sustainability Review Applications of Artificial Intelligence in Transport: An Overview Rusul Abduljabbar *, Hussein Dia *, Sohani Liyanage and Saeed Asadi Bagloee Department of Civil and Construction Engineering; Swinburne University of Technology, Hawthorn, VIC 3122, Australia;
[email protected] (S.L.);
[email protected] (S.A.B.) * Correspondence:
[email protected] (R.A.);
[email protected] (H.D.); Tel.: +61-432-299-979 (R.A.); +61-392-145-280 (H.D.) Received: 4 November 2018; Accepted: 24 December 2018; Published: 2 January 2019 Abstract: The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport. Keywords: Artificial Intelligence; Genetic algorithms; Simulated Annealing; Artificial Immune system; Ant Colony Optimiser; Bee Colony Optimization; public transport; Auto Urban Mobility; traffic management 1. Introduction Artificial intelligence (AI) is a broad area of computer science that makes machines function like a human brain. It is used to address issues that are difficult to clarify using traditional computational techniques. AI was first discovered in 1956 by John McCarthy but failed to achieve its objectives [1], and the lack of technology innovations made it less promising. From 1960 to 1970, researchers explored AI through the Knowledge-based system (KBS) and Artificial Neural network systems (ANNs) [1]. The KBS systems are computers that provide advice using pre-determined rules, according to the knowledge presented to it by humans. The ANNs, on the other hand, are systems of neuron connections designed in various layers, modelled after the human brain which have been used in medicine, biology, and language translation engineering, law, manufacturing, etc. [2,3]. During that period of time, interest in AI diminished due to limited applications of ANNs and lack of data until the 1980a [4]. Sustainability 2019, 11, 189; doi:10.3390/su11010189 www.mdpi.com/journal/sustainability Sustainability 2019, 11, 189 2 of 24 Since the 1980s, many research was conducted to minimize the error of prediction through a method dubbed as gradient descent. This method is referred to as a Backpropagation algorithm for the training of the ANNs and it was applied to solve problems in different domains using few hidden layers [5,6]. Today, the availability of data has introduced the concept of machine learning as a subcategory to AI. Machine learning implies coding the computers to behave like a human brain instead of teaching them everything. It provides the computers with access to big data and extract important features from them to solve complicated problems [7,8]. ANN is the most distinguished AI method used in different applications. One of the first and most common types of ANNs is the Feedforward Neural Network in which the data moves in one direction from the input layer to the hidden layer to the output layer. Other types of ANNs are Convolutional Neural Network (CNN) [9–11] and Recurrent Neural Network [12–14]. The CNN performs better for image processing tasks while RNN Processes a sequence for the input data to become well-suited for many application such as; language, writing and text recognition. They are often referred to as Deep Learning Techniques due to the multiple hidden layers structured in their architectures. There are many uncertainties and gaps within the data that cannot be solved using traditional techniques. Therefore, AI uses those uncertainties and model a relationship between the cause and effect of different real-life scenarios by combining the available data with assumptions and probabilities for a better analysis [15]. Transportation problems become a challenge when the system and users’ behaviour is too difficult to model and predict the travel patterns. Therefore, AI is deemed to be a good fit for transportation systems to overcome the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. These challenges arise from the steady growth of rural and urban traffic due to the increasing number of population, especially in the developing countries. In Australia, the cost of congestion is expected to reach 53.3 billion as the population increase to 30 million by 2031 [16]. In Melbourne, Australia alone, more than 640 km of arterial roads are congested during peak time with a CO2 emission of 2.9 tons per year [17]. Many researchers in the 21st-century attempt to accomplish a more reliable transport system with less effect on people and the environment using cost-effective and more reliable by AI techniques. It has a potential application for the road infrastructure, drivers, road users, and vehicles. The AI applications in transport have been developing and implementing in a variety of ways. Among those, this research paper aims to address three main examples. (i) The use of AI in corporate decision making, planning, and managing. This is important to overcome the issue of a continuously rising demand with limited road supply. This includes better utilization of accurate prediction and detection models aiming to better forecast traffic volume, traffic conditions, and incidents. (ii) Applications of AI aiming to