Network Travel Time Reliability Research Articles

Freeway Network Travel Time Reliability Analysis Methodology and Software Tool Development

Applications in the field often require analysis of freeway travel time reliability (TTR) at the network level. While micro-simulation is suitable for performing network-level analysis, the computational burden can become unreasonable when TTR analysis is factored in. As for macro-simulation, most network analysis studies often use simplified link performance functions to represent the travel time and flow relationship. Such functions are not generally sensitive to the range of geometric and traffic conditions that can influence freeway facility operations. This research extends the Highway Capacity Manual (HCM) freeway TTR analysis methodology to the network level. The proposed freeway network TTR analysis methodology generates scenarios that represent the impacts of origin–destination (OD) demand variations, weather events, incident events, and work zone events on freeway network travel time. For each scenario, the methodology performs user equilibrium (UE) traffic assignment and the HCM freeway facility core methodology is applied to represent the travel time and flow relationship. The method of successive average approach is applied to solve the UE traffic assignment. Finally, scenario travel times (and/or other performance measures) are aggregated into various distributions of interest, such as the network-, facility-, and OD-level distributions, and TTR performance measures are calculated at the three different levels. A software tool is developed using C# language on the .NET Framework. The software tool provides a convenient and efficient approach for transportation planners and researchers to conduct the freeway network TTR analysis, which helps to bridge the gap between research and practice.

Estimating network travel time reliability with network partitioning

Network travel time reliability can be represented by a relationship between network space-mean travel time and the standard deviation of network travel time. The primary objective of this paper is to improve estimation of the network travel time reliability with network partitioning. We partition a heterogeneous large-scale network into homogeneous regions (clusters) with well-defined Network Fundamental Diagrams (NFD) using directional and non-directional partitioning approaches. To estimate the network travel time reliability, a linear relationship is estimated that relates the mean travel time with the standard deviation of travel time per unit of distance at the network level. The impact of different partitioning approaches, as well as the number of clusters, on the network travel time reliability relationship are also explored. To estimate individual vehicle travel times, we use two distinct approaches to allocate vehicle trajectories to different time intervals, namely trajectory and sub-trajectory methods. We apply the proposed framework to a large-scale network of Chicago using a 24-h dynamic traffic simulation. Partitioning and travel time reliability estimation are conducted for both morning and afternoon peak periods to demonstrate the impacts of travel demand pattern variations. The numerical results show that the sub-trajectory method for the network travel time reliability estimation and the directional partitioning with three clusters have the highest performance among other tested methods. The analyses also demonstrate that partitioning a heterogeneous network into homogeneous clusters may improve network travel reliability estimation by estimating an independent relationship for each cluster. Also, comparing morning and afternoon peak periods suggests that the estimated parameter for the linear network travel time reliability relationship is directly related to the coefficient of variation of density as a measure of spatial distribution of congestion across the network.

Travel Time Reliability for Urban Networks: Modelling and Empirics

The importance of travel time reliability in traffic management, control, and network design has received a lot of attention in the past decade. In this paper, a network travel time distribution model based on the Johnson curve system is proposed. The model is applied to field travel time data collected by Automated Number Plate Recognition (ANPR) cameras. We further investigate the network-level travel time reliability by connecting the network reliability measures such as the weighted standard deviation of travel time rate and the weighted skewness of travel time rate distributions with network traffic characteristics (e.g., the network density). The weighting is done with respect to the number of signalized intersections on a trip. A clear linear relation between the weighted average travel time rate and the weighted standard deviation of travel time rate can be observed for different time periods with time-varying demand. Furthermore, both the weighted average travel time rate and the weighted standard deviation of travel time rate increase monotonically with network density. The empirical findings of the relation between network travel time reliability and network traffic characteristics can be possibly applied to assess traffic management and control measures to improve network travel time reliability.

