Vikash V. Gayah

Crash Risk Assessment Using Intelligent Transportation Systems Data and Real-Time Intervention Strategies to Improve Safety on Freeways

This article provides a comprehensive overview of the novel idea of real-time traffic safety improvement on freeways. Crash prone conditions on the freeway mainline and ramps were identified using loop detector data, then intelligent transportation systems (ITS) strategies to reduce the crash risk in real-time are proposed. Separate logistic regression models for assessing the risk of crashes occurring under two speed regimes were estimated. The results show that the variables in the two models are consistent with probable mechanisms of crashes under the respective regimes (high-to-moderate and low speed). This study also discusses the analysis of parameters and conditions that affect crash occurrence on freeway ramps by type (on-/off-ramp) and configurations (diamond, loop, etc.) using five-minute traffic flow data obtained from the loop detectors upstream and downstream of ramps to reflect actual traffic conditions prior to the time of crashes. Finally, several traffic management strategies are evaluated for the resulting traffic safety improvement in real-time using PARAMICS microscopic traffic simulation and the measures of crash potential determined through the logistic regression models. The results show that, while variable speed limit strategies reduced the crash potential under moderate-to-high speed conditions, ramp metering strategies were effective in reducing the crash potential during the low-speed conditions.

Dynamic Variable Speed Limit Strategies for Real-Time Crash Risk Reduction on Freeways

Recent research at the University of Central Florida involving crashes on I-4 in Orlando, Florida, has led to the creation of new statistical models capable of determining the crash risk on the freeway in real time. The potential benefits of variable speed limit (VSL) implementation for reducing the crash risk along the freeway at different loading scenarios was studied. VSL strategies were used in a networkwide attempt to reduce rear-end and lane-change crash risks where speed differences between upstream and downstream vehicles were high. The idea of homogeneous speed zones also was introduced in this study to determine the distance over which variable speed limits should be implemented from a station of interest. This idea is unique because it is the first time a dynamic distance has been considered for VSL implementation. This study shows that VSL is an effective crash prevention strategy when the freeway is operating in uncongested conditions. Specifically, in free-flow conditions and conditions approaching congestion, VSL can be used to reduce crash risk and prevent crash occurrence. It was also confirmed that the effects of crash migration increase as the level of congestion increases, and specific implementation techniques were found to better resist those effects. VSL was not found to effectively reduce crash risk in congested situations. This finding is sustained with logic and is supported by previous research.

Analytical Capacity Comparison of One-Way and Two-Way Signalized Street Networks

Recently cities have been converting traditional one-way downtown street networks to two-way operation partly because one-way networks are seen as confusing and as less conducive to economic activity and a livable environment and they require vehicles to travel longer distances on average. However, one of the main disadvantages of such conversions is thought to be a reduction in the networks ability to serve vehicles. Intersections in two-way networks can serve fewer vehicles per unit time than their one-way counterparts. Several studies have assessed the differences between these two types of networks, but most studies are site specific and do not consider the best possible two-way networks. This paper presents an analytical model that uses macroscopic analysis techniques to compare various one-way and two-way networks using their trip-serving capacities. This metric is a key indicator of network performance. Two-way networks can serve more trips per unit time than one-way networks when average trip lengths are short. This study also found that two-way networks in which left-turn movements were banned at intersections could always serve trips at a higher rate than one-way networks could, even long trips. Thus, the trip-serving capacity of a one-way network can actually be increased when it is converted to two-way operation if left turns are banned. In this way, livability and efficiency objectives can be achieved simultaneously. This framework can be used by planners and engineers to determine how much a networks capacity changes after a conversion, and also to unveil superior conversion options.

The potential of parsimonious models for understanding large scale transportation systems and answering big picture questions

A model with few variables is said to be parsimonious. If it is also analytically tractable, physically realistic, and conceptually insightful, it is said to be effective. Effective parsimonious models have long been used in fields such as economics and applied physics to describe the aggregate behavior of systems as opposed to the behavior of their individual parts. In transportation, these models are particularly well suited to address big picture questions because they provide insights that might be lost when focusing on details. This paper presents an abbreviated history of effective parsimonious models in the transportation field, classified by sub-area: regional and urban economics, traffic flow, queuing theory, network dynamics, town planning, public transportation, logistics, and infrastructure management. The paper also discusses the benefits of these models—fewer data requirements, reduced computational complexity, improved system representation, insightfulness—and ways of constructing them. Two examples, one from logistics and one from urban transportation, are used to illustrate these points. Finally, the paper discusses ways of expanding the application of effective parsimonious models in the transportation field.

A robust optimization approach for dynamic traffic signal control with emission considerations

We consider an analytical signal control problem on a signalized network whose traffic flow dynamic is described by the Lighthill–Whitham–Richards (LWR) model (Lighthill and Whitham, 1955; Richards, 1956). This problem explicitly addresses traffic-derived emissions as constraints or objectives. We seek to tackle this problem using a mixed integer mathematical programming approach. Such class of problems, which we call LWR-Emission (LWR-E), has been analyzed before to certain extent. Since mixed integer programs are practically efficient to solve in many cases (Bertsimas et al., 2011b), the mere fact of having integer variables is not the most significant challenge to solving LWR-E problems; rather, it is the presence of the potentially nonlinear and nonconvex emission-related constraints/objectives that render the program computationally expensive. To address this computational challenge, we proposed a novel reformulation of the LWR-E problem as a mixed integer linear program (MILP). This approach relies on the existence of a statistically valid macroscopic relationship between the aggregate emission rate and the vehicle occupancy on the same link. This relationship is approximated with certain functional forms and the associated uncertainties are handled explicitly using robust optimization (RO) techniques. The RO allows emissions-related constraints and/or objectives to be reformulated as linear forms under mild conditions. To further reduce the computational cost, we employ a link-based LWR model to describe traffic dynamics with the benefit of fewer (integer) variables and less potential traffic holding. The proposed MILP explicitly captures vehicle spillback, avoids traffic holding, and simultaneously minimizes travel delay and addresses emission-related concerns.

