Accurate traffic flow data is important for traffic flow state estimation, real-time traffic
management and control, etc. Raw traffic flow data collected from inductive detectors may be …

When studying the vehicle routing problem, especially for on-time arrivals, the determination
of travel time plays a decisive role in the optimization of logistics companies. Traffic Internet …

We propose a novel approach for network-wide traffic state prediction where the statistical
time series model ARIMA is used to postprocess the residuals out of the fundamental …

This study proposes a short-term traffic flow prediction framework. The vector autoregression
(VAR) model based on econometric theory and the CNN-LSTM hybrid neural network model …

Passenger flow prediction is critical for subway managers to efficiently organize passenger
flow and assign capacity resources. Station closures caused by economic conferences and …

Routing problems play a crucial part in urban transportation network operation and
management. This study addresses the problem of finding a set of non-dominated shortest …

Analyzing the traffic state of large citywide networks is an inherently difficult task. Various
data issues, traffic signals, stops signs and other flow inhibitors of the network-level traffic …

With growing environmental concerns and the exploitation of ubiquitous big data, smart
transportation is transforming logistics business and operations into a more sustainable …

In practice, most of the intelligent transportation systems provide average travel times of all
vehicles on selected paths in real time on a regular basis. However, path travel times of …

Considering that the road short-term traffic flow has strong time series correlation
characteristics, a new long-term and short-term memory neural network (LSTM)-based …