Network Flow Relations and Travel Time Reliability in a Connected Environment

Connected vehicle technology provides the opportunity to create a connected network of vehicles and infrastructure. In such a network, individual vehicles can communicate with each other and with the infrastructure, including a traffic management center. The effects of connectivity on reducing congestion and improving throughput and reliability have been extensively investigated at the segment (facility) level. To complement the segment-level studies and to assess the large-scale effects of connectivity, this paper presents a networkwide evaluation of the effect of connectivity on travel time reliability. This study uses a microscopic simulation framework to establish the speed–density relationships at different market penetration rates (MPRs) of connected vehicles. Calibrated speed–density relationships are then used as inputs to the mesoscopic simulation tools to simulate the networkwide effects of connectivity. The Chicago, Illinois, and Salt Lake City, Utah, networks are simulated. Numerical results from the simulations confirm that the linear relationship between distance-weighted travel time rate and standard deviation holds for both networks and is not affected by either the demand level or the MPR of connected vehicles. In addition, with an increase in the MPR of connected vehicles, the network attains a lower maximum density and gets an increased flow rate for the same density level. Highly connected environment has the potential to help a congested network to recover from a breakdown and avoid gridlock. It is shown that a connected environment can improve a system’s performance by providing increased traffic flow rate and better travel time reliability at all demand levels.

Understanding network travel time reliability with on-demand ride service data

Understanding network travel time reliability with on-demand ride service data

Travel Time Reliability Using the Hasofer–Lind–Rackwitz–Fiessler Algorithm and Kernel Density Estimation

Travel time reliability has emerged as an indicator of roadway performance. Estimation of travel time distribution is an important starting input for measuring travel time reliability. This study used kernel density estimation to estimate travel time distribution. The Hasofer–Lind–Rackwitz–Fiessler algorithm, widely used in the field of reliability engineering, was used in this work to compute the reliability index of a system based on its previous performance. The computing procedure for travel time reliability of corridors on a freeway was introduced, then network travel time reliability was developed. Given probability distributions estimated by the kernel density estimation technique and an anticipated travel time from travelers, the two equations of corridor and network travel time reliability can be used to address the question, “How reliable is my perceived travel time?” The definition of travel time reliability was in the sense of on-time performance, and this study was conducted from the perspective of travelers. The major advantages of the proposed method are as follows: ( a) it demonstrates an alternative way to estimate travel time distributions when the choice of probability distribution family is still uncertain and ( b) it shows its flexibility for application to levels of roadways (e.g., individual roadway segment or network). A user-defined anticipated travel time can be input, and travelers can use the computed travel time reliability information to plan their trips so that they can better manage trip time, reduce costs, and avoid frustration.

Research on Link Travel Time Reliability Model Based on Massive Expressway Toll Data

Massive expressway toll data contained lots of valuable information. However, the skills of mining and analyzing toll data were limited currently. This study explored the modeling method of road network travel time reliability based on massive toll data. Firstly, this study obtained travel time data sample of each link at different months, and analyzed travel time statistical properties preliminarily. Secondly, this study used normal distribution, gamma distribution and Weibull distribution to fit travel time data sample, and different statistical indicators were involved to measure the fitting effect. Fitting results showed that normal distribution for link travel time was more rational and acceptable than the others. Thus, this study established link travel time reliability model, and proposed moment estimation method of calibrating the model parameters. In practical application, the reliability model can be used to judge traffic operating posture for expressway management department, and also can be used to forecast travel time information, to provide valuable reference on decision-making for drivers travel plan or route choice.

Connecting Networkwide Travel Time Reliability and the Network Fundamental Diagram of Traffic Flow

The existence of the network fundamental diagram (NFD) has been established at the urban network scale. It relates three traffic descriptors: speed, density, and flow. However, its deterministic nature does not convey the underlying variability within the network. In contrast, travel time reliability as a network performance descriptor is of growing concern to both the traveling public and traffic managers and policy makers. The objectives of this paper were to extend travel time reliability modeling from the link–path level to the network level and to connect overall network variability to NFD. Robust relationships between travel time variability and network density and flow rate were analytically derived, investigated, and validated with both simulated and real-world trajectory data. The distance-weighted standard deviation of travel time rate, as a measure of travel time variability, was found to increase monotonically with network density. A maximum network flow rate existed beyond which network travel time reliability deteriorated at a much faster pace. The results also suggest that these relationships are inherent network properties (signature) that are independent of demand level. The effects of en route information on the proposed relationships were also studied. The results showed that en route information reduced network travel time variability. The findings provide a strong connection between NFD and travel time variability, and this connection can be used further for modeling of network travel time reliability and assessment of measures intended to improve reliability of travel in a network.

