Gradation segregation strongly affects the service performance of asphalt pavement. However, rare studies focus on the influences of segregation on asphalt mixture as it is difficult to simulate segregation in a laboratory. This paper presents a research aiming to evaluate the effects of gradation segregation using the two dimensional (2D) discrete element method (DEM). To achieve this objective, the gradation segregation levels are defined according to the differences between control and segregated gradations. Based on this, asphalt mixtures with three gradations at three segregation levels were selected. The corresponding 2D DEM models were then built using Particle Flow Code 2D (PFC 2D). The aggregate skeletons in the asphalt mixture were determined separately by means of the contact point and contact force. Based on this, the variations on the aggregate skeleton, aggregate distribution, and mechanical properties of the asphalt mixture under vertical load were studied at three different segregation levels. This study indicates that the aggregate skeleton in asphalt mixture can be affected by the gradation segregation. The correlation between gradation segregation and mechanical property is also determined in this study.

Since widespread applications of wireless sensors networks, low-speed traffic positioning based on the received signal strength indicator (RSSI) from personal devices with WiFi broadcasts has attracted considerable attention. This study presents a new range-based localization method for outdoor pedestrian positioning by using the combination of offline RSSI distance estimation and real-time continuous position fitting, which can achieve high-position accuracy in the urban road environment. At the offline stage, the piecewise polynomial regression model (PPRM) is proposed to formulate the Euclidean distance between the targets and WiFi scanners by replacing the common propagation model (PM). The online stage includes three procedures. Firstly, a constant velocity Kalman filter (CVKF) is developed to smooth the real-time RSSI time series and estimate the target-detector distance. Then, a least squares Taylor series expansion (LS-TSE) is developed to calculate the actual 2-dimensional coordinate with the replacement of existing trilateral localization. Thirdly, a trajectory-based technique of the unscented Kalman filter (UKF) is introduced to smooth estimated positioning points. In tests that used field scenarios from Guangzhou, China, the experiments demonstrate that the combined CVKF and PPRM can achieve the highly accurate distance estimator of <1.98 m error with the probability of 90% or larger, which outperforms the existing propagation model. In addition, the online method can achieve average positioning error of 1.67 m with the much better than classical methods.

Overlapping phase and split phase are two typical phase patterns at signalized intersections in practice. The existing bandwidth-oriented progression control schemes mainly focus on overlapping phase to adopt the various layout of intersections and volumes rather than split phase. To address this issue, a two-way signal progression model, namely Pband, is developed to simultaneously optimize phase pattern choice from overlapping phase or split phase, phase sequence, and offsets. Also, the proposed Pband is formulated with a mixed integer nonlinear programming technique to guarantee an optimal solution. A numerical test based on a field arterial from the county of Jiashan, Jiaxing, China is employed to validate Pband under designed traffic patterns and signal timings. Numerical results have indicated that the Pband can show promise in increasing progression bandwidths and reducing average travel delay and the number of stops via traffic simulation experiments compared with the conventional Multiband model.

The aim of this study is to develop a fast data fusion method for recognizing metro-to-bus transfer trips based on combined data from smart cards and a GPS system. The method is intended to establish station- and time-specific elapsed time thresholds for overcoming the limitations of one-size-fits-all criterion which is not sufficiently convincing for different transfer pairs and personal characteristics. Firstly, a data fusion method with bus smart card data and GPS data is proposed to supplement absent bus boarding information in the smart card data. Then, a model for identifying metro-to-bus interchange trips is derived based on two rules about maximal allowable transfer distance and elapsed transfer time threshold. Finally, in tests that used half-monthly field smart card data and GPS data from Shenzhen, China, the results recognized by the proposed method were more consistent with the actual surveyed group transfer time with a P value of 0.17 determined by Mann–Whitney U test. The comparison analysis showed that the proposed method can be widely applied to successfully identify and interpret metro-to-bus interchange behavior beyond a static transfer time threshold of 30 min.

Improving operational efficiency and safety on freeway segments has long been recognized as a priority in traffic communities. This study presents a comprehensive variable speed limit (VSL) control model for reducing both travel delays and potential crash risks at freeway work zones. The proposed strategy offers the responsible agency a reliable way to determine reasonable speed limits over the control time horizon in response to traffic fluctuations. The control objective of the proposed system is to minimize total operational cost by using embedded functions to estimate total cost with total travel delay and potential crash rate on the targeted freeway segment. This study used a field site in China for its case study to evaluate the effectiveness of the proposed VSL control system. Based on the experimental results, it was observed that the proposed control model shows promise in reducing average delay, number of stops, and average stopped delay on the studied freeway segment. In addition, simulation experiments for sensitivity analysis indicate that the proposed VSL control model is more effective when driver compliance rate exceeds a threshold of 60% and the user-defined benefit threshold ranges 10–20%.