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On freeway corridors, traffic flow is limited by active bottlenecks. Weaving maneuvers (i.e., intensive lane changes) are a major cause of bottlenecks during high-demand periods. To relieve bottleneck severity, ramp metering (RM) is implemented as an active traffic control method. Ample research has been devoted to developing RM control algorithms and to exploring weaving impacts; however, RM control that is considerate of dynamic weaving impact and its evaluation has received little attention in the published literature. This paper aims to bridge that gap by proposing a proactive control algorithm that uses as inputs dynamic weaving capacity (as opposed to traditional fixed capacity values) for RM control at weaving segments. The control goals are to reduce networkwide travel time and improve traffic flow. Capacity and capacity drop were estimated through fundamental diagrams (FDs). Then, capacity drop sensitivities to on-ramp and mainline demand were analyzed within a field-data-based microsimulation model. The findings were applied to dynamically estimate weaving capacity within a macroscopic traffic flow model. The proposed traffic flow model conducted estimation in a model predictive control (MPC) framework. The RM rates were optimized by sequential quadratic programming (SQP). The proposed RM algorithm was evaluated in macrosimulation and compared with a no-control scenario as well as with a control scenario that used static (as opposed to dynamic) weaving capacity. This analysis contributes to efficient and effective field applications and freeway operational improvements. [ABSTRACT FROM AUTHOR]

Wireless communication among vehicles and roadside infrastructure, known as connected vehicles, is expected to provide higher-resolution real-time vehicle data, which will allow more effective traffic monitoring and control. Availability of connected vehicle technology among the vehicle fleet will likely grow gradually, but it will possibly remain limited, with many drivers potentially being unwilling to transmit their locations. This is problematic given that research has indicated that the effectiveness of many connected vehicle mobility applications will depend on the availability of location data from a minimum of 20-30% of roadway vehicles. In an effort to improve the performance of connected vehicle applications at low connected vehicle technology penetration rates, the authors propose a novel technique to estimate the positions of noncommunicating (unequipped) vehicles based on the behaviors of communicating (equipped) vehicles along a signalized arterial. Unequipped vehicle positions are estimated based on observed gaps in a stopped queue, and the forward movements of these estimated vehicles are simulated microscopically using a commercial traffic simulation software package. In simulations, the algorithm made more correct than incorrect estimates of unequipped vehicle positions in the same lane and within 7 m longitudinally. When applied to a previously developed connected vehicle traffic signal control strategy in simulation, the location estimation algorithm produced small improvements in delays, speeds, and stopped delay when compared to an equipped-vehicle-only scenario at penetration rates of 25% or less. The location estimation algorithm is generic, and it could be applied to other connected vehicle applications to improve performance at low penetration rates. [ABSTRACT FROM AUTHOR]

This paper presents the concept of a living laboratory (LL) and how it is applied to transportation operations research through a case study. This case study focuses on calibrating the Wiedemann car-following model parameters specific to freeway work zones. Applying the concept of an LL enables the experimental platform to be in a natural real-world environment. The design of this LL included the development of an instrumented research vehicle (IRV) to capture the natural car-following response of a driver when entering and passing through a freeway work zone. The development of a connected mobile traffic sensing (CMTS) system, which included state-of-the-art intelligent transportation systems (ITS) technologies, supports the LL environment by providing the connectivity, interoperability, and data processing of the natural, real-life setting. The IRV and CMTS system are tools designed based on the research objective to support the concept of an LL which facilitates the experimental environment to capture and calibrate natural driver behavior. This case study shows the application of an LL specific to operations research providing an experimental platform for evaluating the operational performance of a roadway in a real-time, connected, and collaborative natural environment. [ABSTRACT FROM AUTHOR]

Modern urban traffic control (UTC) systems are a key element of intelligent transportation systems (ITS). They represent the answer to the need of increased road network capacity and provide important benefits by reducing delays, stops, and travel times. Traffic parameters, measured or computed in the field, enable an UTC system to operate in a fully adaptive mode. This way, local decision units are able to follow variations in the traffic behavior and produce optimal signaling plans. Therefore, it is highly important to have this information within a distributed approach, where neighboring junctions cooperate to produce optimal control strategies. This paper aims to demonstrate the importance of different traffic parameters and how their absence affects the signaling plans and the accuracy of traffic analyses. The present study uses a local adaptation simulation option embedded in the Synchro 7 modeling tool to assess and classify these parameters according to their impact on performing efficient traffic control; the results can be deployed in any real UTC distributed system to ensure that critical information is successfully exchanged between its local decision units to maintain a fully adaptive operation. [ABSTRACT FROM AUTHOR]

