Sirisha Kothuri

Prompted Recall in Global Positioning System Survey: Proof-of-Concept Study

Modeling procedures in transportation planning depend on the quality of data collected from personal travel surveys, which in turn depend on the data-collection technique. All conventional data-collection techniques rely on respondents to report the time, distance, and location attributes of a trip, among other things. Respondents rarely know addresses that they visit with sufficient detail to permit accurate geocoding. Also, it has been observed that short trips are underreported. Earlier studies proved the feasibility of using the Global Positioning System (GPS) as an alternative to acquire error-free, high-quality information on trip-making behavior. However, all GPS survey methodologies tested relied on the respondent to enter information into a personal data assistant (PDA) as the trip is being made and to intervene in other ways to record all data for each trip. This adds the expense of a PDA and its power supply and puts a burden on the respondent. A method that uses GPS technology with less complexity, involving less cost and minimal user intervention while making the trip, is tested and explained. Additional trip attributes that cannot be recorded by the GPS receiver were obtained after the survey period by prompted recall, in which the respondents were aided with maps displaying their travel paths. Analysis of the data showed that this method performed very well. However, a still-larger survey is needed to estimate the benefits.

Toward Understanding and Reducing Errors in Real-Time Estimation of Travel Times

In recent years, the increased deployment of the infrastructure of intelligent transportation systems has enabled the provision of real-time traveler information to the public. Many states as well as private contractors are providing real-time travel-time estimates to commuters to help improve the quality and efficiency of their trips. Accuracy of travel-time estimates is important: inaccurate estimates can be detrimental to travelers, particularly when such estimates are less accurate than a persons ability to predict traffic on the basis of experience. Improving the accuracy of real-time estimates involves identifying and understanding the sources of error. The errors found during the evaluation of real-time travel-time estimates in Portland, Oregon, were explored and solutions are provided for reducing estimation error. The midpoint algorithm used by the Oregon Department of Transportation was used to estimate travel times from speeds obtained from loop detectors. The estimates were assessed for accuracy by comparisons with ground truth probe vehicle runs. The findings from the study indicate that 85% of the travel-time runs had errors less than 20% and, further, that accuracy varied widely between segments. The evaluation of high-error runs revealed the main causes of errors as transition traffic conditions, failure of detectors, and detector spacing. Potential solutions were identified for each source of error. In addition, a method was tested for evaluating the benefits of additional detectors by simulation of virtual detectors. The results indicated that additional detection helps in reducing the mean average percentage error in most cases, but the location of detectors is critical to error reduction.

Freeway Sensor Spacing and Probe Vehicle Penetration: Impacts on Travel Time Prediction and Estimation Accuracy

Accurate travel time prediction–estimation is important for advanced traveler information systems and advanced traffic management systems. Traffic managers and operators are interested in estimating optimal sensor density for new construction and retrofits. In addition, with the development of vehicle-tracking technologies, they may be interested in estimating optimal probe vehicle percentage. Unlike most studies focusing on data-driven models, this paper extends some limited previous work and describes a concept developed from first principles of traffic flow. The goal is to establish analytical relationships between travel time prediction–estimation accuracy and sensor spacing by means of two basic travel time prediction–estimation algorithms, as well as to probe vehicle penetration rate. The methods are based on computing the magnitude of under- and overprediction–estimation of total travel time (TTT) during shock passages in a time–space plane by using the midpoint method for online travel time prediction and the Coifman method for offline travel time estimation. Three shock wave configurations are assessed with each method so as to consider representative traffic dynamics situations. TTT prediction–estimation errors are calculated and expressed as a function of sensor spacing and probe vehicle percentage. Optimal sensor spacing is calculated with consideration of the tradeoff between TTT estimation error and sensor deployment cost. The results from this study can provide simple and effective support for detector placement and probe vehicle deployment, especially along a freeway corridor with existing detectors. Optimal sensor spacing results are analyzed and compared for various methods of travel time estimation during different types of shock waves.

Development of an ITS data archive application for improving freeway travel time estimation

The dissemination of travel time information has become crucial with the advent of ATIS. This paper summarizes the results of a comparative analysis between two travel time algorithms applied to archived loop detector data. Travel time estimates derived from the algorithms are compared to ground truth probe vehicle data. Our results indicate that Coifmans algorithm is more accurate for estimating travel times than a standard segment midpoint algorithm. However, the accuracy of the travel time estimates was dependent on the location and spacing of detectors and the location and formation of queues with respect to the detector positioning

