Future Road Mobility & Reliability
Analysis of truck volumes on the region’s roadways provides an overview of the routes that drivers use for the movement of road freight within, to, from and through the region. The volume of trucks given a roadway’s capacity, or ability to handle traffic levels at posted speeds, determines whether the level of congestion.
In 2020, at a macro level, I-75 and I-71 were the routes most utilized by trucks. Delving deeper, I-71/I-75 Brent Spence Corridor in Boone and Kenton counties is the most heavily used truck segment in the region. Truck congestion extends further south along this corridor through Boone County to the I-71 split to Louisville. Other important truck routes include the I-75 Mill Creek Expressway between the Brent Spence Bridge and I-275 interchange in Hamilton County, I-75 just north of I-275 in West Chester Township, and I-71 just north of I-275 in Symmes Township.
To forecast future truck volumes for the year 2050, OKI’s Travel Demand Model, an Activity-Based Model (ABM), is used to evaluate the potential regional-level impact on congestion of planned projects included in OKI’s 2050 Metropolitan Transportation Plan (MTP). Truck trips are provided to OKI for incorporation into the ABM by the Ohio Department of Transportation (ODOT) from the statewide travel demand model. The ABM provides a reasonable estimate of future truck trips. It is important to note, however, that truck analysis is not the primary purpose of OKI’s regional travel demand modeling.
The OKI Travel Demand Model predicts that in 2050, regional truck volumes are expected to increase from 2020 counts with the same roadways serving as the predominant routes for the region’s trucks in the future as they do today. Some of the highest percent growth between 2020 and 2050 is forecasted for I-275 between I-74 in western Hamilton County and KY 237 in Boone County (54% growth). Other key segments for high 2050 truck volume growth are I-71 in southern Boone County below the I-75 merge (43%) and in Kenton County north of the I-275 interchange (37%), I-74 west of the I-275 interchange (32-35%), and I-75 in Butler County north of SR 129 (34%).
Truck Vehicles Miles Traveled (VMT)
In 2020, truck vehicle miles traveled was 4.9 million, comprising 9.8%of all vehicle miles traveled on OKI regional roadways. In 2050, 5.7 million truck vehicle miles are forecasted, a 15.5% increase from 2020. However, truck vehicle miles are projected to be only 9.6%of all VMT in 2050. This speaks to the huge growth forecasted for non-truck vehicle miles over the coming decades.
Hamilton County is the largest county in the region in population, total employment, and traffic. The county has about 38 percent of the region’s total VMT and truck VMT in 2020. Based on estimates from the OKI Travel Demand Model, in Hamilton County truck VMT is expected to decrease as a share of total VMT by about 5% (to 33% of total VMT) by 2050.
Future truck trips are estimated from truck trip tables provided by the Ohio Department of Transportation (ODOT) which estimate truck trips within the Ohio statewide model and are overlaid on the OKI Traffic Analysis Zones (TAZ) structure. The maps different colors highlight the truck volumes that are expected to originate (generating truck TAZs) and terminate across the region in 2050.
Looking at the forecasted top 10 generating and top 10 terminating TAZs for year 2050 truck trips shows nine identical locations for truck origins and destinations. The additional TAZ area for the top 10 generating truck locations is located to the east of the Cincinnati/Northern Kentucky International Airport (CVG), along KY 3076 (Mineola Pike) in northeastern Boone County. The additional TAZ area is located in northern Hamilton County near the I-75/I-275 interchange.
In 2050, the TAZ encompassing the CVG is forecasted to have the highest number or concentration of average daily total truck trip origins and destinations. The OKI Travel Demand Model forecasts an average daily total of 19,124 truck trips originating from the CVG TAZ and an average daily total of 18,909 truck trips terminating in the CVG TAZ.
