Abstract
Real-time data about various traffic events and conditions—offences, accidents, dangerous driving, or dangerous road conditions—is crucial for safe and efficient transportation. Unlike roadside infrastructure data which are often limited in scope and quantity, crowdsensing approaches promise much broader and comprehensive coverage of traffic events. However, to ensure safe and efficient traffic operation, assessing trustworthiness of crowdsourced data is of crucial importance; this also includes detection of intentional or unintentional manipulation, deception, and spamming. In this paper, we design and demonstrate a road traffic event detection and source reputation assessment system for unreliable data sources. Special care is taken to adapt the system for operation in decentralized mode, using smart contracts on a Turing-complete blockchain platform, eliminating single authority over such systems and increasing resilience to institutional data manipulation. The proposed solution was evaluated using both a synthetic traffic event dataset and a dataset gathered from real users, using a traffic event reporting mobile application in a professional driving simulator used for driver training. The results show the proposed system can accurately detect a range of manipulative and misreporting behaviors, and quickly converges to the final trust score even in a resource-constrained environment of a blockchain platform virtual machine.
Highlights
Road transport is a cornerstone of modern society; despite many alternative modes of transport, vehicular traffic remains prevalent for personal mobility
We propose a smart-contract-based mechanism for truth discovery in a traffic event-reporting scheme
The proposed event detection and source reputation assessment mechanism was designed with decentralized implementation in mind
Summary
Road transport is a cornerstone of modern society; despite many alternative modes of transport, vehicular traffic remains prevalent for personal mobility. Traffic and road information acquisition is based on two principal models It can be sourced from roadside infrastructure (induction loops, surveillance cameras, speed cameras, Radio Frequency Identification (RFID) tags, etc.); such sources typically produce small amounts of high-quality data. It can leverage crowdsensing, by pooling large quantities of lower-quality data, and applying statistical modeling techniques to clean such data. Many providers use this approach, leveraging mobile terminals with Global Navigation Satellite System (GNSS) support and mobile apps to submit anonymized or pseudonymized data points of people’s location
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