Accuracy of Bicycle Counting with Pneumatic Tubes in Oregon

Interest in counting bicycles and establishing nonmotorized counting programs is increasing, but jurisdictions still struggle with how to integrate bicycle counting into standard practice. In this paper, the authors share findings and recommendations for how to minimize error for bicycle counting from tests conducted in conjunction with the Oregon Department of Transportation. This research studied three types of off-the-shelf pneumatic tube counters for counting bicycles, including equipment from five manufacturers: two bicycle-specific counters, three varieties of motor vehicle classification counters, and one volume-only motor vehicle counter. Tests were conducted both in a controlled environment and in on-road mixed traffic to better identify problems in accuracy. Equipment studied generally undercounted cyclists, especially those in groups. Results from the controlled test with standard bicycles showed that within 10 ft of the counter, the undercounting error ranged from 0% to −12%. In the mixed-traffic test, all the equipment tested tended to undercount with mean percent error ranging from −10% to −73%. Each counter type has pros and cons, but in general, counting accuracy decreased with increases in bicycle and motor vehicle traffic and longer tube lengths. Higher accuracy can be achieved by careful selection of equipment type, classification scheme, and tube configuration. Bicycle speeds given by off-the-shelf pneumatic counting equipment were accurate.

Bicycle and pedestrian counts at signalized intersections using existing infrastructure: opportunities and challenges

Bicycling and walking have gained increased attention recently; however, systematic bicycle and pedestrian counts are still scarce. At intersections, transportation agencies are interested in counting bicycles and pedestrians and leveraging for counting purposes, if possible, existing signal detection equipment. This study evaluated four counting technologies: inductive loops and a thermal camera to count bicycles and passive infrared counters and pedestrian signal actuation data to count pedestrians. The four technologies were tested in a parking lot (controlled environment) and in an intersection (real-world environment). The findings revealed that while the inductive loops and thermal camera counted bicycles accurately in a controlled environment, the loops and cameras failed to do so at an intersection. Passive infrared counters were found to count pedestrians accurately at the intersection sidewalk, and pedestrian signal actuation data could be a cost-effective surrogate for pedestrian demand at signalized intersections.

Lessons from Developing an Archived Data User Service in Portland, Oregon: Who Is Using It?

The value of creating an ITS data archive is somewhat undisputed, and a number exist in states and major metropolitan regions in North America. In addition to providing a secure data storage environment many archives include tools for analyzing the quality of the data and for creating performance measures describing the transportation system both in real time and on a historical basis. This provides a unique opportunity to actually measure how a transportation system operates over time. As part of a research project, an ITS data archive has been developed in Portland, Oregon at relatively low cost, taking advantage of sensors and communications used to operate the system in real time. The value of the data archive is dictated by the quality of the analysis tools provided for users. The objective of this paper is to describe the results of a survey conducted to gauge ADUS user needs and experiences from both the planning and operations perspectives. Recommendations for improvements and next steps are provided.

Annual Average Nonmotorized Traffic Estimates from Manual Counts: Quantifying Error

Across the United States, jurisdictions are investing more in bicycle and pedestrian infrastructure, which can benefit from nonmotorized traffic volume data. The design of nonmotorized counting programs varies. Whereas some agencies use automated counters to collect continuous and short duration counts, the most common type of bicycle and pedestrian counting is manual counting either in the field or from video. The objective of this research is to identify the optimal times of day to conduct manual counts for the purposes of accurately estimating annual average daily nonmotorized traffic (AADNT). This study used continuous bicycle and pedestrian counts from six U.S. cities to estimate AADNT and analyze estimation errors for multiple short duration count scenarios. Using two permanent counters per factor group reduces error substantially (> 50%); afternoon counts seem to be best for reducing error (2:00 to 6:00 p.m.). Error on Sunday is often as good as, if not better than, Saturday, contrary to what others have found. Arlington has the lowest AADNT estimation error (mean absolute percentage error), probably because of better data quality and higher nonmotorized traffic volumes, and Mount Vernon, Washington has the highest. Average AADNT estimation errors for the studied short duration count scenarios range from 30% to 50%. Error is lower for the commute factor group, bicycle-only counts, scenarios in which more peak hours are counted, and when more than one permanent counter is available to estimate adjustment factors.

Leading Pedestrian Intervals Treating the Decision to Implement as a Marginal Benefit-Cost Problem

To improve the safety of people walking at particular signalized intersections, traffic signal engineers may implement leading pedestrian intervals (LPIs) to provide pedestrians with a walk signal for a few seconds before the parallel vehicular green indication. Previous before-and-after studies and simple economic analyses have indicated that LPIs are low-cost tools that can reduce vehicle–pedestrian conflicts and crashes at some signalized intersections. Despite this evidence, municipalities have little guidance for when to implement LPIs. A marginal benefit–cost framework is developed with quantitative metrics and extends the concept of traffic conflicts and marginal safety–delay trade-offs to analyze the appropriateness of implementing an LPI at specific signalized intersections. The method provides guidance to help quantify the probability of a conflict occurring and direction on whether to implement an LPI at a given location from macroscopic-level inputs, including number of turning movements, crash data, and geometry. A case study with sample data indicated that an LPI was cost-effective for the scenario presented.