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Mobility is a critical element of one's quality of life regardless of one's age. Although the challenges for women are more significant than those for men as they age, far less is known about the gender differences in mobility patterns of older adults, especially in the United States (US) context. This paper reports on a study that examined potential gender gaps in mobility patterns of older adults (aged 65 years and over) in the US by analyzing data from the 2017 National Household Travel Survey. Elderly respondents were first classified into one of six clusters based on socio-demographic variables. A Structural Equation Model (SEM) was then estimated and showed that gender gaps existed in the mobility patterns of the elderly, and the differences were diverse across the different clusters. The most substantial gender gap was found in the Senior Elder with Medical Condition(s) cluster, followed by the High-income Workers cluster and the Middle-income Urban Residents cluster. In contrast, females in the Low-Income Single Elder cluster enjoyed statistically significant positive mobility differences with their male counterparts. Our results also found that female elderly in the Senior Elder with Medical Condition(s) and the Low-income Family Elder clusters suffered most after the cessation of driving, with the largest mobility gender gap in the Middle-income Urban Resident cluster. This study will help transportation planners and policymakers understand gender and other socio-demographic differences in elderly mobility. Thus, it will facilitate the development of measures to improve elderly mobility and reduce gender gaps by recognizing and addressing specific target groups' mobility characteristics and needs rather than treating the elderly as a single potential user group.

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.

The toll design problem (TDP) provides a quantitative approach to the design of road pricing schemes. Its practical use, however, can be computationally challenging if the formulated TDP requires time-consuming computer models to evaluate candidate designs, especially if such designs must account for multiple objectives. For TDPs to be of practical relevance to the real-world planning of sustainable transportation networks, efficient TDP solution heuristics must be developed. To this end, two surrogate-based solution heuristics for multi-objective TDPs are proposed in this paper. Surrogate-based optimization uses simple approximations to computationally expensive models in order to accelerate the discovery of good solutions. The general search strategy of the proposed heuristics is as follows. In each iteration of the heuristics, a pool of candidate pricing schemes with unique sets of tolling locations and associated tolling levels is generated. From this pool of designs, the heuristics use the surrogate models to screen for solutions that are expected to be nondominated and that meet a specified selection criterion. Then, these promising designs are evaluated by the computationally expensive models, and the outputs obtained from these evaluations are used to update the surrogate models. Both heuristics repeat this general process until a maximum number of iterations are completed, at which point the best TDP solutions are returned. In addition to the solution heuristics, this paper presents a transportation network paradox that highlights how transportation network interventions intended to reduce traffic emissions could have unintended effects on a population’s exposure to pollutants. The paradox also is used to illustrate the practical complexity of accounting for environmental inequality objectives, as well as the relevance of multi-objective analysis approaches to transportation planning. Formulations of multi-objective TDPs that consider both travel and pollutant exposure-related objectives are also presented, including the objectives of reducing human intake of vehicle-generated air pollutants and of minimizing environmental inequality. The Sioux Falls and Chicago Sketch networks were used in tests that examined the relative performance of the heuristics, as well as the characteristics of pricing configurations obtained under different budget constraints. Among other results, the tests show that a pricing configuration could decrease total pollutant intake and environmental inequality, while at the same time producing an increase in pollutant concentrations in a significant number of pollutant receptor points.

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.

A surrogate-based solution heuristic is presented for single-objective cordon and area-based road pricing problems that consider environmental constraints. In the proposed algorithm, surrogate models are constructed using geometric representations of charging boundaries. A surrogate model is defined here as a simple approximation to computationally expensive models used to simulate road users’ response to pricing. The surrogates are employed as part of a screening procedure to select the most promising candidate schemes for evaluation by potentially time-consuming models. Departing from previous elastic demand-based formulations of congestion charging problems, this study utilizes a set of objective functions that can be easily integrated with commonly used travel demand models. Environmental considerations are introduced to the pricing problem in the form of pollutant concentration constraints. Two constraint handling strategies are presented to account for the pollutant concentration constraints in the solution heuristics. Numerical tests were conducted to explore the surrogate models’ predictive accuracy and their degree of correlation with the model outputs. On average, the surrogate predictions exhibited relatively good correlation with model outputs (correlation coefficients greater than 0.70). Additionally, a sample application of the proposed problem and methods is presented for illustrative purposes. The tests examined the relative performance of the proposed algorithm, the diversity of the design solutions generated, and the impact of the pricing schemes on pollutant concentration in the hypothetical study area.

