Open AccessUsing ALPR data to understand the vehicle use behaviour under TDM measures
Author(s) -
Chang Yujiao,
Duan Zhengyu,
Yang Dongyuan
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5233
Subject(s) - computer science , transport engineering , engineering
In Shanghai, China, two transportation demand management (TDM) measures, auctions of Shanghai vehicle licence plates and a narrow‐time‐based travel restriction policy, have been implemented to control the vehicle ownership and the use of vehicles registered outside Shanghai (VROS). To investigate the impact of these two TDM measures on VROS, vehicle use behaviour is analysed with automatic licence plate recognition (ALPR) data. A two‐step k ‐means clustering algorithm is proposed to classify VROS' use behaviour from ALPR raw data. Moreover, the spatiotemporal patterns of each type of VROS and the structure of total transportation demand are analysed. The results show that VROS in Shanghai expressway network can be classified into five types. Type 1 vehicles are used for commuting (COM), and type 2 vehicles are used with high intensity during both on workdays and non‐workdays (HHI). COM and HHI are mainly used by local Shanghai residents who cannot obtain a local licence plate under the auction policy. These two types of VROS only account for 3.6% of total vehicles but generate 14.2% of total traffic demand. If the users of COM and HHI transferred to public transit, the traffic congestion of expressway network would be greatly alleviated.