improve public transport is also discussed. It is due to the fact that public transportation is regarded as a sustainable mode of mobility. (iii) The next promising AI application in transport is connected and autonomous vehicles, which aims to enhance productivity by reducing the number of accidents on highways. The self-driven cars and small-scale autonomous bus trials that have been initiated, most prominently in Finland, Singapore, and China are overviewed in this paper. The remainder of this paper is divided as follows: Section 2 discusses the application of AI in transport. This section is subdivided into the application of AI for planning, designing and decision making, public transportation, intelligent self-driving cars. It also illustrates real-time incident detection and future traffic state prediction. Section 3 shows that the future of AI is focused on Deep Learning. Section 4 explains the future research work by the authors while Section 5 provides a conclusion of this paper review. 2. Applications of AI in Transport In many cases, it is hard to fully understand the relationships between the characteristics of the transportation system; therefore, AI methods can be presented as a smart solution for such complex Sustainability 2019, 11, 189 3 of 24 systems that can’t be managed using traditional methods. Many researchers have demonstrated the advantages of AI in transport. An example of that includes transforming the traffic sensors on the road into a smart agent that detects accidents automatically and predicts future traffic conditions [18]. Also, there are many AI methods used in transport such as ANNs. ANNs can be used for road planning [19], public transport [20,21] Traffic Incident Detection [22–25]; and predicting traffic conditions [26–33]. It is classified into supervised and unsupervised learning methods. Supervised methods include Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Radial Basis Network (RBN), K-Nearest Neighbors and Decision Tree, etc. while unsupervised NNs include greedy layer-wise and cluster analysis. Many transportation problems lead to an optimization problem that needs bespoke algorithms to make computational analytics easy to solve. They are highly advanced computational algorithms referred to as raster algorithms. The Genetic Algorithm (GA) is an example of those algorithms. It is based on the evolutionary biological concept. It solves complex optimizations problems based on “survival of the fitness” concept and it is a good tool to use in the urban design networks [34–37]. Another algorithm is Simulated Annealing (SA) which is obtained from by simulating the process of annealing of metal [34,38–40]. Ant Colony Optimiser (ACO) is also an AI algorithm developed based on the behaviour of a group of real ants following their path from the nest to food source [41,42]. An artificial Immune system (AIS) which is modelled based on the human immune system [34,43,44]. Bee Colony Optimization (BCO) which solves a hybrid complex optimization problem [44–47]. ACO and BCO are part of swarm intelligence systems [41–44]. Swarm intelligence is an AI system which is inspired by ants and bees working together as a group to reach to an optimised solution. The intelligent computational analytics of these system are able to represent uncertainty, imprecision and vague concepts, hence these techniques are used for route optimization problems in transport [48–51]. Another optimisation technique is Fuzzy Logic Model (FLM). It is applied to solve shortest path optimization [52]. The performance of FLM is compared with Logistic Regression Model (LRM) by [53] when developing a route choice model, and FLM outperformed. Therefore, intelligent techniques such as FLM, GA, ANN, ACO are suitable for prediction, reasoning, and adaptability. Therefore, these are used to solve optimization problems which involve dynamic traffic situations and events. Another novel software paradigm has introduced based on AI theories, called Agent-Based Software Engineering (ABSE). ABSE is capable of allowing the dynamic approach to identifying shortest path through the formation of multi-criteria and multi scenarios [54]. Also, NNs were utilised to integrate the system with the aforementioned algorithms for better results [22,49,55]. For example, references [50,51] showed another tool used for AI as a software and hardware implementation for automated vehicles and trip planning. It is important for transport authorities to determine when and how to use these technologies to make a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of its vital assets. 