# Machine Learning Applications in Surface Transportation Systems: A Literature Review
## 1 Introduction  
Mobility is a multidimensional, heterogeneous concept that involves the movement of goods and people. With advancements in information and communication technology, mobility has provided more comfortable, more efficient, and faster accessibility in recent decades. Although advances in mobility continuously improve the quality of human life, the accompanying changes result in hurdles and setbacks, known as transportation externalities (i.e., air pollution, congestion, noise pollution, and health problems). Due to their internal interactions, transportation ecosystem problems are typically categorized as non-deterministic polynomial-hard (NP-hard) problems [1, 2]. One example of an NP-hard problem is automobiles speeding up mobility while causing traffic congestion, which slows down mobility. NP-hard problems combined with externalities make transportation problems complex system problems. The science, technology, and engineering fields have become involved in the world of transportation engineering to solve some of the complex system problems that have arisen over the past decades (i.e., computer science and systems engineering). These interdisciplinary resolutions have generally been called intelligent transportation systems (ITSs) [3]. Over the past 30 years, ITSs have included dynamic message signs, real-time transit information systems, traveler information systems, electronic toll collection systems, ramp meters, smart parking systems, mapping applications, and advanced traffic signals [4]. Subsequent ITS generation has included automation, connectivity, cybersecurity for ITSs, and enabling technologies such as artificial intelligence (AI) [5]. Artificial intelligence, the application of computers mimicking human decision-making and problem-solving logic capabilities, has been introduced into transportation systems. For instance, AI tools such as machine vision and voice recognition have already been applied in automated vehicle technologies [6]. As a subset of AI, machine learning (ML) is the most sophisticated state-of-the-art knowledge branch offering the potential to solve unsettled or difficult-to-solve problems. Machine learning is a data-driven approach that allows computers to imitate human learning capabilities by gradually improving accuracy [7]. This approach solves some NP-hard problems, including optimization problems, as it does not have the limitations associated with analytical methods [8]. Several learning techniques are used to implement the ML algorithm, including classical learners, neural networks and deep learners, and ensemble learners. Ensemble learners use two or more shallow learners (from classical or neural network learners) to improve their performance. Generally, neural networks and deep learners aim to solve homogeneous data representation problems such as the use of images and video. Instead, classical learners target problems with heterogeneous data, such as independent properties of a particular subject. Consequently, feature engineering could be a performance improvement strategy for classical learners. On the other hand, deep learners perform better with a large amount of input data although they are computationally expensive. Additionally, their results usually are uninterpretable for humans. These specific properties make different ML algorithms suitable for various applications depending on input data, required results, and expected performance. With all the potential and capabilities of ML techniques, academic research and specific technological companies have considered ML for addressing transportation problems. Machine learning models have been widely adapted for forecasting and prediction regarding traffic flow, traffic congestion modeling, capacity analysis, mode choice analysis, and demand prediction [9; 10; 11; 12; 13]. Machine learning has primarily been explored within the academic research world and technology and manufacturing enterprises to accelerate pushing knowledge and products forward.