Titlebook: Advances in Smart Vehicular Technology, Transportation, Communication and Applications; Proceedings of the F Jeng-Shyang Pan,Tsu-Yang Wu,La

aesthetic

Conference proceedings 2018 2017). Held from 6 to 8 November 2017 in Kaohsiung, Taiwan, the conference was co-sponsored by Springer, Fujian University of Technology in China, Fujian Provincial Key Laboratory of Digital Equipment, Fujian Provincial Key Lab of Big Data Mining and Applications, and National Kaohsiung University

HAVOC

Relinquish

impaction

Predicting the Travel Time in Using Recurrent Neural Networks: A Case Study of FuzhouFuzhou taxicab is extracted. Different recurrent networks architectures are applied. Experimental result shows that the deep learning models considering the temporal relation work well on travel time prediction.

Flounder

A Ranging Algorithm for Mobile Vehicles Based on Kalman Filterefficiency. In order to verify the reliability and accuracy of the algorithm, this paper has carried out static and dynamic experiments. According to the experimental results, we can find that the algorithm can meet the requirements of ranging and have good accuracy.

輕打

Railway Passenger Service Mode on “Internet+” of passengers services, extension services, advanced services, an new railway passenger service mode based on “Internet+” is proposed, and railway passenger service information platform technology architecture is put forward based on cloud computing. Thus, a reference for the railway passenger service to achieve “Internet+” can be proposed.

concentrate

Research on Train Delay Diagnosis in Train Diagram Based on Big Data Technologyfect-delay occurred, evaluate redundant capability of station and interval, analyze relationship between train delay and stop time in station or and running time in interval based on big data technology and computer graphic technology.

說明

Thyroid-Gland

Securing Event Logs with Blockchain for IoT mechanism of driving behavior. Research results show that the driving motivation model constructed can estimate the probability of the potential driving motivation topic in each spatial grid, and then the driving trajectory will be transformed from random probability events into driving intention with intrinsic certainty.

calamity

?ukasz Gadomer,Zenon A. Sosnowskipacity loss for time window plan, balanced train line layout for the combined and decomposition problem. For train loading and unloading problem, train plan should be adjusted to meet the working time at the arrived station.