Uncovering the Uncertainties and Variability of Travel Time and Fuel Consumption Using Floating Car Data: A Case Study in Wuhan | Zendy

Wenxin Teng | Zendy; Fei Liu | Zendy; Jianbing Yang | Zendy; Chaoyang Shi | Zendy; Yunfei Zhang | Zendy; Fuqiang Wang | Zendy

Open Access

Uncovering the Uncertainties and Variability of Travel Time and Fuel Consumption Using Floating Car Data: A Case Study in Wuhan

Author(s) -

Wenxin Teng,

Fei Liu,

Jianbing Yang,

Chaoyang Shi,

Yunfei Zhang,

Fuqiang Wang

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3596034

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Understanding the probabilistic behavior of both travel time and fuel consumption is critical for improving urban traffic efficiency and developing reliable urban transportation systems. While previous studies have extensively examined travel time distributions, the joint distributional patterns of fuel consumption remain underexplored, limiting the effectiveness of green routing strategies. A novel data-driven framework, integrating map-matching, second-by-second trajectory interpolation, and microscopic fuel consumption estimation (CMEM) is developed for jointly analyzing the distributions and spatiotemporal variability characteristics of link-level travel time and fuel consumption using floating car data (FCD) in Wuhan, China. The results reveal that hourly travel time follows a Lognormal distribution for over 80% of road links, while fuel consumption is best represented by a Gamma distribution in more than 30% of links. Peak-hour analyses (08:00 and 18:00) indicate substantial increases in both the mean and 90th-percentile values of the two metrics, reflecting heightened uncertainty during congestion. Coefficients of variation are consistently higher on weekdays than on weekends, emphasizing increased variability in urban mobility. Hot spot analysis further shows that areas with dense traffic signals and commercial activities tend to form clusters of high travel time and fuel consumption. These findings highlight the necessity of incorporating reliability and energy efficiency considerations into urban traffic management and eco-routing navigation systems.

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