The current wireless technological advancements in video surveillance are bound to shape the future of online traffic monitoring applications for intelligent transportation systems. The authors present the effect of using 802.11 b/g wireless technology on real-time traffic surveillance systems. Although numerous studies have focused on studying the quality requirements for video streaming using a simulation environment, there is limited information on such performance in a real world environment. This study analyzed key video quality parameters, including packet jitter and packet rate, suggesting necessary guidelines for the transportation agency personnel to use when evaluating and selecting surveillance and communication tools. Study of the packet jitter showed that jitter and skipped video are strongly correlated with one another. Moreover, tolerated jitter ranges were identified that would ensure smooth traffic surveillance video. Findings indicated that to avoid jitter-related problems and to minimize the discontinuity of surveillance video, a 1-s play-out buffer size should be added to traffic surveillance communication systems. Analysis of packet rate suggested that an average value of 23 packets/s can ensure smooth video continuity for traffic surveillance applications. Further analysis of multiple surveillance videos streaming simultaneously revealed the importance of knowing the available bandwidth for video transmission. Multiple video streaming over a wireless network indicated that the available bandwidth plays an important role in deciding the perceptual quality of the video. [ABSTRACT FROM AUTHOR]

Dynamic message signs (DMS) are used in conjunction with other media to communicate traffic conditions, general information, and recommended diversion strategies to motorists. This study uses loop detector data from Richmond, Virginia, to estimate diversion rates attributable to DMS advisory messages on Interstate 95 where Interstate 295 is available as a comparable alternate route, thus evaluating DMS effectiveness in traffic diversion without major concerns of added trip time or quality of the alternate route. The impacts of existing message strategies are investigated to determine those messages that maximize diversion for specific circumstances. Analysis is performed for various message types for the scenario of an incident on the primary freeway, I-95, encouraging diversion of I-95 traffic to an alternate route (I-295). Results show trends where the usage of particular words in messages is more effective than others in causing diversion, and recommendations are made to create a more effective message that promotes diversion. [ABSTRACT FROM AUTHOR]

Traffic sensors have been deployed for decades to freeways to meet the requirements of various traffic/transportation applications, most noticeably traffic control and traveler information applications. A unique feature of traffic sensor deployment is that it is a continuous and evolving process. That is, with new applications that emerge, additional sensors are usually required to be deployed to meet new requirements. This process is also sequential in nature and the new deployment has to consider existing sensors. In this paper, we propose a modeling framework to capture this sequential decision-making process for traffic sensor deployment. The framework is based on our recent findings that (1) optimal sensor deployment for a single application can be determined by a staged process or, more formally, a dynamic programming (DP) method and (2) new sensor locations for new applications can be optimally solved by the DP method via considering existing sensors. We illustrate the framework using two applications: ramp metering control and travel time estimation. It is found that the proposed scheme can appropriately capture the decision-making process of traffic sensor deployment and can generate optimal sensor placement at any stage by considering sensors that have already been deployed. The model is tested using global positioning system enabled cell phone data and traffic simulation on a real-world freeway route in the San Francisco Bay Area. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of modeling and predicting urban traffic flow variability, which involves considerable implications for the deployment of dynamic transportation management systems. Traffic variability is described in terms of a volatility metric, i.e., the conditional variance of traffic flow level, as a latent stochastic (low-order Markov) process. A discrete-time parametric stochastic model, referred to as stochastic volatility (SV) model is employed to provide short-term adaptive forecasts of traffic (speed) variability by using real-time detector measurements of volumes and occupancies in an urban arterial. The predictive performance of the SV model is compared to that of the generalized autoregressive conditional heteroscedasticity (GARCH) model, which has been recently used for the traffic variability forecasting, with regard to different measurement locations, forms of data input, lengths of forecasting horizon and performance measures. The results indicate the potential of the SV model to produce out-of-sample forecasts of speed variability with significantly higher accuracy, in comparison to the GARCH model. [ABSTRACT FROM AUTHOR]

