation, Vol. 20, No. 2, 1993, pp. 83–106. https://doi.org/10.1007/BF01307054.
6.
EmmerinkR. H. M., NijkampP., RietveldP., and Van OmmerenJ. N.. Variable Message Signs and Radio Traffic Information: An Integrated Empirical Analysis of Drivers’ Route Choice Behavior. Transportation Research Part A: Policy and Practice, Vol. 30, No. 2, 1996, pp. 135–153. https://doi.org/10.1016/0965-8564(95)00016-X.
7.
ChatterjeeK., HounsellN. B., FirminP. E., and BonsallP. W.. Driver Response to Variable Message Sign Information in London. Transportation Research Part C: Emerging Technologies, Vol. 10, No. 2, 2002, pp. 149–169. https://doi.org/10.1016/S0968-090X(01)00008-0.
8.
ZhouY. F., and WuJ. P.. The Research on Drivers’ Route Choice Behavior in the Presence of Dynamic Traffic Information. In Proceedings of the IEEE Intelligent Transportation Systems Conference, 2006, pp. 17–20.
9.
ErkeA., SagbergF., and HagmanR.. Effects of Route Guidance Variable Message Signs (VMS) on Driver Behavior. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 10, No. 6, 2007, pp. 447–457. https://doi.org/10.1016/j.trf.2007.03.003.
10.
AbbasM. M., and McCoyP. T.. Optimizing Variable Message Sign Location on Freeways Using Genetic Algorithms. Presented at 78th Annual Meeting of the Transportation Research Board, Washington, D.C., 1999.
11.
ChiuY.-C., HuynhN., and MahmassaniH. S.. Determining Optimal Locations for Variable Message Signs Under Stochastic Incident Scenarios. Presented at 80th Annual Meeting of the Transportation Research Board, Washington, D.C., 2001.
12.
ChiuY., and HuynhN.. Location Configuration Design for Dynamic Message Signs Under Stochastic Incident and ATIS Scenarios. Transportation Research Part C: Emerging Technologies, Vol. 15, No. 1, 2007, pp. 33–50. https://doi.org/10.1016/j.trc.2006.12.001.
13.
LiS., and FanB.. Dynamic and Stochastic Traffic Network Models and Their Application. Tongji University Publication Press, Shanghai, China, 2005 (in Chinese).
14.
FuL., LiS., and HendersonJ.. A Planning Model for Determining Optimal CMS Locations on a Freeway-Arterial Network. Journal of Transportation Systems Engineering and Information Technology, Vol. 5, No. 5, 2005, pp. 101–120.
15.
ZhongS. Q., ZhouL. Z., MaS. F., WangX. L., and JiaN.. Study on the Optimization of VMS Location Based on Drivers’ Guidance Compliance Behaviors. Transport, Vol. 29, No. 2, 2014, pp. 154–164. https://doi.org/10.3846/16484142.2014.929534.
16.
BoylesS. D., and WallerS. T.. Optimal Information Location for Adaptive Routing. Networks and Spatial Economics, Vol. 11, No. 2, 2011, pp. 233–254. https://doi.org/10.1007/s11067-009-9108-9.
17.
BanX., LiY., and MarguliciJ. D.. Optimal Use of Changeable Message Signs for Displaying Travel Times. University of California Press, Berkeley, Calif., 2009.
18.
LiM., LinX., HeF., and JiangH..Optimal Locations and Travel Time Display for Variable Message Signs. Transportation Research Part C: Emerging Technologies, Vol. 69, 2016, pp. 418–435. https://doi.org/10.1016/j.trc.2016.06.016.
19.
MesbahM., SarviM., OuveysiI., and CurrieG.. Optimization of Transit Priority in the Transportation Network Using a Decomposition Methodology. Transportation Research Part C: Emerging Technologies, Vol. 19, No. 2, 2011, pp. 363–373. https://doi.org/10.1016/j.trc.2010.05.020.