2.1. AI in Planning, Designing and Controlling Transportation Network Structures The objective of planning is to identify the community needs and decide on the best approach to meet this demand while utilizing the impact of social, environmental and economic in transportation. Designing an optimal road method for transport planning is part of the Network Design Problem (NDP) [56]. It can be a Continuous problem when the capacity of existing infrastructure changes (extend lane width, median and shoulder area), a Discrete problem is identified when adding more infrastructure and a Mixed of Continuous and Discrete problems. Previous researches in the 90s focused on NNs for road planning, designing and modelling. For example, reference [57] modelled the spatial relationship between transportation and land-use planning by using a parallel neural network system. After that, research was more focused on raster algorithms which are more preferred for urban planning because they don’t require existing links and nodes to find the optimal path [58]. Today, emerging huge amount of data with advanced algorithms become the interest of most research by Sustainability 2019, 11, 189 4 of 24 using Machine-learning to create patterns among the data. Reference [34] addressed the continuous NDP problem as a Bi non-linear model assignment with two levels. They used GA and SA algorithms and compared their efficiency on a simulated network. When the demand is low, SA finds optimal value with less computational effort than GA. However, when more computations are done by GA, it can reach a better optimal solution. This result opposed to [37], in which the authors argued that GA provides better results than SA with less computational time. However, the model considered in this paper was a single-level linear model for a Continuous NDP problem. Another study [21] modelled the safety Management plan for Ankara city in Turkey using ANN and Genetic algorithms. The results obtained from ANN model were better than GA with less error involved. While [59] used the ant colony algorithm for the design of an optimal vehicle path. Moreover, reference [58] combined Cellular Automata (CA) which is a spatial simulation method with Ant Colony optimization technique (ACO). The results showed a planning improvement for urban development based on the simulated industrial land use patterns for several years in China. Furthermore, reference [19] focused on finding a good transportation system management and safety plan to balance the transport demand with a proper distribution of highways, railways, and airways. The author used ANN for accident and injury prediction for a highly congested route from Istanbul to Ankara. Last, reference [33] compared between GA, SA and AIS algorithms to find the best modification for an existing network structure considering it as a Mix NDP problem in Poland. The results demonstrated that SA performs worse than GA and AIS for this class of problem. In addition, planning of routes for vehicles is important to avoid congestion and delays in travel times. Many Authors concluded that ant colony algorithm is a promising solution for the vehicle routing problem [57,60–62]. While [63] focused on solving a routing and wavelength problem using BCO Algorithm. The problem means choosing a path within a network and assigning a wavelength to that path for each connected node to maximize the number of connections established between the nodes in the network. Moreover, recent research focused on utilizing microscopic traffic data for modelling and identifying security breaches and for traffic control systems and management plans of roadways [64]. In terms of finding best paths for public transport users, reference [65] suggests to learn and update the real time path generation system according to the preferences of travellers. Also, to use a utility-based approach which concentrates on different attributes of paths and parameters per each public transport user. An area where AI applications have also seen rapid developments is Intelligent Transport Systems (ITS). These systems aim to alleviate congestion and improve driving experience using a variety of technologies and communication systems. They capture important data that can be integrated with machine learning technology. For example, deep reinforcement learning has been used for real time optimisation of traffic control policies embedded in large scale ITS systems [66]. Similarly, a deep learning system to empower ITS devices with functions for signal processing and fast computing analytics has also been proposed [67]. In the future, data complexity will increase as ITS continues to develop, hence, deep learning techniques will be essential to find patterns and features in these data to achieve a more connected transportation systems. In another example [68], genetic algorithm and fuzzy methods were used to control the traffic signal systems automatically at intersections. The ITS system proposed, ‘NeverStop’, utilised RFID sensors and the findings showed that this system was effective to reduce average waiting times for vehicles. Also, two NNs systems [69] were developed to manage the road more efficiently based on microscopic simulated data. The first system is to control the traffic signal and the second one is to predict future traffic congestion. While [70] demonstrated the feasibility of using NNs to control the traffic by proposing a multi-layer NNs system evaluated in three intersections networks [70]. ANNs are also effective to use in signal traffic control. Reference [71] Developed