literature review on traffic management

Principles and Guidance for Presenting Active Traffic Management Information to Drivers (2021)

Chapter: chapter 3: literature review.

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36 Chapter 3: Literature Review The main objective of this activity was to search through ATM literature and to develop a literature synthesis based on those topics. Figure 40 illustrates an overview of our approach to the literature review. The output of this task was used to identify key research gaps between previous approaches and the research questions from this project. Figure 40: Overview of Literature Review Activities. Methods for the Literature Search To aid the development of our strategy and help to scope our proposed level of effort, we conducted a literature search on ATM topics. The literature search process consisted of two main steps: (a) “broad search,” and (b) “focused search.” The purpose of the “broad search” was to understand research trends on ATM topics and to adjust the search strategy for the second step. The purpose of the “focused search” was to find literature relevant to the 6 key research questions motivating this project. For the broad search, we utilized three resources for the literature search to minimize the possibility of missing key articles due to the constraints of using a single search engine. We used TRID (Transport Research International Documentation), Google Scholar, and MIT library (in that order) as search tools and expanded our reference database by adding new documents. First, we used “Active Traffic Management” as a keyword phrase and found a total of 284 papers from TRID. Second, we repeated this process using Google Scholar, and found a total of 72 papers (among the 72 papers, 27 papers were not covered by the first search, and those were added to our reference database). Finally, we repeated the process using the MIT library15 and found a total of 37 papers. Only four papers among the 37 were not covered by our list, and these four papers were added to the reference database. 15 https://libraries.mit.edu/

37 After the broad search process, two researchers assessed the total of 315 documents through a four-step process: (a) general relevance assessment with respect to ATM, (b) relevance assessment with respect to our research questions, (c) research method classifications (e.g., on- road evaluation, simulation, etc.), and (d) ATM strategy classification. Through these four steps, complete data sources were evaluated to determine if they meet minimum requirements for quality and applicability. Data sources meeting the minimum requirements for quality and applicability were saved for a full review. The results of the general relevance assessment showed that around 23% of the articles that we reviewed were directly related to ATM strategies (44% were not related and 33% were possibly related). We found 36 documents (among the 315) that might be relevant to our research questions (e.g., drivers’ information needs, drivers’ information processing capacity, potential media to deliver information, and effective ways to achieve safe response from drivers), and around 50% of the documents were related to the questions about potential media. However, only a few of the experiments tested alternative/innovative media (e.g., Sykes, 2016). Among the procedures described in the reference database, around 30% of the authors conducted studies related to “VSL,” 17% addressed “shoulder lanes,” and 13% addressed “lane control.” During the broad search process, we found only a few documents that might be relevant to drivers’ information needs and the information processing implications of ATM. Based on the results, we modified our search strategy for the “focused search” process by adding “driver information” as an additional search phrase. For the focused search process, we picked three ATM strategies (“dynamic speed limit,” “shoulder lanes,” and “lane control”), and used these terms as keyword phrases. As our previous results showed a low ratio between the number of documents relevant to our research focus and total number of documents that we found, we combined two keywords—one for ATM strategy and the other for our research focus, “driver information,” and refined the literature search. Another technique that we applied in the focused search was to review Battelle’s previous work, which were generally relevant to the broad topic of “presenting information to drivers, but less relevant to presenting ATM information.” However, some of the questions (e.g., driver information process, information formatting, etc.) can be answered without high relevance with ATM strategies. Especially for question 2 and 4, we have utilized Battelle’s previous work (Campbell et al., 2004; Campbell et al., 2012; Campbell et al., 2016; Lichty et al., 2012; Richard et al., 2015) to find relevant documents to increase search efficiency. From this focused search process, we identified a total of 89 relevant documents. A literature assessment was conducted again for the new additions. After the broad search, focused search, and two rounds of the literature assessment, we had a total of 26 documents representing the final set of relevant data sources. These 26 documents are listed in Appendix B. For the final reference set, the full documents were requested and obtained for further evaluation through the Internet and the Battelle library services. Based on Battelle’s previous experience conducting similar literature reviews during guideline development on a variety of topics, we found that a structured approach to conducting the literature review is essential for developing accurate and unbiased guideline information. A central problem in literature reviews is that the quality and quantity of applicable research findings vary greatly from topic to topic. A

38 systematic approach is therefore needed for the literature review so that only the highest quality and most applicable data available will be used to inform content development of design principles and guidelines. For this reason, we created a review form and reviewer’s guide, which provide reviewers with clear instruction on how to complete the form (see Figure 41 for an example of a completed review). A total of 26 documents were fully reviewed in accordance with the reviewer guide, with relevant information entered into the review form. All the review forms were organized according to research areas or potential guideline topics, and cataloged electronically, allowing for both a project record as well as for future use. This catalog will track the status of individual data sources through the review process and tie the document to the review. Figure 41. Sample document summary template (adapted from McCallum et al., 2006). Literature Synthesis The final step in the literature review process was to develop topic-specific summaries of key findings from the literature, as well as research gaps identified in the reviewed data sources. Table 9 below summarizes key findings related to our research questions (note that Task 3.b focused on research questions 1-4, whereas Task 3.a focused on research questions 3, 5, & 6). Question 1. What information related to ATM strategies does a driver want and need? What characteristics are associated with this information (reliability, timeliness)? From the literature, we have found general characteristics associated with traffic information. Previous studies addressed specific information characteristics and their impact on driver

39 performance and subjective ratings (e.g., Kantowitz et al., 1997), types of general information which drivers need (e.g., Mensonides, 2004), or features of traffic information, such as “accuracy,” “timeliness,” “reliability,” etc. (e.g., Lappin, 2000). However, previous literature did not provide detailed driver needs related to different ATM strategies. As mentioned in the work plan, we aim to understand drivers’ travel/driving-related decisions for specific scenarios related to the key ATM strategies, with details (such as how they make the decisions, what information they needed to make the decisions, where/how they want to obtain that information, etc.). Also, this research question needs to be considered with different ATM mediums (medium type x information type x where/how to obtain). We expect that conducting focus groups will provide specific driver information needs associated with the key ATM strategies. Question 2. How much information can a driver process via the complementary and contrasting modalities (e.g., visual, auditory), given the context and distraction? We have found previous approaches for examining drivers’ information process, range from general legibility and comprehension of the ATM signs (e.g., Jeffers et al., 2015) to effects of in- vehicle alert type/modality on driver behaviors (e.g., Sykes, 2016). In general, “modality comparison” (e.g., visual messages vs. auditory messages) has been studied in the transportation domain, especially for driver-vehicle interface designs. For example, the “Human Factors Design Guidance for Driver-Vehicle Interfaces” (Campbell et al., 2016) describes how to select sensory modalities based on message complexity, receiving locations, information priority, etc., and we expect to utilize these previous guidelines to some extent. What we found as a research gap from the previous literature was that there was little to no systematic comparison among various modalities for ATM strategies. For example, Sykes (2016) compared visual, auditory, and verbal alerts for in-vehicle displays, which disseminated ATM-related information. However, the alert modality was confounded with alert types, and made it unclear whether differences in the outcome measures (e.g., glance behavior) were caused by the modality or alert type (e.g., HOV alert was featured with visual alert, whereas speed limit alert consisted with visual, auditory, and verbal alerts). Based on drivers’ information needs from the research question 1, factorial designs (which systematically manipulate information types/modality) will be applied to evaluate modality effects on driver performances for the specific ATM strategies varying test scenarios. Question 3. What existing and potential media could be used to deliver this information? Media that are under the control of transportation agencies (e.g., electronic signs) are of primary interest but alternative and innovative media (e.g., in-vehicle displays, cell phone applications, geographic information system) and their evolving capabilities and roles must be examined. Potential ATM mediums have been studied by testing alternative options (e.g., Saha et al., 2013; Scarinci et al., 2014; Ishak et al., 2015) or by comparisons with traditional ATM mediums (e.g., Hogema et al., 2000; Craig et al., 2017). In particular, in-vehicle displays have been actively tested as potential mediums in ATM applications (e.g., Sykes, 2016; Hogema et al., 2000). Although we have found more literature related to this topic compared to what we found for the other topic areas, there were only a few studies that directly compared several ATM mediums. In this regard, the Craig et al. (2017) study is noteworthy. Craig et al. (2017) conducted a driving simulator study to evaluate the efficacy of three approaches to presenting a ‘work zone ahead,

