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"Local transit Data processing."
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Smart Public Transportation Sensing: Enhancing Perception and Data Management for Efficient and Safety Operations
The use of cloud computing, big data, IoT, and mobile applications in the public transportation industry has resulted in the generation of vast and complex data, of which the large data volume and data variety have posed several obstacles to effective data sensing and processing with high efficiency in a real-time data-driven public transportation management system. To overcome the above-mentioned challenges and to guarantee optimal data availability for data sensing and processing in public transportation perception, a public transportation sensing platform is proposed to collect, integrate, and organize diverse data from different data sources. The proposed data perception platform connects multiple data systems and some edge intelligent perception devices to enable the collection of various types of data, including traveling information of passengers and transaction data of smart cards. To enable the efficient extraction of precise and detailed traveling behavior, an efficient field-level data lineage exploration method is proposed during logical plan generation and is integrated into the FlinkSQL system seamlessly. Furthermore, a row-level fine-grained permission control mechanism is adopted to support flexible data management. With these two techniques, the proposed data management system can support efficient data processing on large amounts of data and conducts comprehensive analysis and application of business data from numerous different sources to realize the value of the data with high data safety. Through operational testing in real environments, the proposed platform has proven highly efficient and effective in managing organizational operations, data assets, data life cycle, offline development, and backend administration over a large amount of various types of public transportation traffic data.
Journal Article
An Improved Big Data Analytics Architecture Using Federated Learning for IoT-Enabled Urban Intelligent Transportation Systems
The exponential growth of the Internet of Things has precipitated a revolution in Intelligent Transportation Systems, notably in urban environments. An ITS leverages advancements in communication technologies and data analytics to enhance the efficiency and intelligence of transport networks. At the same time, these IoT-enabled ITSs generate a vast array of complex data classified as Big Data. Traditional data analytics frameworks need help to efficiently process these Big Data due to its sheer volume, velocity, variety, and significant data privacy concerns. Federated Learning, known for its privacy-preserving attributes, is a promising technology for implementation within ITSs for IoT-generated Big Data. Nevertheless, the system faces challenges due to the variable nature of devices, the heterogeneity of data, and the dynamic conditions in which ITS operates. Recent efforts to mitigate these challenges focus on the practical selection of an averaging mechanism during the server’s aggregation phase and practical dynamic client training. Despite these efforts, existing research still relies on personalized FL with personalized averaging and client training. This paper presents a personalized architecture, including an optimized Federated Averaging strategy that leverages FL for efficient and real-time Big Data analytics in IoT-enabled ITSs. Various personalization methods are applied to enhance the traditional averaging algorithm. Local fine-tuning and weighted averaging tailor the global model to individual client data. Custom learning rates are utilized to boost the performance further. Regular evaluations are advised to maintain model efficacy. The proposed architecture addresses critical challenges like real-life federated environment settings, data integration, and significant data privacy, offering a comprehensive solution for modern urban transportation systems using Big Data. Using the Udacity Self-Driving Car Dataset foe vehicle detection, we apply the proposed approaches to demonstrate the efficacy of our model. Our empirical findings validate the superiority of our architecture in terms of scalability, real-time decision-making capabilities, and data privacy preservation. We attained accuracy levels of 93.27%, 92.89%, and 92.96% for our proposed model in a Federated Learning architecture with 10 nodes, 20 nodes, and 30 nodes, respectively.
Journal Article
Edge Intelligence-Based Rail Transit Equipment Inspection System
The safe operation of rail transit systems relies heavily on the efficient and reliable maintenance of their equipment, as any malfunction or abnormal operation may pose serious risks to transportation safety. Traditional manual inspection methods are often characterized by high costs, low efficiency, and susceptibility to human error. To address these limitations, this paper presents a rail transit equipment inspection system based on Edge Intelligence (EI) and 5G technology. The proposed system adopts a cloud–edge–end collaborative architecture that integrates Computer Vision (CV) techniques to automate inspection tasks; specifically, a fine-tuned YOLOv8 model is employed for object detection of personnel and equipment, while a ResNet-18 network is utilized for equipment status classification. By implementing an ETSI MEC-compliant framework on edge servers (NVIDIA Jetson AGX Orin), the system enhances data processing efficiency and network performance, while further strengthening security through the use of a 5G private network that isolates critical infrastructure data from the public internet, and improving robustness via distributed edge nodes that eliminate single points of failure. The proposed solution has been deployed and evaluated in real-world scenarios on Beijing Metro Line 6. Experimental results demonstrate that the YOLOv8 model achieves a mean Average Precision (mAP@0.5) of 92.7% ± 0.4% for equipment detection, and the ResNet-18 classifier attains 95.8% ± 0.3% accuracy in distinguishing normal and abnormal statuses. Compared with a cloud-centric architecture, the EI-based system reduces the average end-to-end latency for anomaly detection tasks by 45% (28.5 ms vs. 52.1 ms) and significantly lowers daily bandwidth consumption by approximately 98.1% (from 40.0 GB to 0.76 GB) through an event-triggered evidence upload strategy involving images and short video clips, highlighting its superior real-time performance, security, robustness, and bandwidth efficiency.
