The traffic signal control problem for intersections: a review

In this section, we classify the ITSCP according to its various characteristics. Due to the highly stochastic nature of the ITSCP, problem complexity is a crucial consideration. The complexity of the ITSCP depends on various factors such as the number and shapes of the intersections and the types of vehicles in the network, as well as the real-time strategies used (if any). Analyzing the sources of computational complexities in the ITSCP is relevant to the practical application of optimized traffic signals at intersections. Accordingly, to provide researchers with the insight required to solve the ITSCP and address open problems, we have chosen to mainly focus on the factors affecting problem complexity. The optimization objectives of the ITSCP are also discussed from a practical perspective. Table 2 shows our proposed classification criteria based on the ITSCP characteristics, and the details of each criterion are described in the following subsections. Table 3 summarizes the ITSCP literature with respect to these classification criteria, in which the &#;Network type&#; column defines the network structure, where &#;I&#; indicates a single intersection, &#;A&#; indicates an arterial, and &#;G&#; indicates a general network, while &#;(A&#;×&#;B)&#; indicates that the target network has a grid structure with A rows and B columns, &#;(C TL)&#; indicates that the target network contains C intersections, and &#;(N-leg)&#; indicates that the intersection has N legs. The &#;Lane&#; column defines the maximum number of lanes in a single direction of each road in the studied network. Finally, &#;-&#; means that no information for the given column is provided in the subject paper.

Check now

Full size table

Full size table

4.1

Network type: isolated intersection, arterial network, or general network

As discussed in Section 2, we classified the network types evaluated in ITSCP research into isolated intersections, arterial networks, and general networks. Computational complexity increases dramatically as the number of lanes and intersections increase, or as the intersections are connected in more complex structures. Earlier research therefore covered only ITSCPs at an isolated intersection. For example, Dunne and Potts [14] solved the ITSCP for an isolated intersection with a maximum of two lanes on each leg. Afterwards, the network scope expanded to include isolated intersections with multiple lanes in each direction and various shapes such as T-junctions [7, 26, 39]. Similarly, arterial networks with multiple lanes were studied in detail after Gazis [16] first discussed a 1&#;×&#;2 arterial network consisting of two sequential intersections. Finally, Wong [35] explored a general network containing 15 intersections with one or two lanes on each leg.

As computer hardware and software simulation tools have developed, the computationally affordable network size has increased. Recently, some papers have succeeded in applying algorithms to real-world networks such as a 9&#;×&#;7 grid of intersections in Ottawa, Canada and a general network containing 50 intersections in Tehran city [9, 75]. Nonetheless, the ITSCP is still being actively researched for isolated intersections or small arterial networks. Jin and Ma [73] and Li et al. [77] solved the ITSCP for an isolated intersection and 1&#;×&#;3 arterial network model, respectively. The networks evaluated in both papers considered contained intersections with only one or two lanes on each leg. Such small networks are still being actively researched because of the development of connected vehicles and new solution methods. For example, Christofa et al. [65] proposed a person-based optimization approach on arterial network by considering passenger occupancy of vehicles explicitly in a connected vehicle environment. When the passenger occupancies of vehicles are considered as decision variables, the number of constraints and variables increases with the number of vehicles in the system, necessitating a small network. Additionally, as new solution methods are developed, they are typically first validated using a small network.

4.2

Type of road users and priority consideration

In this review, we assumed that the traffic on the roads consists of passenger cars, pedestrians, transit vehicles and their passengers, emergency vehicles, motorcycles, HGVs, LGVs, and bicycles for the ITSCP. Because it is difficult to take all traffic types into consideration, most researchers have limited the type of traffic modes to specific categories. A large number of papers have considered only one type of passenger car without pedestrians. Improta and Cantarella [28] first expanded the type of road users considered to include pedestrians in addition to a single type of passenger car. Pedestrians are accounted for in the ITSCP in terms of the minimum green light time required for them to cross the road. Some papers dealing with physical queue lengths or the occupancy of the network have accounted for various types of passenger cars [39], and Chandan et al. [71] considered various types of passenger cars as well as HGVs to more precisely estimate emissions. Recently, studies considering bicycles have been conducted as the number of intersections with dedicated bicycle lanes increases to accommodate the growing number of cyclists [69, 79]. Portilla et al. [69] proposed separate vehicle and bicycle models for the ITSCP to reflect the ability of bicycles to be accommodated in smaller spaces as well as the simpler description of the dynamic behavior of bicycles.

