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The Federal Highway Administration (FHWA) vehicle classification scheme is designed to serve various transportation needs such as pavement design, emission estimation, and transportation planning. Many transportation agencies rely on Weigh-In-Motion and Automatic Vehicle Classification sites to collect these essential vehicle classification counts. However, the spatial coverage of these detection sites across the highway network is limited by high installation and maintenance costs. One cost-effective approach has been the use of single inductive loop sensors as an alternative to obtaining FHWA vehicle classification data. However, most data sets used to develop such models are skewed since many classes associated with larger truck configurations are less commonly observed in the roadway network. This makes it more difficult to accurately classify under-represented classes, even though many of these minority classes may have disproportionately adverse effects on pavement infrastructure and the environment. Therefore, previous models have been unable to adequately classify under-represented classes, and the overall performance of the models is often masked by excellent classification accuracy of majority classes, such as passenger vehicles and five-axle tractor-trailers. To resolve the challenge of imbalanced data sets in the FHWA vehicle classification, this paper constructed a bootstrap aggregating deep neural network model on a truck-focused data set using single inductive loop signatures. The proposed method significantly improved the model performance on several truck classes, especially minority classes such as Classes 7 and 11 which were overlooked in previous research. The model was tested on a distinct data set obtained from four spatially independent sites and achieved an accuracy of 0.87 and an average F1 score of 0.72.

Restrictions on travel and in-person commercial activities in many countries (e.g. the U.S., China, European Countries, etc.) due to the global outbreak and rapid spread of the coronavirus disease 2019 (COVID-19) have severely impacted the global supply chain and subsequently affected freight transportation and logistics. This chapter summarizes the findings from the analysis of truck axle and weight data from existing highway detector infrastructure to investigate the impacts of COVID-19 on the freight truck activity. Three aspects of COVID-19 truck impacts were explored: drayage, long and short-haul movements, and payload characteristics. This analysis revealed disparate impacts of this pandemic on freight truck activity because of local and foreign policies, supply chain bottlenecks, and the dynamic changes in consumer behavior. Due to the ongoing effects of COVID-19, it is not yet possible to distinguish between transient and long-term impacts on freight trucking activity. Nonetheless, a future expansion of the study area and the incorporation of other complementary data sources may provide further insights of the pandemic’s impacts on freight movement.

Trucks have distinct driving characteristics in general traffic streams such as lower speeds and limitations in acceleration and deceleration. As a consequence, vehicles keep longer headways or frequently change lane when they follow a truck, which is expected to increase crash risk. This study introduces several traffic measures at the individual vehicle level to capture vehicle interactions between trucks and non-trucks and analyzed how the measures affect crash risk under different traffic conditions. The traffic measures were developed using headways obtained from Inductive Loop Detectors (ILDs). In addition, a truck detection algorithm using a Gaussian Mixture (GM) model was developed to identify trucks and to estimate truck exposure from ILD data. Using the identified vehicle types from the GM model, vehicle interaction metrics were categorized into three groups based on the combination of leading and following vehicle types. The effects of the proposed traffic measures on crash risk were modeled in two different cases of prior- and non-crash using a case-control approach utilizing a conditional logistic regression. Results showed that the vehicle interactions between the leading and following vehicle types were highly associated with crash risk, and further showed different impacts on crash risk by traffic conditions. Specifically, crashes were more likely to occur when a truck following a non-truck had shorter average headway but greater headway variance in heavy traffic while a non-truck following a truck had greater headway variance in light traffic. This study obtained meaningful conclusions that vehicle interactions involved with trucks were significantly related to the crash likelihood rather than the measures that estimate average traffic condition such as total volume or average headway of the traffic stream.