2050 Daily Truck Trips by County
Boone County generates and attracts the largest share of truck volumes (relative to all vehicles) in the region, while Hamilton County generates and attracts the largest number of trucks. This points to Boone County’s role as a center for freight movement in the region, and Hamilton County as the area’s largest population and economic driver. For each OKI county, the number of estimated originating and terminating truck trips in 2050 are similar signaling a balanced truck flow into and out of each respective county.
A Closer Look into Truck Trips to/from CVG
As stated previously, in 2050, the area encompassing the Cincinnati/Northern Kentucky International Airport (CVG) is forecasted to have the highest number or concentration of average daily total truck trip origins and destinations.
Using OKI’s Origin-Destination Geographic Information Systems (GIS) tool and data from the American Transportation Research Institute (ATRI), a truck trip origin and destination analysis was performed as part of this freight plan to determine truck volumes and movements centered on CVG airport. The main objective of this analysis was to determine where freight origins and destinations that were connected to CVG were concentrated, how they were distributed, and what routes were connecting them.
OKI’s Origin-Destination GIS tool was run on six samples of ATRI data, each sample spanning two weeks between March 2021 and June 2022, on a half-mile grid size spanning the OKI region, for truck trips originating or terminating within the CVG TAZ. Both origin and destination outputs were combined to represent truck movements to or from the TAZ.
This data analysis revealed specific corridors whose truck movements create potential corridors for truck electrification due their predictability, repetitiveness, and short distances.
Future Truck Use of Roadway Capacity
In the analysis of existing roadway conditions, OKI uses a measure called the Level of Truck Travel Time Reliability (LOTTTR) to weigh the performance of the roadway freight system and help identify locations where congestion is most severely impacting trucks today. This analysis relies on a large amount of current travel time data that is unavailable for future years.
For purposes of this analysis to determine the importance of roadway segments for future truck movement, truck volume to capacity ratios (V/C ratios) were calculated for the region’s interstate corridors by peak time period in 2050 from truck forecasts generated by the regional travel demand model. These ratios show the degree to which total capacity is consumed by trucks on segments throughout the region and provides a measure of the forecasted truck volumes relative to the designed capacity of the roadway. It provides the level of importance of these segments to trucks and the potential congestion implications resulting from the fact that truck size and speeds require more roadway capacity than passenger vehicles and therefore the more trucks, the less capacity available for other vehicles.
Peak periods are defined by the OKI Travel Demand Model as 6 a.m. to 9 a.m. for AM Peak and 3 p.m. to 7 p.m. for PM Peak. This measure depicts the degree of congestion on interstates in the OKI region. The top 10th percentile of estimated truck V/C ratios per time of day for the AM peak period and PM peak period are highlighted to show the roadways forecasted to have the worst V/C rations in 2050.
Most of the segments with the highest V/C ratios are along I-71 in all counties it traverses in the OKI region. The segments with the highest V/C ratios during both peak periods are observed in Hamilton County on I-75 north of the Brent Spence Bridge, I-71 north of I-471 and the Daniel Carter Beard Bridge (also known as the Big Mac Bridge), and on I-71 south of the intersection with I-275 in Kenton County.
The entire length of I-71 to I-275 in Northern Kentucky, most of I-71 in Hamilton County and I-71 in Warren County are forecasted to have the highest future v/c ratios.
Truck Bottleneck Analysis
Truck hours of delay were calculated as part of this freight plan from regional travel demand model outputs to identify the top interstate future bottlenecks in the OKI region for 2050. The posted speed limit was used to calculate the free flow (FF) travel time for each length of a segment, and the congested speed of traffic was used to calculate the congested (CS) travel time for that segment (Truck Hours Delay = (CS Travel Time – FF Travel Time) X Truck Volume). Using truck volumes per peak period, an estimate for truck-hours of delay for each interstate segment was derived for AM and PM peak periods and the top 10th percentile of segments were highlighted for each peak period.
The most congested truck bottleneck in 2050 is expected in the northern portion of the region along I-75 in Warrant County. Planned improvements are expected to help alleviate many of the truck bottlenecks in the region, most notably the bottleneck at the Brent Spence Bridge. Analysis shows both AM and PM peak periods delay truck traffic along the corridor, with the PM being more severe.
