The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

https://repository.tudelft.nl/islandora/object/uuid%3A2f5f3518-8d71-4609-81b7-a8df0375ed8d/datastream/OBJ/download

Data collection methods for studying pedestrian behaviour: A systematic review

Burst passenger flow in the public transportation system is serious to public safety. Existing works mainly focused on prediction and monitoring of regular passenger flow, which are not suitable for burst passenger flow. In this article, we first formulate the problem as early warning of burst passenger flow. Next, we design a novel framework to solve this problem by our observation that a burst passenger in-flow usually comes after an abnormal passenger out-flow for a subway station, especially when there is a large-scale social crowd event. Our framework consists of two models: (1) Abnormal out-flow detection (AOFD) which detects abnormal out-flows and warns the city administration of the burst in-flow fairly ahead of time. (2) Burst in-flow peak estimation (BIFPE) which estimates burst in-flow peak time and volume. We evaluate our framework with real-world smartcard data of the largest city in China and use large-scale social crowd event data to further explain our model. The result shows that: (1) AOFD can detect abnormal out-flows that would later result in bursts in-flows with better performance and can send warning signal ahead of the time of burst passenger in-flow. (2) BIFPE can effectively estimate the peak time of burst in-flow and can reduce peak volume estimation error compared with the traditional passenger flow prediction models.

Early warning of burst passenger flow in public transportation system

The importance of investigating pedestrian safety has been evaluated repeatedly in safety studies. The present study attempts to evaluate the various methods used by previous researchers in a hierarchical process, to determine the characteristics, advantages, and limitations of each method. Two general analysis approaches (passive and active) were taken into account to categorize 169 previous types of research. In the passive approach, the studied methods were those based on crash databases, questionnaires, and post-crash field observation data; while, in the active approach, the studied methods were those based on driving simulations and videography. The result of the passive approach reveals that road users' features and road characteristics (crash database studies), and error, lapses, intentional and unintentional violations (questionnaire studies) by them were among the most important causes of crashes and conflicts. Furthermore, road users’ distractions also reported a set of factors affecting the possibility of conflicts and crashes based on post-crash field observation studies. Also, results of the active approach showed that risky behaviors are the most important factor in threatening pedestrian safety such as unauthorized speeding, non-compliance with traffic law, unauthorized overtaking by drivers, and illegal crossing. Furthermore, risk perception and decision-making processes are the most important bond between the attitude and behavior of road users in dangerous driving situations. Examining studies through passive approach would lead to identifying the causes of crashes, recognizing the attitude of road users towards safety, and determining road users' behavioral patterns in certain situations, while the active approach has led to a more detailed understanding of behaviors and attitudes of road users. The inference of the findings obtained in this study will lead to a better understanding of the behavior of road users for studies on advanced driving assistance systems (ADAS).

Review and assessment of different perspectives of vehicle-pedestrian conflicts and crashes: Passive and active analysis approaches

Short-term estimation and prediction of pedestrian density in urban hot spots (e.g., railway station, shopping mall, etc.) is an important topic for traffic management and control in densely populated areas. In this paper, we propose a short-term pedestrian density estimation and prediction method based on mobile phone data. Firstly, pedestrian density in hot spots is estimated using mobile phone data. To decrease the positioning errors of mobile phone data, a modified particle filter method, which considers the movements of pedestrians, is applied for pre-processing the data. An efficient spatial access method (i.e., Hilbert R-tree) is adopted to construct pedestrians’ position indexes for realizing the short-term estimation. Secondly, based on the estimation results, the spatiotemporal extended Kalman filter (SEKF) is proposed for the short-term prediction of pedestrian density. A massive mobile phone dataset collected in Nanjing, China is used in the case study. The estimated pedestrian density from Monday to Thursday is used for pedestrian density prediction on Friday. The results show that the proposed method can estimate and predict pedestrian density in hot spots, especially in small-scale sites of hot spots efficiently in a short time. Comparing with classical prediction methods, the proposed SEKF method predicts short-term pedestrian density in urban hot spots more accurately. 

Short-Term Estimation and Prediction of Pedestrian Density in Urban Hot Spots Based on Mobile Phone Data

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.

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

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

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

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