Antoine G. Hobeika

A linear programming approach for synthesizing origin-destination trip tables from link traffic volumes

We present a nonproportional-assignment, user-equilibrium motivated, linear programming model for estimating origin-destination (O-D) trip tables from available data on link traffic volumes. The model is designed to determine a traffic equilibrium network flow solution that reproduces the link volume data, if such a solution exists. However, it recognizes that due to incomplete information, the traffic may not conform to an equilibrium flow pattern, and moreover, there might be inconsistencies in the observed link flow data. Accordingly, the model permits violations in the equilibrium conditions as well as deviations from the observed link flows but at suitable incurred penalties in the objective function. A column generation solution technique is presented to optimally solve the problem. This methodology is extended to the situation in which a specified prior target trip table is available and one is required to find a solution that also has a tendency to match this table as closely as possible. Implementation strategies are discussed and the proposed method is illustrated using some sample test networks from the literature.

Parameter optimization methods for estimating dynamic origin-destination trip-tables

Dynamic origin-destination tables help in on-line control of traffic facilities and, consequently, are of significant use in alleviating traffic congestion. Such tables find useful applications in the contexts of Advanced Traffic Management Systems and Advanced Traveler Information Systems. This paper considers the estimation of split parameters that prescribe an origin-destination trip-table based on dynamic information regarding entering and exiting traffic volumes through an intersection or a small freeway segment. Two models are developed and motivated for this problem, one based on a least-squares estimation approach and the other based on a least absolute norm approach. Both models enhance existing dynamic origin-destination trip-table estimation models in that they also consider freeway segments having differing time-dependent transfer lags between different pairs of entrances and exits. A projected conjugate gradient scheme is employed for solving the constrained least-squares problem and is compared against a standard commercial software. The least absolute norm estimation problem is posed as a linear programming problem and is also solved using a commercial software for the sake of comparison. Computational results are presented on a set of test problems using synthetic as well as realistic simulated data, involving the determination of origin-destination trip tables for both intersection and freeway scenarios, in order to demonstrate the viability of the proposed methods. These results exhibit that, unlike as reported in the literature based on previous efforts, properly designed parameter optimization methods can indeed provide accurate estimates in a real-time implementation framework. Hence, these methods provide competitive alternatives to the iterative statistical techniques that have been heretofore used because of their real-time processing capabilities, despite their inherent inaccuracies.

Time-Dependent, Label-Constrained Shortest Path Problems with Applications

In this paper, we consider a variant of shortest path problems where, in addition to congestion related time-dependent link travel times on a given transportation network, we also have specific labels for each arc denoting particular modes of travel. The problem then involves finding a time-dependent shortest path from an origin node to a destination node that also conforms with some admissible string of labels. This problem arises in theRoute Planner Module of Transportation Analysis Simulation System (TRANSIMS), which is developed by theLos Alamos National Laboratory and is part of a multitrackTravel Model Improvement Program sponsored by the U.S. Department of Transportation (DOT) and the Environmental Protection Agency (EPA). We propose an effective algorithm for this problem by adapting efficient existing partitioned shortest path algorithmic schemes to handle time dependency along with the label constraints. We also develop several heuristics to curtail the search based on various route restrictions, indicators of progress, and projected travel times to complete the trip. The proposed methodology is applied to solve some real multimodal test problems related to the Portland, Oregon, transportation system. Computational results for both the exact method and the heuristic curtailing schemes are provided.

Optimal evacuation planning using staging and routing

Evacuation is an important disaster management tool. Evacuating a large region by automobile (the most commonly used mode) is a difficult task, especially as high levels of traffic congestion often form. This paper studies the use of demand-based strategies, specifically, the staging and routing of evacuees. These strategies attempt to manage demand in order to reduce or eliminate congestion. A strategic mixed-integer programming planning model that accounts for evacuation dynamics and congestion is used to study these strategies. The strategies adopted incorporate different evacuee types based on destination requirements and shelter capacity restrictions. The main objective studied is to minimize the network clearance time. We examine the structure of optimal strategies, yielding insights into the use of staging and routing in evacuation management. These insights are then used to develop effective solution procedures. To demonstrate the efficacy of the proposed solution technique, we provide computational experience using a large realistic example based on Virginia Beach.

Real-time travel time estimation using macroscopic traffic flow models

This paper presents the estimation of travel time on highways based on macroscopic modelling. The focus is on real-time values as compared to average or static values. The macroscopic models are used for distributed and time/space lumped settings and corresponding travel time estimation functions and algorithms are developed. The implications of these algorithms for the implementation of various incident management and traffic control strategies are also discussed.

