Minimum Sampling Size of Floating Cars for Urban Link Travel Time Distribution Estimation

Abstract

Despite the wide application of floating car data (FCD) in urban link travel time estimation, limited efforts have been made to determine the minimum sample size of floating cars appropriate to the requirements for travel time distribution (TTD) estimation. This study develops a framework for seeking the required minimum number of travel time observations generated from FCD for urban link TTD estimation. The basic idea is to test how, with a decreasing the number of observations, the similarities between the distribution of estimated travel time from observations and those from the ground-truth vary. These are measured by employing the Hellinger Distance (HD) and Kolmogorov-Smirnov (KS) tests. Finally, the minimum sample size is determined by the HD value, ensuring that corresponding distribution passes the KS test. The proposed method is validated with the sources of FCD and Radio Frequency Identification Data (RFID) collected from an urban arterial in Nanjing, China. The results indicate that: (1) the average travel times derived from FCD give good estimation accuracy for real-time application; (2) the minimum required sample size range changes with the extent of time-varying fluctuations in traffic flows; (3) the minimum sample size determination is sensitive to whether observations are aggregated near each peak in the multistate distribution; (4) sparse and incomplete observations from FCD in most time periods cannot be used to achieve the minimum sample size. Moreover, this would produce a significant deviation from the ground-truth distributions. Finally, FCD is strongly recommended for better TTD estimation incorporating both historical trends and real-time observations.

Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches

“…Considering a free-flow traffic condition, the travel times of vehicles traversing a signalized link directly depend on the signal controls and their distribution appears to be bimodal [1][2][3][4][5]7], which features two clusters of travel times corresponding to the states of nonstopped and stopped vehicles, respectively. To achieve the state partitions, a two-component mixed normal distribution, rather than the skewed mixture models (e.g., the components are both lognormal distributions), is adopted to obtain the travel times in state 1 and state 2 individually as a tradeoff between computation complexity and explanatory power [18].…”

“…Many works have proposed analytical or data-driven models to infer mean link travel time from this data, but yet research on the estimation of link TTD is still evolving, especially when the travel times appear to be multistate features. In reality, the travel times of vehicles traversing an urban link are heavily affected by traffic lights, interaction among vehicles, and conflicting traffic from cross streets [1][2][3][4][5]. The characteristics of interrupted traffic flow would likely result in different clusters of travel times, which reflect how vehicles experienced traffic states on a link.…”

“…Unfortunately, the spatial distribution of floating cars is not uniform. Taxicabs, which have been suggested as the main types of floating cars [6], are used widely to collect travel time information for urban link travel time and congestion estimation [4,5,7]. However, taxicabs cover mainly densely populated and 2 Journal of Advanced Transportation commercially well-developed areas, since these areas may have higher passenger flow and taxicabs are more likely to pick up passengers.…”

“…RFID sensors scan the vehicle plate with high frequency of 20 milliseconds and mainly record plate number, timestamp, and station number when tagged vehicles pass by the RFID stations. More than 80% of vehicles on arterials are detected by RFID sensors [3][4][5]. Hence RFID can almost obtain the information of all the passing vehicles which constitutes a full-sample data.…”

Copula-Based Travel Time Distribution Estimation Considering Channelization Section Spillover

“…They further used the Gaussian Mixture Model (GMM) to fit the link-level TTD and proved the superiority of GMM in supporting service reliability analysis [10]. Using the sources of floating car data, Yun and Qin developed a framework for determining the minimum sample size of floating cars to estimate TTD in situations when link travel times may follow multistate distributions [11]. Hans and Chiabaut proposed an aggregated diagram that provided a direct assessment of vehicle travel times with respect to their departure and traffic flow [12].…”