Rob Hranac

Decomposition of Travel Time Reliability into Various Sources: Incidents, Weather, Work Zones, Special Events, and Base Capacity

An empirical, corridor-level method is proposed to divide the travel time unreliability or variability over a freeway section into the following components: incidents, weather, work zones, special events, and inadequate base capacity or bottlenecks. The method consists of three steps: (a) corridor-level aggregation of travel time and source data, (b) quantile regression to fit the 95th percentile of travel time on the source variables, and (c) calculation of the contribution of individual sources to the buffer time. It could be applied to other percentile-based travel time reliability measures such as planning time and 90th and 95th percentiles. Once the source data are defined, the method can be automatically applied to any site with minimum calibration. When applied to a 30.5-mi section of northbound I-880 in the San Francisco Bay Area, California, the method revealed that traffic accidents contributed 15.1% during the morning and 25.5% during the afternoon, among others, and most of the remaining reliability came from recurrent bottlenecks. Quantifying the components of travel time variability at individual freeway sites is essential in developing effective strategies to mitigate congestion.

Heuristic Approach for Estimating Arterial Signal Phases and Progression Quality from Vehicle Arrival Data

Automated performance-monitoring systems take in intelligent transportation system sensor data in real time, archive them, and analyze them. These systems are needed to help local agencies identify problem areas, develop improvement plans, and perform before and after evaluations on the impacts of traffic management changes. Research performed in the past few years has demonstrated the utility of these systems for local transportation agencies, particularly for evaluating signal progression quality. However, acquiring the critical data items for existing arterial intelligent transportation systems—signal phase event information—is often a practical challenge because the configuration of the existing system of most arterial systems does not record or communicate signal phase events to a central location. As a solution to that problem, this paper documents an approach to estimate signal phase data with in-pavement vehicle sensors, a data source that is generally available from arterial systems. On many arterial systems, these sensors frequently communicate data from the field to a central traffic management center. The goal of this paper was to make recent arterial progression quality research implementable by developing a method to gather signal phase event data in a way that would be practical for most local transportation agencies, given their existing arterial systems. Two proposed methods were tested on a years worth of data from a 2-mi arterial corridor in Carson, California. Results showed that sensor data from central traffic management centers could be used to develop accurate measurements of signal phase events when coupled with timing plans.

Simulating the Travel Time Impact of Missed Transit Connections

It is well established that transit passengers dislike transferring, in part because of the inherent risk that the connecting vehicle will be missed, a risk that can increase overall travel time. Despite the problems that missed transfers cause, such transfers across a system are rarely tracked in transit performance monitoring programs. The likelihood of a missed transfer depends on combinations of several factors and thus is hard to estimate. In practice, transit systems are most often evaluated according to the performance of individual vehicles, stops, and routes, not the interactions between them. This paper describes a systems approach to quantify the effects of travel time reliability, schedule adherence, and schedule design on missed transit connections, and the resulting travel time distributions. To determine the effects of vehicle interactions on transfers and the role that transfers play in travel time, a series of simulations based on automatic passenger counting data from the bus system in San Diego, California, was performed. Travel times on two transfer trips in downtown San Diego were simulated. The effects of passenger arrival rate, on-time vehicle performance, and schedule design on the likelihood of a transfer being missed were investigated in a sensitivity analysis. This research is expected to lead to a better understanding of the passenger effects of schedule adherence on transfer trips. Practically speaking, this methodology could aid in the identification of pairs of buses whose chronic schedule deviations at a particular location are likely causing missed transfers.

Dashboards for Transportation Operations: Detector Health Case Study

Dashboards are an increasingly common method for executives in the private sector to monitor the effect of business decisions on business operations. These dashboards integrate business intelligence databases into relatively simple web-based reporting mechanisms accessible to multiple decision makers. Some states in the public-sector transportation arena have developed dashboards to monitor delivery on relatively long-term agency processes, such as project budget and schedule metrics. Few efforts have focused on monitoring relatively short-term operational performance measures. Berkeley Transportation Systems, Inc., has worked with the California Department of Transportation to develop an operational performance measure–based dashboard for transportation agency executives using the Performance Measurement System. This paper outlines an application of this dashboard to a specific agency program aimed at improving traffic detector health.