Optimization for Bus Stop Design Based on Cellular Automaton Traffic Model

Transcription

1 Modern Tranportation Volume 6, 2017, PP.1-7 Optimization for Bu Stop Deign Baed on Cellular Automaton Traffic Model Liankui Zhang 1, Chengyou Cui 1 1. Department of Technology Economic and Management, college of economic and management, Yanbian Univerity, Yanji , China chengyoucui@163.com Abtract Bu i a main component of public tranportation, and then the deign of bu top poition, ize and form hould improve efficiency of public tranportation. The optimization of bu top i tudied in thi paper. According to the obervation and analyi of realitic traffic, a micro-cellular automaton mixed traffic flow model with bu top i etablihed. And then a new evaluation index for the bu top deign i introduced. Finally, an optimized bu top cheme i obtained by imulation. Thi reearch i tudied for providing a theoretical bai for the contruction of bu top. Keyword: Urban Traffic; Evaluation Index; Cellular Automata; Bu Stop; Mixed Traffic Flow 1 INTRODUCTION With the continuou development of China, the problem of urban traffic ha become more and more eriou, and the traffic jam ha become one of the mot annoyed problem to urban reident. Public tranport i a reaonable and economical olution to thi problem. A a key node in public tranportation, bu top i the main factor affecting public travel, ervice level and operational efficiency of the traffic ytem. However, many mall and medium citie bu top deign are not reaonable. Thee heavily affect efficiency and the level of ervice of tranportation. The effect of bu top etting on traffic flow ha been tudied by cholar. X.-m. Zhao et al. [1] reearched on the effect of the bu top between two neighboring ignal-controlled interection and pointed out the bu top near an interection erved a a bottleneck. Bin Jia et al. [2] compared, analyzed non-harbor haped, and harbor-haped bu top under different traffic mix proportion. Song Yu-Kun et al. [3] tudied the traffic characteritic of a combined bottleneck with an on-ramp and it nearby bu top and came to the concluion that bu top in the uptream ection of the on-ramp would enhance road capacity. X-j Zhang et al. W. Gu et al. [4] developed formula uing Markov chain to predict the maximum bu flow that could be erved by a elect cla of bu top. Neverthele, there wa no pecified reearch concerning optimization of bu top poition, form and ize. In thi paper, a etting method for bu top i propoed at the micro-level. Firt, a new urban traffic flow model i etablihed baed on cellular automaton. The model include car, truck, bue and take different road condition into account, uch a interection, bu top. Second, a new evaluation index for different bu top etting cheme i et up, and the index value of different bu top are calculated according to the imulation reult of traffic flow model. Finally, the mot reaonable etting cheme can be elected by comparing the evaluation index value. 2 CELLULAR AUTOMATON URBAN AFFIC MODEL Cellular Automaton (CA) i a powerful tool for etablihing model to tudy complicated ytem. It i alo widely applied to traffic flow model. By imple rule definition, many behavior of real traffic can be reproduced. In addition, Cellular Automaton i eay for computer operation. Therefore, CA model ha been widely tudied and Fund upport: National Natural Science Foundation of China( );Science and technology reearch project of Jilin Provincial Department of education in 12th Five-Year( ) - 1 -

2 applied in the reearch of traffic flow. The mot typical CA traffic flow model are Nach model [5]and BML model [6]. Thee model are etablihed for the highway traffic flow and are not uitable for urban road traffic flow. According to the urban road traffic characteritic, thi paper etablihe a CA urban traffic flow model. 2.1 Model Setting The reearch object i a two-lane road between two interection. Vehicle enter thi ection from right hand ide and leave at right hand ide. At the leaving ide, there i a traffic ignal. In thi model, the default ize of one cell i 7.5 meter. Cellular tatu i empty or occupied by a vehicle. The model ha 3 kind of vehicle, car, truck, bue. Their length i 1cell, 2cell, 2 cell repectively. The new inflow vehicle come from the right interection. Vehicle are randomly generated, and each vehicle ha it own flow direction. The inflow vehicle data can be adjuted by the generation probability according to the actual need or import the fixed vehicle data. The model etting can be hown in Fig Moving Rule FIG. 1. ROAD MODEL The moving rule conit of forwarding rule, overtaking rule and pecial moving rule for bu. 1) Forwarding Rule In urban area, the average peed of vehicle i 30~40 km/h. Therefore, the maximum peed i et a two cell/, that i, 54 km/h. Forwarding rule define the common way of driving on the road without changing lane. Aume that the vehicle n i being concerned. The rule are a follow: if ( dn = 0,1), vn = dn (1) 1, pacc < rand() if ( dn = 2), vn = (2) 2, pacc rand() xn = xn + vn (3) dn ---the ditance between vehicle n and the vehicle in front vn --- the peed of vehicle n pacc --- given acceleration probability xn --- the poition of vehicle n 2) Lane-changing Rule. Lane-changing happen when the road i blocked by another vehicle and the adjacent lane driving condition i better than the current lane. For vehicle n, when the followed equation i met, lane-changing happen. d = 0, d = 2, d = 2, rand() < p (4) n n, other n, back t d ---the ditance between vehicle n and the vehicle in front at adjacent lane n, other d --- the ditance between vehicle n and the vehicle behind it at adjacent lane n, back pt ---lane change probability, on behalf of the driver' willingne to change lane 3) Special Lane-changing Rule. In thi model, the vehicle ha three kind of flow direction, which will enter the correponding road at the interection. However, taking into account multi lane, there will be left, traight or right lane lane, o the vehicle - 2 -

