Variability of daily car usage and the frequency of long-distance driving

The limited electric range of battery electric vehicles (BEV) and plug-in hybrid electric vehicles (PHEV) requires an understanding of the variation in day-to-day driving and the frequency of long-distance driving. Existing literature suggests high regularity of human mobility. However, large longitudinal mobility samples for empirical tests are hardly available. Here, we analyze the regularity of daily vehicle kilometers travelled (VKT) of 10,000 vehicles observed between two months and several years and quantify the regularity of daily VKT and the frequency of long-distance driving. Our results indicate limited regularity of daily VKT beyond one day of time lag (mean autocorrelation ≤ 0.11). Long-distance driving with daily km over 100 km (200 km) typically take place on less than 20% (5% for 200 km) of driving days but make up 40% (18%) of annual VKT. Our results have implications for sustainable transport research and the design of travel surveys

Exploring human mobility for multi-pattern passenger prediction : a graph learning framework

Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents with fixed routes and schedules, which leads to latent travel regularity. However, human mobility patterns, specifically the complex relationships between bus passengers, are deeply hidden in this fixed mobility mode. Although many models exist to predict traffic flow, human mobility patterns have not been well explored in this regard. To address this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN). Firstly, we construct a novel sharing-stop network to model relationships between passengers based on bus record data. Then, we employ GCN to extract features from the graph by learning useful topology information and introduce a deep clustering method to recognize mobility patterns hidden in bus passengers. Furthermore, to fully utilize spatio-temporal information, we propose GCN2Flow to predict passenger flow based on various mobility patterns. To the best of our knowledge, this paper is the first work to adopt a multi-pattern approach to predict the bus passenger flow by taking advantage of graph learning. We design a case study for optimizing routes. Extensive experiments upon a real-world bus dataset demonstrate that MPGCN has potential efficacy in passenger flow prediction and route optimization. © 2000-2011 IEEE