The paper is organized as follows. The related works on CAIV, particularly from the perspective of traffic prediction, are presented in the next section. We then give the taxonomy of CAIV approaches and review traditional traffic prediction models used in both V2I and V2V communications. Following that, we propose a novel MCS approach to make traffic predictions. Subsequently, we carry out experiments to verify the proposed approach. Finally, we give some insights for reliable traffic prediction.

As previously described, the emerging technologies for traffic prediction are becoming a reality. In this paper, the goal of our research is to outline the current methods for traffic prediction and propose a novel method based on MCS technology. Our main features and contributions as follows:

Also, we provide a qualitative comparison among three kinds of approaches, and we discuss our analysis and the field’s outlook.

In this paper, we propose a method for utilizing data derived from connected vehicles to improve transportation efficiency. We present new algorithms for the timely estimation and prediction of travel times, and combine the results with accident prediction to support dynamic route choices for drivers to avoid congestion. In addition, this approach could potentially lead to building efficient large scale sensing applications by leveraging smartphones and/or sensor-equipped vehicles [ 8 ]. For example, instead of installing road-side cameras and loop detectors, one could collect traffic data and detect congestion levels using smartphones carried by drivers. Such solutions reduce the cost of deployment of specialized sensing infrastructure.

Using vehicle communication, the roadside unit can ascertain a road’s status in real-time, and, along with the vehicular status, deliver these traffic data to the cloud, which in turn can estimate the average speed and other information. Unfortunately, these valuable traffic data is not being utilized by traffic departments, so no provision has been made for their effective transmission, storage and analysis. Currently, existing travel time prediction and vehicular dynamic route planning models do not analyze traffic data for drivers.

With the convergence of mobile communications and intelligent terminal technology, the transportation system is provided with a new approach in alleviating the traffic congestion through Mobile Crowd Sensing (MCS) technology, which is based on the power of various mobile devices such as smartphones and/or sensor-equipped vehicles [ 6 7 ]. In this sensing paradigm, participants, such as the drivers, can forward the traffic data obtained from mobile devices to the traffic monitoring system’s cloud. Then, traffic data analysis is carried out to inform drivers or related traffic authorities of the traffic situation.

Recently, cloud computing technology has emerged as a new information technology infrastructure for the fast developing IT industry. In [ 2 ], Liu proposed that, from the network perspective, the IoV system is a three-level “Client-Connection-Cloud” system, which includes the client, the connection and the cloud, respectively. Cloud-Assisted IoV (CAIV) is becoming a hot topic; it refers to representing physical system components, such as sensor-equipped vehicles and other devices in the cloud virtually, accessing (e.g., monitoring, actuating and navigating) those physical components through their virtual representations, and processing and managing the large amount of data collected from physical components in a scalable, real-time, on-demand, efficient and reliable manner [ 4 5 ]. Specifically, the integration of cloud computing techniques (e.g., virtualization, elastic re-configuration, and multi-tenancy of resources) with IoV techniques (e.g., vehicle social networks, and efficient big data analysis) appears to be a promising approach to advancing the state of the art, and allows previously unrealizable applications and services to be built, deployed, and managed effectively. Therefore, we believe that vehicular networking as a nascent form of IoV constitutes a very basic scenario of IoV. Recently, an increasing number of system technologies and system intelligence designs have been developed to make transportation cleaner, safer and more efficient. The IoV will play an important role in the clean traffic environment in the future. For example, reliable traffic prediction is very beneficial in saving travel time, reducing pollution, and improving traffic efficiency.

The Internet of Things technology is driving the evolution of VehicleNetworks (VANETs) into the Internet of Vehicles (IoV) paradigm. IoV is an emerging field that crosses multiple disciplines, such as automotive, transportation, information and communications technology. The different perception of the vehicle in VANETs and the IoV makes these two scenarios differ fundamentally in the device, communications, networking, and services aspects. A vehicle in VANET is mainly considered as a node to disseminate messages among other vehicles, thus forming an inter-vehicle communication network. However, in the IoV paradigm, a VANET forms only a portion of the communication network. In addition to VANETs, dynamic vehicular mobile communication systems include inner-vehicle communication systems, such as Vehicle to Sensor (V2S), and communication with other entities, such Vehicle to Infrastructure (V2I), Vehicle to Human (V2H) and Vehicle to Internet [ 1 3 ].

These projects have shown the feasibility of using GPS equipped mobile devices in sensing traffic situation. They indicate that even with a relatively low penetration of mobile devices, it is possible to get more detailed information and broader coverage of the traffic situation than solely with stationary sensors. Moreover, the experiments given rise to a variety of approaches for noisy GPS data processing and verification. Still, the central focus of these experiments has been on private vehicles and the route choices of individual users. However, the superiority of cloud-assisted MCS for transportation has not been studied. In this paper, we first review geographical data aggregation, and then propose a specific algorithm to realize reliable traffic prediction.