3 need to change baed on with their own flow direction in advance. There i a pecial lane changing area in the model, and the vehicle need to complete the lane change before it. Special lane-changing rule i hown a follow: 2.3 Special Moving Rule for Bu d = 2, d 0 (5) n, back n, other In thi model, taking into account the 2 form of bu top. Stop can be et up different ize. Unit ize i 2 cell. In bu top area, moving rule i a little bit different. For vehicle like car and truck, they jut treat bu top area a normal area. However, for bue, they have to park at bu top for ome time to load and unload paenger. So, pecial rule need to be et. Step 1 After the bu into the top area, it would check the top. If there i vacant, enter the top nearet exit; If not, wait. If the form i harbor, the bu need to change lane into top. Step 2 Stop at bu top for paenger to get on and off the bu. The top time i T. It can be et a a random time that obey a probability ditribution according to the actual invetigation or it can be et to a fixed time. Step 1 After tep 2 i completed, the bu move forward according to the etablihed rule. If the form i harbor, the bu need to change lane. 3 EVALUATION OF BUS STOP SETTING SCHEME In traffic flow theory, everal indicator are ued to repreent traffic condition uch a throughput, velocity and traffic denity. All thee common indicator can be meaured by monitor in road network. However, there i no unified performance index for the influence on road traffic caued by bu top. It i important to introduce an evaluation index for road traffic with bu top to determine the bet bu top deign. Thi paper put forward an evaluation index baed on bu ervice capacity and road traffic capacity. It can determine the optimal bu top etting on a certain road, through the imulation of CA traffic flow model 3.1 Evaluation Index When bu top etting i reaonable, it ervice capability and road traffic hould be well. For thi reaon, the paper take the travel time of bue and car, ervice ability and the road capacity into account, and put forward a new evaluation index---a. It i defined by Eq. (1). A = α11 rt1 + β2rt 2 2 (7) α and β are adjutment factor for bue and car, which repreent the importance of bue and car. It can be determined according to the degree of emphai on public tranport and private tranportation. 1 i the ervice level of the bu top. It can be determined according to the ervice radiu of the top, number of erviced people, ervice efficiency, ervice time and cot, etc. 2 i the road ervice level, It can be determined according to the average peed, the accident rate, the inflow condition of vehicle etc. r and 1 r2 are the pa ratio of bue and car, which i through the road vehicle and the proportion of all vehicle. T1 i bu travel time. T2 i car travel time Scheme Selection Proce The modified cellular automaton traffic model and evaluation index are ued to get the mot optimal bu top cheme. The procedure of the optimization for bu top i in hown Fig.2. Step1: et the parameter of the CA model - 3 -

4 The initial macro parameter of the CA model hould be et firtly, including the length of the road, the inflow rate of the vehicle, the compoition of the vehicle, the proportion of different driving direction, the acceleration rate, the imulation time, the parameter of the traffic light, etc. Star Set parameter of the CA model Set parameter of the bu top(ize, poition, form) Simulation Record the elevation data Finih all cheme N Y Determine the bet bu top. Stop FIG. 2. PROCEDURE OF THE OPTIMIZATION Step2: et bu top cheme According to the bu top cheme, et the parameter of the bu top, including ize, poition and form. Step3: imulation After the parameter are et, the program i tarted to run the imulation. And then the output data i recorded. Step 4: record the evaluation data. Step 5: if imulate all bu top, then continue the next tep, ele go to tep 2. Step6: elect the bet cheme According to the imulation output data, the evaluation index value i calculated, and then the bet cheme can be elected. 4 SIMULATION EXERCISES AND ANALYSIS In order to verify the propoed evaluation index to chooe the bu top cheme, a JAVA imulation interface i compiled, according to the cellular automaton traffic flow model. It i hown in Fig

