Driver Side and Traffic Based Evaluation Model for On-Street Parking Solutions

TL;DR

This paper introduces DSTBM, a comprehensive evaluation model for on-street parking solutions, focusing on practical assessment from drivers' perspectives and comparing fixed and mobile sensing methods.

Contribution

It develops a standardized evaluation scheme for parking solutions, addressing the gap between theoretical accuracy and practical applicability.

Findings

DSTBM effectively evaluates parking solutions from drivers' perspective.

Fixed sensing outperforms mobile sensing in prediction accuracy.

DSTBM is compatible with existing evaluation schemes.

Abstract

Parking has been a painful problem for urban drivers. The parking pain exacerbates as more people tend to live in cities in the context of global urbanization. Thus, it is demanding to find a solution to mitigate d rivers' parking headaches. Many solutions tried to resolve the parking issue by predicting parking occupancy. Their focuses were on the accuracy of the theoretical side but lacked a standardized model to evaluate these proposals in practice. This paper develops a Driver Side and Traffic Based Evaluation Model (DSTBM), which provides a general evaluation scheme for different parking solutions. Two common parking detection methods, fixed sensing and mobile sensing are analyzed using DSTBM. The results indicate first, DSTBM examines different solutions from the driver's perspective and has no conflicts with other evaluation schemes; second, DSTBM confirms that fixed sensing performs better than mobile sensing in terms of prediction accuracy.