Future Roadway-Oriented Technologies
As we discuss challenges associated with future road freight, there are a growing number of transportation technological advances being developed and implemented to address mobility and reliability. In the brief overview of roadway-oriented technologies, particularly those showing the greatest potential positive benefits for truck mobility and reliability, we should note that the advantages of these technologies extend to positively impact other freight goals of safety, infrastructure maintenance, environmental sustainability, and economic competitiveness.
Freight Traveler Information
Freight traveler information applications use real time data to allow drivers and operators to make informed trip planning decisions to achieve greatest efficiency. This efficiency equates to travel time savings and enhanced reliability through avoidance of congestion and safety concerns. Furthermore, other benefits can be achieved including freight-specific trip planning, drayage optimization, and dynamic eco routing to minimize fuel consumption and emissions.
Freight traveler information can come from several different sources: roadside signage, pre-trip planning or guidance from centralized dispatch, and in-vehicle equipment such as electronic logging devices (ELDs), mobile devices, and after-market global positioning system (GPS) navigation systems. Enabling infrastructure to share information with a traveler could come from two different sources:
to collect, synthesize, and distribute data to third party applications, roadside signage, or directly to end users
to convey information to drivers, either in the form of signage or connected vehicle roadside units (RSUs) that send messages to in-vehicle devices. Note that most roadside infrastructure will require some degree of investment in the back-office system described above.
The level of investment in roadside infrastructure will depend largely on how technology is adopted within vehicles. For example, if connected vehicle technology (in its strictest definition) never achieves high market penetration, investment in RSUs will serve little purpose. In the meantime, planning for the platforms to support the influx of data—regardless of its source—is likely to remain a worthwhile investment.
Example of Freight Traveler Information Deployment
(Source: High Plains Journal. Great Plains Rural Freight Technology project will shore up essential Kansas supply chain. September 8, 2022)
Weigh-in-Motion/Weigh Station Bypass
Weigh-in-motion technologies use intelligent transportation system (ITS) infrastructure that measure the weight of a vehicle as it passes over roadway sensors. This electronic clearance allows wireless screening of trucks at heading speeds, eliminating the need for trucks to pull over at weigh stations. Alternately, weigh station bypass technology includes an in-vehicle app or transponder that allows trucks meeting specified criteria to skip a weigh station.
Weigh-in-motion/weigh station bypass could require several types of field infrastructure depending on the application:
Inductive loops indicate when a truck has entered and exited the weigh station, which may also measure vehicle length or assess the vehicle classification.
Embedded scales or roadway sensors
Embedded scales or roadway sensors weigh trucks as they pass, which may include a variety of sensor types to indicate weight.
License plate recognition
License plate recognition uses a camera to confirm measured weight against allowable weight for the vehicle and capture evidence of violations.
Communications backhaul and back-office platform
Communications backhaul and back-office platforms collects and analyzes the data from each type of infrastructure above and if necessary, sends notifications of violations to police. For weigh station bypass facilities, this will also include a platform for trucking companies to input information for verification at individual weigh stations.
Dynamic message signage
Dynamic message signage indicates when a weigh station is open or closed and provide instructions to drivers.
In-vehicle devices provide drivers indication of when bypass has been granted.
Example of Weight-in-Motion Deployment
In spring 2022, INDOT committed to a long-term agreement with International Road Dynamics Inc. to begin the installation of 56 weigh-in-motion systems and 23 Virtual Weigh-in-Motion systems to be operated by the INDOT. The systems will not only provide traffic and load data for state planning purposes, they will enforce compliance with weight requirements. Drivewyze, a provider of connected truck services, supports weigh station bypass at more than 800 sites nationally, including five locations in the OKI region. When approaching a weigh station, drivers meeting the size, height, weight, and cargo requirements of a state or facility may receive a “green” signal to bypass the station. PrePass offers a similar service to truckers.
