1992Transportation Research Record Journal of the Transportation Research BoardRequires access

DWELL TIME RELATIONSHIPS FOR LIGHT RAIL SYSTEMS

Tyh-ming Lin, Nigel H. M. Wilson

Open publisher page 128 citations

Abstract

Vehicle dwell time is an important determinant in the capacity and performance characteristics of high-frequency, high-ridership light rail lines that are common in Europe. In the United States these systems are best exemplified by the Green Line of the Massachusetts Bay Transportation Authority (MBTA). In such systems cumulative dwell time can represent a significant proportion of total train running time and can contribute greatly to headway variability, which in turn affects passenger service quality. Models are estimated for both one- and two-car trains based on data gathered for the MBTA Green Line. These models explain about 70% of the observed variation in dwell times using three explanatory variables: passengers boarding, passengers alighting, and passengers on board. The effect of passenger crowding is statistically significant in most models, and adding crowding variables to reflect congestion on board the vehicle significantly improves the explanatory power of most models. Nonlinear forms of the crowding effect were also estimated, and genrally these forms performed better than the corresponding linear forms.

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Vehicle dwell time is an important determinant in the capacity and performance characteristics of high-frequency, high-ridership light rail lines that are common in Europe. In the United States these systems are best exemplified by the Green Line of the Massachusetts Bay Transportation Authority (MBTA). In such systems cumulative dwell time can represent a significant proportion of total train running time and can contribute greatly to headway variability, which in turn affects passenger service quality. Models are estimated for both one- and two-car trains based on data gathered for the MBTA Green Line. These models explain about 70% of the observed variation in dwell times using three explanatory variables: passengers boarding, passengers alighting, and passengers on board. The effect of passenger crowding is statistically significant in most models, and adding crowding variables to reflect congestion on board the vehicle significantly improves the explanatory power of most models. Nonlinear forms of the crowding effect were also estimated, and genrally these forms performed better than the corresponding linear forms.

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Available abstract

Vehicle dwell time is an important determinant in the capacity and performance characteristics of high-frequency, high-ridership light rail lines that are common in Europe. In the United States these systems are best exemplified by the Green Line of the Massachusetts Bay Transportation Authority (MBTA). In such systems cumulative dwell time can represent a significant proportion of total train running time and can contribute greatly to headway variability, which in turn affects passenger service quality. Models are estimated for both one- and two-car trains based on data gathered for the MBTA Green Line. These models explain about 70% of the observed variation in dwell times using three explanatory variables: passengers boarding, passengers alighting, and passengers on board. The effect of passenger crowding is statistically significant in most models, and adding crowding variables to reflect congestion on board the vehicle significantly improves the explanatory power of most models. Nonlinear forms of the crowding effect were also estimated, and genrally these forms performed better than the corresponding linear forms.

Key concepts: Dwell time, Crowding, Train, Transport engineering, Explanatory power, Headway, Level of service, Econometrics

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