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.

This paper presents an analysis of data from the 2017 National Household Travel Survey to examine the factors influencing the adoption and the frequency of use of on-demand ride-hailing services such as Uber and Lyft among older adults. Using a zero-inflated negative binomial model (ZINB), the results indicate that the determinants of adoption of on-demand ride-hailing services (users versus non-users) are different from the determinants of the frequency of use of these services among older adult users. Seniors that are younger, living alone, urban dwelling, more highly educated, more affluent, or male with a medical condition that results in asking others for rides, are more likely to be adopters of ride-hailing services. However, seniors who are middle elderly, less educated, or are carless older adults, are more likely to be frequent users of on-demand ride-hailing services as long as they adopt these services. In addition, smartphone possession plays an important role in the adoption behavior of on-demand ride-hailing services among older adults. Results of bivariate analysis showed that older adult ride-hailing users make more transit trips than their non-user counterparts, suggesting that ride-hailing services have the potential to serve as a complementary form of public transportation for older adults. The findings of this research will help ride-hailing operators in identifying potential market segments of their services and in developing campaign strategies for potential adopters.

A Clean Trucks Program (CTP) has been enacted at California’s San Pedro Bay Ports (SPBP) of Long Beach and Los Angeles, to help address major environmental issues associated with port operations. “Clean trucks” (meeting 2007 model year emission standards) that utilized public funds to replace older, polluting drayage trucks were required to be fitted with GPS units for compliance monitoring, with an expectation that freight truck movements could be investigated more precisely. Such implementation also served as a prototype of emerging smart freight mobility concepts, which are often heavily data-driven processes, but which should provide data and insights that are useful to both researchers and practitioners. Accordingly, this paper reports on research to develop a comprehensive framework for processing SPBP clean truck GPS data, to both interpret tour behavior of clean drayage trucks, and to prepare sufficient tour data for clean truck modeling at the SPBP. An important finding is that clean trucks at the SPBP have distinct tour characteristics. First, most completed a tour within one day, but one day of travel behavior is not necessarily representative of any other day. Second, the identified tour types contain repetitive trip patterns while other commercial trucks mostly tend to travel as circulative patterns. These insights into clean truck behavior at the SPBP potentially provide more accurate depictions of current conditions and better projections of future conditions for freight related improvement plans and models.

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).

The traffic state estimation process estimates various traffic states from available data in a road network and provides valuable information for travelers and decision makers to improve both travel experience and system performance. In many existing methods, model parameters and initial states have to be given in order to estimate traffic states, which limits the accuracy of the results as well as their transferability to different locations and times. In this paper, we propose a new framework to simultaneously estimate model parameters and traffic states for a congested road segment based on Newell’s simplified kinematic wave model (Newell, 1993). Given both Eulerian traffic count data and Lagrangian vehicle reidentification data, we formulate a single optimization problem in terms of the initial number of vehicles and model parameters. Then we decouple the optimization problem such that the initial number of vehicles can be analytically solved with a closed-form formula, and the model parameters, including the jam density and the shock wave speed in congested traffic, can be computed with the Gauss-Newton method. Based on Newell’s model, we can calculate individual vehicles’ trajectories as well as the average densities, speeds, and flow-rates inside the road segment. We also theoretically show that the optimization problem can have multiple solutions under absolutely steady traffic conditions. We apply the proposed method to the NGSIM datasets, verifying the validity of the method and showing that this method yields better results in the estimation of average densities than existing methods.

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.

