Fangfang Zheng

Uncertainty and Predictability of Urban Link Travel Time: Delay Distribution–Based Analysis

Travel times that vehicles experience in urban road networks are intrinsically uncertain because of the stochastic character of delays at signalized intersections. The ability to capture delay characteristics at signalized intersections is critical for estimating and predicting travel times on urban links. Much research has been done to predict travel time on urban links on the basis of traffic state information (e.g., volumes or speeds). However, the results have not been promising. One important reason is that delays experienced by vehicles on urban links are uncertain because of both traffic conditions and traffic control at intersections. This paper addresses the causes of travel time uncertainty. A probabilistic delay distribution model with stochastic arrivals and departures is proposed to investigate delay uncertainty in both undersaturated and oversaturated conditions. The delay distributions with the Poisson arrival process and with the binomial arrival process are compared. Results show that different arrival patterns have little influence on the delay distribution in undersaturated conditions, although they have significant influence on the delay distribution in oversaturated conditions. The delay distributions under different degrees of saturation are overlapping, which indicates that it is difficult to determine the traffic state for a given single-valued delay and vice versa. The “width” of the delay distribution based on percentiles is used to quantify the delay uncertainty at signalized intersections. The dynamics of delay uncertainty and of delay uncertainty under different degrees of saturation are investigated.

Using Probe Vehicle Data for Traffic State Estimation in Signalized Urban Networks

Probe Vehicle Data (PVD) is becoming more and more common for the collection of information about the traffic state. In most cases, the information that can be obtained from a probe vehicle refers to the position, the speed and the direction of movement at certain time intervals. Especially in urban networks, the raw GPS data needs a cleaning process to map the measured position to the road network. The cleaned information about positions in the network at fixed moments where GPS signals are collected can be used to derive travel time along certain routes.

Modeling Variability of Urban Travel Times by Analyzing Delay Distribution for Multiple Signalized Intersections

It is widely accepted that travel times in urban road networks can vary greatly within a short period as well as from period to period in a single day. One important cause for this variability in urban travel times is the delay that drivers experience at intersections, particularly signalized intersections. This delay can be due to the time a driver needs to wait for a red light or the time needed for a queue of waiting vehicles at an intersection to dissolve, or both. As a result, the ability to capture the characteristics of delay at signalized intersections is a critical, yet challenging, step in estimating and predicting urban link travel times. The uncertainties in these experienced delays are caused by traffic conditions and traffic control at the intersections. In this paper a delay distribution model is developed for an urban trip over a route with two consecutive fixed-time controlled intersections. The proposed model captures the stochasticity in the arrival and departure process at the first intersection. An example illustrates how these stochastic factors influence the realized delay distribution for the two consecutive signalized intersections. Delay distributions are analyzed for different levels of mismatch of the two controlled intersections (from well coordinated to badly coordinated) under different degrees of saturation. Finally, the predicted delay distributions are validated with the VISSIM simulation model and show that the proposed (analytical) model can represent the VISSIM simulation data.

The Development and Calibration of a Model for Urban Travel Time Distributions

Travel times on the urban roadways are intrinsically uncertain. For known traffic conditions, a wide travel time distribution can be observed. Among all the components of travel times, delays incurred when approaching intersections constitute a large part of travel times that vehicles experience in urban trips. In this article, a model is presented for the delay distribution function for an urban trip with two fixed-time controlled intersections. Most parameters of the model are related to traffic control and flows, which can be directly calibrated from observations. The overflow queue distribution provides important parameters in the delay distribution function that have to be calibrated indirectly from traffic measurements, for example, from the measured delays and flows. Based on the directly observed and estimated model parameters, the delay distribution can be reconstructed. This article discusses the calibration procedure for delays. Two parameter estimation methods, namely, maximum likelihood (ML) and least squares (LS), are applied to estimate the overflow queue distribution from sample data simulated by VISSIM. Results show that estimation accuracy is not so susceptible to the choice of the estimation methods, sampling techniques, and sample size. The proposed delay model is able to successfully capture the delay process.

Investigating the Feasibility of Urban Link Travel Time Estimation Based on Probe Vehicle Data

For the past several years, the interest in using probe vehicles for monitoring is growing. In this paper, vehicles equipped with GPS are used as traffic sensors to collect traffic data (speed, position and time stamps) on urban road networks. A method developed (by Bruce Hellinga, 2008) is adopted to decompose travel times collected by probe vehicles between two consecutive time stamps into individual links. The estimated link travel times are compared with travel times from video cameras. The results show that link travel times estimated using Hellingas method deviate significantly from travel times measured by cameras, especially when there is congestion or vehicles need to wait for the red light on the intersection. From the measured travel time, the authors also notice that travel times on urban links are quite variable during different times of day. There seems no clear travel time pattern which puts a great challenge for the travel time estimation and prediction on the urban road network.

