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1. Security and Privacy Issues in Intelligent Transportation Systems: Classification and Challenges

   https://doi.org/10.1109/MITS.2019.2898973  

   Dalton A. Hahn, Arslan Munir (April 2021, IEEE intelligent transportation systems magazine)

   null (Ed.)

   Intelligent Transportation Systems (ITS) aim at integrating sensing, control, analysis, and communication technologies into travel infrastructure and transportation to improve mobility, comfort, safety, and efficiency. Car manufacturers are continuously creating smarter vehicles, and advancements in roadways and infrastructure are changing the feel of travel. Traveling is becoming more efficient and reliable with a range of novel technologies, and research and development in ITS. Safer vehicles are introduced every year with greater considerations for passenger and pedestrian safety, nevertheless, the new technology and increasing connectivity in ITS present unique attack vectors for malicious actors. Smart cities with connected public transportation systems introduce new privacy concerns with the data collected about passengers and their travel habits. In this paper, we provide a comprehensive classification of security and privacy vulnerabilities in ITS. Furthermore, we discuss challenges in addressing security and privacy issues in ITS and contemplate potential mitigation techniques. Finally, we highlight future research directions to make ITS more safe, secure, and privacy-preserving. 

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2. Urban Walkability and Pedestrian Stress: A Sensor-Based Study Across Three Sites

   Begum, R; Aqlan, F; Brockman, J; Marie, H; Wood, D (June 2025, ASEE Annual Conference)

   This study investigates the relationship between urban walkability and human stress across three distinct sites, utilizing data collected from wearable sensors. The objective is to assess how urban design and environmental factors influence human stress while walking. Participants were equipped with wearable sensors to monitor physiological indicators of stress (e.g., heart rate variability, etc.) as they walked through different urban environments. Data was collected in realtime to capture fluctuations in stress levels and provide insights into how specific urban design features impact pedestrian well-being. To facilitate data collection and analysis, walking areas were divided into blocks, and urban design features were grouped into six categories such as imageability, enclosure, human scale, transparency, complexity, and safety. Each city has different features, depending on the issues that were considered most pressing for that city. To supplement sensor stress data, the study also utilized surveys to gather participants’ perceptions of safety, comfort, and environmental quality. Using regression analysis, researchers identified the urban design categories that have a significant impact on stress scores and their frequency. Machine learning models were built to predict stress scores based on the urban design aspects and air quality data as input features. Results showed that increased stress is correlated with poorly designed walkways, while lower stress was linked to well-maintained paths and green spaces. Transparency and enclosure were identified as significant contributors to pedestrian stress. The findings from one of the three cities add another dimension to the understanding of walkability and stress, highlighting that there are factors beyond basic infrastructure, such as noise levels and tree canopy can play a significant role in influencing pedestrian well-being. Findings from this research can facilitate targeted infrastructure planning and investment, better mobility, and ultimately improve the quality of life in urban areas. Future research should consider a wider range of environmental and social factors and how different factors interact over time to influence stress levels. 

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3. Understanding pedestrian movement using urban sensing technologies: the promise of audio-based sensors

   https://doi.org/10.1007/s44212-024-00053-9  

   Han, Chaeyeon; Seshadri, Pavan; Ding, Yiwei; Posner, Noah; Koo, Bon_Woo; Agrawal, Animesh; Lerch, Alexander; Guhathakurta, Subhrajit (July 2024, Urban Informatics)

   Abstract While various sensors have been deployed to monitor vehicular flows, sensing pedestrian movement is still nascent. Yet walking is a significant mode of travel in many cities, especially those in Europe, Africa, and Asia. Understanding pedestrian volumes and flows is essential for designing safer and more attractive pedestrian infrastructure and for controlling periodic overcrowding. This study discusses a new approach to scale up urban sensing of people with the help of novel audio-based technology. It assesses the benefits and limitations of microphone-based sensors as compared to other forms of pedestrian sensing. A large-scale dataset called ASPED is presented, which includes high-quality audio recordings along with video recordings used for labeling the pedestrian count data. The baseline analyses highlight the promise of using audio sensors for pedestrian tracking, although algorithmic and technological improvements to make the sensors practically usable continue. This study also demonstrates how the data can be leveraged to predict pedestrian trajectories. Finally, it discusses the use cases and scenarios where audio-based pedestrian sensing can support better urban and transportation planning. 

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4. Microscopic modeling of attention-based movement behaviors

   Li, D; Schwartz, M; Sohn, M; Yoon, S; Pavlovic, V; Kapadia, M (September 2025, Transportation research Part C Emerging technologies)

   For transportation hubs, leveraging pedestrian flows for commercial activities presents an effective strategy for funding maintenance and infrastructure improvements. However, this introduces new challenges, as consumer behaviors can disrupt pedestrian flow and efficiency. To optimize both retail potential and pedestrian efficiency, careful strategic planning in store layout and facility dimensions was done by expert judgement due to the complexity in pedestrian dynamics in the retail areas of transportation hubs. This paper introduces an attention-based movement model to simulate these dynamics. By simulating retail potential of an area through the duration of visual attention it receives, and pedestrian efficiency via speed loss in pedestrian walking behaviors, the study further explores how design features can influence the retail potential and pedestrian efficiency in a bi-directional corridor inside a transportation hub. 

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5. Mining Spatiotemporal Mobility Patterns Using Improved Deep Time Series Clustering

   https://doi.org/10.3390/ijgi13110374  

   Zhang, Ziyi; Li, Diya; Zhang, Zhe; Duffield, Nick (November 2024, ISPRS International Journal of Geo-Information)

   Mining spatiotemporal mobility patterns is crucial for optimizing urban planning, enhancing transportation systems, and improving public safety by providing useful insights into human movement and behavior over space and time. As an unsupervised learning technique, time series clustering has gained considerable attention due to its efficiency. However, the existing literature has often overlooked the inherent characteristics of mobility data, including high-dimensionality, noise, outliers, and time distortions. This oversight can lead to potentially large computational costs and inaccurate patterns. To address these challenges, this paper proposes a novel neural network-based method integrating temporal autoencoder and dynamic time warping-based K-means clustering algorithm to mutually promote each other for mining spatiotemporal mobility patterns. Comparative results showed that our proposed method outperformed several time series clustering techniques in accurately identifying mobility patterns on both synthetic and real-world data, which provides a reliable foundation for data-driven decision-making. Furthermore, we applied the method to monthly county-level mobility data during the COVID-19 pandemic in the U.S., revealing significant differences in mobility changes between rural and urban areas, as well as the impact of public response and health considerations on mobility patterns. 

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