Incorporating Uncertainty into the Estimation of the Passing Sight Distance Requirements

Passing sight distance (PSD) is provided to ensure the safety of passing maneuvers on 2-lane, 2-way roads. Many random variables determine the minimum length required for a safe passing maneuver. Current PSD design practices replace these random variables by single-value means in the calculation process, disregarding their inherent variations, which results in a single-value PSD design criteria. The main aim of this paper is to derive a PSD distribution that accounts for the variations in the contributing random variables. Two models are devised, a Monte-Carlo simulation model used to obtain the PSD distribution and a closed form analytical estimation model used for verification purposes. The Monte-Carlo simulation model uses random sampling to select values of the contributing parameters from their corresponding distributions in each run. The analytical model accounts for each parameter variation by using their means and standard deviations in a closed form estimation method. The means and standard deviations of the PSD using both models are compared for verification purposes. Both models use the same PSD formulation. Analysis is conducted for a design speed of 50 mph. A PSD distribution is developed accordingly. Results of both models differ only by less than 2%. The obtained distribution is used to estimate the reliability index of the current PSD standards at a design speed of 50 mph.

Databases and Needs for Risk Assessment of Hazardous Materials Shipments by Trucks

Risk assessment of hazardous materials movement by trucks is data intensive, and its analysis depends on several data sources and on the quality of the input data. This paper reviews the sources of available data at both federal and state levels for major components of risk assessment. It evaluates the adequacy as well as the structural problems associated with accident, incident and exposure databases. Particular emphasis is placed on accident and incident databases. The differences and similarities in risk assessment results obtained using federal and state data are highlighted. Pennsylvania and Missouri were used as case studies. Also, the differences in the state data available for California, Illinois, and Michigan are discussed. These comparisons show the impact of utilizing national default values for truck accident rates and the probability of hazardous materials releases in performing routing analyses.

Application of Dynamic Value Pricing Through Enhancements to TRANSIMS

In the past decade, transportation agencies have become increasingly interested in a high-occupancy toll (HOT) lane value pricing system, especially one with a dynamically varying toll system that depends on the congestion levels in the HOT lanes. HOT lane users are influenced by many factors, including toll prices, savings in time, and purpose of the trip. The current methodologies that forecast the demand for a HOT lane value pricing system are aggregate and do not consider individual traveler socioeconomic characteristics, particularly the value of time (VOT). This paper applies heterogeneous VOT for each individual into the generalized travel time function. This approach relaxes the conventional assumption of constant VOT. The generalized travel time function combined with VOT is used as a route choice for each traveler between the two choices (a toll lane versus a nontoll lane) in a HOT lane value pricing system. With this function, each driver is assigned to the network on the basis of a dynamic traffic assignment. This approach is implemented by using TRANSIMS on a large transportation network. TRANSIMS has a microscopic simulation model that enables the user to capture route choice behavior, including each travelers response to dynamic tolls through heterogeneous VOT. To initiate this capability, TRANSIMS was enhanced to support dynamic toll pricing. It is now capable of presenting the 15-min dynamic toll rates. In addition, the impacts of various tolls on route choice can be analyzed on the basis of socioeconomic and trip characteristics of each traveler.

Integrated Stochastic Approach for Risk and Service Estimation: Passing Sight Distance Application

The geometric design of highway elements is based on a set of standards that do not consider the risk levels associated with these designs. Safety elements including risk measures need to be identified and incorporated into the design process. The focus of this paper is a procedure that can be used by practitioners as a framework to conduct trade-off analyses between risk and service measures of specific design elements. The procedure builds on the randomness of parameters affecting the design attributes of any highway element. By accounting for the inherent variability of the determining parameters, random distributions of the design element can be derived. Then, using stochastic simulation, each of the design values can be tested to assess its risk measures. As a result, a risk index can then be attached to every design value in the random distribution. Finally, level of service (LOS) measures can be estimated and a trade-off analysis between LOS and safety could be conducted. The application focused on the design of the passing sight distance (PSD), which is an example of a highway element traditionally analyzed with no risk measures. Within the analysis, the risks and LOS measures associated with the use of the current PSD standards were assessed. The American Association of State Highways and Transportation Officials (AASHTO) Green Book values were shown to be conservative. The Manual on Uniform Traffic Control Devices (MUTCD) values fall within the acceptable risk and level of service (LOS) levels. Language: en

ESTIMATION OF TRAVEL TIMES ON URBAN FREEWAYS UNDER INCIDENT CONDITIONS

Travelers on urban freeways are interested in knowing how long it will take them to reach their destinations, especially under bottleneck, congested, and incident conditions. Travel time information is important for advanced traveler information systems (ATIS) applications and for assessing the performance of the transportation network. Though many advances have been made in the field of traffic engineering and intelligent transportation system applications, practical travel time estimation procedures for ATIS applications under incident conditions are lacking. An algorithm for travel time estimation on urban freeways for ATIS applications under incident conditions is presented. The algorithm is based on point estimates of traffic variables obtained from detectors placed on every link of the freeway network. The output required from the detectors is flow and occupancy aggregated for a short time interval (5 min). The system for travel time estimation is based on traffic flow theory rather than on statistical methods. The travel times calculated using this system are compared with the results obtained using the FHWA microsimulation computer package CORSIM in the Traffic Software Integrated System (TSIS) Version 5.0. The algorithm results are found to be reasonable and accurate compared with CORSIM results.