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To avoid bias caused by the high percentage of rear-end collisions in the generic models, only the observed sideswipe collisions on Highway I-5 in the central Puget Sound, Washington State, area are used to analyze the matched case-control logistic regression model. Considering the spatial-temporal characteristics of traffic flow and short-term variation of the sideswipe collision occurrence, a comprehensive analysis of sideswipe collision occurrence and its relationship with the freeway flow across lanes and detector locations are studied. The results imply that sideswipe collisions are more likely to occur at straight and level segments of multilane freeways in off-peak hours. High average occupancy and low average flow and speed variance upstream of collision location tend to increase the probability of sideswipe collision in congested scenarios. In contrast, high average speed and coefficient of variation in speed, low speed variance and coefficient of variation in occupancy, and high average absolute difference in speed between adjacent lanes downstream of collision location may decrease the likelihood of sideswipe collisions in uncongested conditions. Furthermore, the average absolute difference in occupancy and speed between upstream and downstream of the collisions location leads to an increase in the occurrence of sideswipe collisions, while standard deviation of absolute difference in occupancies and speeds exerts opposite effects. The sideswipe collision model proposed is validated using sideswipe collision data of SR 520 freeway, and both the transferability and stability of the proposed sideswipe collision model are statistically tested. [ABSTRACT FROM AUTHOR]

Congestion has become highly recognized as a worldwide traffic problem, as traffic demand has grown steadily over the past few decades. Variable speed limits (VSLs) are an intelligent transportation system (ITS) measure that limits mainline flow to mitigate bottleneck congestion. Currently, VSLs have become proactive based on short-term prediction. Proactive VSLs succeed in simulation evaluations, but few have been deployed in the field and their real-world effectiveness has not been proven. Various factors may lead to this limitation, such as the absence of reliable field application software, accuracy of prediction models, and high computation time for proactive control. To address this research gap, this study reports a preliminary VSL test and details its implementation results on Whitemud Drive, Edmonton, Canada. First, based on field traffic measurements before VSL control, recurrent bottleneck locations are identified. Second, the proactive control algorithm is briefly introduced. Then, a software application is designed to realize all necessary functions for VSL field implementation.With all these in hand, the preliminary field test was conducted and the VSL control performance and reliability are evaluated. Finally, the results for before and after VSL control implementation are analyzed in depth. The analysis compares average traffic speed, standard deviation of speed, total travel time, and total travel distance. The results from this study confirm that proactive VSL can relieve recurrent traffic congestion effectively. [ABSTRACT FROM AUTHOR]

Conditions at a freeway bottleneck are predicted with a two-dimensional stochastic differential equation (SDE) model of flow and speed. Stochastic behavior is assumed to be standard Brownian motion. To fully describe conditions observed in the field, it is necessary to couple the SDE model with a stochastic capacity model, thus forming a stochastic capacity-differential equation (SCDE) model. The model contains mechanisms to (1) trigger a breakdown event when the traffic flow reaches or exceeds the freeway capacity, and (2) identify when recovery takes place. Measures of freeway performance are derived from SCDE model predictions. They include breakdown probability, expressed as a function of traffic flow and time-of-day, and recovery time, i.e., the length of time the freeway remains in a congested state before returning to a free-flow state. To achieve dependable model forecasts, it is necessary to impose constraints when fitting the stochastic capacity model. The constraint condition consists of matching the breakdown and prebreakdown flow distributions derived from field data and stochastic capacity model forecasts. Sensitivity analyses are performed to test validity of the model-fitting procedure and to test the dependability of the SCDE model forecasts. An empirical study of recurrent workday freeway congestion on I-93 in Salem, NH is used to demonstrate the approach. The approach demonstrates the need to treat breakdown as a random process. [ABSTRACT FROM AUTHOR]

