Accounting for taxi service conditions in estimating route travel time from floating car data using Markov chain model

Abstract

Recognising the variations in driving behaviour between taxis in the empty and carry conditions is pivotal for enhancing the accuracy of route travel time estimations using floating car data. However, existing methods largely overlook this distinction. In light of this, this study aims to harness these variations for more precise estimations. Utilising taxi data, we segmented the information by service conditions and executed distinct estimations for each segment. The route travel time was deduced through convolutional operation, complemented by a Markov chain model to discern correlations between travel times across various links. Our innovative approach realised a substantial enhancement in accuracy. Notably, when accounting for distinct service conditions, there was a reduction of 51.44% in mean absolute error and a 46.83% decline in maximum percentage error. By providing more accurate and reliable travel time predictions, our methodology enables better-informed traffic management decisions. Accurate travel time estimations are essential for optimising traffic signal timings, planning efficient routing strategies, and managing road network usage. These improvements in traffic management can lead to smoother traffic flow, reduced travel times, and ultimately, diminished congestion in urban areas.

Keywords

• Floating car data
• Service conditions
• Route travel time
• Markov chain model
• Driving behaviour