Newell’s simplified kinematic wave model has been widely used in studying traffic dynamics in a road network. However, it cannot account for First-In-First-Out violation among vehicles on multi-lane roads. In this study, we present an extension of the model in the Lagrangian coordinates. In particular, we define a new variable of a vehicle’s order as its corresponding cumulative flows at different times and locations. The new kinematic wave model is consistent at the aggregate level with Newell’s simplified kinematic wave model, but allows for FIFO violation among individual vehicles. Based on the new model, we then present two algorithms to estimate vehicles’ trajectories from Eulerian data (cumulative flows provided by loop detectors) and Lagrangian data (the vehicle’s entry and exit times provided by reidentification technologies) at two boundaries of a road segment. In the first algorithm, we assume that vehicles follow the First-In-First-Out (FIFO) principle on a multi-lane road, and their orders are constant and equal the average of the entry and exit orders; in the second algorithm, we introduce a linear order-changing model to interpolate a vehicle’s orders at different times according to its entry and exit times. With Next Generation Simulation (NGSIM) datasets, we demonstrate that both algorithms are effective, but the second one, with an estimation error of around 10%, greatly outperforms the first one, with an estimation error of around 13%. This verifies the advantage of incorporating FIFO violation in the new model.

This chapter discusses innovations in Intelligent Transportation Systems that are contributing to the transformation of mobility in the United States (US) and the creation of a US transportation system that effectively integrates rapidly evolving advancing technologies to produce significantly safer and more efficient transportation of goods and people. It identifies the specific technological innovations and developments that are transforming mobility in the US and address the role of government in ensuring that the country will effectively harness the rapidly emerging advanced technologies to remain competitive in the global marketplace. Connected Vehicles (CVs) are vehicles that communicate with other vehicles, the infrastructure and mobile devices carried by individuals to accomplish a two-way wireless exchange of data. The massive amounts of data that will be exchanged as CVs expand in operation have the potential to create many positive impacts. The introduction of autonomous vehicles is largely a private sector automobile industry effort.

Timely and reliable truck activity data are of importance for transportation planning and investment analysis, traffic management, environmental and safety analyses, and operation and maintenance of infrastructure. Therefore, state and regional agencies have used various types of sensors to obtain truck flows. To measure accurate traffic flows with the use of sensors, considerable effort has been made to determine optimal sensor locations. The most common approach has been to focus on capturing the maximum number of origin–destinations (O-Ds) and routes at the sensor locations; this approach is referred to as the observability problem. However, when the long travel distances of trucks are considered, sensor locations that seek observability of O-Ds and routes may not acquire meaningful proportions of truck movements in a large-size truck network when budgets are limited. Therefore, this study provides a decision model that optimally locates sensors to capture the maximum O-D and route flow. A goal-programming approach with different weights that prioritize O-Ds or route flow, depending on a prior objective, was investigated. The proposed model was implemented in a real network in Los Angeles, California, with actual truck flow data obtained from sampled truck GPS trajectories. Results showed that significantly larger numbers of O-Ds and route flows were captured by the proposed flow-capturing model than with conventional observability approaches. In addition, trade-offs between O-D and route flow capturing depended on the applied weights and budgets. Particularly in the Los Angeles truck network, equally applied weight provided balanced capturing of O-D and route flow when sufficient numbers of sensors were located in the network. The proposed goal-programming approach can provide insights for researchers and practitioners for alternative sensor location solutions.

The emissions generated by motor vehicles remain a major source of air pollutants that affect public health and contribute to anthropogenic climate change. These negative externalities can be reduced, in part, with the implementation of environmentally oriented road pricing schemes, which can be designed using optimization-based approaches. In this paper, a toll design problem is proposed for determining toll locations and levels that minimize the expected human exposure to air pollutants and the related environmental inequalities, subject to constraints on pollutant concentration levels and implementation costs. The practical use of the proposed problem is hindered in most real-world applications by the computational costs associated with the evaluation of candidate solutions, as is common for network design problems. Furthermore, the problem cannot be expressed analytically given the multiple types of models (e.g., traffic assignment, emissions, air dispersion models) that would be required to e...

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.