40 reduce speed message’ to drivers. Messaging conditions included: (1) an audio message, (2) an audio message + visual message, and (3) a roadside display. Interestingly, the in-vehicle visual message was an image of an icon with text messages, presented within an image of a smart phone. The entire message was presented on an LCD; i.e., the LCD was used to mimic a smart phone. The roadside display was a temporary changeable message sign programmed into the driving route. Overall, the research indicated that the in-vehicle message conditions were associated with better driving performance on key measures, less mental workload, improved perceived usability, and better event recall relative to the roadside display condition. This was perhaps the most directly-relevant study that we reviewed; it demonstrates the potential offered by a shift from infrastructure based ATM messaging to an in-vehicle approach. We had expected that studies employing multiple medium types to disseminate ATM information (e.g., electronic signs and in-vehicle displays) would have been seen in the literature. However, we have not found any literature which considers the harmonization of multiple types of ATM, such as (a) how to coordinate the alternative media (e.g., in-vehicle displays with traditional electronic signs); (b) whether they need to disseminate the same information or need to compensate for each other by minimizing redundancy; (c) how to match ATM information types and delivery medium; or (d) how to maximize the efficiency of information dissemination when deploying multiple ATM mediums. Question 4. Given a particular message and medium, what are effective ways to prioritize, format, and present the information to achieve a desired and safe response by drivers? As mentioned in the work plan, previously Battelle has reviewed original data sources, conducted analytical and empirical studies, and generated entire handbooks to provide effective ways to prioritize, format, and present information to drivers (e.g., Campbell et al., 2012). Conclusions As seen in the State of Practice and Literature Review, relatively little research data is available to support the development of rigorous design guidelines for the six (6) key research questions that animate this project. The state of practice review identified a wide range of deployment for virtually all types of ATM systems. However, these deployments do not reflect findings or conclusions from specific research studies. In general, State DOT staff identified technology and messaging solutions that worked for them, implemented them, and then made changes over time as needed. They generated solutions that worked for their state; often, other states adopted all or part of these solutions. This accounts for the wide variety of practices across the states, yet it provides little in the way of rigorous guidance or even best practices. The literature review identified data sources that provide some very general design guidance for ATM messages. We had hoped to identify data sources/studies more focused on this study’s objectives that could be used to provide answers to key research questions 1-4. However, rigorous answers to key research questions 1-4 were not provided by the existing literature.

41 Table 9: Research synthesis matrix. Kantowitz (1997) Long (2014) Mensonides (2004) Lappin (2000) 1. What information related to ATM strategies does a driver want and need? What characteristics are associated with this information? Information "accuracy" influenced on driver performance and subjective rating for advanced information system. "Dissatisfaction" with VSL signs could be reduced by improving public understanding/operations by modifying current settings. Road users preferred general information about "accidents," "road maintenance," "daily congestion," "events," "slipperiness," "fog." Information features: "accuracy," "timeliness," "reliability," "cost," "degree of decision guidance and personalization," "convenience," "safety.” Janssen (1999) Sykes (2016) Luoma (1999) Jefferes (2015) 2. How much information can a driver process via the complementary and contrasting modalities given the context and distraction? Comparison across six in- vehicle systems (e.g., visual vs. auditory) under car- following and braking tasks. Examination of driver behavior associated with in- vehicle systems by varying alert type and modality (visual, auditory, and verbal). Test whether variable message signs divert driver attention from adjacent fixed signing. Test legibility distance for CMS signs and ATM sign comprehension with static images. Balke (1992) Ishak (2015) Craig (2017) Hogema (2000) 3. What existing and potential media could be used to deliver this information? Changeable message signs, Lane control signals, Highway advisory radio, Commercial radio traffic report. Highway Advisory Radio, DMS, Telephone Information Services. Traditional CMS sign vs. ‘cell phone’ work zone information. Traditional VMS signs vs. in-car speed information. Hourdos (2016) Luoma (1999) Jeffers (2015) 4. Given a particular message and medium, what are effective ways to prioritize, format, and present the information to achieve a desired and safe response by drivers? Specific sign configuration induces more lane changes than others. Drivers pay more attention to highly effective signs using fiber-optic technology than to fixed signs. Comparison of ATM message formats: Washington gantry vs. Minnesota gantry for comprehension and legibility.

Active Traffic Management (ATM) strategies have become more common in the United States as state departments of transportation grapple with increasing congestion and fewer dollars available to add capacity to keep pace.

The TRB National Cooperative Highway Research Program's NCHRP Web-Only Document 286: Principles and Guidance for Presenting Active Traffic Management Information to Drivers develops and details principles and guidance for presenting drivers with dynamic information that can be frequently updated based on real-time conditions.

These principles and guidance should improve the effectiveness of ATM strategies, which include systems to manage congestion, incidents, weather, special events, and work zones.

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An Open Access Journal

• Open access
• Published: 09 October 2020

State-of-art review of traffic signal control methods: challenges and opportunities

• Syed Shah Sultan Mohiuddin Qadri   ORCID: orcid.org/0000-0002-2950-3993 1 ,
• Mahmut Ali Gökçe 1 &
• Erdinç Öner 1  

European Transport Research Review volume  12 , Article number:  55 ( 2020 ) Cite this article

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Metrics details

Introduction

Due to the menacing increase in the number of vehicles on a daily basis, abating road congestion is becoming a key challenge these years. To cope-up with the prevailing traffic scenarios and to meet the ever-increasing demand for traffic, the urban transportation system needs effective solution methodologies. Changes made in the urban infrastructure will take years, sometimes may not even be feasible. For this reason, traffic signal timing (TST) optimization is one of the fastest and most economical ways to curtail congestion at the intersections and improve traffic flow in the urban network.

Researchers have been working on using a variety of approaches along with the exploitation of technology to improve TST. This article is intended to analyze the recent literature published between January 2015 and January 2020 for the computational intelligence (CI) based simulation approaches and CI-based approaches for optimizing TST and Traffic Signal Control (TSC) systems, provide insights, research gaps and possible directions for future work for researchers interested in the field.

In analyzing the complex dynamic behavior of traffic streams, simulation tools have a prominent place. Nowadays, microsimulation tools are frequently used in TST related researches. For this reason, a critical review of some of the widely used microsimulation packages is provided in this paper.

Our review also shows that approximately 77% of the papers included, utilizes a microsimulation tool in some form. Therefore, it seems useful to include a review, categorization, and comparison of the most commonly used microsimulation tools for future work. We conclude by providing insights into the future of research in these areas.

1 Introduction

One of the biggest challenges for urban management is managing and mitigating traffic congestion. The number of vehicles in the urban network increasing day to day basis, which results in deterioration of traffic conditions. Due to this increase, blockage and long queues of vehicles form at intersections, leading commuters to lose valuable time, especially during rush hours. Apart from this, congestion of traffic also has detrimental effects on the health, environment, and the state’s economy.

As to health, traffic congestion causes excessive fatigue, mental illnesses, and problems related to cardiovascular systems, such as the respiratory system and nervous system, resulting in a lower standard of life. Different pollutants are added to the environment through the heavy use of automobiles, which are one of the main reasons for different health-related issues and ecological damages [ 1 , 2 ]. Environmentally, congestion of traffic leads to increased noise and air pollution [ 3 , 4 ].

In terms of economy, it decelerates the transportation rate of services and goods’ merchandising, the consequences of which have to be borne by the consumers in terms of higher prices. Due to the aforementioned facts, there is always a need for an effective methodology that can tackle congestion resulting from the contemporary demand for urban traffic [ 5 ].

One of the main purposes of signal timing settings is to move people safely and efficiently through an intersection. Achieving this goal requires an accommodation plan to different users that assign the right-of-way. The plan should be able to adapt according to the fluctuations in demand. Many signal timing parameters affect the performance of the intersection. These parameters include cycle length, green time, change interval, phase sequencing, etc. These parameters are further defined under section 3.1. Regulating the timing of the traffic signals is one of the fastest and most economical ways to curtail congestion at the intersection and improve traffic flow in urban streets. Therefore it is necessary to update the timing of the traffic signal control (TSC) system to cope with the prevailing urban traffic conditions [ 6 , 7 , 8 ].

