2016Transportation Research Board 95th Annual MeetingTransportation Research BoardRequires access

Modeling Bus Dwell Time for Bus Stops

Chao Wang, Zhirui Ye, Yuan Wang, Yueru Xu, Wei Wang

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Abstract

Bus dwell time at bus stops is a major component of bus travel time, and it is of great importance to estimate capacity of a bus stop. The primary objective of this study was to develop a quantitative approach to estimate bus stop dwell time. It included acceleration and deceleration time, dead time, and the time for serving boarding and alighting passengers at bus stops. A polynomial model incorporating kinematics of a particle was derived for estimating bus acceleration and deceleration time. In addition, 8 descriptive statistics methods were used to analyze dead time, which involved aveage delay for re-entering the car stream, boarding lost time, bus stop failure time, and traffic signal delay. A case study was conducted to show the applicability of the proposed model with data collected from the seven most common types of bus stops in China. Linear regression analysis between calculated and observed bus dwell time showed that they had a strong relationship (R-square value of 0.8840 for non-peak period and 0.8386 for peak period). The results of R-square values and Mean Absolute 15 Percentage Error (MAPE) indicated the method was well validated and could be practically used for the analysis and estimation of bus dwell time in China. Sensitivity analyses were also conducted to investigate the effects of bus stop locations on the bus dwell time.

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What this paper is about

Bus dwell time at bus stops is a major component of bus travel time, and it is of great importance to estimate capacity of a bus stop. The primary objective of this study was to develop a quantitative approach to estimate bus stop dwell time. It included acceleration and deceleration time, dead time, and the time for serving boarding and alighting passengers at bus stops. A polynomial model incorporating kinematics of a particle was derived for estimating bus acceleration and deceleration time. In addition, 8 descriptive statistics methods were used to analyze dead time, which involved aveage delay for re-entering the car stream, boarding lost time, bus stop failure time, and traffic signal delay. A case study was conducted to show the applicability of the proposed model with data collected from the seven most common types of bus stops in China. Linear regression analysis between calculated and observed bus dwell time showed that they had a strong relationship (R-square value of 0.8840 for non-peak period and 0.8386 for peak period). The results of R-square values and Mean Absolute 15 Percentage Error (MAPE) indicated the method was well validated and could be practically used for the analysis and estimation of bus dwell time in China. Sensitivity analyses were also conducted to investigate the effects of bus stop locations on the bus dwell time.

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Available abstract

Bus dwell time at bus stops is a major component of bus travel time, and it is of great importance to estimate capacity of a bus stop. The primary objective of this study was to develop a quantitative approach to estimate bus stop dwell time. It included acceleration and deceleration time, dead time, and the time for serving boarding and alighting passengers at bus stops. A polynomial model incorporating kinematics of a particle was derived for estimating bus acceleration and deceleration time. In addition, 8 descriptive statistics methods were used to analyze dead time, which involved aveage delay for re-entering the car stream, boarding lost time, bus stop failure time, and traffic signal delay. A case study was conducted to show the applicability of the proposed model with data collected from the seven most common types of bus stops in China. Linear regression analysis between calculated and observed bus dwell time showed that they had a strong relationship (R-square value of 0.8840 for non-peak period and 0.8386 for peak period). The results of R-square values and Mean Absolute 15 Percentage Error (MAPE) indicated the method was well validated and could be practically used for the analysis and estimation of bus dwell time in China. Sensitivity analyses were also conducted to investigate the effects of bus stop locations on the bus dwell time.

Key concepts: Dwell time, Real-time computing, Simulation, Computer science, Statistics, Engineering, Mathematics, Medicine

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