Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

Abstract

Travel time is a basic measure based on which intelligent transportation systems such as traveller information systems, traffic management systems, public transportation systems are developed. Although many methodologies have been proposed, they have not yet adequately solved many challenges associated with travel time, in particular, travel time estimation for all links in a large and dynamic urban traffic network is still an open problem that needs addressing. Typically focus is placed on major roads such as motorways and main city arteries but there is an increasing need to know accurate travel times for minor urban roads. Such information is crucial for tackling air quality problems, accommodate the growing number of cars and provide accurate information for routing. This study aims to address the aforementioned challenges by introducing a methodology, namely Similar Model Searching (SMS), to estimate travel times by using historical sparse travel time data. The SMS learns the temporal and spatial relationship between the travel time of adjacent links and utilise labelled data of similar models in order to improve its overall performance. The effectiveness of the proposed method is evaluated on a section of Leicestershire traffic network in the UK. The obtained results show that SMS efficiently estimates travel time of target links using models of adjacent traffic links.

Urban Origin–Destination Travel Time Estimation Using K-Nearest-Neighbor-Based Methods

“…Relying solely on GPS data has limitations, as it may not capture the complete picture of traffic conditions, especially in areas with limited GPS coverage or when the sample size is insufficient [18] and/or sparse [19,20]. Therefore, there are cases where there is partial information, such as the location of origin (O) and destination (D), and it is possible to estimate the travel times between O and D, but not the complete trajectory of the trip.…”