An automated approach from GPS traces to complete trip information

Transportation Research Part C: Emerging Technologies

2020

Self Cite

Blockchain has the potential to render the transaction of information more secure and transparent. Nowadays, transportation data are shared across multiple entities using heterogeneous mediums, from paper collected data to smartphone. Most of this data are stored in central servers that are susceptible to hacks. In some cases shady actors who may have access to such sources, share the mobility data with unwanted third parties. A multi-layered Blockchain framework for Smart Mobility Data-market (BSMD) is presented for addressing the associated privacy, security, management, and scalability challenges. Each participant shares their encrypted data to the blockchain network and can transact information with other participants as long as both parties agree to the transaction rules issued by the owner of the data. Data ownership, transparency, auditability and access control are the core principles of the proposed blockchain for smart mobility data-market. In a case study of real-time mobility data sharing, we demonstrate the performance of BSMD on a 370 nodes blockchain running on heterogeneous and geographically-separated devices communicating on a physical network. We also demonstrate how BSMD ensures the cybersecurity and privacy of individual by safeguarding against spoofing and message interception attacks and providing information access management control. Service 1 that "...(Waze will) share personal information with companies or organizations connected or affiliated with Waze...". So all the information collected by Waze could be shared with other organizations associated with Waze. However, the Terms of Service do not make it clear if the user can track with whom their information has been shared.Another point related to privacy is the access control of the information that is provided by the users. It was recently discovered that Google keeps collecting user location data even if they explicitly deactivate the tracking system in their mobiles [26]. Another example of disclosing information without the user's consent is the infamous Facebook-Cambridge Analytica scandal [9].The General Data Protection Regulation 2 (GDPR) is a step forward for privacy. Although the GDPR is valid only in the European Union, it is still expected to push multinational companies to be more transparent on how they manage people's private information. In the authors opinion, no matter how many rules and fines governments apply, given the current centralized ways of collecting people's data, some entities will always find a loophole in legislation or hackers will be able to tamper with these centralized data systems. In addition to laws, individuals have to have total control and ownership of their information. We need to be the guardians and responsible of our own privacy, unfortunately nowadays we have to trust third parties for that.Distributed ledger technologies like blockchain have the potential to give the people full control of their information, protect individual's personal mobility information and guard their privacy. The ...

Section: Discussionmentioning

confidence: 99%

A multi-layered blockchain framework for smart mobility data-markets

López

Transportation Research Part C: Emerging Technologies

2020

Self Cite

“…To assess the prediction accuracy of the CNN models and ensemble methods in this study, we developed a Decision Tree (DT) as a classical machine learning algorithm as well as a Random Forest (RF) model, which is an ensemble machine learning approach. We used several hand-crafted features, as explained in [18], including average speed (km/h), 85th percentile speed, maximum and minimum acceleration, travel time, etc. Like CNN models, there are hyper-parameters for DT and RF.…”

Section: Comparison With Classical Machine Learning Models and Prementioning

confidence: 99%

Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey

Yazdizadeh

Patterson

IEEE Trans. Intell. Transport. Syst.

2020

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We develop ensemble Convolutional Neural Networks (CNNs) to classify the transportation mode of trip data collected as part of a large-scale smartphone travel survey in Montreal, Canada. Our proposed ensemble library is composed of a series of CNN models with different hyper-parameter values and CNN architectures. In our final model, we combine the output of CNN models using "average voting", "majority voting" and "optimal weights" methods. Furthermore, we exploit the ensemble library by deploying a Random Forest model as a meta-learner. The ensemble method with random forest as meta-learner shows an accuracy of 91.8% which surpasses the other three ensemble combination methods, as well as other comparable models reported in the literature. The "majority voting" and "optimal weights" combination methods result in prediction accuracy rates around 89%, while "average voting" is able to achieve an accuracy of only 85%.

“…In a more recent study, Bantis and Haworth (2017) incorporated multiple smartphone data, as well as sociodemographic characteristic of travelers to develop a mode detection framework using Hidden Markov Models. Similarly, Yazdizadeh et al (2019a) trained three random forest classifies on GPS data enhanced with network information and social media data. Despite the higher accuracy achieved when adding sociodemographic information or social media data, gathering such data is not always feasible and can only be used as a complementary tool, and not as a replacement, to mobility surveys.…”

Section: Literature Reviewmentioning

confidence: 99%

A Semi-Supervised Deep Residual Network for Mode Detection in Wi-Fi Signals

Kalatian

J. Big Data Anal. Transp.

2020

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Inferring transportation mode of users in a network is of paramount importance in planning, designing, and operating intelligent transportation systems. Previous studies in the literature have mainly utilized GPS data. However, albeit the successful performances of models built upon such data, being limited to certain participants and the requirement of their involvement makes large scale implementations difficult. Due to their ubiquitous and pervasive nature, Wi-Fi networks have the potential to collect large scale, low-cost, passive and disaggregate data on multimodal transportation. In this study, by a passive collection of Wi-Fi network data on a congested urban road in downtown Toronto, we attempt to tackle the aforementioned problems. We develop a semi-supervised deep residual network (ResNet) framework to utilize Wi-Fi communications obtained from smartphones. Our semi-supervised framework enables utilization of an ample amount of easily collected low-cost unlabelled data, coupled with a relatively small-sized labelled data. By incorporating a ResNet architecture as the core of the framework, we take advantage of the high-level features not considered in the traditional machine learning frameworks. The proposed framework shows a promising performance on the collected data, with a prediction precision of 81.4% for walking, 80.5% for biking and 84.9% for the driving mode.