Estimating Uncertainty of Bus Arrival Times and Passenger Occupancies

Travel time reliability and the availability of seating and boarding space are important indicators of bus service quality and strongly influence users’ satisfaction and attitudes towards bus transit systems. With Automated Vehicle Location (AVL) and Automated Passenger Counter (APC) units becoming common on buses, some agencies have begun to provide real-time bus location and passenger occupancy information as a means to improve perceived transit reliability. Travel time prediction models have also been established based on AVL and APC data. However, existing travel time prediction models fail to provide an indication of the uncertainty associated with these estimates. This can cause a false sense of precision, which can lead to experiences associated with unreliable service. Furthermore, no existing models are available to predict individual bus occupancies at downstream stops to help travelers understand if there will be space available to board. The purpose of this project was to develop modeling frameworks to predict travel times (and associated uncertainties) as well as individual bus passenger occupancies. For travel times, accelerated failure-time survival models were used to predict the entire distribution of travel times expected. The survival models were found to be just as accurate as models developed using traditional linear regression techniques. However, the survival models were found to have smaller variances associated with predictions. For passenger occupancies, linear and count regression models were compared. The linear regression models were found to outperform count regression models, perhaps due to the additive nature of the passenger boarding process. Various modeling frameworks were tested and the best frameworks were identified for predictions at near stops (within five stops downstream) and far stops (further than eight stops). Overall, these results can be integrated into existing real-time transit information systems to improve the quality of information provided to passengers

Evaluating the reliability of automatically generated pedestrian and bicycle crash surrogates

Vulnerable road users (VRUs), such as pedestrians and bicyclists, are at a higher risk of being involved in crashes with motor vehicles, and crashes involving VRUs also are more likely to result in severe injuries or fatalities. Signalized intersections are a major safety concern for VRUs due to their complex and dynamic nature, highlighting the need to understand how these road users interact with motor vehicles and deploy evidence-based countermeasures to improve safety performance. Crashes involving VRUs are relatively infrequent, making it difficult to understand the underlying contributing factors. An alternative is to identify and use conflicts between VRUs and motorized vehicles as a surrogate for safety performance. Automatically detecting these conflicts using a video-based systems is a crucial step in developing smart infrastructure to enhance VRU safety. The Pennsylvania Department of Transportation conducted a study using video-based event monitoring system to assess VRU and motor vehicle interactions at fifteen signalized intersections across Pennsylvania to improve VRU safety performance. This research builds on that study to assess the reliability of automatically generated surrogates in predicting confirmed conflicts using advanced data-driven models. The surrogate data used for analysis include automatically collectable variables such as vehicular and VRU speeds, movements, post-encroachment time, in addition to manually collected variables like signal states, lighting, and weather conditions. The findings highlight the varying importance of specific surrogates in predicting true conflicts, some being more informative than others. The findings can assist transportation agencies to collect the right types of data to help prioritize infrastructure investments, such as bike lanes and crosswalks, and evaluate their effectiveness

Interchange Comparison Safety Tool User Guide

DTFH6116D00040This user guide is intended for use alongside the Federal Highway Administration (FHWA) report Safety Comparisons Between Interchange Types (forthcoming) and the spreadsheet tool FHWA Interchange Configuration Safety Comparison Tool. (1,2) This user guide provides an overview of the data needs and workflow for using the spreadsheet tool. Additionally, it provides information on the interchange configurations for which this tool can be used and the ranges of characteristics to which it applies. Further, this guide provides an overview for finding results within the tool

Evaluating Route Diversion as a Strategy for Reduction of Real-Time Crash Risk on Freeways Using Microscopic Simulation

The objective of this study is to evaluate route diversion as a strategy for reducing crash risk on freeways using Microsimulation. Traffic simulation environment provides the \u27loop detector data\u27 which in turn are the inputs to the models used for real-time crash risk estimation. It has been found that rear-end crashes are more accurately described as occurring within one of two distinct traffic regimes. Hence, the crash risk estimates for rear-end crashes belonging to Regime 1 and Regime 2 are output posterior probabilities from two different models which are not directly comparable. A method was proposed to transform the output from the two models into a single measure of rear-end crash risk, which can be used to assess the crash risk during simulation environment even when traffic conditions change from Regime 1 to 2 or vice versa. Using the information obtained from simulation package the crash risk estimates for the base case (No route diversion) and cases with route diversion(s) were compared. Route diversion was found to decrease the overall rear-end and lane-change crash risk on the freeway sections with free-flow conditions to low levels of congestion. However, a crash migration phenomenon was observed at higher levels of congestion

Evaluation of Real-Time Transit Information Systems: An information demand and supply approach

This study assesses current needs in the implementation of Real-Time Transit Information Systems. Web surveys are used to better understand information supply and demand, defined as the attitudes and experiences with real-time information of transit passengers and agencies, respectively. The most valued types of information demanded were found to be related to vehicle location while the least valued information relates to vehicle characteristics, like seating availability. Smartphone applications were found to be the preferred medium for receiving information followed by Internet/websites and dynamic message signs. The surveys also revealed that demographic and socioeconomic status influence preferences for real-time information. The information supply survey found that approximately 70 percent of surveyed agencies currently offer real-time information. The largest constraint to providing or improving Real-Time Transit Information Systems (RTTISs) was found to be funding, followed by staffing needs. A comparison between the survey results found that the information currently being provided by transit agencies is mostly in line with the information most valued by transit passengers. The few differences that exist are generally because agencies do not provide information on the media preferred most by passengers. To address these differences, several suggestions are made to improve the implementation of real-time information. This information can be used to better develop and prioritize investment in real-time information systems

Clockwise Hysteresis Loops in the Macroscopic Fundamental Diagram

A recent study reported that the Macroscopic Fundamental Diagram of a medium size city exhibited a clockwise hysteresis loop on a day in which a major disturbance caused many drivers to switch to unfamiliar routes. This paper shows that clockwise loops are to be expected when there are disturbances, especially if the disturbances cause a significant fraction of the drivers to not change routes adaptively. It is shown that when drivers are not adaptive networks are inherently more unstable as they recover from congestion than as they are loaded. In other words, during recovery congestion tends more strongly toward unevenness because very congested areas clear more slowly than less congested areas. Since it is known that uneven congestion distributions reduce network flows, it follows that lower network flows should arise during recovery, resulting in clockwise loops. Fortunately, in sufficient numbers, drivers that choose routes adaptively to avoid congested areas help to even out congestion during recovery, increasing flow. Thus, clockwise loops are less likely to occur when driver adaptivity is high