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In this paper, two new adaptive traffic signal control algorithms are proposed based on data from probe vehicles to realize the coordinated signal control of arterial roads. One is an iterative signal control algorithm, and the other is an optimized signal control algorithm. The proportion of vehicles in the nonstop group is selected as the indicator of the traffic state. The value for this indicator can be accurately estimated by data from probe vehicles. Our goal is to ease traffic congestion and enhance the capacities of traffic networks. Compared with the Webster fixed-time signal control algorithm, these two new adaptive signal control algorithms are evaluated on a microscopic simulation platform. Simulation results show that the average travel time is reduced by 32% under the iterative signal control algorithm and by 23% under the optimized signal control algorithm, and the average delay times are reduced by 36% and 35%, respectively. In the meantime, the average number of stops under the iterative signal control algorithm is reduced by 43%, and under the optimized signal control algorithm, by 67%. They indicate that the two new adaptive signal control algorithms are effective for easing traffic congestion and achieve the adaptive signal control objectives using real-time traffic information. [ABSTRACT FROM AUTHOR]

Traffic prediction is critical for the success of intelligent transportation systems (ITS). However, most spatio-temporal models suffer from high mathematical complexity and low tune-up flexibility. This article presents a novel spatio-temporal random effects (STRE) model that has a reduced computational complexity due to mathematical dimension reduction, with additional tune-up flexibility provided by a basis function capable of taking traffic patterns into account. Bellevue, WA, was selected as the model test site due to its widespread deployment of loop detectors. Data collected during the 2 weeks of July 2007 from 105 detectors in the downtown area were used in the modeling process and traffic volumes predicted for 14 detectors for the entire month of July 2008. The results show that the STRE model not only effectively predicts traffic volume but also outperforms three well-established volume prediction models, the enhanced versions of autoregressive moving average (ARMA) and spatiotemporal ARMA, and artificial neural network. Even without further model tuning, all the experimental links produced mean absolute percentage errors between 8% and 16% except for three atypical locations. Based on lessons learned, recommendations are provided for future applications and tune-up of the proposed STRE model. [ABSTRACT FROM PUBLISHER]

Optimization of traffic network performance using dynamic traffic management (DTM) measures can be viewed as a specific example of solving a network design problem (NDP). Decision variables are the specific settings of DTM measures. DTM measures have been identified as powerful instruments not only to increase network efficiency, but also to improve externalities. As a result, in the optimization the focus is not only on efficiency, but also on climate, air quality, traffic safety, and noise. These assessment criteria are determined using the output of a dynamic traffic assignment model. This results in a dynamic multi-objective NDP, which is solved as a bilevel optimization problem; it results in a Pareto optimal set. This set provides valuable information for the decision-making process, which would not have been available if the compensation principle would have been chosen in advance. Knowledge obtained by optimization of realistic cases can be used to attain knowledge about incorporation of externalities as an objective when optimizing traffic systems using DTM measures. A case study for a realistic network of the city of Almelo shows that the objectives efficiency, climate, and air quality are mainly aligned and mainly opposed to traffic safety and noise. However, there is not a single solution that optimizes all three aligned objectives. Based on the Pareto optimal set, the trade-offs are determined and using cluster analysis the solutions and results are further analyzed for network segments. [ABSTRACT FROM AUTHOR]

Travel time for an itinerary constitutes one of the most relevant roadway traffic metrics. Numerous studies have been conducted to estimate travel times on the basis of data from loop detectors. This article focuses on evaluating the performance of a set of estimation methods—algorithms that estimate route travel times using specific speed data from dual loop detectors for real-time applications, such as displaying travel times on changeable message signs. The travel-time estimates are evaluated against probe vehicle data obtained from FasTrak in the San Francisco Bay Area to determine the accuracy of the estimates. The study reveals that (a) the dispersion of travel time is often larger in off-peak periods than in peak periods for heavily congested freeways; (b) assessed against the same set of speed data, the evaluated algorithms have similar performance; (c) the accuracy of travel-time estimates on the basis of loop-detector data is better in off-peak periods than in peak periods; (d) lane-by-lane speeds from loop detectors may be used to improve travel time estimation; and (e) travel time estimation quality is more sensitive to actual sensor locations if sensors are sparse. [ABSTRACT FROM AUTHOR]

Usually, benefits from deploying an adaptive traffic control system are measured by comparing data collected in the field before the system is installed with those collected after the system is fully operational. Such an approach is costly because it requires installation of a system before potential benefits can be observed. Nowadays to investigate effectiveness of many alternatives that were traditionally investigated in field, microsimulation is used. However, how much can one rely on results from microsimulation? There has not been a single study in which benefits of adaptive traffic control systems measured in the field are compared with those obtained through microsimulation. This study presents such a research effort. The authors compared performance measures from a field evaluation of Sydney Coordinated Adaptive Traffic System in Park City, Utah, to their counterparts from microsimulation. They collected a significant amount of data to assess system performance in the field and to help build a microsimulation model. A 14-intersection model of the Park City network was developed, calibrated, and validated on the basis of multiple data sources. The results show that a validated microsimulation model can accurately reflect field conditions although such an effort can be very challenging. [ABSTRACT FROM AUTHOR]

