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Berkeley, CA, United States

Kwon J.,Berkeley Transportation Systems Inc. | Hranac R.,Berkeley Transportation Systems Inc. | Petty K.,Berkeley Transportation Systems Inc. | Compin N.,120 N Street
Transportation Research Record | Year: 2011

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. Source


Kwon J.,Berkeley Transportation Systems Inc. | Petty K.,Berkeley Transportation Systems Inc.
17th ITS World Congress | Year: 2010

We have developed a method for using the vehicle signature data from the general-purpose weigh-in-motion (WIM) infrastructure for two novel applications: truck travel time measurement and WIM sensor calibration. WIM sensors provide vehicle classification, truck axle weights and spacing data for individual vehicles. These are traditionally used for producing traffic data inputs to design of new and rehabilitated pavement structures. Our proposed approach consists of four steps: (i) to collect vehicle signatures at upstream and downstream points; (ii) anonymously re-identify vehicles using the Minimum Distance with Screening (MDS) method we have developed; (iii) estimate truck travel times from the matched vehicles; and (iv) calibrate WIM sensor bias for axle weights and spacing by comparing measurements from matched vehicles. The MDS method is extremely simple to implement, does not require ground truth data, and performs very well, comparable to more sophisticated methods. The approach is applied to the data collected from WIM stations in California, USA, to reliably estimate the truck travel times and identify/calibrate sensor measurement biases for multiple sensors pairs, some of which are as far as 30 miles apart. Source


Barkley T.,Berkeley Transportation Systems Inc. | Hranac R.,Berkeley Transportation Systems Inc. | Fuentes K.,South Bay Cities Council of Governments | Law P.,Southern California Association of Governments
Transportation Research Record | Year: 2011

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 year's 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. Source


Barkley T.,Berkeley Transportation Systems Inc. | Hranac R.,Berkeley Transportation Systems Inc. | Ciccarelli A.,Berkeley Transportation Systems Inc.
18th World Congress on Intelligent Transport Systems and ITS America Annual Meeting 2011 | Year: 2011

Arterial performance measurement is of growing interest to agencies, as they seek toquantify the return on investment for projects, provide enhanced information to travelers, and better integrate arterial operations into multi-modal network management practices.Quality performance measurement requires agencies to collect data from ITS infrastructure, archive it, and compute meaningful and actionable metrics from it. This paper takes a case study approach to illustrate how an arterial performance measurement software system can programmatically compute and report consistent performance measures for different detection types, in a way that is scalable to different network sizes. It is based on deployments of Berkeley Transportation Systems, Inc's Arterial Performance Measurement System (A-PeMS) at three sites that differ in sensor type, sensor placement, and deployment scale.The paper also details the unique computational and visualization approaches taken to capitalize on the performance measurement potential of each site's detection capabilities. Source

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