5 FIG. 3. SIMULATION INTERFACE 4.1 Simulation Condition There are 10 different bu route, bu interval of about 10 minute. The imulation parameter of road are hown in Table 1. In order to enure conitency, each imulation ue the ame traffic flow data, the pecific traffic flow and compoition a hown in table 2. TAB.1 ROAD PARAMETERS Road length 525m(70cell) Signal light time (green light, red light) 45,30 Bu ervice time T 30 Acceleration probability P t 0.85 Turn left Car TAB. 2 THE INFLOW DATA (VEHICLE/H) Go traight and turn right There are 36 bu top cheme. Their parameter are hown in table Evaluation Index Decription TAB. 3 SCHEME PARAMETERS Form Linear type, Harbor type Poition 75m,150m,225m,300m,375m,450m Size 1,2,3 In thi paper, α i 0.8 and β i 0.2, which are obtained according to the number of paenger in one cell. 1 i the ervice level of the bu top. It i determined baed on ervice time in an hour. Specific claification i hown in Table 4 TAB. 4 SERVICE COEFFICIENT OF STOP Service coefficient Service time() 1 > <800 2 i the road ervice level. Reference to the United State "Highway Capacity Manual", it i baed on the average peed of the vehicle and i hown in Table 5 Bu Truck - 5 -

6 TAB. 5 SERVICE COEFFICIENT OF ROAD Service coefficient Average peed(km/h) 1 > <10 r 1 i calculated according to the ratio of the inflow and outflow of the bu. r 2 i calculated baed on the ratio of the inflow and outflow of car. 4.3 Reult Analyi According to the above etting, 36 different bu top etting cheme are imulated, and the imulation reult are hown in table 6. TAB. 6 SIMULATION RESULT form poition ize 1 r 1 evaluation index 2 r 2 A linear harbor Table 6 how the following concluion: (1) The average value of evaluation index of harbor bu top i 73.14, the linear i Harbor top i better than linear top. Becaue harbor top doe not occupy the driveway and the traffic flow i le affected. However, the harbor bu top requirement more pace and higher cot. If the actual condition allow, the contruction of the harbor bu top i a better choice. (2) No matter i the harbor or the linear, the average value of the ite i mall at the ditance of 225m-375m (

7 50cell).That indicate that top hould be cloe to the central road. If built on both ide of the road, the top will block the traffic. Becaue the top will caue jam at the entrance and exit. That not only affect the road, will alo affect the adjacent ection. According to the micro traffic flow imulation model, which i etablihed in thi paper, the bet cheme can be obtained. The optimal deign cheme of linear top i that length i 2cell and poition i at the exit of the road 300m (40cell) and the optimal deign of the harbor top i that that length i 4cell and poition i at the exit of the road 300m (40cell). 5 CONCLUSION Thi paper etablihe a micro bu top CA mixed traffic flow model, and put forward a new evaluation index according to the road traffic and top ervice and other factor. By the imulation, the general principle and the optimal bu top etting cheme are get. However, the model till need to be improved; the evaluation method i not perfect. Future work i to imulate baed on the actual data and to tudy different road condition. REFERENCES [1] Zhao X, Gao Z, Li K (2008), The capacity of two neighbor interection conidering the influence of the bu top. Phyica A: Statitical Mechanic and it Application 387(18): [2] JIA B, LI X G, JIANG R (2009), et al. The influence of bu top on the dynamic of traffic flow. Acta Phy Sin 58(10): [3] Yu-Kun S, Xiao-Mei Z (2009), Combined bottleneck effect of on-ramp and bu top in a cellular automaton model. Chinee Phyic B 18(12): [4] Gu W, Caidy M J, Li Y (2014). Model of bu queueing at curbide top[j]. Tranportation Science 49(2): [5] Nagel K, Schreckenberg M(1992), A cellular automaton model for freeway traffic[j].phy.i(france) [6] Biham O, Middleton A A(1992), Levine D A. elf-organtion and a dynamical tranition intraffic flow model. Phyical Review A 46: AUTHORS Liankui Zhang(1991-) :Male, mater, reearch direction: Logitic and upply chain. lainkuizhang@126.com Chengyou Cui(1983-) : Male, doctor, aociate profeor, reearch direction: Operation Reearch and Optimization. chengyoucui@163.com - 7 -