Randy B Machemehl

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.

Tabu Search Strategies for the Public Transportation Network Optimizations with Variable Transit Demand

In this article, systematic tabu search (TS)-based heuristic methods are put forward and applied for the design of public transportation networks with variable demand. A multi-objective nonlinear mixed integer model is formulated. Solution methodologies are proposed, which consist of 3 main components: an initial candidate route set generation procedure that generates all feasible routes incorporating practical bus transit industry guidelines; a network analysis procedure that decides transit demand matrix, assigns transit trips, determines service frequencies, and computes performance measures; and a Tabu search method (TSM) that combines these 2 parts, guides the candidate solution generation process, and selects an optimal set of routes from the huge solution space. Comprehensive tests are conducted and sensitivity analyses are performed. Characteristics analyses are undertaken and solution qualities from different algorithms are compared. Numerical results indicate that the preferred TSM outperforms the genetic algorithm used as a benchmark for the optimal bus transit route network design problem without zone demand aggregation.

Development and Evaluation of Transit Signal Priority Strategies

Research on the effectiveness of providing signal priority to bus transit vehicles is presented in this report. Results from previous studies indicate the effectiveness of transit signal priority depends on a number of factors, including the type of transit route, the level of transit usage, and the time of day priority is used. This research describes and evaluates several methods for providing transit signal priority during both peak and off-peak times. Results indicate that providing signal priority during off-peak times is often justified, due to the excess capacity available within the transit network. However, during peak times, use of transit signal priority is only justified when the level of transit patronage is high.

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.

A tabu search based heuristic method for the transit route network design problem

Systematic tabu search based meta-heuristic algorithms are designed and implemented for the transit route network design problem. A multi-objective nonlinear mixed integer model is formulated. Solution methodologies based on three variations of tabu search methods are proposed and tested using a small experimental network as a pilot study. Sensitivity analysis is performed, a comprehensive characteristics analysis is conducted and numerical results indicate that the preferred tabu search method outperforms the genetic algorithm used as a benchmark.

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

Impact of Rising Fuel Prices on U.S. Transit Ridership

As gasoline prices have risen to unprecedented levels over the past 2 years, many transit agencies have claimed that higher fuel prices have driven ridership growth. It is determined first whether such a correlation is substantiated by the available data and then, if such correlation exists, the nature of this relationship. Five U.S. cities were selected for analysis on the basis of their level of automobile orientation and the extent and variety of transit services: Atlanta, Georgia; Dallas, Texas; Los Angeles, California; San Francisco, California; and Washington, D.C. Most of the transit systems in the five cities analyzed have experienced ridership growth since early 2004. Exceptions include the Atlanta bus and heavy rail systems and the San Francisco bus systems. With the use of time series analysis, seasonal indices, and correlation coefficients, ridership trends are evaluated and compared with corresponding national fuel prices. With the exceptions of the modes cited above and the Virginia Railway Express commuter rail in Washington, D.C., the correlation between ridership and fuel prices is statistically significant for all systems. This finding indicates that fuel price increases have indeed played a role in encouraging transit use in historically automobile-oriented American cities. Finally, the empirical relationships between fuel price and transit demand are explored. Results indicate that, on average, as fuel price increases by 1%, transit demand increases on the order of 0.24%; in other words, ridership increases approximately 0.09% for each

Do transit users just wait for buses or wait with strategies? Some numerical results that transit planners should see

0.01 increase in fuel price.

Effects of On-Street Bicycle Facility Configuration on Bicyclist and Motorist Behavior

The effects of bus line headway, schedule reliability index, and many other potential predictors on passenger waiting times were investigated. A large amount of waiting time data, including passenger-related attributes and transit system operational characteristics, was collected by direct observation and by videotaping during a 6-month period in the city of Austin, Texas. Various mathematical models were developed, and a preferred model was identified. A linear regression model that uses bus line headway as the only independent variable to predict passenger waiting times was given for transit planning purposes. A great difference between the traditional random model and the study model was examined. With the use of structural breaks on the basis of an econometric analysis approach for the first time, several insights into the numerical results were provided. An 11-min vehicle headway was identified to mark the transition from practically random to less random passenger arrivals, and all transit users can be regarded as coordinated arrivals after 38-min bus headway. The benefits of coordinated arrivals compared with random arrivals were also presented.

Bi-Level Optimization Model for Public Transportation Network Redesign Problem: Accounting for Equity Issues

Growing awareness of environmental and public health problems associated with motorized transportation has led to a recent effort to promote nonmotorized modes of travel. Previous studies have shown that facility design plays a large role in encouraging bicycling. With the aim of defining the roadway configurations that lead to safe motorist and bicyclist behavior, this research examines the impact of design elements, including the type and width of the bicycle facility, the presence of adjacent motor vehicle traffic, parking turnover rate, land use, and the type of motorist-bicyclist interaction. Observational studies conducted at 48 sites in three large Texas cities characterize bicyclist and motorist behavior through lateral position measurements and instances of motorist encroachment on an adjacent lane. These observations were used to build two multivariate regression models and allowed for direct site-to-site comparisons. Notable results include the observation that bicycle lanes create a safer and more predictable riding environment relative to wide outside lanes, and that the provision of a buffer between parked cars and bicycle lanes is the only reliable method for ensuring that bicyclists do not put themselves at risk of being hit by opening car doors.