Journal Article
Towards Green Innovation in Smart Cities: Leveraging Traffic Flow Prediction with Machine Learning Algorithms for Sustainable Transportation Systems
The emergence of smart cities has presented the prospect of transforming urban transportation systems into more sustainable and environmentally friendly entities. A pivotal facet of achieving this transformation lies in the efficient management of traffic flow. This paper explores the utilization of machine learning techniques for predicting traffic flow and its application in supporting sustainable transportation management strategies in smart cities based on data from the TRAFFIC CENSUS of the Hong Kong Transport Department. By analyzing anticipated traffic conditions, the government can implement proactive measures to alleviate congestion, reduce fuel consumption, minimize emissions, and ultimately improve quality of life for urban residents. This study proposes a way to develop traffic flow prediction methods with different methodologies in machine learning with a comparison with other results. This research aims to highlight the importance of leveraging machine learning technology in traffic flow prediction and its potential impact on sustainable transportation systems for the green innovation paradigm. The findings of this research have practical implications for transportation planners, policymakers, and urban designers. The predictive models demonstrated can support decision-making processes, enabling proactive measures to optimize traffic flow, reduce emissions, and improve the overall sustainability of transportation systems.
Journal Article
A Survey on Directed Acyclic Graph-Based Blockchain in Smart Mobility
This systematic review examines the integration of directed acyclic graph (DAG)-based blockchain technology in smart mobility ecosystems, focusing on electric vehicles (EVs), robotic systems, and drone swarms. Adhering to PRISMA guidelines, we conducted a comprehensive literature search across Web of Science, Scopus, IEEE Xplore, and ACM Digital Library, screening 1248 records to identify 47 eligible studies. Our analysis demonstrates that DAG-based blockchain addresses critical limitations of traditional blockchains by enabling parallel transaction processing, achieving high throughput (>1000 TPS), and reducing latency (<1 s), which are essential for real-time applications like autonomous vehicle coordination and microtransactions in EV charging. Key technical challenges include consensus mechanism complexity, probabilistic finality, and vulnerabilities to attacks such as double-spending and Sybil attacks. This study identifies five research priorities: (1) standardized performance benchmarks, (2) formal security proofs for DAG protocols, (3) hybrid consensus models combining DAG with Byzantine fault tolerance, (4) privacy-preserving cryptographic techniques, and (5) optimization of feeless microtransactions. These advancements are critical for deploying robust, scalable DAG-based solutions in smart mobility, and fostering secure and efficient urban transportation networks.
Journal Article
Effects of bus transit-oriented development (BTOD) on single-family property value in Seattle metropolitan area
Transit-Oriented Development (TOD) is considered to be a powerful model intended to achieve sustainable urban development. A well-designed TOD enhances the accessibility of different kinds of activities, reduces transportation costs and improves the comfort and safety of travel for the neighbourhood as whole, thereby increasing the willingness to pay for real estate properties located nearby. This study examines the housing price premiums of bus transit-oriented development (BTOD), a particular type of TOD that has become quite common in practice, especially in cities where public transportation is provided primarily through a bus system instead of a metro or light rail system. BTOD projects are built at major nodes of a bus network and typically include housing units and commercial services. Our research focuses on four completed BTODs in the Seattle metropolitan area, and employs data on sales prices, physical attributes, neighbourhood characteristics and location features for almost 7000 single-family homes located within a 1.5-mile radius. Using Hedonic price analysis, we find that these BTODs have generated significant positive effects on the values of adjacent homes, especially those located within 0.5 miles. Results from a more sophisticated longitudinal analysis using the data for Renton, one of the BTODs, confirm the price premiums while gaining additional insights about the temporal variations. These findings have an important policy implication, which is especially relevant for cities with an extensive bus transit system: local governments can generate additional tax revenues while advancing sustainability through bus transit-oriented developments.
