Humanoid traffic control robot

A traffic control device in the form of a humanoid character robot, doll or dummy is used to warn driver of danger ahead on the road. The device can be used on roads, streets and in other sites where there are moving vehicles. The robotic device informs drivers of impending danger by moving its arms and sounding an acoustic alarm. In this way the robot can simulate a policeman or road flagging operator. The device may also include speed detection and preferably speed indication means. The robot may make decisions based on the detected speed of a vehicle and the limit for the area in operating the arms and sound warning means. The robot may also be equipped with a camera or video. The robot may also be controlled wirelessly.

Dividing traffic sub-areas based on a parallel K-means algorithm

In order to alleviate the traffic congestion and reduce the complexity of traffic control and management, it is necessary to exploit traffic sub-areas division which should be effective in planing traffic. Some researchers applied the K-Means algorithm to divide traffic sub-areas on the taxi trajectories. However, the traditional K-Means algorithms faced difficulties in processing large-scale Global Position System(GPS) trajectories of taxicabs with the restrictions of memory, I/O, computing performance. This paper proposes a Parallel Traffic Sub-Areas Division(PTSD) method which consists of two stages, on the basis of the Parallel K-Means(PKM) algorithm. During the first stage, we develop a process to cluster traffic sub-areas based on the PKM algorithm. Then, the second stage, we identify boundary of traffic sub-areas on the base of cluster result. According to this method, we divide traffic sub-areas of Beijing on the real-word (GPS) trajectories of taxicabs. The experiment and discussion show that the method is effective in dividing traffic sub-areas.

A flow control methodology for the rapid generation of vehicle traffic models

This paper presents a conveyor-based methodology to model complex vehicle flows common to factory and distribution warehouse facilities. The AGV and human path modelling techniques available in many commercial discrete event simulation packages require extensive knowledge and time to implement even the simplest flow control rules for multiple vehicle interaction. Although discrete event simulation is accepted as an effective tool to model vehicle delivery movements, human paths and delivery schedules for modern assembly lines, the time to generate accurate models is a significant limitation of existing simulation-based optimisation methodologies. The flow control method has been successfully implemented using two commercial simulation packages. It provides a realistic visual representation, as well as accurate statistical results, and reduces the model development process cost.

Intelligent traffic light control of isolated intersections using machine learning methods

Traffic congestion is one of the major problems in modern cities. This study applies machine learning methods to determine green times in order to minimize in an isolated intersection. Q-learning and neural networks are applied here to set signal light times and minimize total delays. It is assumed that an intersection behaves in a similar fashion to an intelligent agent learning how to set green times in each cycle based on traffic information. Here, a comparison between Q-learning and neural network is presented. In Q-learning, considering continuous green time requires a large state space, making the learning process practically impossible. In contrast to Q-learning methods, the neural network model can easily set the appropriate green time to fit the traffic demand. The performance of the proposed neural network is compared with two traditional alternatives for controlling traffic lights. Simulation results indicate that the application of the proposed method greatly reduces the total delay in the network compared to the alternative methods.

Speed control and policing in a cellular mobile network: speedNet

In this paper, we describe SpeedNet, a GSM network variant which resembles an ad hoc wireless mobile network where base stations keep track of the velocities of mobile users (cars). SpeedNet is intended to track mobile users and their speed passively for both speed policing and control of traffic. The speed of the vehicle is controlled in a speed critical zone by means of an electro-mechanical control system, suitably referred to as VVLS (Vehicular Velocity Limiting System). VVLS is mounted on the vehicle and responds to the command signals generated by the base station. It also determines the next base station to handoff, in order to improve the connection reliability and bandwidth efficiency of the underlying network. Robust Extended Kalman Filter (REKF) is used as a passive velocity estimator of the mobile user with the widely used proportional and integral controller speed control. We demonstrate through simulation and analysis that our prediction algorithm can successfully estimate the mobile user’s velocity with low system complexity as it requires two closest mobile base station measurements and also it is robust against system uncertainties due to the inherent deterministic nature in the mobility model.

Mobility prediction based routing for minimizing control overhead in mobile ad hoc networks

Routing in ad hoc networks faces significant challenges due to node mobility and dynamic network topology. In this work we propose the use of mobility prediction to reduce the search space required for route discovery. A method of mobility prediction making use of a sectorized cluster structure is described with the proposal of the Prediction based Location Aided routing (P-LAR) protocol. Simulation study and analytical results of the of P-LAR find it to offer considerable saving in the amount of routing traffic generated during the route discovery phase.

A performance comparison of three wireless multi hop ad-hoc network routing protocols when streaming MPEG4 traffic

Mobile ad-hoc networks are characterised by constant topology changes, the absence of fixed infrastructure and lack of any centralised control. Traditional routing algorithms prove to be inefficient in such a changing environment. Ad-hoc routing protocols such as dynamic source routing (DSR), ad-hoc on-demand distance vector routing (AODV) and destination-sequence distance vector (DSDV) have been proposed to solve the multi hop routing problem in ad-hoc networks. Performance studies of these routing protocols have assumed constant bit rate (CBR) traffic. Real-time multimedia traffic generated by video-on demand and teleconferencing services are mostly variable bit rate (VBR) traffic. Most of these multimedia traffic is encoded using the MPEG standard. (ISO moving picture expert group). When video traffic is transferred over MANETs a series of performance issues arise. In this paper we present a performance comparison of three ad-hoc routing protocols - DSR, AODV and DSDV when streaming MPEG4 traffic. Simulation studies show that DSDV performs better than AODV and DSR. However all three protocols fail to provide good performance in large, highly mobile network environments. Further study is required to improve the performance of these protocols in mobile ad-hoc networks offering VBR services.

