Nicholas E Lownes

Carsharing: Dynamic Decision-Making Problem for Vehicle Allocation

Carsharing provides members access to a fleet of shared-use vehicles in a network of locations on a short-term, as-needed basis. It allows individuals to gain the benefits of private vehicle use without the costs and responsibilities of ownership. The dynamic vehicle allocation problem is addressed in a carsharing context, that is, as a decision-making problem for vehicle fleet management in both time and space to maximize profits for the carsharing service operator. A multistage stochastic linear integer model with recourse is formulated that can account for system uncertainties such as carsharing demand variation. A stochastic optimization method based on Monte Carlo sampling is proposed to solve the carsharing dynamic vehicle allocation problem. Preliminary results are discussed and related insights are presented on the basis of a five-stage experimental network pilot study.Carsharing provides members access to a fleet of shared-use vehicles in a network of locations on a short-term, as-needed basis. It allows individuals to gain the benefits of private vehicle use without the costs and responsibilities of ownership. The dynamic vehicle allocation problem is addressed in a carsharing context, that is, as a decision-making problem for vehicle fleet management in both time and space to maximize profits for the carsharing service operator. A multistage stochastic linear integer model with recourse is formulated that can account for system uncertainties such as carsharing demand variation. A stochastic optimization method based on Monte Carlo sampling is proposed to solve the carsharing dynamic vehicle allocation problem. Preliminary results are discussed and related insights are presented on the basis of a five-stage experimental network pilot study.

Sensitivity of Simulated Capacity to Modification of VISSIM Driver Behavior Parameters

This paper presents a sensitivity analysis of VISSIM simulation capacity output under various values of driver behavior parameters. The analysis is undertaken for a simulation developed for the interchange of US-75 and SH-190 (George Bush Turnpike) north of Dallas, Texas. For each driver behavior parameter, including look-back distance, variations in simulated capacity are noted as the particular parameter is modified. The analysis of each parameter is completed with all parameters other than the one under investigation held at their calibrated values. The information presented in this paper is intended to aid practitioners developing and calibrating VISSIM simulation models in understanding the particular driver behavior parameters and the impacts on capacity of modifying the parameters. This work is also intended as a building block in the possible development of a quantitative, optimization-based calibration method for the VISSIM driver behavior parameters.

VISSIM: a multi-parameter sensitivity analysis

Traffic microsimulation is increasingly a preferred method of traffic analysis for todays transportation professionals. The importance of properly calibrating these traffic simulations is evidenced by the adoption of microsimulation calibration standards by several state and federal transportation authorities. A component of the calibration process is the calibration of the simulation for capacity. Capacity is a high-level measurement that is a function of many lower level user-defined input parameters. VISSIM utilizes psychophysical car-following models that rely on ten user-defined parameters to represent freeway driving behavior. Several VISSIM driver behavior parameters have been shown to have a significant impact on roadway capacity. This paper seeks further understanding of the performance of the VISSIM traffic microsimulator by investigating the impact of driver behavior parameter combinations on a measure of freeway capacity. This paper is intended to provide insight useful for manual calibration of VISSIM microsimulation or the development of calibration algorithms

A Composite Index of Public Transit Accessibility

Measuring ease of access to transit services is important in evaluating existing services, predicting travel demands, allocating transportation investments, and making decisions on land development. A composite index to assessing accessibility of public transit is described. It involves use of readily-available methods and represents a more holistic measure of transit accessibility, integrating developer, planner, and operator perspectives. The paper reviews previous and current methods of measuring accessibility and selects three methods for application in a case study in Meriden, Connecticut. Inconsistencies are noted across the methods, and a consistent grading scale is presented to standardize scores. Finally, this paper proposes weighting factors for individual methods to formulate a composite measure based on individual accessibility component measures. The approach aims to provide a robust and uniformly applicable measure that can be interpreted easily by planners to identify shortcomings in service coverage and promote equity in transit accessibility in the community.

Measuring Service Gaps: Accessibility-Based Transit Need Index

The integration of transit needs into transit accessibility indexing is important for evaluating existing transportation systems and service gaps and for identifying priority areas for investments in transportation infrastructure. This study detailed an indexing model for accessibility of transit need and focused on the necessity of evaluating transit needs and transit accessibility simultaneously. A need index was developed to identify areas in high need of public transit services from economic and sociodemographic information, and a composite accessibility index was developed to identify levels of access to transit services and shortcomings in providing service. The need for transit service was then modeled as the lack of transit accessibility, and the model correlated different access indicators with their ability to predict transit service need. This model mapped areas with different levels of transit accessibility and transit needs by using a single score, which may be easily interpreted by planners who examine transit equity. The model was applied to the city of Meriden, Connecticut, and results were compared with a general approach for consistency and effectiveness. The usefulness of the model was also highlighted through a representative example of the models application.