Traffic incidents are a major contributor to congestion on US freeways, resulting in millions of dollars in property damage and significant deaths and injuries each year. Because a single incident can cause traffic delays, equally devastating secondary incidents can result. Thus, the faster an incident is detected, verified, and cleared, the less significant the impact. Traffic cameras and other technologies are widely used for this purpose. This work examined the effectiveness of traffic cameras at five metropolitan freeway sites in South Carolina using the traffic simulation software PARAMICS to simulate various incident scenarios and traffic camera operations through application programming interfaces. The authors used these interfaces to produce random spatial and temporal occurrence of incidents, including the incident start times, durations and locations. A benefit-cost analysis based on the simulation results suggested traffic cameras returned $12 for every dollar spent under the prevailing conditions at the study sites. A sensitivity study, varying different benefit- and cost-related parameters, such as deployment costs, number of incidents, and discount rate, revealed benefit-cost values that were always above one. [ABSTRACT FROM AUTHOR]

Flow of traffic on freeways and limited access highways can be represented as a series of kinemetic waves. Solutions to these systems of equations become problematic under congested traffic flow conditions, and under complicated (real-world) networks. A simplified theory of kinematics waves (KWaves) was previously proposed. Simplifying elements includes translation of the problem to moving coordinate system, adoption of triangular speed-density relationships, and adoption of restrictive constraints at the on- and off-ramps. However, these simplifying assumptions preclude application of this technique to most practical situations. By directly addressing the limitations of the original theory, this article proposes a simplified Kwaves model for network traffic (N-KWaves). Several key constraints of the original theory are relaxed. For example, the original merge model, which gives full priority to on-ramp traffic, is relaxed and replaced with a capacity-based weighted queuing (CBWFQ) merge model. The original diverge model, which blocks upstream traffic as a whole when a downstream queue exceeds the diverge, is also relaxed and replaced with a contribution-based weighted splitting (CBWS) diverge model. Based on the above, the original theory is reformulated and extended to address network traffic. Central to the N-KWaves model is a five-step computational procedure based on a generic building block. It is assumed that a freeway network can be represented by the combination of some special cases of the generic building block. An empirical field study showed satisfactory results. The N-KWaves model is best suited for modeling traffic operation in a regional freeway network and has a strong connection to Intelligent Transportation Systems (ITS). [ABSTRACT FROM AUTHOR]

Video object tracking is an important method of traffic detection in Intelligent Transportation Systems. In video traffic tracking systems the matching method is often used to find the position of moving objects. In this article an improved algorithm of corner feature extraction is presented and corner points are tracked as the feature points of traffic objects. The tracking precision is mainly decided by matching algorithms. If the matching is not accurate, good tracking results cannot be achieved. In this article Kalman Filtering is used to track the moving traffic objects. In this system two kinds of data are used: One is from the general matching algorithm, which is the representation of the target's position; the other is detected by a spatial filtering velocimeter, containing the rough flow velocity of the targets. Though neither kind of data are highly accurate, Kalman Filtering is capable of integrating both position and velocity data to obtain better tracking results. [ABSTRACT FROM AUTHOR]

This article presents a discrete-event stochastic microscopic simulation model specifically developed to evaluate the operational performance of toll plazas. The model has been calibrated, validated, and applied to toll plazas equipped with Electronic Toll Collection (ETC) in Orlando, Florida. Traffic behavior is represented using a set of mathematical and logic algorithms that control the conflicts among vehicles within the toll plaza area. Modified versions of Car-Following and Lane-Changing Algorithms and a new Toll-Lane Selection Algorithm are integrated into this new model to simulate traffic operation at toll plazas. The model output includes Measures of Effectiveness that can be used to evaluate the performance of existing and future individual toll lanes and the entire toll plaza system. Real-life data collected at the busiest toll plaza in the Orlando-Orange County Expressway Authority (OOCEA) system was compared with the model output to validate the developed model. Statistical tests indicated that there is no significant difference at the 95% confidence level between Measures of Effectiveness obtained from the model and those collected in the real world. [ABSTRACT FROM AUTHOR]

In this article a nonlinear model predictive control approach to the problem of coordinated ramp metering is presented. The previously designed optimal control tool Advanced Motorway Optimal Control (AMOC) is used within the framework of a hierarchical control structure which consists of three basic layers: the estimation/prediction layer, the optimization layer, and the direct control layer. More emphasis is given to the last two layers where the control actions on a network-wide and on a local level, respectively, are decided. The hierarchical control strategy combines AMOC's coordinated ramp metering control with local feedback Asservissement LInéaire d'Entré Autoroutière (ALINEA) control in an efficient way. Simulation investigations for the Amsterdam ring-road are reported whereby the results are compared with those obtained by applying ALINEA as a stand-alone strategy. It is shown that the proposed control scheme is efficient, fair, and real-time feasible. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITS) are playing increasingly important roles in addressing traffic congestion, safety, and environmental concerns. Archiving and “reusing” the vast amounts of data initially collected by ITS for real-time operations holds the potential to significantly improve a wide range of transportation analyses. Effectively archiving and deriving information from ITS data requires the application of technology and algorithms recently developed and proven in research. This paper describes and summarizes this research, particularly in areas such as data warehousing, complex systems development, traffic data aggregation, traffic data imputation, and traffic data characterization. The paper concludes with a list of key future research needs required to allow expanded use of ITS data archives. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the authors discuss various reports within the issue on topics including generating emission information for transport routing, environment advantages of time-dependent green routing in the Greater Buffalo-Niagara Region in New York, and the significance of eco-route planning algorithm.