# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
## 8 Discussion  
From the reviewed papers can be concluded that TSC is a topic that produces high research interest in the field of traffic engineering. Although TSC is a well-founded research topic, TSC is still a focus of interest for many researchers applying new findings from optimization, control and machine learning, and with the emergence of CVs and CAVs the field of TSC gained new application possibilities because of CVs and CAVs communication/data sharing capabilities. Regarding the applicability of CVs and CAVs in TSC, questions such as the influence of the penetration rates, data sampling rates, and data processing methodologies can be singled out. In [21], it is stated that CAVs can increase road capacity because of their capability to obtain more precise driving condition parameters compared to HDVs. The authors observed that the increase of the road capacity happens gradually before the CAVs penetration rate reaches 30% penetration rate, and above 30% penetration rate the increase of road capacity depends on CAV capability to maintain the minimum time gap. A similar observation is noticed in research [60, 61, 62]. Although the authors conducted the research independently on different models, they observed that a CV penetration rate of 20% improves system performance. Considering the CV's capability to share onboard data, data-driven methods and streaming-data-driven methods are reasonable choices for real-time traffic control applications even with their pros and cons. While data-driven methods can provide information about conditions on turn-level [54] or even on network level [45], they rely on historical data which means they are prone to failure if an irregular event occurs (e.g., traffic accident). However, streaming-data-driven methods are relying on streaming data and weak assumptions which require large amounts of data. They are more robust to unexpected events and include data from various sensors [41,59]. Driven by the results of the STM applications in [42], it is evident that STMs are one approach for processing real-time data from CVs and CAVs. Thus, future work will be focused on TSC systems for mixed traffic flows, especially the exploration of CV \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Paper** & **Year** & **Data Source** & **Type** & **Applied Method** & **Impact** & **Benchmark** \\ \hline
[66] & 2020 &
\begin{tabular}{c} Simulation, \\ real-world data \\ \end{tabular} & Model-driven method & Traffic signal & Improved traffic signal & Compared to existing \\  & & & formulated as MINLP & performance & actuated signal timing \\
[69] & 2018 &
\begin{tabular}{c} Real-world \\ data \\ \end{tabular} & Model-driven method & Back-pressure based & Reduced average & Fixed-cycle and \\  & & & ATSC & vehicle travelling time & Backpressure \\  & & & Distributed- & The algorithm & algorithms \\
[67] & 2017 & Simulation & Model-driven method & Methodology for & controlled queue length, & Tested on different \\  & & & signal timing & throughput and & simulation \\  & & & optimization & reduced travel time & \\
[65] & 2017 &
\begin{tabular}{c} Real-world \\ data \\ \end{tabular} & Model-driven method & Two-level & Offset optimization & Actuated- \\  & & & coordination & along corridor & coordinated signal \\  & & & algorithm & control by Vissim \\
[71] & 2017 & Simulation & Data-driven method & Platoon-based & Reduce average delay & Evaluation in \\  & & & intersection & per vehicle by up to 50\% & simulated \\  & & & & environment \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Tabular overview of referenced papers regarding CV and CAV based TSC. applications in TSC, with the final goal of developing STM-based TSC. The idea of the development will be how to apply the STM to estimate the state of the intersection with the final goal to achieve ATSC, meaning that the obtained information about the state of the intersection will be used as input for the TSC.