A novel arterial travel time distribution estimation model and its application to energy/emissions estimation

Abstract

Arterial travel time information is crucial to advanced traffic management systems and advanced traveler information systems. An effective way to represent this information is the estimation of travel time distribution. In this paper, we develop a modified Gaussian mixture model in order to estimate link travel time distributions along arterial with signalized intersections. The proposed model is applicable to traffic data from either fixed-location sensors or mobile sensors. The model performance is validated using real-world traffic data (more than 1,400 vehicles) collected by the wireless magnetic sensors and digital image recognition in the field. The proposed model shows high potential (i.e., the correction rate are above 0.9) to satisfactorily estimate travel time statistics and classify vehicle stop versus non-stop movements. In addition, the resultant movement classification application can significantly improve the estimation of traffic-related energy and emissions along arterial.

Analysis on Link Travel Time Estimation considering Time Headway Based on Urban Road RFID Data

“…Jenelius and Koutsopoulos [16] used a multivariate probabilistic statistical principal component analysis model to predict the travel time of urban roads based on floating car data. From the perspective of energy/emission estimation, Yang et al [17] proposed an improved Gaussian mixture model to fit urban road travel time distribution characteristics, and the actual collected data were analysed and studied. Ma et al [18] used the Markov process to estimate the probability distribution of travel time based on the correlation between time and space.…”

Understanding Factors Affecting Arterial Reliability Performance Metrics

“…Of the arterial-related work, areas of focus include travel time prediction, (Polus, 1979;Sen et al, 1996;Liu et al, 2006a;Liu et al, 2006b); travel time estimation (H. X. Liu and Ma, 2009;Chan et al, 2009;Hans et al, 2015;Skabardonis and Geroliminis, 2005); and travel time distribution (Hans et al, 2015;Chen et al, 2017;Zheng et al, 2017;Ramezani and Geroliminis, 2012;Yang et al, 2018). Although these works provide significant insights as it pertains to travel time on arterials, the effects of specific factors on expected travel time and travel time standard deviation remain unknown.…”

Urban travel time reliability at different traffic conditions

“…Chen et al (2017) proposed a copula-based model to estimation path travel time for urban arterial roads considering stochastic characteristics of segments. Yang et al (2017) proposed a Gaussian mixture model to estimate travel time distributions for urban arterial road. Apart from modeling travel time variability with field data, Kim et al (2013) employed traffic simulation models to derive travel time distributions under different scenarios considering various demand and supply uncertainty factors, such as weather, traffic incidents, work zones, traffic control.…”