Hillel Bar-Gera

Origin-Based Algorithm for the Traffic Assignment Problem

We present an origin-based algorithm for the traffic assignment problem, which is similar conceptually to the algorithm proposed by Gallager and Bertsekas for routing in telecommunication networks. Apart from being origin-based, the algorithm is different from other algorithms used so far for the traffic assignment problem by its restriction to acyclic solutions and by the use of approach proportions as solution variables. Projected quasi-Newton search directions are used to shift flows effectively and to eliminate residual flows. Experimental results comparing the proposed algorithm with the state-of-the-practice algorithm of Frankand Wolfe demonstrate the algorithms excellent convergence performance, especially when highly accurate solutions are needed. Reasonable memory requirements make this algorithm applicable to large-scale networks. The resulting solution has an immediate route flow interpretation, thus providing equivalent detail to route-based solutions.

Origin-based algorithms for combined travel forecasting models

Consistent transportation forecasting models that combine travel demand and network assignment are receiving more attention in recent years. A fixed point formulation for the general combined model is presented. Measures for solution accuracy are discussed. An origin-based algorithm for solving combined models is proposed. Experimental results demonstrate the efficiency of the algorithm in comparison with prevailing alternatives.

Primal Method for Determining the Most Likely Route Flows in Large Road Networks

This paper presents a method to identify the set of routes and their flows in a user-equilibrium traffic assignment solution. We present a general consistency condition that is satisfied by any set of minimum-cost routes, and show how it can be used in choosing a set of routes that is likely to be similar to the set of user-equilibrium routes. The proposed consistency condition is also essential for finding the entropy-maximizing route flows solution, which may be regarded as the most likely one. An efficient method for finding the entropy-maximizing solution is presented. Numerical results on several networks, including one of large scale, demonstrate the effectiveness of the proposed method. In most cases the method achieves a duality gap of practically zero in a short computation time.

Validation of Multiclass Urban Travel Forecasting Models Combining Origin–Destination, Mode, and Route Choices

The formulation, estimation, and validation of combined models for making detailed urban travel forecasts are described. These models combine origin-destination, mode, and auto route choices into a consistent forecasting method for multiple user classes for the Chicago Region. Household Travel Survey and Census Transportation Planning Package data for 1990, respectively, are used to estimate and validate the model.

Multiclass Combined Models for Urban Travel Forecasting

Progress in formulating, solving and implementing models with multiple user classes that combine several travel choices into a single, consistent mathematical formulation is reviewed. Models in which the travel times and costs on the road network are link flow-dependent are considered; such models seek to represent congestion endogenously. The paper briefly summarizes the origins of this field in the 1950s and its evolution through the development of solution algorithms in the 1970s. The primary emphasis of the review is on the implementation and application of multiclass models. The paper concludes with a brief discussion of prospects for improved solution algorithms.

Model-Based Application of Abbreviated Injury Scale to Police-Reported Crash Injuries

The evaluation of crash severities and the estimation of crash costs are key elements in evidence-based traffic safety improvement programs. Police records include information on injury severity estimated at the scene either by a police officer or by a medical emergency unit. At a hospital, the accident injuries are examined and documented more thoroughly and thus provide a better basis for cost estimation. Using hospital injury data for priority ranking of transportation projects and in other decision-making processes requires linking medical and crash records. Unfortunately, only a modest portion of crash records can be linked to corresponding medical records, even if a person is hospitalized, because missing data hamper the linking process. This study proposes a new method to overcome these difficulties and to estimate the expected level of injury of individuals included in all police-reported crashes. This method is accomplished by developing statistical models based on the linked medical and crash records and by applying these models to the entire crash data set. A fundamental problem to overcome was the selectivity bias present in the linked data and caused by the injury criteria for directing individuals to hospitals. The concept and the development of the proposed method are presented. The method is illustrated with its components developed with Indiana linked data and applied to Indiana crash data for 2005 to 2006.

