Algorithms for Studying the Impact of Travel Time Reliability along Multisegment Trucking Freight Corridors

Abstract

Performance measures allow planners and engineers to monitor and evaluate transportation facilities or projects and to justify the allocation of funds among alternative transportation improvement options. To capture the impact of corridor congestion on freight vehicles, new tools and methodologies are developed to analyze data from commercial vehicles and produce performance measures like travel time, speed and travel time reliability. Since long freight corridors are comprised of segments with potentially different reliability characteristics, the objective of this paper is to develop a programming logic that will use available truck GPS (Geographical Positioning System) data to: (a) identify natural segments or regions in a corridor between urban centers, interstate junctions, or rural areas and (b) estimate corridor wide impacts of travel time unreliability. The case study presented here investigates the Interstate 5 (I-5) corridor in Oregon. After identifying corridor segments, this research applies statistical techniques to compute vehicle travel time and reliability for freight movements within each segment. The proposed methodology has been used successfully to indentify distinct segments and characteristics of travel time reliability in freight corridors. Travel time information was used to compute cost impacts of delays within rural and urban areas along the I-5 corridor. This research presents an advance in the processing and aggregation of GPS truck data to produce succinct yet informative performance measures and segments.

Truck Traffic Speed Prediction Under Non-Recurrent Congestion: Based on Optimized Deep Learning Algorithms and GPS Data

“…In terms of prediction and estimation methods of traffic flow parameter, Wang et al [8] proposed a pragmatic multi-regime speed-density relationship-based approach to improve travel time prediction. Figliozzi et al [10] applied statistical techniques to calculate the travel time and reliability of freight vehicles. Wang and Goodchild [11] developed a set of logit models to determine the influencing factors of freight car routes and estimate travel time.…”

“…Spoel et al [9] built prediction models of truck arrival time using actual traffic and weather data. Figliozzi et al [10], Wang and Goodchild [11], Flaskou et al [12], Greaves and Figliozzi [13], and Zhao and Goodchild [14] all used GPS data to study the travel time of trucks.…”

The Application of Smart Phone, Weight-Mile Truck Data to Support Freight-Modeling, Performance Measures and Planning

“…Mobility is one of the primary concerns for highway systems as it has direct connection to levels of congestion, travel-time variability, and increased emissions, not to mention significant costs to commercial truck companies (i.e., penalties for late deliveries) (NCFRP, 2011;Figliozzi et al, 2011;Wheeler and Figliozzi, 2011). According to NCFRP Report 10, "Negative impacts of freight bottlenecks are expected to become more severe as the demand for freight transportation continues to grow and peak period congestion increases."…”

“…According to The Oregon Freight Plan, Oregon is the ninth most trade-dependent state in the nation and is expected to see significant increases in freight flows in the future (Cambridge Systematics Inc. and ODOT Freight Mobility Unit, 2011). This trade dependency comes with pressures on the general transportation system, as well as the freight-specific system, such as congestion, travel unreliability, environmental and health concerns, and increased transport costs Figliozzi et al, 2011. In this regard, recent research stresses the importance of freight performance measures and associated data-management processes in sustaining an affective transportation planning system (McMullen and Monsere, 2010;NCFRP, 2011).…”

Calibrating the Highway Capacity Manual Arterial Travel Time Reliability Model

“…The LOS is intended to simplify the communication of quantitative performance metrics related to measures of central tendency such as average density or average delay, for example (Roess and Prassas 2014). Logistics companies and commuters are, however, interested not only in the measures of central tendency but also in the measures of dispersion (e.g., variance) because both affect their arrival/travel times (Figliozzi et al 2011). Consequently, travel time reliability (TTR) metrics, which combine components in measures of central tendency and measures of dispersion, have attracted considerable research interest over the past decade (Taylor 2013).…”