Examination of factors affecting freeway incident clearance times: a comparison of the generalized F model and several alternative nested models

Traffic incidents are a principal cause of congestion on urban freeways, reducing capacity and creating risks for both involved motorists and incident response personnel. As incident durations increase, the risk of secondary incidents or crashes also becomes problematic. In response to these issues, many road agencies in metropolitan areas have initiated incident management programs aimed at detecting, responding to, and clearing incidents to restore freeways to full capacity as quickly and safely as possible. This study examined those factors that impact the time required by the Michigan Department of Transportation Freeway Courtesy Patrol to clear incidents that occurred on the southeastern Michigan freeway network. These models were developed using traffic flow data, roadway geometry information, and an extensive incident inventory database. A series of parametric hazard duration models were developed, each assuming a different underlying probability distribution for the hazard function. Although each modeling framework provided results that were similar in terms of the direction of factor effects, there was significant variability in terms of the estimated magnitude of these impacts. The generalized F distribution was shown to provide the best fit to the incident clearance time data, and the use of poorer fitting distributions was shown to result in severe over‐estimation or under‐estimation of factor effects. Those factors that were found to impact incident clearance times included the time of day and month when the incident occurred, the geometric and traffic characteristics of the freeway segment, and the characteristics of each incident. Copyright © 2012 John Wiley & Sons, Ltd.     

Competing risks analysis on traffic accident duration time

Different clearance methods in traffic accident management lead to varied duration distributions. Apart from investigating the influence of various factors associated with accidents on the duration of such accidents using different clearance methods, this study also examines the cumulative incidence probability. We used traffic accident data obtained for 12 months from the Fourth Ring Expressway main line in Beijing to develop a subdistribution hazard regression model, which can assess the risk factors of two clearance methods. The regression results show that the different factors have statistically significant effects on the duration of two accident groups with different clearance methods; furthermore, opposite effects occur even for some factors that have a strong effect on both accident groups. For example, an accident involving a taxi extends the duration time with clearance method 1; in comparison, the accident is shorter with clearance method 2. The predicted cumulative incidence curves of the two types of clearance methods are shown as examples, with stratification based on the influence factors (taxi involved, season). Finally, the Gray test of the cumulative incidence functions and the log‐rank test of the Kaplan–Meier estimates of the survival functions are compared, in order to demonstrate the importance of using proper methods for analyses. Copyright © 2014 John Wiley & Sons, Ltd.     

The acceptability of road pricing in Vienna: the preference patterns of car drivers

The acceptability of road pricing has attracted considerable attention among researchers over the last decade, as is evident in the amount of literature about transport and environmental economics. The general conclusion from these studies has been that there is low acceptability for road pricing among car users. In this paper, we add more knowledge to the existing literature by conducting an acceptability study of road pricing in Vienna, where such a study has never been conducted before. We used a replication study approach where a previous approach used in the EU research project AFFORD (acceptability of fiscal and financial measures and organisational requirements for demand management) was replicated for Vienna and further supplemented with a conjoint analysis. In order to examining whether the Vienna study confirms previous findings. We investigated the acceptability of two concrete policy packages factors influencing this acceptability, and preference patterns that can be used in designing a road pricing policy for Vienna. The survey reveals a higher acceptability if road pricing schemes lead to perceived personal benefits. According to the multivariate analyses, the “personal outcome expectations”, “social norm” and “perceived effectiveness” variables account for more than 50 % of the criterion variance and therefore these are the most influential factors. Road pricing schemes can be an effective transport management instrument for a city particularly if they are associated with direct investment in public transport and public infrastructure. Thus, personal benefits can be perceived more easily and direct effects can be expected.     

Incident characteristics,frequency, and duration on a high volume urban freeway

Rapidly deteriorating travel conditions in U.S. metropolitan areas have led to renewed interest in more effectively managing nonrecurrent congestion. Effective incident management requires an understanding of incident patterns, frequency, and duration. However, such information is limited. This paper presents an analysis of incidents using data from a major Los Angeles, California freeway. Incident patterns are described, and duration is analyzed as a function of incident characteristics. Results indicate that accidents make up a very small proportion of all incidents, but account for a relatively greater share of all incident duration. Major explanatory factors of incident duration include incident type, time of day, truck involvement, and lane closures. The paper concludes with a discussion of alternative approaches to reducing the congestion impacts of incidents.     

