Real-time road traffic state prediction based on ARIMA and Kalman filter

The realization of road traffic prediction not only provides real-time and effective information for travelers, but also helps them select the optimal route to reduce travel time. Road traffic prediction offers traffic guidance for travelers and relieves traffic jams. In this paper, a real-time road traffic state prediction based on autoregressive integrated moving average （ARIMA） and the Kalman filter is proposed. First, an ARIMA model of road traffic data in a time series is built on the basis of historical road traffic data. Second, this ARIMA model is combined with the Kalman filter to construct a road traffic state prediction algorithm, which can acquire the state, measurement, and updating equations of the Kalman filter. Third, the optimal parameters of the algorithm are discussed on the basis of historical road traffic data. Finally, four road segments in Beijing are adopted for case studies. Experimental results show that the real-time road traffic state prediction based on ARIMA and the Kalman filter is feasible and can achieve high accuracy.

A two-layer optimization model for high-speed railway line planning

Line planning is the first important strategic element in the railway operation planning process,which will directly affect the successive planning to determine the efficiency of the whole railway system.A two-layer optimization model is proposed within a simulation framework to deal with the high-speed railway (HSR) line planning problem.In the model,the top layer aims at achieving an optimal stop-schedule set with the service frequencies,and is formulated as a nonlinear program,solved by genetic algorithm.The objective of top layer is tominimize the total operation cost and unserved passenger volume.Given a specific stop-schedule,the bottom layer focuses on weighted passenger flow assignment,formulated as a mixed integer program with the objective of maximizing the served passenger volume andminimizing the total travel time for all passengers.The case study on Taiwan HSR shows that the proposed two-layer model is better than the existing techniques.In addition,this model is also illustrated with the Beijing-Shanghai HSR in China.The result shows that the two-layer optimization model can reduce computation complexity and that an optimal set of stop-schedules can always be generated with less calculation time.

A vehicle re-identification algorithm based on multi-sensor correlation

Magnetic sensors can be applied in vehicle recognition.Most of the existing vehicle recognition algorithms use one sensor node to measure a vehicle’s signature.However,vehicle speed variation and environmental disturbances usually cause errors during such a process.In this paper we propose a method using multiple sensor nodes to accomplish vehicle recognition.Based on the matching result of one vehicle’s signature obtained by different nodes,this method determines vehicle status and corrects signature segmentation.The co-relationship between signatures is also obtained,and the time offset is corrected by such a co-relationship.The corrected signatures are fused via maximum likelihood estimation,so as to obtain more accurate vehicle signatures.Examples show that the proposed algorithm can provide input parameters with higher accuracy.It improves the average accuracy of vehicle recognition from 94.0%to 96.1%,and especially the bus recognition accuracy from 77.6%to 92.8%.