Alixandra Demers

Probes as Path Seekers: A New Paradigm

In a recent field experiment, a fleet of Global Positioning System—equipped vehicles shared real-time data about network travel times over a wireless network and had their path choices automatically updated accordingly. This paper describes the behavior of these 200 vehicles during the 3-month experiment and illustrates the kind of information that can be derived from the data archive that the vehicles created. Much can be learned about real-time traffic-responsive path choice, travel times, compliance, and more. Planners, designers, and system operators can learn much about the way a system behaves and can sharpen their ability to create systems that work effectively and efficiently under all ranges of use. True path seeking by probe-equipped vehicles will arise when such vehicles become a common part of the vehicle fleet.

Experimenting with Real-Time ATIS: Stepping Forward from ADVANCE

In the early 1990’s an in-vehicle navigation and route guidance project called ADVANCE was conducted in the northeastern suburbs of Chicago. It proved that travel time data could be updated on in-vehicle devices (albeit not in real-time) to assist drivers in choosing faster routes to their destinations. This past spring, about a decade later, a more progressive but similar 3-moonth field experiment was conducted in upstate New York. Nearly 200 participants used state-of-the-art, in-vehicle navigation and route guidance technology in conjunction with GPS tracking and broadband wireless to share travel time data and pick the shortest paths through a congested network. The route guidance devices observed travel times, uploaded them to a central server that updated a travel time database, and then downloaded every minute to each of the probe vehicles to ensure the latest travel time information was being used while enroute. The experiment resulted in a total of 4,111,210 latitude-longitude position/speed/time points. The largest number of location points per user was 98,018 while the smallest was 117; the average per user was just over 26,000 location points, or 325.5 points per trip. There were 12,629 probe trips for a traveled distance of 147,316 miles over a duration of 3,945.8 hours. This paper presents a discussion of the Capital District ATIS project including the parallels and differences with the ADVANCE effort. Areas covered are: travel time data, project background, description of the study data, participant statistics, experimental design, sample results, and a summary with future research directions.

Hardware-in-the-Loop Simulation: Challenges and Solutions

This paper discusses some complex Hardware-In-the-Loop configurations used for evaluating three distinct control strategies aimed at minimizing dilemma zone problems at high-speed, rural intersections. Five intersections were studied; three configurations were tested. The authors show that effective Hardware-In-the-Loop Simulations can be created for complex signal control configurations. They demonstrate that it is important to become familiar with the electrical details of the components and then create software and hardware splices that tie the devices together in such a way that each one functions in the manner intended. The authors show that for speed traps, the time step in simulation models dictates longer-than-field-based separations, about 29 m (95’) instead of 6.6 m (20’) (as in the field). This clearly suggests that time step duration has a significant impact on the results obtained for HILS, and hence suggests it worthwhile to push that frontier further, striving for even smaller time steps so that HILS can be effective.

Three Dilemma Zone Strategies for High-Speed Rural Intersections: Comparison of Field Results

High-speed, rural intersections with both passenger car and truck flows present dilemma zone challenges. Three signal-timing strategies were tested in the field at three intersections of varying geometries with the goal of minimizing the occurrence of dilemma zones without sacrificing efficient operation. The strategies focused on hardware changes impacting vehicle sensing as well as mainline green phase extension and termination. The base strategy was volume-density control. The second one (NQ4) added advance detection of high-speed trucks triggering a fixed green extension. The final strategy replaced the second system with a sophisticated advance detection and control system (D-CS) logic based on the work of Bonneson et al. (2002). Results indicate simple volume-density control is surpassed in safety by both other strategies; moreover, the detection-control system dramatically reduced the number of vehicles trapped in dilemma zones at the onset of amber. The second strategy works fairly well in the field. The drawbacks to the NQ4 system are most noticeable at high-volume intersections — it does not actually find times when no vehicles are in dilemma zones, vehicle speeds are not directly used for computing main street hold times (which is accomplished in the D-CS control strategy resulting in improved efficiency), and it is a bit cumbersome and expensive to install. Based on our findings, the addition of the DC-S algorithm into a controller is a worthwhile investment for mixed-traffic, high-speed, rural intersections with dilemma zone issues.

Hybrid Traffic Flow Forecasting Model Based on MRA

The presence of complex scaling behavior in traffic makes accurate forecasting of traffic a challenging task. This paper proposes a multi-scale decomposition & reconstruction approach for real-time traffic prediction. The proposed scheme combines the superior characteristics of wavelet neural networks, ARIMA and MRA. This multi-scale decomposition and reconstruction approach can better capture the correlations within traffic flows caused by different mechanisms, which may not be obvious when examining the raw data directly. The proposed hybrid prediction algorithm is applied to real-time traffic data from a large metropolitan area. It is shown that the proposed algorithm generally outperforms traffic prediction using a single prediction model approach and gives more accurate results.