Some Amazing Properties of Road Traffic Network Equilibria

One of the first mathematical models of a physical network interacting with human behavior was the model of road traffic equilibria with variable flow (demand) formulated by Martin Beckmann and colleagues in 1954. Beckmann applied the recently-proved theorem of Kuhn and Tucker to incorporate an assumption and two hypotheses concerning road traffic into a single mathematical formulation. The model considers a road network consisting of nodes and links. Associated with each directional link is an increasing function relating its travel time, or generalized travel cost, to its flow. The behavioral hypotheses represented by the model are as follows:

The effect of signalised intersections on dynamic traffic assignment solution stability

This article examines a continuous-flow analytic dynamic traffic assignment (DTA) model with consideration of isolated uncoordinated traffic signals. The dynamic network loading component of the DTA model relies on trajectories and anticipated arrival order to nodes in order to achieve consistency between flow propagation along routes and kinematic waves (physical queue) representation of single link traffic behaviour. We compare results between a model where queues with random arrivals and deterministic departure rates are captured by non-stationary cycle-to-cycle Markov chains, and a model where only exit capacity effects of traffic signals are considered. Numerical examples illustrate that overall the model with Markov chains behaves properly, and captures interesting impacts of random queuing traffic signal delays on route choice and network level DTA solutions. A particular focus of this article is the issue of solution stability and its relationship to model specification and discretisation. We discuss the connection between DTA models and general finite element models in this respect, particularly regarding lag options in the discrete form of the equilibrium condition. Our results regarding these options, known as ‘forward’ versus ‘backward’ Euler method, or as ‘reactive’ versus ‘predictive’ user-equilibrium, confirm previous findings. In addition, the results show that the model specification with Markov chain representation of traffic signals enhances solution instability, and exhibits spurious oscillations even if backward Euler method is used. The results suggest that longer route choice intervals reduce oscillations, contrary to the typical behaviour in finite element models where stability generally improves when the resolution is refined.

Quantifying the yellow signal driver behavior based on naturalistic data from digital enforcement cameras.

The yellow signal driver behavior, reflecting the dilemma zone behavior, is analyzed using naturalistic data from digital enforcement cameras. The key variable in the analysis is the entrance time after the yellow onset, and its distribution. This distribution can assist in determining two critical outcomes: the safety outcome related to red-light-running angle accidents, and the efficiency outcome. The connection to other approaches for evaluating the yellow signal driver behavior is also discussed. The dataset was obtained from 37 digital enforcement cameras at non-urban signalized intersections in Israel, over a period of nearly two years. The data contain more than 200 million vehicle entrances, of which 2.3% (∼5million vehicles) entered the intersection during the yellow phase. In all non-urban signalized intersections in Israel the green phase ends with 3s of flashing green, followed by 3s of yellow. In most non-urban signalized roads in Israel the posted speed limit is 90km/h. Our analysis focuses on crossings during the yellow phase and the first 1.5s of the red phase. The analysis method consists of two stages. In the first stage we tested whether the frequency of crossings is constant at the beginning of the yellow phase. We found that the pattern was stable (i.e., the frequencies were constant) at 18 intersections, nearly stable at 13 intersections and unstable at 6 intersections. In addition to the 6 intersections with unstable patterns, two other outlying intersections were excluded from subsequent analysis. Logistic regression models were fitted for each of the remaining 29 intersection. We examined both standard (exponential) logistic regression and four parameters logistic regression. The results show a clear advantage for the former. The estimated parameters show that the time when the frequency of crossing reduces to half ranges from1.7 to 2.3s after yellow onset. The duration of the reduction of the relative frequency from 0.9 to 0.1 ranged from 1.9 to 2.9s.

Origin-based Network Assignment

Most solution methods for the traffic assignment problem can be categorized as either link-based or route-based. Only a few attempts have followed the intermediate, origin-base dapproach. This paper describes the main concepts of a new, origin-based method for the static user equilibrium traffic assignment problem. Computational efficiency in time and memory makes this method suitable for large-scale networks of practical interest. Experimental results show that the new method is especially efficient in finding highly accurate solutions.