20.
MiandoabchiE., FarahaniR. Z., DullaertW., and SzetoW. Y.. Hybrid Evolutionary Metaheuristics for Concurrent Multi-Objective Design of Urban Road and Public Transit Networks. Networks and Spatial Economics, Vol. 12, No. 3, 2012, pp. 441–480. https://doi.org/10.1007/s11067-011-9163-x.
21.
YaoJ., ShiF., ZhouZ., and QinJ.. Combinatorial Optimization of Exclusive Bus Lanes and Bus Frequencies in Multi-Modal Transportation Network. Journal of Transportation Engineering, Vol. 138, No. 12, 2012, pp. 1422–1429. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000475.
22.
BonsallP., FirminP., AndersonM., PalmerI., and BalmforthP.. Validating the Results of a Route Choice Simulator. Transportation Research Part C: Emerging Technologies, Vol. 5, No. 6, 1997, pp. 371–387. https://doi.org/10.1016/S0968-090X(98)00003-5.
23.
GanH. C., SunL. J., ChenJ. Y., and YuanW. P.. Advanced Traveler Information System for Metropolitan Expressways in Shanghai, China. Transportation Research Record: Journal of the Transportation Research Board, No. 1944, 2006, pp. 35–40. https://doi.org/10.3141/1944-05.
24.
ZhongS. Q., ZhouL. Z., MaS. F., and JiaN.. Effects of Different Factors on Drivers’ Guidance Compliance Behaviors Under Road Condition Information Shown on VMS. Transportation Research Part A: Policy and Practice, Vol. 46, No. 9, 2012, pp. 1490–1505. https://doi.org/10.1016/j.tra.2012.05.022.
25.
ThomasH. C., CharlesE. L., RonaldR. R., and CliffS.. Introduction to Algorithms. McGraw-Hill, New York, 1990.
26.
Beijing Transportation Research Center. Beijing Transportation Annual Report 2015. http://www.bjtrc.org.cn (in Chinese).
27.
FanL. L., ChenS. K., and GuanW.. Temporal Pattern Analysis of Beijing Traffic Guidance Information Released by VMS. Procedia: Social and Behavioral Sciences, Vol. 96, 2013, pp. 2595–2601. https://doi.org/10.1016/j.sbspro.2013.08.290.
28.
KraanM., van der ZijppN., TutertB., VonkT., and van MegenD.. Evaluating Networkwide Effects of Variable Message Signs in the Netherlands. Transportation Research Record: Journal of the Transportation Research Board, No. 1689, 1999, pp. 60–67. https://doi.org/10.3141/1689-09.
29.
XuT., SunL., and PengZ.-R.. Empirical Analysis and Modeling of Drivers’ Response to Variable Message Signs in Shanghai, China. Transportation Research Record: Journal of the Transportation Research Board, No. 2243, 2011, pp. 99–107. https://doi.org/10.3141/2243-12.
30.
FooS., AbdulhaiB., and HallF. L.. Impacts on Traffic Diversion Rates of Changed Message on Changeable Message Sign. Transportation Research Record: Journal of the Transportation Research Board, No. 2047, 2008, pp. 11–18. https://doi.org/10.3141/2047-02.
31.
BrandD.Criteria and Methods for Evaluating Intelligent Transportation System Plans and Operational Tests. Transportation Research Record, No. 1453, 1994, pp. 1–15.
32.
HiggingsT. J.Congestion Management Systems: Evaluation Issues and Methods. Transportation Quarterly, Vol. 49, 1995, pp. 25–42.
33.
HeZ., ZhengL., ChenP., and GuanW.. Mapping to Cells: A Simple Method to Extract Traffic Dynamics from Probe Vehicle Data. Computer-Aided Civil and Infrastructure Engineering, Vol. 32, No. 3, 2017, pp. 252–267. https://doi.org/10.1111/mice.12251.