Trucks are an essential element in freight movements, transporting 73% of freight tonnage among all modes. However, they are also associated with severe adverse impacts on roadway congestion, safety, and air pollution. Truck speed by truck body types has been considered as an indicator of traffic conditions and roadway emissions. Even though vehicle speed estimation has been researched for decades, there exists a gap in estimating truck speeds particularly at the individual vehicle level. The wide diversity of vehicle lengths associated with trucks makes it especially challenging to estimate truck speeds from conventional inductive loop detector data. This paper presents a new speed estimation model which uses detailed vehicle signature data from single inductive loop sensors equipped with advanced detectors to provide accurate truck speed estimates. This model uses new inductive signature features that show a strong correlation with truck speed. A modified feature weighting K-means algorithm was used to cluster vehicle length related features into 16 specific groups. Individual vehicle speed regression models were then developed within each cluster. Finally, a multi-layer perceptron neural network model was used to assign single loop signatures to the pre-determined speed related clusters. The new model delivered promising estimation results on both a truck-focused dataset and a general traffic dataset.

Understanding the spatiotemporal distribution of commercial vehicles is essential for facilitating strategic pavement design, freight planning, and policy making. Hence, transportation agencies have been increasingly interested in collecting truck body configuration data due to its strong association with industries and freight commodities, to better understand their distinct operational characteristics and impacts on infrastructure and the environment. The rapid advancement of Light Detection and Ranging (LiDAR) technology has facilitated the development of non-intrusive detection solutions that are able to accurately classify truck body types in detail. This paper proposes a new truck classification method using a LiDAR sensor oriented to provide a wide field-of-view of roadways. In order to enrich the sparse point cloud obtained from the sensor, point clouds originating from the same truck across consecutive frames were grouped and combined using a two-stage vehicle reconstruction framework to generate a dense three-dimensional (3D) point cloud representation of each truck. Subsequently, PointNet – a deep representation learning algorithm – was adopted to train the classification model from reconstructed point clouds. The model utilizes low-level features extracted from the 3D point clouds and detects key features associated with each truck class. Finally, model ensemble techniques were explored to reduce the generalization error by averaging the results of seven PointNet models and further enhancing the overall model performance. The optimal number of models in the ensemble was determined through a comprehensive sensitivity analysis with the consideration of the average correct classification rate (CCR), the variability of the prediction results, and the computation efficiency. The developed model is capable of distinguishing passenger vehicles and 29 different truck body configurations with an average CCR of 83 percent. The average correct classification rate of the developed method on the test dataset was 90 percent for trucks pulling a large trailer(s).

Truck flow patterns are known to vary by season and time-of-day, and to have important implications for freight modeling, highway infrastructure design and operation, and energy and environmental impacts. However, such variations cannot be captured by current truck data sources such as surveys or point detectors. To facilitate development of detailed truck flow pattern data, this paper describes a new truck tracking algorithm that was developed to estimate path flows of trucks by adopting a linear data fusion method utilizing weigh-in-motion (WIM) and inductive loop point detectors. A Selective Weighted Bayesian Model (SWBM) was developed to match individual vehicles between two detector locations using truck physical attributes and inductive waveform signatures. Key feature variables were identified and weighted via Bayesian modeling to improve vehicle matching performance. Data for model development were collected from two WIM sites spanning 26 miles in California where only 11 percent of trucks observed at the downstream site traversed the whole corridor. The tracking model showed 81 percent of correct matching rate to the trucks declared as through trucks from the algorithm. This high accuracy showed that the tracking model is capable of not only correctly matching through vehicles but also successfully filtering out non-through vehicles on this relatively long distance corridor. In addition, the results showed that a Bayesian approach with full integration of two complementary detector data types could successfully track trucks over long distances by minimizing the impacts of measurement variations or errors from the detection systems employed in the tracking process. In a separate case study, the algorithm was implemented over an even longer 65-mile freeway section and demonstrated that the proposed algorithm is capable of providing valuable insights into truck travel patterns and industrial affiliation to yield a comprehensive truck activity data source.

