Verification and Employment of Crowd-Sourcing Data in Road Safety Assessment

In recent years, a variety of crowd-sourcing data have been addressed in road safety studies with the popularization of mobile intelligent terminals. In contrast to traditional crash record data, crowd-sourcing data have the potential to reflect more detailed insights into road safety performance. In this paper, we proposed a novel method to conduct road safety assessments based on crowd-sourcing data provided by AutoNavi software Co. Ltd. Basically, with the user-report crash records as a safety indicator, we adopted both a traditional crash prediction model and a machine learning model to evaluate the safety performance by taking various risk factors into account. Alternatively, some dangerous driving behaviors such as speeding, rapid accelerations or sharp turn may also be considered as safety indicators. We thus examined the correlations of these indicators and traffic crash records, and in further, innovatively revealed the substitutability and applicability of dangerous driving indicators for particular road types. Findings in this paper can be used to disclose alternative indicators for traffic crashes and emergencies. We also believe that crowd-sourcing data are worth further exploration to bring its full potential in road safety assessments.