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Review

Research Progress and Prospects of Transit Priority Signal Intersection Control Considering Carbon Emissions in a Connected Vehicle Environment

by
Xinghui Chen
1,
Xinghua Hu
1,2,*,
Ran Wang
3 and
Jiahao Zhao
1,2
1
School of Traffic &Transportation, Chongqing Jiaotong University, Chongqing 400074, China
2
Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System, Chongqing Jiaotong University, Chongqing 400074, China
3
Chongqing YouLiang Science & Technology Co., Ltd., Chongqing 401336, China
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2024, 15(4), 135; https://doi.org/10.3390/wevj15040135
Submission received: 29 February 2024 / Revised: 25 March 2024 / Accepted: 26 March 2024 / Published: 27 March 2024
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Abstract

:
Transit priority control is not only an important means for improving the operating speed and reliability of public transport systems, but it is also a key measure for promoting green and sustainable urban transportation development. A review of signal intersection transit priority control strategy in a connected vehicle environment is conducive to discovering important research results on transit priority control at home and abroad and will promote further developments in urban public transport. This study analyzed and reviewed signal intersection transit priority control at four levels: traffic control sub-area divisions, transit signal priority (TSP) strategy, speed guidance strategy, and the impacts of intersection signal control on carbon emissions. In summary, the findings were the following: (1) In traffic control sub-area divisions, the existing methods were mainly based on the similarity of traffic characteristics and used clustering or search methods to divide the intersections with high similarity into the same control sub-areas. (2) The existing studies on the TSP control strategy have mainly focused on transit priority control based on fixed phase sequences or phase combinations under the condition of exclusive bus lanes. (3) Studies on speed guidance strategy were mainly based on using constant bus speeds to predict bus arrival times at intersection stop lines, and it was common to guide only based on bus speed. (4) The carbon emissions model for vehicles within the intersection mainly considered two types of vehicles, namely, fuel vehicles and pure electric vehicles. Finally, by analyzing deficiencies in the existing studies, future development directions for transit priority control are proposed.

1. Introduction

In recent years, with the continuous growth in the number of motor vehicles, problems such as urban traffic congestion and traffic pollution have become prominent. As a result of its advantages of low energy consumption, low pollution, large carrying capacity, and low per capita occupation of road resources, urban public transport plays an indispensable role in residents’ commuting traffic as a major mode. Giving priority to the development of urban public transport and promoting transit priority is of great significance in alleviating urban traffic congestion, reducing energy consumption and emissions, enhancing the level of public transport services, and promoting the sustainable development of transportation [1]. Urban transport is one of the major sources of carbon emissions, and vehicle carbon emissions from intersections are one of the major sources of carbon emissions from transport. Increasing attention is being paid to the overall green and low-carbon transition, optimizing the energy structure of transport and improving the efficiency of transport organization. These measures call for optimizing and adjusting the structure of public transport in urban areas, promoting and popularizing electric buses or other zero- or low-emission new-energy buses, adhering to transit priority, and creating a “transit metropolis”, thus tapping into the low-carbon space to realize sustainable urban development goals.
When a bus arrives at a signal intersection, it stops and waits due to the influence of signal control and only starts accelerating when the green light in its phase is turned on again. During this process, the driving conditions of the bus change, and due to the effects of deceleration, acceleration, and idling conditions, the carbon emissions and delays are much higher than those generated by buses cruising normally. Previous studies have shown that delays caused by signal blocking in buses traveling in the upstream section of an intersection accounted for 10% to 20% of bus travel time and accounted for more than half of the total delays in the operation process [2]. Transit signal priority (TSP), as one of the main strategies of transit priority control, interacts with intelligent roadside units through their real-time data (including bus location, speed, and other information) through V2I communication and determines whether the buses need to be prioritized or not, according to the set conditions in the cooperative vehicle infrastructure environment. For vehicles that need priority, speed guidance and signal timing parameter optimization give them priority at the intersection, which effectively reduces the carbon emissions and delays of buses in the driving process [3].
The concept of TSP first appeared in 1967 [4], mainly by adjusting the duration of signal lights to ensure that buses can pass faster at the intersections. The active priority strategy for single-point intersections was mainly studied in mixed traffic lanes (where buses were mixed with social vehicles). By the 1990s [5], research on TSP strategy gradually became dominated by real-time prioritization and more accurate transit priority control was achieved through the use of advanced sensors and communication technologies. Since the beginning of the 21st century [6,7], with the rapid development of artificial intelligence and big data technology, the transit signal priority system has become more intelligent and efficient.
As the coordination and control function unit of the urban traffic control system, the results of the traffic control sub-area divisions directly affect the formulation of regional coordination control strategy and control effects. Speed guidance and TSP strategy have significant positive effects on enhancing the operation efficiency of public transport and promoting the green and sustainable development of urban transport. Therefore, this study aims to systematically review the relevant research results at home and abroad from four aspects: traffic control sub-area divisions, TSP control strategy, speed guidance strategy, and the impacts of intersection signal control on carbon emissions. The framework of the transit priority control strategy at signal intersections in a connected environment is shown in Figure 1.

