[雙語翻譯]交通燈外文翻譯--基于優(yōu)化工具的交通燈管理（英文）

1、 Procedia - Social and Behavioral Sciences 238 ( 2018 ) 323 – 330 Available online at www.sciencedirect.com ScienceDirect1877-0428 © 2018 Published by Elsevier Ltd. This is an open access article under the CC

2、 BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of SIM 2017 / 14th International Symposium in Management. doi: 10.1016/j.sbspro.2018.04.008 SIM 2017 / 14th Intern

3、ational Symposium in Management Traffic Lights Management Using Optimization Tool M?d?lin-Dorin Pop * Politehnica University of Timi?oara, Bd. Vasile Pârvan 2, Timi?oara 300223, Romania Abstract The increase of traf

4、fic is one of today’s problems. Numerous cities are affected by traffic congestion and the increase of emissions from fuel use. They have a negative impact on economy, environment and overall on the quality of life. It

5、is necessary to find intelligent solutions for road traffic management. The interest for intelligent traffic systems appeared at the beginning of the 20th century. There are various methods available for traffic manage

6、ment such as inductive loop detection, infrared sensors, video data analysis etc. The purpose of this paper is to present an approach of optimization tool using in order to decrease the traffic congestion and the cross

7、ing time of a road network. © 2018 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of SIM 2017 / 14th International Symposium in Management. Keywords: intelligent transportation systems; op

8、timization; traffic lights 1. Introduction “Intelligent Transportation Systems, or ITS, can be defined as the application of computing, information, and communications technologies to the real-time management of vehicle

9、s and networks involving the movement of people, goods, and services ” ( Samadi, S., Rad, A.P., Kazemi, F.M., E-mail address: madalinpop20@yahoo.com © 2018 Published by Elsevier Ltd. This is an open access articl

10、e under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of SIM 2017 / 14th International Symposium in Management. 325Vladimir Gritsenko / Procedia - Social

11、 and Behavioral Sciences 238 ( 2018 ) 323 – 330 3. Related work Within traffic systems, it is desirable to obtain optimal solutions for the fluidization of traffic. The trend is to develop the most efficient algor

12、ithms of adaptive traffic control using concepts such as parameter estimation, fuzzy logic, or artificial intelligence. An innovative approach in traffic lights management is to take in account also the pedestrians. “G

13、enetic algorithm is introduced in the traffic control system to provide an intelligent green interval response based on dynamic traffic load inputs, thereby overcoming the inefficiencies of conventional traffic control

14、lers. In this way, the challenges are resolved as the number of vehicles are read from sensors put at every lane in a four-way, two-lane junction and pedestrians are monitored at the road junction” ( Turky, A.M., Ahmad

15、, M.S., ? links; ? lane choice; ? vehicle-following. Based on this microscopic representation, an online adaptive traffic signal control algorithm is developed and the behavior of traffic lights is studied. The syst

16、em is modeled by using a state called traffic status. The current number of vehicles that form a queue at the level of a lane is represented by a vector. Also, a vector corresponding to the traffic lights signals is de

17、fined. Action is the decision the system takes at a time t and is denoted in vectorial form. For each moving lane, it is possible to decide whether the color of the traffic light signal should be maintained or changed

18、based on action vector values. “The network loading model and traffic signal control model are generally constructed in discrete-time, as much as possible to the real circumstance. By comparing different methods, result

19、s show that approximate dynamic programming (ADP) with adaptive phase sequence (APS) mode has a quite good performance of queue length and traffic delay reductions” (Yin, B., Dridi, M., & El Moudni, A., 2015). Can

20、be noticed that this approach has a good potential for autonomous intersection management. In 2012-2013, the implementation of the pilot safety model for vehicles connected using wireless communication networks takes p

21、lace in Ann Arbor, Michigan. Testing concluded that Vehicle-to-Vehicle (V2V) technology could significantly reduce the number of accidents through safety applications that can warn drivers of possible collisions with o

22、ther vehicles or even automate braking to avoid accidents. This method was applied on a single traffic intersection, with eigth possible directions for vehicle movement. Was developed a Greedy forwarding algorithm that

23、 succeeds to reduced the delays experienced by the vehicles when are tryin to cross a road network. For collecting necessary data was used a sensor network. “The broadcast medium is the 5.9–5.95-GHz radio spectrum, and

24、 the communication standards are defined in the IEEE 802.11p standards. The information consists of speed and position data collected from vehicles. Speed data can be gathered from the vehicle speedometers, and position

25、 data can be gathered using GPS receivers fitted to the vehicles. In our implementation, the following data are gathered and encapsulated in data packets that are broadcast over the wireless medium” (Yugapriya, R., Dhi