Owing to its mathematical elegance and empirical accuracy, the speed-density model is critical in solving macroscopic traffic problems. This study developed an improved general-logistic-based speed-density model, which is a new method in macroscopic traffic flow theory. This article extensively discusses the properties of the general-logistic-based speed-density model. The physical meanings and values of all the parameters were determined based on the effect of heavy vehicles and the method for the linear and nonlinear regression analysis. The accuracy and versatility of the developed model were also found to be excellent based on the field data and relative error.

Contraflow is a common traffic strategy used to improve the capacity of outbound roads during mass evacuation. Previous studies have focused on the contraflow network configuration, travel time, and number of evacuated vehicles on a macroscopic level. Only a few researchers have considered microscopic factors, such as the contraflow characteristics and moving bottlenecks caused by coaches and trucks. In this study, the effects of the contraflow strategy were investigated through field experiments and traffic simulations. Traffic data were collected from highway segments where trucks were forbidden under regular and contraflow conditions for analysis of the traffic characteristics and the effects of coach moving bottlenecks. The results demonstrate that the capacity and flow speed of contraflow lanes are lower than normal lanes, owing to the narrow cross sections and unfamiliar driving environment. The moving bottlenecks also reduced the speed of passenger car platoons by approximately 5–20 km/h. Four different contraflow schemes were developed and evaluated by Vissim to examine their effectiveness for minimizing the effect of truck moving bottlenecks. The findings revealed an obvious negative effect of trucks on the performance of the contraflow strategy, indicating the need for specific schemes when the truck ratio is large.

We investigated the effects of four safety facilities in expressway tunnels—information boards, flashing lights, human-voice broadcasts, and siren broadcasts—on driver distance perception by questionnaire surveys and field experiments. Results from a survey questionnaire given to 436 drivers indicated that each of the facilities, except the human-voice broadcast, was perceived to increase the driving safety. Consistently, results from field experiments involving 150 participants in China’s Xingshuliang Tunnel indicated that information boards, flashing lights, and siren broadcasts increased the distance perception accuracy of drivers, while human-voice broadcasts decreased this accuracy. The results of human-voice broadcasts may be due to the fact that drivers could not catch and understand the information they heard from human-voice broadcasts while driving in tunnels. This research can assist engineers in identifying the effective safety facilities in tunnels and provide a basis for prioritizing the implementation of these facilities, ultimately increasing driver distance perception accuracy and decreasing rear-end collisions.

Apart from private traffic, the evacuation of transit-dependent population is also an essential component of emergency preparedness, especially under no-notice evacuation scenarios with limit evacuation horizon. In literature, most bus-based evacuation models for no-notice evacuation are established under implicit assumptions of uniform evacuation horizon among different pick-up locations or fixed bus fleet in the evacuation area. These constraints will distance their models from real-world situations, where evacuation horizon is various due to spatial distribution of pick-up locations and fleet size of bus available for allocation will increase over time in no-notice evacuation. This research presents a risk-based bus schedule model which is differentiated from the vehicle routing problem (VRP) and bus evacuation problem (BEP) in literature, including the objective and the time-dependent parameters. A quantified definition of evacuation risk for pick-up location with concerns of disaster dynamics and time-varying supply-demand conditions is proposed in this paper as a criterion for bus allocation, also acting as a reflection of social equity to some extent. A notion of time-evolving disadvantageous evacuation units (DEU) is introduced to represent the pick-up locations selected for bus allocation with limited resource. The binary integer linear programming (BILP) named risk-based bus schedule model incorporated with DEU notion can provide a reference for resource allocation in stage of both evacuation planning and operation for transit-dependent population. The proposed model structure can effectively capture the changes of evacuation risk among pick-up locations over time to realize real-time bus schedule. Numerical experiments are conducted using the transportation network of the city of Xi’an, China, to test the performance of the model. The applicability and comparison of different bus evacuation models are also discussed in this paper. This research provides insights into dealing with disaster dynamics and time-varying supply conditions in realistic bus-based no-notice evacuation operations.

Speed estimation for the out-of-control truck on a downhill grade is essential for passive safety features like truck escape ramps to promote traffic safety. This paper presents a method for estimating the speed of out-of-control trucks based on Newton’s Laws of Motion. First of all, we analyze gravity effort, aerodynamics, and rolling resistance through a free body diagram of an out-of-control truck on a downhill grade. Further, we select the speed as the dependent variable, with the following road and vehicle characteristics as independent variables: road surface type, grade, grade length, truck size, truck weight, and tire type. Finally, we estimate the speed and acceleration according to Newton’s Laws of Motion. The results show that the factors that significantly affect the out-of-control truck’s speed include tire type, road surface coefficient, grade, and grade length. TruckMaker simulation results demonstrate that the model is valid at a 99% confidence level.