2. Division of Traffic Control Sub-Areas

The traffic control sub-areas are the coordinated control functional unit in an urban traffic signal control system, and its division method directly determines the selection of regional signal coordinated control strategies and the advantages and disadvantages of control effects. The study of the correlation degree between intersections, as the theoretical basis for the division of control sub-areas, is the basic premise for the study of signal-coordinated control theory and methodology.

2.1. Correlation Degree Analysis of Intersections

The intersection correlation degree is generally categorized into two types: static and dynamic. The static correlation degree is measured from the perspective of road network topology, which is the road characteristics of the degree of correlation between intersections, such as intersection spacing. The dynamic correlation degree is measured from the perspective of roadway traffic flow and signal timing parameters of the dynamic correlation between intersections.
Tian and Urbanik [8] constructed a calculation model for the correlation degree between intersections in a road network area by considering the influencing factors, such as intersection spacing, traffic flow, and signal cycle ratio, and realized the coordinated control of intersections through heuristic algorithms. Lu et al. [9] considered the number of queuing vehicles on road sections, the number of running vehicles, and the cycle length and established a calculation model for the degree of correlation between two adjacent intersections and the combined correlation degree of multiple intersections. Bie et al. [10,11] investigated the effects of cycle time differences between adjacent intersections and fleet length on the benefits of coordinated control and established a comprehensive correlation degree model for two adjacent intersections based on cycle time and fleet length correlation degree. Tian et al. [12] quantitatively analyzed the intersection correlation degree and established a total correlation degree model of adjacent intersections by considering the distance between adjacent intersections in the road network, traffic flow, travel time, discrete characteristics of traffic flow, and signal period. Ke et al. [13] established a comprehensive correlation degree model between intersections by calculating the impact of adjacent intersections’ traffic flow, signal period, and road segment density. By combining the existing research results on intersection correlation, the calculation indexes of intersection correlation are clarified, as shown in Table 1. From Table 1, it can be seen that intersection spacing, traffic flow, and signal cycle ratio are the common indicators studied by scholars. Based on this, the relationship between the above three indicators and the correlation degree of intersections is drawn, as shown in Figure 2.

2.2. Method of Dividing Control Sub-Areas

In terms of sub-area division control methods, Ma et al. [14] proposed three automatic division methods, namely, equal division, division by mean value, and division by distance; they used a spectral method to take the correlation degree of adjacent intersections as the basis of division. With the help of complex network theory, Wang et al. [15] researched the control sub-area optimization method based on a cohesive community discovery algorithm, using community modularity as an indicator. Shen et al. [16] analyzed the influence of road section distance, traffic flow density, and signal cycle on intersection correlation and established a sub-area division method based on a fuzzy algorithm. Xu et al. [17] proposed a dynamic division method for road networks based on different saturation degrees for different states of intersections, established the correlation and similarity model between intersections within each sub-area, and utilized the spectrogram theory to dynamically divide road sections and intersections. Ambühl et al. [18] proposed a control sub-area division method based on the Monte Carlo algorithm, according to the traffic characteristic parameters set by fixed sensors. Ding et al. [19] proposed a continuous division method for controlling sub-areas based on the heterogeneity difference theory of road networks. Fang et al. [20] proposed a spectral clustering algorithm for homogeneous partitioning of heterogeneous networks, taking into account road similarity, cluster values, and associations between adjacent roads. By combining the existing research results, the division methods of the control sub-areas are summarized, as shown in Table 2. Based on the division methods of the control sub-areas, the design of the traffic control sub-area divisions process is shown in Figure 3.