Transportation is an obvious application area for MCS. Recently, some crowd sensing experiments were carried out to determine traffic congestion levels, traffic delays, and road condition problems (e.g., potholes). There are several notable examples:

Even if the areas of interest are not covered by RSE, we can still carry out traffic predictions. The vehicle can cache traffic data in its memory and, once it passes by a RSE, it will transmit cached traffic information such as its origin, destination, and vehicle trajectory data, to the RSE. A lot of useful information, such as volume, speed, density, acceleration/deceleration rates, and travel times of upstream segments, can be processed by appropriate data analysis algorithms. This information can then be correlated with traffic data obtained from upstream/downstream RSE and traditional loop detectors. A mathematical relationship can then be established using the tensor regression method of [ 14 ]. The RSE mines the trajectory and loop detector data and continuously provides estimates and predictions on the state of traffic in areas not covered by RSE.

A vehicle in a VANET not only connects the RSE, but also connects to other vehicles. Connected vehicles and roadside infrastructure can generate some traffic data, such as vehicular spatio-temporal trajectories, which allow a brand new perspective on addressing some key issues of traffic prediction, including: (a) how to accurately aggregate traffic data and predict future traffic conditions using the VANET; and (b) how to improve efficiency of traffic management by mining huge amounts of traffic data. In [ 12 ], Minpresented a new method for real-time road traffic prediction with spatio-temporal correlations. The method takes into account the spatial characteristics of a road network in a way that reflects not only the distance but also the average speed of the links. In [ 13 ], Liproposed a real-time and reliable communication architecture for connected vehicles based on traffic responsiveness, a field theory model based on connected vehicles, and networked vehicle routing algorithms. In this study, a new method for traffic prediction was proposed through the combination of temporal and spatial traffic flow data (e.g., volume, density, and speed), which was simultaneously based on tensor feature regression. In [ 13 ], Wangave some insights from the point of view of context-aware, and carried out a simplified experiment for traffic prediction.

In order to perform hierarchical aggregation, landmarks are assigned a level in a hierarchy. Landmarks of a higher level are also members of all the lower levels. The destination of a trip will not always be a high-level landmark position. Nevertheless, the aggregated information can of course be used for route planning. In order to plan a route, a navigation system “fills in” the missing information between the final destination and close-by landmarks by using standard travel times hardcoded in the map data. This is reasonable, because a final decision on the last part of the route is not yet required at this stage—it is sufficient if a good choice for the immediately upcoming routing decisions can be made. As the vehicle approaches its destination, the route can be updated and refined as more detailed information becomes available.

With the support of V2I communication, vehicles passing a road segment make an observation of the current travel time between two neighboring landmarks. This information is subsequently distributed to nodes within their close surroundings. It is then used by vehicles to calculate travel times between landmarks of the next higher level, thereby summarizing the travel times in the area. The details of landmark-based aggregation were analyzed in [ 11 ].

For the travel time aggregation process, communication channels are established between dynamic mobile systems based on vehicles and RSE units. In [ 10 ], Locherttackled the aggregation problem for the specific case of travel time data supporting road navigation decisions. Essentially, the travel time aggregation, including landmark- and hierarchical landmark-based aggregation, is achieved through compressing all available information on all possible paths between two landmarks to a “virtual” link connecting them. The basic idea of the aggregation scheme is based on landmarks such as junctions and intersections. Landmarks are defined on multiple levels of hierarchy in the road network. At the highest level, these are junctions of the main roads or highways, while lower levels include higher level landmarks and an increasing number of smaller street intersections. The lowest level is a representation of the full road network.

However, VANETs introduced a novel, more timely and interactive way of collecting traffic data. A VANET uses vehicles and/or smartphones as mobile nodes in a mobilenetwork to create a mobile network [ 9 ]. In this manner, Roadside Equipment (RSE) deployed at strategic locations can exchange information with smartphones carried by the drivers. RSE and proximate smartphones are interconnected and share traffic information (e.g., traffic congestion levels). Vehicles outside the range of any RSE may still be connected to the rest of the vehicle and infrastructure network via neighboring vehicles. This network can generate accurate real-time traffic information in great detail, based on which some fundamental traffic problems regarding efficiency can be addressed from a brand new perspective.