An exploratory hazard-based analysis of highway incident duration

The statistical analysis of highway incident duration has become an increasingly import research topic due to the impact that highway incidents (vehicle accidents and disablements) have on traffic congestion. In addition, there is a growing need to evaluate incident management programs that seek to reduce incident duration and incident-induced traffic congestion. We apply hazard-based duration models to statistically evaluate the time it takes detect/report, respond to, and clear incidents. Two-year data from Washington State's incident response team program were used to estimate the hazard models. The model estimation results show that a wide variety of factors significantly affect incident times (i.e. detection/reporting, response, and clearance times), and that different distributional assumptions for the hazard function are appropriate for the different incident times being considered. It was also found that the estimated coefficients were not stable between the two years of data used in model estimation. The findings of this paper provide an important demonstration of method and an empirical basis to assess incident management programs.     

Stability analysis on a dynamical model of route choice in a connected vehicle environment

Research on connected vehicle environment has been growing rapidly to investigate the effects of real-time exchange of kinetic information between vehicles and road condition information from the infrastructure through radio communication technologies. A fully connected vehicle environment can substantially reduce the latency in response caused by human perception-reaction time with the prospect of improving both safety and comfort. This study presents a dynamical model of route choice under a connected vehicle environment. We analyze the stability of headways by perturbing various factors in the microscopic traffic flow model and traffic flow dynamics in the car-following model and dynamical model of route choice. The advantage of this approach is that it complements the macroscopic traffic assignment model of route choice with microscopic elements that represent the important features of connected vehicles. The gaps between cars can be decreased and stabilized even in the presence of perturbations caused by incidents. The reduction in gaps will be helpful to optimize the traffic flow dynamics more easily with safe and stable conditions. The results show that the dynamics under the connected vehicle environment have equilibria. The approach presented in this study will be helpful to identify the important properties of a connected vehicle environment and to evaluate its benefits.     

Competing risk mixture model and text analysis for sequential incident duration prediction

Predicting the duration of traffic incidents sequentially during the incident clearance period is helpful in deploying efficient measures and minimizing traffic congestion related to such incidents. This study proposes a competing risk mixture hazard-based model to analyze the effect of various factors on traffic incident duration and predict the duration sequentially. First, topic modeling, a text analysis technique, is used to process the textual features of the traffic incident to extract time-dependent topics. Given four specific clearance methods and the uncertainty of these methods when used during traffic incidents, the proposed mixture model uses the multinomial logistic model and parametric hazard-based model to assess the influence of covariates on the probability of clearance methods and on the duration of the incident. Subsequently, the performance of estimated mixture model in sequentially predicting the incident duration is compared with that of the non-mixture model. The prediction results show that the presented mixture model outperforms the non-mixture model.     

Modeling the time to the next primary and secondary incident: A semi-Markov stochastic process approach

Incidents are notorious for their delays to road users. Secondary incidents – i.e., incidents that occur within a certain temporal and spatial distance from the first/primary incident – can further complicate clearance and add to delays. While there are numerous studies on the empirical analysis of incident data, to the best of our knowledge, an analytical model that can be used for primary and secondary incident management planning that explicitly considers both the stochastic as well as the dynamic nature of traffic does not exist. In this paper, we present such a complementary model using a semi-Markov stochastic process approach. The model allows for unprecedented generality in the modeling of stochastics during incidents on freeways. Particularly, we relax the oftentimes restrictive Poisson assumption (in the modeling of vehicle arrivals, vehicle travel times, and incidence occurrence and recovery times) and explicitly model secondary incidents. Numerical case studies are provided to illustrate the proposed model.     