2.3. Research Review

(1)
When analyzing the correlation degree of intersections, most of the existing research established a correlation degree calculation model based on considering the intersection spacing, traffic flow of road sections, signal cycle, and other factors; few researchers considered the overall correlation magnitude between multiple intersections and did not effectively synthesize the various factors affecting the correlation degree of intersections. The indexes were often concentrated on the selection of one or a few kinds of traffic parameters and did not take into account the global characteristics of the road network.
(2)
In the traffic control sub-area divisions, most of the existing methods were based on similarities of traffic characteristics, using clustering or search methods to divide high-similarity road sections and intersections into the same sub-areas. For the setting of some model parameters in the sub-area division methods, there was a lack of actual traffic data support, and the sub-area divisions in some of the studies did not reflect the dynamic changes in the modes of traffic operation well.
(3)
Most of the studies on traffic control sub-areas focused on the normal traffic state, and there were relatively few studies on the division of sub-areas under the oversaturated traffic state. In the oversaturated traffic state, the vehicle traffic efficiency was low, and the effective traffic control method is extremely important to enhance vehicular traffic efficiency.

3. Transit Signal Priority Control Strategy at the Intersections

Intersection TSP control is a time-priority control method that prioritizes the operation of buses passing through intersections. The schematic diagram of transit priority control at a signal intersection in a connected vehicle environment is shown in Figure 4. In recent years, domestic and foreign scholars have conducted extensive research on intersection TSP strategy, which can be mainly divided into three types: passive priority, active priority, and real-time priority.

3.1. Passive Priority Control

The passive priority control method is a pre-set control method that gives more passing time and passing opportunities to transit vehicles by combining the historical traffic data of the intersections.
Passive priority control for transit signals first appeared in the United Kingdom [21] and Japan [22]. Skabardonis [23] proposed three kinds of intersection bus preemptive signaling schemes and provided a specific formula for the signal timing and the effect before and after the implementation of each scheme. Baker et al. [24] summarized the results of the TSP research, pointing out that passive priority control can only show its advantage when the bus flow is relatively high, and the adaptability of passive priority is poorer in the case of lower flow. Zhang et al. [25] proposed a passive signal timing transit priority optimization method by optimizing the signal cycle to minimize the total delay of vehicles at intersections. Ma and Yang [26] analyzed the characteristics of China’s public transport system and comparatively studied the applicability of passive and active priority strategies in China. They proposed a single-point intersection public transport passive priority control method for the optimization of traffic time and space and established a passive priority control model based on the optimization of a combination of spatial–temporal resources [27], as well as a passive priority method for signal intersections based on bus departure frequency [28]. Ma et al. [29] proposed a comprehensive TSP optimization method based on passenger capacity, which considered passive signal priority along with the effect of exclusive bus lane settings. A summary of the research results regarding passive priority control is shown in Table 3.

3.2. Active Priority Control

Active priority control is a control strategy that adjusts the signal control scheme based on the arrival time of buses and the signal phase status of intersections, ensuring that buses pass through intersections first. It mainly includes control strategies such as green extension (GE), red truncation (RT), and phase insertion (PI). The control process of the three strategies is shown in Figure 5.
Active transit priority control experiments in Los Angeles, USA, confirmed that active priority control can effectively reduce bus travel time [30]. Furth and Muller [31] pointed out that conditional active priority has the smallest impact on whole intersections. Byrne et al. [32] considered stochastic traffic demand and proposed an optimization model for transit priority control with the optimization objective of maximizing the capacity of vehicles. Bie et al. [33] proposed a transit priority strategy, with and without green light time compensation, for non-priority phases at single-point unsaturated intersections. Cesme et al. [34] considered the overall access benefits of buses and social vehicles for the problem of multi-bus conflicting priority requests under exclusive bus lane conditions. Hu et al. [35] proposed a green time loss equalization method under priority strategies to equalize the effects of GE, RT, and PI strategies on non-priority phases. Wolput et al. [36] proposed a formula for calculating the optimal signal cycle length to adapt to transit priority strategies such as GE and phase rotation. Islam et al. [37] considered transit priority under exclusive bus lane scenarios in terms of right-of-way fairness. Wunderlich et al. [38] proposed a dynamic transit signal scheduling method based on a maximum weight matching algorithm. Xu et al. [39] considered GE and RT strategies for BRT systems to achieve maximum stability and reliable bus system service for social vehicles. Li et al. [40] proposed an arterial transit priority signal coordination method that took into account the multi-path coordination of social vehicles. The active priority control research results are combined, as shown in Table 4.