In recent years, On-Board Equipment (OBE) is being used to detect the status of vehicle using vehicular sensors. A GPS receiver can obtain the location; the speedometer can measure the vehicle’s speed; the odometer can obtain the distance travelled within an interval and various other inner sensors can obtain information about the vehicle’s condition. These traffic data can be delivered to a data center though a cellular network.

In the past decade’s approaches, researchers usually made some measurements to collect traffic flow information. The most common intrusive and non-intrusive detection technologies are loop detectors and road-side cameras, respectively. A loop detector is buried underground and detects the pressure exerted by vehicles to count the number of vehicles passing over it. Traffic cameras not only count the number of vehicles, but can also identify plate numbers. These two approaches incur enormous infrastructure deployment costs, but they have been widely used in transportation systems for traffic flow monitoring. Once a slow traffic flow or an unexpected standstill is detected, the traffic situation regarding the particular road may be published dynamically on nearby billboards to inform drivers.

In order to improve traffic conditions, some researchers have proposed traffic mitigation techniques. In this section, we review the state-of-art technologies on traffic data collection and traffic prediction.

From the standpoint of the service relationship between cloud computing and vehicular networks, the architecture of CAIV can be divided into three primary architecture types: Vehicles to Clouds (VTC), Vehicles as Clouds (VAC), and Vehicles with Clouds (VWC). With MCC support, intelligent transportation systems can provide more elastic services, and even facilitate traffic prediction. In this paper, we analyze the service relationship between cloud computing and IoV, and mainly focus on how to utilize the traffic cloud to achieve traffic prediction. For VTC, vehicles can access cloud services from gateways deployed along the roadside infrastructure. VAC is composed of a set of connected passengers and/or vehicles, initially located in the same area as other users. Subsequently, they may opt to allocate their computing resources to other users, forming datacenters.

Recently, a few research projects conducted studies on the combination of cloud computing with vehicular networks. In [ 24 ], researchers proposed architectures of vehicular clouds, vehicles using clouds, and hybrid clouds. In [ 25 26 ], a hierarchical cloud architecture for vehicular networks was introduced, and the proposed architecture included a vehicular cloud, a roadside cloud, and a central cloud. Mobile Cloud Computing (MCC) technology, with its features of scalability and virtualization, can handle massive computing, storage and software services in a flexible manner [ 27 28 ]. The integration of IoV and MCC can promote the development of cost-effective, scalable transportation systems. CAIV is a promising approach that highlights some emerging applications and services, and it is hoped that a number of strategies for improving traffic efficiency and road safety and enabling a clean traffic environment will be introduced through this approach.

As mentioned above, RSE deployed at strategic locations can exchange traffic data with vehicles [ 20 22 ]. V2I connectivity is critical to avoid or mitigate the effects of road accidents, and to enable the efficient management of intelligent transportation systems [ 23 ].

4. MCS for Traffic Prediction by VWC

Automatic Sensing and Uploading Approaches : According to the Mobile Century and Mobile Millennium projects, the results suggest that a 2%–3% penetration of smartphones in the driver population is enough to provide accurate measurements of traffic conditions. Therefore, at the early stage of the project implementation, we can make use of administrative means to obtain the participation of, for example, taxi drivers for the purposes of the experiments. Smartphones carried by taxi drivers can periodically forward data (e.g., mobileId, location, speed, and direction ) to the traffic cloud through the mobile network. The duration of the period should be a tradeoff between energy consumption, data traffic and data reliability.

Service Process : Figure 2 shows the logic flowchart of a cloud-assisted MCS traffic congestion control algorithm. MCS, as an emerging category of internet-of-things applications, leverages the sensors and computing power in mobile devices opportunistically to sense environmental conditions. In this paradigm, we achieve abundant cloud services by using V2V, V2I, V2H and V2S interactions to form a VWC architecture. The following describes the sensing methods and service process.

Figure 2. Flowchart of a cloud-assisted MCS traffic congestion control algorithm.

Figure 2. Flowchart of a cloud-assisted MCS traffic congestion control algorithm.

Algorithm 1. input: T : The sample period; s d : The destination station begin Set the weight parameters α , β and γ ; Construct all values of d i,j and q i,j according to the distances among stations; T c ← current time; T l ← T c ; while conditions are satisfied do if ( T c - T l ≥ T ) then Construct all values of v i,j and r i,j which are obtained from cloud servers; Construct the weight W using Equations (3) and (4); Get the next station s next ; Obtain and report the shortest path from s next to s d using Dijkstra’s algorithm; T c ← current time; T l ← T c else T c ← current time; endif endwhile end. The drivers or passengers can quickly obtain traffic congestion levels by various smart terminals, such as smartphones, PDAs, or sensor-equipped vehicles. If a service request is derived from the participants, the system will automatically enable the incentive mechanism. The algorithm pseudocode for the service process is given in Algorithm 1.