An investigation of incident frequency,duration and lanes blockage for determining traffic delay

Traffic delay caused by incidents is closely related to three variables: incident frequency, incident duration, and the number of lanes blocked by an incident that is directly related to the bottleneck capacity. Relatively, incident duration has been more extensively studied than incident frequency and the number of lanes blocked in an incident. In this study, we provide an investigation of the influencing factors for all of these three variables based on an incident data set that was collected in New York City (NYC). The information about the incidents derived from the identification can be used by incident management agencies in NYC for strategic policy decision making and daily incident management and traffic operation. In identifying the influencing factors for incident frequency, a set of models, including Poisson and Negative Binomial regression models and their zero‐inflated models, were considered. An appropriate model was determined based on a model decision‐making tree. The influencing factors for incident duration were identified based on hazard‐based models where Exponential, Weibull, Log‐logistic, and Log‐normal distributions were considered for incident duration. For the number of lanes blocked in an incident, the identification of the influencing factors was based on an Ordered Probit model which can better capture the order inherent in the number of lanes blocked in an incident. As identified in this study, rain is the only factor that significantly influenced incident frequency. For incident duration and the number of lanes blocked in an incident, various factors had significant impact. As concluded in this study, there is a strong need to identify the influencing factors in terms of different types of incidents and the roadways where the incidents occured.     

Equilibrium analysis of the scheduling of tours in congested networks

In this paper, we develop a model of travel in tours that joins several locations by travel through a congested network. We develop a microscopic analysis in continuous time of individual benefits obtained by spending time at each of the locations and costs incurred through travel between them. This is combined with a continuous time macroscopic equilibrium model of travel during congested peak periods to show how individuals' travel choices are influenced by the congestion that result from corresponding choices made by others. We show how different travellers can achieve identical net utilities by making different combinations of choices within the equilibrium. The resulting model can be used to investigate the effect on travel behaviour and individual utility of various transport interventions, and we illustrate this by considering the effect of a peak‐period charge that eliminates congestion.     

The validation of a microscopic simulation model: a methodological case study

The use of microscopic simulation models to assess the likely effects of new traffic management applications and changes in vehicle technology is becoming increasingly popular. However the validity of the models is a topic of increasing concern, as the quality of the presentation often exceeds the models ability to predict what is likely to happen.Traditionally, model validity has been ascertained through comparing outputs aggregated at a macroscopic level such as speed flow and lane use, against real data. Little microscopic comparison is generally possible and, where this is done there is often no separation of the calibration and validation process. This paper demonstrates how microscopic validation may be undertaken when suitable data is available, in this case time series data collected by an instrumented vehicle, and its use in the validation of the car following performance of a fuzzy logic based car following model. Good agreement has been attained between the simulated model and observed data, primarily using a root mean square error indicator. Lastly, a brief comparison of the new model with the performance of a number of existing formulations has also been undertaken.     

Average time gaps in congested freeway flow

Characteristics of time gaps (that is, the time separation between the rear of the lead vehicle and the front of the following vehicle) in congested freeway flow provide an important link between microscopic and macroscopic traffic flow. Although individual time gaps are a microscopic phenomenon, average time gaps can easily be determined from commonly collected macroscopic traffic flow data. Data from San Diego freeways and the Queen Elizabeth Way in Ontario, Canada are analyzed to show that average time gaps in congested flow are essentially constant with respect to speed; that they vary considerably between lanes at a single location and, for the same lane, from site to site; that they display considerable scatter; and that at some sites there is a distinct increase in average time gaps in the median lane in the transition to congested flow but at others there is no change or a slight reduction. The variability of average time gaps is not easily explained, although differences in driver populations may partly explain differences among different sites. Hysteresis due to acceleration and deceleration does not appear to be an explanation for the high degree of scatter in average time gaps, since no positive correlation was found between speed changes and average time gaps.     