3.3. Real-Time Priority Control

Real-time priority is mainly studied in terms of delay analysis and optimization, intersection control, arterial coordination control, and network control.
Yagar et al. [41,42] proposed a real-time transit priority control method for signal intersections under consideration of transit interference factors. Mirchandani et al. [43] used the RHODES traffic adaptive signal control system to optimize the signal phase timing of transit priority at intersections based on real-time bus status and GPS positioning data under the conditions of exclusive bus lanes and analyzed the per capita delay of bus passengers. Dion et al. [44] proposed a real-time priority control strategy for transit signals based on an adaptive bus signal control system and evaluated it through simulation experiments. Li et al. [45] proposed an adaptive TSP optimization model that optimizes the green time splitting for three consecutive cycles to minimize the weighted sum of bus delays and other traffic delays, taking into account safety and other optimization constraints under the double-loop structure of signal control. Li et al. [46] simulated the physical queuing phenomenon of vehicles by considering a dynamic traffic model with bus and car interactions and proposed an adaptive transit priority signal optimization setting method. Moghimi et al. [47] proposed an adaptive signal control system with self-organizing logic based on exclusive bus lanes, which reduced bus delays and improved bus operation reliability. Yin et al. [48] proposed an optional phase optimization framework for TSP and developed a real-time TSP algorithm by improving the dual-ring phase structure and adding several optional bus phases to form multiple phase configuration scenarios. The real-time priority control research results are combined, as shown in Table 5.

3.4. Research Review

(1)
In the research for TSP control strategy in a connected environment, it is common to consider only the case where buses are intelligently connected. With the development of intelligent connected technology, few studies have been able to explore the effects of TSP control strategy on heterogeneous traffic groups when there are both intelligent connected vehicles (ICVs) and human-driven vehicles (HDVs) in the social vehicle fleet.
(2)
Most of the existing studies were based on fixed phase sequences or phase combinations for transit priority control without considering the dynamic changes in traffic flow characteristics and lack of flexibility, while in an intelligent network environment, the signal phase sequences and phase lengths can be dynamically adjusted to adapt to changes in traffic demand by obtaining real-time intersection information.
(3)
In the existing research on TSP control strategy, most of the studies were conducted under the conditions of exclusive bus lanes, and there was relatively little research on TSP control under non-exclusive bus lanes. Currently, most of the transit priority is focused on improving the efficiency of bus operations, ignoring the problem of impaired rights and interests of social vehicles within the intersection.

4. Speed Guidance Control Strategy

The situations that buses may encounter when entering signal intersections are shown in Table 6. In the cooperative vehicle infrastructure environment, real-time speed and location information of vehicles are uploaded to the cloud service center. Based on the distance from the current position of the vehicle to the stopping line at the intersection, the ideal driving speed of the vehicle is calculated, and the information is transmitted to the driver in real-time to induce the driver to adjust the speed so that the vehicle can pass the intersection without stopping.

4.1. Travel Time Prediction Model

Accurate and real-time prediction of bus arrival times at intersection stop lines is the key to realizing the TSP control strategy, and it is also a great concern for scholars in the field of transit priority research.
Liu et al. [49] used the binocular vision principle to measure the distance from a bus to a signal intersection stop line and established an adaptive-historical prediction model to predict the travel time of the bus to reach the signal intersection stop line. Behera et al. [50] proposed an adaptive Kalman filtering prediction model. Rahman et al. [51] developed a prediction model based on long- and short-term memory neural networks and particle filtering. Li et al. [52] proposed a method to dynamically predict bus travel time using a combination of an adaptive asymptotic Kalman filter and a wavelet neural network model based on RFID electronic license plate data in Chongqing. Huang et al. [53] proposed a data-driven approach based on Fisher’s discriminant analysis and Bayesian support vector regression for predicting bus travel time. Tran et al. [54] applied a nonlinear time series regression model to predict bus travel time. Shan et al. [55] developed a bus travel time prediction model based on stochastic time series and Markov chains and validated the model using historical GPS data. A compendium of research results on travel time prediction modeling is shown in Table 7.