Macroscopic Travel Time Reliability Diagrams for Freeway Networks

Travel time reliability is considered to be one of the key indicators of transport system performance. Knowledge of the mechanisms of travel time unreliability enables the derivation of explanatory models with which travel time reliability can be predicted and utilized in traffic management. Inspired by the macroscopic fundamental diagram (MFD), describing the relationship between production (average flow completing the trips) and vehicle accumulation (average density) in a traffic network, this paper investigates a macroscopic travel time reliability diagram (MRD) relating the travel time reliability to the network accumulation. The MFD relationship has potential because it characterizes the state of an entire traffic network with just two (production and accumulation) or three (adding spatial variability of accumulation) state variables. Likewise, the MRD describes the network travel time reliability as a function of just one independent state variable (network accumulation). Empirical analyses were performed to investigate the variability in MFD as seen in scatters and to show the travel time reliability in relation to the network accumulations. Traffic data from Dutch freeway networks were employed to facilitate the analyses. With the MRD for different freeway networks, it was found that a critical travel time reliability accumulation existed, below which network accumulation had little or no impact on travel time reliability and above which it had a significant impact. Also, the critical travel time reliability accumulation was, in general, lower than the critical MFD accumulation. These findings provide insights for road authorities in how to make trade-offs in traffic management between maximum production and travel time reliability.

Impact of Traveler Information on Road Network Travel Time Reliability

To investigate the provision of traffic information on road network travel time reliability, the travelers are divided into two classes: travelers equipped with advanced traveler information systems (ATIS) and travelers unequipped with ATIS. A route choice behavior model considering information guidance is formulated as a mathematical program based on the mixed stochastic user equilibrium modeling approach in conjunction with the assumption is that two classes of travelers made their route choices in a Logit-based stochastic manner. The link capacities are treated as truncated normal random variables considering the fact that link capacity can only vary between 0 to link design capacity. With the above considerations, a travel time reliability evaluation method with degradable link capacity under influence of traveler information is proposed using the Monte Carlo simulation technique and network equilibrium flow solution algorithm. Some numerical studies on a small road network are conducted to validate the proposed method, the results show that: road network travel time reliability increases with the quality of information and/or the market penetration of ATIS, but the marginal effects of them are decreasing; for a road network with more higher traffic demand, the provision of traveler information can significantly improve road network travel time reliability.

Measuring Network Reliability by considering Paradoxes

Traditionally, game theoretic approaches to measuring transport network reliability have relied on the outcome of a game played between network users seeking to minimize their travel costs and an origin-destination-specific (O-D-specific) demon that seeks the opposite by damaging links in the network. This problem assumes the presence of only one demon in each O-D pair and assumes the capacity reduction to be 50% if the link is selected for damage by one or more O-D-specific demons. The game is typically expressed as a path-based formulation, which is computationally intensive since the formulation requires path enumeration. With relaxation of the assumptions on the O-D-specific nature of the demons and the capacity reduction, a link-based multiple network demon formulation is proposed by the nonlinear complementarity problem approach, in which each demon is free to select any link to damage. Within this framework, the effects of the proposed model on total expected network cost and reliability measures are examined, and specific examples demonstrate the paradoxical phenomenon that if one adds a road to a network, then all travelers may be worse off for total expected network cost or travel time reliability. Overall, the results indicate the importance of assumptions used to total expected network cost and reliability measures and provide some insights into the problem of ignoring these paradoxical phenomena in reliable and robust network design.

