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We provide a comprehensive review of the literature on inferring activity-travel patterns (ATP) using multi-source big data; the increasing number of publications over time on this subject, demonstrates the importance of big data in this task. Our aims are to identify the advantages and research gaps in ATP inference and to promote further developments in this field. We clarify the fundamental concepts (i.e. ATP and its components), commonly used data sources, and inference processes employed in ATP inference studies. Emphasis is placed on two prominent big data sources: mobile phone data and smart card data. We outline the various approaches involved in the inference process, and we highlight existing shortcomings in data sources, ATP inference methodologies, and result validation. Based on the review, it is evident that future research should address several limitations in ATP inference. Firstly, it is necessary to improve the comprehensive understanding of ATP and understand the interrelationships among its different components. Secondly, it is necessary to integrate different data sources and leverage their respective strengths to gain deeper insights into activity-travel behaviour. Lastly, further investigation into emerging technologies such as artificial intelligence in ATP inference is warranted to improve inference accuracy. The findings of this study could provide valuable insights for policy makers, enabling them to gain a deeper understanding of activity-travel choice behaviour and develop more effective policies related to transportation system. [ABSTRACT FROM AUTHOR]

Bus rapid transit systems (BRT) have been an indispensable public transportation pillar, especially in densely populated regions. Accurate insight into the BRT network's utilization is vital in BRT resource allocation planning contexts. This research focuses on how operators can utilize passengers' smart card data to develop origin-destination (OD) matrix-based business analytics. This research proposes a hybrid approach combining trip chaining, direct pairing, mode estimation methods, and visual analytics development. The novel approach is robust in handling incomplete smart card data transactions to generate origin-destination matrices and corresponding visual analytics as decision support systems for the BRT operators. As a case study, we applied and validated the proposed analytics to more than 20.6 million smart card transactions in one of the largest global BRT systems in Jakarta, Indonesia. [ABSTRACT FROM AUTHOR]

Transfer behavior is a critical factor influencing the travel efficiency of public transportation passengers. To address the potential group heterogeneity, the present work developed an integrated Classification and Regression Tree-Multiple-Cox Proportional Hazards (CART-Multi-Cox) model for transfer behavior analysis using smart card data in Shenzhen, China. Specifically, passengers are first grouped into different types based on transfer behavior features, and the influence of various independent variables on the transfer duration of different passenger groups is then examined. The results reveal that the proposed CART-Multi-Cox model is able to account for the heterogeneity effect and provides a deeper understanding about passengers’ transfer behavior and its underlying influencing mechanism. The findings offer valuable references for refined transfer behavior management and help enhancing the competitiveness of public transportation. [ABSTRACT FROM AUTHOR]

There are various new-age crime rackets reported with the help of digital technology and such cases are routinely investigated by the police and digital forensics analysts. In the course of investigation of different rackets such as Aadhaar card, ATM card, PAN card, ration card, voter identity card, passport, fake Indian currency note, various electronic gadgets such as mobile phones, laptops, desktops, biometric scanners, GPS devices, SIM cards, portable skimmers and hi-tech printers are usually taken from the fraudsters for digital forensics analysis. In one case, some Aadhaar cards, mobile phones, laptops, SIM cards and memory card were seized from some suspected airline passengers by the law enforcement agency. All the key probe elements were received for digital forensics examination. In this paper, we propose a digital forensics model for decoding a distorted QR code and specialized Android app used in the creation of fake Aadhaar cards. This model comprised a new algorithm to decode QR codes and recovery of background data including resources associated with the QR code. The proposed model was tested with a QR code retrieved from a suspect's laptop and Aadhaar card document. The Android app used for preparation of fake Aadhaar card was analyzed in our model and also checked in a mobile phone to verify data generated through the app. The result of the analysis detects fake Aadhaar cards. Further, examination of mobile phones and laptops revealed various technical data, supporting link evidence for preparing disputed fake Aadhaar cards for ID proof. [ABSTRACT FROM AUTHOR]

Human mobility is subject to collective dynamics that are the outcome of numerous individual choices. Smart card data which originated as a means of facilitating automated fare collection has emerged as an invaluable source for analysing mobility patterns. A variety of clustering and segmentation techniques has been adopted and adapted for applications ranging from market segmentation to the analysis of urban activity locations. In this paper we provide a systematic review of the state-of-the-art on clustering public transport users based on their temporal or spatial-temporal characteristics as well as studies that use the latter to characterise individual stations, lines or urban areas. Furthermore, a critical review of the literature reveals an important distinction between studies focusing on the intra-personal variability of travel patterns versus those concerned with the inter-personal variability of travel patterns. We synthesise the key analysis approaches as well as substantive findings and subsequently identify common trends and shortcomings and outline related directions for further research. [ABSTRACT FROM AUTHOR]