Transit vehicle have been considered important road users in the ITSCP since Salter and Shahi [80] demonstrated that giving priority to buses reduced bus delay at the cost of increasing passenger car delay. Subsequent research efforts have been dedicated to finding more advanced transit signal priority logic considering the performance indices of the vehicles in the network. Ekeila et al. [29] proposed an algorithm to minimize the delay of transit vehicles while preventing negative impacts on street traffic. Christofa et al. [46] approached the problem from the perspective of the individual, especially the drivers of passenger cars and passengers of transit vehicles. He et al. [41] gave priority not only to transit vehicles, but also to emergency vehicles. With the advent of connected vehicles, it is now possible to obtain additional information about the network state and vehicle operations [54]. Using vehicle-to-infrastructure communication systems, the traffic signal control system can receive requests from appropriately equipped vehicles and pedestrians to generate an optimized signal timing plan that accommodates all of the active requests. As communication technology continues to rapidly develop, more research into solving the ITSCP with priority consideration is expected.

4.3

Real-time strategies: fixed-time, actuated, or adaptive

Three major traffic control strategies can be used when solving an ITSCP: fixed-time, actuated, and adaptive [57]. The fixed-time strategy establishes optimal signal plans for fixed signal phase sequences with a fixed time duration for each phase. Adopting the fixed-timed strategy assumes that traffic demand remains similar at all times to calculate the optimal signal plans based on historical traffic information. Gazis [16] and Smith [26] used the fixed-time strategy for a 1&#;×&#;2 arterial network and an isolated intersection, respectively.

The actuated strategy collects real-time data from infrastructure-based sensors and applies a simple logic criterion such as green light extension, gap out, or max out. Green light extension prolongs the green phase based on traffic flow rate. Gap out terminates a phase when the time interval between consecutive activations of a vehicle detector exceeds an established threshold. Max out terminates the green phase when it exceeds the established maximum green phase duration. Since Dunne and Potts [14] first adopted the actuated strategy of green light extension assuming a constant arrival rate per experiment, actuated strategies have been consistently applied in research [40, 54, 60, 75].

The adaptive strategy is similar to the actuated strategy, but utilizes predicted traffic conditions in the near future. Dell&#;Olmo and Mirchandani [33] identified vehicle platoons and predicted their movements in the network using the Approximate Prediction in Response to a Signal Network (APRES-NET) model. The adaptive strategy has been implemented using various other prediction algorithms, and several adaptive signal control systems have been developed accordingly. These systems include ACS-Lite [81], SCATS [82], SCOOT [83], OPAC [84], MOTION [85], UTOPIA [86], and RHODES [6]. Recently, Lee et al. [8] predicted information including lane-to-lane turning proportions, adjustment factors, queue lengths, and arrival and discharge rates using a rolling-horizon process and then calculated an optimized signal plan using a proactive global optimization method. Because adaptive strategies require highly accurate prediction algorithms as well as good signal plan optimization, developing algorithms based on an adaptive strategy could be more difficult than when doing so based on an actuated strategy.

4.4

Objectives

Lee and Park [87] discussed two measures for evaluating the performance of traffic signal control algorithms: mobility and sustainability. Mobility measures consist of the average total delay, average total throughput, average total travel time, average total number of vehicle stops, and average queue length. Sustainability measures consist of emissions and fuel consumption. Most research into the ITSCP has primarily used mobility measures.

