## 3 Results and Discussion
### The Most Influential Documents
The results show a great reduction in vehicle fuel consumption and emissions from these velocity profiles compared with those from a typical velocity profile of vehicles without velocity planning. In the paper from [PERSON] et al. [81], an adaptive traffic signal time manipulation algorithm is proposed on single and multiple road intersections by using a wireless sensor network (WSN). A traffic system communication algorithm (TSCA) and a traffic signal time manipulation algorithm (TSTMA) are implemented to provide the system with adaptive and efficient traffic estimation represented by the dynamic change in the traffic signals' flow sequence and traffic variation. The results of simulated scenarios and a real implementation show the efficiency of the system in solving traffic congestion by reducing the average waiting time and the average queue length both for isolated intersections and multiple intersections. [PERSON] et al. [82] present a traffic-light-to-vehicle communication (TLVC) system that has the potential to reduce the environmental impact of traffic flow by helping drivers to avoid braking and accelerating maneuvers at traffic lights. They used a detailed emission model to identify key factors influencing TLVC and evaluate the level of detail required for the different simulation components. The results of a sensitivity analysis identify the gear choice and the distance from the traffic light at which vehicles are informed as key influencing factors. The authors generated the driving cycles using a microscopic traffic simulator, using a real-world inner-city road network, in which they integrated the speed-adaptation algorithm as well as a communication model. The work highlights that the driver's behavior, e.g., gear choice and compliance, plays an important role in the impact advantage of the TLVC on the environment. [PERSON] et al. [83] propose an adaptive traffic light control algorithm to minimize the average waiting times at intersections and optimize the traffic throughput. Three main steps compose the algorithm: the vehicle detection, the green light sequence determination and the light length determination. The algorithm performances are affected by the traffic information detected for each vehicle traversing the intersection (e.g., distance, speed, vehicle's length). The simulation results show that the proposed algorithm can achieve higher throughput and lower average waiting time compared to a fixed-time control algorithm and an actuated control algorithm. [PERSON] et al. [84] studied how to estimate real-time queue lengths at signalized intersections using intersection travel times collected from mobile traffic sensors. The estimation methodology is based on the assumption that critical pattern changes of intersection travel times or delays indicate signal timing or queue length changes. By detecting these critical points, the real-time queue length can be estimated with a reverse thinking process. The model and algorithm were tested in a field experiment and in a simulation environment. [PERSON] et al. [85] evaluated a novel software (SignalGuru) that, by collecting mobile phone probes, detects and predicts traffic signal schedules. The data collected allow the drivers to adjust their speed arriving at the end of the signalized intersection when the signal ahead will turn green. To use SignalGuru, the phones must be mounted on the car windshield in order to detect current traffic signals with their cameras, collaboratively communicate and learn traffic signal schedule patterns and predict their future schedule. Results from two applications of SignalGuru in Cambridge (MA, USA) and Singapore show that traffic signal schedules can be predicted accurately. [PERSON] et al. [86] present a green light optimized speed advisory (GLOSA) system to reduce traffic congestion by decreasing the average stop time behind traffic lights. The authors intended to evaluate the impacts of GLOSA on both fuel consumption and CO2 emissions.