Crash type is an informative indicator to infer driving behaviors and conditions that cause a crash. For example, rear-end and side-swipe crashes are typically caused by improper vehicle interaction such as sudden lane-changing or speed control while hit-object crashes are likely the result of a single driver's mistake. This study investigated the impact of vehicles travelling as a group (platoon) and its configuration (i.e., types of vehicles consisting of the platoon) on crash type and severity since the vehicles could affect each other when travelling in close proximity. This study applied Generalized Structure Equation Modeling (GSEM) to capture the complex relationships among the various crash factors such as traffic condition, driver characteristics, environmental conditions, and vehicle interaction to the crash attributes including type and severity. This study collected over 3 million individual vehicle data from 39 traffic count sites in California to estimate the vehicle interactions and driving behaviors. The microscopic traffic data are matched to 1417 crash reports. Results showed that vehicles traveling in platoons are associated with more rear-end and side-swipe crashes. Speed difference in the platoon had a positive effect on hit-object crashes if the platoon comprises vehicles of homogeneous type - either trucks or non-trucks. In addition, human factors such as age and gender were identified as significant influential factors in all type of crashes, however truck involvement particularly played an important role amongst side-swipe crashes. Crash severity was negatively affected by total flow, and rear-end crashes were more likely to be severe compared with hit-object crashes. Based on findings, this study suggests practical operational strategies to reduce traffic instability associated with platooned vehicle patterns. Understanding the high-risk factors for different crash types and severities would provide valuable insights for decision-makers and transportation engineers to develop targeted intervention strategies in consideration of road users and traffic conditions such as fleet mix and speed.

Transportation agencies tasked with forecasting freight movements, creating and evaluating policy to mitigate transportation impacts on infrastructure and air quality, and furnishing the data necessary for performance driven investment depend on quality, detailed, and ubiquitous vehicle data. Unfortunately in the US, currently available commercial vehicle data contain critical gaps when it comes to linking vehicle and operational characteristics. Leveraging existing traffic sensor infrastructure, we developed a novel, readily implementable approach of integrating two complementary data collection devices, Weigh-in-Motion (WIM) systems and advanced inductive loop detectors (ILD), to produce high resolution truck data. For each vehicle traversing a WIM site, an inductive signature was collected along with WIM measurements such as axle spacing and weight which were then used as inputs to a series of truck body classification models that encompass all truck classes in the most common axle-based Federal Highway Administration (FHWA) classification scheme in the US. Since body configuration can be linked to commodity carried, drive and duty cycle, and other distinct operating characteristics, body class data is undeniably useful for freight planning and air quality monitoring. A multiple classifier systems (MCS) method was adopted to increase the classification accuracy for minority body classes. In all, eight separate body classifications models were developed from an extensive data set of 18,967 truck records distinguishing an unprecedented total of 23 single unit truck and 31 single and semi-trailer body configurations, each with over 80% correct classification rates (CCR). Remarkably, the body class model for five axle semi-tractor trailers – the most diverse truck category – achieved MCS CCRs above 85% for several industry specific classes including refrigerated and non-refrigerated intermodal containers, livestock, and logging trailers.

With the discontinuation of the national Vehicle Inventory and Use Survey (VIUS) in the United States in 2002, insufficient data have been available for well more than a decade on commercial vehicle activity. The goal of this pilot survey effort was to develop a preliminary design for a proposed California Vehicle Inventory and Use Survey (Cal-VIUS) and to test it with a scaled-down sample to provide guidance on the full-scale survey design. The sample was drawn from vehicle records obtained from the California Department of Motor Vehicles and International Registration Plan data sets by using a stratified sampling technique to capture intrastate and Interstate commercial vehicle activity in California. Limitations identified in the 2002 VIUS were addressed in the Cal-VIUS pilot survey questionnaire, which was administered on an online survey platform ( http://surveyanalytics.com ). The questionnaire was designed to collect annual and trip-based activity data through two complementary surveys: a web-based fleet manager survey and a smartphone app-based driver survey (with web-based option). These surveys were conducted between December 29, 2014, and February 28, 2015, and between February 24 and February 26, 2015, respectively. Results from the web-based fleet manager survey showed that the stratification design was adequate to describe the heterogeneous characteristics of vehicle activities between strata with respect to vehicle miles traveled within California. The driver survey was not fully tested because of limited response. Results from the pilot survey are expected to provide valuable insights to those who are developing future truck-related survey studies.