4.4.1

Mobility measures

A fundamental performance measure when solving an ITSCP is the delay per vehicle, the minimization of which serves to minimize the average waiting time of vehicles at an intersection due to a red signal. This performance measure is the most commonly used in ITSCP design as indicated by the fact that 61 of the 72 papers in this review treated delay as the fundamental performance index. Some papers considered a weighted delay as a performance measure. Prashanth and Bhatnagar [36] gave a higher weight to main road traffic delay, and Murat and Gedizlioglu [76] proposed a weighted average delay considering traffic volumes in each direction as an objective value. For situations considering different traffic mode priorities, some researchers minimized the delay of transit vehicles [29, 41], and some considered weighted personal delays for both passenger cars and transit vehicles according to their respective passenger occupancies [46, 65].

Another important concept when evaluating traffic signal systems is the throughput of the network. In the ITSCP, throughput is the capacity of the network, defined as the number of vehicles passing through the network. Smith [26] attempted to maximize the throughput. Later, some researchers combined capacity maximization in terms of throughput with other measures [34, 36, 39, 52, 67, 70, 73].

The total travel time of a vehicle is the duration of time it moves in the network. Wong and Yang [45] considered the total travel time of vehicles as a performance index when solving both a signal setting optimization problem and traffic assignment problem. They attempted to take into account the fact that the equilibrium pattern flow of a network is strongly related to signal settings. Some studies conducted within an assumed connected vehicle environment have also used the total travel time of vehicles as a performance index [67].

Minimizing the total number of vehicle stops in a network has also been used as a mobility measure. Vehicle stops, which occur due to a red light or accumulated queue, are directly related to driver satisfaction. Some studies developed flexible models that minimize either the average delay or total number of stops [13, 37, 49, 51], and some studies combined the two performance measures using weighted combinations [35, 60, 64, 72].

To balance each traffic signal phase and each direction in an intersection, the concept of queue length, defined as the total number of vehicles waiting on the roads at each intersection, has been used. Queue length is correlated to the delay or number of stops and as such is typically applied together with these performance measures. Spall and Chin [10], De Schutter and De Moor [42], and Feng et al. [57] proposed the minimization of the average queue length as an additional objective of an ITSCP. Specifically, Feng et al. [57] verified that the minimization of queue length can lower the variance of vehicle delay in each phase. To balance queue length for all roads in the subject network, Sen and Head [37] and De Schutter [62] minimized the maximum queue length.

4.4.2

Sustainability measures

As awareness of the importance of environmental protection has grown, researchers have begun to investigate the environmental impacts of traffic signalization. Aslani et al. [75] employed a microscopic emissions/fuel consumption model to minimize both exhaust products such as carbon dioxide and fuel consumption to improve sustainability. Models evaluating emissions/fuel consumption require the assumption of some constraints on vehicular speed, deceleration, and acceleration. As stated by Han et al. [63], emissions-related objectives make traffic signal optimization problems more difficult due to their nonlinearity and non-convexity.

4.4.3

Other measures

Additionally, various performance indices have been used in accordance with different assumptions and problem-solving methods. For arterial networks, the bandwidth, or portion of a signal cycle during which a vehicle can progress through the signals without stops, has been maximized [31, 33]. Improta and Cantarella [28] and Wong and Wong [70] considered cycle-time minimization as a secondary objective, claiming that if two signal time plans output similar levels of delay and capacity, the plan with the shorter cycle time is better. Arel et al. [30] minimized the likelihood of intersection cross-blocking. Some authors compared the aggregated average speed of vehicles [9] and the number of vehicles in bottleneck links [59]. When accounting for cyclists in the ITSCP, Wang et al. [79] attempted to maximize safety by proposing a traffic conflict index estimated based on the probability of vehicle crossing and the potential traffic conflict severity.

4.5

Signal timing constraints

In this sub-section, we summarize ITSCP constraints regarding cycle length, green phase duration, and phase sequence signal timing constraints.

If you are looking for more details, kindly visit FAMA.

