Xia Jin

Methodologies for Estimating Vehicle Queue Length at Metered On-Ramps

The ability to monitor vehicle queue length accurately at metered on-ramps can improve ramp meter performance and help to create improved ramp metering algorithms. The queue length distribution can be considered as a continuous analog signal, which consists of both deterministic and stochastic components. Three types of methods for the estimation of on-ramp queue length are discussed: Kalman filter, linear occupancy, and Highway Capacity Manual (HCM) back of queue. Queue data estimated with these methods are compared with field-observed queue data and random number samples. The comparisons indicate that the Kalman filter and linear occupancy methods are usable for real-world operations, but both of them have limitations. The HCM back-of-queue method does not produce reliable estimates for on-ramp queue length.

Experiment to Improve Estimation of Vehicle Queue Length at Metered On-Ramps

Two types of algorithms for on-ramp queue estimation are discussed; a Kalman filter and a conservation model. A volume-balancing ratio is introduced to both models to account for unavoidable detector miscounting behavior. Estimation results are compared with queue data observed in the field. The volume-balancing ratio improves both models. Although the conservation model may provide more accurate prediction with balanced volumes, the Kalman filter tends to provide better estimation when the volume-balancing ratio deviates from 1. Generally the Kalman filter provides a better prediction, but the conservation model is simpler to implement. Attempts to improve the Kalman filter further are also explored.

Propensity to Telecommute: Exploring the National Household Travel Survey

Telecommuting is the substitution for work at the workplace with work at home or other locations close to home. The interest in telecommuting stems from its potential benefits in trip reduction, congestion mitigation, cost saving for office spaces, increased productivity, and better home–work balance. The factors that influence peoples telecommuting behavior were explored by using data from the 1995 Nationwide Personal Transportation Survey and 2001 and 2009 National Household Travel Survey. A comprehensive analysis was undertaken, and the trends over several years were examined. The analysis advances the understanding of the characteristics of workers who telecommute according to detailed categories of telecommuting frequency. The findings are essential as a first step toward estimating and incorporating telecommuting in the travel demand forecasting process.

Tour-Based Time-of-Day Choices for Weekend Nonwork Activities

Research on time-of-day (TOD) choices has predominantly focused on weekday trips or activities, with few studies covering the behavior of TOD choices on weekends. This paper examines TOD choices on weekends using a tour-based approach. A multinomial discrete choice model was calibrated to explore the effects that household and individual socio-demographics have on TOD choice behavior. In light of unique travel patterns on weekends, the authors have combined both Saturday and Sunday into a single travel period, which is then divided into six TOD segments. The methodology is based on the observation that, unlike on weekdays, what individuals do on Saturdays is less likely to be repeated on Sundays with the same TOD pattern. This manuscript demonstrates the application of a tour-based TOD weekend forecast model using the 2001 Atlanta Household Survey data from Georgia. The survey contains detailed travel information on weekends. The study presents the exploratory analysis of weekend travel patterns at both trip-based and tour-based levels in Atlanta. A brief comparison of trip-based and tour-based models is also given. The study validates the suggestion that a tour-based model improves the overall goodness-of-fit of the model and produces a better forecast.