4.2. Co-Optimization of Speed Guidance and Signal Control

In the continuous development of intelligent connected technology, intersection speed guidance and signal collaborative control are achieved by adopting speed guidance and TSP strategy under the premise of fixed phase sequence and cycle time so that the bus arrives at the intersection stop line in a green phase, thereby reducing bus delays. The bus speed guidance control strategy diagram is shown in Figure 6.
Li et al. [56] proposed a single-lane traffic signal–vehicle trajectory co-optimization method for single-intersection scenarios. Wu et al. [57] proposed a combined speed-guided and TSP control strategy in a connected vehicle environment to improve the bus punctuality rate and reduce overall delays at the intersection. Feng et al. [58] developed a staged optimization model, where signal timing optimization is used in the first stage, while the speed optimization of the moving fleet is carried out in the second stage. Zhang et al. [59] used BRT speed guidance and signal timing dual compensation to correct deviations between actual BRT departure times and the timetable. Lu et al. [60] proposed a cooperative optimization method using regional green wave speed and signal timing. Kamal et al. [61] proposed a co-optimization method for mixed-traffic environments to minimize the delay of all vehicles at an intersection for signal optimization. Hu et al. [62] established a bi-level optimization model for single-intersection transit priority control based on the stochastic characteristics of intersection vehicle speed. The research results of speed guidance and signal control co-optimization are sorted out, as shown in Table 8.

4.3. Research Review

(1)
Bus travel time prediction methods can be generally divided into two kinds: statistical regression and machine learning, in which the prediction effect of machine learning algorithm models was better than that of statistical regression models. However, although the prediction model based on machine learning can better fit the nonlinear and non-stationary time series data in the bus travel time prediction problem, the traditional machine learning algorithm had higher computational complexity and lower prediction efficiency.
(2)
Existing studies have generally only explored the prediction of transit arrival times at intersection stop lines based on constant transit speeds under exclusive bus lane conditions. There was insufficient support for the prediction of transit priority duration when the intersection traffic density is high, i.e., under the strong random arrival characteristics of buses.
(3)
Existing studies exploring the speed guidance strategy under transit priority control generally only focused on bus speed guidance, and few studies have been able to explore the impact of bus–social vehicle group collaborative guidance strategies on the model optimization objectives under the all-elementary connected environment or mixed-connected environment, with insufficient consideration of the speed guidance and traffic efficiency of the vehicle group.

5. The Impact of Signal Control at Intersections on Carbon Emissions

With the increasingly prominent problem of mobile source pollution in urban transportation, many scholars have carried out corresponding research on the intersection signal control problem, considering the carbon emissions factor under the new development mode of deeply practicing green concepts and promoting energy conservation and emission reduction in the transport field.

5.1. Research on the Impact of Signal Control on Carbon Emissions

Karekla et al. [63] considered metrics such as transit energy consumption and carbon emissions; they developed a TSP control optimization model under exclusive bus lane conditions and used a genetic algorithm to solve it. Kwak et al. [64] concluded that traffic signal parameter settings can significantly affect vehicle delays and traffic emissions at signal intersections and that optimal traffic signal timing can reduce traffic carbon emissions by 8% to 20%. Abudayyeh et al. [65] proposed a multi-objective transit priority control model based on two metrics—passenger travel time benefit and environmental benefit—and the results of the study showed a relationship between pollutant emissions and signal timing at intersections. Kang et al. [66] analyzed carbon emissions using convolutional neural networks and long- and short-term memory network methods and verified that signal timing would have an impact on carbon emissions in road networks. Hu et al. [67,68] constructed a low-carbon control optimization model of TSP at intersections under stable arrival characteristics as well as strong stochastic arrival characteristics for two scenarios of exclusive and non-exclusive bus lanes in a bus connected environment, with the objectives of optimizing carbon emissions and reducing vehicle delays at intersections, respectively. A compendium of research results on the impact of intersection signal control on carbon emissions is shown in Table 9.

