Florida DOT, in collaboration with Miami-Dade County Public Works Department, conducted an assessment of a vision-based collision warning system for transit buses. Several key scenarios of interest, including transit vehicle-pedestrian and rear-end conflicts, were assessed by comparing driver responses when the system was active, and inactive but recording. In the rear-end scenario, operators were observed to drive more conservatively when the system was active, resulting in a reduced frequency of hard braking events (once every 166 miles vs. every 105 miles, on average). With the system active, overall yielding behavior to pedestrians increased from 58 percent to 88 percent.
The project installed a camera-vision collision warning system on board ten transit buses using a randomized experimental design. Five buses acted as a control group, where the system was silent (“stealth mode”) and did not present alerts or warning to the bus operator. In the other five buses, the system was active and presented alerts to the driver as designed. Drivers were assigned randomly to each group. Several evaluation objectives were planned with corresponding performance measures:
- Objective 1: Assess the system effectiveness in reducing rear-end crashes
- Objective 2: Assess the system effectiveness in reducing pedestrian and bicycle crashes
- Objective 3: Assess the user acceptance of the system
- Objective 4: Assess the ease of the installations and operations of the devices
- Objective 5: Assess the cost-effectiveness of the technology.
The project analyzed three years of historical incident data for the Miami-Dade Transportation and Public Works buses to determine potential benefits from avoided crashes. The analysis used records from 5,164 collisions and determined that 38 percent of crashes occurred at intersections, and 28 percent occurred near bus stops. At least 15 percent of crashes resulted in injuries, and 38 percent were associated with inappropriate bus operator actions. Route specific data was also used to assess the distribution of potential crashes to be prevented by collision avoidance systems.
The project team analyzed the system performance based on recorded data and a sampled video confirmation for alerts. For example, recorded data streams were used to compare the time of alerts and the time the bus speed started decreasing, if there was an operator reaction. Video data were used to observe the external environment such as presence of actual conflict or not, and the yielding behavior to pedestrians.
During the study, there were route changes that affected the ability to assess early data using the original experimental design. As a result, most of the analyses used the period after the route changes.
The project team analyzed the potential return on investment for a five-year period using a cost value of $120,000 for fatality/injury crashes and $10,000 for property damage only crashes as it was felt that reliable fatality rate data was not available. The analysis estimated that the benefit-to-cost ratio for installation on all buses was 0.60. If installed only on buses servicing routes with higher historical crash rates, the benefit-cost ratio ranged from 1.24 to 1.86.
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