Developing public transportation and giving priority to buses is a feasible solution for improving the level of public transportation service, which facilitates congestion alleviation and prevention, and contributes to urban development and city sustainability. This paper presents a novel bus operation control strategy including both holding control and speed control to improve the level of service of transit systems within a connected vehicle environment. Most previous work focuses on optimization of signal timing to decrease the bus signal delay by assuming that holding control is not applied; the speed of buses is given as a constant input and the acceleration and deceleration processes of buses can be neglected. This paper explores the benefits of a bus operation control strategy to minimize the total cost, which includes bus signal delay, bus holding delay, bus travel delay, acceleration cost due to frequent stops and intense driving. A set of formulations are developed to explicitly capture the interaction between bus holding control and speed control. Experimental analysisand simulation tests have shown that the proposed integrated operational model outperforms the traditional control, speed control only, or holding control only strategies in terms of reducing the total cost of buses. The sensitivity analysis has further demonstrated the potential effectiveness of the proposed approach to be applied in a real-time bus operation control system under different levels of traffic demand, bus stop locations, and speed limits.

https://www.mdpi.com/2071-1050/8/11/1170/pdf

Designing Sustainable Public Transportation: Integrated Optimization of Bus Speed and Holding Time in a Connected Vehicle Environment

In this paper, a class of neutral stochastic functional evolution equations with infinite delay is investigated. Under some suitable assumptions, the existence and uniqueness of quadratic mean almost periodic mild solutions for these equations are discussed by means of semigroups of operators and the fixed point method. Moreover, two examples are given to illustrate our results.

On almost periodic mild solutions for neutral stochastic evolution equations with infinite delay

Real-time traffic signal control is an integral part of an urban traffic control system. It can control traffic signals online according to variations of traffic flow. In this paper we propose a new method for a real-time traffic signal control system. The system uses a cellular automaton model and a Bayesian network model to predict probabilistic distributions of standing vehicles, and uses particle swarm optimization to calculate the optimal traffic signals. A simulation based on real traffic data was carried out to show the effectiveness of the proposed CAPSOBN real-time traffic signal control system using a micro traffic simulator. © 2012 Wiley Periodicals, Inc. Electron Comm Jpn, 96(1): 1–13, 2013; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.11436

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecj.11436

Real-time traffic signal control for optimization of traffic jam probability

Intelligent Transportation Systems (ITS) and Advanced Traveller Information Systems (ATIS) are the emerging areas of research. They focus with keen interest to solve the issues in traffic management and planning and designing infrastructure to meet the demanding needs of the general public. Many research articles focus on developing video surveillance algorithms for processing video data captured at real-time traffic scenes, but as there is a huge demand for more sophisticated software systems, complexity of the algorithms gets increased in terms of data storage and large scale processing. This research article focuses on refining a framework for large scale video analytics while incorporating the simple, light-weight aspects of a video surveillance algorithm, and makes an insight by adopting blob tracking based video surveillance algorithm for large scale video analytics. The proposed system uses hadoop’ map-reduce function to clean and pre-process the hours of traffic video captured in the local site stores. It summarizes and transmits the key frames of the video data to the central computing server to analyses the video frames. The key frame differencing method has been justified as a pronouncing method for data preprocessing and cleaning. Further this system follows the Blob detection, Identification and Tracking using connected components algorithm to determine the correlation between the vehicles moving in the real road scene.

/pdf/an-intelligent-video-surveillance-framework-with-big-data-iorzix0rfe.pdf

An Intelligent Video Surveillance Framework with Big Data Management for Indian Road Traffic System

Actually the urban traffic is the main pollution generator in most of the crowded cities. Thus, the traffic management and the enhancements of the transportation efficiency should be the target for any municipality. The intelligent management systems for transports bring along with the evaluation from the eco-efficiency perspective, a walk-through the two most used methods of improving the road traffic with direct impact in fuel savings and consequently to the road network efficiency. The authors has deeply studied the road traffic management technologies and realised into this article an analysis of the two main methods, applied separately, looking test the improvements and the impact of fuel consumption and CO 2 emissions reduce. The first analysed method - the static electronic management system - analyses the road classic electronically signalization systems. The second method, a dynamic optimisation model, analyses a intelligent traffic management system which uses real time adaptive technologies. As case study, the authors has researched by results of an already implemented system - Bucharest Traffic Management System or BTMS, in Bucharest, Romania, by comparing data before and after the system was implemented, considering similar general conditions (road geometry and traffic load).

Analisys of improvements the urban transport conditions by using electronic intelligent transports systems - Case study: Urban transportation

Real-time traffic signal control is an integral part of urban traffic control system. It can control traffic signals online according to variation of traffic flow. In this paper, we propose a new method for the real-time traffic signal control system. The system uses a Cellular Automaton model and a Bayesian Network model to predict probabilistic distributions of standing vehicles, and uses a Particle Swarm Optimization method to calculate optimal traffic signals. A simulation based on real traffic data was carried out to show the effectiveness of the proposed real-time traffic signal control system CAPSOBN using a micro traffic simulator.

Optimization of urban logistics terminal distribution based on cellular automaton model

In this article, we propose a new method of traffic signal control based on the predicted distribution of traffic jams. First, we built a forecasting model to predict the probability distribution of vehicles being in a traffic jam during each period of the traffic signals. A dynamic Bayesian network was used as the forecasting model, and this predicted the probability distribution of the number of standing vehicles in a traffic jam. According to calculations by the dynamic Bayesian network, a prediction of the probability distribution of the number of standing vehicles at each time will be obtained, and a control rule to adjust the split and cycle of the signals to maintain the probability of a lower limit and a ceiling of standing vehicles is deduced. Through a simulation using the actual traffic data of a city, the effectiveness of our method is shown.

Traffic signal control based on a predicted traffic jam distribution

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Real-time traffic signal control for optimization of traffic jam probability

Real-time traffic signal control for optimization of traffic jam probability

Optimization of urban logistics terminal distribution based on cellular automaton model

Traffic signal control based on a predicted traffic jam distribution

Optimization of urban logistics terminal distribution based on cellular automaton model