公交导向式开发 (TOD) 被认为是旨在实现城市可持续发展的一种强大模式。精心设计的 TOD 增强了各种活动的可及性，降低了交通成本，提高了整个街区的出行舒适度和安全性，从而增加了人们对附近房地产的支付意愿。本研宄考察了公共汽车导向式开发 (BTOD) 带来的住房溢价。 BTOD 是特定类型的 TOD, 在现实中已经变得相当普遍，特别 是在主要通过公共汽车系统而不是地铁或轻轨系统提供公共交通的城市。 BTOD 项目建 在公交车网络的主要节点上，通常包括住房单元和商业服务。我们的研宄重点是西雅图大 都会地区四个完整的 BTOD, 并运用了距离 1.5 英里半径范围内的近 7,000 家单户住宅的 销售价格、物理属性、街区特征和位置特征数据。我们使用特征价格分析，发现这些 BTOD 对周边房屋的价值产生了显著的积极影响，特别是位于 0.5 英里距离内的房屋。通过使用其中一个 BTOD 项目 Renton 的数据进行更精细的纵向分析，结果确认了溢价，同 时获得了关于时间性变化的更多洞见。这些发现具有重要的政策意义，这对于拥有广泛的 公共汽车交通系统的城市尤为重要：地方政府可以通过公共汽车导向式开发推动可持续发 展，从而增加税收。
Journal Article
Efficient Large-Scale GPS Trajectory Compression on Spark: A Pipeline-Based Approach
Every day, hundreds of thousands of vehicles, including buses, taxis, and ride-hailing cars, continuously generate GPS positioning records. Simultaneously, the traffic big data platform of urban transportation systems has already collected a large amount of GPS trajectory datasets. These incremental and historical GPS datasets require more and more storage space, placing unprecedented cost pressure on the big data platform. Therefore, it is imperative to efficiently compress these large-scale GPS trajectory datasets, saving storage cost and subsequent computing cost. However, a set of classical trajectory compression algorithms can only be executed in a single-threaded manner and are limited to running in a single-node environment. Therefore, these trajectory compression algorithms are insufficient to compress this incremental data, which often amounts to hundreds of gigabytes, within an acceptable time frame. This paper utilizes Spark, a popular big data processing engine, to parallelize a set of classical trajectory compression algorithms. These algorithms consist of the DP (Douglas–Peucker), the TD-TR (Top-Down Time-Ratio), the SW (Sliding Window), SQUISH (Spatial Quality Simplification Heuristic), and the V-DP (Velocity-Aware Douglas–Peucker). We systematically evaluate these parallelized algorithms on a very large GPS trajectory dataset, which contains 117.5 GB of data produced by 20,000 taxis. The experimental results show that: (1) It takes only 438 s to compress this dataset in a Spark cluster with 14 nodes; (2) These parallelized algorithms can save an average of 26% on storage cost, and up to 40%. In addition, we design and implement a pipeline-based solution that automatically performs preprocessing and compression for continuous GPS trajectories on the Spark platform.
Journal Article
MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems
Highly populated cities depend highly on intelligent transportation systems (ITSs) for reliable and efficient resource utilization and traffic management. Current transportation systems struggle to meet different stakeholder expectations while trying their best to optimize resources in providing various transport services. This paper proposes a Microservice-Oriented Big Data Architecture (MOBDA) incorporating data processing techniques, such as predictive modelling for achieving smart transportation and analytics microservices required towards smart cities of the future. We postulate key transportation metrics applied on various sources of transportation data to serve this objective. A novel hybrid architecture is proposed to combine stream processing and batch processing of big data for a smart computation of microservice-oriented transportation metrics that can serve the different needs of stakeholders. Development of such an architecture for smart transportation and analytics will improve the predictability of transport supply for transport providers and transport authority as well as enhance consumer satisfaction during peak periods.
Journal Article
Combining sensor tracking with a GPS-based mobility survey to better measure physical activity in trips: public transport generates walking
Background
Policymakers need accurate data to develop efficient interventions to promote transport physical activity. Given the imprecise assessment of physical activity in trips, our aim was to illustrate novel advances in the measurement of walking in trips, including in trips incorporating non-walking modes.
Methods
We used data of 285 participants (RECORD MultiSensor Study, 2013–2015, Paris region) who carried GPS receivers and accelerometers over 7 days and underwent a phone-administered web mobility survey on the basis of algorithm-processed GPS data. With this mobility survey, we decomposed trips into unimodal trip stages with their start/end times, validated information on travel modes, and manually complemented and cleaned GPS tracks. This strategy enabled to quantify walking in trips with different modes with two alternative metrics: distance walked and accelerometry-derived number of steps taken.
Results
Compared with GPS-based mobility survey data, algorithm-only processed GPS data indicated that the median distance covered by participants per day was 25.3 km (rather than 23.4 km); correctly identified transport time vs. time at visited places in 72.7% of time; and correctly identified the transport mode in 67% of time (and only in 55% of time for public transport). The 285 participants provided data for 8983 trips (21,163 segments of observation). Participants spent a median of 7.0% of their total time in trips. The median distance walked per trip was 0.40 km for entirely walked trips and 0.85 km for public transport trips (the median number of accelerometer steps were 425 and 1352 in the corresponding trips). Overall, 33.8% of the total distance walked in trips and 37.3% of the accelerometer steps in trips were accumulated during public transport trips. Residents of the far suburbs cumulated a 1.7 times lower distance walked per day and a 1.6 times lower number of steps during trips per 8 h of wear time than residents of the Paris core city.
Conclusions
Our approach complementing GPS and accelerometer tracking with a GPS-based mobility survey substantially improved transport mode detection. Our findings suggest that promoting public transport use should be one of the cornerstones of policies to promote physical activity.
Journal Article