An application of variance reduction techniques in freeway simulation

Computer simulation models are used in a variety of applications in transportation engineering and have become a prime aid in decision making. The applications range from evaluating traffic control strategies for single intersections to such complex decision processes as evaluating the impact of removing toll facilities at the George Washington Bridge in New York City. While it is widely accepted that simulation offers an unmatchable capability of evaluating alternate control policies, the high variance of the output variable presents a critical problem in such comparative analyses. The simulation models with high output variance must be run longer or replicated many times to achieve a desired precision level, and that corresponds to increased cost of computer resources. This paper describes and illustrates the application of variance reduction concepts that can improve the reliability and efficiency of the simulation experimental process by taking advantage of the simulation model structure. The two variance reduction concepts (common random numbers and antithetic variates) reduce the variance of the output variable by replacing the original sampling procedure with a new procedure that yields the same expected value but with a smaller variance. The application of the variance reduction concept was illustrated using results from experiments with a freeway simulation model. The results indicate that both common random numbers and antithetic variates sampling procedures appreciably reduce the variance of the simulation output measure.     

A computer model for finding the time-dependent minimum path in a transit system with fixed schedules

This paper describes the development of a computer model and algorithms for finding the time-dependent minimum path between two stations in a multi-route, multi-mode transit system running to fixed schedules. Selection of the minimum path can be based either on journey time or on weighted time. A worked example using a simple transit network is given to illustrate how the model works. The model has several applications in transport planning: it can be used for generating route schedule information to guide transit users, for assisting in route schedule coordination, and for analyzing transit system accessibility.     

Performance indicators for transit management

Transit performance can be evaluated through quantitative indicators. As the provision of efficient and effective transit service are appropriate goals to be encouraged by federal and state governments, these goals are used to develop performance indicators.Three efficiency and four effectiveness indicators are described, together with two overall indicators. These nine indicators are analyzed for comparability utilizing operating and financial data collected from public transit agencies in California.Performance indicators selected for this study should not be viewed as final. Twenty-one performance indicators proposed by previous studies were reviewed. Theoretical considerations and unavailability or unreliability of data caused omission of several useful measures like passenger-miles. Circumstances such as improved data, emphasis upon goals other than efficiency and effectiveness, and local conditions might warrant the inclusion of indicators deleted from this research.This paper is based on work conducted for the Urban Mass Transportation Administration under University Research and Training Grant CA-11-0014, Development of Performance Indicators for Transit. The views expressed herein are those of the authors and not necessarily those of the University of California or the United States Government. We are indebted to John Feren for assistance with the statistical processing and data gathering.     

Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems

Map-matching (MM) algorithms integrate positioning data from a Global Positioning System (or a number of other positioning sensors) with a spatial road map with the aim of identifying the road segment on which a user (or a vehicle) is travelling and the location on that segment. Amongst the family of MM algorithms consisting of geometric, topological, probabilistic and advanced, topological MM (tMM) algorithms are relatively simple, easy and quick, enabling them to be implemented in real-time. Therefore, a tMM algorithm is used in many navigation devices manufactured by industry. However, existing tMM algorithms have a number of limitations which affect their performance relative to advanced MM algorithms. This paper demonstrates that it is possible by addressing these issues to significantly improve the performance of a tMM algorithm. This paper describes the development of an enhanced weight-based tMM algorithm in which the weights are determined from real-world field data using an optimisation technique. Two new weights for turn-restriction at junctions and link connectivity are introduced to improve the performance of matching, especially at junctions. A new procedure is developed for the initial map-matching process. Two consistency checks are introduced to minimise mismatches. The enhanced map-matching algorithm was tested using field data from dense urban areas and suburban areas. The algorithm identified 96.8% and 95.93% of the links correctly for positioning data collected in urban areas of central London and Washington, DC, respectively. In case of suburban area, in the west of London, the algorithm succeeded with 96.71% correct link identification with a horizontal accuracy of 9.81 m (2σ). This is superior to most existing topological MM algorithms and has the potential to support the navigation modules of many Intelligent Transport System (ITS) services.