Role of street patterns in zone-based traffic safety analysis

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Predicting crash frequency using an optimised radial basis function neural network model

With the enormous losses to society that result from highway crashes, gaining a better understanding of the risk factors that affect traffic crash occurrence has long been a prominent focus of safety research. In this study, we develop an optimised radial basis function neural network (RBFNN) model to approximate the nonlinear relationships between crash frequency and the relevant risk factors. Our case study compares the performance of the RBFNN model with that of the traditional negative binomial (NB) and back-propagation neural network (BPNN) models for crash frequency prediction on road segments in Hong Kong. The results indicate that the RBFNN has better fitting and prediction performance than the NB and BPNN models. After the RBFNN is optimised, its approximation performance improves, although several factors are found to hardly influence the frequency of crash occurrence for the crash data that we use. Furthermore, we conduct a sensitivity analysis to determine the effects of the remaining input variables of the optimised RBFNN on the outcome. The results reveal that there are nonlinear relationships between most of the risk factors and crash frequency, and they provide a deeper insight into the risk factors’ effects than the NB model, supporting the use of the modified RBFNN models for road safety analysis.postprin

Computation offloading and resource allocation for wireless powered mobile edge computing with latency constraint

In this letter, we consider a multi-user wireless powered mobile edge computing (MEC) system, in which a base station (BS) integrated with an MEC server transfers energy to wireless devices (WDs) as an incentive to encourage them to offload computing tasks to the MEC server. We formulate an optimization problem to contemporaneously maximize the data utility and minimize the energy consumption of the operator under the offloaded delay constraint, by jointly controlling wireless-power allocation at the BS as well as offloaded data size and power allocation at the WDs. To solve this problem, the offloaded delay constraint is first transformed into an offloaded data rate constraint. Then an iterative algorithm is designed to obtain the optimal offloaded data size and power allocation at the WDs by using Lagrangian dual method. The results are applied to derive the optimal wireless-power allocation at the BS. Finally, simulation results show that our algorithm outperforms existing schemes in terms of operator’s reward

Active Suspension Control of Full-car Systems without Function Approximation

This paper proposes a new control approach for full-car active suspension systems with unknown nonlinearities. The main advantage of this approach is that the uncertainties and nonlinearities in the system can be handled without using any function approximator (e.g., neural networks (NNs), fuzzy logic systems (FLSs)), and the associated online adaptation. Hence, the heavy computational costs and sluggish learning phase to achieve convergence can be remedied. To maintain the transient and steady-state suspension responses, a coordinate suspension error transformation with prescribed performance functions (PPF) is adopted. Then an approximation-free control (AFC) is developed to achieve stabilization of the transformed system so as to retain predefined suspension response. Extreme Value Theorem is used together with Lyapunov theorem to prove the stability and convergence of the closed-loop control system. To validate the proposed method and show its practical applicability, a dynamic simulator is built by using a commercial vehicle software, Carsim, where an E-SUV type vehicle is configured to describe realistic vehicle dynamics. Simulation results reveal that the proposed control can achieve better suspension performance and require less model information compared with some existing approaches