two NNs systems to manage the road more efficiently based on microscopic simulated data. The first system is to control traffic signal and the second one is to predict future traffic congestion. While, reference [72] Sustainability 2019, 11, 189 5 of 24 demonstrated the feasibility of using NNs to control the traffic by proposing a multi-layer NNs system evaluated in three intersections networks [72]. Moreover, reinforcement learning NNs is used to update the parameters and cycle length of the system as the flow changes periodically [64,65]. AI is a dynamic research area that keeps on improving and new methods and applications are introduced frequently to utilize the strength of AI to improve the planning, decision making and management of the road. 2.1.1. Incident Detection Many attempts were established to identify, the time, location and the severity of an incident to support traffic managers to mitigate congestion. These attempts are ranging from manual reports to automated algorithms to Neural Networks. Manual reports that are written by humans can have a delay in detecting incidents and cost-effective. On the contrary, algorithms can measure the flow characteristics before and after the incident through data collected from sensors along the road. Algorithms for incident detection has been first implemented using statistical techniques such as California Algorithm. However, it is difficult to use an algorithm on arterial roads, because of the street parking and traffic signals. For this reason, algorithms have been developed to neural networks approaches. A classification neural network algorithm was evaluated to detect incident occurrence in a freeway [22]. Other research considered finding a good software package to detect all objects of vehicles on real-time [73]. The author suggested using AdaBoost software for accurate image detection. Reference [24] proposed IMM ENKF algorithm to detect incidents for hybrid state problems. Then, a more accurate algorithm named “Efficient Multiple Model Particle Filter (EMMPF) was developed to detect incidents on the highway using both simulated and field data [23]. Moreover, incidents in real time can be detected from social media as discussed in [74]. It showed that Twitter is a cost-effective and efficient technique to acknowledge incident occurrence on both, freeway and arterial roads. 2.1.2. Predictive Models The rapid development of intelligent transport systems (ITS) has increased the need to propose advanced methods to Predict traffic information. These methods play an important role in the success of ITS subsystems such as advanced traveller information systems, advanced traffic management systems, advanced public transportation systems, and commercial vehicle operations. Intelligent predictive systems are developed using historical data extracted from sensors attached to the roads. Then, these data becomes an input to machine learning and AI algorithms for a real-time, short-term and long-term predictions [75]. In the past, research focused for short-term flow prediction by using simple feedforward neural network. Reference [28] integrated a neural network system with one hidden layer to the overall urban traffic control system. The author demonstrated the strong possibility of predicting traffic flow up to 1 min by using simulated data only. While, reference [25] used field data from a 1.5 km section of a highway in Queensland, Australia. An object-oriented neural network model was developed with a time-lag recurrent network (TLRN). The model was able to predict speed for 5 min into the future with 90–94% accuracy. Also, when using speed and flow as an input to the network, it predicted travel time up to 15 min with that same accuracy as speed prediction. In another example [29], the authors developed a deep neural network to predict the traffic flow up to 60 min into the future. The traffic flow data were collected from freeways across California. The authors used an unsupervised stack of auto-encoders named (SAE) model and train it by using a greedy layer-wise algorithm. It extracts important features of traffic flow as each output is fed back as an input to the network. Then, a supervised logistic regression layer is applied for prediction. The model showed a superior performance for nonlinear spatial and temporal traffic data correlation when compared to other machine learning methods such as SVM and Multi-layer NN/Back Propagation NN. While [26] proposed unsupervised Deep Belief Network (DBN) trained using a greedy layer-wise algorithm to learn important features from the flow pattern. Then applied a regression Sustainability 2019, 11, 189 6 of 24 supervised layer for prediction. However, the authors suggest a multitasking regression layer with aSustainability weight grouping method to join multiple tasks together and train the model. This showed an 2018, 10, x FOR PEER REVIEW 6 of 24 improvement in the accuracy of the model. In addition, reference [31] developed a deep learning model by usinginField data collected from California to capture the the long-term traffic fl...