Reconstruction of delay distribution at signalized intersections based on traffic measurements

In an urban road network, travel times are not uniquely determined by the traffic states due to stochastic properties of traffic flow, stochastic arrivals and departures at intersections and traffic signal control. As a result, for a given traffic state, a range of travel times (delays) is found. This can be represented by a distribution of travel times (delays). Calibrating a model for the travel time only for the expectation value gives a large ‘noise’ such that the model will have little value for the prediction purpose. In this paper, the delay distribution function as derived from the analytical model under different circumstances is introduced. The overflow queue distribution which is the parameter in the delay distribution function is estimated based on traffic measurements, e.g., the measured delays, flows and cycle time. The Least Squares (LS) and Maximum Likelihood (ML) Estimation are used to perform the parameter estimation in the delay distribution. The Genetic Algorithm (GA) is applied to find the optimal solution for the objective functions in terms of minimizing square error and maximizing the likelihood function. Based on the estimated model parameters, the delay distribution is reconstructed. The estimated delay distribution is compared with that obtained from VISSIM simulation. Results show that both ML and LS estimation methods perform well in the undersaturated condition. While in the oversaturated condition, the ML method performs considerably better than the LS method.

Comparison of Urban Link Travel Time Estimation Models Based on Probe Vehicle Data

The information collected by probe vehicles on the urban signalized network is widely used for traffic monitoring. Traffic conditions could be inferred based on the data collected by these probe vehicles. In recent years, the attempts to estimate link travel time based on probe vehicle data (e.g. positions, time stamps and speeds) are arising. However, due to the low polling frequencies (e.g. 1min or 5min), travel times recorded by probe vehicles provide only partial link or route travel times. In this paper, a three-layer Artificial Neural Network (ANN) model is proposed to estimate the complete link travel time for each individual probe vehicle. The information including positions, time stamps and speeds is input into the model and the output is the complete link travel time. The model is evaluated using the data from vissim simulation model. The results are compared with those from Hellinga’s model and distance-proportion model. The evaluation results show that the proposed ANN model performs much better than Hellinga’s model and distance-proportion model both in undersaturated conditions and oversaturated conditions.

A Methodological Framework of Travel Time Distribution Estimation for Urban Signalized Arterial Roads

Urban travel times are rather variable as a result of a lot of stochastic factors both in traffic flows, signals, and other conditions on the infrastructure. However, the most common way both in literature and practice is to estimate or predict only expected travel times, not travel time distributions. By doing so, it fails to provide full insight into the travel time dynamics and variability on urban roads. Another limitation of this common approach is that the effect of traffic measures on travel time reliability cannot be evaluated. In this paper, an analytical travel time distribution model is presented especially for urban roads with fixed-time controlled intersections by investigating the underlying mechanisms of urban travel times. Different from mean travel time models or deterministic travel time models, the proposed model takes stochastic properties of traffic flow, stochastic arrivals and departures at intersections, and traffic signal coordination between adjacent intersections into account, a...

Trip travel time distribution prediction for urban signalized arterials

Travel time prediction is a challenge, especially if we consider urban trips. For freeways well-known models for traffic flow and speeds are applicable, e.g., based on physical models inspired by hydrodynamic or statistical models ranging from more conventional to more advanced AI approaches. While for urban trips the traffic flow models are more complicated because, next to vehicle-vehicle interaction, also the influence of traffic signals has to be modeled. In this paper, a trip travel time distribution model for urban roads with fixed-time controlled intersections is introduced. The model explicitly considers urban traffic characteristics, including stochastic traffic processes at intersections, stochastic properties of traffic flow and signal coordination between intersections. Based on the proposed model, a trip travel time distribution prediction procedure is discussed, which considers time-varying demand and traffic control schemes. The model predicted results are further compared with those from VISSIM simulation data. It shows that the proposed trip travel time distribution prediction model can perform well both for undersaturated conditions and oversaturated conditions.

Urban travel time reliability at different traffic conditions

ABSTRACT The decision making of travelers for route choice and departure time choice depends on the expected travel time and its reliability. A common understanding of reliability is that it is related to several statistical properties of the travel time distribution, especially to the standard deviation of the travel time and also to the skewness. For an important corridor in Changsha (P.R. China) the travel time reliability has been evaluated and a linear model is proposed for the relationship between travel time, standard deviation, skewness, and some other traffic characteristics. Statistical analysis is done for both simulation data from a delay distribution model and for real life data from automated number plate recognition (ANPR) cameras. ANPR data give unbiased travel time data, which is more representative than probe vehicles. The relationship between the mean travel time and its standard deviation is verified with an analytical model for travel time distributions as well as with the ANPR travel times. Average travel time and the standard deviation are linearly correlated for single links as well as corridors. Other influence factors are related to skewness and travel time standard deviations, such as vehicle density and degree of saturation. Skewness appears to be less well to explain from traffic characteristics than the standard deviation is.