Using Mobile Probe Data and the Macroscopic Fundamental Diagram to Estimate Network Densities: Tests Using Microsimulation

Recent advances in urban traffic network modeling have led to the proposal of several large-scale control strategies aimed at improving network efficiency, including metering vehicle entry, pricing network use, and allocating limited street space between multiple modes. However, these strategies typically require accurate real-time predictions of networkwide traffic conditions to be implemented, and it is often taken for granted that this information is available. In practice, this is not a trivial issue, because measuring traffic conditions across a large urban network in real time is not straightforward. For that purpose, this paper presents a method of indirectly estimating average vehicle densities across a network in real time by combining travel speed information from a few circulating probe vehicles with the macroscopic fundamental diagram (MFD) of urban traffic. The proposed method is advantageous because it requires relatively little data and involves few calculations. Tests of this methodology on a simulated network showed that the results were not accurate when the network was uncongested, but reliable density estimates could be obtained when the network was congested or approaching congestion, even if only a small fraction of vehicles served as probes. This result is promising because congested states are the most critical. Therefore, this methodology seems useful as a traffic-monitoring scheme to complement networkwide control strategies, provided that the network exhibits a well-defined and reproducible MFD.

Effects of Turning Maneuvers and Route Choice on a Simple Network

A simple symmetric network that consists of two tangent rings on which vehicles obey the kinematic wave theory of traffic flow and can switch rings at the point of tangency is studied. An online adaptive simulation reveals that if there is any turning whatsoever, the two-ring system becomes unevenly loaded for densities greater than the optimal density, and reduces traffic flow. Furthermore, the two-ring system jams at significantly lower densities than the maximum density possible.

On the continuum approximation of the on-and-off signal control on dynamic traffic networks

In the modeling of traffic networks, a signalized junction is typically treated using a binary variable to model the on-and-off nature of signal operation. While accurate, the use of binary variables can cause problems when studying large networks with many intersections. Instead, the signal control can be approximated through a continuum approach where the on-and-off control variable is replaced by a continuous priority parameter. Advantages of such approximation include elimination of the need for binary variables, lower time resolution requirements, and more flexibility and robustness in a decision environment. It also resolves the issue of discontinuous travel time functions arising from the context of dynamic traffic assignment.

Accuracy of Networkwide Traffic States Estimated from Mobile Probe Data

Reproducible relationships between networkwide averages of local traffic metrics can be used to describe network dynamics and to inform large-scale strategies for traffic control. Even though relationships such as the network fundamental diagram or macroscopic fundamental diagram (MFD), which relates average vehicle flow and density, have been verified with empirical data, these relationships remain notoriously difficult to estimate in practice. Data from mobile probe vehicles are proposed for use in estimating the relevant networkwide traffic metrics, which include the average flow, density, speed, accumulation, and exit flow of vehicles. The method has low data requirements: only the distances traveled by probes at various times and the probe and nonprobe vehicle counts at fixed locations. This information is becoming increasingly available because of advances in intelligent transportation systems, GPS, and mobile computing. Probe and nonprobe vehicle counts at fixed locations can be estimated by combining probe vehicle data with data from fixed detectors. The uncertainty of these measurements also can be estimated from probe vehicle data. This information then can be used to estimate the MFD and other networkwide relationships directly or to monitor traffic in real time. This method was tested on a microsimulated network and was accurate when mobile probe penetration rates reached about 20%.

Relationship between mean and day-to-day variation in travel time in urban networks

The day-to-day reliability of transportation facilities significantly affects travel behavior. To better understand how travelers use these facilities, it is critical to understand and characterize this reliability for different facilities. Early work in this area assumed that the variance of day-to-day travel times (a measure of the inverse of reliability) increases proportionally with the mean travel time; i.e., as the mean travel time increases, travel time reliability decreases. However, recent empirical data for a single bottleneck facility and a small urban network suggest a more complex relationship that exhibits hysteresis. When this phenomenon is present, the variance in travel time is larger as the mean travel time decreases (congestion recovery) than as the mean travel time increases (congestion onset). This paper presents an elegant theoretical model to describe the variance of travel times across many days in an urban network. This formulation shows that the hysteresis behavior observed in empirical floating car data on urban networks should not be unexpected, and that it is linked to the hysteresis loops that often exist in the Macroscopic Fundamental Diagram of urban traffic. To verify the validity of this formulation, data from a micro-simulation of the City of Orlando, Florida, are used to derive an observed relationship with which to compare to theory. The simulated data are shown to match the theoretical predictions very well, and confirm the existence of hysteresis in the relationship between the mean and variance of travel times that is suggested by theory. These results can be used as a first step to more accurately represent travel time reliability in future models of traveler decision-making.