Travel Time Distribution under Interrupted Flow and Application to Travel Time Reliability

Abstract

The travel time distribution under interrupted flow based on radio frequency identification–detected data is analyzed. The urban road network studied is in downtown Nanjing, Jiangsu Province, in China, where video cameras and radio frequency equipment are installed at some arterial links to acquire traffic flow data including vehicle type, passing time, and spot speed. On the basis of the radio frequency identification data, the travel time distribution under interrupted flow shows a bimodal curve instead of a unimodal curve. This finding suggests that travel times under interrupted flow are not independent and identically distributed and can be divided into two components, the uninterrupted component and the interrupted component, according to whether vehicles experience intersection control delay. Six convex distribution models are presented, and an expectation–maximization algorithm is adopted to obtain the best distribution for each link unit. The results indicate that travel time under interrupted flow generally follows a bimodal distribution. The parameters of the models can distinguish travel times between those encountering delay and those without delay. This finding demonstrates that intersection control delay plays a predominant role in travel times under interrupted flow. Two main travel time reliability indexes under interrupted flow obtained by the parameters of the bimodal distribution, expected travel time and RATIO, are presented as applications. Expected travel times indicate the average levels of travel times of two independent components, and RATIO represents the ratio of the uncertain part of the travel times. The analysis indicates that these two indexes can capture the characteristics of interrupted-flow travel time reliability, which is mainly caused by link length, number of intersections, and mixed traffic flow.

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].…”

“…Signal delay is the primary factor that influences travel time patterns on an urban link [3]. Unfortunately, in most cases, the signal control plans at intersections are generally unknown.…”

“…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.…”

“…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.…”

Effect of Signal Control on Bimodal Travel Time Distributions

“…Susilawati et al (9) found that bimodal travel times were more likely to occur on shorter links with coordinated signal control, but did not investigate the impact of the traffic control scheme on the distribution. Other research has focused on model development (10), and the relationship between bimodal travel time distributions and travel time reliability (11,12) and congestion identification (13).…”