Studies on the economic impact of air cargo traffic have been gaining traction in recent years. The slowed growth of air cargo traffic at California’s airports, however, has raised more pressing questions amongst airport planners and policy makers regarding the determinants of air cargo traffic. Specifically, it would be useful to know howCalifornia’s air cargo traffic is affected by urban economic characteristics surrounding airports. Accordingly, this study estimates the socioeconomic determinants of air cargo traffic across cities in California. We construct a 7-year panel (2003-2009) using quarterly employment, wage, population, and traffic data for metro areas in the state. Our results reveal that the concentration of service and manufacturing employment impacts the volume of outbound air cargo. Total air cargo traffic is found to grow faster than population, while the corresponding domestic traffic grows less than proportionally to city size. Wages play a significant role in determining both total and domestic air cargo movement. We provide point estimates for the traffic diversion between cities, showing that 80 percent of air cargo traffic is diverted away from a small city located within 100 miles of a large one. Using socioeconomic and demographic forecasts prepared for California’s Department of Transportation, we also forecast metro-level total and domestic air cargo tonnage for the years 2010-2040. Our forecasts for this period indicate that California’s total (domestic) air cargo traffic will increase at an average rate of 5.9 percent (4.4 percent) per year.

Weigh-in-motion (WIM) systems measure truck volumes, assist in pavement design and management, and enforce truck size and weight regulations. Although WIM systems provide truck classification based on the FHWA axle configuration classification scheme, more specific vehicle characteristics such as body configuration are necessary for freight planning and pollution monitoring. A modified decision tree model was developed to estimate truck volumes and gross vehicle weight (GVW) distributions by body configuration for five-axle semi-tractor trailers (3S2) with the use of existing WIM system measurements such as axle spacing and vehicle length. This method allows more information to be extracted from axle-based measurement data to leverage the significant investments in existing WIM systems better. Data for model development were collected at three WIM sites spanning rural and urban locations in California and described more than 7,500 3S2 trucks stratified into five trailer body categories: vans, tanks, platforms, 40-ft intermodal containers, and other. Model estimates of trailer body configuration volumes differ by only 8% from actual volumes when averaged across all body configurations on an independent test data set. A normalization procedure was designed to improve the model's robustness against systematic and random calibration inaccuracies at WIM sites. An algorithm based on Gaussian mixture models was developed to estimate GVW distribution by body configuration. Results show that estimated GVW distributions statistically capture the actual GVW distribution of each body configuration and are temporally and spatially transferable.

Freight transportation has a multifaceted impact on the economy, and the importance of understanding freight demand is increasing. There is a significant need to access a wide array of data sources for freight modeling and analysis. However, current data sources are not always easily accessible even with the availability of the Internet. Among the reasons are differing user interfaces, unavailability of data type definition, data format incompatibility, and inability to assess the scope of data conveniently. The repository developed in this study, the California Freight Data Repository, is a user-centered online tool designed from a systems perspective with several objectives. First, it facilitates convenient access, standardized interface, and a centralized location for obtaining freight data. Data dictionaries and lookup tables are provided for each data source to allow users to understand the scope of the data source and to give a clear definition of terms found in the data. A quality assessment summary is also provided to inform users of the strengths and limitations associated with each data source. Second, the repository is equipped with several geographic information system–based visualization tools intended to allow users to perform preliminary evaluation of data to determine their suitability for specific modeling or analysis needs. Third, the repository is designed with a customized search engine to retrieve web resources specifically associated with freight modeling and analysis. This paper presents the metadata architecture used for identifying data sources, the assessment framework used to evaluate selected data sources, and the system and interface design of the California Freight Data Repository. Several use cases are presented to demonstrate the applicability of this resource.