Automated counting technologies are one of the fastest growing sources of data in the non-motorized transportation field. Although automated counts make it possible to collect data for longer time periods and to document temporal variations in volumes more effectively than manual counts, all of the technologies being used are subject to systematic miscount rates that must be accounted for to generate accurate volume estimates. In this paper, accuracy and precision rates are tested for six automated pedestrian and bicycle counting technologies: passive infrared, active infrared, radio beam, pneumatic tubes, inductive loops, and piezoelectric strips. For some technologies, multiple products are tested. Counting devices were installed at 13 sites in seven cities to introduce variation in environmental (weather) conditions and volume levels, and manual validation counts were conducted based on video footage taken at each of the test sites. Correction functions are developed for each technology to increase accuracy of volume estimates. Various environmental conditions including temperature, rain, and lighting are tested in the development of the correction functions. For most technologies, a net undercount effect was observed that appears to worsen at higher volumes. Average error rates (average percentage deviation) for the tested technologies range from 0.55% for inductive loops to -17.38% for pneumatic tubes. However, after applying correction functions accuracy improves for nearly all technologies. [ABSTRACT FROM AUTHOR]

This study proposes a methodology to determine the optimal cross-sectional design of a high-occupancy vehicle (HOV) facility for safety performance by evaluating the effects of each relevant design element on safety and tradeoffs among multiple design variables. This aspect of geometric design is critical because it is often difficult to acquire sufficient right-of-way for retrofitting HOV lanes to existing freeway systems with recommended cross-sectional design. Detailed collision data of concurrent-flow buffer-separated HOV lanes, along with their geometric features and traffic flow data, were analyzed to establish collision predictive models by injury types for HOV and the adjacent general-purpose lanes. These models were used to determine the set of cross-sectional design elements that minimizes the expected collision occurrences. As a case study, a real freeway corridor where a conversion from contiguous to buffer-separated types was recently completed was selected to demonstrate the applicability of the proposed method. The evaluation through this case study shows that the proposed methodology is useful in determining the cross-sectional design of HOV facilities for safety based on selective use of available geometric space. [ABSTRACT FROM AUTHOR]

This paper presents an investigation of the accurate methodology of motorcycle equivalent units (MEUs) in mixed traffic flow by considering the characteristics of moving vehicles, such as velocity and effective space. In addition, the effective space of each kind of vehicle is computed with consideration of the influences of velocity, physical size of the subject vehicle, and the surrounding motorcycles in the proposed methodology. The field data were collected in Hanoi, Vietnam in the case study. The results indicated that the MEU values of cars, buses, minibuses, and bicycles are 3.4, 10.5, 8.3 and 1.4, respectively. On the other hand, the collection of the field data of vehicle flow and speed passing through road segments was conducted in three groups of divided roads with raised medians. The fundamental diagrams of vehicle speed-flow-density relationships, ranging from stable traffic flow to unstable conditions, were plotted in this paper. The values of capacity, maximum motorcycle flow, critical mean stream speed, and critical density of traffic flow were computed. Results indicated that the capacity values of urban roads with two, three, and four lanes per direction are 13,358, 21,725 and 24,335 (motorcycles per hour) respectively. The capacity, therefore, increases with the number of lanes of urban roads. [ABSTRACT FROM AUTHOR]

The seasonal autoregressive integrated moving average (SARIMA) model is one of the popular univariate time-series models in the field of short-term traffic flow forecasting. The parameters of the SARIMA model are commonly estimated using classical (maximum likelihood estimate and/or least-squares estimate) methods. In this paper, instead of using classical inference the Bayesian method is employed to estimate the parameters of the SARIMA model considered for modeling. In Bayesian analysis the Markov chain Monte Carlo method is used to solve the posterior integration problem in high dimension. Each of the estimated parameters from the Bayesian method has a probability density function conditional to the observed traffic volumes. The forecasts from the Bayesian model can better match the traffic behavior of extreme peaks and rapid fluctuation. Similar to the estimated parameters, each forecast has a probability density curve with the maximum probable value as the point forecast. Individual probability density curves provide a time-varying prediction interval unlike the constant prediction interval from the classical inference. The time-series data used for fitting the SARIMA model are obtained from a certain junction in the city center of Dublin. [ABSTRACT FROM AUTHOR]