A multitude of new technologies (ranging from guidance systems to speed-limit exceeding systems and to fatigue detection systems) are emerging, many of which are either explicitly targeted to older drivers or expected to benefit them the most. However, these same older drivers are more likely to find adapting to the use of such technologies challenging. Therefore, understanding older drivers’ perception of such devices will allow experts to take the necessary steps to ensure their smoother acceptance and complete success of their deployment. Using Greek drivers’ attitude data collected within the scope of an extensive recent survey in 23 European countries (the SARTRE-3 data set), a statistical analysis of the perception of usefulness and acceptance of new technologies by older drivers is presented. The results of the developed ordered logit models provide insight into the human-factors’ aspect of the introduction of advanced technologies with respect to these more sensitive segments of the driver population. For example, older respondents are, in general, more supportive of the considered in-vehicle technologies, while female respondents also show a higher willingness to adopt them. [ABSTRACT FROM AUTHOR]

The ability to measure or estimate accurate speed data are of great importance to a large number of transportation system operations applications. Estimating speeds from the widely used single inductive loop sensor has been a difficult, yet important challenge for transportation engineers. Based on empirical evidence observed from sensor data collected in two metropolitan regions in Virginia and California, this research developed a Kalman filter model to perform speed estimation for congested traffic. Taking advantage of the coexistence of dual loop and single loop stations in many freeway management systems, a calibration procedure was developed to seed and initiate the algorithm. Finally, the paper presents an evaluation that illustrates that the proposed algorithm can produce acceptable speed estimates under congested traffic conditions, consistently outperforming the conventional g-factor approach. [ABSTRACT FROM AUTHOR]

Travel time is an important parameter in evaluating the operating efficiency of traffic networks, in assessing the performance of traffic management strategies, and as input to many intelligent transportation systems applications such as advanced traveler information systems. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as inductance loop detectors. Because of the widespread deployment of loop detectors, they are one of the most widely used inputs to travel time estimation techniques. There are different methods available to calculate the travel time from loop detector data, such as extrapolation of the point speed values, statistical methods, and models based on traffic flow theory. However, most of these methods fail during the transition period between the normal and congested flow conditions. The present study proposes several modifications to an existing traffic flow theory based model for travel time estimation on freeways, such that the model can estimate travel time for varying traffic flow conditions, including transition period, directly from the loop detector data. Field data collected from the I-35 freeway in San Antonio, Tex., USA, are used for illustrating the results. Automatic vehicle identification data collected from the same location are used for validating the results. Simulated data using CORSIM simulation software are also used for the validation of the model. [ABSTRACT FROM AUTHOR]

Video detection has become an increasingly popular technology for vehicle detection at signalized intersections. Among the potential disadvantages of this technology is the tendency of video detectors to activate early at night due to headlight reflection on the pavement. This early activation results in a dramatic increase in the length of the effective vehicle detection zone. This observed variation in the effective length of the vehicle detection zone that varies by ambient lighting condition and camera placement presents a very serious impediment for traffic engineers to design vehicle extension intervals that operate consistently during day, night, and transition periods. Further, the stochastic variation in the length of the vehicle detection zone length has the potential to create driver expectancy issues. Tables are included that report the observed average and range of detection zone length variations for 16 observed video cameras that were extensively calibrated by the manufacturer at the test site. The paper concludes by recommending near-side placement of video detection devices to reduce the stochastic variation in detection zone length. [ABSTRACT FROM AUTHOR]

As transportation agencies seek to better manage and operate the transportation system, there is an increasing need for traffic condition data across the roadway network. Wireless location technology (WLT)-based traffic monitoring offers the opportunity to directly measure link travel times on a significantly larger portion of the roadway system than is currently monitored. In WLT-based monitoring, the locations of wireless devices (such as cellular phones) in vehicles are monitored. A series of these positions are then used to derive vehicle speeds on links, effectively making the wireless devices probes in the traffic stream. While this idea is conceptually appealing, previous field deployments have not been able to successfully implement this concept. This paper describes the results of a study that used simulation to examine the efficacy of WLT-based monitoring systems under various configurations. Two case studies were used to investigate how WLT system design and roadway network configuration impact the overall effectiveness of WLT-based monitoring systems. Conclusions on how these systems should be designed and where they are likely to function best are also included. The results of this paper show that while first-generation WLT-based monitoring systems can be used to monitor relatively simple networks, additional design refinements are needed before they can be used across a broad spectrum of roadway conditions. [ABSTRACT FROM AUTHOR]

