Cross-cutting Issues

Reliable estimates of conditions on arterials are the holy grail of traffic performance measurement.  The Chicago Arterial Performance Measurement System was developed by Chicago DOT and the Chicago Transit Authority to cost effectively monitor traffic conditions on arterials.  CAPMS gathers and processes real-time performance data using existing infrastructure and innovative technologies to measure traffic congestion on Chicago streets.  Design considerations included network segmentation, associating bus probe GPS data with segments, traffic condition algorithms, travel time estimation procedures, map displays, and modular software architecture.  CAPMS is the analytical and data processing engine that drives the Chicago Traffic Tracker traveler information service.  CAPMS is also a key subsystem in the design of the Chicago Traffic Management Center (TMC) Advanced Traffic Management System.  Current traffic conditions on major arterials are calculated and displayed on roadway segments and for community areas throughout Chicago.

Authors: David Zavattero, Abraham Emmanuel

Presented at the 18th World Congress on ITS, October 2011, Orlando, Florida

This study develops two on-line stochastic routing policies/algorithms which identify the optimal next action at the current decision node (intersection) for travelers preferring either the shortest path or the most reliable path given the current network conditions. Considering that real-time traffic information is usually available with a certain level of accuracy, the proposed on-line routing policy integrates an existing information fusion model which provides real-time short-term link travel time distributions by considering information accuracy. Further, this study uses numerical experiments to demonstrate the efficiency of the proposed routing policies/algorithms as well as the impacts of realtime information accuracy on the on-line stochastic routing.

NEXTRANS

Presented at the 18th World Congress on ITS, October 2011, Orlando, Florida

Under the aegis of Intelligent Transportation Systems (ITS), real-time information provision strategies are being proposed to manage traffic congestion, alleviate the effects of incidents, enhance response efficiency after disasters, and improve the multimodal/intermodal travel experience of travelers. While there have been at least two decades of theoretical studies and limited scope field test beds, a key gap has been in terms of adequately understanding the role of human behavior in real-world contexts. That is, either restrictive assumptions have been made on how travelers would respond to information in analytical/simulation models or the role of behavior has found only limited emphasis, if any, in the design of testbed experiments. Since traveler response to the real-time information provided is a fundamental determinant of the evolution of the traffic system, a key issue is the reliability of the traffic conditions predicted by these approaches relative to generating effective control strategies in the field. The proposed study seeks to address this issue by designing field experiments in which the role of human behavior is analyzed to understand the potential benefits of real-time information to travelers. The ability to do so has several dimensions of significant impact on practice. First, as real-time information systems enter the realm of widespread deployment, state and local transportation agencies require realistic and reliable assessments of the potential benefits of these systems given the infrastructure cost investments required and the need for coordination across multiple public, and possibly private sector entities. Second, in order to assess the potential benefits, there is a need to determine meaningful performance measures beyond just the putative travel time savings. Third, the ability to explicitly quantify the human behavior dimension provides a broader set of parameters to public and private sector entities relative to the evolution of the travel information market. This is especially critical given that USDOT’s IntelliDrive program seeks to leverage public sector investment with private sector participation given the monetary investments required for fruition of the program vision.

Over the past five years, the City of Los Angeles Department of Transportation (LADOT) has been testing and deploying new parking technology in an effort to improve customer service, reliability, and efficiency, while providing staff the tools necessary to begin the intelligent management of parking in Los Angeles. Through the deployment of wirelessly-connected sensors, parking meters, and pay stations in select areas of the City, parking management staff is routed directly to maintenance and enforcement needs, and customers can find the closest available parking, all in real-time. This new parking technology is now set to blanket all of Downtown Los Angeles, and, combined with an intelligent parking management system, become an innovative congestion management tool through demand-based parking pricing and a comprehensive parking guidance system.

City of Los Angeles DOT

Presented at the 18th World Congress on ITS, October 2011, Orlando, Florida

In 2009, the Minnesota Department of Transportation selected an Arterial Travel Time
Measurement System using Bluetooth Technology as part of its 2009-2010 ITS Innovative
Idea Program. The project deploys 8 Bluetooth readers along CSAH 81 together with a
central web based analysis software. One key goal of this project is to demonstrate how the
travel time information may be used as a performance measure for arterial traffic
management and operations. This paper provides the results of this deployment and draws
conclusions for the use of Bluetooth based data collection for arterial traffic operations and
performance management.

Author: Michael Wieck

Presented at the 18th World Congress on ITS, October 2011, Orlando, Florida