Karl Petty

Accurate estimation of travel times from single-loop detectors

As advanced traveler information systems become increasingly prevalent the importance of accurately estimating link travel times grows. Unfortunately, the predominant source of highway traffic information comes from single-loop loop detectors which do not directly measure vehicle speed. The conventional method of estimating speed, and hence travel time, from the single-loop data is to make a common vehicle length assumption and to use a resulting identity relating density, flow, and speed. Hall and Persaud (Transportation Research Record 1232, 9-16, 1989) and Pushkar et al. (Transportation Research Record 1457, 149-157, 1994) show that these speed estimates are flawed. In this paper we present a methodology to estimate link travl times directly from the single-loop loop detector flow and occupancy data without heavy reliance on the flawed speed calculations. Our methods arise naturally from an intuitive stochastic model of traffic flow. We demonstrate by example on data collected on I-880 data (Skabardonis et al. Technical Report UCB-ITS-PRR-95-S, Institute of Transportation Studies, University of California, 1994) that when the loop detector data has a fine resolution (about one second), the single-loop based estimates of travel time can accurately track the true travel time through many degrees of congestion. Probe vehicle data and double-loop based travel time estimates corroborate the accuracy of our methods in our examples.

Freeway performance Measurement System. Mining loop detector data

Performance Measurement System (PeMS) is a freeway performance measurement system for all of California. It processes 2 GB/day of 30-s loop detector data in real time to produce useful information. At any time managers can have a uniform, comprehensive assessment of freeway performance. Traffic engineers can base their operational decisions on knowledge of the current status of the freeway network. Planners can determine whether congestion bottlenecks can be alleviated by improving operations or by minor capital improvements. Travelers can obtain the current shortest route and travel time estimates. Researchers can validate their theory and calibrate simulation models. PeMS, which has been in stable operation for 18 months, is a low-cost system. It uses the California Department of Transportation (Caltrans) network for data acquisition and is easy to deploy and maintain. It takes under 6 weeks to bring a Caltrans district online, and functionality can be added incrementally. PeMS applications are accessed over the World Wide Web; custom applications can work directly with the PeMS database. Built as a prototype, PeMS can be transitioned into a 7 × 24 production system. The PeMS architecture and use are described.

I-880 Field Experiment: Data-Base Development and Incident Delay Estimation Procedures

The collection and processing of field data on freeway incidents and operating conditions are described, and the development and application of a methodology for estimating incident delays are presented. The data were collected on a section of the I-880 freeway in the San Francisco Bay Area before and after the implementation of freeway service patrols (FSPs). Incident characteristics were obtained through observations of probe vehicle drivers traveling at an average of 7-min headways. Travel times were obtained from the specially instrumented probe vehicles. Speeds, flows, and occupancies at 1-sec intervals were collected from closely spaced loop detectors on the freeway main line and the ramps. Software was developed to process the data and create a computerized data base. The I-880 data base consists of 276 hours of field data that are uniquely linked to provide a complete representation of the freeway operating conditions at the test site. Improved procedures for estimating incident delay and other pe...

An extended macroscopic model for traffic flow

This paper presents a model of traffic on a highway based on the macroscopic description of traffic as a compressible fluid. We take the computationally efficient model of Papageorgiou and test it on field data. We extend the model to flow under the influence of traffic-obstructing incidents. In applications where the interest is in mass phenomena and real time computation, a macroscopic model is preferable over microscopic models. This model also permits systematic parameter identification, which makes it more useful for real traffic systems. The influence of incidents on the highway is included in the model and it is possible to tune its parameters for flow under incidents. This allows the model to compute the effect of incidents on flow capacity using field data.

I-880 FIELD EXPERIMENT: ANALYSIS OF INCIDENT DATA

The I-880 field experiment has produced one of the largest data bases on incidents and freeway traffic-flow characteristics ever compiled. Field data on incidents were collected through observations of probe-vehicle drivers before and after the implementation of freeway service patrols (FSPs) over a freeway section. Supplementary information was collected from the California Highway Patrol’s computer-aided dispatch system, FSPs, and tow-truck company logs. The incident patterns are described and the major factors affecting incident frequency and duration are identified. FSPs significantly reduced the response times but did not have a significant effect on the duration of all incidents.

Travel Time Prediction Algorithm Scalable to Freeway Networks with Many Nodes with Arbitrary Travel Routes

A travel time prediction algorithm scalable to large freeway networks with many nodes with arbitrary travel routes is proposed. Instead of constructing separate predictors for individual routes, it first predicts the whole future space-time field of travel times and then traverses the required subsection of the predicted travel time field to compute the travel time estimate for the requested route. Compared with the traditional approach that offers the same flexibility, the proposed method substantially reduces storage and computation time requirements at the relatively small computational cost at the time of actual prediction. It is first established that travel times computed by traversing travel time fields are compatible with more direct measurements of travel times from a vehicle reidentification technique based on electronic toll collection tags. This provides a conceptual justification of the proposed approach. When applied to the loop data from an 8.7-mi section of the I-80 freeway, the proposed a...

Los Angeles I-10 Field Experiment: Incident Patterns

The Los Angeles I-10 field experiment has produced a large, comprehensive database on incidents and freeway traffic flow characteristics. Field data on incidents were collected through observations of probe vehicle drivers over a 12.5-km (7.8-mi) freeway section. Supplementary information was collected from the California Highway Patrol computeraided dispatch (CHP/CAD) system and freeway service patrol logs. The incident patterns at the study area are described, and the major factors affecting incident frequency and duration are identified. Comparisons of the findings with other databases are discussed. The effects of incidents on traffic flow are also investigated using the empirical data to determine the proportion and characteristics of delay-causing incidents.

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.

Probe Vehicle Runs or Loop Detectors?: Effect of Detector Spacing and Sample Size on Accuracy of Freeway Congestion Monitoring

Freeway congestion monitoring can be based either on sampling-based methods, such as probe vehicle runs, or on continuous data from loop detector infrastructure. Sample size, in terms of the number of days sampled, affects the accuracy of sampling-based methods; detector spacing or detector density affects the accuracy of the detector-based method. This paper presents an empirical model of the effect of the two parameters—sample size and detector spacing—on the accuracy of both methods in estimating the annual average of three congestion parameters: total delay, average duration of congestion, and average spatial extent of congestion. The model is developed with data from four urban freeway corridors in California. Among other conclusions, the model predicts that to measure the congestion parameters with 10% error, 4 to 6 days’ worth of good probe vehicle data or loop detector data with half-mile spacing is needed. The proposed model facilitates comparison of the two alternatives in regard to the cost for achieving the same target accuracy. The result can also be used as a guide to determine the sample size or detector spacing in planning new congestion monitoring.

Formal verification of the PATHO real-time operating system

We present several models of PATHO, a real-time operating system for an automatically controlled vehicle. The models are simple and scalable, thus they can be used to evaluate real-time verification tools. We describe the verification of PATHO using real-time extensions of HSIS verification system. Experiments show that user-supplied guidelines are crucial for successful verification.<<ETX>>