Operational impacts of incident quick clearance legislation: a simulation analysis

Many states in the US have enacted quick clearance laws requiring drivers of vehicles involved in minor incidents to move their vehicles from travel lanes prior to the arrival of first responders. Since little is known about the effectiveness of these laws, this research sought to find the benefit–cost ratio of advertising quick clearance legislation to improve driver compliance, and compare it with benefit–cost ratios of other incident management strategies, particularly traffic cameras, freeway service patrols, and traffic sensors. The analysis used traffic simulation that applied application programming interfaces to produce random spatial and temporal occurrence of incidents, including incident start times, durations, and locations, based on normal distributions developed from field data, to test before and after the law scenarios. The results provide decision makers with support for prioritizing funding between these incident management strategies and indicated that investments in the advertisement of this law was beneficial. Copyright © 2011 John Wiley & Sons, Ltd.     

Empirical flow-density and speed-spacing relationships: Evidence of vehicle length dependency

Traffic flow theory has come to a point where conventional, fixed time averaged data are limiting our insight into critical behavior both at the macroscopic and microscopic scales. This paper develops a methodology to measure relationships of density and vehicle spacing on freeways. These relationships are central to most traffic flow theories but have historically been difficult to measure empirically. The work leads to macroscopic flow-density and microscopic speed-spacing relationships in the congested regime derived entirely from dual loop detector data and then verified against the NGSIM data set. The methodology eliminates the need to seek out stationary conditions and yields clean relationships that do not depend on prior assumptions of the curve shape before fitting the data. Upon review of the clean empirical relationships a key finding of this work is the fact that many of the critical parameters of the macroscopic flow-density and microscopic speed-spacing relationships depend on vehicle length, e.g., upstream moving waves should travel through long vehicles faster than through short vehicles. Thus, the commonly used assumption of a homogeneous vehicle fleet likely obscures these important phenomena. More broadly, if waves travel faster or slower depending on the length of the vehicles through which the waves pass, then the way traffic is modeled should be updated to explicitly account for inhomogeneous vehicle lengths.     

Evacuation Modelling in the United States: Does the Demand Model Choice Matter?

Abstract

This paper reviews the literature on the evacuation demand problem, with an emphasis on the impact of various modelling approaches on network‐wide evacuation performance measures. First, a number of important factors that affect evacuee behaviour are summarized. Evacuation software packages and tools are also investigated in terms of the demand generation model they use. The most widely used models are then selected for performing sensitivity analysis. Next, a cell‐transmission‐based system optimal dynamic traffic assignment (SO‐DTA) model is employed to assess the effects of the demand model choice on the clearance time and average travel time. It is concluded that evacuation demand models should be selected with care, and policy makers should make sure the selected demand curve can replicate real‐life conditions with relatively high fidelity for the study region to be able to develop reliable and realistic evacuation plans.     

Macroscopic traffic flow propagation stability for adaptive cruise controlled vehicles

Traffic flow propagation stability is concerned about whether a traffic flow perturbation will propagate and form a traffic shockwave. In this paper, we discuss a general approach to the macroscopic traffic flow propagation stability for adaptive cruise controlled (ACC) vehicles. We present a macroscopic model with velocity saturation for traffic flow in which each individual vehicle is controlled by an adaptive cruise control spacing policy. A nonlinear traffic flow stability criterion is investigated using a wavefront expansion technique. Quantitative relationships between traffic flow stability and model parameters (such as traffic flow and speed, etc.) are derived for a generalized ACC traffic flow model. The newly derived stability results are in agreement with previously derived results that were obtained using both microscopic and macroscopic models with a constant time headway (CTH) policy. Moreover, the stability results derived in this paper provide sufficient and necessary conditions for ACC traffic flow stability and can be used to design other ACC spacing policies.     