Researchers have been working on the use of numerous approaches for optimizing TST. Several good quality reviews have been written previously within the problem area of TST or TSC settings [ 9 , 10 ]. are the two most recently published survey papers published in the year 2015, covering different methodological areas utilized until 2014 in this problem field [ 9 ]. reviewed the work, concentrating solely on fuzzy logic strategies whereas [ 10 ] shed light on fuzzy logic as well as some of the important work of Q-learning, & neural network approaches used in the domain of TSC setting [ 5 , 11 , 12 ]. are some of the other review papers covering the application of frequently used CI-based methodologies for controlling the flow of traffic at the urban traffic networks. But in terms of technological advancement, the period from January 2015 to January 2020 is huge and significantly important. There is a dearth of a good literature review paper that should cover the literature published in these years regarding TSC and TST settings.

The remainder of this paper is structured as follows. Section 2 describes the research methodology of the paper, section 3 covers the background of the TST while giving the structural design of TST and a review of some frequently used microsimulation tools respectively. Section 4 comprises a classification of TST optimization approaches along with a concise review of the related approaches. The discussion and the analysis of approaches are presented in Section 5, whereas the last section concludes with promising suggestions for future research directions.

2 Research methodology

This article analyzes the recent literature for the simulation-optimization, and CI-based approaches for optimizing TST and TSC systems, which have been published from January 2015 to January 2020 in terms of journal papers and conference proceedings. One of the main reasons for covering the research that has been published in the most recent years is technological advancement. These publications were selected by meticulously browsing through databases including Scopus, Web of Science, IEEE Xplore, and Google Scholar. Keywords used to explore the databases were: “TST optimization”, “traffic congestion optimization”, “TSC settings”, “microscopic traffic simulation-based optimization (SimOpt)”, “dynamic traffic management system”, and “signalized urban intersection”. From the reference list of selected publications, further publications were added, which were published in the above mentioned period. Table  1 and Table  2 lists the search parameters used in this study and the total number of papers that we have, respectively.

Table 2 also illustrates the fact that several studies with one of the key phrases, i.e. TST optimization, has seen a significant increase over the past years, due to the continuous advancement in technology and the increasing number of vehicles on urban traffic. In shortlisting, only the studies about TST and TSC have been included, which are either dealing with one of the parameters or the combination of them. These parameters include cycle length of the traffic signal, green phase timing of the traffic signal, offset and phase sequencing of the traffic signal. Studies that are related to the connected vehicles, pedestrians, simulation model calibration, as well as macroscopic and mesoscopic traffic simulation are excluded from this study. Figure  1 illustrates the number of publications included in this study after shortlisting.

Journal and conference papers included in this study by year

The shortlisted publications have been further classified into two categories, that are “microsimulation-based optimization models” and “computational intelligence models”. In the 1st category, papers integrated any one of the microsimulation tools for solving the TST problem with the proposed strategy, are included. Whereas, the papers in the 2nd category uses some sort of an estimation function to evaluate potential solutions during the search process, in the hope of finding an optimal or near-optimal solution. Some of the papers in this category also employed a microsimulation tool but only for the demonstration of the solution approach (see section 4 for further detail).

Microsimulation tools are frequently used in TST related researches. Our review also shows that approximately 77% of the papers included in this study utilizes a microsimulation tool. There is a wide range of microsimulation tools available and utilized for research. Therefore, it is useful to include a review, categorization, and comparison of the most commonly used microsimulation tools for future work.

3 Background on traffic signal timing

This section sketches out the idea around which the settings and the evaluation of TST and the controllers revolve. In subsection 3.1, we describe the important parameters of TST. Understanding of TSC’s structural design is provided in section 3.2. Lastly, section 3.3 presents a critical review of the most commonly used microsimulation tools along with their common and unique features.

3.1 Traffic signal timing parameters

One of the main purposes of TST settings is to move people safely and efficiently through an intersection by assigning the right-of-way. Some of the TST setting parameters should be able to adapt to the fluctuations in traffic demand and some should be controlled by the traffic management authority. These control parameters are

Green Time: The time duration in seconds, during which a given traffic movement at signalized intersection proceed at a saturation flow rate

Cycle Length: Time required by a signal to complete one cycle.

Pha se Sequence : The order of the signal phases during the signal cycle.

Change Interval: It is also known as the clearance interval. It is the interval of yellow and red signal timing between phases of traffic signals to provide clearance at an intersection before the onset of conflicting traffic movements.

Offset: The relationship between coordinated phases in terms of time.

The number of stopped vehicles and the delay can be reduced by increasing the green phase timing for a particular movement. However, an increase in the green time of one traffic movement usually occurs at the expense of increased delay and the number of stopped vehicles in other movements. Therefore, a good signal timing plan is one that allocates time so that overall traffic performance, e.g. average wait delay time, is optimized.

3.2 Structural Design of Traffic Signal Control

Traffic signal control (TSC) strategies have been classified into fixed-time, adaptive, and actuated control strategies [ 5 , 13 , 14 ]. The main reason behind this classification is the type of data and the algorithms they employ to optimize traffic signal plan.

Fixed time TSC strategies are mainly appropriate for the traffic signals, where the flow of traffic is somewhat stable and consistent. Based on previously observed traffic data, these strategies make use of offline optimization algorithms for TST and end up with predetermined cycle length, split setting plan. The main objective of this strategy is to achieve an overall goal, such as minimizing average delay, maximizing the capacity of a network, etc. [ 15 , 16 ] developed the initial models, which laid down the foundation of fixed-time traffic control strategies by minimizing the average delay. As the traffic system is exceptionally dynamic in urban areas, any small disturbance in terms of a traffic collision, construction work, etc. can suddenly alter traffic volumes and render the overall performance of a predetermined traffic signal plan insufficient.

On the contrary to fixed-time control strategies, the main aim of the adaptive control strategies is to optimize the TST plan according to the present-time traffic situations in every phase. Hence the use of sensor technologies came into practice. Initial sensor technologies were capable of discerning vehicular presence when they cross it.

Later on, visual systems came into heavy use. The actuated control strategies also use sensors and the actuated controller decides the cycle length based on past information. Whereas the adaptive strategies are the modified form of actuated control, which uses the present data to predict the cycle length of real-time traffic conditions. These sensors are placed at every road within the bounds of the signalized network. However, these strategies cannot perform as much detailed analysis as the fixed time strategies, because they have to regulate traffic signal plans instantly [ 17 ].

The traffic systems, especially in urban areas, are exceptionally dynamic. Any small disturbance can suddenly alter traffic volumes. So, it is more beneficial if the system is capable of reducing traffic congestion in real-time. Now in today’s world, with the use of sensor technologies and additional strategies, the collection of data in real-time is no more a challenging task. With the abundance of data and the use of available computational power, instant traffic management, or prediction of traffic scenarios can be possible.

3.3 Review of frequently used microsimulation tools

Although analytical models are useful in terms of providing insights into more general system behavior, simulation tools have a prominent place in analyzing responses of traffic systems under a variety of conditions. A tool like simulation is very helpful in traffic engineering to analyze the complex dynamic behavior of a traffic stream. Simulation can be defined as the imitation of real-world systems or processes for conveniently acquiring the information through analogous traffic flow models. These models assist in describing the physical propagation of the flow of traffic. The use of traffic simulation models is crucial for a comprehensive investigation of the urban transportation system in a safe and suitable environment.

As a whole, traffic simulation can be dichotomized broadly into microscopic and macroscopic approaches. The microscopic simulation approach contemplates the individual behavior of the driver along with the interaction with other vehicles or pedestrians, whereas the macroscopic approach considers the vehicular flow as a whole. Mesoscopic is another approach, which is the hybrid of the previously mentioned approaches. AIMSUN, CORSIM, MATSim, Paramics, SUMO, VISSIM are some of the widely used microsimulation packages for exploring the wide array of dynamic problems in urban traffic. There is a large number of papers included in this study dealing with microsimulation in some form (either evaluation or as a part of the model). Therefore authors at this stage, consider it necessary to discuss the characteristics of some of the most commonly used microsimulation packages for the readers.

Ratrout and Rahman [ 18 ] evaluated the attributes and characteristics of various commonly used traffic simulation packages and also provided the relative analysis by focusing on some special features. They found VISSIM, AIMSUN , and CORSIM suitable for modeling features like arterial, freeway congestion, and integrated network of freeways and streets, and AIMSUN, CORSIM, and PARAMICS for intelligent transportation systems (ITS) [ 19 ]. compared the results of TransModeler, AIMSUN, and VISSIM at an International Bridge connecting the US-Mexico border cities.

We categorize and compare the seven most widely used microsimulation tools based on eleven (11) features and present the results in Table  3 . Concerning the graphical user interface (GUI), all of the above-mentioned packages are found to be equally user-friendly and adequate. For the lane closures and the modeling of work zones, TransModeler has great advantages over AIMSUN and VISSIM . In terms of the decision modeling of vehicular routing, VISSIM allows easy input of a large amount of data through Excel spreadsheets. AIMSUN , VISSIM , and SUMO are the simulation tools that allow the user to build and control models with an application programming interface (API) by an external programming language. The “ AIMSUN Next ” by AIMSUN provide the toolkit for the automation of task through programming environment. Whereas, C omponent O bject M odel (COM) and Tra ffic C ontrol I nterface (TraCI) are the programming interfaces provided by VISSIM and SUMO respectively. The availability of a hierarchical VISSIM-COM object model makes it easier to code a network with VISSIM as compared to SUMO . Previously AIMSUN did not have any feature of API. One of the most prominent features of AIMSUN Next is its speed, which provides its supremacy to all other current microscopic simulation tools. Saying AIMSUN tried to overcome all of its shortcomings through AIMSUN Next would not be wrong. Except for AIMSUN Next , it is recommended that the modeler must have moderate knowledge about programming on Python, C++, or VBA while using VISSIM and SUMO for modeling, otherwise, AIMSUN might be the optimal one. Except for the MatSim, all of the above-mentioned microsimulation tools provide the facility for both 2D and 3D visualization.

Figure  2 indicates that, within the search parameters used in this paper, researchers have chosen VISSIM and SUMO (either for evaluation or as a part of the model) over all other available microsimulation tools in their research published over the last five years. This is a clear proof of the superiority in the usage of both software. The number of times these tools are used is also mentioned at the top of each bar.

Number of publications utilizing microsimulation tool

4 Categorization of methodologies for traffic signal timing settings

Traffic signal control is one of the most efficient methods for urban traffic management, providing a smoother and more secure traffic flow at every intersection. From the time when the simple automatic signal controller has been introduced, the TSC system has been going through interminable improvements to address the factors that cause impediments in TSC. Some of these impediments are inadequate transport infrastructure, an increasing number of vehicles, weather conditions, traffic network structure, etc. Each cause is notable in itself and has the full potential to generate congestion at any time in the network. As a whole, mitigating traffic congestion caused by these reasons is a challenging, complex, and nonlinear stochastic problem for engineers and researchers to solve [ 5 ]. In the following subsections, we review traffic signal control papers in two major categories. These are papers utilizing “microSimOpt (Microsimulation-Based Optimization) models” and “computational intelligence (CI) models”. Each subsection includes a detailed table summarizing papers belonging to that subsection based on several carefully selected parameters. These parameters are context/objectives, methods and parameters employed, type of intersection studied, control system strategy, source of data, micro-simulation tool used as well as additional comments.

In section 4.1, researches on the SimOpt model with a focus on microscopic traffic simulation are introduced. The analysis of papers using CI methods is given in section 4.2. If one of the studies included in this review can be categorized into more than one (sub)section, it is included in the section based on the most dominant approach.

4.1 Microsimulation-based optimization model

SimOpt, in general, is regarded as a field in which the optimization techniques are integrated with simulation analysis [ 20 ]. The reason for doing this is to find the best decision variable values among all possibilities without explicitly evaluating (simulating) each possibility [ 21 ]. In this sub-section, papers in which a microsimulation tool is integrated with the proposed strategy for solving the TST problem and have been utilized as an evaluation function in an optimization loop are reviewed. Simulation-optimization for TST is important because evaluating the effects of minor changes in decision variables regarding TST can be assessed accurately through microsimulation without actual implementation. For understanding the concept; the proposed strategy/algorithm asks the microsimulation model of the network to evaluate the current solution and the results from each simulation run are fed back to the proposed algorithm until some stopping criterion(a). Researchers have used numerous approaches of CI methods along with traffic simulation tools in the domain of TSC. These approaches include but not limited to machine learning approaches, fuzzy logic, and computational strategies such as Evolutionary Computation (EC), Swarm Intelligence (SI), and other population based-metaheuristic algorithms.

Optimization of TST is a complex problem yet cost-effective to mitigate traffic congestion and to smooth the vehicular traffic flow. This optimization problem has been widely addressed in the context of stochastic equilibrium network design with different approaches like deterministic and heuristic methods. Due to the presence of a large number of local optimum points in the convoluted solution space of the problem, deterministic approaches like gradient-based methods are not effective. On the other hand, despite the nonconvex nature of the problem, heuristic approaches like the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) are quite successful in the exploration of the search space. However, they may spend a comparatively long time in finding the global optimum solution. In the next subsections, categorization, and evaluation of recent literature on simulation-optimization based TST approaches are given.

The detailed analysis of the researches published over the past five years is included in Appendix 1 . Below the categorization and evaluation of relevant recent literature based on SimOpt. approaches are presented.

4.1.1 Artificial intelligence-based approaches

The papers [ 22 , 23 , 24 , 25 , 26 , 27 , 28 ] are employed a type of artificial learning algorithm for solving the TST problem. Among these studies, Neural Networks, Adaptive Neuro-Fuzzy Inference System, Q-Learning, fuzzy logic, and Deep Reinforcement learning are the adapted machine learning algorithms. Different objectives have been used in these studies including minimization of average delay [ 22 , 27 ], total travel time [ 24 , 25 ], average queue length [ 26 ], optimization of TST plan [ 23 ], and maximization of the flow rate [ 28 ].

Araghi, et al. [ 22 ] utilized a different nature-inspired algorithm called the cuckoo search algorithm for the first time to tune the parameters of intelligent controller optimally. The Intelligent controllers implemented in this study were a Neural Network (NN) and an Adaptive Neuro-Fuzzy Inference System (ANFIS). The fuzzy logic-based control system developed by Jin, et al. [ 23 ] was capable of providing traffic light indication during real-time operations after receiving the messages from the signal controller hardware. The signal control and optimization toolboxes were integrated into the software embedded in the controller’s device. In [ 24 ] Araghi et al. assessed the performance of three meta-heuristic algorithms, which were Simulated Annealing (SA), GA, and the CS on a complex advanced interval type-2 adaptive neuro-fuzzy inference system (IT2ANFIS) based traffic signal controller. Miletić et al. [ 25 ] compared the effectiveness of two different approaches used in premature traffic light control systems through six different scenarios of microsimulation models using real-time data. The first method used in the comparison was operated by fixed values for vehicle arrival time and queue length ranges, while the second was based on fuzzy logic and, therefore it was more adaptive.

4.1.2 Metaheuristics based approaches

Reference numbers [ 29 , 30 , 31 , 32 , 33 , 34 ] employ meta-heuristic methods for optimization along with a micro-simulation tool. Among the meta-heuristics implementations, population-based methods were mostly employed, where PSO, ACO, and GA are the most heavily utilized methods. In addition to some common objectives that are mentioned in the previous sub(section), Elgarej et al. [ 32 , 33 ] came up with a different objective of finding the shortest effective green time.

Gökçe et al. [ 29 ], Dabiri and Abbas [ 30 ], Panovski and Zaharia [ 31 ], Chuo et al. [ 35 ] utilized PSO for the fulfillment of their objectives. Among them, [ 29 ] is the only study that has been carried out for the signalized roundabout containing 28 signal heads, whereas [ 30 , 31 ] worked on optimizing the arterial traffic signals having three intersections and the issues related with traffic flow management in the urban areas respectively. Jintamuttha, et al. [ 33 ] proposed a finite-interval model to achieve the objective regarding TST. A different swarm-based algorithm, Bat algorithm, was utilized to relax the computational complexity. Chuo et al. [ 35 ] developed a multiple-intersection TST system. PSO with the small adjustment was employed for the consistency of the results.

4.1.3 Multi-objective based approaches

Nguyen et al. [ 36 ], Hatri and Boumhidi [ 37 ], Zheng et al. [ 38 ] are the only papers that employ a multi-objective simulation-optimization approach. Although a relevant approach, there seems to be a research void in implementing multi-objective SimOpt for the TST problem.

Nguyen et al. [ 36 ] integrated a local search (LS) algorithm with the iterations of NSGA-II in a way that the output of LS was becoming the next generation’s parents in their study. Results of the proposed NSGA-II-LS were compared with NSGA-II and multi-objective differential evolutionary algorithms and found that the proposed algorithm was better than the other two approaches and good simulation results were achievable in the early phase of the optimization procedure. To balance the equity and efficiency of traffic flow in the urban network Zheng et al. [ 38 ] presented a bi-objective stochastic SimOpt approach. Two types of surrogate models were also used to capture the mapping relationship between decision variables and objectives. VISSIM was used to model the case study network and the results demonstrated that the proposed model outperformed three other counterparts including NSGA- II.

4.1.4 Bi-level programming based approaches

There are only two studies [ 39 , 40 ] that employed a bi-level programming approach, where signal settings are determined by the upper and lower level optimization tasks. With an objective function of maximizing the weighted trip, Hajbabaie and Benekohal in [ 39 ] formulated a program to optimize TST and system optimal traffic assignment simultaneously. By relaxing network loading and traffic assignment constraints, the study also proposed a framework for the calculation of the upper-bound value of the objective function.

To obtain the optimal TST setting, Li et al. [ 40 ] designed a framework in such a way that the settings of traffic signals were determined by the upper level. The upper level was intended to minimize the average travel time of drivers, whereas the task for achieving the network’s equilibrium was attained by the lower level through the settings provided by the upper level.

4.1.5 Miscellaneous approaches

References [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ] employ a mathematical optimization method along with micro-simulation. The methods employed range from dynamic programming to backpressure to optimal control. These approaches are more likely to be useful for design phase problems rather than operational, due to the difficulties involved in solving them for large instances in an acceptable amount of time. Based on a dynamic programming approach with NEMA configuration, the real-time signal control algorithm was formulated by Chen et al. [ 41 ]. Dakic et al. [ 43 ] also proposed two signal control algorithms based on the backpressure model to maximize throughput through an urban traffic network. These two models were initialized and modified backpressure. Results revealed that the proposed algorithms outperformed the fixed time and actuated control strategies

To deal with the reliable TSC problem, Chen et al. [ 45 ] presented an approach in which the higher-order distributional information that was derived from a stochastic microscopic simulator was used. The TSC problem was based on a linear combination of the expectation of total travel time and its standard deviation. For enhancing the computational efficiency of the algorithm, the analytical approximation of the simulated metrics was combined with the simulated data. According to the authors, such kinds of approaches can be utilized to inform the design and operation of the transportation system.

4.2 Computational intelligence based models

The researches, which incorporates the CI-based approaches, are analyzed in this section. Papers in this section, use some sort of an estimation function to evaluate potential solutions during the search process, in the hope of finding an optimal or near-optimal solution. Some of the papers also utilize microsimulation tools. But the usage of microsimulation tools here is not for the development of the proposed solution but rather to demonstrate possible or potential benefits of the proposed solution. In this category, the approaches like fuzzy models, neural networks, machine learning algorithms, evolutionary computation (EC), swarm intelligence (SI), and other population-based metaheuristic algorithms are discussed. Similar to Appendix 1 , Appendix 2 presents a detailed analysis of work in this field under the same categories.

One of the strategies to optimize the traffic in urban areas is the use of ITS, which implements the CI method to facilitate problem-solving that previously seemed very difficult. CI is a collection of “intelligence” methods, including evolutionary computing, fuzzy logic, and artificial neural networks with a claim of being the successor of Artificial Intelligence [ 49 ]. Moreover, CI can also capitalize on other approaches, like swarm intelligence and reinforcement learning, etc.

The inspiration for both EC and SI algorithms often comes from nature, like biological evolution. Classical ECs, encompass evolution strategies, evolutionary programming, GA, and genetic programming (GP). They all are metaheuristic optimization techniques for finding the optimal or near-optimal solutions to the non-linear complex problem within an acceptable time limit. They imitate natural processes, such as natural evolution under the principle of fit or adapted to the environmental condition best, well known as the phenomenon of the survival of the fittest [ 49 ]. The origin of SI algorithms is from the behaviors of some social living beings such as birds, ants, and fishes [ 50 ]. The main strength of SI based research mainly depends on two families of algorithm namely ant colony optimization and particle swarm optimization. They all are very successful in various kinds of optimization problems.

One of the motivations behind the development of EC was the growing demand for robust automated problem solvers in the second half of the twentieth century, which should apply to a wide range of problems without the need for much tailoring, along with satisfactory performance [ 51 ]. Montana et al. [ 52 ] presented one of the initial works through an evolutionary approach for intelligent TSC. A hybrid approach of GA and strongly typed GP [ 53 ] was employed to optimize fixed cycle signal timings. On the comparison, of both the strategies for three small different network settings, it was found that in all the cases, GP’s performance was better than that of GA. These were some details of the preeminent early work that came into the category of CI-based optimization for TST. The detailed summaries of the work that fits with our literature search parameters are given in Appendix 2 . This table reflects the diversity of TST optimization in terms of solution methodology and lists the details of recently published research.

Below the categorization and evaluation of relevant recent literature are presented based on CI methods utilized.

4.2.1 Artificial intelligence-based approaches

A variety of different AI-based methods [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 ] were used for eliminating bottlenecks or to increase the throughput at the signalized intersections.

Xiang and Chen [ 54 ] proposed a Back Propagation neural network-based Grey Qualitative Reinforcement Learning algorithm to eliminate bottlenecks and to avoid reducing traffic flow and timing plan function relationships. Benhamza et al. [ 55 ] used a multi-agent framework for the development of an adaptive TST scheme for multiple intersections. In the developed scheme, each intersection was managed by an autonomous agent.

Vidhate et al. [ 56 ] and Genders et al. [ 57 ] modeled TSC using the RL algorithm based on real-time traffic data whereas Liang et al. [ 58 ] proposed a deep RL model to decide the TST and to control the cycle length of traffic signal based on the data collected through different sensors. Ozan et al. [ 61 ] presented a modified RL algorithm that was based on Q-Learning. The algorithm was further combined with Transfyt-7F for finding the optimal TST of the coordinated network. The proposed approach was better than other RL based algorithms because of its ability to produce a sub-environment in each learning event. The similarity in terms of size was kept constant among the new and the original environment using the best solution obtained from the previous learning event.

A decentralized TSC strategy based on the data collected from sensors was introduced by Bemas et al. [ 59 ]. A neuroevolution strategy was used to improve the coupling configuration of the introduced NN and SUMO was employed for the extensive microsimulation based investigation of the proposed model.

4.2.2 Metaheuristics based approaches

Metaheuristic approaches are one of the widely implemented by researchers in the optimization of TSC strategies. References [ 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ] implemented metaheuristic algorithms such as SI, SA, GA, Bee colony, memetic algorithm, PSO, differential evolution, HS, etc. Our analysis shows that the population-based algorithms are the most widely used metaheuristic algorithms in optimizing TSC strategies.

Li et al. [ 62 ] presented a hybrid solution algorithm for arterial TST optimization based on SA and GA. Gao et al. [ 63 ] and Gao et al. [ 72 ] considered the scheduling of urban traffic light as the model-based optimization problem. To solve this problem, the discrete harmony search algorithm was employed in [ 63 ], whereas, five metaheuristics were implemented in [ 72 ]. Bie et al. [ 64 ], Guo et al. [ 71 ] and Tan et al. [ 65 ] developed GA to optimize the TST settings of the respective networks and objective functions. Jovanović et al. [ 66 ] used the BC algorithm to solve TST of isolated intersection in an undersaturated and oversaturated traffic conditions.

To control the flow of traffic, Manandhar and Joshi [ 68 ] developed a hybrid system that incorporated the Statistical Multiplexing technique and PSO. Based on PSO Tarek et al. [ 67 ] also developed a TST control strategy for the signalized roundabout that was combined with the three different sub-controllers.

4.2.3 Multi-objective based approaches

Due to conflict among different objectives of TST optimization, some of the research in the literature considered multi-objective models to optimize the TST problem. References [ 76 , 77 , 78 ] refer to the problem of TST optimization with multi-objective models. Optimization of traffic capacity was the common objective among the above-mentioned studies. Other than this, popular objectives were the minimization of vehicle delay, stopping time, and vehicle emission.

Yu et al. [ 76 ] employed a fuzzy compromise programming approach. In this approach, different weight coefficients were assigned to various optimization objectives. These weights could be different depending on the states of the traffic flow ratio. After assigning the weights, the multi-objective function was converted to a single objective which was solved through GA. Zhao et al. [ 77 ] and Jia et al. [ 78 ] used PSO with some improvement for their multi-objective TST models.

4.2.4 Dynamic, MILP & non-linear programming based approaches

References, [ 79 , 80 , 81 , 82 , 83 ] formulated the TST optimization problem as mixed-integer linear programming (MILP) whereas references [ 84 , 85 ] presented as the non-linear programming models

Countering the oversaturated condition problem of TSC, He et al. [ 79 ] introduced the partial grade separation at a signalized intersection. A lane-based optimization model for lane configuration and TST settings was formulated as MILP, which was solved by branch and bound method. Mehrabipour et al. [ 80 ] also modeled TST of network-level as MILP. To find the near-optimal TST parameters, a rolling horizon solution methodology was developed. Based on the vehicle trajectory data in urban road networks, Yan et al. [ 82 ] formulated a network-level multiband signal coordination scheme as MILP to provide progression bands for major traffic streams. For optimizing TST parameters Köhler et al. [ 81 ] presented an approach based on a cyclically time-expanded network model. The model was able to optimize traffic assignment problems at the same time. The MILP model was for optimizing the control parameters.

Mohebifard et al. [ 84 ] formulated the network-level TST optimization problem as a Mixed-Integer Non-Linear Program (MINLP) which was based on the Cell Transmission Model (CTM) and presented a customized methodology to solve it with a tight optimality gap. Yu et al. [ 85 ] put forward a non-linear programming model for an optimal TSC setting. A new aspect of this model was combining the double queue traffic flow model to the signal-controlled traffic network to record the traffic dynamics and queue spillback in real-time. In [ 86 ] a convex (quadratic) programming approach was utilized to optimize the pedestrian as well as vehicular TST at an isolated intersection.

4.2.5 Miscellaneous approaches

References [ 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 ] present miscellaneous approaches to optimize TST settings and TSC strategies. These approaches include stochastic programming, semi-analytic, stage-based sequencing, elimination pairing, etc.

A multi-stage stochastic program for the adaptive TSC system, which was based on phase clearance reliability, was proposed by Ma et al. [ 87 ]. In the first stage, a base timing plan that included the cycle length and green timing of each phase was developed, while in the second stage, the green splits and offsets were adapted to the current traffic conditions. Different from the existing methods, Jiao et al. [ 88 ] proposed a model that was intended to minimize average delay time per person, rather than the delay of vehicles from traffic intersection. In the first stage, the curves of the accumulative arriving and departing vehicles were fitted and the delay functions of the vehicles were formulated during each phase of the signal. Subsequently, the delay time of the vehicle was shifted to personal delay time, taking into account the passenger load of the vehicles. This personal delay time was further employed as the objective function and proposed a TST optimization model for attaining real-time signal parameters. Simoni et al. [ 89 ] introduced a Hamilton–Jacobi formulation to model the TST over the arterial road based on the Lighthill Whitham Richards model.

Keeping the focus on Network Signal Setting Design, Memoli et al. [ 90 ] introduced scheduled synchronization. This scheduled synchronization included the scheduling of green phase timing and synchronization to a single optimization problem. The stage-based method was proposed to solve the problem, which was the extension of the synchronization method and the flow model of traffic. Eriskin et al. [ 91 ] proposed a new method, elimination pairing system, for designing TST at the oversaturated condition. Afdelghaffar et al. presented the idea of an isolated and adaptable decentralized cycle free TST controller in [ 92 , 93 ]. The optimization of TST was achieved through the Nash-Bargaining game theory approach. Wu et al. [ 96 ] presented a distributed TSC strategy for the traffic lights in the network. Tang et al. [ 97 ] formulated a non-centralized TSC paradigm to control phase timing based on fog computing.

5 Discussion

5.1 analysis of findings.

TST optimization is a difficult and complex problem to solve. It usually involves stochasticity due to the randomness of demand for flow and behavior of players. Realistic problems’ solution space is so vast that searching for optimal or near-optimal solutions is a challenging task, to say the least.

Jin et al. [ 23 ], Vogel et al [ 26 ] developed a fuzzy logic based traffic light controller for an isolated traffic intersection. Results were very encouraging in terms of congestion, travel time, etc. But according to [ 31 , 75 ] fuzzy logic and machine-learning-based traffic controllers are not economically feasible and require a large investment for their configuration and maintenance. In terms of parameters, it has also been observed that the offset in the network of intersections has been targeted for optimization in a very limited number of studies.

Figure  3 shows that the number of studies using a SimOpt model over the past 5 years is relatively fewer in number. Our analysis shows that the population-based algorithms are the most widely used metaheuristic algorithms in optimizing TSC strategies. In terms of approaches developed for both the control strategies that are fixed time and real-time, the majority of the researchers preferred to work on fixed time controls over the real-time systems. Metaheuristic approaches are utilized mostly for optimization, in 69% of studies with fixed-time control strategies and 49% of studies with real-time control strategies. It is also quite clear that the solution to the problem of optimizing TST lies in the real-time traffic control strategy that can deal with the fluctuation of traffic.

Publications per year according to the category

We also find out that microsimulation tools are used at an increasing rate. Among the many available microsimulation tools, VISSIM and SUMO have been used in 60% of the studies either for evaluation or as a part of the model published over the last five years. This is a clear proof of the superiority in the usage of both software.

5.1.1 Analytical vs simulation-optimization and CI methods

As mentioned in section 2, 77% of studies that were analyzed utilized a microsimulation tool. Besides, we have also come across some papers utilizing analytical methods. The search for 2015–2020 resulted in a total of 10 such papers [ 76 , 84 , 88 , 91 , 101 , 102 , 103 , 104 , 105 , 106 ] utilizing an analytical approach. Only 3 papers [ 84 , 103 , 105 ] considered a network problem, and the remaining 7 considered a single network problem. But the size of the realistic problems and the amount of interaction that needs to be included in the model for them to be interesting, make use of analytical methods significantly less practical (promising) for TST problems. Analytical models are useful to gain insights into the problem but getting useful results is difficult for two reasons. Either the number of interactions that need to be included in the model or the solution time required, make use of these analytical models prohibitive. For these reasons, analytical models are not included in this review paper.

5.1.2 Single vs network of intersections

Traffic is a very much a flow problem. Therefore, it may not be enough to improve traffic at a single intersection or roundabout when the next intersection is blocked. Traffic conditions are very much affected by driver characteristics, roadway conditions, and environmental conditions. Therefore effective TST methods should be able to solve for a network of intersections, representing problematic areas in an urban setting. Our analysis shows that only about 53.5% of simulation-optimization papers and 54.3% of CI papers worked on some sort of network of intersections.

5.1.3 Real-time vs. fixed-time control

Our analysis finds out that, still a significant amount of research is performed for fixed-time controllers, rather than real-time controllers. Among simulation-optimization methods, 63% of papers utilized fixed-time compared to 37% of papers utilizing real-time control. For papers in the CI category, 42% of papers worked on a fixed-time control problem compared to 58% of papers on real-time control.

Furthermore, Fig.  4 epitomizes the types of approaches developed for both the control strategies that are fixed time and real-time. We observe the dominance of Metaheuristic approaches in both strategies and machine learning algorithms, especially for real-time control.

Research publication according to approaches

5.1.4 Signalized roundabouts

Although in many countries, roundabouts used to be non-signalized, more and more countries are adapting signalization of roundabouts led by the UK, France, Sweden, and Turkey [ 107 , 108 ]. United States Department of Transportation Federal Highway Administration discourages the use of fully signalized roundabouts but also concedes that unexpected demand may require signalization [ 109 , 110 ]. In any case, one should expect a significant amount of literature on the signal timing of roundabouts. We have come across only two studies [ 29 , 67 ] on signalized roundabouts.

5.1.5 Experimentation with realistic conditions

Testing proposed solution methods to TST is important to truly evaluate its effectiveness. Traffic flow data is complex and presents particular challenges in imitating. That is probably one of the reasons, why there are no well-established data sets that one could test his/her proposed method, like the ones found, e.g. for scheduling problems. For this reason, testing with real-life data is important. From our analysis, we found that about 44.4% of researches utilizing simulation-optimization methods tested with real-life data compared to about 42.2% of research utilizing CI methods.

5.1.6 Evaluation of solutions

Appendices 1 and 2 list the objective functions used to compare solutions in search of the best. We find out that 61% of papers use average delay, average travel time, queue length, and flow rate (or some function of them) for this purpose.

5.2 Implications for practice

In this subsection, we summarize some of the implications for practice from the analysis of findings.

Real-time controllers are more flexible to adapt to ever-changing requirements. We believe that fixed-time controllers will have a less practical impact and therefore more research effort can be expected to real-time control strategies.

Working with real-time control strategies, with real data over a large network of intersections all contribute to already high computational requirements for solving TST problems. This means difficulty for analytical approaches and increased utilization of heuristic approaches. There are studies [ 7 , 62 , 65 , 69 , 71 , 96 , 97 ], especially under the heading of metaheuristic-based approaches (subsection 4.2), that utilize heuristics; though these applications are not enough and most of them found so far, are far away from being extensively customized. In addition, few utilize customized representations and data structures, which can be crucial in performance. Also due to the nature of the traffic problem, the management of a large network of intersections’ TST becomes important. We realize larger the network, the more difficult solution or even representation of a solution is.

6 Conclusion, directions for future research

Based on the categorization and analysis of state-of-art for TST, the authors propose a number of directions for future research, associated opportunities, and challenges.

Today’s urban traffic system comes under great stress during sudden transient peak demand that forms, either with or without prior information. These events diverge from the regular traffic in important characteristics; like being transient, specific to a region, resulting from an emergency, disaster requiring evacuation, or a large public event. Modeling and solution for TST after such events is a research gap that must be filled.

Our analysis found out that only two papers dealing with signalized roundabouts. Roundabouts have a different flow dynamic compared to regular intersections. With the increasing use of signalized roundabouts, especially in metropolitan areas, we believe TST for signalized roundabouts presents a particular research gap within this area.

We found that still less than half of the papers on TST optimization are performed utilizing real-life data. The lack of standardized data sets and the complexity of traffic flow data suggests studies tested with real-life data will have more impact in the field.

Although there are studies made using real-life data and real-time control, there are few or almost no mention of findings and/or methods being applied or adapted in real life. One of the important challenges for the researchers in this area would be getting these methods to the decision-makers and adapters.

Papers studying fixed time controllers are made to set the best possible timings for expected traffic flow. The rest of the studies are made to employ real-time data (either using sensors or cameras) and react to the congestion and try to mitigate the resulting problem.

Recent years have witnessed a significant increase in the advancement of prediction algorithms, computing power, and the availability of real-time data. These facts along with advances in the heuristic algorithms can lead to proactive models, which may now be successfully developed. Proactive models can predict a traffic flow problem before it happens and calculate necessary changes in the TSC system to either prevent it totally or decrease the adverse effects. These proactive models may also be combined with a new area of simulation-optimization. The concept of a digital twin has been gaining popularity for the manufacturing environment. By definition, “Digital twins integrate internet of things, AI, machine learning, and software analytics with spatial network graphs to create living digital simulation models that update and change as their physical counterparts change.” [ 111 ]. With the aforementioned advances, it is possible and interesting to apply the idea of a digital twin to an urban traffic model. The use of a digital twin for the urban traffic system within the correct framework may enable estimating possible problems earlier and lead to an improvement in the computational power almost real-time.

The great majority of papers found on the relevant topic singles out traffic signal timing and its effects on usually average delay and/or emissions. An important part of heavy traffic intersections, especially in metropolitan areas are pedestrians. Except for [ 86 , 98 ], pedestrians and the effect of their behavior are not modeled into the studies. The same goes for driver behavior. An important avenue of research would be to analyze the effects of pedestrian and driver behavior on the models.

There is a big increase in the number of studies dealing with autonomous vehicles and technologies. To the best of the author’s knowledge, all these studies exclusively study the general area of how autonomous vehicles, flow in traffic safely and/or efficiently and/or environmentally friendly, etc. However, we did not come across studies that benefit from autonomous vehicles and related technologies for TST optimization. A great majority of research on autonomous technologies and TST are studied in rural highway environments, rather than urban. We believe there is a research opportunity to study TST in urban settings with the heavy use of autonomous vehicles.

Availability of data and materials

Not Applicable.

Abbreviations

Artificial Bee Colony Algorithm

Ant Colony Optimization

Advanced Interactive Microscopic Simulator for Urban and non-urban Networks

Adaptive Neuro-Fuzzy Inference System

Bee Colony algorithm

Computational Intelligence

Cuckoo Search

Cell Transmission Model

CORridor SIMulation

Cycle Length

Differential Evolution

Decentralized GA

Evolutionary Algorithm

Evolutionary Computation

Genetic Algorithm

Genetic Programming

Graphical User Interface

Harmony Search

Interval Type-2 Adaptive Neuro-Fuzzy Inference System

Intelligent Transportation System

Local Search algorithm

Memetic Algorithm

Multi-Agent Transport SIMulation

Mixed Integer Linear Programming

Multi-Objective Differential Evolutionary Algorithm

Mixed Integer Non Linear Programming

Neural Network

Non-dominated Sorting Genetic Algorithm

PARAllel MICroscopic Simulation

Phase Sequence

Particle Swarm Optimization

Reinforcement Learning

Simulated Annealing

Swarm Intelligence

Simultaneous Perturbation Stochastic Approximation

Simulation of Urban Mobility

SIMulation-based OPTimization

Tabu Search

Traffic Signal Control

Traffic Signal Timing

TRAffic Network StudY Tool, version 7F

Urban Traffic Optimization by Integrated Automation

Verkehr In Städten -SIMulationsmodell

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Syed Shah Sultan Mohiuddin Qadri, Mahmut Ali Gökçe & Erdinç Öner

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1.1 Categorization of existing literature on the SimOpt mode

1.1 Categorization of existing literature on CI based model

The researchers from the transportation field working in the area of TST are moving towards the implementation of hybrid algorithms. The main idea behind this is to overcome flaws in one algorithm, especially to reduce complexity and speed up the processes so that they can be more useful in optimizing TST. Sign of addition i.e. “+” between the two different strategies in the “Method / Tool” column of summary in Appendix 2 showing the hybrid of those two strategies, like [ 62 , 75 ] presented the hybrid of SA-GA and TS-ABC based algorithms respectively for solving the problem. Additionally, the table also indicates whether the results generated from the underexamined strategy is verified by any simulation means or not.

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Qadri, S.S.S.M., Gökçe, M.A. & Öner, E. State-of-art review of traffic signal control methods: challenges and opportunities. Eur. Transp. Res. Rev. 12 , 55 (2020). https://doi.org/10.1186/s12544-020-00439-1

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• Chengaou S El Yassini K Oufaska K (2023) Simulation of an intelligent traffic management model Proceedings of the 6th International Conference on Networking, Intelligent Systems & Security 10.1145/3607720.3607758 (1-4) Online publication date: 24-May-2023 https://dl.acm.org/doi/10.1145/3607720.3607758
• Han Y Lee H Kim Y (2023) Extensible prototype learning for real‐time traffic signal control Computer-Aided Civil and Infrastructure Engineering 10.1111/mice.12955 38 :9 (1181-1198) Online publication date: 14-May-2023 https://dl.acm.org/doi/10.1111/mice.12955

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A systematic literature review of autonomous and connected vehicles in traffic management.

1. Introduction

1.1. prior research, 1.2. research goal, 1.3. contribution and layout.

• Through early November 2022, 140 critical papers on connected and autonomous vehicle traffic management were discovered. This work can be a foundation for future, more in-depth scientific studies in this area.
• Then, 100 significant studies were selected that adhered to our criteria for the quality evaluation stage. When compared to other research of a similar sort, these investigations can offer valuable data.
• Then, the data from 100 research were carefully analyzed, and data were obtained to pinpoint concepts and problems related to designs for AV and CV traffic control methods.
• In this regard, this study provides a meta-analysis of traffic management techniques and goals to enhance intelligent transportation systems and emerging technologies.
• In addition to researching different methods for directing CAVs traffic at junctions, it is crucial to compare and evaluate how well each method achieves its objectives in order to spot any shortcomings and help the researchers for the gap in this field.
• At the end, the study describes the constraints and offers suggestions to help further study in this field.

