KLD ADAPTIVE CONTROL SYSTEM ACDSS is a comprehensive system that is a fully integrated module of TransSuite TCS, highlighted by:

• Fully NTCIP compatible, works with existing traffic control infrastructure, leveraging basic NEMA-TS2 actuated control operations, updating cycle, offset and splits dynamically in real-time;
• Does not require any ‘black box’ equipment to be installed in cabinet for operation;
• Uses data objects of standard Management Information Base (MIB), and requires minimal modification to traffic controllers to support dynamic cycle length, offset and split changes;
• Flexible detector requirements, supports sparse detector placement with detectors strategically deployed;
• Multi-regime, variable-objective signal optimization, handling both unsaturated and oversaturated traffic conditions;
• Applicable for stand-alone, small cluster of intersections, arterials, diamond interchanges, closely spaced intersections, as well as grid networks which include hundreds of intersections.

The license for ACDSS will be exclusive to the Client generally without restriction on the number of intersections operating under adaptive control. The flexible licensing allows for the Client to expand adaptive operations at any time themselves they may choose without expensive per intersection license fees and external professional engineering services.

Optimal Detection Requirements

ACDSS is compatible with any reliable detection technology that collect real-time volume and occupancy data. Commonly this includes inductive loops, magnetometers, radar and video detection. Additionally, if real-time travel time data (e.g., collected by RFID or Bluetooth) is available, ACDSS can use this data as well. Travel time data is used as supplemental data to enhance signal optimization.

ADVANCE DETECTION: ACDSS requires advance per-lane detection at critical approaches. A critical approach is defined as an approach that has a significant role in traffic on the corridor (i.e. a major diversion point or bottleneck location queue spillbacks), or an approach that is characteristic of traffic conditions at upstream or downstream approaches. The Critical approaches help play a critical role in how ACDSS responds to traffic.

Advanced detection is used to detect volume, occupancy, and estimate queue lengths on a per-lane basis to trigger different adaptive strategies. Per-lane basis is important because it provides a detailed picture of traffic on the approach, as well as the needed system redundancy should some detector fails. Alternatively, a detection zone in the busiest lane of the approach can be used, should the number of detection zones have to be minimized.

Protected left turn bays are given additional priority in ACDSS to manage queue storage and preventing spillover into the through lanes. The principle of the left turn detector placement is to determine turning movement demand, as opposed to proper actuations for actuated control.

The guidelines for determining advance detection placement are provided.

STOPLINE DETECTION: Stopline detection is not required for most of the signal optimization algorithms in ACDSS Signal Optimization Algorithms Repository, though it is useful as complementary data when maintaining actuated control at intersections. In many cases, the agency prefers that at the local controller level, phases are still actuated based on actuated control logic, while the intersection cycle, offset and splits are dynamically adjusted. Splits essentially determine the force-off points. By virtue of keeping the actuated behavior while systematically adjusting cycle, offset and splits, the system better addresses local variations in light demand, as well as proactively adjusts these parameters to network-wide change.

TRAVEL TIME DATA: Travel Time Data is either per-trip records or aggregated as average travel time for a roadway segment that is typically collected using RFID or AWAM Bluetooth technology. ACDSS does not require travel times but if such data is available, either via XML feeds, or a Telnet server, ACDSS can employ the data in the adaptive signal optimization.