Traffic congestion is a critical concern in most cities. Inefficient traffic control wastes time and fuel, and causes harmful carbon emissions, road accidents, and many economic problems. This paper proposes a cooperative traffic control framework for optimizing the global throughput and travel time for multiple intersections. Adjacent intersections are considered in analyzing their joint passing rates and attempting to maximize the number of vehicles traveling through a road network. The proposed framework provides fairness for each road segment and realizes the green wave concept for arterial roads. This paper extends previous studies by considering the passing rates of continuous road segments and coordinating traffic signals of multiple intersections. The simulation results show that the approach outperforms existing schemes in that it achieves a high global throughput, reduces the average waiting time, lowers the total travel time, and decreases average CO ₂ emissions. To verify the feasibility of the proposed framework, a wireless access in vehicular environments/dedicated short-range communications-based prototype for lane-level dynamic traffic control is designed and implemented.

In this paper, we design a driver behavior monitoring and warning (DBMW) framework to detect dangerous driving for enhancing road safety through the Internet of Vehicles (IoV). The designed DBMW framework applies onboard image sensors and wearable devices to detect the deviation degree of vehicles and trace the head motion of drivers, respectively. According to our review of relevant research, DBMW is the first framework for driver behavior monitoring and warning that provides the following features: 1) DBMW can keep recognizing the located lane lines and estimating the power spectral density of lane deviation for a vehicle through image sensors, 2) DBMW can keep monitoring driver behaviors and measuring the anomaly level of a driver through wearable devices, and 3) DBMW can instantly send the warning messages of potential dangerous driving to neighboring vehicles and nearby pedestrians through IoV communications as necessary, which makes vehicles and pedestrians be aware of the existence of surrounding dangerous drivers in advance to keep alerting and avoid potential accidents/collisions. In particular, the prototype consisting of an Android-based sensing unit and an Arduino-based wearable device is implemented to verify the feasibility and superiority of DBMW. Experimental results show that DBMW outperforms existing methods and can significantly improve the detection accuracy and false alarm rates of dangerous driving behaviors.

This paper presents a centimeter-grade metropolitan positioning (CGMP) framework for achieving lane-level localization through Internet of Vehicles (IoV) communications. The CGMP framework integrates vehicle-to-roadside (V2R) and vehicle-to-vehicle (V2V) communications with installed roadside/intersection cameras to provide positioning information to vehicles on the road. This framework can be used to estimate the precise position of a vehicle and detect the lane of each vehicle. The estimated positions and detected lanes can be broadcast to nearby vehicles through V2R and V2V communications. According to our review of relevant research, CGMP is the first positioning system to provide the following features: 1) centimeter-grade positioning accuracy through IoV communications that can detect a vehicle's current lane; 2) the employment of existing roadside/intersection cameras, thus keeping additional construction costs low; 3) an innovative infrastructure for the application of next-generation intelligent transportation systems that can enable lane-level traffic control, collision avoidance, and vehicle navigation. A WAVE/DSRC-based prototype is implemented to verify the feasibility and effectiveness of the proposed framework. The experimental results indicate that fully-deployed CGMP can achieve centimeter-grade positioning accuracy with the error around 10 cm and partially-deployed CGMP can significantly reduce the positioning errors of existing localization methods.

In this paper, we propose a time-efficient indoor navigation and evacuation (TINE) framework to minimize moving time for mobile users based on Internet of Things (IoT) technologies. In normal time, the proposed TINE framework can estimate the density of mobile users in each area and determine the moving speeds to pass through different areas. Based on the determined moving speed of each area, an indoor navigation path can be planned to provide the shortest moving time for a mobile user. In emergent time, TINE can accurately estimate the escaping time for groups of mobile users by jointly considering the length and moving time of passageways, the capacity of passageways/doors/exits, the present distribution and parallel moving of mobile users, and the possible congestion caused by other groups. Based on the estimated escaping time, TINE can efficiently alleviate the congestion of all passageways/exits and evenly distribute the evacuation load among exits to minimize the total escaping time. According to our review of relevant research, this is the first solution that can both provide the fastest navigation path to arbitrary target places and evacuate all groups of mobile users to safe places in the shortest escaping time. Simulation results show that TINE outperforms existing schemes and can significantly reduce the total walking and escaping times of indoor navigation and evacuation, respectively. In particular, an Android-based prototype with iBeacon IoT localization is implemented to verify the feasibility of our TINE system.

This paper proposes a group-based framework with dedicated path planning for emergency guiding based on Internet of Things (IoT) technologies. The proposed framework can model the spatiotemporal mobility of indoor people to determine and relieve the congestion of corridors and exits. A dedicated path can be determined to provide the shortest evacuation time for each group of nearby people. The corridor and exit capacities, corridor lengths, clustering motion of a group, concurrent moving of different groups, and up-to-date distribution of group people are considered together to accurately estimate the evacuation time for each group. Based on the estimated evacuation time, evacuation load can be evenly distributed among corridors and exits to alleviate the congestion of all corridors and exits for minimizing total evacuation time. The performance of the proposed framework is evaluated by conducting mathematical analysis and computer simulations, which outperforms existing schemes and can achieve the shortest evacuation time for group-based emergency guiding. In addition, an Android-based prototype with indoor IoT localization technologies is implemented to verify the feasibility of our framework.

We consider vehicular sensor networks (VSNs) consisting of a large number of sensor nodes deployed on vehicles to facilitate vehicular applications. We try to apply such VSNs to preventing rear-end collisions that are common accidents due to sharp stops. An infrastructure-less framework is proposed, which only relies on vehicles' onboard sensors to prevent such accidents. The proposed framework consists of a distributed warning protocol and a location-based backoff scheme. Vehicle-to-vehicle communications is used to form warning groups, where a warning group is a set of vehicles that drive along the same lane and every pair of adjacent cars is within a certain distance. Only single-hop transmissions are needed to join and leave a group, thus keeping the group maintenance overhead low. When a sudden brake event is detected in a warning group, the location-based backoff scheme can quickly propagate warning messages among its group members. Simulation results show that the proposed approach outperforms existing schemes.