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In October 2010, a special issue on “Recent Advances in Traffic Control and Management” was published in the Journal of Advanced Transportation [1]. In past years, great strides have been made continuously in traffic control and management for alleviation of traffic congestion in urban areas [2-5]. New approaches have been developed to augment and improve existing ones, or to seek new modeling paradigms for mixed traffic or heterogeneous vehicles in urban areas [6, 7]. It is timely to take inventory of what we have developed in recent years and provide a preview of what lies ahead for further research on this important topic.

Traffic control and management are generally considered as economically, financially, and environmentally sustainable measures for alleviation of traffic congestion in major cities. Promoting traffic control and management has long been one of the top priorities for both developed and developing countries. The performance of traffic control and management is usually evaluated in terms of a number of important attributes such as cost, time, reliability, safety and emission, ranging from a more localized perspective of “efficiency” to a wider perspective of “effectiveness”. An effective and efficient traffic control and management system benefits a society at large by reducing fuel consumption, preserving the environment, fostering development, reducing traffic congestion, and improving safety. However, various impacts of traffic control and management measures are difficult to be quantified [8]. This special issue is aimed to cover some recent methodological advances in traffic control and management. Seven papers are collected in this special issue, as summarized in the paragraphs that follow.

Ren et al. propose an integrated model for determining flows on emergency evacuation routes and traffic signals at intersections when the background travel demands are uncertain under evacuation condition. They adopt a bi-objective bi-level programming approach to optimize traffic signal system, subject to various restraints; the upper-level sub-program seeks to minimize the total travel time of evacuation flows and performance index of the whole network, whereas the lower-level sub-program is a logit-based stochastic user equilibrium assignment model with background demand constraint. A heuristic solution method based on Genetic Algorithm is used to solve the bi-level program. A case study on the Jianye network around the Nanjing Olympics Sports Center in Nanjing, China, shows the applicability of the proposed model and solution method.

Yao investigates the impacts of short left-turn lanes on isolated signalized intersections and proposes joint optimization models for optimizing the signal cycle length and the short left-lane length under different scenarios. Case study is carried out on the basis of the field data collected in Dalian, China, for assessing the impacts of the signal cycles and short left-turn lanes under different flow conditions.

Chiou and Huang propose an approach of using stepwise genetic fuzzy logic controller for optimizing signal control at sequential intersections under different signal coordinated systems (i.e., simultaneous, progressive, alternate, and independent signal control systems). In the proposed approach, a modified cell transmission model is presented for capturing the behaviors of cars and motorcycles at signalized intersections under mixed traffic conditions. Case study in Changhua City of Taiwan is conducted to estimate the impacts of optimized green time extensions on traffic throughput and queue lengths of cars and motorcycles under different signal coordinated systems.

Kattan and Saidi review recent advances and prospective research on ramp metering approaches for minimizing the system travel times including both the travel time on the freeway and the waiting time on the ramps over an extended horizon. A Quadstone PARAMICS micro-simulation model is used to evaluate the performance of three ramp metering approaches, namely, probe-based, detector-based, and pre-timed ramp metering algorithms. A comparative analysis of the three ramp metering approaches is carried out on an 8-km freeway section on Highway No. 2 in Calgary, Alberta, Canada. It indicates that the probe-based ramp metering approach consistently outperformed the other two existing algorithms in terms of the performance measures, even the probe vehicle penetration rates are reduced to 3% in this case study.

It follows with a paper prepared by Chen et al. proposing alternative capacity reliability measures for assessing the adequacy of a degradable transportation network. Three different concepts of capacity (i.e., reserved capacity, ultimate capacity, and practical capacity) are used to develop three respective models for evaluating alternative capacity reliabilities of the transportation network. It is noted that the reserve capacity model can only capture the changes in traffic demand volume by uniformly scaling all origin-destination pairs with the same multiplier. It implies that the demand pattern is fixed. However, the practical capacity model estimates how much more traffic demand volume that can be added to a fixed demand pattern by allowing the additional demand to deviate from the fixed demand pattern. On the other hand, the ultimate capacity model determines the maximum network capacity by allowing all users in the network to choose their destinations and routes. As a result, both the practical capacity and ultimate capacity models can be used to assess the changes in demand volume and variations in demand pattern.

The last two papers are concerned with the fundamental relationships between travel time and traffic flow, speed, and density for evaluation of the network performance. These are the basic inputs for assessing the impacts of different traffic control and management measures. Long et al. propose two categories of route travel time models—one based on route cumulative traffic flow curves and the other one based on link cumulative traffic flow curves. It is proved that all the proposed models can satisfy some desirable properties such as route first-in-first-out and continuity. Finally, Wang et al. present a stochastic speed-density model to capture the effects of traffic fluctuation and stochastic congestion in reality. It can be used to perform real-time probabilistic prediction of traffic congestion once the relevant traffic data are detected on real-time basis.

Owing to the diversity of research on traffic control and management, the papers presented in this special issue are by no means exhaustive. However, they do provide general coverage of various important areas of recent methodological advances on this subject. This issue will bring the latest state-of-the-art methodologies for modeling the impacts of various traffic control and management measures to the attention of practicing engineers and researchers. The editor hopes that it will inspire and stimulate new research opportunities and efforts for further extensions in the field.

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