Open AccessEffects of predictive horizon on network performance under short‐term predictive information
Author(s) -
Phusittrakool Apirath,
Jeenanunta Chawalit,
Prathombutr Passakon
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.2017.0007
Subject(s) - term (time) , time horizon , computer science , horizon , predictive value , model predictive control , operations research , engineering , mathematical optimization , artificial intelligence , mathematics , medicine , physics , geometry , control (management) , quantum mechanics
Providing predictive information is expected to be an effective way in reducing congestion because travellers can have information of future traffic conditions and plan their travel ahead of time. However, due to the complex and computational expense of traffic flow forecasting, only short horizon future travel times can be generated and provided, which seems not sufficient for travellers to accurately plan their travel along an entire path. In this study, the authors have developed a simulation model to investigate the impact that limited time‐span predictions have on the effectiveness of the predictive information. The results indicate that even though the predicted horizon is very short, the predictive information can still perform better than the current prevailing information. Sensitivity analysis of market penetration rates and prediction horizons is also discussed. This study is expected to be guidance for practical implementation and operation of short‐term predictive information scenarios.