In Torino, Italy, a simulation study found that an automated speed control system designed to optimize travel speeds between green lights can reduce fuel consumption by 8.3 to 13.8 percent, reduce CO2 emissions by 3.9 to 5.4 percent; reduce hydrocarbon emissions by 4.2 to 6.9 percent, and reduce NOx emissions by 7.9 to 11.3 percent.
The roadway equipment used during the study consisted of buried magnetic loop detectors and wireless communication beacons. Data from these roadway sensors was transmitted to an integrated traffic management center (TMC) where computers calculated speed and driving recommendations for each ITS-vehicle in the network. On-board UDC systems consisted of computers, communications equipment, cameras, sensors (electromagnetic RADAR and optical LIDAR), and actuators for automatic headway detection and following distance control. The integrated UDC-TMC system attempted to optimize vehicle speeds for maximal flow between green lights.
Test drivers were selected from the general public. The study area consisted of 24 inter-urban roads and 12 urban roads, and included ITS as well as non-ITS vehicles. A driving instructor and technician were present in each ITS-vehicle to assist drivers in case of unsafe conditions.
The following three scenarios were investigated:
- Highway Mobility – drivers used ITS vehicles with ACC and S&G on the freeway between the city of Torino and the Airport.
- Urban Mobility – drivers used ITS vehicles with S&G on the south Carriageway of Coroso Grosseto.
- Urban Assisted Driving – drivers used ITS vehicles with S&G and TLC on the North Carriageway of Corso Grossseto.
Quantitative data was collected using on-board and roadside equipment and information from the Traffic Management Center to calculate travel times, number of stops at red traffic lights, time spent at red traffic lights, lane changes, speed, acceleration, throttle position, brake pressure, headway time, relative distance, and time to collision.
Qualitative data was collected using driver questionnaires, interviews, and instructor observation checklists.
In addition to field trails, computer simulations were run. The NEMIS simulation model produced additional data by modeling the activity of 25 vehicles on a single lane roadway having traffic lights and driving restrictions (no overtaking). Each vehicle had 30 meters of headway and was projected into the simulation 225 meters before the first traffic light. Each traffic light cycle was started at time zero with a red light. The subsequent green lights lasted 26 seconds. Vehicle emissions were calculated using a speed parameter of 16 meters per second with vehicles travelling from time zero until 150 meters past the intersection.
- Pollutant Estimation – TLC reduced carbon dioxide emissions by 3.9 to 5.4 percent; reduced hydrocarbon emissions by 4.2 to 6.9 percent; and reduced nitrogen oxides emissions by 7.9 to 11.3 percent.
- Fuel Consumption – TLC reduced fuel consumption by 8.3 to 13.8 percent.
Evaluation Framework and Assessment of Urban Drive Control Applications
Author: Sala, Gianguido and Lorenzo Mussone
Published By: Paper presented at the 6th World Congress Conference on ITS. Toronto, Canada
Source Date: 8-12 November 1999
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intelligent cruise control, ICC, ACC, Intelligent Speed Adaptation, ISA, longitudinal control, Longitudinal Control