Goods movement is one of the fastest growing transportation sectors, affecting both economic and environmental sustainability, particularly in dense urban areas with traffic congestion and air pollution. To meet this challenge, urban public agencies have paid attention to policies and systems to facilitate efficient and sustainable city logistics. This paper proposes a modeling framework to consider both spatial–temporal constraints and a means to calibrate the model from observable data, based on an adaptation of an activity-based passenger model called the household activity pattern problem. Conceptual comparisons with a state-of-the-art freight forecasting methodology are made using an example. Application of the model is illustrated through formulating and implementing a Sequential Selective Vehicle Routing Problem associated with drayage truck activities at the San Pedro Bay Ports in Southern California.

An approach is presented to determine the most likely tour distributions and model behavior for investigating drayage truck movements in a coastal region. This was done by implementing a revised form of entropy maximization based on truck tours to model and better understand drayage truck tour behavior at the San Pedro Bay Ports (SPBPs) complex in Southern California. The drayage trucks at the SPBPs have features that are distinct from other commercial trucks. The tour-based entropy maximization model proposed in this paper provides an opportunity to incorporate periodically updated GPS data collected in Southern California into a large-scale tour-based model. With the dataset, four models were estimated by cargo movement: (1) year-based, (2) low period, (3) medium period, and (4) high period models. The findings were consistent with the tour patterns varying by season and by cargo movement. Furthermore, the medium period, which represented relatively steady cargo movement, indicated a better MAPE (mean absolute percent error) than did other models. This proposed approach provides a significant advantage in that the most recent touring information obtained from advanced technologies could be directly applied to the tour-based model and subsequently used to assess various strategies.

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.

In the aggregate freight demand modeling literature, temporal assignment (annual to daily flows) is often oversimplified or neglected altogether. Unlike passenger flows, freight flows over the course of a year are not uniform and can vary significantly as the result of trade-offs between inventory and transportation cost management. We introduce the first temporal assignment model that explicitly considers these trade-offs for aggregate freight forecasting. A two-stage model is proposed that first decomposes aggregate annual zonal flows to firm group annual flows using a supply chain network model, which are then temporally assigned by simulating purchase order transactions throughout supply chains. Lot sizes are estimated with an Economic Order Quantity (EOQ) model and calibrated with monthly inventory data. The result is an aggregate-disaggregate-aggregate model that fits into aggregate freight forecasting models but makes use of more disaggregate logistical data. The model is illustrated with a simple replicable example, followed by a case study conducted with California statewide data to break out the distributed zonal flows into average daily volumes for network assignment. Calibration results using 2007 IMPLAN data showed a median percentage difference of simulated annual flows from FAF 3 data of 2.38%, and a median percentage difference of simulated inventories from IMPLAN data of 4.85%, which suggests an excellent fit. Empirical validation results showed the model outperforms fixed factor approaches in mean value accuracy by 15–31%.

General lack of awareness of the transportation profession and of its diversity of content has been cited as critical issues affecting transportation workforce recruitment. University engineering programs have a crucial role in recruitment and training of the next generation of transportation professionals, but in focusing mostly on automobile travel, undergraduate transportation engineering courses do not properly introduce students to the multimodal, environmental, and public service facets of the profession. Moreover, several surveys have cited that engineering students aspire to careers in which they can make a difference in the world, improve the lives of others, and better the natural environment. Service learning is one way to link course content better with students' motivation to study a subject while introducing non-traditional transportation engineering concepts in an effort to increase students' awareness of the diverse nature of the profession. This paper showcases how a transit feasibility study for Joshua Tree National Park was framed as a service-learning project and documents the lessons learned in facilitating student and community stakeholder involvement. A survey of student job placement after graduation showed that many of the participating students went on to transportation-related careers, as compared with their peers who had not participated. Although this unique service-learning project cannot be replicated exactly, it is a good model for forming future projects that highlight the multifaceted roles of transportation professionals and better leverage students' intrinsic motivation so that they persist through their degree programs to become members of the transportation workforce.

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.