Many-to-Many Game-Theoretic Approach for the Measurement of Transportation Network Vulnerability

The vulnerability of a transportation network is strongly correlated with the ability of the network to withstand shocks and disruptions. A robust network with strategic redundancy allows traffic to be redistributed or reassigned without unduly compromising system performance. High-volume edges with limited alternative paths represent system vulnerabilities—a feature of transportation networks that has been exploited to identify critical components. A mixed-strategy, stochastic game-theoretic approach is presented for the measurement of network vulnerability. This method is designed to incorporate all origins and destinations in a network in a computationally efficient manner. The presented method differs from previous efforts in that it provides a many-to-many measure of vulnerability and edge-based disruptions that may not reside on a common path. A game that considers all possible origin–destination pairs is constructed between a router, which seeks minimum cost paths for travelers, and a network tester, which maximizes travel cost by disabling edges within the network. The method of successive averages is used for routing probabilities, and a weighted entropy function is employed to compute edge-disruption probabilities. The method is demonstrated on a small example network and then applied to the Sioux Falls, South Dakota, network. Results indicate good correspondence with a previous method that used equilibrium assignment and rapid solution convergence.

Developing Safety Performance Function for Freeways by Considering Interactions Between Speed Limit and Geometric Variables

Safety performance functions (SPFs) are crash prediction models that quantitatively relate the expected number of crash counts with traffic volume and roadway and roadside geometries. SPFs help traffic safety officials identify unsafe locations and take appropriate counteractive measures. A study assembled crash and roadway geometry data of freeways (only Interstate highway data were used for this study) in Connecticut for development of SPFs. Models were estimated separately for single-vehicle and multivehicle crashes. Total and fatal and injury crashes were considered for model estimation for both single-vehicle and multivehicle crashes. For each crash category, three model estimations were performed with negative binomial distribution models with all geometric variables, with speed limit only, and with interaction between speed limit and roadway geometric variables. The best models were selected for each crash category through a comparison of goodness-of-fit measures (Akaike information criterion). Interaction models were found to be the best models for all crash categories. This finding suggests the importance of incorporating the interaction effect between variables, in particular between speed limit and geometric variables such as number of lanes, shoulder width, and median type, for estimation of crash prediction models.

Complex Network Method of Evaluating Resilience in Surface Transportation Networks

A complex network analysis methodology was adopted to evaluate structural resilience in surface transportation networks with the use of examples of the U.S. highway and Interstate networks in Connecticut and the Indiana interurban railroad network. Resilience in these networks was evaluated alongside that of a biological network, which through millions of years of evolution had developed an adaptive behavior in which cost, efficiency, and resilience were optimized in the feeding network that was constructed. Disruptions in the networks were simulated by using link-based targeted and random strategies. With simulation results, network performance under disruption was assessed by using two metrics: global efficiency and the relative size of the giant component for each disruption strategy. The biological network exhibits superior resistance to disruption regardless of strategy, a quality attributed to its redundant and cyclic weblike network structure and its innate ability to adapt to disruptions by developing network structures that have been honed through millions of years of evolution. In addition, linear correlations between network structural metrics such as the average degree, density, and average clustering coefficient were explored and analyzed.

Effects of Scale and Boundary Selection in Assessing Equity of Transit Supply Distribution

The equitable distribution of transit services is a major concern of transportation planners and policy makers worldwide. In the United States, planners are required by executive order to consider equity concerns when investing in new transportation infrastructure and services. However, equity can be difficult to assess in a consistent, objective, and quantitative way. Australian researchers recently developed a single, systemwide measure that reflects the equity of transit service distribution in a metropolitan area. This measure, known as a Gini coefficient, measures specifically how well transit supply meets transit demand. Transit supply is measured with a modified form of an established supply index, and demand is estimated with population and employment data. Although the idea of a single measure for assessing the equity of a transit system is appealing, researchers and practitioners must be careful when they implement Gini coefficients for comparative purposes. This research investigated the effect of selecting various scales, boundaries, and demand measures for calculating Gini scores for interregional comparisons. Gini coefficients were calculated for six urban transit systems within two boundaries (metropolitan statistical area and transit service area) at two scales (census tract and block group) with two demand measures (population and population plus employment). Results suggest that calculations with Gini coefficients on the basis of various boundary definitions can lead to drastically different comparative results, whereas the different scales and demand measures had little impact on interregional comparisons.

Characteristics of Multimodal Conflicts in Urban On-Street Bicycle Lanes

In urban areas, bicycles that travel in bicycle lanes encounter a variety of obstructions, including pedestrians and various types of motor vehicles. Earlier studies focused on the frequency of such events. The goal of this study was to characterize the obstructions. Data were collected in the Manhattan and Brooklyn boroughs of New York City to evaluate specific characteristics (e.g., bicycle lane designs, curb regulations, land use) that might influence the frequency of specific conflict types. A method is described for evaluating the frequency of conflicts between bicycles that travel in on-street bicycle lanes and various other transportation modes and for identifying factors that might affect the frequency of such conflicts.