5.2. Research Review

(1)
In the studies on the impacts of intersection signal control on carbon emissions, it was common to take intersection traffic efficiency indicators such as delays, queue lengths, and capacity as the optimization objectives of transit priority control. With the deepening of global attention to greenhouse gas emissions reduction, few studies have been able to explore the impacts of speed guidance and TSP control strategies on intersection vehicle carbon emissions.
(2)
For the carbon emissions calculation model of vehicles within the intersection, the existing research generally only investigated two types of vehicles—fuel and pure electric vehicles—without investigating the actual ratio of vehicle mixing at the intersection, and there was a certain deviation between the carbon emissions model calculation results and actual road scenarios.

6. Conclusions and Prospects

6.1. Conclusions

Urban traffic congestion and traffic pollution barriers to the sustainable development of China’s cities present serious challenges, giving priority and significance to the development of urban public transportation and promoting it to alleviate urban traffic congestion and promote green and sustainable development of urban transport. This study combed through the latest research results of transit priority control at signal intersections, analyzed the division methods of traffic control sub-areas based on the intersection correlation degree, explored speed guidance and TSP strategies, and clarified the impacts of intersection signal control on carbon emissions. The findings are as follows: (1) In the traffic control sub-area divisions, most of the existing methods used clustering or search methods to divide intersections with high similarity into the same sub-areas, and some of the model parameters in the sub-area division method were set without the support of actual traffic data. (2) The existing results mainly focused on the bus-connected environment, and there was a general lack of exploration of the impacts of TSP control strategies on heterogeneous traffic groups when ICVs and HDVs were simultaneously present in the social vehicles. (3) Existing studies exploring speed guidance strategy generally only focused on bus speed guidance, and few studies were able to explore the impacts of the bus–social vehicles group cooperative guidance strategy on model optimization objectives under the full-element connected environment or hybrid connected environment, with insufficient consideration of the traffic benefits of the vehicle group. (4) Existing studies generally took intersection traffic efficiency indicators, such as delays, queue lengths, and capacity, as the optimization objectives of transit priority control. Few studies have explored the effects of speed guidance and TSP combined control strategies on intersection vehicle carbon emissions.

6.2. Prospects

(1)
Division of traffic control sub-areas
For the calculation of the correlation degree between intersection nodes, it is common that only correlations between neighboring nodes are considered, ignoring the indirect role of non-neighboring nodes. In terms of the division of control sub-districts, the influence factors considered by the correlation model are not comprehensive enough, and in subsequent research, a complete dynamic division technology system for traffic control sub-areas should be formed by designing sub-area division algorithms for the whole road network.
(2)
TSP control strategy at the intersection
Existing studies have mainly focused on exploring the effect of TSP on the model optimization objective in a transit-connected environment. In the subsequent studies, the effect of the TSP control strategy on heterogeneous traffic groups when both ICV and HDV are present in social vehicles can be explored with the aim of realizing the TSP control of intersections in complex traffic environments.
(3)
Speed guidance control strategy
For the prediction of transit travel times in speed guidance strategy, the existing methods generally only considered the recursive relationship of macro travel time, while bus travel times between intersections are also affected by factors such as bus stopping times, social vehicles, uncertainties of roadway traffic flow, etc. In the next step of the study, microscopic travel time prediction models can be further developed to reduce prediction errors of the model and enhance the applicability of the model to the actual transport environment.
(4)
The impact of signal control at intersections on carbon emissions
In the existing research, the carbon emissions model of vehicles within intersection areas generally explored only two types of vehicles: fuel and pure electric vehicles. In subsequent studies, the carbon emissions model can be performed on the basis of the existing studies, taking into account actual traffic situations and considering a variety of vehicles with different energy sources to reduce the deviation between the model calculation results and the actual carbon emission results.

Author Contributions

Conceptualization, X.C. and X.H.; formal analysis, J.Z.; resources, R.W.; writing—original draft preparation, X.C.; writing—review and editing, X.C. and X.H.; funding acquisition, X.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Sichuan Science and Technology Program (No. 2022YFG0132); Chongqing Transportation Science and Technology Project (No. CQJT-CZKJ2023-10); Chongqing Social Science Program Office (No. 2023PY27); Scientific and Technological Research Program of Chongqing Municipal Education Commission (No. KJQN202300741); Chongqing Postgraduate Joint Training Base Project (Chongqing Jiaotong University-Chongqing YouLiang Science & Technology Co., Ltd. Joint Training Base for Postgraduates in Transportation) [No. JDLHPYJD2019007].

