# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### ML and IoT Applications in Smart Transportation
#### 2.2.1 Route Optimization  
Traffic congestion is a common issue in urban areas and it is only getting worse as the number of vehicles increases. Route optimization is a method to propose the best route for a specified destination, in order to minimize traffic congestion. By minimizing the traffic congestion, both the traveling time and vehicle emissions are reduced [13]. The route optimization problem has been widely challenged in the literature by various ML approaches over the IoT infrastructure. Namely, in [36], an MDP algorithm and V2V communication have been used for a group routing to minimize traffic congestion. In [12], a combination of k-Means and DBN is preferred to optimize the traffic network configuration. Short-term traffic forecasting is addressed in [18], and it is faced with multiple approaches such as a BN-SARIMA, an FF-NN, and a NARX model. Short-term along with long-term traffic predictions have been also challenged in [27] comparing the performance of four ML methods: RF, a baseline predictor, a regression tree, and an FF-NN. An FF-NN trained with a BP is also selected in [28] to calculate time-travel variations and estimate the total travel time. The authors in [23] dealt with traffic congestion predictions comparing a Logistic Regression with SVM, a decision tree, an RF, and an MLP method. Finally, ref. [31] performs traffic flow predictions based on big data, using a Stacked Auto Encoder approach. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **ITS Application** & **References-ML** & **References-No ML** \\ \hline
1 & Route Optimization-Navigation & [12; 18; 23; 27; 28; 31; 36] & [13; 58; 59; 60; 61; 62] \\
2 & Parking & [14; 20; 38] & [63; 64; 65; 66; 67; 68] \\
3 & Lights & & [69; 70; 71] \\
4 & Accident Detection & [17; 19; 22; 24; 25; 29; 32; 37] & [72] \\
5 & Road Anomalies & & [21; 30; 33; 34; 35] & \\
6 & Infrastructure & & [16; 73; 74] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: References to IoT smart transportation applications. The idea in [58] is to explore the capabilities of Mobile Crowd-Sensing for Intelligent Transportation Systems, by using a swarm intelligence algorithm for route optimization. The authors implement a Modified Crowd-Sensing version of the Ant Colony Optimization algorithm (MoCSACO). Users will exchange information with each other and will navigate to less congested routes, following the messages received by other users, similar to the way ants follow pheromone paths when searching for food. An innovative approach to support route optimization using IoT technology is described in [13]. Scalable Enhanced Road Side Units (SERSU) are used alongside with a Master Control Center (MCC). The SERSU consists of, a camera, weather condition sensors, pollution sensors, a wireless modem, and a Radio Frequency module, and is capable of monitoring the traffic and the environmental conditions and sending the data to the MCC through the 4G network. The MCC processes the data and can adapt the speed limit of each road based on the congestion, the weather conditions, and the pollution levels and send the new speed limits and route suggestions back to the SERSU in order to notify the drivers. In this way, traffic congestion can be diminished and possible accidents due to bad weather conditions can be avoided. A Vehicular Ad-hoc Network (VANET)-based algorithm for enhanced route planning is implemented in [59]. The authors design a mobile navigation application that collects traffic data and proposes the optimum route for the shortest travel time or the least fuel consumption. In a VANET, the cars exchange information when neighbouring cars are within a IEEE 802.11p range, in order to give real-time updates about the status of a road even when an abnormal event has occurred in a route. Traffic information is also collected from the Google Maps application for the routes that no VANET information is available. The traffic information from the two sources is combined and the best route is proposed to the user.