2. Research Methodology

2.1. primary studies selection.

(“AV” OR “autonomous vehicle” OR “self-driven” OR “driverless vehicle” + “interchange” OR “intersection” OR “roundabout” + “urban” OR “suburban” OR “rural” + “congestion” OR “capacity” OR “safety” OR “management” OR “detection”)

2.2. Inclusion and Exclusion Criteria

2.3. selection results, 2.4. quality assesment, 2.5. data extraction, 2.6. data analysis, 2.6.1. publication overtime, 2.6.2. substantial keyword distribution, 3. research analysis, 3.1. driving objectives perspective, 3.2. traffic management methodologies consisiting of primary goals, 3.2.1. efficiency, 3.2.2. safety, 3.2.3. safety and efficiency, 3.2.4. efficiency and ecology, 3.2.5. ecology, passenger comfort, and safety, 3.2.6. efficiency, safety, and ecology, 3.2.7. efficiency, safety, and passenger comfort, 3.2.8. efficiency, safety, ecology, and passenger comfort, 3.2.9. other: data sharing, 4. discussion, 5. conclusions.

• A comprehensive review of 315 publications that were published between 2012 and 2022 was given in this study. In the end, this research examined 100 studies on traffic management, including AVs and CVs at junctions, interchanges, and roundabouts that had passed the quality evaluation. According to statistics on the number of research papers published on this subject each year from 2018 to 2022, additional research is anticipated in 2023–2024, particularly in machine learning techniques.
• The primary goal of this literature review was to describe the most recent publications in the field of connected and autonomous vehicles to understand current traffic management techniques and identify difficulties and limitations. The study addressed three research questions, as per the analytical discussions. The approach recommended by [ 107 ] generated the maximum performance for the techniques described in this research out of all the articles considered in this evaluation. However, Due to the inability of human-driven cars to rationally communicate and cooperate with other road users, mixed traffic at unsignalized intersections may be difficult to evaluate in such a technique. Rule-based approaches made up 34% of the papers chosen, followed by optimization techniques at 39%, hybrid methodologies at 13%, and 14% of the publications that were chosen employed ML techniques.
• The study assessed the behavior of the recommended approaches associated with effectiveness, safety, environmental effects, and passenger ease, and the study’s findings were published. Investigators utilized numerical testing, math, simulators, mathematics, numerical testing, and other techniques in 95% of the selected articles to support their theories, whereas 5% used toy vehicles, actual automobiles, or field tests. It is recommended that AI-based traffic management structures may minimize some of the issues said by optimizing the data collection method. This may include learning traffic characteristics and human behaviors, projecting traffic attributes, and creating more effective traffic-management decisions. The recommended approaches should be more extensively assessed to cope with sensor variation, since car manufacturers install various sensor types with varying features and quality to collect data.
• Finally, RQ3 was addressed by discussing the primary research’s remaining shortcomings and gaps while considering various factors, such as methodology and validation environment. In total, 90% of research has focused on pure AVs, in contrast to the reality, which will soon involve a combination of human-driven automobiles, AVs, pedestrians, and bicycles.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest, abbreviations.

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Alanazi, F. A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management. Appl. Sci. 2023 , 13 , 1789. https://doi.org/10.3390/app13031789

Alanazi F. A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management. Applied Sciences . 2023; 13(3):1789. https://doi.org/10.3390/app13031789

Alanazi, Fayez. 2023. "A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management" Applied Sciences 13, no. 3: 1789. https://doi.org/10.3390/app13031789

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Traffic light control design approaches: a systematic literature review

2022, International Journal of Electrical and Computer Engineering (IJECE)

To assess different approaches to traffic light control design, a systematic literature review was conducted, covering publications from 2006 to 2020. The review's aim was to gather and examine all studies that looked at road traffic and congestion issues. As well, it aims to extract and analyze protruding techniques from selected research articles in order to provide researchers and practitioners with recommendations and solutions. The research approach has placed a strong emphasis on planning, performing the analysis, and reporting the results. According to the results of the study, there has yet to be developed a specific design that senses road traffic and provides intelligent solutions. Dynamic time intervals, learning capability, emergency priority management, and intelligent functionality are all missing from the conventional design approach. While learning skills in the adaptive self-organization strategy were missed. Nonetheless, the vast majority of intelligent design approach papers lacked intelligent fear tires and learning abilities.

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International Journal IJRITCC

Citation/Export MLA Shubhada P. Mone, Sachin Wankhede, Rohini Kadam, Aditya Mahakulkar, Poonam Kauthale, “An Intelligent Traffic Light Controlling System”, March 15 Volume 3 Issue 3 , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 940 - 943, DOI: 10.17762/ijritcc2321-8169.150309 APA Shubhada P. Mone, Sachin Wankhede, Rohini Kadam, Aditya Mahakulkar, Poonam Kauthale, March 15 Volume 3 Issue 3, “An Intelligent Traffic Light Controlling System”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 940 - 943, DOI: 10.17762/ijritcc2321-8169.150309

International Research Group - IJET JOURNAL

Maythem Abbas

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Building a new traffic infrastructure system is very expensive, thus the generally acceptable solution is to improve the utilization of the existing resources by moving towards Intelligent Transportation Systems (ITS) for traffic management and control. As traffic increases throughout the globe, it becomes more and more apparent that the most appropriate response to its stochastic nature is real-time adaptive traffic control. While actuated systems to a large extent adjust well to traffic conditions, they cannot adjust fully to the hectic nature of traffic in real-time as while they are responsive to the presence of vehicles, they are not sensitive to the traffic demand therefore performing poorly in saturated or nearly-saturated environments. The purpose of this paper is to put forth a heuristic method to dealing with the traffic problem and to prove that adaptive approach fits for purpose for traffic light optimization problem. Adaptive traffic control system is a promising approach to deal with the stochastic nature of the traffic problem. Case study introduced and developed for validating the value of the proposed algorithm via set of metrics and KPIs like average delay per vehicle, and average and number of stops per vehicle to show the value and gain achieved by the proposed approach.

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IJERT Journal

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ISSN Online 2321-2004 ISSN Print 2321-5526

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Keywords: Traffic Management, Internet of Things, RFID, Artificial Intelligence, Machine learning, Neural Networks

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Influence of local geological data and geographical parameters to assess regional health impact in LCA. Tomsk oblast’, Russian Federation application case

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• Published: 08 July 2022
• Volume 29 , pages 87281–87297, ( 2022 )

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• Alexandra Belyanovskaya   ORCID: orcid.org/0000-0003-4320-7637 1 , 2 ,
• Bulat Soktoev 1 ,
• Bertrand Laratte 3 ,
• Elena Ageeva 1 ,
• Natalia Baranovskaya 1 &
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The research paper is aimed to modify the human health impact assessment of Cr in soils. The current article presents the input of several critical parameters for the human health Impact Score (IS hum ) assessment in soils. The modification of the IS hum is derived using geological data — results of neutron activation analysis of soils are used in the IS hum calculation; research area is divided using the watersheds and population size and density. Watersheds reflect the local environmental conditions of the territory unlike the administrative units (geographical areas of the studied region) due to their geological independence. The calculations of the characterization factor value underestimate the influence of the population size and density on the final result. Default characterization factor values cannot be considered during the assessment of the potential human health impact for the big sparsely inhabited areas. In case of very low population density, the result will be overrated and underestimated in the opposite case. The current approach demonstrates that the geographical separation in the USEtox model should be specified. The same approach can be utilized for other geo zones due to the accessibility of this information (area size, population size, and density, geological, and landscape features).

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Alexandra Belyanovskaya, Bulat Soktoev, Elena Ageeva & Natalia Baranovskaya

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Belyanovskaya, A., Soktoev, B., Laratte, B. et al. Influence of local geological data and geographical parameters to assess regional health impact in LCA. Tomsk oblast’, Russian Federation application case. Environ Sci Pollut Res 29 , 87281–87297 (2022). https://doi.org/10.1007/s11356-022-21784-9

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