Network reliability‐based optimal toll design

AbstractOptimal toll design from a network reliability point of view is addressed in this paper. Improving network reliability is proposed as a policy objective of road pricing. A reliability‐based optimal toll design model, where on the upper level network performance including travel time reliability is optimized, while on the lower level a dynamic user‐equilibrium is achieved, is presented. Road authorities aim to optimize network travel time reliability by setting tolls in a network design problem. Travelers are influenced by these tolls and make route and trip decisions by considering travel times and tolls. Network performance reliability is analyzed for a degradable network with elastic and fluctuated travel demand, which integrates reliability and uncertainty, dynamic network equilibrium models, and Monte Carlo methods. The proposed model is applied to a small hypothesized network for which optimal tolls are derived. The network travel time reliability is indeed improved after implementing optimal tolling system. Trips may have a somewhat higher, but more reliable, travel time.

IMPACT OF ROAD PRICING ON THE NETWORK RELIABILITY

In this paper, the impact of road pricing on the transport network reliability is investigated with the use of a newly developed reliability-based user equilibrium (RUE) model. The RUE model can be adopted to overcome the difficulty in predicting the flow pattern in a congested and unreliable road network. In this RUE model, both the travel time and travel time reliability are taken into account for drivers' route choices by introducing a concept of path preference index (PI) to quantify the attractiveness of each alternative path for travel between a particular origin-destination pair. The PI is a weight sum of the path travel time index (TI) and the path travel time reliability index (RI), which represent the travel time and travel time reliability on a particular path respectively. The RUE model can be applied to investigate the effects of road pricing on travel time and reliability in urban road networks, in which drivers would consider both the travel time and its reliability for their route choices. In this paper, the RUE model is extended to maximize the network reliability by road pricing. The results demonstrated the trade-off between the total network travel time and the network travel time reliability. The effects of the toll on the total network travel time and the network travel time reliability are investigated.

Computing Travel Time Reliability in Transportation Networks with Multistates and Dependent Link Failures

The measurement of transportation network travel time reliability is imperative to provide drivers with accurate route guidance information and to generate the shortest path (or alternative paths) connecting origins and destinations, especially under conditions of varying demands and limited capacities. Previous studies assumed that link failures in a road network are statistically independent and that reliability probabilities are perfectly determinable. In real life, these assumptions are dubious, because the failure of a link in one particular area does not necessarily result in the complete failure of the neighboring link, but may lead to deterioration of its performance. This paper presents a new methodology to study the multistate system reliability analysis of transportation networks for which one cannot formulate an all or nothing type of failure criterion and in which dependent link failures are considered. The methodology is presented, using a numerical example, for computing the probability that travel time between an origin and a destination may exceed a threshold.

Modelling network travel time reliability under stochastic demand

A technique is proposed for estimating the probability distribution of total network travel time, in the light of normal day-to-day variations in the travel demand matrix over a road traffic network. A solution method is proposed, based on a single run of a standard traffic assignment model, which operates in two stages. In stage one, moments of the total travel time distribution are computed by an analytic method, based on the multivariate moments of the link flow vector. In stage two, a flexible family of density functions is fitted to these moments. It is discussed how the resulting distribution may in practice be used to characterise unreliability. Illustrative numerical tests are reported on a simple network, where the method is seen to provide a means for identifying sensitive or vulnerable links, and for examining the impact on network reliability of changes to link capacities. Computational considerations for large networks, and directions for further research, are discussed.

Basic concepts and future directions of road network reliability analysis

AbstractThe stability of road networks has become an increasingly important issue in recent times, since the value of time has increased considerably and unexpected delay can results in substantial loss to road users. Road network reliability has now become an important performance measure for evaluating road networks, especially when considering changes in OD traffic demand and link flow capacity over time.This paper outlines the basic concepts, remaining problems and future directions of road network reliability analysis. There are two common definitions of road network reliability, namely, connectivity reliability and travel time reliability. As well, reliability analysis is generally undertaken in both normal and abnormal situations. In order to analyse the reliability of a road network, the reliability of the links within the network must be first determined. A method for estimating the reliability of links within road networks is also suggested in this paper.