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to privacy.

Conflicts of Interest

Ran Wang is an employee of Chongqing YouLiang Science & Technology Co., Ltd. The paper reflects the views of the scientists and not the company. The authors declare no conflicts of interest.

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Figure 1. Framework for transit priority control strategy at signal intersections.
Figure 1. Framework for transit priority control strategy at signal intersections.
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Figure 2. Impacts of various correlation factors on the correlation degree of adjacent intersections.
Figure 2. Impacts of various correlation factors on the correlation degree of adjacent intersections.
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Figure 3. Traffic control sub-area divisions process.
Figure 3. Traffic control sub-area divisions process.
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Figure 4. Schematic diagram of transit priority control at a signal intersection.
Figure 4. Schematic diagram of transit priority control at a signal intersection.
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Figure 5. Three strategy control processes.
Figure 5. Three strategy control processes.
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Figure 6. Schematic diagram of bus speed guidance control.
Figure 6. Schematic diagram of bus speed guidance control.
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Table 1. Summary of intersection correlation degree research.
Table 1. Summary of intersection correlation degree research.
AuthorYearCorrelation Degree Indicators
Tian and
Urbanik [8]		2007Intersection spacing, road traffic flow, signal cycle ratio
Lu et al. [9]2009Number of vehicles in the queue, number of vehicles in operation, cycle length
Bie et al. [10,11] 2011, 2013Cycle length, fleet length
Tian et al. [12]2019Intersection spacing, traffic flow, travel time, discrete characteristics of traffic flow, signal cycle
Ke et al. [13]2022Adjacent intersection flows, signal cycle, roadway density
Table 2. Research summary of traffic control sub-area division methods.
Table 2. Research summary of traffic control sub-area division methods.
AuthorYearSub-Area Division Method
Ma et al. [14]2010Spectral methods
Wang et al. [15]2012Complex network community discovery algorithms
Shen et al. [16]2016Fuzzy algorithms
Xu et al. [17]2017Spectral graph theory
Ambühl et al. [18]2019Monte Carlo algorithm
Ding et al. [19]2022Road network heterogeneity difference theory
Fang et al. [20]2023Spectral clustering segmentation algorithm
Table 3. Summary of passive priority control studies.
Table 3. Summary of passive priority control studies.
AuthorYearMain Methodology
Furth et al. [21], Ma et al. [22]1991, 2014Adjustment of cycle length
Skabardonis et al. [23]2000Adjustment of phase sequence
Baker et al. [24]2004Cancel the left turn phase
Zhang et al. [25], Ma and Yang. [26,27,28]2004, 2007Adjustment of green signal ratio
Table 4. Summary of active priority control studies.
Table 4. Summary of active priority control studies.
AuthorYearMain Methodology
Furth and Muller [31], Byrne et al. [32], Bei et al. [33], Cesme et al. [34], Hu et al. [35], Wolput et al. [36], Islam et al. [37], Wunderlich et al. [38], Xu et al. [39]2000, 2005, 2008, 2011, 2014, 2016, 2016, 2022, 2023GE
Byrne et al. [32], Bei et al. [33], Hu et al. [35], Islam et al. [37], Wunderlich et al. [38], Xu et al. [39], Xu et al. [40]2005, 2008, 2011, 2016, 2018, 2022, 2023RT
Hu et al. [35], Islam et al. [37], Wunderlich et al. [38]2008, 2016, 2018PI
Table 5. Summary of real-time priority control research.
Table 5. Summary of real-time priority control research.
AuthorYearMain Methodology
Yagar et al. [41,42], Mirchandani et al. [43], Li et al. [45], Moghimi et al. [47], Yin et al. [48]1993, 1994, 2001, 2017, 2020, 2023Optimization-based real-time prioritization
Dion et al. [44], Li et al. [46]2005, 2011Rule-based real-time prioritization
Table 6. Driving situations of buses entering signal intersections.
Table 6. Driving situations of buses entering signal intersections.
Situation ClassificationSituation Description
Direct PassageThe light is green at the moment of vehicle arrival, and the remaining time is sufficient for the bus to pass at its current speed, at a constant speed.
Accelerated PassageThe light is green at the moment of vehicle arrival, and the remaining time does not allow the bus to pass, but it can pass by accelerating within the remaining time of the green light.
