DwaRa: A Deep Learning-Based Dynamic Toll Pricing Scheme for Intelligent Transportation Systems

【Author】 Shukla, Arpit; Bhattacharya, Pronaya; Tanwar, Sudeep; Kumar, Neeraj; Guizani, Mohsen

【Source】IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY

【影响因子】6.239

【Abstract】In Internet-of-Vehicles (IoV) ecosystems, intelligent toll gates (ITGs) connect nearby metropolitan cities through smart highways. At ITGs, existing solutions integrate blockchain (BC) and deep-learning schemes to leverage trusted and responsive analytics support for connected smart vehicles (CSVs) at ITGs. BC eliminates third-party intermediaries, and secures payments between vehicle owners (VO) and governing authorities (GA). Deep-Learning, on the other hand, facilitates accurate predictions for diverse and complex urban traffic conditions. However, due to fixed toll pricing schemes based on connected smart vehicles (CSV) type, VOs suffer from variable delays at different lanes due to dynamic congestion scenarios. To address the research gaps of such a fixed pricing schemes, we propose a BC-envisioned scheme DwaRa, that operates in three phases. In the first phase, future traffic is predicted based on Markov queues to balance the congestion at different lanes at ITGs efficiently. Then, we propose a novel spatially induced-long-short term memory (SI-LSTM) model to predict current traffic and weather based on historical repositories. Second, based on inputs by the Markov model, SI-LSTM, lane type, and vehicle type, a dynamic pricing algorithm is presented to improve the quality of experience (QoE) of the VO. Finally, based on dynamic price fixation between the VO and the GA, smart contracts (SCs) are executed and transactional data is secured through BC. The proposed scheme is compared against parameters like average mean-squared error (MSE), predicted traffic, scalability, interplanetary file system (IPFS) storage, computation (CC), and communication cost (CCM). At n = 100 test samples, and arrival rate beta = 80, the obtained MSE is 0.0012, with a peak average value of 0.00526. The overall CC is 45.88 milliseconds (ms) and CCM is 53 bytes that indicate the proposed scheme efficacy against conventional approaches.

【Keywords】Vehicle dynamics; Pricing; Heuristic algorithms; Predictive models; Logic gates; Markov processes; Prediction algorithms; Intelligent transportation systems; deep-learning; blockchain; Markov queues; smart contracts; IPFS

【发表时间】2020 NOV

【收录时间】2022-01-02

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