For enhanced efficiency and sustainability, transportation must rely on new technologies. Researchers and engineers work nowadays on many valuable and green transportation ideas. One such idea creates vehicle platoons on freeways to achieve multiple benefits: reductions in gas consumption, vehicle emissions, and traffic congestion through enhanced aerodynamics and road space utilization, as well as improvements in safety and driver comfort through fail-safe mechanisms and moderation of collision damages given similar vehicle speed. The technological enhancements are built into the vehicles. The existing freeway infrastructures do not necessitate modification. Still, the interactions between the human factors, or lack thereof, and the new technologies may directly impact the traditional guidelines for freeway design. Over time, the dedicated freeways of the future, whether rehabilitated or built anew, could satisfy guidelines derived specifically for road-trains. Further, freeways built to current guidelines may accommodate travel speeds higher than those originally anticipated per design. It remains to understand (1) why existing freeway lanes can accommodate road-trains, (2) whether design guidelines for road-trains are significantly different from current guidelines, and (3) whether road-train operation results in significant a posteriori design speed increases. This article seeks to answer these very questions. The investigation of a continuum of transitory to end state scenarios concluded that road-trains promote both shorter required and longer available sight distances, thereby promoting the adequacy of existing freeways. It is shown that road-trains reduce minimum length requirements and increase a posteriori design speeds for all curves. Changes are less drastic for horizontal than vertical curves.

This paper presents an innovative rule-based intelligent network simulation model (INSIM) expert system (IES) which simulates real-time mode choice decision-making process of commuters in the presence of multimodal traveler information. The IES captures interactions among available modes and decides on the commuter’s mode based on a commuter’s socioeconomic traits and prevailing travel condition. The commuter’s mode choice behavior is modeled and represented by cognitive rules in the rule-base of the IES. Two important characteristics of the IES, the reliability and the adaptive learning, are highlighted. Three different models, i.e., (1) pure rule-based model (PRB), (2) discrete choice model (DCM), and (3) probabilistic model (COM) are introduced to formulate the mode choice decisions. Simulation results show that the highest level of accuracy can be achieved by applying the PRB model to generate mode choice decisions.

The ability to predict the impact of typhoons on transportation infrastructure is important as it can help to avoid serious delays and dangers when roads are closed due to such events. This research uses support vector regression (SVR) to predict the impact of typhoons on transportation infrastructure. It first integrates and examines the infrastructure and precipitation data from different authorities. An SVR model is constructed to solve a nonlinear prediction problem for small size data. The SVR model is calibrated and validated by a heuristic process. The calibrated and validated results are then applied to predict closed roads in a real network through a simulation assignment model. Several traffic management strategies are developed to reduce the negative impacts of typhoons. The results show that the mean absolute percentage error (MAPE) of SVR prediction is 9.7%. The impact of typhoons on transportation networks can thus be predicted and simulated based on the calibrated SVR model, and appropriate strategies can then be developed in order to reduce both delays and risks.

Lane changing has received much attention as it is a significant component of microscopic traffic simulation. Many studies have focused on the details of the lane changing maneuver from external observation-based data which do not consider the type of driver performing the maneuver. The research reported in this paper relates the physical details of freeway lane changing to the type of driver performing the maneuver. Forty-six research participants drove an instrumented vehicle and performed a combined total of 726 freeway lane changes. Each research participant was categorized into one of four groups ranging from conservative to aggressive based on cluster analysis. The data were analyzed to identify any trends between the different driver types and their lane changing characteristics, specifically lane changing duration and gap acceptance characteristics. In general, more conservative drivers have greater lane changing durations than aggressive drivers. The gap acceptance comparison among driver types did not yield any conclusive trend. In addition, distributions were fitted to lane changing duration and gap acceptance histograms. The results suggest that driver type impacts freeway lane changing behavior, and therefore should be taken into account when developing or refining simulation-based lane changing models.

Purolator is a Canadian courier company that recently introduced hybrid electric vehicles (HEVs) into their fleets. This paper estimates the fuel savings and greenhouse gas reductions for Purolator’s hybrid trucks using a life-cycle assessment executed with GHGenius, a Government of Canada model. Overall, it was found that Purolator’s hybrid diesel trucks reduce GHG emissions by 23% and 8% for city and highway driving, respectively. The results confirm that switching to an HEV fleet could reduce ${\mathrm{CO}}_2$ emissions during vehicle operations by 25%, as the vehicle manufacturer (Azure Dynamics) has advertised. Moreover, the modeled emission reductions (1,668 t ${\mathrm{CO}}_2\text{-}\mathrm{eq}$) and fuel savings (609,000 L) over a distance of 5,200,000 km are similar to Purolator’s advertised values (1,900 t ${\mathrm{CO}}_2\text{-}\mathrm{eq}$, 645,000 L). The life-cycle costs of hybrid delivery trucks do not presently make them financially favorable alternatives to conventional diesel trucks, but their financial competitiveness is sensitive to vehicle service life, diesel fuel price, assumed discount rate, and incremental hybrid cost.

There is a significant difference on the setting temperature gradient when concrete paving is conducted under different conditions. The strain distribution through the depth of portland cement concrete slab changes greatly after considering the setting temperature profile. Currently, the consideration on built-in curling is simplified to the temperature difference between the top and bottom of portland cement concrete slab. The primary objective of this paper was to develop an approach that is capable of incorporating the nonlinearity of the setting temperature gradient into the determination of nonlinear curling stress. Typical weather conditions in summer and fall were also selected to illustrate the influence of built-in curling on the curling stress determination. Furthermore, to better understand the effect of paving conditions on pavement performance, comparisons of transverse cracking and longitudinal cracking between 47 test sections constructed in the summer and 43 sections constructed in the cooler seasons in long-term pavement performance database were performed. Pavement performance corresponded with the curling stress analysis in the research reported in this paper.