TY  - JOUR
PY  - 2021//
TI  - A data-driven urban metro management approach for crowd density control
JO  - Journal of advanced transportation
A1  - Zhou, Hui
A1  - Zheng, Zhihao
A1  - Cen, Xuekai
A1  - Huang, Zhiren
A1  - Wang, Pu
SP  - e6675605
EP  - e6675605
VL  - 2021
IS  - 
N2  - Large crowding events in big cities pose great challenges to local governments since crowd disasters may occur when crowd density exceeds the safety threshold. We develop an optimization model to generate the emergent train stop-skipping schemes during large crowding events, which can postpone the arrival of crowds. A two-layer transportation network, which includes a pedestrian network and the urban metro network, is proposed to better simulate the crowd gathering process. Urban smartcard data is used to obtain actual passenger travel demand. The objective function of the developed model minimizes the passengers' total waiting time cost and travel time cost under the pedestrian density constraint and the crowd density constraint. The developed model is tested in an actual case of large crowding events occurred in Shenzhen, a major southern city of China. The obtained train stop-skipping schemes can effectively maintain crowd density in its safety range.<p />  <p>Language: en</p>
LA  - en
SN  - 0197-6729
UR  - http://dx.doi.org/10.1155/2021/6675605
ID  - ref1
ER  -