4.5.1

Cycle length

Constraints on cycle length can be classified into four types: fixed, limited minimum length, limited maximum length, and unrestricted. Dell&#;Olmo and Mirchandani [33] optimized traffic signals while maintaining the total time of one complete phase sequence; in other words, they assumed a fixed cycle length. Some papers limited the minimum and maximum cycle length [31], whereas others limited only the maximum cycle length, such as Gallivan and Heydecker [7], who limited it to 120&#;s. Spall and Chin [10], however, calculated the total cycle length, red&#;green splits, and offset times without any restrictions.

4.5.2

Green phase duration

In a similar fashion, the green phase duration can either be constrained by a minimum/maximum limit or allowed to be any value. The selection of the minimum and maximum green phase durations is dependent on the traffic characteristics of the study area and the space available for vehicles to queue [76].

A minimum limit on the green phase duration is normally required for safety and to guarantee that no phase is skipped [65], and is also relevant to pedestrians. Because signals from different road directions are entangled in a single traffic signal system at an intersection, a minimum limit on the green phase duration in one direction can accordingly be interpreted as a minimum limit on the red phase duration in the perpendicular direction. To ensure that pedestrians can cross the road comfortably, this red phase should not be too short [39]. When setting the minimum green time to accommodate a pedestrian crossing, the minimum duration depends on the width of the crossing and the assumed walking speed of the pedestrians [35].

A maximum limit on the green phase duration is usually defined to limit the green extension for signal groups [73]. Most papers started from a minimum green time value and extended the green phase duration until reaching the maximum limit. Though some papers defined only a minimum limit on the green phase duration and allowed a long green phase [35], it is normal practice to constrain the maximum green phase or the total cycle length.

4.5.3

Phase sequence

The signal phase sequence represents a kind of rule between vehicle drivers and traffic signals. Some researchers have claimed that the control system should use a fixed signal phase sequence so as not to confuse drivers, while others have argued against a fixed phase sequence for the sake of performance improvement.

In the early years of ITSCP study, most researchers treated the phase sequence as a given parameter and formulated the problem using fixed phases [14, 16, 18, 20, 22, 24, 26, 28]. Ross et al. [18] addressed the problem using only two phases whereas Wong [35] generalized it to multiple phases. The assumption that the sequence of phases is fixed enforces safety and fairness constraints [6, 88].

Some studies predefined signal phase groups in which compatibility was assured and selected a proper signal phase sequence from among these groups at each rolling horizon. For example, Lee et al. [8] proposed a multi-resolution strategy for updating the elements of the signal plans that included a cycle-by-cycle signal phase sequence and adjusted the current second-by-second green signal timing. Some studies formulated the existing group-based signal as an agent and applied a multi-agent system strategy [55, 73]. The advantages of using predefined signal groups include a high degree of flexibility when specifying signal plans and the ability to deal with a wide range of traffic patterns in a systematic way [8]. Dynamic programming has been widely used to choose phase sequences because the ITSCP can be solved recursively without fixed phase constraints within affordable limits of computational complexity [6, 27, 37].

When not using fixed phase sequences, Dell&#;Olmo and Mirchandani [33] claimed that any sequence of phases and their associated phase durations could be considered for signal plans. In this case, the ITSCP involves a choice of phase sequences and timings to optimize a specified performance index. For example, Haddad et al. [32] simplified traffic flow as a set of vehicle movements at an isolated intersection and determined when to switch the green&#;red signal for each vehicle movement. By using flexible phase sequences, phase pictures were generated considering real-time traffic patterns so the travel delay caused by inefficient phase formulations could be reduced [55].

There is a common misconception that the sensors located at traffic signals, such as video cameras, loop detectors in the pavement and/or radar/microwave sensors, cause the traffic signals to adapt to changing traffic conditions. In reality, this is not always the case. There is a difference between pretimed traffic signals, actuated traffic signals, and adaptive traffic signals.

Pretimed traffic signals don&#;t use any vehicle or pedestrian detection, and every movement gets served for a fixed amount of time. This type of operations is common for central business districts or areas with a lot of pedestrians. Actuated traffic signals use detection for vehicles and pedestrians and serve minor movements only when there is demand and can shorten the movement duration if the demand drops to zero while it is being served. Adaptive traffic signals use detection data and special algorithms to adjust signal timing parameters in real-time or semi real-time for different conditions.

Not all traffic signals are coordinated to other traffic signals. Some traffic signals operate solely based on demand at the intersection, but these traffic signals are most likely not close to other traffic signals, or the demand fluctuates so much that coordination is not possible. Coordinated traffic signals allow the traffic signals to operate as a group, in which there are synchronized movements that are progressed through a series of intersections without stopping.

Most of the traffic signals in the United States operate on a time-of-day sequence, meaning that predetermined timing plans go into effect during certain times of the day. An example is creating an AM Peak timing plan (6 &#; 9 a.m.), MID Peak timing plan (9 a.m. &#; 3 p.m., 7 p.m. &#; 9 p.m.), and PM Peak timing plan (3 &#; 7 p.m.) and uncoordinated timing plan (Midnight &#; 6 a.m., 9 p.m. &#; Midnight) for the weekdays. The sensors located at a traffic signal do make the intersection smarter, because minor movements that don&#;t have any demand can get skipped and some unused time can move to the next movement. However, there are a lot of limitations in the existing methodologies that don&#;t cause the traffic signal to truly adapt, so this is not called adaptive traffic signal control.

One form of advanced traffic signal operations between time-of-day sequences and adaptive traffic signal control is traffic responsive control, where the same predetermined plans in a time-of-day sequence are used, but are selected based on traffic conditions. Special detectors in the field feed a customized traffic responsive algorithm to select one of the predetermined timing plans. The benefits of this type of application are for holidays or areas where the peak period durations can fluctuate. For example, if Independence Day falls during the week, the detectors will sense the lower volumes and may not run the AM Peak timing plan or PM Peak timing plan, or for a much smaller duration if it does, thus reducing the cycle length and delay.

Unlike time-of-day sequences or traffic responsive control, which use the predetermined signal timing plans, adaptive traffic signal systems can adjust all coordination parameters, within certain constraints imposed by an agency, based on what the traffic requires. As time goes on, traffic evolves (it can be quickly in minutes or slowly in days, months or years), and variability is created in which adaptive traffic signal algorithms can handle the variability up to the point of oversaturation (a traffic condition in which the demand exceeds the capacity of the intersection without adding more lanes of travel).

In other countries, or regions outside the United States, there is more use of adaptive traffic signal systems, which is shown in Table 1 (Source: NCHRP Synthesis 403 &#; Adaptive Traffic Control Systems: Domestic and Foreign).

If adaptive traffic signal systems are popular in other areas of the world and handle traffic variations better than traditional traffic signal operations, why are they not used much in the United States?

The answer to that question is focused on some key elements, including, systems engineering, funding, control over the system and perception of a &#;set it and forget it&#; mentality. The funding element is embedded in the other three elements and adaptive traffic signal systems do have a higher cost up front; however, during evaluation, the key areas where adaptive traffic signals provide a bigger benefit are often not studied. Adaptive traffic signal systems provide much benefit when traffic fluctuates the most, like special events, incidents, holidays or business centers like shopping malls. It is hard for studies to be kept open for a longer period of time to study these types of traffic fluctuations because they don&#;t happen as often and there has to be a good apples to apples comparison, meaning the events have to be similar.

Systems Engineering

There is a great need to go through the Systems Engineering process when selecting an adaptive traffic signal system, which is a costly and time-consuming process. Analyzing the data, getting to really understand the corridors and applying the G-COST (goals, context, objectives, strategies and tactics) methodology will better meet expectations of the adaptive traffic signal system (see Figure 1).

The goal is the broad statement that describes the desired outcome or the &#;what&#; the agency is trying to achieve. The goal addresses different areas of the traffic signal system and can include: reliability, efficiency, quality of transit service, preservation, safety, etc. The context defines the types of objectives that are appropriate for the traffic signal system. An example is &#;efficiency,&#; which can be applied to almost all areas of government, including transportation, libraries, parks, etc. By restricting the context to objectives related to different traffic signal system topics, it prevents the analysis from growing into an unmanageable task.

The objective is more specific than the goal and can be measurable. It defines the &#;what&#; the agency needs to do in order to accomplish the goal. It should include at least one performance measure to validate the achievement of the result. A strategy is guidance put in place to achieve an objective. An example could be to operate traffic signals to accommodate all modes of transportation. The tactic is the specific method to implement a strategy. It is usually the action or tool used to implement the strategy to achieve the objective to attain the goal. An example of a tactic to the strategy of operating traffic signals to accommodate all modes of transportation could be to use simulation software to coordinate for bicycle progression.

It is important not to have a predetermined adaptive traffic signal system in mind when going through this process because achieving good mobility means recognizing the interactions between different stakeholders and the policies and procedures that are part of each stakeholder. If the main objective is equitable distribution of green time, but the adaptive traffic signal system is programmed or geared towards smooth flow, then the adaptive traffic signal system is set up for failure at the start.

• Equitable Distribution of Green Time &#; This objective is to equitably serve all traffic movements and modes at each intersection. Coordination is provided along the mainline, but not at the expense of side street, left turn, pedestrian, bicycle and transit traffic.
• Smooth Flow &#; This objective is more geared towards a primary arterial road, in one or both directions, where a big green band is used to keep a platoon that has started moving together and rarely stop at intersections. This may involve holding a platoon at a larger intersection with heavier side street traffic until it can be released and not experience any downstream stops. It may also involve holding the side street at a higher degree of saturation or providing only enough green time to serve queued traffic demand. This allows the mainline to flow more &#;smoothly&#; and allows for less split failures for mainline left turns, which will prevent any backup to the mainline through direction and prevent the slowing down of the through vehicles.

Control Over the Adaptive Traffic Signal System

There are multiple different adaptive traffic signal systems available on the market. Certain adaptive systems can &#;adapt&#; more than others. If an adaptive traffic signal system needs to fit within existing controller coordination technology, there are inherent limitations like transition. Transition is the process of either entering into a coordinated timing plan from uncoordinated operations or changing between two different coordinated timing plans. It is required because not all coordinated timing plans operate with the same patterns and cycle lengths. Transition is not a friend of adaptive traffic signal systems, which makes for slower adaptive abilities, but the adaptive methodology does utilize methodologies that are known to traffic signal engineers and allow for more control over the adaptive traffic signal system. The adaptive traffic signal systems that can &#;adapt&#; more tend to be &#;black box&#; solutions, which means more proprietary algorithms and producing results without revealing any information about the internal workings. When the traffic community can find out the tolerance for control and accept what comes with it, then we will be happier with the adaptive traffic signal system chosen.

Adaptive Traffic Signal Systems Are Not &#;Set and Forget&#;

There is the &#;set and forget&#; mentality when it comes to adaptive traffic signal systems; however, goals and objectives will change over time and then the adaptive traffic signal system priorities need to change with it. For example, if the land use changes dramatically or new big traffic generators are built, the chances are the adaptive traffic signal system will need modification to the parameters because the goals and objectives may have to be changed. If the agency has a new multi-modal priority, then the adaptive traffic signal system may need to be modified to more of an equitable distribution of green time objective. If an improvement project adds capacity to a corridor, then the objective may need to be changed from queue management to smooth flow or equitable distribution of green time objective. The extra maintenance doesn&#;t mean the benefit to cost ratio goes down dramatically, as the adaptive traffic signal system still provides great value between these maintenance steps. The more acceptance the traffic community has regarding the maintenance required and the extra benefits it provides, the happier we will be with the adaptive traffic signal system chosen.

The company is the world’s best intelligent traffic signal controller supplier. We are your one-stop shop for all needs. Our staff are highly-specialized and will help you find the product you need.