[雙語(yǔ)翻譯]--外文翻譯--長(zhǎng)隧道交通管理策略的仿真研究（原文）

1、Simulation studies of traffic management strategies for a long tunnelTsai-Yun Liao a, Ta-Yin Hu b,?, Wei-Ming Ho ba Graduate Institute of Marketing and Logistics/Transportation, National Chiayi Univ., No. 580, Sinmin Rd.

2、, Chiayi City 60054, Taiwan, ROC b Department of Transportation and Communication Management Science, National Cheng Kung University, No. 1, Ta-Hsueh Road, Tainan 701, Taiwan, ROCa r t i c l e i n f oArticle history:Rece

3、ived 10 March 2009Received in revised form 18 May 2011Accepted 23 August 2011Available online 23 September 2011Keywords:The Hsueh-Shan TunnelAdvanced traffic managementDynaTAIWANa b s t r a c tThe Hsueh-Shan Tunnel, the

4、fifth long tunnel in the world, has a total distance of 12.9 km. After the open-ing of the tunnel, traffic demand between Taipei and I-Lan increased dramatically. However, efficient traf-fic management strategies for the

5、 Hsueh-Shan Tunnel need to be developed and evaluated accordingly inorder to alleviate traffic congestion due to high demand and/or possible incidents. The research aims toevaluate possible traffic management strategies

6、based on available control devices through simulation-assignment techniques. Possible traffic management strategies, including access control (ramp metering),lane control and route guidance, are proposed and examined thr

7、ough numerical experiments. In order toanalyze traffic management strategies based on simulation, DynaTAIWAN, a simulation-assignmentmodel, is developed to simulate traffic control strategies and reflect driver’s respons

8、e to route guidance.Several indexes, including vehicle queue length, average density and average speed, are used in the com-parisons. The results show that ramp control provides the best benefits compared to other strate

9、gies andcan reduce the average queue length by about 18%.? 2011 Elsevier Ltd. All rights reserved.1. IntroductionTaiwan is a mountainous island, and tunnels play an important role in developing new freeways. The length o

10、f the freeway con- necting Taipei and I-Lan in the northern part of Taiwan is about 55 km, and 25% of the freeway is tunnel. The Hsueh-Shan Tunnel, the fifth longest tunnel in the world, has a total distance of 12.9 km.

11、The design speed is 80 km/h, and there are two lanes for each direction. The Tunnel opened on June 16, 2006. After the opening of the tunnel, traffic demand between Taipei and I-Lan in- creased dramatically and average d

12、aily demand has reached 50,000–60,000 passenger car units (PCU) (TANFB, http:// www.freeway.gov.tw/). Several methods have been proposed to ensure safety in road tunnels, and these methods are classified into two categor

13、ies (Mashimo, 2002), namely, reduction of the proba- bility of an incident and reduction of the consequence of events such as accidents and fires. The latter is achieved though a highly automated surveillance and control

14、 system. However, how to uti- lize new traffic control devices for traffic management is a critical issue. Traffic accidents in tunnels have been extensively studied. Ma et al. (in press) conducted a study of traffic acc

15、idents based on po- lice-reports from four tunnels and summarized temporal and spa- tial distribution characteristics of traffic accidents. The analysisshowed that most of the problems are speeding and the failure to mai

16、ntain a safe distance to the vehicle in front. Bari and Naser (2005) presented simulation studies of smoke from a burning vehi- cle in a tunnel and the time-averaged equations for velocity, pres- sure, temperature, and m

17、ass fraction of emission are solved. Their simulation results showed that the emissions released from the vehicles in the jam posed a threat to human health and quick evac- uation of the passengers is essential in the ev

18、ent of a fire in the tunnel. Under Intelligent Transportation Systems (ITS), traffic manage- ment strategies can be developed based on new detection technol- ogies and traffic control devices. Real-time traffic condition

19、s could be observed through traffic data, such as flow, speed, and occu- pancy, from monitoring and surveillance systems. Thus, real-time traffic operations or traffic management strategies could respond to real-time tra

20、ffic conditions to avoid and/or relieve traffic congestions. In order to avoid possible loss and delay due to special charac- teristics, tunnel operation and management needs to be carefully developed and planned in adva

21、nce in case of any possible inci- dents. Since there is no prior experience in managing long tunnels in Taiwan, especially under very high demand, advanced traffic management strategies for long tunnels need to be evalua

