Agent-based microscopic pedestrian-flow simulation models are promising tools for designers or engineers to evaluate the level of safety or comfort of crowded pedestrian traffic facilities. Existing models tend to simulate movement direction choice behaviors of a virtual agent based on a joint effect of several physical, psychological, and sociological factors dominating the real-world pedestrian walking behaviors. Challenging questions remain for this type of model, including how to control and balance the influences among these behavioral factors and how to naturally avoid collisions without losing the effect of the behavior factors considered. This article presents an improved utility-maximization approach to determine the movement direction of individuals in an agent-based pedestrian-flow simulation model. A new utility function is proposed. An explicit collision detection and avoidance technique is used as a supplementary rule together with the utility maximization method to improve the collision avoidance behaviors in the model. Simulation experiments are carried out for detailed analyses of agent-movement direction-choice behaviors under the influence of utility values and behavioral factors. It is shown that the new utility function can control and balance the influences among the behavioral factors better and avoid unrealistic direction choices. In addition, simulations of intersecting pedestrian flow based a real pedestrian flow experiment are designed, and simulation results are compared with the experiment results. The comparison demonstrates the improvements of using the collision detection and avoidance technique, and shows that well-configured simulations could be close to the experiment both qualitatively and quantitatively.

Crashes present different collision types at freeway diverge areas. The research reported in this paper applies the multivariate modeling technique to evaluate the crash risks by collision type. Three years crash data are obtained from 282 freeway exit ramps. Three types of crashes are considered [i.e., (1) rear-end, (2) sideswipe, and (3) angle collisions]. A multivariate Poisson-lognormal (MVPLN) model is estimated to jointly evaluate the impacts of explanatory variables on different collision risks. For comparison purpose, univariate negative binomial (NB) models are also estimated based on the same dataset. The results show that the MVPLN model successfully captures the correlation of latent effects among the crash counts of different collision types. Thus, the MNPLN model estimates the impacts of variables more accurately than the NB model. The MVPLN model is found outperform the NB models in predicting the crash count of each collision type. Findings of this paper can help better understand how variables affect the risks of different collisions and propose accurate crash prediction models at freeway diverge areas.

The importance of reliable estimates of travel demand for effective planning, design, and management of roads and facilities is well known by transportation engineers. A review of current short-term traffic monitoring practices shows that most transportation agencies simply use road functional class as the criteria to assign short-term traffic counts (STTCs) to permanent traffic counter (PTC) factor groups, or they commit significant resources to implement other data intensive methods, such as regression analysis. The improved methods described in this study estimate average annual daily traffic (AADT) with higher accuracy using all historical counts collected to date for a short-term counting site to create its seasonal traffic pattern and assign it to a PTC or a PTC group without imposing additional data collection cost. Two pattern-matching methods, and their combination with Bayesian statistics, are proposed and tested using PTC data from Alberta, and their results are compared to the Federal Highway Administration (FHWA) method. Study results show that, compared to the FHWA method, the proposed methods reduce the $95\mathrm{t}\mathrm{h}$ percentile of the absolute percent AADT estimation errors (P95) by 0.5 to 31.9 when applied to different testing sites.

For determining highly disaggregate details about traffic dynamics, microscopic traffic simulation has long proven to be a valuable tool for the evaluation of development plans and operation/control strategies. With recent advances in computing capabilities, research interest in large-scale microscopic simulation has never been greater. This case study develops a 24-h large-scale microscopic traffic simulation model for the Washington, DC, metropolitan area. The model consists of over 7,000 links, 3,500 nodes, 400 signalized intersections, and over 40,000 origin-destination pairs. Various field measurements, such as time-dependent traffic counts and corridor travel times, have been used for model calibration/validation. The EPA’s Motor Vehicle Emission Simulator is linked with the microscopic simulation model for the estimation of environmental impacts. The calibrated model system has been used to comprehensively evaluate a newly built toll road in Maryland, the Intercounty Connector. Various network-level and corridor-level performance measures are quantified. The case study demonstrates the feasibility and capability of large-scale microscopic simulation in transportation applications. It establishes an example for modelers and practitioners who are interested in constructing a large-scale model system. The developed 24-h simulation model system of traffic and emissions has the potential to serve as a test bed for integration with other analysis tools, such as behavioral and optimization models.

A study is conducted to compare two simulation methods for estimating conflicts between road users. An improved cellular automata (CA) model is proposed to estimate the occurrences and severity of traffic conflicts (both vehicle–vehicle and vehicle–pedestrian) at signalized intersections. The proposed CA model is compared with a calibrated method of a surrogate safety assessment model (SSAM) based on Vissim. Simulated conflicts from both methods are compared with observed vehicle conflicts from automated vehicle tracking for both occurrences and severity. Simulation results show that the CA approach is able to replicate realistic conflicts. However, SSAM tends to overestimate occurrences and underestimate the severity of rear-end and lane-change conflicts. SSAM has also been found to overestimate the severity of crossing conflicts. Furthermore, the proposed CA model is able to estimate conflicts between vehicles and pedestrians.

Stresses in the concrete surrounding dowel bars is a major factor that contributes to transverse joint distress in jointed plain concrete pavement (JPCP). In this study, a distribution of stresses around dowel bars was analyzed with special attention to compressive and tensile stresses, which are responsible for cracks’ initiation and propagation. Calculations considering diameter, spacing, and length of dowels in JPCP were made using the finite-element method (FEM). Sliding interfaces with friction that permit separation between dowels and surface concrete were modeled using the full cylindrical surface between each dowel and the surrounding concrete. Practical functional relationships among the studied parameters may be useful in the calculation of maximal stresses in concrete around a dowel bar in dependence on its characteristics. Knowledge about the mechanical behavior of dowel bars at their contact with concrete is important to develop effective doweled joints and to improve the load transfer efficiency (LTE). It was also shown that permissible tensile stresses in concrete may be exceeded for dowel diameters $<0.04\mathrm{m}$, length 0.5 m, and distances $>0.30\mathrm{m}$. Based on derived empirical equations and relationships, it is possible to calculate stresses around dowels for different diameters, length, and spacing for a selected concrete pavement. They may be useful in a design of the pavement.