ABSTRACT
In the last few years, traffic congestion has become a growing concern due to increasing vehicle ownerships in urban areas. Intersections are one of the major bottlenecks that contribute to urban traffic congestion. Traditional traffic signal control systems cannot adjust the timing pattern depending on road traffic demand. This results in excessive delays for road users.
Adaptive traffic signal control in a connected vehicle environment has shown a powerful ability to effectively alleviate urban traffic congestions to achieve desirable objectives (e.g.,delayminimization). Connected vehicle technology, as an emerging technology, is a mobile data platform that enables the real-time data exchange among vehicles and between vehicles and infrastructure. Although several reviews about traffic signal control or connected vehicles have been written, a systemic review of adaptive traffic signal control in a connected vehicle environment has not been made.
Twenty-six eligible studies searched from six databases constitute the review. A quality evaluation was established based on previous research instruments and applied to the current review. The purpose of this paper is to critically review the existing methods of adaptive traffic signal control in a connected vehicle environment and to compare the advantages or disadvantages of those methods. Further,a systematic framework on connected vehicle based adaptive traffic signal control is summarized to support the future research. Future research is needed to develop more efficient and generic adaptive traffic signal control methods in a connected vehicle environment.
METHODS
On the basis of the PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines, six databases were searched in May 2017 for peer-reviewed papers with regard to adaptive traffic signal control in a connected vehicle environment. These included Web of Science, Science Direct, Academic Search Complete, Springer Link, IEEE Xplore, and TRID. The first four of these are comprehensive databases, and the other databases include journals for various disciplines, such as engineering, mathematics, statistics, computer science, and transportation.
At least one term from each of the three categories of search terms or keywords must be used to contain: (1) signal control, traffic signal control, adaptive signal control, real-time signal control, intersection control, traffic light; (2) intersection, isolated intersection, signalized intersection; and (3) connected vehicle, Vehicular Adhoc Networks, VANET, Internet of vehicle, cooperative vehicle, vehicle to vehicle communication, vehicle to infrastructure communication, Intelligent Traffic System, ITS. The item search forms were adjusted to match the specific structure and requirement of each database. Duplicate and irrelevant papers were eliminated and reference lists within selected papers were also researched for further studies.
SYSTEMATIC REVIEW PROCESS
Figure 1: The flowchart of systematic review process
The search and retrieval process is shown in Figure 1. The number of papers collected from each database above were 635 (Web of Science), 1672 (Science Direct), 903 (Springer Link), 618 (IEEE Xplore), 1365 (TRID) and 29 (Academic Search Complete). After duplicates were removed, a total of 5223 different records were extracted from six databases, of which 357 were identified following the screening of titles and abstracts.
There were three reasons for eliminating irrelevant and ineligible papers: not about signal control; not in a connected vehicle environment; and the full text is not available. Thus, the full text of 23 publications was retrieved. The reference lists of excluded reviews were reviewed and potential papers were gathered. Finally, 26 published papers matching all the criteria were included in this review, as shown in Table 2.
QUALITY AND REVIEWED STUDIES
The scores of the quality of each eligible paper range from 3 to 11, as shown in Table 5. According to Table 5, only 3 studies (11.54%) estimated the unequipped vehicle status in an imperfect connected vehicle penetration rate. In the selected papers, 14 studies (53.85%) employed more than two objective functions to optimize the proposed traffic signal control method. All studies have been simulated to verify the reliability of the proposed method.
However, in all selected papers, the simulations were not all based on the field data and field scenarios. 9 studies (45.00%) and 13 studies (56.52%), respectively, used the field data and field scenarios to simulate the adaptive traffic signal control method. In view of penetration rate, 8 studies (30.77%) took into account the performance of the proposed method under different penetration rates. Nearly all studies(n=25,96.15%) compared results with other traffic signal control methods in order to highlight the superiority of the proposed method.
Table 5. Distribution of quality characteristics across reviewed studies
LIMITATIONS AND STRENGTHS
The following limitations should be considered when interpreting the existing results. First, we limited our search to papers published in English, thus, relevant literature published in other languages was excluded. Second, all included studies were adaptive traffic signal control, while other traffic signal control methods applied in a connected vehicle environment were not be discussed. This study had several strengths. First, it used an extensive search strategy to locate papers in six databases and rigorously screened papers through well-defined inclusion/exclusion criteria. Second, the quality of included papers was assessed in a standardized way.
DISCUSSION
An adaptive traffic signal control framework is summarized based on the existing research, with aims to support future research, as shown in Figure 2. The framework is divided into three modules: input, optimization, and output. In the input module, basic information including vehicle information, intersection information, proposed intersection signal control method, and objective functions will provide a basic input setting for optimization module. It is to be noted that the car-following model in a connected vehicle environment may be different from traditional models.
In the optimization module, the simulation software will calculate the related parameters, estimate unequipped vehicle status, and determine the arrival time of each vehicle. Based on the above process, the adaptive traffic signal control will be optimized based on the objective functions. In the output module, the optimum parameter will be sent to the optimization module, including optimum trajectory of each vehicle and other pre-setting output parameters. Moreover, the module provides the results of a given objective function for the authors to evaluate the proposed methods.
Figure 2. The framework of adaptive traffic signal control in a connected vehicle environment
CONCLUSIONS
In this paper, we present a thorough and systematic review on adaptive traffic signal control in a connected vehicle environment. In order to have a strict evaluation process, this review has provided a detailed discussion and analysis of adaptive traffic signal control methods, such as the method implemented in the selected papers, the estimation of unequipped vehicle status, and the simulation platform employed in those papers. The review has also carefully discussed advantages and disadvantages of the different methods or strategies used in the selected papers.
To our knowledge, this is the first systematic review of the existing methods of adaptive traffic signal control in a connected vehicle environment. The existing adaptive signal control methods mainly focus on two research directions: one is to optimize the signal timing and the other is to optimize the queue. The best available evidence indicates that adaptive traffic signal control can significantly reduce the delay and improve the road traffic efficiency. The present systematic review shows that adaptive traffic signal control research in a connected vehicle environment is in its infancy.
Limited by the development of connected vehicle technology and hardware support, the proposed methods can only be verified by simulation experiments. Future work examining their adaptability and validity based on the field testing is warranted. Finally, further research is needed to develop efficient and generic adaptive traffic signal control methods in a connected vehicle environment.
FUTURE WORK
Based on the literature review, a thorough analysis of adaptive traffic signal control in a connected vehicle environment suggests that there are significant opportunities for innovation in adaptive traffic signal control research within this domain. These include:
The existing signal control models and optimization methods are based primarily on unsaturated traffic flow. With the rapid increase in motorization level, the road traffic congestion has become a common problem all over the world. Although the connected vehicle technology will reduce the traffic congestion in a certain degree, traffic congestion remains a problem in the period ahead. Therefore, traffic signal control models and strategies for saturated and over-saturated intersections are one of the important directions for future research.
Source: Jiangsu University
Authors: Peng Jing | Hao Huang | Long Chen