Short-term traffic flow prediction has long been regarded as a critical concern for intelligent transportation systems. On the basis of many existing prediction models, each having good performance only in a particular period, an improved approach is to combine these single predictors together for prediction in a span of periods. In this paper, a neural network model is introduced that combines the prediction from single neural network predictors according to an adaptive and heuristic credit assignment algorithm based on the theory of conditional probability and Bayes’ rule. Two single predictors, i.e., the back propagation and the radial basis function neural networks are designed and combined linearly into a Bayesian combined neural network model. The credit value for each predictor in the combined model is calculated according to the proposed credit assignment algorithm and largely depends on the accumulative prediction performance of these predictors during the previous prediction intervals. For experimental test, two data sets comprising traffic flow rates in 15-min time intervals have been collected from Singapore’s Ayer Rajah Expressway. One data set is used to train the two single neural networks and the other to test and compare the performances between the combined and singular models. Three indices, i.e., the mean absolute percentage error, the variance of absolute percentage error, and the probability of percentage error, are employed to compare the forecasting performance. It is found that most of the time, the combined model outperforms the singular predictors. More importantly, for a given time period, it is the role of this newly proposed model to track the predictors’ performance online, so as to always select and combine the best-performing predictors for prediction. [ABSTRACT FROM AUTHOR]

Traffic engineering studies such as validating Highway Capacity Manual (HCM) models require complete and reliable field data. However, the wealth of intelligent transportation systems (ITS) data is sometimes rendered useless for these purposes because of missing values in the data. Many imputation techniques have been developed in the past with virtually all of them imputing a single value for a missing datum. While this provides somewhat simple and fast estimates, it does not eliminate the possibility of producing biased results and it also fails to account for the uncertainty brought about by missing data. To overcome these limitations, a multiple imputation scheme is developed which provides multiple estimates for a missing value, simulating multiple draws from a population to estimate the unknown parameter. This paper also develops a framework of imputation which gives a broad perspective so that one can relate imputation methods to each other. [ABSTRACT FROM AUTHOR]

Accurate and timely forecasting of traffic flow is of paramount importance for effective management of traffic congestion in intelligent transportation systems. In this paper, a novel nonparametric dynamic time-delay recurrent wavelet neural network model is presented for forecasting traffic flow. The model incorporates the self-similar, singular, and fractal properties discovered in the traffic flow. The concept of wavelet frame is introduced and exploited in the model to provide flexibility in the design of wavelets and to add extra features such as adaptable translation parameters desirable in traffic flow forecasting. The statistical autocorrelation function is used for selection of the optimum input dimension of traffic flow time series. The model incorporates both the time of the day and the day of the week of the prediction time. As such, it can be used for long-term traffic flow forecasting in addition to short-term forecasting. The model has been validated using actual freeway traffic flow data. The model can assist traffic engineers and highway agencies to create effective traffic management plans for alleviating freeway congestions. [ABSTRACT FROM AUTHOR]

Active warning systems are traffic control devices consisting of variable signs or flashing beacons with conventional warning signs, which are activated by sensors detecting real-time roadway, environmental, and operational hazards. Currently most active warning systems detect and warn drivers of variable weather or pavement conditions, high-risk vehicle operating speeds, or the presence of intermittent hazards such as other vehicles, pedestrians, or wild animals. Within the Intelligent Transportation Systems framework, active warning systems applications detect these and other variable hazards and communicate these warnings directly to the vehicle or operator. Because most active warning systems are relatively recent, limited long-term system performance data are available. Testing and evaluation of active warning signs should continue to more firmly establish their benefits to highway safety and traffic operations. This paper provides an overview of available active warning systems and discusses their implementation requirements and potential benefits based on results and recommendations from evaluation studies. [ABSTRACT FROM AUTHOR]

An emerging technology known as wireless location technology has been developed to allow mobile wireless devices (the most common of which are cellular telephones) to be geolocated. Over the past decade, a number of research studies and operational tests have attempted to develop probe-based traffic monitoring systems using wireless location technology, but there still exist significant misperceptions concerning its capabilities. To address this problem, in this paper we seek to provide an assessment of wireless location technology-based traffic monitoring. The specific purposes of this paper are to (1) describe the concept of wireless location technology-based traffic monitoring, (2) present a critical assessment of past studies of this technology, (3) document the evaluation of one of the most recent operational tests—the 2001 Virginia Department of Transportation, Maryland State Highway Administration, and U.S. Wireless Corporation effort in the Washington, D.C., region, and (4) discuss the challenges that these systems pose to traffic engineering. [ABSTRACT FROM AUTHOR]