Joseph Perrin

Need for Culvert Asset Management

Through Governmental Accounting Standards Board Statement No. 34 (GASB 34), departments of transportation (DOTs) are required to track their infrastructure costs and conditions through asset management practices. GASB 34 is applied primarily to roadways and bridges; access and inspection of that visible infrastructure are comparatively easy. However, underground culvert infrastructure is also a critical component in asset management. With the growing problem of culvert deterioration across the country, it is important to consider these underground assets in the inventory and inspection process. As part of this report, all 50 U.S. state DOTs were sent a survey concerning culvert asset management issues. The resulting 28 responses identified that several states are working toward developing an inventory database and planning to implement an inspection program. Many are not yet considering applying asset management practices to their culvert infrastructure. Several agencies did identify failure as a primary reason for developing the inventory and inspection programs. Because culvert failures are often the motivation for agencies to respond, some recent failure examples are reviewed. Previous research recommended a national tracking of culvert failures to understand the risks and costs better. This paper builds on that idea and identifies the benefits of culvert asset management. These benefits include up-to-date inventory, reduced failures through regular inspection, reduced emergency repair costs and unplanned financial burden, better budget planning for repair and replacement, and long-term ability to identify actual life cycle and performance of various pipe materials.

SCOOT and incidents: Performance evaluation in simulated environment

SCOOT is a widely used adaptive signal control system. There have been many evaluations of SCOOT during normal traffic conditions. It is hypothesized that SCOOTs ability to adapt to varying traffic during incidents provides an added benefit over its normal congestion-relieving capability. Through an interface between CORSIM and an actual SCOOT system, this study evaluates, in a simulated environment, SCOOTs performance during incidents to quantify these additional benefits. The evaluation is made over a range of volumes and incident durations. A theoretical test network and two real-world networks are simulated. Network and intersection delay, travel time, and queue length are used as measures of effectiveness (MOEs) throughout the comparison to quantify total and marginal benefits. During a 45-min incident within the Salt Lake City downtown area network, SCOOT reduced network delay, travel time, intersection delay, and queue length by 28.3%, 22.8%, 30.7 %, and 24.2 %, respectively, relative to the optimized plan-based control. Similar results were observed on the other real-world network. Although adaptive control has benefits above plan-based signal control, the findings indicate that during incidents, SCOOT provides an additional increase in benefits. The findings indicate that average SCOOT MOEs improve by 7% for a 15-min incident, by 12% for a 30-min incident, and by 18% for a 45-min incident depending on congestion level. The additional benefits that SCOOT adaptive control provides during incidents are quantified through the systems inherent ability to respond to traffic conditions in real time.

MONITORING COMMUTER CONGESTION ON SURFACE STREETS IN REAL TIME

As public agencies focus on optimized signal timing to reduce congestion, few have the ability to monitor network operations in real time. Little work has been done to provide a graphic presentation of surface street congestion. A way is introduced here to identify and monitor commuter congestion on surface street arterials without using specialized equipment. Existing system detectors have been combined with the signal timing information to classify traffic congestion levels. The Utah Department of Transportation Traffic Operation Center is incorporating the results to provide a real-time arterial congestion map. Occupancy, flow, and signal information are reported to the traffic operation center every 5 min. Then, a commuter congestion algorithm compares the actual measured volume with an estimated approach capacity. It is inappropriate to assume a fixed-green time per cycle because the network’s signalized intersections operate on coordinated–actuated control. The algorithm compares the estimated approach capacity with the measured approach volume to give an estimated real-time volume/capacity ratio. The approach capacity is estimated by an equation developed through simulating network intersections under a range of congested conditions with various cycle lengths and approach green times. The methodology has been verified against field data from congested locations with actual volume and signal timing during peak periods. The predicted approach volumes were within 5% of observed congested conditions. This method allows traffic engineers to monitor and identify the congestion of a signal-controlled surface street network in real time without the need for new technologies.

Connecting SCOOT to CORSIM: real-time signal optimization simulation

Adaptive signal control systems react to traffic in real-time and adjust the signal timings to improve signal efficiency. SCOOT is the most widely implemented system. It comes from the UK with over 200 installations worldwide including the US. Average delay reductions of 20 percent have been shown in urban networks that employ adaptive signal control systems. However, these benefits vary between cities. Until now, no commercially available adaptive signal control system could be modeled across a city-specific network prior to installation. The University of Utah has developed a simulation modeling connection between the Federal Highways CORSIM model and the SCOOT adaptive control system. SCOOT runs on the VMS operating system, CORSIM on Windows NT. The two are connected via Ethernet with a dynamic link library interface that extracts the signal state and detector information from CORSIM and converts it to a format that SCOOT understands. SCOOT processes the information and sends it back across the Ethernet. In a completed loop, the optimized signal timing is then communicated from SCOOT to CORSIM, which implements the timing and updates the traffic simulation. This work offers traffic engineers the opportunity to evaluate the impact of SCOOT in a simulated environment prior to installation of the system. This paper reports the findings of the simulation of an actual urban network.