The Impact of Experience in Service Virtualization on Travel Intention - The Case of Forbidden City Tour

The advent of Internet and home shopping economy in the recent years has reduced the intention of people to leave home for sightseeing. This has significantly impacted the growth of physical tourism industry. This paper utilizes the virtual tour of Forbidden City to conduct a sequence of experiments in tourism experience. Before using the system, Theory of Planned Behavior and Involvement are employed to measure the intention of traveling. After then, two constructs, emotion and system, are adopted to explore how the experiential value of virtual tourism impacts the intention of travelling. The experience of tour virtualization allows customers to create unforgettable feelings in the virtual world. It can affect not only the customer’s experiential value of virtual tourism, but also intention of traveling in the future

B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction

Federated Learning (FL) is a privacy-preserving machine learning (ML) technology that enables collaborative training and learning of a global ML model based on aggregating distributed local model updates. However, security and privacy guarantees could be compromised due to malicious participants and the centralized FL server. This article proposed a bi-level blockchained architecture for secure federated learning-based traffic prediction. The bottom and top layer blockchain store the local model and global aggregated parameters accordingly, and the distributed homomorphic-encrypted federated averaging (DHFA) scheme addresses the secure computation problems. We propose the partial private key distribution protocol and a partially homomorphic encryption/decryption scheme to achieve the distributed privacy-preserving federated averaging model. We conduct extensive experiments to measure the running time of DHFA operations, quantify the read and write performance of the blockchain network, and elucidate the impacts of varying regional group sizes and model complexities on the resulting prediction accuracy for the online traffic flow prediction task. The results indicate that the proposed system can facilitate secure and decentralized federated learning for real-world traffic prediction tasks.Comment: Paper accepted for publication in IEEE Transactions on Services Computing (TSC

Data Collection and Processing Methods for the Evaluation of Vehicle Road Departure Detection Systems

Road departure detection systems (RDDSs) for avoiding/mitigating road departure crashes have been developed and included on some production vehicles in recent years. In order to support and provide a standardized and objective performance evaluation of RDDSs, this paper describes the development of the data acquisition and data post-processing systems for testing RDDSs. Seven parameters are used to describe road departure test scenarios. The overall structure and specific components of data collection system and data post-processing system for evaluating vehicle RDDSs is devised and presented. Experimental results showed sensing system and data post-processing system could capture all needed signals and display vehicle motion profile from the testing vehicle accurately. Test track testing under different scenarios demonstrates the effective operations of the proposed data collection system

Alternate Interchange Signing Study for Indiana Highways

The main objectives of this research were to (1) understand signing issues from the perspective of drivers and (2) develop recommendations for improving interchange signing in Indiana to aid driver understanding and increase the safety and efficiency of highway traffic operations. An online survey with specific questions was designed and distributed through email, social media, online newspapers, and a survey company with the goal of better understanding driver thinking when approaching decision-making areas on the interstate. The analysis of the survey results revealed the following. Drivers usually do not know the interchange types as they approach an interchange on the freeway. Drivers are most interested in which lanes they should be in when approaching an interchange, even in advance of typical signing locations. Drivers do not like signs that require cognitive work since it will delay their driving decision by creating uncertainty. Different drivers need different types of information from signs, such as cardinal direction, destination name, road name, and lane assignments. Therefore, a perfect sign for one driver may be confusing or information overload for another driver. In some instances, a driver who is familiar with the area is confused by the signs because the sign information contradicts the driver’s knowledge

BFRT: Blockchained Federated Learning for Real-time Traffic Flow Prediction

Accurate real-time traffic flow prediction can be leveraged to relieve traffic congestion and associated negative impacts. The existing centralized deep learning methodologies have demonstrated high prediction accuracy, but suffer from privacy concerns due to the sensitive nature of transportation data. Moreover, the emerging literature on traffic prediction by distributed learning approaches, including federated learning, primarily focuses on offline learning. This paper proposes BFRT, a blockchained federated learning architecture for online traffic flow prediction using real-time data and edge computing. The proposed approach provides privacy for the underlying data, while enabling decentralized model training in real-time at the Internet of Vehicles edge. We federate GRU and LSTM models and conduct extensive experiments with dynamically collected arterial traffic data shards. We prototype the proposed permissioned blockchain network on Hyperledger Fabric and perform extensive tests using virtual machines to simulate the edge nodes. Experimental results outperform the centralized models, highlighting the feasibility of our approach for facilitating privacy-preserving and decentralized real-time traffic flow prediction.Comment: Published in 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid

A Wearable Data Collection System for Studying Micro-Level E-Scooter Behavior in Naturalistic Road Environment

As one of the most popular micro-mobility options, e-scooters are spreading in hundreds of big cities and college towns in the US and worldwide. In the meantime, e-scooters are also posing new challenges to traffic safety. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than the pedestrains and bicyclists. These features make e-scooters challenging for human drivers, pedestrians, vehicle active safety modules, and self-driving modules to see and interact. To study this new mobility option and address e-scooter riders' and other road users' safety concerns, this paper proposes a wearable data collection system for investigating the micro-level e-Scooter motion behavior in a Naturalistic road environment. An e-Scooter-based data acquisition system has been developed by integrating LiDAR, cameras, and GPS using the robot operating system (ROS). Software frameworks are developed to support hardware interfaces, sensor operation, sensor synchronization, and data saving. The integrated system can collect data continuously for hours, meeting all the requirements including calibration accuracy and capability of collecting the vehicle and e-Scooter encountering data.Comment: Conference: Fast-zero'21, Kanazawa, Japan Date of publication: Sep 2021 Publisher: JSA

Collision-Free Path Planning for Automated Vehicles Risk Assessment via Predictive Occupancy Map

Vehicle collision avoidance system (CAS) is a control system that can guide the vehicle into a collision-free safe region in the presence of other objects on road. Common CAS functions, such as forward-collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However, these CASs focus on mitigating or avoiding potential crashes with the preceding cars and objects. They are not effective for crash scenarios with vehicles from the rear-end or lateral directions. This paper proposes a novel collision avoidance system that will provide the vehicle with all-around (360-degree) collision avoidance capability. A risk evaluation model is developed to calculate potential risk levels by considering surrounding vehicles (according to their relative positions, velocities, and accelerations) and using a predictive occupancy map (POM). By using the POM, the safest path with the minimum risk values is chosen from 12 acceleration-based trajectory directions. The global optimal trajectory is then planned using the optimal rapidly exploring random tree (RRT*) algorithm. The planned vehicle motion profile is generated as the reference for future control. Simulation results show that the developed POM-based CAS demonstrates effective operations to mitigate the potential crashes in both lateral and rear-end crash scenarios