Section-related or link-based traffic sensor data can provide reliable and accurate inputs for traffic-performance-measurement systems. Section performance measurements can easily be generated via a vehicle-reidentification system. Inductive loop-detector (ILD)-based systems are cost effective because ILDs are widely installed in the field (with fewer market penetration concerns) and provide essentially anonymous surveillance with few, if any, privacy concerns. Accordingly, the authors have recently developed an algorithm, i.e., RTREID-2, using inductive loop signature-based methods for vehicle reidentification (ILD-VReID) and which was dedicated to meet the needs for real-time implementation and section-performance measurement. RTREID-2 was developed by utilizing a piecewise slope rate (PSR) approach to transform the raw vehicle signatures obtained from square loops (only). This paper reports the results of a 10.0-km (6.2-mi) freeway corridor implementation of RTREID-2 under congested morning peak-period conditions. Although RTREID-2 has been designed for real-time operation, this initial corridor investigation was conducted offline. The corridor contained mostly round inductive loop sensors with some square loops, providing an opportunity to assess the applicability and transferability of RTREID-2 to homogenous and heterogeneous loop-sensor systems. Analyses of travel time and speed at both freeway corridor and individual freeway section levels were conducted, and excellent results were obtained compared with Global Positioning System (GPS) measurements from control vehicles. The results suggest that RTREID-2 has the potential to successfully be implemented in a congested freeway corridor, utilizing either or both round or square inductive loop sensors.

The Highway Capacity Manual provides a method for determining the level of service (LOS) on freeways to evaluate freeway performance. Apart from being essentially an off-line decision support tool for planning and design, it is also based on point measurements from loop detectors, which may not provide an accurate assessment of freeway section performance. In order to meet user requirements of advanced traffic management and information systems, new LOS criteria based on section measures are required for real-time freeway analysis. The main aim of this research was to demonstrate a technique for development of such LOS criteria. The study uses a new measure of effectiveness, called reidentified median section speed (RMSS), derived from analysis of inductive vehicle signatures and reidentification of vehicles traveling through a major section of freeway in the City of Irvine, CA. Two main issues regarding real-time LOS criteria were addressed. The first was how to determine the threshold values partitioning the LOS categories. To provide reliable real-time traffic information, the threshold values should be decided such that RMSSs within the same LOS category represent similar traffic conditions as much as possible. In addition, RMSSs in different LOS categories should represent dissimilar traffic conditions. The second issue concerned the aggregation interval to use for deriving LOS categories. Two clustering techniques were then employed to derive LOS categories, namely, k-means and fuzzy approaches. Wilk's Lambda analysis and LOS stability analysis were performed to design new LOS criteria. Six LOS categories defined in terms of RMSS over a fixed 240-s interval were identified as the best solution to meet two major considerations described above. The procedures used in this study are readily transferable to other similarly equipped freeway sections for the derivation of real-time LOS.

The need for accurate, comprehensive and timely traffic surveillance information is critical to ensure optimal traffic operations and management for advanced traffic management systems (ATMS). This paper describes an on-going study that involves the design and implementation of the section-based freeway real-time traffic performance measurement system (RTPMS)-an advanced surveillance system based on inductive vehicle signature technologies. Unlike traffic performance measurement systems that depend on point measures, RTPMS provides section-based travel time measures via matching of inductive vehicle signatures obtained at two adjacent detector station locations. Hence, the performance measures account for traffic conditions spanning an entire section, not just at a local detector station. In addition, each re-identified vehicle is also classified in RTPMS, yielding detailed section-based performance measures of different vehicle classes. This gives the ability to obtain more accurate travel statistics and vehicle exposure rates, such as those of commercial vehicles.

Understanding truck activity is an essential component of strategic freight planning and programming. However, recent studies have revealed a significant void in the availability of detailed truck activity data. Although some existing detectors are capable of providing truck counts by axle configuration, higher-resolution data that indicate truck body configuration, industry served, and commodity carried cannot be obtained from existing sensors. This paper presents the newly developed Truck Activity Monitoring System, which leverages existing in-pavement traffic sensors to provide truck activity data in California. Existing inductive loop detector sites were updated with inductive signature technology and advanced truck classification models were implemented to provide detailed truck count data with more than 40 truck body configurations. The system has been deployed to more than 90 detector locations in California to provide coverage at state borders, regional cordons, and significant metropolitan truck corridors. An interactive geographic information system website provides users with advanced visual analytics and access to archived data across all deployed locations. The case studies presented in this paper demonstrate the potential of the data obtained from this system in analyzing and understanding current and historical industry-specific truck activity.