activity
20132023
most citedPredicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach

442 citations · 482 across the 7 of their papers we have counts for

collaborators

7 papers

physics.soc-ph2023★ 2 cited

Speed Limit: Obey, or Not Obey?

Zhengbing He, Mirco Nanni, Luca Pappalardo +2

It is commonly expected that drivers maintain a driving speed that is lower than or around the posted speed limit, as failure to obey may result in safety risks and fines. By takin…

physics.soc-ph2021

An interactive city choice model and its application for measuring the intercity interaction

Xiang-Yu Jia, Er-Jian Liu, Chun-Yan Chen +1

Measuring the interaction between cities is an important research topic in many disciplines, such as sociology, geography, economics and transportation science. The traditional and…

eess.SP2020★ 15 cited

Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method

Jinlei Zhang, Hongshu Che, Feng Chen +2

Short-term origin-destination (OD) flow prediction in urban rail transit (URT) plays a crucial role in smart and real-time URT operation and management. Different from other short-…

physics.soc-ph2020★ 3 cited

Portraying ride-hailing mobility using multi-day trip order data: A case study of Beijing, China

Zhengbing He

As a newly-emerging travel mode in the era of mobile internet, ride-hailing that connects passengers with private-car drivers via an online platform has been very popular all over…

stat.ML2017★ 442 cited

Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach

Lei Lin, Zhengbing He, Srinivas Peeta

This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between sta…

cs.LG2017★ 2 cited

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

Xiaolei Ma, Zhuang Dai, Zhengbing He +3

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spati…

physics.soc-ph2013★ 18 cited

Route guidance strategies revisited: Comparison and evaluation in an asymmetric two-route traffic system

Zhengbing He, Ning Jia, Wei Guan

To alleviate traffic congestion, a variety of route guidance strategies has been proposed for intelligent transportation systems. A number of the strategies are proposed and invest…