Decelerate to passUnder normal circumstances, the light is red when the vehicle reaches the stop line, but it is possible to slow down so that the bus reaches the stop line with a green light and thus passes through smoothly.
Wait for the next green lightVehicles are unable to accelerate or decelerate through intersections.
Table 7. Summary of research on travel time prediction models.
Table 7. Summary of research on travel time prediction models.
AuthorYearPrediction Models
Liu et al. [49]2016Adaptive-historical predictive modeling.
Behera et al. [50]2017Adaptive Kalman filter prediction model.
Rahman et al. [51]2018Long- and short-term memory neural network–particle filter prediction modeling.
Li et al. [52]2019Combined prediction model with adaptive asymptotic Kalman filter and wavelet neural network.
Huang et al. [53]2021Fisher discriminant analysis and Bayesian support vector regression predictive modeling.
Tran et al. [54]2022Nonlinear time series regression forecasting model.
Shan et al. [55]2023Combined forecasting models for stochastic time series and Markov chains.
Table 8. Summary of speed guidance and signal control co-optimization study.
Table 8. Summary of speed guidance and signal control co-optimization study.
AuthorYearMain Points
Li et al. [56]2014Traffic signal–vehicle trajectory cooperative optimization in single-intersection scenario.
Wu et al. [57]2016A combined control strategy of speed guidance and TSP in a connected environment is proposed.
Feng et al. [58]2018A two-stage approach for the co-optimization of signal timing and fleet speed is proposed.
Zhang et al. [59]2020BRT speed guidance and signal timing dual compensation correction for deviation between actual BRT departure time and timetable.
Lu et al. [60]2021Aiming at the problem of coordinated control of regional green waves, a cooperative optimization method of ICV green wave speed and signal timing is proposed.
Kamal et al. [61]2022Intersection signal timing and ICV trajectory optimization in ICV–HDV mixed traffic environment.
Hu et al. [62]2023Introducing bus speed probability density function to predict bus priority duration and constructing a bi-level optimization model for single intersection bus priority control.
Table 9. Summary of research on the impacts of intersection signal control on carbon emissions.
Table 9. Summary of research on the impacts of intersection signal control on carbon emissions.
AuthorYearMain Points
Karekla et al. [63]2018Studied the impact of TSP strategies on bus energy consumption and carbon emissions under the condition of exclusive bus lanes.
Kwak et al. [64]2019Traffic signal parameter settings can significantly affect vehicle delays and emissions at signal intersections, and optimal traffic signal timing can reduce carbon emissions by 8% to 20%.
Abudayyeh et al. [65]2021There was a relationship between pollutant emissions at intersections and signal timing, and pollutant emissions were not necessarily minimized when intersection capacity was maximized.
Kang et al. [66]2023Carbon emissions were calculated using convolutional neural networks and long- and short-term memory network methods, and it was verified that signal timing has an impact on carbon emissions in the road network.
Hu et al. [67,68]2023An optimization method was proposed for low-carbon control of TSP at intersections with stable arrival characteristics as well as strong stochastic arrival characteristics.
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Chen, X.; Hu, X.; Wang, R.; Zhao, J. Research Progress and Prospects of Transit Priority Signal Intersection Control Considering Carbon Emissions in a Connected Vehicle Environment. World Electr. Veh. J. 2024, 15, 135. https://doi.org/10.3390/wevj15040135

AMA Style

Chen X, Hu X, Wang R, Zhao J. Research Progress and Prospects of Transit Priority Signal Intersection Control Considering Carbon Emissions in a Connected Vehicle Environment. World Electric Vehicle Journal. 2024; 15(4):135. https://doi.org/10.3390/wevj15040135

Chicago/Turabian Style

Chen, Xinghui, Xinghua Hu, Ran Wang, and Jiahao Zhao. 2024. "Research Progress and Prospects of Transit Priority Signal Intersection Control Considering Carbon Emissions in a Connected Vehicle Environment" World Electric Vehicle Journal 15, no. 4: 135. https://doi.org/10.3390/wevj15040135

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