Identification and Prediction of Large Pedestrian Flow in Urban Areas Based on a Hybrid Detection Approach

Abstract

Abstract:Recently, population density has grown quickly with the increasing acceleration of urbanization. At the same time, overcrowded situations are more likely to occur in populous urban areas, increasing the risk of accidents. This paper proposes a synthetic approach to recognize and identify the large pedestrian flow. In particular, a hybrid pedestrian flow detection model was constructed by analyzing real data from major mobile phone operators in China, including information from smartphones and base stations (BS). With the hybrid model, the Log Distance Path Loss (LDPL) model was used to estimate the pedestrian density from raw network data, and retrieve information with the Gaussian Progress (GP) through supervised learning. Temporal-spatial prediction of the pedestrian data was carried out with Machine Learning (ML) approaches. Finally, a case study of a real Central Business District (CBD) scenario in Shanghai, China using records of millions of cell phone users was conducted. The results showed that the new approach significantly increases the utility and capacity of the mobile network. A more reasonable overcrowding detection and alert system can be developed to improve safety in subway lines and other hotspot landmark areas, such as the Bundle, People's Square or Disneyland, where a large passenger flow generally exists.

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“…Ahas et al [19] researched seasonal tourism space consumption patterns of a city using mobile phone data. Zhang et al [20] estimated large pedestrian flow in urban areas based on a hybrid detection approach. Langford [21] used open access ancillary data to estimate small area population.…”

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“…Crosswalks, raised medians, and pedestrian refuge islands should be introduced at locations with high frequencies of severe pedestrian injury. Hybrid detection approach techniques could be helpful in determining large pedestrian flows in urban areas to determine suitable locations to implement pedestrian facilities [74].…”

Real-Time Pedestrian Flow Analysis Using Networked Sensors for a Smart Subway System

“…Ban et al [18]; Zou et al [19]; Pratama et al [20] Fusion Improves accuracy through a combination of technologies, such as the integration of GPS and INS. Kumar et al [21]; Chen et al [22] The triangulation method calculates the distance between three or more AP devices and mobile devices. The position of the AP observation instrument is fixed and thus known in advance.…”

“…For example, the GPS signal attenuation due to ground obstructions and RF interference causes instantaneous signal loss and thus reduces the accuracy of the position determined using GPS. Under these conditions, a combination of GPS with an inertial navigation system (INS) is an effective solution for determining the exact location of a target [21]. In this scenario, the GPS provides location, velocity, and time data based on satellite signals, and the INS compensates for GPS signal problems using device gyros to measure the angular rate of change in inertial space and accelerometers to achieve high reliability and a constant power ratio by measuring the linear acceleration of the inertial system [22].…”