Several methods have been proposed to disaggregate Freight Analysis Framework (FAF) commodity flows to zonal structures of greater geographical detail. This disaggregation is usually performed on the basis of explanatory variables related to the supply and demand of goods. This paper studied a complementary procedure to determine the mode splits of disaggregated FAF flows. A goal programming approach was proposed to allocate FAF mode flow data on the basis of mode-related variables. The formulated goal programming problem minimized the deviation between the mode flow decision variables and target mode flow values, subject to given FAF mode flow information. The use of mode split models was proposed to define the problem's target values. In a sample application of the procedure, a method to estimate aggregate mode split models with FAF data was discussed. Mode split models could be used by transportation organizations that did not have access to freight mode choice models to define the goal programming problem's target mode flow values. In addition, an optimization problem was formulated to account for FAF mode flow data in the disaggregation of total commodity flows. Last, validation procedures for FAF disaggregation and mode allocation results were discussed, and an example of a validation approach was presented.

Freight forecasting models are data intensive and require many explanatory variables to be accurate. One problem, particularly in the United States, is that public data sources are mostly at highly aggregate geographic levels but models with more disaggregate geographic levels are required for regional freight transportation planning. A second problem is that supply chain effects are often ignored or modeled with economic input–output models that lack explanatory power. This study addressed these challenges with a structural equation modeling approach that was not confined to a specific spatial structure, as spatial regression models would have been, and allowed correlations between commodities. A model for structural commodity generation that was based on freight analysis framework was specified, estimated, and shown to provide a better fit to the data than did independent regression models for each commodity. Three features of the model are discussed: indirect effects, supply chain elasticity, and intrazonal supply–demand interactions. A goal programming method was used with imputed data to validate the geographic scalability of the model.

In this paper, vehicle microscopic simulation and emission models were combined with an air pollutant dispersion model and a health assessment tool to quantify some social costs resulting from urban freight transportation in the Alameda corridor that links the Ports of Los Angeles and Long Beach to downtown Los Angeles. Traffic on two busy freeways, the I-710 and the I-110, and some heavily trafficked arterial roads was analyzed to estimate the health impacts caused by drayage truck emissions of particulate matter (PM) for four different years: 2005, which serves as a baseline for various pollution inventories, as well as 2008, 2010 and 2012. These years correspond to deadlines for the Clean Truck Program (CTP), which was put in place to improve air quality in the Alameda corridor. Results show that the health costs from particulate matter (PM) emitted by drayage trucks exceeded 440 million dollars in 2005. However, these costs decreased by 36%, 90%, and 96% after accounting for the requirements of the 2008, 2010, and 2012 CTP deadlines. These results quantify the magnitude of the social costs generated by drayage trucks in the Alameda corridor, suggest that these costs justified replacing drayage trucks operating there, and indicate that the Clean Truck Program likely exceeded its target.

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.

Escalating concerns about air quality in southern California have led authorities at the Ports of Los Angeles and Long Beach, also known as the San Pedro Bay Ports (SPBP), to adopt a number of measures designed to mitigate emissions. One way to do this is to shift some of the containers currently transported by drayage trucks to trains. This alternative is attractive because it would decrease congestion and air pollution on the area's main freeways (I-710 and I-110) and the arterials that serve the SPBP. In addition, it would increase road safety along the busy Alameda freight corridor between the SPBP and downtown Los Angeles. One drawback would be an increase in pollutant emissions from train operations in the Alameda corridor; however, trains tend to pollute less than trucks per ton-mile, and new federal regulations are tightening the emissions standards for diesel locomotives. The goal of this paper is to quantify the net impact of such a modal shift on the emission of particulate matter (PM) and nitrogen oxides (NOx), the two air pollutants of most concern in the SPBP area. This analysis relies on microscopic simulation to capture emissions resulting from stop-and-go traffic on the freeways serving the SPBP. It was found that emissions of both NOx and particles less than 2.5 μm in diameter (PM2.5) can be significantly reduced by switching from drayage trucks to trains. This finding suggests that a modal shift should be encouraged, especially if there is unused train capacity and if the shift does not conflict with the shippers' interests.