22、ted accordingly in order to alleviate traffic congestion due to high de- mand and/or possible incidents (Jha et al., 1999; Ben-Akiva et al., 2001, 2003). Main traffic management devices in the Hsueh-Shan Tunnel in- clude

23、 Variable Message Signs (VMS), Lane Control Signal (LCS), and0886-7798/$ - see front matter ? 2011 Elsevier Ltd. All rights reserved.doi:10.1016/j.tust.2011.08.004? Corresponding author.E-mail addresses: tyliao@mail.ncyu

24、.edu.tw (T.Y. Liao), tyhu@mail.ncku.edu.tw(T.-Y. Hu).Tunnelling and Underground Space Technology 27 (2012) 123–132Contents lists available at SciVerse ScienceDirectTunnelling and Underground Space Technologyjournal homep

25、age: www.elsevier.com/locate/tustcan be achieved through simulation results, including system-wide performance and traffic characteristics for specific links.3.2. DynaTAIWANDynaTAIWAN was developed to simulate and predic

26、t traffic flow patterns in order to develop appropriate ATMS/ATIS strategies for traveler information as well as real-time traffic control (Hu et al., 2007). The overall framework of DynaTAIWAN is shown in Fig. 3. It is

27、composed of two layers, namely simulation-layer and real-time control layer. The simulation layer consists of two major components, demand and supply. The demand component pro- cesses tripmaker’s decisions on mode, depar

28、ture time, and route, thus time-dependent OD trip tables are formed. The supply com- ponent includes roadway geometric data, traffic network configu- ration, and traffic control measures. Thus, vehicles and associated pa

29、ths are generated and simulated in a realistic traffic environment. Vehicles and associated attributes are generated through the de- manddata, and areloaded into the networkaccordingto the selected departure time and rou

30、tes. Vehicles are moved individually accord- ing to prevailing local speeds, consistent with mesoscopic flow rela- tions across network. While the vehicles are moving in the network, travel information is provided throug

31、h Variable Message Signs (VMS) as well as in-vehicle information systems such as radio. In the real-time layer, it is assumed that real-time data from traffic surveillance systems is obtained in terms of flow and speed,

32、then the real-time data can be used to estimate and predict short- term origin–destination (O–D) patterns, resulting in O–D trip ta- bles. The time-dependent O–D trip tables are loaded into the sim- ulation layer. The pr

33、edicted flows are obtained through simulation according to the updated O–D tables and the resulting flow distri- butions could be utilized for advanced traveler information sys- tems as well as real-time traffic control

34、systems. In cases where the real-time flow data is not available because it does not cover the whole network, flow data from the simulation layer could be used in its absence. Vehicular movements in the traffic network a

35、re simulated through two processes: link movement and intersection transferprocesses. In the link movement process, the Greenshields mod- el, a linear speed-density relationship, is assumed. The transfer process performs

36、 the link to link transfer of vehicles at nodes. The transfer process is controlled by several factors: link satura- tion flow rate, possible capacity, and possible green time. For interrupted link flow, the transfer pro

37、cess appropriately allocates the right of way according to the prevailing control strategy at intersections. DynaTAIWAN provides the ability to explicitly model an array of control elements for both surface streets and f

38、reeways in urban areas, including ramp control, mainline speed control, VMS, and signal control. Validations of DynaTAIWAN have been conducted in different research projects (Hu et al., 2007, 2008, 2009) and the main foc

39、us is to compare actual flow distributions with simulated flow distributions from DynaTAIWAN. In general, the flow counts from VDs for every 5 min are com- pared with the simulated flow counts from DynaTAIWAN. One of the

40、 studies shows that the average errors between the simulated flows and the observed flow are within the range of ±40% and large errors are observed near the boundary of study areas.3.3. Traffic management strategies

41、Based on available control devices in the Hsueh-Shan Tunnel, traffic management strategies considered for the tunnel include ac- cess control, route guidance, lane control, and mixed strategies. The mixed strategies refe

42、r to multiple control strategies, including access control, route guidance and lane control, which are applied at the same time. These control strategies as well as the models developed in DynaTAIWAN are discussed hereaf

43、ter.(1) Access controlRamp control is a classic example of access control and a large number of ramp control algorithms ranging from local control to area control have been developed. Two major strategies applied in this

44、 study are ramp closure and on-ramp metering. ALINEA (Papageorgiou et al., 1991), a feedback local control algorithm for on-ramp metering, is applied in this study. The feedback algorithm is described as follows:Fig. 1.