A Data-Driven Urban Metro Management Approach for Crowd Density Control

Abstract

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.

Risk Assessment of Crowd-Gathering in Urban Open Public Spaces Supported by Spatio-Temporal Big Data

“…The experiment results showed that the coefficients b 0 and b 1 of the TUD-population calibration method (Equation ( 2)) were 27,654 and 55. 4, respectively. The fitting results showed that the R-square of the regression equation was 0.76 (Figure 5).…”

“…In open public spaces, which are open-boundary places, crowd accessing and leaving are unregulated, making them more likely to have large and risky crowd-gatherings compared to other places. Due to the high density of the crowd, once a crowd accident occurs, it will cause severe casualties [4][5][6]. For example, in the Love Parade accident of 2010 in Duisburg, Germany, 21 people died and more than 500 people were injured [7]; in the stampede that occurred in the Chenyi Square of Shanghai, 36 people died and 49 people were injured [8].…”

Sensing Multi-modal Mobility Patterns: A Case Study of Helsinki using Bluetooth Beacons and a Mobile Application

“…Second, the present analysis primarily relies on daily-based data collection, thus limiting the provision of real-time prediction or early warning capabilities. However, integrating real-time data sources and applying predictive analytics could provide early warnings [15], [16], [18] and proactive management options [21], [22], [31]. This study has the potential to contribute to the development of ITS capable of managing the complexities of large-scale urban events.…”

“…For example, how many people use public transport (PT) to arrive at critical locations related with a special event could be used for PT operation planning. Hence, crowd management [5], [19] and evacuation design [20], [21], [22] could benefit from travel mode information concerning crowded events and special celebrations.…”

A vector field approach for identifying anomalous human mobility

“…Previous works suggested that larger volume of trips can be attracted on the event day than on ordinary days [56]. Therefore, we define that the anomalous mobility fluxes are the large mobility fluxes which greatly exceed their ordinary values.…”