No abstract

Estimation of short-term bus travel time is an essential component of effective intelligent transportation systems (ITS), including traveler information systems and transit signal priority (TSP) strategies. Several technologies, such as automated vehicle location (AVL) systems, can provide real-time information for estimation of bus travel time. However, low resolution of data from such technologies presents a challenge to accurate estimation of travel time. Several models for estimation of bus travel time at signalized urban arterials were developed and tested. These models used low-frequency AVL data and required only knowledge of network specifications such as locations of bus stops and intersections. First, a linear regression model was developed; it decomposed total travel time into its components, including running travel time, dwell time at bus stops, and delay at signalized intersections. Second, various machine learning models, including support vector regression (SVR) with nonlinear kernel, ridge, Lasso, decision tree, and Bayesian ridge were trained by using Python libraries such as scikit-learn and evaluated. A segment of Washington Street in Boston, Massachusetts, was selected as the study site. The results indicate that the SVR model outperformed other regression models in generalized error measures, in particular those of mean absolute error and root mean square error. The findings of this study can lead to improved traveler information systems and more-efficient TSP strategies and, overall, can contribute to better transit quality of service.

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Transit preferential treatments (TPTs) enhance transit service by reducing transit travel time and improving transit reliability. In this study analytical and simulation models were developed to evaluate various space TPTs with person-based measures such as person delay and person discharge flow. The focus was placed on the evaluation of dedicated bus lanes and queue jumper lanes. This study extended previous research to differentiate between a queue jumper lane and a dedicated bus lane when an analytical model was used to estimate delays. In addition, the proposed model accounted for the effect of nearside bus stops on auto and transit vehicle delays. The performance of these space TPTs was evaluated with the analytical model and microsimulation tests at a signalized intersection that was part of a larger signalized arterial. Results indicated that the analytical model provided estimates of person delay and person discharge flow that were consistent and comparable with the estimates from microsimulation tests.

Quality data are vital to the planning and operation of traffic systems. High occupancy vehicle (HOV) lanes, for instance, must comply with federal performance standards. If an agency fails to meet the standards, the facility is considered to be “degraded” and the agency is required to undertake actions that would return the facility to satisfactory operation. This could include removing exempted vehicles (e.g., low-emission vehicles) or increasing toll prices and passenger occupancy limits. Such policy changes may be costly, and may affect related policy goals, such as promoting clean air vehicles. Owing to constant changes in the system (e.g., roadwork, system upgrades), some of the thousands of HOV sensors in California’s transportation system are misconfigured, such as being labeled as general-purpose lanes. In this situation, HOV lane data may be mistakenly aggregated with general-purpose lane data and vice versa, causing a HOV facility to be erroneously reported as degraded and requiring unnecessary policy action. Detecting these misconfigurations is challenging and labor-intensive to accomplish manually. The purpose of this research was to utilize machine learning techniques to detect sensor misconfigurations and to understand the extent to which they affect performance reporting of HOV lanes. The results for Caltrans District 7 (Los Angeles and Ventura counties) showed that about 5% to 8% of Performance Measurement System HOV sensors are misconfigured. Therefore approximately 27 to 44 mi of HOV lanes are erroneously measured, with approximately 10 to 16 mi (38%) of those reporting an erroneously high degradation rating.