Speed control and policing in a cellular mobile network: SpeedNet

In this paper, we describe SpeedNet, a GSM network variant which resembles an ad hoc wireless mobile network where base stations (possibly other vehicles in the network) keep track of the velocities of mobile users (cars). SpeedNet is intended to track mobile users and their speed passively for both speed policing and control of traffic. The speed of the vehicle is controlled in a speed critical zone by means of an electro-mechanical control system, suitably referred to as VVLS (vehicular velocity limiting system). VVLS is mounted in the vehicle and responds to the command signals generated by the base station. It also determines the next basestation to handoff, in order to improve the connection reliability and bandwidth efficiency of the underlying network. Robust extended Kalman filter (REKF) is used as a passive velocity estimator of the mobile user with the widely used proportional and integral controller speed control. We demonstrate through simulation and analysis that our prediction algorithm can successfully estimate the mobile users velocity with low system complexity as it requires two closet mobile-base station measurement and also it is robust against system uncertainties due to the inherent deterministic nature in the mobility model.

Applying fair intelligent congestion control in a hybrid QoS architecture for wireless environment

This simple and scalable Differentiated Services (DiffServ) QoS control model is acceptable for the core of the network. However, more explicit and stringent admission and reservation based QoS mechanisms are required in the wireless access segment of the network, where available resources are severely limited and the degree of traffic aggregation is not significant, thus rendering the DiffServ principles less effective. In this paper we present a suitable hybrid QoS architecture framework to address the problem. At the wireless access end, the local QoS mechanism is designed in the context of IEEE 802.11 WLAN with 802.11e QoS extensions. At the edge and over the DiffServ domain, the Fair Intelligent Congestion Control (FICC) algorithm is applied to provide fairness among traffic aggregates and control congestion at the bottleneck interface between the wireless link and the network core.

A study on look-ahead control and energy management strategies in hybrid electric vehicles

Fuel efficiency in a hybrid electric vehicle requires a fine balance between usage of combustion engine and battery power. Information about the geometry of the road and traffic ahead can have a great impact on optimized control and the power split between the main parts of a hybrid electric vehicle. This paper provides a survey on the existing methods of control and energy management emphasizing on those that consider the look-ahead road situation and trajectory information. Then it presents the future trends in the control and energy management of hybrid electric vehicles.

Simulation-based learning for control of complex conveyor networks

This research investigated the problem of path planning in complex conveyor networks. A reinforcement learning approach was applied to derive a control strategy for routing traffic. The derived strategy was verified in real world systems and was found to improve network performance by prioritising traffic flows and balancing network load.

Multimedia stream rate control over MANET based on router feedback

Streaming applications over Mobile Ad-hoc Networks (MANET) require a smooth transmission rate. The Internet is unable to provide this service during traffic congestion in the network. Designing congestion control for these applications is challenging, because the standard TCP congestion control mechanism is not able to handle the special properties of a shared wireless multi hop channel well. In particular, the frequent changes to the network topology and the shared nature of the wireless channel pose major challenges. In this paper, we propose a novel approach, which allows a quick increase of throughput by using explicit feedback from routers.

Video driven traffic modelling

We propose Video Driven Traffic Modelling (VDTM) for accurate simulation of real-world traffic behaviours with detailed information and low-cost model development and maintenance. Computer vision techniques are employed to estimate traffic parameters. These parameters are used to build and update a traffic system model. The model is simulated using the Paramics traffic simulation platform. Based on the simulation techniques, effects of traffic interventions can be evaluated in order to achieve better decision makings for traffic management authorities. In this paper, traffic parameters such as vehicle types, times of starting trips and corresponding origin-destinations are extracted from a video. A road network is manually defined according to the traffic composition in the video, and individual vehicles associated with extracted properties are modelled and simulated within the defined road network using Paramics. VDTM has widespread potential applications in supporting traffic decision-makings. To demonstrate the effectiveness, we apply it in optimizing a traffic signal control system, which adaptively adjusts green times of signals at an intersection to reduce traffic congestion.

Drivers display anger-congruent attention to potential traffic hazards

Previous research has suggested that angry drivers may respond differently to potential hazards. This study replicates and extends these findings. Under simulated driving conditions, two groups of drivers experienced conditions that would either increase angry mood (N=12; men =6) or not (control group, N =12; men=6). All drivers then performed a neutral drive, during which they encountered a number of traffic events not experienced in the initial drive. These included vehicles emerging from driveways into their path and jaywalking pedestrians. Subjective anger, eye-movement behaviour and driving behaviours (speed and reaction times) were measured as drivers drove. Subjective moods (Profile of Mood States) were assessed before and after each drive. Anger-provoked drivers reported reliably higher increases in angry mood when compared with the control group after the initial drive, and these increases remained stable across the subsequent neutral drive. During the neutral drive, anger provoked drivers demonstrated evidence of more heuristic style processing of potential hazards, with shorter initial gazes at less apparent hazards and longer latencies to look back at jaywalking pedestrians obscured by parked vehicles. Anger-provoked drivers also took longer to make corrective actions to avoid potential collisions. It is concluded that anger-provoked drivers may initially make more superficial assessments of certain driving situations and consequently underestimate the inherent risk.