Within day rescheduling microsimulation combined with macrosimulated traffic

The concept of rescheduling is essential to activity-based modeling in order to calculate effects of both unexpected incidents and adaptation of individuals to traffic demand management measures. When collaboration between individuals is involved or timetable based public transportation modes are chosen, rescheduling becomes complex. This paper describes a new framework to investigate algorithms for rescheduling at a large scale. The framework allows to explicitly model the information flow between traffic information services and travelers. It combines macroscopic traffic assignment with microscopic simulation of agents adapting their schedules. Perception filtering is introduced to allow for traveler specific interpretation of perceived macroscopic data and for information going unnoticed; perception filters feed person specific short term predictions about the environment required for schedule adaptation. Individuals are assumed to maximize schedule utility. Initial agendas are created by the FEATHERS activity-based schedule generator for mutually independent individuals using an undisturbed loaded transportation network. The new framework allows both actor behavior and external phenomena to influence the transportation network state; individuals interpret the state changes via perception filtering and start adapting their schedules, again affecting the network via updated traffic demand. The first rescheduling mechanism that has been investigated uses marginal utility that monotonically decreases with activity duration and a monotonically converging relaxation algorithm to efficiently determine the new activity timing. The current framework implementation is aimed to support re-timing, re-location and activity re-sequencing; re-routing at the level of the individual however, requires microscopic travel simulation.     

Enhancing the universality and transferability of freeway incident detection using a Bayesian-based neural network

Development of a universal freeway incident detection algorithm is a task that remains unfulfilled despite the promising approaches that have been recently explored. Incident detection researchers are realizing that an operationally successful detection framework needs to fulfill a full set of recognized needs. In this paper we attempt to define one possible set of universality requirements. Among the set of requirements, a freeway incident detection algorithm needs to be operationally accurate and transferable. Guided by the envisioned requirements, we introduce a new algorithm with potential for enhanced performance. The algorithm is a modified form of the Bayesian-based Probabilistic Neural Network (PNN) that utilizes the concept of statistical distance. The paper is divided into three main sections. The first section is a detailed definition of the attributes and capabilities that a potentially universal freeway incident detection framework should possess. The second section discusses the training and testing of the PNN. In the third section, we evaluate the PNN relative to the universality template previously defined. In addition to a large set of simulated incidents, we utilize a fairly large real incident databases from the I-880 freeway in California and the I-35W in Minnesota to comparatively evaluate the performance and transferability of different algorithms, including the PNN. Experimental results indicate that the new PNN-based algorithm is competitive with the Multi Layer Feed Forward (MLF) architecture, which was found in previous studies to yield superior incident detection performance, while being significantly faster to train. In addition, results also point to the possibility of utilizing the real-time learning capability of this new architecture to produce a transferable incident detection algorithm without the need for explicit off-line retraining in the new site. In this respect, and unlike existing algorithms, the PNN has been found to markedly improve in performance with time in service as it retrains itself on captured incident data, verified by the Traffic Management Center (TMC) operator. Moreover, the overall PNN-based framework promises potential enhancements towards the envisioned universality requirements.     

RENAISSANCE – A unified macroscopic model-based approach to real-time freeway network traffic surveillance

The paper presents a unified macroscopic model-based approach to real-time freeway network traffic surveillance as well as a software tool RENAISSANCE that has been recently developed to implement this approach for field applications. RENAISSANCE is designed on the basis of stochastic macroscopic freeway network traffic flow modeling, extended Kalman filtering, and a number of traffic surveillance algorithms. Fed with a limited amount of real-time traffic measurements, RENAISSANCE enables a number of freeway network traffic surveillance tasks, including traffic state estimation and short-term traffic state prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction, and incident alarm. The traffic state estimation and prediction lay the operating foundation of RENAISSANCE since RENAISSANCE bases the other traffic surveillance tasks on its traffic state estimation or prediction results. The paper first introduces the utilized stochastic macroscopic freeway network traffic flow model and a real-time traffic measurement model, upon which the complete dynamic system model of RENAISSANCE is established with special attention to the handling of some important model parameters. The algorithms for the various traffic surveillance tasks addressed are described along with the functional architecture of the tool. A simulation test was conducted via application of RENAISSANCE to a hypothetical freeway network example with a sparse detector configuration, and the testing results are presented in some detail. Final conclusions and future work are outlined.     

Microsimulation models incorporating both demand and supply dynamics

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework.The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers’ decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers’ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space–time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamics.