Narrow Results

In order to improve the efficiency of traffic control system in the era of big data, a new variable cycle control model based on multi-source information is presented for intersection in this paper. Firstly, with consideration of multi-source information, a unified framework based on cyber-physical system is proposed. Secondly, taking into account the variable length of cell, hysteresis phenomenon of traffic flow and the characteristics of lane group, a Lane group-based Cell Transmission Model is established to describe the physical properties of traffic flow under different traffic signal control schemes. Thirdly, the variable cycle control problem is abstracted into a bi-level programming model. The upper level model is put forward for cycle length optimization considering traffic capacity and delay. The lower level model is a dynamic signal control decision model based on fairness analysis. Then, a Hybrid Intelligent Optimization Algorithm is raised to solve the proposed model. Finally, a case study shows the efficiency and applicability of the proposed model and algorithm.

Mainline freeway traffic flow control is one of the primary methods of traffic management, which can present the best network situation. In this paper, we integrate variable speed limit (VSL) strategy into the cell transmission model (CTM). Then the implementation of the integrated model on freeway traffic network is discussed. A novel optimal model of controlling freeway traffic flow is proposed for minimizing the total travelling time in the network. A solution algorithm is designed by using a simulation method. Considering the main purpose of the speed limit strategy is to control the mainstream flow, we compare the case where the VSL is used with the one without VSL. A simulation is implemented to show that the control strategy is efficient in describing system’s dynamic performance and the dynamic speed limit strategy significantly alleviates congestion.

Wireless Multimedia Sensor Networks (WMSNs) are mostly affected by bottleneck issues, high packet loss, increased delay, and minimum throughput. One of the most effective schemes towards controlling the bottleneck on the web is traffic control. The WMSNs handle different types of data, hence QoS is essential to afford trustworthy as well as reasonable services towards these kinds of data. The existing congestion control methods, FTLP and FEWPBRC consider the frequency of packet transmission and decide on the output transmission rate of the sink. In the Fuzzy-Based QoS Alert Bottleneck Mechanism, the probability of congestion is predicted by using a fuzzy inference system with three special congestion indicators, and the traffic rate is adjusted based on the priority of the real-time and non-real-time applications. The FBQACC is simulated using the NS2 simulator and it gives an improvement in the average throughput FTLP and FEWPBRC by 7.1499% and 6.3327%, respectively. Similarly, FBQACC reduces average delay compared to FTLP and FEWPBRC by 11.074% and 7.8128%, respectively. The proposed work also gives a minimized average packet loss percentage compared to the existing congestion control methods.

In many countries, energy-saving and emissions mitigation for urban travel and public transportation are important for smart city developments. It is essential to understand the impact of smart transportation (ST) in public transportation in the context of energy savings in smart cities. The general strategy and significant ideas in developing ST for smart cities, focusing on deep learning technologies, simulation experiments, and simultaneous formulation, are in progress. This study hence presents simultaneous transportation monitoring and management frameworks (STMF ). STMF has the potential to be extended to the next generation of smart transportation infrastructure. The proposed framework consists of community signal and community traffic, ST platforms and applications, agent-based traffic control, and transportation expertise augmentation. Experimental outcomes exhibit better quality metrics of the proposed STMF technique in energy saving and emissions mitigation for urban travel and public transportation than other conventional approaches. The deployed system improves the accuracy, consistency, and F-1 measure by 27.50%, 28.81%, and 31.12%. It minimizes the error rate by 75.35%.

This paper addresses the issue of managing the traffic flow at traffic-light controlled junctions adopting a mixed, nonlinear programming model. More precisely, we adopt an adaptive, acyclic control system based on the genetic algorithm (GA), a system that allows to accurately decide in real time on the light switching pattern that minimizes the traffic waiting time. Equally importantly, the inherent crossroads constraints, namely, the security of their users and the structure thereof, also referred to as the antagonism principle, are considered in the proceeding of this method.

The implementation of this model on two crossroads significantly decreases the waiting time and regulates the traffic flow. Therefore, the adaptive control system based on the GA proves efficient compared to the nonadaptive system.

The urban expressway network is one of the city’s major traffic arteries. However, the urban road network near the overpass is the most congested area. Improving the traffic situation in this area has become an important issue. This paper presents a new real-time traffic estimation and prediction system, i.e., DynaCHINA which reproduces the traffic propagation by simulating the vehicle movement on the network. The improved mesoscopic traffic flow models are the key technologies that include some models such as static queuing model and the vehicle movement model. Moreover, a coordinated control approach is proposed to determine the traffic states by maximizing the total traffic flow of the network in the simulation period. Also, the variable speed limits can be implemented near the overpass area. The results demonstrated the feasibility of the models. In some cases, the proposed variable speed limits is an effective way to alleviate the traffic congestion in urban road networks.