Freight Signal Priority
Freight signal priority uses infrastructure-based sensors, which could include connected vehicle radios to detect the presence of trucks, as well as extend green lights to allow their passage. This type of technology would enable platoons of freight trucks pass through signals as a group. This improves the flow of drayage traffic while still being able to handle additional vehicle traffic. Freight signal priority has been demonstrated to reduce truck travel times by 10%.
Freight signal priority can be accomplished using a number of different methods for detection and decision-making:
Detection of freight vehicles
Several different detection methods exist. Video-based detection can be implemented without the need for vehicle-based communications but may be less accurate. Optical detectors, localized radio units, and connected vehicle technology can facilitate direct communications between vehicles and signals but will require adoption and installation by the freight industry.
Priority decisions can be made directly by the signal controller cabinet, with the risk of potential disruptions to the broader transportation network.
With interconnectivity to a traffic management center (TMC), signal priority decisions can be made based on real-time traffic conditions throughout the network. Central priority is more costly to implement due to backhaul communication requirements.
Example of Freight Signal Priority Deployment
The Colorado Department of Transportation (CODOT) installed commercial vehicle signal priority (CVSP) technology at critical intersections to promote greater safety, efficiency, and reliability for commercial vehicles. CODOT launched a proof-of-concept project that gathered pre- and post-deployment data measures to analyze the performance of CVSP. The effort examined safety impacts through the number of red-light violations; efficiency outcomes through the number of commercial vehicles stopped at intersections; and changes to reliability through the planning time index and vehicles’ travel time.
The proof-of-concept project used radar-based detection technology (Wavetronix) fixed on traffic signal mast arms to identify commercial vehicles within 900 feet of the traffic signal. To maintain the level of service, the cycle length was not adjusted. Instead, green time was reduced from side streets and added to the mainline to extend the length of the green signal for commercial vehicles on major roadways. For both phases of testing, significant improvements were identified with a 63-74% improvement in the reduction of commercial vehicles running red lights and a 26-31% reduction in the number of commercial vehicles stopped at intersections. Mobility and reliability improvements were minor.
Drayage optimization applications enable information exchanges between all intermodal parties to provide current drayage truck load matching and container availability and appointment scheduling at railroad and steamship line terminals. It establishes a link between drivers and a freight management systems dispatcher to an intermodal terminal reservation system and integrates an appointment function with terminal queue status and load matching.
As part of their effort to deploy information-sharing capabilities in freight movement, the USDOT developed the Freight Advanced Traveler Information System (FRATIS) in 2014 to test freight applications for improved operations efficiencies. This system was tested during the Drayage Optimization Proof of Concept Application, a series of pilot programs that allowed the system to evolve with lessons learned from each pilot deployment. The overall aim of this project was to create optimal routes that reduced the number of unproductive moves for trucks in drayage operations. An optimization algorithm was created that enables the assignment and sequencing of freight orders to minimize driven miles while also considering operational constraints. These constraints include appointment times, driving and duty hour limits for drivers, starting location, and overall configuration of orders.
The algorithm is launched using a web interface that allows dispatchers or integrated order management systems to upload orders and run the optimization algorithm a day in advance of the planned trip execution. After dispatchers have reviewed the orders and adjusted them as necessary, the resulting optimized orders are then sent to drivers and communicated through a driver mobile application that allows the driver to view their itinerary and update order statuses in real time. The pilot deployments saw significant reductions in unproductive miles, fuel consumption, and emissions while also demonstrating improvements in driving time utilization and productivity.
Drayage optimization may involve deployment of some of the previously discussed technologies including freight traveler information, truck parking availability, and freight signal priority. Coordination among these strategies will require:
Integrated back-office platform
Interpreting the data from the above technologies and any other traffic detection equipment will require a sophisticated platform to manage drayage operations.
Web-based interface for shippers
Operators/dispatchers will need a tool to plan and monitor drayage shipments. In-cab messaging via app or other vehicle-based device may also be required.