N stations, s 1 , s 2 , …, s N . The stations are usually deployed at intersections or junctions; the detailed diagram is shown in R ( s i , s j ) represents road segment from station s i to station s j . The distance between s i and s j is denoted by d i,j , which remains constant after the corresponding stations have been deployed. The variable q i,j is used to express the quality of the road, the value which lies in [ 0 , 1 ] . We set the value as follows: q i , j = { 0 , R ( s i , s j ) i s a f i r s t c l a s s h i g h w a y ; 0.5 R ( s i , s j ) i s a s e c o n d c l a s s h i g h w a y ; 1 , R ( s i , s j ) i s a t h i r d c l a s s h i g h w a y ; + ∞ , R ( s i , s j ) c a n n o t b e u s e d . (1) We further study the algorithm to realize traffic prediction with MCS technology. Let us assume the network under consideration hasstations,, …,. The stations are usually deployed at intersections or junctions; the detailed diagram is shown in Figure 3 ) represents road segment from stationto station. The distance betweenandis denoted by, which remains constant after the corresponding stations have been deployed. The variableis used to express the quality of the road, the value which lies in. We set the value as follows:

R ( s 1 , s 2 ), R ( s 2 , s 3 ) are 0.5 and 0, respectively. The variable r i,j is used to express the existence of an event that causes congestion. We call such an event an adverse event; a collision is typical such event. The value of r i,j is assigned as follows: r i , j = { + ∞ , a n a d v e r s e e v e n t h a s t a k e n p l a c e ; 0 , o t h e r w i s e . (2) Therefore, as shown in the figure, the quality values of roads),) are 0.5 and 0, respectively. The variableis used to express the existence of an event that causes congestion. We call such an event an adverse event; a collision is typical such event. The value ofis assigned as follows:

The average road speed v i,j is derived in the traditional manner, i.e. , by dividing the distance d i,j from station s 1 to station s 2 by the vehicle travel time. This method is simple, but does not consider vehicle parking. If the driver goes shopping and the vehicle parks in a parking lot between station s 1 and station s 2 , the shopping time is accumulated with the travel time, so the value of v i,j is inaccurate.

In Algorithm 1, we can obtain the real-time speed using speedometer measurements in a specific period T . If the vehicle is located on the road, which can be verified using a GPS receiver and map matching software, we consider the speed value to be qualified at that moment. The average speed v i,j is the expectation of all qualified real-time speed values. It is underlined that, when the vehicle does not lie on a given road, the corresponding real-time speed value must be discarded. In the above-mentioned situation, speed measurements during shopping time should be discarded.

Figure 3. Traffic prediction based on VWC.

Figure 3. Traffic prediction based on VWC.

G = ( V , E , φ) as follows. Let V = ( s 1 , s 2 , …, s N ), and E = {( s i , s j ) |, there exists a direct path between s 1 and s j }, and φ be a function: E → R + such that: φ ( s i , s j ) = α v i , j + β d i , j + γ q i , j + r i , j (3) α , β , γ are the prescribed weights, and φ ( s i , s j ) is assigned to be + ∞ if the value of v i,j is detected to be zero. We then obtain a weighting matrix W N × N such that: W i , j = φ ( s i , s j ) (4) Subsequently, we construct a weighted directed graph= (, φ) as follows. Let= (, …,), and= {() |, there exists a direct path betweenand}, and φ be a function:such that:whereare the prescribed weights, andis assigned to beif the value ofis detected to be zero. We then obtain a weighting matrixsuch that:

If we want to obtain a path plan to the destination from any given location, we need to reach the next station s next first. Then, we obtain a routing from the station s next to the destination s d . Also, we may form an updated version of the path within a given period T . In other words, we obtain a new path every T seconds.

In order to reach this goal, we first set the parameters α , β , γ , the period T , the destination station s d , the distances and the road quality between stations. Then together with the information about vehicle speeds and events between stations, which is obtained from the cloud server, we obtain a weighting matrix W for the paths between stations and thus we form a directed weighted traffic network. Now, when asked to give the optimum path to a destination s d , we first reach the next station s next and compute the desired path from s next to s d .

When the traffic network status changes at higher frequencies, a shorter period period T must be set. When we set T , we also need to consider the ability of the cloud servers. Normally, we can set T = 300 seconds. The values of the parameters α , β , γ are then prescribed according to practical needs. If we ignore the speeds of vehicles or the quality of the road, then α and γ can be set to be zero, respectively. Nornally, we must consider the distances, so the parameter β should be positive.